Vira Nur Rahmawati, Eko Mulyanto Yuniarto, Supeno Mardi Susiki Nugroho
Pencak silat, besides from being useful for self-protection, also has many other benefits, such as increasing physical strength, maintaining posture, and maintaining heart health. Due to the recent pandemic, practicing pencak silat is difficult to do together. Even when there is study material on pencak silat at school, it is difficult for the sports teacher to teach the movements directly. Pencak silat exercises that are practiced alone without a coach can cause injury if the movements are not correct. Therefore, this study builds a system to recognize pencak silat movements. The system was built using the bodypose-based CNN method. Bodypose estimation is used to detect human body keypoints, then these keypoints are used as a feature for input to CNN to recognize movement in each frame. This system uses CNN because it requires fewer parameters and less computing power so that it can be more easily applied for further studies. The accuracy obtained reaches 77% when tested on data that has never been used. This model can be used as a starting point for creating an easy-to-use system to help people practice pencak silat with more recognizable moves.
{"title":"Pencak Silat Movement Classification Using CNN Based On Body Pose","authors":"Vira Nur Rahmawati, Eko Mulyanto Yuniarto, Supeno Mardi Susiki Nugroho","doi":"10.12962/jaree.v7i2.369","DOIUrl":"https://doi.org/10.12962/jaree.v7i2.369","url":null,"abstract":"Pencak silat, besides from being useful for self-protection, also has many other benefits, such as increasing physical strength, maintaining posture, and maintaining heart health. Due to the recent pandemic, practicing pencak silat is difficult to do together. Even when there is study material on pencak silat at school, it is difficult for the sports teacher to teach the movements directly. Pencak silat exercises that are practiced alone without a coach can cause injury if the movements are not correct. Therefore, this study builds a system to recognize pencak silat movements. The system was built using the bodypose-based CNN method. Bodypose estimation is used to detect human body keypoints, then these keypoints are used as a feature for input to CNN to recognize movement in each frame. This system uses CNN because it requires fewer parameters and less computing power so that it can be more easily applied for further studies. The accuracy obtained reaches 77% when tested on data that has never been used. This model can be used as a starting point for creating an easy-to-use system to help people practice pencak silat with more recognizable moves.","PeriodicalId":32708,"journal":{"name":"JAREE Journal on Advanced Research in Electrical Engineering","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135314850","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}
Zubair Saeed, Haseeb Ur Rehman, Abdul Haseeb, Rabia Taseen, Muhammad Shahzaib Shah, Inam Ul Hasan Shaikh, Muhammad Zulqarnain Haider Ali
In this research, for the load frequency control (LFC) challenge, we provide a few possible approaches to building an optimum PID controller. This scheme employs the Quadratic Regulator Approach with Compensating Pole (QRAWCP) approach. In both multi-area and single-area power systems, this control law is used to solve load frequency concerns. And the other scenario that is considerable, the controller's robustness is evaluated on the same systems in terms of non-linearities, external disturbances, and parametric uncertainty such as the Governor Dead Band (GDB) as well as the Generation Rate Constraint (GRC). The performance of the control method is evaluated using Simulink simulations.
{"title":"Load Frequency Control by Quadratic Regulator Approach with Compensating Pole using SIMULINK","authors":"Zubair Saeed, Haseeb Ur Rehman, Abdul Haseeb, Rabia Taseen, Muhammad Shahzaib Shah, Inam Ul Hasan Shaikh, Muhammad Zulqarnain Haider Ali","doi":"10.12962/jaree.v7i2.356","DOIUrl":"https://doi.org/10.12962/jaree.v7i2.356","url":null,"abstract":"In this research, for the load frequency control (LFC) challenge, we provide a few possible approaches to building an optimum PID controller. This scheme employs the Quadratic Regulator Approach with Compensating Pole (QRAWCP) approach. In both multi-area and single-area power systems, this control law is used to solve load frequency concerns. And the other scenario that is considerable, the controller's robustness is evaluated on the same systems in terms of non-linearities, external disturbances, and parametric uncertainty such as the Governor Dead Band (GDB) as well as the Generation Rate Constraint (GRC). The performance of the control method is evaluated using Simulink simulations.","PeriodicalId":32708,"journal":{"name":"JAREE Journal on Advanced Research in Electrical Engineering","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135314851","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}
A new lossless passive snubber for pulse width modulation converters is introduced in this paper. This snubber is established zero current switching condition for main switch in the converters for turning on distance. This snubber does not impose any additional current stress on the switch. The snubber can be applied to all single-switch DC-DC converters. A boost converter with the proposed snubber is analyzed and to verify theoretical analysis a 200 W sample is implemented and tested, also the experimental results are presented. In order to investigate the effect of snubber circuit in the converter in terms of efficiency, the boost converter with the proposed snubber has been compared in terms of efficiency with the conventional boost converter and the results show that the efficiency has increased by about 7% despite the snubber.
