The security of communication networks can be compromised through both known and novel attack methods. Protection against such attacks may be achieved through the use of an intrusion detection system (IDS), which can be designed by training machine learning models to detect cyberattacks. In this paper, the KOMIG (knapsack optimization and mutual information gain) IDS was developed to detect network intrusions. The KOMIG IDS combined the strengths of optimization and machine learning together to achieve a high intrusion detection performance. Specifically, KOMIG IDS comprises a 2-stage feature selection procedure; the first was accomplished with a knapsack optimization algorithm and the second with a mutual information gain filter. In particular, we developed an optimization model for the selection of the most important features from a network intrusion dataset. Then, a new set of features was synthesized from the selected features and combined with the selected features to form a candidate features set. Next, we applied an information gain filter to the candidate features set to prune out redundant features, leaving only the features that possess the maximum information gain, which were used to train machine learning models. The proposed KOMIG IDS was applied to the UNSW-NB15 dataset, which is a well-known network intrusion evaluation dataset, and the resulting data, after optimization operation, were used to train four machine learning models, namely, logistic regression (LR), random forest (RF), decision tree (DT), and K-nearest neighbors (KNN). Simulation experiments were conducted, and the results revealed that our proposed KNN-based KOMIG IDS outperformed comparative schemes by achieving an accuracy score of 97.14%, a recall score of 99.46%, a precision score of 95.53%, and an F1 score of 97.46%.
通信网络的安全可能会通过已知和新颖的攻击方法受到破坏。可通过使用入侵检测系统（IDS）来防范此类攻击，该系统可通过训练机器学习模型来检测网络攻击。本文开发了 KOMIG（knapsack optimization and mutual information gain）入侵检测系统来检测网络入侵。KOMIG IDS 将优化和机器学习的优势结合在一起，实现了较高的入侵检测性能。具体来说，KOMIG IDS 包括一个两阶段的特征选择程序；第一阶段采用 Knapsack 优化算法，第二阶段采用互信息增益过滤器。具体而言，我们开发了一个优化模型，用于从网络入侵数据集中选择最重要的特征。然后，从所选特征中合成一组新特征，并与所选特征相结合，形成候选特征集。接着，我们对候选特征集进行信息增益过滤，剪除冗余特征，只留下具有最大信息增益的特征，用于训练机器学习模型。我们将所提出的 KOMIG IDS 应用于 UNSW-NB15 数据集（这是一个著名的网络入侵评估数据集），并将优化后的数据用于训练四个机器学习模型，即逻辑回归（LR）、随机森林（RF）、决策树（DT）和 K 近邻（KNN）。仿真实验结果表明，我们提出的基于 KNN 的 KOMIG IDS 的准确率为 97.14%，召回率为 99.46%，精确率为 95.53%，F1 分数为 97.46%，优于同类方案。
{"title":"Network Intrusion Detection Using Knapsack Optimization, Mutual Information Gain, and Machine Learning","authors":"A. Afolabi, O. A. Akinola","doi":"10.1155/2024/7302909","DOIUrl":"https://doi.org/10.1155/2024/7302909","url":null,"abstract":"The security of communication networks can be compromised through both known and novel attack methods. Protection against such attacks may be achieved through the use of an intrusion detection system (IDS), which can be designed by training machine learning models to detect cyberattacks. In this paper, the KOMIG (knapsack optimization and mutual information gain) IDS was developed to detect network intrusions. The KOMIG IDS combined the strengths of optimization and machine learning together to achieve a high intrusion detection performance. Specifically, KOMIG IDS comprises a 2-stage feature selection procedure; the first was accomplished with a knapsack optimization algorithm and the second with a mutual information gain filter. In particular, we developed an optimization model for the selection of the most important features from a network intrusion dataset. Then, a new set of features was synthesized from the selected features and combined with the selected features to form a candidate features set. Next, we applied an information gain filter to the candidate features set to prune out redundant features, leaving only the features that possess the maximum information gain, which were used to train machine learning models. The proposed KOMIG IDS was applied to the UNSW-NB15 dataset, which is a well-known network intrusion evaluation dataset, and the resulting data, after optimization operation, were used to train four machine learning models, namely, logistic regression (LR), random forest (RF), decision tree (DT), and K-nearest neighbors (KNN). Simulation experiments were conducted, and the results revealed that our proposed KNN-based KOMIG IDS outperformed comparative schemes by achieving an accuracy score of 97.14%, a recall score of 99.46%, a precision score of 95.53%, and an F1 score of 97.46%.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141277173","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 capacitance multiplier is an active circuit designed specifically to increase the capacitance of a passive capacitor to a significantly higher capacitance level. In this paper, the use of a voltage differencing differential difference amplifier (VDDDA), an electronically controllable active device for designing grounded and floating capacitance multipliers, is proposed. The capacitance multipliers proposed in this study are extremely simple and consist of a VDDDA, a resistor, and a capacitor. The multiplication factor (Kc) can be electronically controlled by adjusting the external bias current (IB). It offers an easy way of controlling it by utilizing a microcontroller for modern analog signal processing systems. The multiplication factor has the potential to be adjusted to a value that is either less than or greater than one, hence widening the variety of uses. The grounded capacitance multiplier can be easily transformed into a floating one by utilizing Zc-VDDDA. PSpice simulation and experimentation with a VDDDA realized from commercially available integrated circuits were used to test the performance of the proposed capacitance multipliers. The multiplication factor is electronically adjustable, ranging in approximation from 0.56 to 13.94. The operating frequency range is approximately three frequency decades. The realization of the lagging and leading phase shifters using the proposed capacitance multiplier is also examined and proven. The results reveal that the lagging and leading phase shifts are electronically tuned via the multiplication factor of the proposed capacitance multipliers.
