The sliding innovation filter (SIF) is a recently developed estimation technique that has gained widespread use. It is a predictor-corrector filter that utilizes a hyperplane and applies a force to allow estimates to fluctuate about it. SIF belongs to the same family as the smooth variable structure filter and sliding mode observer, and it is stable and robust in the face of uncertainties. This paper discusses the use of SIF for estimating the states of Power Converters, which play a crucial role in Electric Vehicles (EVs) by converting high-voltage DC from the battery to low-voltage AC used by the motor. One of the main challenges in Power Converters is accurately estimating their states, such as input voltage, output voltage, and inductor current, which are critical for optimal control and efficient operation. The SIF has demonstrated promising results in addressing this challenge.
Capacitive deionization is a promising electrochemical technology employed in water treatment applications. Among the various water desalination and treatment technologies, capacitive deionization technology has many advantages and appreciably increases desalination efficiency. CDI desalinates the Water via the electrosorption of ions inside the porous structure of two oppositely charged electrodes. The electrodes are considered the core of the CDI system. The carbon flow electrode is a new design for improving salt removal efficiency (SRE). Thus, developing a numerical model to predict CDI salt removal efficiency (SRE) and understanding how electrodes jointly contribute to desalination is crucial for rational FCDI system design. This paper demonstrates the concept of using Artificial intelligence-based modeling to predict the electrosorption capacity of FCDI with reasonable accuracy based on the important flow electrode and process features. The contribution and relative importance of each feature in deionization and the cost analysis framework of FCDI are determined and validated. This study shows that artificial neural networks (ANN) have strong abilities in predicting the nonlinear behavior of the CDI system and in revealing each featureโs role of the electrode in desalination. Two hidden layers with 14 and 11 neurons in the first and second hidden layers have been used. The model has good regression of 100% for training, 99.67% for validation 99.809% for testing, and 99.908% for the overall system. The ๐ ๐๐๐ธ, ๐๐ด๐ธ, and ๐ ๐๐๐ธ%๐ธ๐๐๐๐ were significantly small.
With the modernization of cities, the concept of the Internet of Things (IoT) is gaining popularity and becoming a vital source of smart developments. An added advantage of solar energy systems, IoT applications enable automatic and remote sensing, processing, and execution. IoT ensures that information is easily available and accessible from any location around the world. The IoT applications improve the visibility, scalability, and cost-effectiveness of solar energy generation and service. A bibliometric analysis of scientific publications in the field of solar PV and IoT applications was conducted using the Scopus database between the years 2011 and 2023. Many studies of technological development have been discovered, and some insights can still be approached in such a way that the practical implementation of photovoltaic solar systems is improved. Since 2013, there has been an increase in the rate of publications. The majority of these studies were conducted in India, and the most common IoT applications reported were in the fields of computer science and engineering. This article identifies knowledge gaps to inform the community, industry, and government officials about IoT research directions in the solar energy field.
KEYWORDS: Signal filtering, Tunable filters, Control systems, Electronic filtering, Nonlinear filtering, Engineering, Optical engineering, Digital filtering, Signal processing
Currently, microgrids are frequently used and various control algorithms have been applied to improve their performance in both grid-connected and islanded modes. However, research has shown that incorporating a filtering technique into the controller can lead to even better performance. As a result, a simple controller with a filter can perform just as well as a complex controller that operates alone. This study focuses on the performance of a microgrid using a new filter called the sliding innovation filter, which is known for its robustness and stability. The filter is an excellent option for estimating performance under various conditions, such as load and injected powers. To demonstrate the filter's advantages, modeling uncertainties are introduced into the system while the filter is estimating states. The filter's performance is evaluated using a MATLABยฉ simulation environment.
Fault detection and identification strategies utilize knowledge of the systems and measurements to accurately and quickly predict faults. These strategies are important to mitigate full system failures, and are particularly important for the safe and reliable operation of aerospace systems. In this paper, a relatively new estimation method called the sliding innovation filter (SIF) is combined with the interacting multiple model (IMM) method. The corresponding method, referred to as the SIF-IMM, is applied on a magnetorheological actuator which was built for experimentation. These types of actuators are similar to hydraulic-based ones, which are commonly found in aerospace systems. The method is shown to accurately identify faults in the system. The results are compared and discussed with other popular nonlinear estimation strategies including the extended and unscented Kalman filters.
An information filter is one that propagates the inverse of the state error covariance, which is used in the state and parameter estimation process. The term โinformationโ is based on the Cramer-Rao lower bound (CRLB), which states that the mean square error of an estimator cannot be smaller than an amount based on its corresponding likelihood function. The most common information filter (IF) is derived based on the inverse of the Kalman filter (KF) covariance. This paper introduces preliminary work completed on developing the information form of the sliding innovation filter. The SIF is a relatively new type of predictor-corrector estimator based on sliding mode concepts. In this brief paper, the recursive equations used in the sliding innovation information filter (SIIF) are derived and summarized. Preliminary results of application to a target tracking problem are also studied.
Estimating the position of a unmanned ground vehicle (UGV) that is navigating a complex road is a challenging task. Numerous algorithms have been developed to estimate the maneuvering status of the UGV. In this study, a newly developed filtering technique called the sliding innovation filter (SIF) is combined with multiple model technique to improve the estimation accuracy. The SIF uses the measured states as a discontinuous hyperplane to constrain the estimates to stay close to it. By combining the benefits of both methods, the proposed filter minimizes chatter during position estimation when the UGV is maneuvering. The effectiveness of the proposed method is evaluated on a UGV navigating an S-shaped road, and the results are compared to those obtained using the standard SIF.
KEYWORDS: Field programmable gate arrays, Tunable filters, Signal filtering, Image processing, Signal processing, Control systems, Artificial intelligence, Electronic filtering, Design and modelling, Robotics
Field programmable gate arrays (FPGAs) are increasingly popular due to their customizability, which enables them to be tailored to specific applications, resulting in minimal resource usage that saves energy and space. In this work, we used an FPGA with a Z-board from Xilinx to simulate the application of the sliding innovation filter (SIF) to a robotic arm. SIF is a predictor-corrector filter used for both linear and nonlinear systems to estimate states and/or parameters. It shares similar principles with sliding mode observer and smooth variable structure filter (SVSF) and uses a correction gain derived to satisfy Lyapunov stability, keeping the estimates near the measurements. We tested SIF on a manipulator with two joints (rotational and prismatic), using FPGA to run the simulation while tracking resource utilization. We compared the results with those of SVSF.