{"title":"A new lossless passive snubber with simple structure for pulse width modulation DC-DC converters","authors":"Mahmood Vesali","doi":"10.12962/jaree.v7i2.358","DOIUrl":"https://doi.org/10.12962/jaree.v7i2.358","url":null,"abstract":"A new lossless passive snubber for pulse width modulation converters is introduced in this paper. This snubber is established zero current switching condition for main switch in the converters for turning on distance. This snubber does not impose any additional current stress on the switch. The snubber can be applied to all single-switch DC-DC converters. A boost converter with the proposed snubber is analyzed and to verify theoretical analysis a 200 W sample is implemented and tested, also the experimental results are presented. In order to investigate the effect of snubber circuit in the converter in terms of efficiency, the boost converter with the proposed snubber has been compared in terms of efficiency with the conventional boost converter and the results show that the efficiency has increased by about 7% despite the snubber.","PeriodicalId":32708,"journal":{"name":"JAREE Journal on Advanced Research in Electrical Engineering","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135314848","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}
Yusdiar Sandy, Ardyono Priyadi, Vita Lystianingrum
Educational buildings have the potential to support government programs in efforts to reduce carbon emissions. Installing photovoltaics and providing charging stations can reduce the use of fossil fuels and increase the number of electric vehicle users. This paper aims to optimize educational buildings when implementing a prosumer scheme and integrating Plug-in Electric Vehicles (PEV) to meet building electricity demands. Optimization is carried out through two case studies, namely the application of a prosumer scheme with independent photovoltaic generators with and without PEV integration. The optimization process uses the Modified Firefly Algorithm. The results obtained by applying the prosumer scheme to educational buildings, the two case studies can produce LCOE cheaper than just buying electricity from the grid. Optimizing results show that photovoltaic installation and charging stations in educational buildings can be beneficial when implementing a prosumer scheme.
{"title":"Prosumer-Based Optimization of Educational Building Grid Connected with Plug-in Electric Vehicle Integration using Modified Firefly Algorithm","authors":"Yusdiar Sandy, Ardyono Priyadi, Vita Lystianingrum","doi":"10.12962/jaree.v7i2.350","DOIUrl":"https://doi.org/10.12962/jaree.v7i2.350","url":null,"abstract":"Educational buildings have the potential to support government programs in efforts to reduce carbon emissions. Installing photovoltaics and providing charging stations can reduce the use of fossil fuels and increase the number of electric vehicle users. This paper aims to optimize educational buildings when implementing a prosumer scheme and integrating Plug-in Electric Vehicles (PEV) to meet building electricity demands. Optimization is carried out through two case studies, namely the application of a prosumer scheme with independent photovoltaic generators with and without PEV integration. The optimization process uses the Modified Firefly Algorithm. The results obtained by applying the prosumer scheme to educational buildings, the two case studies can produce LCOE cheaper than just buying electricity from the grid. Optimizing results show that photovoltaic installation and charging stations in educational buildings can be beneficial when implementing a prosumer scheme.","PeriodicalId":32708,"journal":{"name":"JAREE Journal on Advanced Research in Electrical Engineering","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135314849","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}
Intelligent Car (I-Car) ITS is an autonomous car prototype where one of the main localization methods is obtained through reading GPS data. However the accuracy of GPS readings is influenced by the availability of the information from GPS satellites, in which it often depends on the conditions of the place at that time, such as weather or atmospheric conditions, signal blockage, and density of a land. In this paper we propose the solution to overcome the unavailability of GPS localization information based on the omnidirectional camera visual data through environmental recognition around the ITS campus using Deep Neural Network. The process of recognition is to take GPS coordinate data to be used as an output reference point when the omnidirectional camera takes images of the surrounding environment. Visual localization trials were carried out in the ITS environment with a total of 200 GPS coordinates, where each GPS coordinate represents one class so that there are 200 classes for classification. Each coordinate/class has 96 training images. This condition is achieved for a vehicle speed of 20 km/h, with an image acquisition speed of 30 fps from the omnidirectional camera. By using AlexNet architecture, the result of visual localization accuracy is 49-54%. The test results were obtained by using a learning rate parameter of 0.00001, data augmentation, and the Drop Out technique to prevent overfitting and improve accuracy stability.
