Pub Date : 2020-12-15DOI: 10.1109/ICCES51560.2020.9334582
Mahmoud Fathy Al-Sawah, M. El-Mahlawy, M. Abbass
In this paper, the application of the board-level external testing using the pseudo-exhaustive testing (PET) was explored. The new design approach to reconFigure the hardware of the pseudo-exhaustive test pattern generator (PETPG), based on the permutated convolved linear feedback shift register/shift register (LFSR/SR), is developed. The permutated convolved LFSR/SR is considered a superset of all previously published output-specific PETPGs in the PET with low test application time (TAT). The PET segments digital circuit-under-test (CUT) into several output cones. The proposed test system can stimulate all combinational hard faults in each output cone using the reconfigured PETPG without the need of the fault simulator, and to compact test responses of digital circuits for signature generation. The simulation results using some digital circuits, compared to previously published works, illustrate the effectiveness of the presented test approach to detect target faults with reduction of TAT.
{"title":"Reconfigurable PETPG for External Testing of Digital Circuits","authors":"Mahmoud Fathy Al-Sawah, M. El-Mahlawy, M. Abbass","doi":"10.1109/ICCES51560.2020.9334582","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334582","url":null,"abstract":"In this paper, the application of the board-level external testing using the pseudo-exhaustive testing (PET) was explored. The new design approach to reconFigure the hardware of the pseudo-exhaustive test pattern generator (PETPG), based on the permutated convolved linear feedback shift register/shift register (LFSR/SR), is developed. The permutated convolved LFSR/SR is considered a superset of all previously published output-specific PETPGs in the PET with low test application time (TAT). The PET segments digital circuit-under-test (CUT) into several output cones. The proposed test system can stimulate all combinational hard faults in each output cone using the reconfigured PETPG without the need of the fault simulator, and to compact test responses of digital circuits for signature generation. The simulation results using some digital circuits, compared to previously published works, illustrate the effectiveness of the presented test approach to detect target faults with reduction of TAT.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121907850","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 : 2020-12-15DOI: 10.1109/ICCES51560.2020.9334644
Mohamed Elkashlan, Marianne A. Azer
There is an increase in the usage of the Internet of Things (IoT) devices which focus on, efficiency and automation of different tasks to minimize the user intervention during COVID-19 pandemic. These IoT devices incur seamless excessive data exchange, Therefore, there is need for reinforcing the security, authentication and privacy to be more resilient to the various types of attacks which are the key concern for many organizations, especially cloud based networks carrying sensitive data. This paper presents a review of the challenges facing the IoT ecosystem along with the attack vectors threatening the IoT environment. A proposed solution for defending security threats found in IoT using blockchain technology is also discussed.
{"title":"Mitigating IoT Security Challenges Using Blockchain","authors":"Mohamed Elkashlan, Marianne A. Azer","doi":"10.1109/ICCES51560.2020.9334644","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334644","url":null,"abstract":"There is an increase in the usage of the Internet of Things (IoT) devices which focus on, efficiency and automation of different tasks to minimize the user intervention during COVID-19 pandemic. These IoT devices incur seamless excessive data exchange, Therefore, there is need for reinforcing the security, authentication and privacy to be more resilient to the various types of attacks which are the key concern for many organizations, especially cloud based networks carrying sensitive data. This paper presents a review of the challenges facing the IoT ecosystem along with the attack vectors threatening the IoT environment. A proposed solution for defending security threats found in IoT using blockchain technology is also discussed.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116798806","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 : 2020-12-15DOI: 10.1109/ICCES51560.2020.9334574
A. Elsherif, Ahmed Karaman, Omar Ahmed, Omar Magdy, R. Shouman, Rita El-Noumier, Ahmed M. Hamed, Hany Eldawlatly, S. Eldawlatly
Human error is considered one of the major causes of car accidents. One potential approach to reduce human driving errors is to continuously monitor the driver’s performance while driving. This could help in detecting potential risks and thus reduce the likelihood of accidents. In this paper, we introduce a machine learning system that analyzes the driver’s brain activity to monitor and predict the driver’s performance. While driving, the system monitors the driver’s mental state by analyzing acquired Electroencephalography (EEG) signals. Additionally, the proposed system acquires EEG activity from the driver before driving and predicts the driving performance along the intended route. The proposed system is tailored for the Automotive Open System Architecture (AUTOSAR) framework. Our results demonstrate the ability of the system to classify the mental state of the driver in real-time into three states (focused, unfocused, and drowsy) with a mean accuracy of 96.5% across three examined subjects. The system also predicts the driver’s performance before driving from the recorded EEG signals with a mean accuracy of 85%. These results indicate the utility of EEG signals analysis in enhancing the safety of futuristic automotive applications.