Recent interest in unmanned aerial vehicles (UAVs) has grown due to the wide range of possible civilian uses for these aircraft. However, present robot navigation technologies still need to be improved in various situations. Researchers are particularly interested in the 'Sense and Avoid' capacity as a critical issue. UAVs operating in civilian areas must have this functionality to do so safely. Numerous path planning and navigation algorithms have been developed for autonomous decision-making and control of UAVs. These path-planning algorithms are divided into either heuristic and non-heuristic or accurate methods. Both existing UAV route planning algorithms for the first and second techniques will be thoroughly compared in this work. Each algorithm is put through its paces in three diverse obstacle scenarios. Each method has been evaluated under various global and local obstacle information availability conditions while comparing the computational time and solution optimality.
In this paper, Chameleon Swarm Algorithm, which is a new metaheuristic algorithm, is used to design a speed reducer gearbox. The gearbox is used in an autonomous vehicle, and it is supposed to take in consideration the total weight of the gear sets along with other contains that include the stresses; including the bending and surface stresses for the gears and the stresses of the shaft, and he deflections of the shafts, beside other contains. The algorithm must find the optimal solution while satisfying the eleven constraints, which makes it a multi-objective optimization problem. The algorithm minimizes the energy dissipation in the gears as the design is optimal. The algorithm will be tested in Monte Carlo simulation to show its stability and robustness in such an application. The computer resources are examined, and the results are compared to the particle swarm optimization. The proposed method shows better results and better performance compared to the PSO.
Path planning and obstacle avoidance are crucial tasks in the robotics and autonomous industry. Path planning seeks to determine the most efficient path between a start and an end point, whereas obstacle avoidance seeks to avoid collisions with static or dynamic obstacles in the environment. On this work, we utilize the Chameleon Swarm Algorithm (CSA), which is a metaheuristic approach, for path planning and obstacle avoidance on a predetermined map with static obstacles. This CSA extracted the optimal path from several possible different paths, and the results showed that it has slightly superior performance compared to PSO.
Robotic manipulators involve the motion in 3D space. The motion of the manipulator is usually done through rotational movements using rotational joints, and/or through translational movements using prismatic joints. In this work, a prismatic-prismatic robotic arm is modeled in a matrix form and then it is simulated through MATLABยฉ. The simulation solves the dynamic of the system numerically. The motion of the manipulatorโs end-effector of the double prismatic joints is observed.
In this paper, the design of vehicle door weight is minimized using a newly developed machine learning algorithm that is referred to as Grey Wolf optimizer (GWO). GWO is a metaheuristic technique that show good and robust performance in solving optimization applications. It is a nature-inspired algorithm that is based on the hunting grey wolf while hunting and catching the prey. The algorithm is known to have simple, yet efficient, structure. On the other hand, the design of the carโs door during an impact is known to be a multi-objective optimization problem that is based the European Enhanced Vehicle-Safety Committee. The algorithm needs to minimize the door weight will be satisfying several constrains. The design depends on 11 parameters including the B-pillar inner, B-pillar reinforcement, floor side inner, cross members, door beam, door beltline reinforcement, roof rail, materials of B-pillar inner floor side inner barrier height and hitting position. Monte Carlo simulation is used to test the method and its robustness and stability.
The concept of an autonomous rover system to perform maintenance, investigations, and data collection in remote or inaccessible locations has seen an increased demand recently. In this work, an autonomous rover is developed to detect radioactive contamination. The rover utilizes a gas tube radiation detector as an active sensing element and onboard modules to command and control the rover, such as a GNSS receiver, Autopilot controller, and a microcontroller as an onboard controller a communication module. The rover could be controlled by a human operator or autonomous control. In both cases, the operator would be far away from the scene. The rover has many potentially valuable applications, such as radiometric survey and mapping, locating survivors, or aiding in recovering victims after a CBRN disaster. This paper discusses the concept of operations and the design of the autonomous rover.
The most prevalent kind of cardiovascular illness is a heart attack, which may or may not have symptoms. The damage to the heart muscle increases with delayed treatment, which increases the risk of mortality. More than 10 million people die each year from heart attacks, and many of them may be avoided if heart attacks could be accurately predicted. To estimate the likelihood of suffering a heart attack, five different machine learning algorithms are used on the Public Health Heart Attack dataset. Several evaluation metrics, including accuracy, recall, precision, ROC curve, and F-score, were used to evaluate the models. All the modelsโMLP, RBF, SVM, KNN, and RFโ achieved significant accuracies of more than 75%, with KNN having the greatest overall performance
KEYWORDS: Performance modeling, Retina, Data modeling, Convolutional neural networks, Machine learning, Deep learning, Visual process modeling, Image classification, Binary data
Due to high blood sugar levels, diabetic retinopathy (DR), a complication of diabetes, affects the retina in the back of the eye. It may cause blindness if undiagnosed and mistreated. The early detection and treatment of DR are made easier by retinal screening. This paper proposes using an image-based dataset to build different convolution neural network (CNN) models to detect DR in its early stages to ease the screening procedure. The accuracy achieved was 0.9615 using the VGG model and 0.9712 using the Inception-ResNet model. This study demonstrates the effectiveness of using deep learning techniques to aid in diagnosing and predicting diabetic retinopathy.
KEYWORDS: Biomimetics, Sensors, Prototyping, Servomechanisms, Design and modelling, Muscles, Microcontrollers, Signal detection, 3D printing, Electromyography
Bionic limbs have transformed the lives of individuals with missing or damaged limbs, enabling them to regain independence using electronic sensors and motors. Over the years, significant advancements have been made in prosthetic devices, with some reaching a level of sophistication that is almost indistinguishable from natural limbs. However, not all amputees have equal access to cutting-edge technology, which motivates the research and development presented in this paper. In this study, we have designed and developed a bionic arm that can be easily manufactured using additive manufacturing, paired with a wearable sensor suit that commands the actuators to execute movements. The use of gesturecontrolled wearable sensors allows for the creation of sophisticated bionic arms with applications in both civilian and military contexts. Furthermore, the team is exploring the use of advanced computer algorithms to enable fast and fluid movements, facilitating the performance of complex tasks with prosthetic limbs. This paper provides a general design overview of the bionic arm and its sensor suit, showcasing the potential of this innovative approach in revolutionizing the field of prosthetics. The use of additive manufacturing and wearable sensor technology opens up new possibilities for providing accessible and advanced prosthetic solutions for individuals with limb loss.
Artificial Neural Network (ANN) is a powerful tool to model a system using only the inputs and outputs of that system. In this paper, ANN is used to model the relation between the subjectโs gender to its performance while been excited in a whole-body vibration machine (WBV). For training the ANN, 20 male and 20 female subjects were observed during an experimental setup using a WBV at different vibration frequencies in the range of 20 to 45 Hz. The apparent mass was measured for the subjects at different frequencies. The input to the ANN includes body mass index, mass, and gender of the subjects along with and the excitation frequency. The ANN shows a good performance and extract the relationship with a performance that has a root mean squared error of the relative percentage error less than 9%.
