Pub Date : 2020-10-24DOI: 10.1109/NILES50944.2020.9257893
H. Ali, Samaa Khaled, Omar M. Shehata, E. I. Morgan
The rapid population growth and increase in vehicle numbers over the last few decades have caused traffic congestion worldwide, as intersections significantly impact the efficiency of traffic networks in urban areas, this paper focuses on intelligent traffic management at intersections by integrating the technologies of Intelligent Transportation Systems (ITS) and Autonomous Vehicles (AV). In this framework, a physical model represents each vehicle taking into account the dynamic limits. A decentralized Nonlinear Model Predictive Control (NMPC) is proposed to help coordinate the traffic flow of the AV at the intersection. The controller solves a quadratic cost function for the vehicle to ensure a smooth trajectory and minimum energy consumption, while avoiding collisions that is guaranteed using linear constraints and two developed priority assignment methods, Predicted Arrival Time (PAT) and First-Come First-Served (FCFS). The two methods are tested using a developed simulation environment on MATLAB/Simulink and the results are compared, the predicted arrival time method outperformed the FCFS method.
{"title":"Decentralized Intersection Management of Autonomous Vehicles Using Nonlinear MPC","authors":"H. Ali, Samaa Khaled, Omar M. Shehata, E. I. Morgan","doi":"10.1109/NILES50944.2020.9257893","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257893","url":null,"abstract":"The rapid population growth and increase in vehicle numbers over the last few decades have caused traffic congestion worldwide, as intersections significantly impact the efficiency of traffic networks in urban areas, this paper focuses on intelligent traffic management at intersections by integrating the technologies of Intelligent Transportation Systems (ITS) and Autonomous Vehicles (AV). In this framework, a physical model represents each vehicle taking into account the dynamic limits. A decentralized Nonlinear Model Predictive Control (NMPC) is proposed to help coordinate the traffic flow of the AV at the intersection. The controller solves a quadratic cost function for the vehicle to ensure a smooth trajectory and minimum energy consumption, while avoiding collisions that is guaranteed using linear constraints and two developed priority assignment methods, Predicted Arrival Time (PAT) and First-Come First-Served (FCFS). The two methods are tested using a developed simulation environment on MATLAB/Simulink and the results are compared, the predicted arrival time method outperformed the FCFS method.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"5 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114099286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-24DOI: 10.1109/NILES50944.2020.9257892
F. A. Mohamed, Cherif R. Salama, A. Yousef, Ashraf Salem
Recently, research studies were directed to the construction of a universal defect prediction model. Such models are trained using different projects to have enough training data and be generic. One of the main challenges in the construction of a universal model is the different distributions of metrics in various projects. In this study, we aim to build a universal defect prediction model to predict software defective classes. We also aim to validate the Object-Oriented Cognitive Complexity metrics suite (CC metrics) for its association with fault-proneness. Finally, this study aims to compare the prediction performances of the CC metrics and the Chidamber and Kemerer metrics suite (CK metrics), taking into account the effect of preprocessing techniques. A neural network model is constructed using these 2 metrics suites (CK & CC metrics suites). We apply different preprocessing techniques on these metrics to overcome variations in their distributions. The results show that the CK metrics perform well whether a preprocessing is applied or not, while CC metrics’ performance is significantly affected by different preprocessing techniques. The CC metrics always outperform in the recall, while the CK metrics usually outperform in other performance metrics. Normalization preprocessing results in the highest recall values using either of the two metrics suites.