{"title":"Monitoring and Predicting Driving Performance Using EEG Activity","authors":"A. Elsherif, Ahmed Karaman, Omar Ahmed, Omar Magdy, R. Shouman, Rita El-Noumier, Ahmed M. Hamed, Hany Eldawlatly, S. Eldawlatly","doi":"10.1109/ICCES51560.2020.9334574","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334574","url":null,"abstract":"Human error is considered one of the major causes of car accidents. One potential approach to reduce human driving errors is to continuously monitor the driver’s performance while driving. This could help in detecting potential risks and thus reduce the likelihood of accidents. In this paper, we introduce a machine learning system that analyzes the driver’s brain activity to monitor and predict the driver’s performance. While driving, the system monitors the driver’s mental state by analyzing acquired Electroencephalography (EEG) signals. Additionally, the proposed system acquires EEG activity from the driver before driving and predicts the driving performance along the intended route. The proposed system is tailored for the Automotive Open System Architecture (AUTOSAR) framework. Our results demonstrate the ability of the system to classify the mental state of the driver in real-time into three states (focused, unfocused, and drowsy) with a mean accuracy of 96.5% across three examined subjects. The system also predicts the driver’s performance before driving from the recorded EEG signals with a mean accuracy of 85%. These results indicate the utility of EEG signals analysis in enhancing the safety of futuristic automotive applications.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130089845","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 : 2020-12-15DOI: 10.1109/ICCES51560.2020.9334659
Iman M. Shawky, M. Sadek, H. Elhennawy
Multiuser scheduling enables users to share the same time and frequency resources while exploiting spatial diversity through the use of multiple antennas. In this paper, we propose a machine learning (ML) approach that decides on multiuser scheduling through solving a system capacity optimization problem. More specifically, we use a support vector machine (SVM). The proposed algorithm takes as an input the signal to noise ratio (SNR) and uplink channel information of a predetermined set of users. The output is a decision as to which users, if any, can be scheduled in the same time slot and frequency band. We show that the resulting system capacity is comparable to the optimal capacity obtained through exhaustive search, with significantly lower algorithm complexity. Moreover, building on the crucial importance of feature-engineering in ML models and capitalizing on the domain-expert knowledge of our problem, we work on tailoring the information available at the scheduler to further enhance the performance of our proposed approach.
{"title":"Uplink Multiuser Scheduling Using Machine Learning","authors":"Iman M. Shawky, M. Sadek, H. Elhennawy","doi":"10.1109/ICCES51560.2020.9334659","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334659","url":null,"abstract":"Multiuser scheduling enables users to share the same time and frequency resources while exploiting spatial diversity through the use of multiple antennas. In this paper, we propose a machine learning (ML) approach that decides on multiuser scheduling through solving a system capacity optimization problem. More specifically, we use a support vector machine (SVM). The proposed algorithm takes as an input the signal to noise ratio (SNR) and uplink channel information of a predetermined set of users. The output is a decision as to which users, if any, can be scheduled in the same time slot and frequency band. We show that the resulting system capacity is comparable to the optimal capacity obtained through exhaustive search, with significantly lower algorithm complexity. Moreover, building on the crucial importance of feature-engineering in ML models and capitalizing on the domain-expert knowledge of our problem, we work on tailoring the information available at the scheduler to further enhance the performance of our proposed approach.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130676401","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 : 2020-12-15DOI: 10.1109/ICCES51560.2020.9334671
Asmaa M. Hafez, Amany Abdelsamea, A. El-Moursy, S. Nassar, M. Fayek
Container is an evolving lightweight virtualization innovation that attempts to perfectly capture a function and its library dependencies to be executed seamlessly at the operating system level without pre-installations or s/w setup. Placement of containers at the appropriate platform is essential in the utilization optimization of resources in cloud infrastructures. Efficient resource utilization can be achieved only when the containers are optimally mapped to VMs. Poor placement may cause a bottleneck in the cloud if VMs are loaded heavily and this may affect the response time of a given set of tasks. The ant colony optimization technique was used to schedule tasks and containers on VMs and PMs in the cloud. The disadvantage of typical ACO is its tendency to schedule tasks to the most used (high pheromone intensity) node. If the node is carrying a big load it will have an issue of overhead. By tracking preceding scheduling, this hassle could be solved by lowering the processing time and tracking load on each VM. With concerning the challenges and difficulty of the container placement, this paper proposes Modified Ant Colony Optimization Technique (MACO) for the placement of containers. The new proposal takes into consideration the scheduling history to enhance the scheduling decision. The results of MACO are compared with the basic Ant Colony Optimization technique (ACO) and First Come First Serve algorithm (FCFS). The experimental results show that the MACO is better than FCFS and the basic ACO in terms of response time and throughput.