The most notable advancement in the 21st Century has been in artificial intelligence (AI). Despite how far AI has progressed, how it applies to healthcare remains a significant challenge for brilliant minds all over the world. A neurological condition known as epilepsy can strike a person at any time in their life. An individual with epilepsy therefore experiences frequent to infrequent seizures, which can occasionally result in death. Electroencephalogram (EEG) signals aid in the diagnosis of this condition. However, lengthy EEG signals frequently take a day or longer to detect this disorder, even for trained neurologists, and may even cause human error. Therefore, it is essential to create a reliable and computationally efficient system. This study aims to classify seizures by creating Convolutional Neural Network (CNN) Inception ResNet V2 and short-time Fourier transform (STFT) to extract the time-frequency plane from time domain signals. This study helped to better classify health and seizures by achieving up to 100% the highest classification accuracy.
Artificial intelligence (AI) i n h ealthcare i s a constantly evolving field that must be explored. Be cause of its practicality and usefulness in estimating various ailments, focused research on AI, specifically deep l earning, is dominating. High blood pressure (BP), also known as hypertension, is a serious health condition. It causes serious issues such as heart attacks, strokes, and even death. As a result, blood pressure should be constantly monitored. The proposed study uses famous CNN models for blood pressure detection and states the results of two main CNN models. Inception-V4 and Xception achieved an accuracy of 96% and 98.8%, respectively. Other performance metrics have been calculated and discussed.This study demonstrates the effectiveness of using deep learning techniques to aid in the diagnosis and prediction of hypertension.
Epilepsy is a neurological condition caused by sudden onsets of electrical activity in the brain. This results in frequent, uncommon seizures, which can lead to severe physical consequences. In a clinical setting, data recorded using EEG (Electroencephalogram) is used to help diagnose the condition. This research focuses on the use of Short-Term Fourier transform (STFT) and feature extraction in the EEG data for the use in a majority voting model using logistic regression (LR) to detect the presence of epileptic seizures in the five EEG frequency bands ( i.e. Alpha, Beta, Gamma, Delta, and Theta). To quantify, a number of evaluation metrics have been calculated. Overall, the model was able to achieve an accuracy of up to 92%.
Sleep apnea is a disorder that has the potential to be life-threatening, that is characterized by irregular breathing patterns. In order to improve the diagnosis and prediction of sleep apnea, a study was conducted to develop a high-accuracy detection method using machine learning. This method involved the use of a convolutional neural network classifier, which was trained using public data sets of ECG signals from both apnea patients and healthy volunteers. The CNN model was able to attain a level of accuracy of 94.12% using the Xception model and 91.18% using the ResNet50 model. According to the studyโs findings, using deep learning techniques can be a helpful strategy to enhance sleep apnea diagnosis and prediction.
The sliding innovation filter is a newly developed filter that was derived in 2020 to be a predictor-corrector filter. The filter uses the measurement as a hyperplane, and then applies a force that makes the estimates fluctuating around it. The filter works on systems with full ranked measurement matrix (all states are measured). However, once the rank becomes partial, the filter depends highly on the pseudo inverse of the measurement matrix. This means that if the measurement matrix does not have a direct link to the hidden states, then these states will not be correctly estimated. When the system is nonlinear, the problem becomes worse as the Jacobean matrix must be calculated for the measurement matrix before the pseudo inverse is applied. To solve this issue, this paper proposes a new formulation of the SIF that is based on the extended Luenberger filter. The proposed method is tested on extracting the damping ration for a third order system.
KEYWORDS: Tunable filters, Covariance, Signal filtering, Simulations, Gain switching, Covariance matrices, Systems modeling, Modeling, Electronic filtering, Monte Carlo methods
State estimation strategies play an essential role in the effective operation of dynamic systems by extracting relevant information about the systemโs state when faced with limited measurement capability, sensor noise, or uncertain dynamics. The Kalman filter (KF) is one of the most commonly used filters and provides an optimal estimate for linear state estimation problems. However, the KF lacks robustness as it does not perform well in the face of modelling uncertainties and disturbances. The sliding innovation filter (SIF) is a newly proposed filter that uses a switching gain and innovation term, and unlike the KF, it only results in a sub-optimal estimate. However, the SIF has been proven to be robust to modelling uncertainties, disturbances, and ill-conditioned problems. In this work, we propose an adaptive SIF and KF (SIF-KF) estimation algorithm that can detect faulty or uncertain conditions and switch between the KF and SIF gain in the absence or presence of such conditions, respectively. A fault detection mechanism based on the normalized innovation squares (NIS) metric is also presented, which is responsible for triggering the activation of the respective gain in the proposed SIF-KF strategy. Experimental simulations are carried out on a simple harmonic oscillator subject to a fault to demonstrate the proposed SIF-KFโs effectiveness over traditional approaches.
In this work, a well-known mechanism that is referred to as piston lever mechanism is design to control the wingโs flange. The design targets the mechanism components and their location while maintaining the minimum oil level required to lift the flanges from 0 to 45 degrees. The design is considered a multi-objective optimization problem; in which we propose the Giza Pyramid Algorithm (GPA) to optimize. GPA is a newly developed metaheuristic technique of type ancientinspired that was developed in 2021. The GPA shall obtain the best design while satisfying four constrains. The algorithm will be tested in Monte Carlo simulation to show its effectiveness in terms of stability and robustness. The performance is then compared to the particle swarm optimization. The proposed method shows a superior result compared to the PSO. Keywords: Giza pyramid, metaheuristic, optimization, design.
KEYWORDS: Signal filtering, Control systems, Nonlinear filtering, Electronic filtering, Aerospace engineering, Systems modeling, Signal processing, Engineering
This study presents the development of a new filter, the sequential sliding innovation filter (SSIF), designed for estimating quantities of interest from noisy measurements. The SIF is formulated in a sequential manner, allowing for multiple updates of estimates, making it well-suited for systems with multiple measured states. The filter is applied to an unmanned ground vehicle (UGV) maneuvering in 2-D path in this study, and the results demonstrate that the SSIF outperforms conventional filter and Kalman Filter (KF) in terms of accuracy and efficiency. The SSIF has the potential for use in signal processing, tracking, and surveillance, making it a valuable tool in various fields.