{"title":"A Universal Model for Defective Classes Prediction Using Different Object-Oriented Metrics Suites","authors":"F. A. Mohamed, Cherif R. Salama, A. Yousef, Ashraf Salem","doi":"10.1109/NILES50944.2020.9257892","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257892","url":null,"abstract":"Recently, research studies were directed to the construction of a universal defect prediction model. Such models are trained using different projects to have enough training data and be generic. One of the main challenges in the construction of a universal model is the different distributions of metrics in various projects. In this study, we aim to build a universal defect prediction model to predict software defective classes. We also aim to validate the Object-Oriented Cognitive Complexity metrics suite (CC metrics) for its association with fault-proneness. Finally, this study aims to compare the prediction performances of the CC metrics and the Chidamber and Kemerer metrics suite (CK metrics), taking into account the effect of preprocessing techniques. A neural network model is constructed using these 2 metrics suites (CK & CC metrics suites). We apply different preprocessing techniques on these metrics to overcome variations in their distributions. The results show that the CK metrics perform well whether a preprocessing is applied or not, while CC metrics’ performance is significantly affected by different preprocessing techniques. The CC metrics always outperform in the recall, while the CK metrics usually outperform in other performance metrics. Normalization preprocessing results in the highest recall values using either of the two metrics suites.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114962584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-24DOI: 10.1109/NILES50944.2020.9257894
Beatrice Shokry, G. Alkady, H. Amer, R. Daoud, I. Adly, H. Elsayed
In safety-critical applications, it is very important for the system to be very reliable. This paper focuses on such applications when implemented with pipelined architectures on SRAM-based FPGAs. The fault model consists of Hard Faults and Single Event Upsets (SEUs). Three different architectures are proposed to add fault detection and/or recovery in order to increase system reliability. It is shown that these improvements are made at a small cost in terms of area, power consumption and performance. An Altera Cyclone IV E FPGA is used to explain the design and the architectures’ behaviors while Markov models are used to calculate reliability increase.
在安全关键型应用中，系统的可靠性是非常重要的。本文的重点是在基于sram的fpga上实现流水线架构时的应用。故障模型包括硬故障(Hard fault)和单事件异常(Single Event Upsets)。提出了三种不同的体系结构来增加故障检测和/或恢复，以提高系统的可靠性。结果表明，这些改进在面积、功耗和性能方面的成本很小。采用Altera Cyclone IV E FPGA对设计和结构行为进行解释，采用马尔可夫模型对可靠性增量进行计算。
{"title":"Error Detection and Recovery in FPGA-based Pipelined Architectures","authors":"Beatrice Shokry, G. Alkady, H. Amer, R. Daoud, I. Adly, H. Elsayed","doi":"10.1109/NILES50944.2020.9257894","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257894","url":null,"abstract":"In safety-critical applications, it is very important for the system to be very reliable. This paper focuses on such applications when implemented with pipelined architectures on SRAM-based FPGAs. The fault model consists of Hard Faults and Single Event Upsets (SEUs). Three different architectures are proposed to add fault detection and/or recovery in order to increase system reliability. It is shown that these improvements are made at a small cost in terms of area, power consumption and performance. An Altera Cyclone IV E FPGA is used to explain the design and the architectures’ behaviors while Markov models are used to calculate reliability increase.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123100224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-24DOI: 10.1109/NILES50944.2020.9257963
Omnia Abd Al-Azeem Hussieny, M. El-Beltagy, Samah El-Tantawy
Prediction and forecasting is preserved to be an important stage in diverse problems. The main aim of our manuscript is to forecast the wind speed and the temperature based on data collected months ago. The data and calculations we obtained for the temperatures in about 4 years ago from 2015 till 2018, whereas the statistics calculated for the wind speed were about 20 years from 1996 until 2015. The data of the wind speed and the temperature collected in different regions of Egypt East coast and Alsheihkzayid. The system used for prediction is based on three different methods which are Artificial Neural network (ANN), Genetic algorithm fused with artificial neural network (GPANN) and Adaptive Neuro-fuzzy inference system (ANFIS). They were used to forecast the future temperature and the future wind speed. The results proved that the system is robust, and it can be applicable during real time. The performance of ANFIS with the trapezoidal membership function proved to obtain the highest performance over all other methods. The optimal mean square error (MSE) reached for the wind speed was 7.2989 m/s and for the temperature is 3.8364 C°.