{"title":"Modified Ant Colony Placement Algorithm for Containers","authors":"Asmaa M. Hafez, Amany Abdelsamea, A. El-Moursy, S. Nassar, M. Fayek","doi":"10.1109/ICCES51560.2020.9334671","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334671","url":null,"abstract":"Container is an evolving lightweight virtualization innovation that attempts to perfectly capture a function and its library dependencies to be executed seamlessly at the operating system level without pre-installations or s/w setup. Placement of containers at the appropriate platform is essential in the utilization optimization of resources in cloud infrastructures. Efficient resource utilization can be achieved only when the containers are optimally mapped to VMs. Poor placement may cause a bottleneck in the cloud if VMs are loaded heavily and this may affect the response time of a given set of tasks. The ant colony optimization technique was used to schedule tasks and containers on VMs and PMs in the cloud. The disadvantage of typical ACO is its tendency to schedule tasks to the most used (high pheromone intensity) node. If the node is carrying a big load it will have an issue of overhead. By tracking preceding scheduling, this hassle could be solved by lowering the processing time and tracking load on each VM. With concerning the challenges and difficulty of the container placement, this paper proposes Modified Ant Colony Optimization Technique (MACO) for the placement of containers. The new proposal takes into consideration the scheduling history to enhance the scheduling decision. The results of MACO are compared with the basic Ant Colony Optimization technique (ACO) and First Come First Serve algorithm (FCFS). The experimental results show that the MACO is better than FCFS and the basic ACO in terms of response time and throughput.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132022013","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 : 2020-12-15DOI: 10.1109/ICCES51560.2020.9334581
Eman Hegazy, W. Saad, M. Shokair
Wireless sensor network has become an increasing interest for research. The major limitation in the WSN nodes design is finite battery capacity that can only operate for finite lifetime depending on the duty cycle of the operation. In this paper, a solar photovoltaic PV will be studied with its nonlinear characteristics curves. It can be employed in a solar energy harvesting system to enhance its performance and to address the problem of finite battery capacity in WSN. The solar panels with small size areas linked with a low power harvesting circuits can supply an alternative power source for its node. Simulation of the DC-DC Boost converter will be included. The efficient design for the solar harvesting system components which are PV panel, DCDC converters, Control Unit, and WSN node will be investigated. In general, there are two efficient ways for solar energy harvesting algorithms which are; Pulse Width Modulation and Maximum Power Point Tracking control algorithms. The desired system in this paper focuses on increasing the output voltage, output power and overall solar harvesting system efficiency using two control techniques than the other related works. The design of the solar energy harvesting system models are simulated using MATLAB/SIMULINK. From the simulation results, the desired solar harvesting system model has a higher overall efficiency using maximum power point control algorithm.