{"title":"Deep Neural Network for Visual Localization of Autonomous Car in ITS Campus Environment","authors":"Rudy Dikairono, Hendra Kusuma, Arnold Prajna","doi":"10.12962/jaree.v7i2.365","DOIUrl":"https://doi.org/10.12962/jaree.v7i2.365","url":null,"abstract":"Intelligent Car (I-Car) ITS is an autonomous car prototype where one of the main localization methods is obtained through reading GPS data. However the accuracy of GPS readings is influenced by the availability of the information from GPS satellites, in which it often depends on the conditions of the place at that time, such as weather or atmospheric conditions, signal blockage, and density of a land. In this paper we propose the solution to overcome the unavailability of GPS localization information based on the omnidirectional camera visual data through environmental recognition around the ITS campus using Deep Neural Network. The process of recognition is to take GPS coordinate data to be used as an output reference point when the omnidirectional camera takes images of the surrounding environment. Visual localization trials were carried out in the ITS environment with a total of 200 GPS coordinates, where each GPS coordinate represents one class so that there are 200 classes for classification. Each coordinate/class has 96 training images. This condition is achieved for a vehicle speed of 20 km/h, with an image acquisition speed of 30 fps from the omnidirectional camera. By using AlexNet architecture, the result of visual localization accuracy is 49-54%. The test results were obtained by using a learning rate parameter of 0.00001, data augmentation, and the Drop Out technique to prevent overfitting and improve accuracy stability.","PeriodicalId":32708,"journal":{"name":"JAREE Journal on Advanced Research in Electrical Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135314933","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}
Mochammad Berliano Putra Ramadhan, Moh. Zaenal Efendi, Syechu Dwitya Nugraha
Cubicle 20 kV is a crucial electrical equipment in the 20 kV power distribution system. Often, cubicle issues arise due to excessively low or high temperatures and humidity, which can lead to the presence of water spots and corrosion on the components inside the 20 kV cubicle. One of the efforts to maintain the reliability of this 20 kV cubicle is to ensure that the temperature and humidity inside the cubicle remain within their operational limits. To achieve this, a system is needed to control the temperature and humidity in the 20 kV cubicle. The system can monitor and control the temperature and humidity inside the cubicle by adjusting the activation angle of the exhaust fan and heater. Control is achieved using a multiple-input, multiple-output fuzzy logic controller. The main components of this system are the STM32 microcontroller, DHT22 sensor, and ESP8266 module for monitoring temperature and humidity via a website. System has successfully controlled temperature and humidity d with a set point value of 35 °C and humidity of 60% RH. This implementation of fuzzy multiple input multiple outputs has performed well and resulted in only a small error of 0.55% for temperature and 1.05% for humidity.With the presence of this device, the temperature and humidity in the cubicle 20 kV can be controlled, and it enables the PLN operator to easily monitor and maintain the cubicle 20 kV.