{"title":"Forecasting of renewable energy using ANN, GPANN and ANFIS (A comparative study and performance analysis)","authors":"Omnia Abd Al-Azeem Hussieny, M. El-Beltagy, Samah El-Tantawy","doi":"10.1109/NILES50944.2020.9257963","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257963","url":null,"abstract":"Prediction and forecasting is preserved to be an important stage in diverse problems. The main aim of our manuscript is to forecast the wind speed and the temperature based on data collected months ago. The data and calculations we obtained for the temperatures in about 4 years ago from 2015 till 2018, whereas the statistics calculated for the wind speed were about 20 years from 1996 until 2015. The data of the wind speed and the temperature collected in different regions of Egypt East coast and Alsheihkzayid. The system used for prediction is based on three different methods which are Artificial Neural network (ANN), Genetic algorithm fused with artificial neural network (GPANN) and Adaptive Neuro-fuzzy inference system (ANFIS). They were used to forecast the future temperature and the future wind speed. The results proved that the system is robust, and it can be applicable during real time. The performance of ANFIS with the trapezoidal membership function proved to obtain the highest performance over all other methods. The optimal mean square error (MSE) reached for the wind speed was 7.2989 m/s and for the temperature is 3.8364 C°.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124892604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-24DOI: 10.1109/NILES50944.2020.9257879
Omar M. Khairy, Omar M. Shehata, E. I. Morgan
Multi-depot Vehicle Routing Problem is one of the most important and challenging variations of the classical Vehicle Routing Problem, where the goal is to find the routes for a fleet of vehicles to serve a number of customers, travelling from and to several depots. Due to the complexity of solving such problems, meta-heuristic algorithms are used. The Most Valuable Player algorithm is a recent technique used to solve continuous optimization problems. This study uses the Genetic Algorithm and the Ant Colony Optimization to solve the Multi-Depot Vehicle Routing Problem. A Hybrid Most Valuable Player algorithm is also proposed to solve the multi-depot vehicle routing problem. The algorithm was tested on 10 different problems and compared to two well-known techniques, Genetic Algorithm and Ant Colony Optimization. Results of the developed algorithm were satisfactory for small sized problems, however Genetic Algorithm surpassed both other algorithms in most test cases.
{"title":"Meta-heuristic Algorithms for Solving the Multi-Depot Vehicle Routing Problem","authors":"Omar M. Khairy, Omar M. Shehata, E. I. Morgan","doi":"10.1109/NILES50944.2020.9257879","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257879","url":null,"abstract":"Multi-depot Vehicle Routing Problem is one of the most important and challenging variations of the classical Vehicle Routing Problem, where the goal is to find the routes for a fleet of vehicles to serve a number of customers, travelling from and to several depots. Due to the complexity of solving such problems, meta-heuristic algorithms are used. The Most Valuable Player algorithm is a recent technique used to solve continuous optimization problems. This study uses the Genetic Algorithm and the Ant Colony Optimization to solve the Multi-Depot Vehicle Routing Problem. A Hybrid Most Valuable Player algorithm is also proposed to solve the multi-depot vehicle routing problem. The algorithm was tested on 10 different problems and compared to two well-known techniques, Genetic Algorithm and Ant Colony Optimization. Results of the developed algorithm were satisfactory for small sized problems, however Genetic Algorithm surpassed both other algorithms in most test cases.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124934865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-24DOI: 10.1109/NILES50944.2020.9257971
Yusuf T. Elbadry, A. Elshafei, H. Ammar, M. Boraey, A. Guaily
Laminar unsteady incompressible flow past two-cylinders in tandem is investigated numerically. The vortex shedding over the cylinders’ arrangement is studied at various Reynolds numbers and blockage ratios while changing the distance between the two cylinders. The output from the numerical simulations is used to feed different regression methodologies to find the optimal approach for the proposed system modeling and identification. Artificial Neural Network (ANN) using Levenberg-Marquardt Algorithm (LM) training algorithm is used, as well as Takagi-Sugeno (T-S) fuzzy model are used and optimized using Particle swarm optimizer (PSO) in order to enhance the system model features. A comparison analysis is performed between the proposed ANN and T-S fuzzy models shows the superior ability of nonlinear modeling of T-S fuzzy with PSO over ANN.
{"title":"Prediction of Internal Flow’s Characteristics Around Two Cylinders in Tandem using optimal T-S fuzzy","authors":"Yusuf T. Elbadry, A. Elshafei, H. Ammar, M. Boraey, A. Guaily","doi":"10.1109/NILES50944.2020.9257971","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257971","url":null,"abstract":"Laminar unsteady incompressible flow past two-cylinders in tandem is investigated numerically. The vortex shedding over the cylinders’ arrangement is studied at various Reynolds numbers and blockage ratios while changing the distance between the two cylinders. The output from the numerical simulations is used to feed different regression methodologies to find the optimal approach for the proposed system modeling and identification. Artificial Neural Network (ANN) using Levenberg-Marquardt Algorithm (LM) training algorithm is used, as well as Takagi-Sugeno (T-S) fuzzy model are used and optimized using Particle swarm optimizer (PSO) in order to enhance the system model features. A comparison analysis is performed between the proposed ANN and T-S fuzzy models shows the superior ability of nonlinear modeling of T-S fuzzy with PSO over ANN.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125348417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-24DOI: 10.1109/NILES50944.2020.9257909
A. Ebied, A. Awadallah, M. Abbass, Y. El-Sharkawy
Muscle fatigue is a biochemical process that causes several effects, one of them is changes in muscular electrical activity. Electromyography (EMG) signals detect these changes in the form of frequency shift and amplitude variation. In this study, forearm muscle fatigue has been investigated using 8-channel EMG signal recorded from 15 healthy subjects during isometric contraction. We utilise Median Frequency (MDF) and Root-Mean-Square (RMS) to quantify the fatigue effects on frequency and amplitude, respectively. The changes of both (∆MDF ) and (∆RMS) are the parameters to assess fatigue across subjects and channels. Statistical analysis has been carried out on the results to evaluate the vulnerability of subjects and channels to fatigue. Our methods were able to identify and assess the most and least susceptible subjects and channels to fatigue.