{"title":"Studying the Effect of Using a Low Power PV and DC-DC Boost Converter on the Performance of the Solar Energy PV System","authors":"Eman Hegazy, W. Saad, M. Shokair","doi":"10.1109/ICCES51560.2020.9334581","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334581","url":null,"abstract":"Wireless sensor network has become an increasing interest for research. The major limitation in the WSN nodes design is finite battery capacity that can only operate for finite lifetime depending on the duty cycle of the operation. In this paper, a solar photovoltaic PV will be studied with its nonlinear characteristics curves. It can be employed in a solar energy harvesting system to enhance its performance and to address the problem of finite battery capacity in WSN. The solar panels with small size areas linked with a low power harvesting circuits can supply an alternative power source for its node. Simulation of the DC-DC Boost converter will be included. The efficient design for the solar harvesting system components which are PV panel, DCDC converters, Control Unit, and WSN node will be investigated. In general, there are two efficient ways for solar energy harvesting algorithms which are; Pulse Width Modulation and Maximum Power Point Tracking control algorithms. The desired system in this paper focuses on increasing the output voltage, output power and overall solar harvesting system efficiency using two control techniques than the other related works. The design of the solar energy harvesting system models are simulated using MATLAB/SIMULINK. From the simulation results, the desired solar harvesting system model has a higher overall efficiency using maximum power point control algorithm.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125372370","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 : 2020-12-15DOI: 10.1109/ICCES51560.2020.9334561
Alhossin K. Aljadai, M. Manna, A. Kharaz
The relay-selection technique has become one of the key-technologies and one of the most functional approaches of modern wireless communication engineering to improve the wireless systems performance. In this work, we introduce a wireless cooperative network utilizing a relay selection (RS) technique and examine the outage probability of the system under the channel environment conditions. The RS are assigned to choose only the optimum 4-relays from K-available relays to improve the performance of our proposed scheme. Probability density function (PDF) and cumulative density function (CDF) have been theoretically used to compute the outage probability of the relay paths in Rayleigh fading channels. It is shown by computer simulations, the proposed distributed extended orthogonal space time block coding (D-EO-STBC) with RS provides significant improvement in the outage probability as compared to the conventional D-EO-STBC in [11].
{"title":"Outage Probability Analysis of One-way Distributed Cooperative Relay Selection Networks","authors":"Alhossin K. Aljadai, M. Manna, A. Kharaz","doi":"10.1109/ICCES51560.2020.9334561","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334561","url":null,"abstract":"The relay-selection technique has become one of the key-technologies and one of the most functional approaches of modern wireless communication engineering to improve the wireless systems performance. In this work, we introduce a wireless cooperative network utilizing a relay selection (RS) technique and examine the outage probability of the system under the channel environment conditions. The RS are assigned to choose only the optimum 4-relays from K-available relays to improve the performance of our proposed scheme. Probability density function (PDF) and cumulative density function (CDF) have been theoretically used to compute the outage probability of the relay paths in Rayleigh fading channels. It is shown by computer simulations, the proposed distributed extended orthogonal space time block coding (D-EO-STBC) with RS provides significant improvement in the outage probability as compared to the conventional D-EO-STBC in [11].","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126815455","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 : 2020-12-15DOI: 10.1109/ICCES51560.2020.9334557
G. Attia
Electrocardiogram (ECG) instrument is used to provide diagnostic information about the critical condition of the patient’s heart, but its performance sometimes suffers from some sources of noise such as: 50Hz line frequency, Hum and high frequency (HF) noise. These sources of noise affect the performance of the ECG and hence cause false diagnosis recording that tricks the doctor who uses this machine. The current paper; proposes to tackle the problem of false diagnoses recordings by employing adaptive digital filter based least mean square (LMS) error algorithm in order to refine the ECG signals from the disturbing sources of noise. Based matlab programming; I have studied two different cases of mixing random noise that disturb the performance of the ECG instrument. The first kind of noise is mixing the line frequency 50 Hz with the ECG signal; the second kind of noise is mixing the hum and high frequency noise with the ECG signal. Numerical values for digital filter parameters have been used as: number of taps or order (M = 16), step size (μ = 0.005), sampling frequency (Fs = 1000Hz), interfering line frequency 50Hz, hum noise, and HF noise. The experimental results using Matlab simulation show that; the proposed scheme of employing digital filter based LMS algorithm; can tackle the problem of false diagnoses that causes frustration for the patient and tricks the doctor. The proposed scheme has several advantages such as; simplicity, reliability, practical applicability, adaptability to the change in signal characteristics and cost affordability.