The COVID-19 epidemic forced governments to adopt worldwide lockdowns in order to limit the virus's spread. Wearing a face mask, it is said, would reduce the possibility of transmission. Due to the growing urban population, proper city management is more important than ever in the modern day to reduce the impacts of COVID-19 infection. To check the mask in public places, however, would require incredibly long lineups and delays. Therefore, it is necessary for an autonomous mask detection system to assess whether someone is wearing a face mask. On the face mask dataset, three different machine learning methods are applied to determine the likelihood of wearing a face mask. The models were assessed using a number of measures, including accuracy, recall, and ROC curve. The main objective of the study is to detect the presence of face masks using deep learning, machine learning, and image processing approaches. All three modelsโNB, KNN, and CNNโachieved noteworthy accuracy of more than 80%, with CNN showing the best overall performance.
Heart diseases are ranked the first cause of death in the world. Australia has the highest incidence of heart disease. Approximately 125 lives every single day thatโs one life every 12 minutes. Heart disease describes a range of conditions that affect the heart or blood vessels and can affect anyone at any age. Also, a major concern, heart disease could cause a heart attack or stroke. Some symptoms may include chest pain, shortness of death, dizziness, fatigue, or nausea. Other serious symptoms, such as diabetes and high cholesterol, may lead to heart attacks. A healthy lifestyle, quitting smoking, and exercising are small steps to avoid heart disease. Heart diseases are easier to treat when detected early. In this paper, an effective heart disease framework is proposed. Support Vector Machine (SVM), Multilayer Perceptron (MLP), Random Forest (RF), and Radial Basic Function (RBF) techniques for classification are used. Moreover, Feature selection is performed to minimize the features to have better accuracy. Info Gain Attribute Eval โ Ranker algorithm is used for feature selection. In addition, classification techniques and feature selection algorithms are applied to the LIAC heart stat log dataset which depends on the heart diseases dataset. The resultโs effectiveness is described by accuracy, precision, recall, and ROC Curve.
Marine pollution is a major environmental hazard and a serious healthcare, economic, and social issue. Machine learning (ML) and deep learning (DL) techniques can be used to automate marine waste removal and make the cleanup process more efficient. The proposed study uses image classification to help categorize the level of marine pollution in ocean underwater regions. The performance of two deep convolutional neural networks (VGG19 and ResNet50) is investigated in this study and VGG19 reported an accuracy of 98.1%.
Plastic pollution has emerged as one of the biggest environmentally threatening issues. Using image classification, the proposed study aids in categorizing the level of marine pollution in ocean underwater regions. This study classified the amount of pollution in the ocean using the two variants of Inception Convolutional Neural Network (CNN) models i.e., Inception- ResNet V2, and InceptionV3. High accuracies of up to 96.4% have been reported. This study will help researchers working in the field of water quality detection.
Short Messaging Service (SMS) becomes a more easy, affordable way to communicate and increasingly replace phone calls. Spam is any kind of unwanted, random unsolicited message that gets sent without any authorization from the receiver. hackers use spam SMS to get their important information. Effective spam detection is an essential tool for assisting users in determining whether an SMS is a spam or not. Different machine learning methods such as Deep Learning techniques have attempted to distinguish between spam and ham SMS texts. This paper proposes the Spam-Ham Classification method using Recurrent Neural Networks (RNN) and Long Short-Term Memories (LSTM). The proposed model utilizes Keras and TensorFlow to detect Spam SMS. The dataset used is SpamSMSCollection from the UCI machine learning repository. The dataset contains a set of 5574 SMS messages. The dataset is preprocessed using tokenization, Lemmatization, padding, and stopword removal. The overall accuracy of the proposed model is 98%. The performance of the proposed method is compared with different machine learning algorithms such as Support Vector Machine (SVM), K-nearest neighbors (KNN), and Multi-layer Perceptron (MLP).
KEYWORDS: Deep learning, Control systems, Education and training, Information technology, Telecommunications, Machine learning, Neural networks, Photonic integrated circuits, Internet of things, Infrared sensors
Cyber Physical Systems (CPS) security within industrial fields have enforced itself, due to its deployment critical infrastructure. The complexity, and diversity are evolved with these CPS systems. While connectivity demands for these systems to communicate with each other increases, their attack surface expands. The impact of cybersecurity has on business continuity increase. ICS can run real time critical function, where firewall inspection delay can fail the process. Hence a special firewall consideration needs to be implemented. The Uptime requirements for these ICS is extremely high, which means the normal maintenance or security patches is out of discussion. Beside a modification on any ICS due to firmware update, can trigger revalidations for all interconnected ICS. Perimeter defense firewall is one of the common strategies to protect these ICS systems. The firewall will inspect and detect ingress traffic. Internal firewall will be more enhanced way to protect also from internal attacks within the network. Hence, a need for more efficient ways to detect these attacks, based on Deep Learning (DL) approach with a good source of (Industrial Internet of Things) IIoT dataset. This conference paper evaluates Deep learning approach using Bi-directional Long Short-Term Memory (BLSTM) on resent publicly available dataset โEdge-IIoTsetโ. This dataset has realistic dataset of IIoT applications. With more than 10 types of sensors/devices uses in ICS systems. With fourteen attacks including DoS/DDoS attack. In this research paper, we consider utilizing deep learning algorithms (BLSTM) to detect and protect the service availability of Critical Infrastructure (CI) and Industrial Control Systems (ICS) from Denial of Service (DoS)attack. The research proposal considers most recent dataset with packet compared with flow format to train our module. The benchmarking with common metrics is used as baseline to compare algorithm efficiency, where accuracy of 99.877 was achieved and validation time of 18millisconds.
In this work, the newly developed filtering technique referred to as the sliding innovation filter (SIF) is combined with multiple model strategies to enhance the performance of the filter when the system changes its structure and/or parameters. This is particularly useful for a system, such as an aerospace system, experiences a fault and continued operation is critical. The proposed method is tested on an aerospace actuator system and the results are discussed.
In modern robotics and automation systems, control and estimation techniques are essential tasks. In this paper, the mathematical model of a non-linear RR manipulator is developed. To realize it, the circuit design of the system is described first in detail, and then implemented on the FPGA (field programmer gate array) prototyping board. The results show that the implementation of the system requires a minimal amount of FPGA resources.
The sliding innovation filter (SIF) is a newly developed filter that may be applied to both linear and non-linear systems. The SIF shares similar principles with sliding mode observers (SMO) and other variable structure filters such as the smooth variable structure filter (SVSF). The SIF utilizes the true trajectory as a hyperplane and forces the estimates to stay within a region of the hyperplane through the use of a discontinuous correction gain. In this paper, the SIF is applied to the well-known complex road estimation problem with nonlinear system function. The results of the application are compared with the SVSF, and future work is discussed.