{"title":"Upper Limb Muscle Fatigue Analysis Using Multi-channel Surface EMG","authors":"A. Ebied, A. Awadallah, M. Abbass, Y. El-Sharkawy","doi":"10.1109/NILES50944.2020.9257909","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257909","url":null,"abstract":"Muscle fatigue is a biochemical process that causes several effects, one of them is changes in muscular electrical activity. Electromyography (EMG) signals detect these changes in the form of frequency shift and amplitude variation. In this study, forearm muscle fatigue has been investigated using 8-channel EMG signal recorded from 15 healthy subjects during isometric contraction. We utilise Median Frequency (MDF) and Root-Mean-Square (RMS) to quantify the fatigue effects on frequency and amplitude, respectively. The changes of both (∆MDF ) and (∆RMS) are the parameters to assess fatigue across subjects and channels. Statistical analysis has been carried out on the results to evaluate the vulnerability of subjects and channels to fatigue. Our methods were able to identify and assess the most and least susceptible subjects and channels to fatigue.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129137246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-24DOI: 10.1109/NILES50944.2020.9257973
Rawan A. Abdellatif, A. El-Badawy
This paper’s objective is to navigate multiple quadrotors using Artificial Potential Field (APF) technique for obstacle avoidance in a known environment. The idea is to use the dynamic potential field concept that does not depend on the quadrotor’s dynamics to ensure avoiding collisions among quadrotors. Each quadrotor navigates with repulsion force surrounding it to avoid obstacles including other quadrotors. The quadrotor follows its free-collision path using Model Predictive Control (MPC) as the trajectory tracker. The MPC controller task is to control each quadrotor independently to follow the planned path.
{"title":"Artificial Potential Field for Dynamic Obstacle Avoidance with MPC-Based Trajectory Tracking for Multiple Quadrotors","authors":"Rawan A. Abdellatif, A. El-Badawy","doi":"10.1109/NILES50944.2020.9257973","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257973","url":null,"abstract":"This paper’s objective is to navigate multiple quadrotors using Artificial Potential Field (APF) technique for obstacle avoidance in a known environment. The idea is to use the dynamic potential field concept that does not depend on the quadrotor’s dynamics to ensure avoiding collisions among quadrotors. Each quadrotor navigates with repulsion force surrounding it to avoid obstacles including other quadrotors. The quadrotor follows its free-collision path using Model Predictive Control (MPC) as the trajectory tracker. The MPC controller task is to control each quadrotor independently to follow the planned path.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128593203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-24DOI: 10.1109/NILES50944.2020.9257956
M. Barakat, H. Ammar, M. Elsamanty
The skid steering tracked robot is consider one of the famous robots that used in the autonomous agricultural field. The robot model is considered as a coupled nonlinear model. So, a real kinematic model is required to develop the robot motion which will improve the high quality and quantity of the cultivated crops. So, in this research a mathematical model for the skid steering mobile robot (SSMR) and a mathamtical model has been presented to simulate the robot. The model has been validated based on experimental data for the Skid Steering model. The robot motion as position and velocity has been measured using Inertial Measurement Unit (IMU) and fused with the External Camera measurements. These data used to train a neural network model to develop the equivalent kinematic model that replace the nonlinear model. Then, PID controller was used to perform position and speed control for the two tracks and then to the whole robot body pose. Moreover, metaheuristic techniques were used to improve the PID response by tuning gains based on Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). The system response for the path tracking and its precision has been optimized based on the developed techniques. Moreover, the extracted results show the high performance of tracking path based on a tuned PID controller based on ABC optimization technique.