{"title":"Correction of False Diagnosis Recording in the Electrocardiograph Signal by Adaptive Digital Filter","authors":"G. Attia","doi":"10.1109/ICCES51560.2020.9334557","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334557","url":null,"abstract":"Electrocardiogram (ECG) instrument is used to provide diagnostic information about the critical condition of the patient’s heart, but its performance sometimes suffers from some sources of noise such as: 50Hz line frequency, Hum and high frequency (HF) noise. These sources of noise affect the performance of the ECG and hence cause false diagnosis recording that tricks the doctor who uses this machine. The current paper; proposes to tackle the problem of false diagnoses recordings by employing adaptive digital filter based least mean square (LMS) error algorithm in order to refine the ECG signals from the disturbing sources of noise. Based matlab programming; I have studied two different cases of mixing random noise that disturb the performance of the ECG instrument. The first kind of noise is mixing the line frequency 50 Hz with the ECG signal; the second kind of noise is mixing the hum and high frequency noise with the ECG signal. Numerical values for digital filter parameters have been used as: number of taps or order (M = 16), step size (μ = 0.005), sampling frequency (Fs = 1000Hz), interfering line frequency 50Hz, hum noise, and HF noise. The experimental results using Matlab simulation show that; the proposed scheme of employing digital filter based LMS algorithm; can tackle the problem of false diagnoses that causes frustration for the patient and tricks the doctor. The proposed scheme has several advantages such as; simplicity, reliability, practical applicability, adaptability to the change in signal characteristics and cost affordability.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133677769","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 : 2020-12-15DOI: 10.1109/ICCES51560.2020.9334607
Ahmed M. Shawqran, A. El-Marhomy, M. Attia
Wind energy is one of the fastest-growing energy sources of green energy in the world. Research efforts are aimed to address the challenges to greater use of wind energy. Thus, the paper presents a blade angle control based on a new modified adaptive PI controller. The controller relies on teaching learning-based optimization algorithms (TLBO) to optimally evaluate its initials. The effectiveness of the proposed controller is verified by simulation results for six 1.5-MW wind turbines doubly fed induction generator (DFIG) system. The validation composes a comparison with conventional adaptive controller under normal and faulty conditions. The modified adaptive PI showed improved mechanical and electrical behaviors for the wind turbine relying on its second order amplifier. The amplifier works as analogue filter that improves the system dynamic characteristics. The new controller showed robustness to the changes in system parameters and the nonlinearity of the wind turbine systems. The superiority of the new controller has been proved when compared with conventional PID controller.
{"title":"Blade Angle Control Using TLBO Based Modified Adaptive Controller","authors":"Ahmed M. Shawqran, A. El-Marhomy, M. Attia","doi":"10.1109/ICCES51560.2020.9334607","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334607","url":null,"abstract":"Wind energy is one of the fastest-growing energy sources of green energy in the world. Research efforts are aimed to address the challenges to greater use of wind energy. Thus, the paper presents a blade angle control based on a new modified adaptive PI controller. The controller relies on teaching learning-based optimization algorithms (TLBO) to optimally evaluate its initials. The effectiveness of the proposed controller is verified by simulation results for six 1.5-MW wind turbines doubly fed induction generator (DFIG) system. The validation composes a comparison with conventional adaptive controller under normal and faulty conditions. The modified adaptive PI showed improved mechanical and electrical behaviors for the wind turbine relying on its second order amplifier. The amplifier works as analogue filter that improves the system dynamic characteristics. The new controller showed robustness to the changes in system parameters and the nonlinearity of the wind turbine systems. The superiority of the new controller has been proved when compared with conventional PID controller.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114588982","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 : 2020-12-15DOI: 10.1109/icces51560.2020.9334639
Bassem A. Abdullah
Software becomes a key corner in the automotive industry. More than 70% of the price of a vehicle is software components on its electronic control units namely ECUs. Vendors of ECUs agree on the concept of “cooperate on standard and compete in the implementation”. Standardization is needed to enable smooth communication of these ECUs in different vehicles.In this session, we discuss the evolution of software in the automotive industry and focus on the famous standard of AUTOSAR.
{"title":"Tutorial I: Automotive Software Emerging Standard","authors":"Bassem A. Abdullah","doi":"10.1109/icces51560.2020.9334639","DOIUrl":"https://doi.org/10.1109/icces51560.2020.9334639","url":null,"abstract":"Software becomes a key corner in the automotive industry. More than 70% of the price of a vehicle is software components on its electronic control units namely ECUs. Vendors of ECUs agree on the concept of “cooperate on standard and compete in the implementation”. Standardization is needed to enable smooth communication of these ECUs in different vehicles.In this session, we discuss the evolution of software in the automotive industry and focus on the famous standard of AUTOSAR.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122109881","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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