In this paper, the newly developed sliding innovation filter (SIF) is reformulated to accommodate the ability of extracting the hidden states. This is accomplished by using the well-known Luenberger technique, which is commonly used by observers. In this paper, the SIF is applied to a linear system, which has fewer measurements than states. The results show that the proposed filter extracts the hidden state with small RMSE, as low as 0.1, and small MAE, as low as 1.
In this paper, a new state and parameter estimation method is introduced based on the particle filter (PF) and the sliding innovation filter (SIF). The PF is a popular estimation method, which makes use of distributed point masses to form an approximation of the probability distribution function (PDF). The SIF is a relatively new estimation strategy based on sliding mode concepts, formulated in a predictor-corrector format. It has been shown to be very robust to modeling errors and uncertainties. The combined method (PF-SIF) utilizes the estimates and state error covariance of the SIF to formulate the proposal distribution which generates the particles used by the PF. The PF-SIF method is applied on a nonlinear target tracking problem, where the results are compared with other popular estimation methods.
The sliding innovation filter (SIF) is a state and parameter estimation strategy based on sliding mode concepts. It has seen significant development and research activity in recent years. In an effort to improve upon the numerical stability of the SIF, a square-root formulation is derived. The square-root SIF is based on Potterโs algorithm. The proposed formulation is computationally more efficient and reduces the risks of failure due to numerical instability. The new strategy is applied on target tracking scenarios for the purposes of state estimation. The results are compared with the popular Kalman filter.
KEYWORDS: Field programmable gate arrays, Simulink, Filtering (signal processing), Mathematical modeling, Digital signal processing, Systems modeling, Roads, Nonlinear filtering
In this paper, the unscented Kalman filter (UKF) is used to estimate the states of a vehicle while it is moving on a road with different speed. Field programmer gate array (FPGA) prototyping board is used to implement the filter. The resources of FPGA is optimized using different techniques. The overall filter performance is examined in further details.
KEYWORDS: Heart, Machine learning, Detection and tracking algorithms, Data modeling, Binary data, Feature selection, Performance modeling, MATLAB, Data conversion, Medical research
Heart failure (HF) is a common health condition that affects more than 600,000 Americans every year and results in their death. Luckily, machine learning classification, regression and prediction models are key approaches and techniques that can be used to detect and predict the cases of heart disease or failure. The study included in this paper based on a dataset that contains 918 instances or rows of various medical records. This research paper attempts to use these medical records to improve heart failure disease prediction accuracy. For that, multiple popular machine learning models were used to understand the data and provide a better prediction and results, based on different evaluation metrics. Furthermore, the results section in this study shows a better accuracy score compared with other related work using different machine learning algorithms and software. Finally, RStudio and Weka software are used in this paper to perform some of the algorithms and the best model results were using the random forest and logistic regression algorithms. These tools assisted us in better understanding of the data and data preprocessing.
Breast cancer is the second most type of cancer diagnosed in women; it is also the leading cause of cancer caused deaths in women after lung cancer. Breast lumps can be classified as cancerous and non-cancerous. Non-cancerous breast lump development is very common in women. It is important to correctly diagnose the type of breast lump to administer the correct treatment and give the needed care and attention. Intensive research is being done to improve the diagnosis of the type of breast lumps. In this paper we will study different machine learning algorithms for the diagnosis of breast tumors and to predict whether its cancerous or non-cancerous. In this paper we will be building four different classification methods SVM, KNN, RF and CART. We will be using the breast cancer Wisconsin (diagnostic) dataset to train the models. We will base the performance of our models based on the accuracy and other classification evaluation parameters. For the final model we were able to achieve a prediction model with an f1 score of 0.9927.
Diabetes mellitus, also known as just diabetes, is a medical condition marked by a high blood sugar level over long period of time. If diabetes left untreated it can result in damaging the nerves, kidney diseases, foot ulcers, damaging eyes, and in worst cases diabetes leads to death. The purpose of this study is to examine and compare numerous machine learning algorithms in order to determine the best forecasting algorithm based on various metrics such as accuracy, precision, recall, F-measure, kappa, sensitivity, and specificity. Four machine learning algorithms will be investigated in this paper such as Random Forest (RF), Support Victor Machine (SVM), K nearest neighbor (k-NN), and Classification and Regression Trees (CART). Algorithms are used in a comprehensive investigation on diabetes dataset. The obtained findings suggest that, when compared to other algorithms, RF provides more accurate predictions.
The state-of-charge (SOC) of Li-ion batteries is an important parameter for regulating and ensuring the safety of batteries, especially for Electric Vehicle applications, where accurate SOC estimation is important for remaining driving range prediction. The SOC is conventionally obtained through different indirect measurement methods that are either impractical or inaccurate. Data-driven approaches for SOC estimation are becoming more popular as an alternative to the conventional estimation methods due to their accuracy and low complexity. In this work, we apply 4 machine learning algorithms: Multiple Linear Regression (MLR), Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Random Forest (RF) to predict the SOC using voltage, current, and temperature measurements from mixed driving cycles datasets, with 50000 instances. Out of the 4 models, the Random forest model performed the best with an MAE of only 0.82%.
Artificial feedforward neural networks (ANN) have been traditionally trained by backpropagation algorithms involving gradient descent algorithms. This is in order to optimize the networkโs weights and parameters in the training phase to minimize the out of sample error in the output during testing. However, gradient descent (GD) has been proven to be slow and computationally inefficient in comparison with studies implementing the extended Kalman filter (EKF) and unscented Kalman filter (UKF) as optimizers in ANNs. In this paper, a new method of training ANNs is proposed utilizing the sliding innovation filter (SIF). The SIF by Gadsden et al. has demonstrated to be a more robust predictor-corrector than the Kalman filters, especially in ill-conditioned situations or the presence of modelling uncertainties. In this paper, we propose implementing the SIF as an optimizer for training ANNs. The ANN proposed is trained with the SIF to predict the Mackey-Glass Chaotic series, and results demonstrate that the proposed method results in improved computation time compared to current estimation strategies for training ANNs while achieving results comparable to a UKF-trained neural network.
In this article, an Artificial Neural Network (ANN) has been used to model the relationship between the gender of human subject to its response to whole-body vibration (WBV). To train, validate and test the model, an experiment was conducted on 20 female and 20 male subjects at different vibration frequencies in the range of 20 to 45 Hz. The response was measured by taking the ratio between the subject headโs horizontal acceleration to the platformโs vertical acceleration. The subjectsโ body mass index, mass, height, gender, and age, and the excitation frequency were used as inputs to the ANN. The ANN model showed a good performance of 98.645% matching regression, and RMSE and MAE of 0.0128 and 0.0483, respectively.
In this paper, the extended Kalman filter is used to estimate the states of an electro-hydrostatic actuator. The filter is then programmed using very high-speed hardware description language (VHDL) and realized using a field programmer gate array (FPGA) prototyping board from Xilinx. The design is then optimized to improve the performance using different techniques. The results show that the implementation of the system requires a minimal amount of FPGA resources.