{"title":"Temperature and Humidity Control System for 20 kV of Cubicle with Multiple Input Multiple Output Fuzzy Logic Controller","authors":"Mochammad Berliano Putra Ramadhan, Moh. Zaenal Efendi, Syechu Dwitya Nugraha","doi":"10.12962/jaree.v7i2.367","DOIUrl":"https://doi.org/10.12962/jaree.v7i2.367","url":null,"abstract":"Cubicle 20 kV is a crucial electrical equipment in the 20 kV power distribution system. Often, cubicle issues arise due to excessively low or high temperatures and humidity, which can lead to the presence of water spots and corrosion on the components inside the 20 kV cubicle. One of the efforts to maintain the reliability of this 20 kV cubicle is to ensure that the temperature and humidity inside the cubicle remain within their operational limits. To achieve this, a system is needed to control the temperature and humidity in the 20 kV cubicle. The system can monitor and control the temperature and humidity inside the cubicle by adjusting the activation angle of the exhaust fan and heater. Control is achieved using a multiple-input, multiple-output fuzzy logic controller. The main components of this system are the STM32 microcontroller, DHT22 sensor, and ESP8266 module for monitoring temperature and humidity via a website. System has successfully controlled temperature and humidity d with a set point value of 35 °C and humidity of 60% RH. This implementation of fuzzy multiple input multiple outputs has performed well and resulted in only a small error of 0.55% for temperature and 1.05% for humidity.With the presence of this device, the temperature and humidity in the cubicle 20 kV can be controlled, and it enables the PLN operator to easily monitor and maintain the cubicle 20 kV.","PeriodicalId":32708,"journal":{"name":"JAREE Journal on Advanced Research in Electrical Engineering","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135314857","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}
Mohammad Nasrul Mubin, Hendra Kusuma, Muhammad Rivai
Implementation of automation greatly facilitates the work of a system. This research automates the search for perspective transformation coordinates. In previous study, the process was done manually and was considered time-consuming and costly. The search for these coordinates is carried out with the help of red circles at several points in the parking area to be identified. There are two cases of images to be automated, namely the image of the parking area without obstacles and with obstacles. In the unobstructed images, the identification of transformation coordinates is carried out by identifying the coordinates of the auxiliary circle. Whereas in the images with obstructions, the identification of the transformation coordinates also involves the intersection equations of lines. The process of identifying the coordinates is done with the condition of the parking lot without a single vehicle. Once the coordinates are obtained, all coordinates are stored and will be used in the perspective transformation process in status parking slot identification stage. The identification stage is same with previous study. The proposed system 100% able to identify the transformation coordinates and carry out the perspective transformation process as expected. Of the 900 samples in each case, we acquire 100% recall, and most of the parking slot identification status being above 85% precision and accuracy. Compared to previous studies, the proposed system is more effective, with recall, precision, and accuracy values at 100%. The effectiveness of the proposed system is even more evident with average data automation time is 31.689 seconds.
{"title":"Perspective Transformation Automation In Identification Of Parking Lot Status With Blob Detection","authors":"Mohammad Nasrul Mubin, Hendra Kusuma, Muhammad Rivai","doi":"10.12962/jaree.v7i2.364","DOIUrl":"https://doi.org/10.12962/jaree.v7i2.364","url":null,"abstract":"Implementation of automation greatly facilitates the work of a system. This research automates the search for perspective transformation coordinates. In previous study, the process was done manually and was considered time-consuming and costly. The search for these coordinates is carried out with the help of red circles at several points in the parking area to be identified. There are two cases of images to be automated, namely the image of the parking area without obstacles and with obstacles. In the unobstructed images, the identification of transformation coordinates is carried out by identifying the coordinates of the auxiliary circle. Whereas in the images with obstructions, the identification of the transformation coordinates also involves the intersection equations of lines. The process of identifying the coordinates is done with the condition of the parking lot without a single vehicle. Once the coordinates are obtained, all coordinates are stored and will be used in the perspective transformation process in status parking slot identification stage. The identification stage is same with previous study. The proposed system 100% able to identify the transformation coordinates and carry out the perspective transformation process as expected. Of the 900 samples in each case, we acquire 100% recall, and most of the parking slot identification status being above 85% precision and accuracy. Compared to previous studies, the proposed system is more effective, with recall, precision, and accuracy values at 100%. The effectiveness of the proposed system is even more evident with average data automation time is 31.689 seconds.","PeriodicalId":32708,"journal":{"name":"JAREE Journal on Advanced Research in Electrical Engineering","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135314937","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}
Arcing is one of the causes of fire disasters that occur quite a lot in the world. The danger of arcing causes enormous losses. The fault current caused by arcing usually occurs in a very short duration, so that safety equipment such as Miniature Circuit Breaker (MCB) and Fuse cannot detect the disturbance. If arcing takes place continuously, there will be heat that can damage the equipment and cause a fire. In this study, an evaluation of the use of mother wavelets in the Discrete Wavelet Transform (DWT) method on low voltage detection equipment will be carried out. DWT is a mathematical function that is the most successful in the field of signal processing. Signal processing using the DWT method will be analyzed and compared its performance with mother wavelets Daubechies-1, Daubechies-4, Coiflet-4, and Symlet-4. Then applied to low voltage arcing detection equipment. From the results of testing and analysis obtained the mother wavelet Daubechies-4 produces a high level of accuracy and sensitivity. Mother wavelet Coiflet-4 and Symlet-4 produce the lowest level of accuracy at 400W and 700W load. Meanwhile, on the mother wavelet Daubechies-1, the normal signal amplitude value is close to the arcing signal amplitude value, so it will be difficult to determine the threshold value. In this study, the results obtained are arcing detection equipment using the mother wavelet Daubechies-4 method can detect arcing disturbances very well and produces a high level of accuracy and sensitivity compared to the mother wavelet Daubechies-1, Coiflet-4 , and Symlet-4.