{"title":"Experimental Path tracking optimization and control of a nonlinear skid steering tracked mobile robot","authors":"M. Barakat, H. Ammar, M. Elsamanty","doi":"10.1109/NILES50944.2020.9257956","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257956","url":null,"abstract":"The skid steering tracked robot is consider one of the famous robots that used in the autonomous agricultural field. The robot model is considered as a coupled nonlinear model. So, a real kinematic model is required to develop the robot motion which will improve the high quality and quantity of the cultivated crops. So, in this research a mathematical model for the skid steering mobile robot (SSMR) and a mathamtical model has been presented to simulate the robot. The model has been validated based on experimental data for the Skid Steering model. The robot motion as position and velocity has been measured using Inertial Measurement Unit (IMU) and fused with the External Camera measurements. These data used to train a neural network model to develop the equivalent kinematic model that replace the nonlinear model. Then, PID controller was used to perform position and speed control for the two tracks and then to the whole robot body pose. Moreover, metaheuristic techniques were used to improve the PID response by tuning gains based on Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). The system response for the path tracking and its precision has been optimized based on the developed techniques. Moreover, the extracted results show the high performance of tracking path based on a tuned PID controller based on ABC optimization technique.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124451669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-24DOI: 10.1109/NILES50944.2020.9257910
Alaa A. Embaby, Zaki B. Nosseir, Hesham Badr
The pancreas of patients with Type 1 Diabetes Mellitus (T1DM) is unable to produce insulin. Thus, insulin therapy is required for T1DM to maintain Blood Glucose (BG) levels within the normal range. The Artificial Pancreas (AP) is a closed-loop control system that is used by T1D patients to maintain their BG levels at the normal range during daily life. In this work, an Adaptive Nonlinear Model Predictive Control (AMPC) algorithm for BG regulation in T1D patients is developed. The proposed technique uses the Feed Forward Neural Network (FFNN) as a nonlinear blood glucose prediction model to handle the delay between the moment of insulin injection and the moment of insulin interaction with the blood glucose. Also, it uses the Fuzzy Logic Controller (FLC) as a control algorithm to determine the amount of insulin required for regulating the BG level. An adaptation method is also included to adjust the proposed system to compensate for physiological differences among patients. The limits of the output membership functions for the FLC are optimized using the Genetic algorithm (GA). Simulation results for a 36h scenario are demonstrated in nine virtual adult patients. The master findings are the average percentages of these patients for the time spent in the normal range, hypo-, and hyperglycemia. Our results indicate that the proposed closed-loop control system increases the time that BG is in the normal range and causes less hyperglycemia as compared to a published technique studied in a similar scenario and population.
{"title":"Adaptive Nonlinear Model Predictive Control algorithm for blood glucose regulation in type 1 diabetic patients","authors":"Alaa A. Embaby, Zaki B. Nosseir, Hesham Badr","doi":"10.1109/NILES50944.2020.9257910","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257910","url":null,"abstract":"The pancreas of patients with Type 1 Diabetes Mellitus (T1DM) is unable to produce insulin. Thus, insulin therapy is required for T1DM to maintain Blood Glucose (BG) levels within the normal range. The Artificial Pancreas (AP) is a closed-loop control system that is used by T1D patients to maintain their BG levels at the normal range during daily life. In this work, an Adaptive Nonlinear Model Predictive Control (AMPC) algorithm for BG regulation in T1D patients is developed. The proposed technique uses the Feed Forward Neural Network (FFNN) as a nonlinear blood glucose prediction model to handle the delay between the moment of insulin injection and the moment of insulin interaction with the blood glucose. Also, it uses the Fuzzy Logic Controller (FLC) as a control algorithm to determine the amount of insulin required for regulating the BG level. An adaptation method is also included to adjust the proposed system to compensate for physiological differences among patients. The limits of the output membership functions for the FLC are optimized using the Genetic algorithm (GA). Simulation results for a 36h scenario are demonstrated in nine virtual adult patients. The master findings are the average percentages of these patients for the time spent in the normal range, hypo-, and hyperglycemia. Our results indicate that the proposed closed-loop control system increases the time that BG is in the normal range and causes less hyperglycemia as compared to a published technique studied in a similar scenario and population.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132648050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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