Emission of greenhouse gases such as CO2 is highly dependent on the energy systems of the countries. In this regard, for accurate analysis of CO2 emission it is necessary to take into account the factors related to energy consumption. In the present paper, three countries including Turkey, Bulgaria and Greece are considered as case studies to model their CO2 by using an economic indicator (GDP) and consumptions of different energy sources. In this regard, Group Method of Data Handling (GMDH) is applied as the method and the required data for modeling between 2000 and 2019 are gathered. The results indicated that R2 values of the proposed model for training, test and overall datasets are 0.9997, 0.9991 and 0.9995, respectively. In addition, AARD of the mentioned datasets were around 0.71%, 1.18% and 0.85%, respectively. These values reveal significant exactness of the proposed which can be attributed to proper selection of both inputs and modeling method.
In this paper, the Smooth Variable Structure Filter is used to extract the position and speed of a vehicle on a complex road. The filter is realized using FPGA (field programmer gate array). The FPGAโs resources and speed are examined at an optimal configuration. FPGA (Field Programmer Gate Array) Z-board from Xilinx is used in this work. The performance is examined and presented.
Water is the new oil of the 21st century due to increased consumption and demand. High-quality water with free of contamination is vital for human beings and many industries such as oil and gas, petrochemicals, pharmaceuticals, and food. To meet the massive amount of freshwater production, United Arab Emirates (UAE) relies on the energy-intensive Multi-Stage Flash (MSF) and Multi-Effect Distillation (MED) technologies to provide fresh water for various applications. These energy-intensive processes consume a significant share of UAE oil and gas. In general, thermal desalination is an energy-intensive process. UAE is shifting to use Reverse Osmosis (RO) desalination for the freshwater production. To reduce the energy consumptionand the pretreatment stages onsite monitoring of the seawater intake has to be intensively recorded. This study discusses a design of a seawater test station, which has a sensor network to measure the quality parameters of seawater, including the waterโs pH.
Water Desalination is the process of desalinating seawater into freshwater. The desalination process is typically done by introducing seawater into the plant by an offshore pipeline. Multiple water properties need to be measured and analyzed to assure the feed seawater is suitable for desalination processing to prevent fouling, scaling, and corrosion of equipment and reduce operational costs. These parameters include seawater temperature, total dissolved oxygen, turbidity, conductivity, total dissolved solids, and pH. This paper will discuss developing and integrating a low-cost, highly scalable sensor subsystem measuring water conductivity in the Arabian Gulf.
We study the non-exponential, power-law charge/voltage-time behavior of a supercapacitor when it is discharged into a constant resistive load. The standard evolution equation dn(t)=dt = -λn(t) where λ is an inverse time constant and n(t) can represent here charge or voltage is modifed instead to be dn(t)=dt = -λ[n(t)]q relating the rate of change of n(t) to a power law function of n(t). This leads to the q-exponential function which shows much better fitting capability to the experimental results obtained on a commercial capacitive device when compared with the traditional exponential decay.
Heat transfer improvement has gained significant importance in the recent decades. In this regard, it is preferred to enhance the thermophysical properties of the fluids that affecting the heat transfer characteristics. To reach this goal, nanofluids have been introduced to be applied in thermal devices due to their relatively higher thermal conductivity that can cause remarkable augmentation in convective heat transfer. Thermal conductivity of these types of fluids is influenced by some elements including the temperature and volume fraction. Considering this fact, these factors must be considered for modeling this property of nanofluids. In the present article, thermal conductivity of the nanofluids with SiC particles is modeled by using artificial neural network as an intelligent method. It is observed that thermal conductivity of the nanofluids is forecasted with high precision. Mean Squared Error (MSE) of the model in optimal architecture was around 2.65ร 10โ5, for this network the R2 is 0.9986 revealing significant closeness of the forecasted data and corresponding experimental values.
Kalman filtering (KF) is a widely used filtering technique in highly predictable temporal-mechanical systems where system noise can be modelled with a gaussian function. Improving the signal quality during acquisition is conventionally accomplished by increasing integration time in acquisition. However, this increases the signal acquisition time in photonic systems. In high noise applications, acquisition time is low, and this post-process filtering technique can be applied to increase signal quality. This work explores the comparison of the KF, and nonlinear filtering methods to a simulated blackbody radiation signal where gaussian noise is added to mimic electrical interference. Three filters are selected for comparison on the ability to improve the root mean square error (RMSE) of a simulated measured signal with respect to a simulated actual signal. The filters that are compared in this work are the Extended Kalman Filter (EKF), the Unscented Kalman (UKF), and the Extended Sliding Innovation Filter (ESIF). The filters use a calibration temperature that the filter model uses to determine expected values. To compare the filters, the RMSE is evaluated when error is introduced to the simulation by changing the actual temperature to values equal, below, and above the calibration temperature. Two additional scenarios were considered to test filter robustness. The first scenario uses changes in model temperature occurring as a function of wavelength (i.e., temperature change mid-scan). The second scenario introduces impurities with different emission values. The ESIF demonstrated favorable performance over the other considered filters, showing promise in optical applications.
This paper gives an overview about the available geothermal power plants. The second part there is a comparison between Geothermal Energy with other sources of Renewable Energy. The advantages and disadvantages of geothermal energy and power plant are discussed. Finally, a case study of a geothermal power plant located in Paris, France was simulated using System Advisor Model to observe the results. All the data input was obtained from a published research paper. Moreover, this study reviews the main functions of dry, flash and binary geothermal power plants.
Double rotor wind turbines are studied for improving wind energy harvesting. The location, size and number of blades of the second rotor are important factors which affect performance of the double rotor wind turbines. These and other blade parameters may influence the drag and output power characteristics of the wind turbine. In the present work, the drag forces acting on two double rotor wind turbine configurations are experimentally investigated using wind tunnel testing. The two configurations are cocurrent and counter current double rotor wind turbines. A single rotor wind turbine is used as a comparison reference to compare with the two double rotor wind turbine configurations. Models of the three horizontal axis wind turbines were produced using 3D printing technology and were tested in the wind tunnel while wind power augmentation was also evaluated. The experimental results revealed an increase in the value of drag coefficient when a second rotor is added. The increase on the drag coefficient depends on the configuration, the size and location of the second rotor. The drag coefficient for the counter current rotation double rotor is close to the single rotor wind turbine; however, an increase of about 25% on drag coefficient is observed for the case of cocurrent double rotor wind turbine.