{"title":"Evaluation Of Digital Wavelet Filter On Low Voltage Arcing Detection Equipment","authors":"Anton Putra Widyatama, Dimas Anton Asfani","doi":"10.12962/jaree.v7i2.311","DOIUrl":"https://doi.org/10.12962/jaree.v7i2.311","url":null,"abstract":"Arcing is one of the causes of fire disasters that occur quite a lot in the world. The danger of arcing causes enormous losses. The fault current caused by arcing usually occurs in a very short duration, so that safety equipment such as Miniature Circuit Breaker (MCB) and Fuse cannot detect the disturbance. If arcing takes place continuously, there will be heat that can damage the equipment and cause a fire. In this study, an evaluation of the use of mother wavelets in the Discrete Wavelet Transform (DWT) method on low voltage detection equipment will be carried out. DWT is a mathematical function that is the most successful in the field of signal processing. Signal processing using the DWT method will be analyzed and compared its performance with mother wavelets Daubechies-1, Daubechies-4, Coiflet-4, and Symlet-4. Then applied to low voltage arcing detection equipment. From the results of testing and analysis obtained the mother wavelet Daubechies-4 produces a high level of accuracy and sensitivity. Mother wavelet Coiflet-4 and Symlet-4 produce the lowest level of accuracy at 400W and 700W load. Meanwhile, on the mother wavelet Daubechies-1, the normal signal amplitude value is close to the arcing signal amplitude value, so it will be difficult to determine the threshold value. In this study, the results obtained are arcing detection equipment using the mother wavelet Daubechies-4 method can detect arcing disturbances very well and produces a high level of accuracy and sensitivity compared to the mother wavelet Daubechies-1, Coiflet-4 , and Symlet-4.","PeriodicalId":32708,"journal":{"name":"JAREE Journal on Advanced Research in Electrical Engineering","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135314931","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}
Connected and Autonomous Vehicles (CAVs) are a crucial breakthrough in the automotive industry and a magnificent step toward a safe, secure, and intelligent transportation system (ITS). CAVs offer tremendous benefits to our society and environment, such as mitigation of traffic accidents, reduction in traffic congestion, fewer emissions of harmful gases, etc. However, emerging automotive technology also has some serious safety concerns. One of them is cyber security. Conventional vehicles are less prone to cyber-attacks, but CAVs are more susceptible to such events as they communicate with the surrounding infrastructure and other vehicles. To gather data for a better perception of their surroundings, CAVs are outfitted with state-of-the-art sensors and modules like LiDAR, GPS, RADAR, onboard computers, cameras, etc. Hackers, terrorist organizations, and vandals can manipulate this sensor data or may access the primary control by cyber-attack, which may result in enormous fatalities. The automotive industry must put up a rigid framework against cyber invasions to make CAVs a more reliable and secure means of transportation. This paper provides an overview of cybersecurity challenges in CAVs at the module and software levels. The sources of active and passive threats are analyzed. Finally, a feasible solution is recommended to cope with such threats
{"title":"A Review: Cybersecurity Challenges and their Solutions in Connected and Autonomous Vehicles (CAVs)","authors":"Zubair Saeed, Mubashir Masood, Misha Urooj Khan","doi":"10.12962/jaree.v7i1.322","DOIUrl":"https://doi.org/10.12962/jaree.v7i1.322","url":null,"abstract":"Connected and Autonomous Vehicles (CAVs) are a crucial breakthrough in the automotive industry and a magnificent step toward a safe, secure, and intelligent transportation system (ITS). CAVs offer tremendous benefits to our society and environment, such as mitigation of traffic accidents, reduction in traffic congestion, fewer emissions of harmful gases, etc. However, emerging automotive technology also has some serious safety concerns. One of them is cyber security. Conventional vehicles are less prone to cyber-attacks, but CAVs are more susceptible to such events as they communicate with the surrounding infrastructure and other vehicles. To gather data for a better perception of their surroundings, CAVs are outfitted with state-of-the-art sensors and modules like LiDAR, GPS, RADAR, onboard computers, cameras, etc. Hackers, terrorist organizations, and vandals can manipulate this sensor data or may access the primary control by cyber-attack, which may result in enormous fatalities. The automotive industry must put up a rigid framework against cyber invasions to make CAVs a more reliable and secure means of transportation. This paper provides an overview of cybersecurity challenges in CAVs at the module and software levels. The sources of active and passive threats are analyzed. Finally, a feasible solution is recommended to cope with such threats","PeriodicalId":32708,"journal":{"name":"JAREE Journal on Advanced Research in Electrical Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135441449","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}