This work introduces a monte carlo based technique powered with the ability to estimate the individual uncertainty contributions of each model parameter. The proposed technique utilizes the so-called parameter space analysis to identify the importance of influential Degrees of Freedom (DoFs) with respect to the uncertainty quantification problem. Once determined, these DoFs can be used to define and solve a linear system of equations based on linearizing the model of interest to determine the uncertainty contribution of each DoFs in conjunction with the monte carlo based samples.
Spent Nuclear Fuel (SNF) management is one of the major challenges in the nuclear power field. Several disposals, reprocessing and recycling techniques and concepts are proposed and implemented, however, the associated challenges have not been completely resolved yet. Therefore, in this work another useful application of SNF in space applications is explored. The overarching goal of this work is to explore the possibility of using nuclear spent fuel in the so-called ion-thrusters. The proposed design consists of a jet engine that utilizes the extraordinary radioactivity from SNF to ionize a propellant that is used as the thrust.
A preliminary basic design is proposed and then evaluated based on simulation predictions. MCNP is used to model a simplified design of the proposed Spent Nuclear Fuel Ion Propulsion Engine (SNIP) and estimate the ionization reaction rate and therefore the thrust exit velocity and specific impulse of the thruster.
The sliding innovation filter is a new type of predictor-corrector estimation method. The strategy is used to estimate relevant states of interests and has been found to be robust to modeling uncertainties and disturbances. In this paper, a second-order formulation of the sliding innovation filter is presented to improve its estimation performance in terms of accuracy while maintaining robustness. The strategy is applied to an aerospace system that has been designed for experimentation. The results are compared with the well-known Kalman filter, and future work is considered.
This paper contains a comparison of several sigma-point Kalman filters, including the unscented Kalman filter (UKF), the cubature Kalman filter (CKF), and the central difference Kalman filter (CDKF). The comparison is based on a simulated electro-hydrostatic actuator, which is commonly used for flight surface actuation in aerospace systems. This brief study compares the response, convergence rate, root mean square error, the maximum absolute error, and the stability of these sigma-point Kalman filters.
This brief work introduces the use of the relatively new sliding innovation filter in the field of fault detection and diagnosis. This important area is part of signal processing techniques that are widely used in industrial practice, telecommunications, optical systems, and robotics, to name a few. This filter overcomes robustness issues during faults caused by modeling uncertainties. This brief work explores the properties and quality of the filter outputs applied on an electromechanical system. The results are compared with the well-known and studied Kalman Filter.
In this brief work, a novel filtering technique that combines the newly developed sliding innovation filter with a multiple model strategy is proposed. Introduced in 2020, the sliding innovation filter is a relatively new filter used for state and parameter estimation. Based on variable structure techniques, it shares the same principles with sliding mode observers. The filter is robust and stable under system modeling uncertainties. The proposed method multiple model-based sliding innovation filter is tested on an electrohydrostatic actuator (EHA) and the results are discussed.
The sliding innovation filter (SIF) is a newly developed filter that shares similar principles with sliding mode observers and variable structure techniques. The SIF is formulated as a predictor-corrector method that uses the innovation or measurement error as a switching hyperplane and forces the states to remain within a region of its state trajectory. In this brief paper, the SIF is reformulated as a two-pass smoother to reduce the effects of noise and improve the overall performance. The proposed method, known as the sliding innovation smoother (SIS), is applied on an aerospace flight surface actuator, and the results are compared to the original filter.
The applications of unmanned aerial systems (UASs) have grown in popularity due to their simplicity and availability. The quality of UASโs performance depends usually on adding several sensors and controllers that improve accuracy and flight performance. However, this typically increases the overall cost of the system. In this paper, a technique to enhance the performance while maintaining UAS affordability is proposed. This technique involves the use of an estimation strategy to extract hidden information from only a few sensors while improving the quality of the achieved signal. The simulation results of this method show strong performance, and are compared with another well-known estimation method.
This work investigates design of a drone for transporting valuable objects. All the required components for building the drone and discussed. The drone can carry a maximum payload of 10 kg for a long duration over reasonable distance. This is achieved by using a hybrid mechanism that combines two 25cc fuel engines with four electrical motors operating by two 6S lithium polymer (LiPo) batteries. The hybrid mechanism is chosen as it tackles the electric drone main problems which are: short flight duration and low payload-carrying ability.
Given the growing global demands on energy and fresh water, nuclear energy has become a promising source of power and freshwater production. Maximizing the nuclear power plant efficiency requires running the plant at maximum power capacity, however, the actual load might not require such huge power supply (1000 MWe +). Power plants operation with high to maximum efficiency has a profound effect on financial prices and environmental conditions for clear reasons which commands the attention towards various expensive and not efficient energy storage techniques (thermal, electrical and hydro). In this work, energy storage is substituted by a desalination plant that utilizes the excess energy to power the desalination unit. Therefore, this work explores the potential of water desalination as a proxy for energy storage systems in nuclear power plants. Various water desalination technologies are examined and compared in terms of economy, water quality and production capacity. Barakah nuclear power plant is used as a case study with APR1400 reactor design. On the desalination side, Reverse Osmosis (RO), Multi-Stage Flash (MSF), Multi-Effect Distillation (MED) and hybrid combinations are studied.
As the world develops, new and more advanced ways of transportation are invented; i.e. drones. Drones are used in several applications. However, the drone market does not utilize the need of medical emergency drones today, where these drones can be used to save countless lives in severe cases, e.g. sudden cardiac arrest. In case of cardiac arrest, defibrillators may save the life if it reaches the victims within short time. It raises the survival rate exponentially. Nonetheless, reaching the victims in a short period of time is challenging as the weight of the equipment is large. This work aims to design an autonomous drone that will be able to carry heavy payloads (portable medical equipment) while being fast and agile. The medical equipment/components are studied to choose the most fit for the proposed design in terms of efficiency and weight. The droneโs components are compared and studied in detail, allowing to choose the fittest motors, ESCs, frame, battery, and propellers. After which the quadcopterโs ability is expected to successfully achieve the objective of trying to save victim life in the city of Sharjah. In addition, the work includes a SolidWorks analysis to the design of the droneโs mechanical components to estimate the possibility of failure.
The Earth is made up of 71% water, but the world still has water shortage, so what is the reason? The answer to that question is that 97% of the water available is salty water, and all of it has high salt content, which makes is impossible for drinking, consuming, and irrigation. The solution for this problem is desalination, it is the only way that we can get drinkable water, other than fresh resources. But desalination usually consumes a huge amount of electricity, so other sustainable sources to help in the process of desalination in a cleaner and more cost-effective way should be considered. In this work, two main technologies for water desalination using geothermal-powered systems are presented and discussed. These technologies are promising, especially in gulf region, where geothermal energy is available generously.