Mu'thiana Gusnam, Hendra Kusuma, Tri Arief Sardjono
Cardiac Activity forms a signal of electrical potential waves in the heart that can be recorded using an Electrocardiogram (ECG). The results of the ECG signal can determine the conditions and abnormalities experienced by the heart, such as arrhythmias. Medical personnel diagnoses normal and arrhythmia heart conditions by looking at R peaks and R-R interval features. Normal conditions have regular R peaks and R-R intervals, whereas arrhythmias are irregular. The challenges in diagnosing ECG signals are that sometimes the signal has some noises that need reducing noise (denoising) are not required in the signal so it can be easier to detect abnormalities. This paper is a brief study of the comparison of the best performance in detecting ECG signals using various wavelet transforms and optimal threshold values based on empirical methods to obtain R peaks and R-R interval features. Wavelet transform describes the signals that can compress the ECG signal and reduce noise without losing important clinical information that can be achieved by medical personnel. The wavelet transform is suitable for approaching data with a discontinuity signal, so the frequency component will increase if noise or anomalies occur in the ECG signal. The various wavelet transforms used Daubechies (db4), Symlets (sym4), Coiflets (coif4), and Biorthogonal (bior3.7) with four types of Detail and Approximate levels; they are Level 1, 2, 3, and 4. The comparison result for the best performance of the various wavelet transforms is using Daubechies wavelet, and biorthogonal wavelet with an accuracy percentage of 100% at level 2 for diagnosing arrhythmia and 93.1% at level 1 for normal diagnosis from 31 data for arrhythmia and 18 for Normal sourced of the MIT-BIH Database. Hence, the total accuracy results obtained from all the data tested is 96.55%.
{"title":"Comparative Performance of Various Wavelet Transformation for the Detection of Normal and Arrhythmia ECG Signal","authors":"Mu'thiana Gusnam, Hendra Kusuma, Tri Arief Sardjono","doi":"10.12962/jaree.v7i1.343","DOIUrl":"https://doi.org/10.12962/jaree.v7i1.343","url":null,"abstract":"Cardiac Activity forms a signal of electrical potential waves in the heart that can be recorded using an Electrocardiogram (ECG). The results of the ECG signal can determine the conditions and abnormalities experienced by the heart, such as arrhythmias. Medical personnel diagnoses normal and arrhythmia heart conditions by looking at R peaks and R-R interval features. Normal conditions have regular R peaks and R-R intervals, whereas arrhythmias are irregular. The challenges in diagnosing ECG signals are that sometimes the signal has some noises that need reducing noise (denoising) are not required in the signal so it can be easier to detect abnormalities. This paper is a brief study of the comparison of the best performance in detecting ECG signals using various wavelet transforms and optimal threshold values based on empirical methods to obtain R peaks and R-R interval features. Wavelet transform describes the signals that can compress the ECG signal and reduce noise without losing important clinical information that can be achieved by medical personnel. The wavelet transform is suitable for approaching data with a discontinuity signal, so the frequency component will increase if noise or anomalies occur in the ECG signal. The various wavelet transforms used Daubechies (db4), Symlets (sym4), Coiflets (coif4), and Biorthogonal (bior3.7) with four types of Detail and Approximate levels; they are Level 1, 2, 3, and 4. The comparison result for the best performance of the various wavelet transforms is using Daubechies wavelet, and biorthogonal wavelet with an accuracy percentage of 100% at level 2 for diagnosing arrhythmia and 93.1% at level 1 for normal diagnosis from 31 data for arrhythmia and 18 for Normal sourced of the MIT-BIH Database. Hence, the total accuracy results obtained from all the data tested is 96.55%.","PeriodicalId":32708,"journal":{"name":"JAREE Journal on Advanced Research in Electrical Engineering","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135441447","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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