Geothermal energy is one of the most attractive clean, sustainable and renewable energy sources due to its independency on weather conditions as the case for solar and wind energy. A hybrid geothermal/solar system for power production is proposed. The proposed system could be considered as highly efficient and cost-effective system. A concentrated solar thermal power generation (CSP) of type parabolic trough collector (PTC) is selected to improve the efficiency of the cycle and increase the electricity output by increasing the temperature of the incoming geothermal fluid. By using this system, the net power generation will increase up to 10% in a month compared to normal systems, and 7.6% in a year.
The modern farm is a technological marvel, from smart tractors to genetically modified organisms (GMOs), along with chemical pesticides and fertilizer. Farms today have continuously increased production by utilizing these various techniques. Many farms on the east coast of North America are growing dent or field corn while also rotating crops between soybeans of various types and winter wheat. These crops have become symbiotic in nature due to the need for specific soil nutrients of the crops and the practice of no till farming. More recently, schools with farm programs have started researching the use of drone technologies and multispectral analysis as a means to reduce chemical usage thereby saving farmers annual chemical costs. This paper investigates the use of drones in capstone projects for undergraduate engineering and computer science programs. Undergraduate capstone projects usually require a design and build element to satisfy ABET accreditation requirements. Therefore, the students needed to design and build an airframe capable of surveying farms with a multispectral camera. In the course of the aircraft design process it was discovered that the students needed to have a broader understanding of federal regulations, experimentation, and a robust understanding of how the drones and data would be used to benefit a typical farm. In addition, we look at the results obtained and discuss the problems associated with making the data and analysis accessible to the farmers who participated in our study. In the process we also discovered other potential uses for the images we created.
In this paper, an experimental study is performed to find the relation between the current of a battery and the power thrust of an electric-powered ducted fan. Electric-powered duct fans are becoming increasingly popular in unmanned aerial vehicles (UAVs) and are controlled by a pulse position modulation controller. Three different measurements are taken by three transducers, namely: a multimeter with a range of 0 to 400 DC Amps that measures the input current feeding the electric speed controller from the batteries; a load cell with a range of 0 to 45 KG to measure the thrust output of each of the motor; and, a thermocouple to measure the temperature of the Li-Po batteries. Once the data was obtained, an artificial neural network was trained and tested to obtain the relationship between the input (pulse position modulation) and output (the thrust). The effects of battery current on an electric-powered ducted fan are then summarized.
Heat is one of the major setbacks to a Li-Po batterieโs efficiency and performance, being an un-avoidable factor, the increase in heat shortens the cycling life of the battery considerably. Moreover, the heat increases the rate of unwanted chemical reactions inside the battery, which in turn increases the risk of swelling, sparking or even catching fire. This study measures the effect of temperature on the performance of the battery power output. The battery is connected to an electric ducted fan that draws approximately 150 Amp DC. The rapid discharge of the Li-Po battery generates heat that affects the power output of battery, and in turn affects the Electric Ducted Fan performance. The temperature of the battery is managed by either emerging it in a cooling bath or by the room HVAC system. The performance of the battery is measured by analyzing the thrust obtained by the electric ducted fan in relation to the power provided by the battery. Once the data is obtained, an artificial neural network is trained to obtain the relationship between the temperature to the battery performance.
The modern farm is a technological marvel, from smart tractors to genetically modified organisms (GMOs), along with chemical pesticides and fertilizer. Farms today have continuously increased production by utilizing these various techniques. Many farms on the east coast of North America are growing dent or field corn while also rotating crops between soybeans of various types and winter wheat. These crops have become symbiotic in nature due to the need for specific soil nutrients of the crops and the practice of no till farming. More recently, schools with farm programs have started researching the use of drone technologies and multispectral analysis as a means to reduce chemical usage thereby saving farmers annual chemical costs. This paper investigates the use of drones in capstone projects for undergraduate engineering and computer science programs. Undergraduate capstone projects usually require a design and build element to satisfy ABET accreditation requirements. Therefore, the students needed to design and build an airframe capable of surveying farms with a multispectral camera. In the course of the aircraft design process it was discovered that the students needed to have a broader understanding of federal regulations, experimentation, and a robust understanding of how the drones and data would be used to benefit a typical farm. In addition, we look at the results obtained and discuss the problems associated with making the data and analysis accessible to the farmers who participated in our study. In the process we also discovered other potential uses for the images we created.
In this paper, a comprehensive comparison is made of the following sigma-point Kalman filters: unscented Kalman filter (UKF), cubature Kalman filter (CKF), and the central difference Kalman filter (CDKF). A simulation based on a complex maneuvering road (an s-path) is used as a benchmark problem. This paper studies the response, stability, robustness, convergence, and computational complexity of the filters. Future work will look at implementing the methods on a robot built for experimentation.
Electric motors are becoming increasingly popular for the propulsion and control of unmanned systems. In order to optimize power generation and energy use for unmanned systems, it is important to understand the dynamics of electric motors and the corresponding powertrain. This paper provides an early, preliminary study on an electric motor used for unmanned aerial systems (UASโ). An electric motor dynamometer is used for collecting data on the motor, and trends are discussed. Future work will look at implementing mathematical models in an unmanned ground system built for experimentation.
The smooth variable structure filter (SVSF) has seen significant development and research activity in recent years. It is based on sliding mode concepts, which utilizes a switching gain that brings an inherent amount of stability to the estimation process. In this paper, the SVSF is reformulated to present a two-pass smoother based on the SVSF gain. The proposed method is applied on an aerospace flight surface actuator, and the results are compared with the popular Kalman-based two-pass smoother.
The smooth variable structure filter (SVSF) is a state and parameter estimation strategy based on sliding mode concepts. It has seen significant development and research activity in recent years. In an effort to improve upon the numerical stability of the SVSF, a square-root formulation is derived. The square-root SVSF is based on Potterโs algorithm. The proposed formulation is computationally more efficient and reduces the risks of failure due to numerical instability. The new strategy is applied on target tracking scenarios for the purposes of state estimation. The results are compared with the popular Kalman filter.
Unmanned aerial systems (UAS) are becoming increasingly popular in industry, military, and social environments. An UAS that provides good operating performance and robustness to disturbances is often quite expensive and prohibitive to the general public. To improve UAS performance without affecting the overall cost, an estimation strategy can be implemented on the internal controller. The use of an estimation strategy or filter reduces the number of required sensors and power requirement, and improves the controller performance. UAS devices are highly nonlinear, and implementation of filters can be quite challenging. This paper presents the implementation of the relatively new cubature smooth variable structure filter (CSVSF) on a quadrotor controller. The results are compared with other state and parameter estimation strategies.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens usersโplease
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.