{"title":"Electronically Tunable Grounded and Floating Capacitance Multipliers Using a Single Active Element","authors":"Nuttapon Seechaiya, Winai Jaikla, Amornchai Chaichana, Phamorn Silapan, Piya Supavarasuwat, Peerawut Suwanjan","doi":"10.1155/2024/6628863","DOIUrl":"https://doi.org/10.1155/2024/6628863","url":null,"abstract":"A capacitance multiplier is an active circuit designed specifically to increase the capacitance of a passive capacitor to a significantly higher capacitance level. In this paper, the use of a voltage differencing differential difference amplifier (VDDDA), an electronically controllable active device for designing grounded and floating capacitance multipliers, is proposed. The capacitance multipliers proposed in this study are extremely simple and consist of a VDDDA, a resistor, and a capacitor. The multiplication factor (Kc) can be electronically controlled by adjusting the external bias current (IB). It offers an easy way of controlling it by utilizing a microcontroller for modern analog signal processing systems. The multiplication factor has the potential to be adjusted to a value that is either less than or greater than one, hence widening the variety of uses. The grounded capacitance multiplier can be easily transformed into a floating one by utilizing Zc-VDDDA. PSpice simulation and experimentation with a VDDDA realized from commercially available integrated circuits were used to test the performance of the proposed capacitance multipliers. The multiplication factor is electronically adjustable, ranging in approximation from 0.56 to 13.94. The operating frequency range is approximately three frequency decades. The realization of the lagging and leading phase shifters using the proposed capacitance multiplier is also examined and proven. The results reveal that the lagging and leading phase shifts are electronically tuned via the multiplication factor of the proposed capacitance multipliers.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141104279","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}
Face recognition is one of the important elements that can be used for securing the facilities, emotion recognition, sentiment exploration, fraud analysis, and traffic pattern analysis. Intelligent face recognition has yielded excellent accuracy in a controlled environment whereas vice versa in an uncontrolled environment. However, conventional methods can no longer satisfy the demand at present due to their low recognition accuracy and restrictions on many occasions. This study proposed an optimal deep learning-based face recognition system that improves the security of the model developed in the IoT cloud environment. Initially, the dataset of images was gathered from the public repository. The captured images are explored using image processing techniques like image preprocessing employing the Gaussian filter technique for removing the noise and smoothing the image. The histogram of oriented gradients (HOGs) is used for the image segmentation. The processed images are preserved at the cloud service layer. Extract features were linked to facial activities using the spatial-temporal interest point (STIP). On the other hand, the extracted feature vectors are investigated using galactic swarm optimization (GSO) techniques that give optimized feature vectors. The necessary features are selected using the gray level co-occurrence matrix (GLCM), which separates the statistical texture features. The GSO output is fed into the deep convolutional neural network (DCNN) that effectively trains the captured face images. This will allow the effectiveness of the GSO-CNN technique to be assessed in terms of recognition accuracy, recall, precision, and error rate.
{"title":"A Novel Technique for Facial Recognition Based on the GSO-CNN Deep Learning Algorithm","authors":"Rana H. Al-Abboodi, A. Al-Ani","doi":"10.1155/2024/3443028","DOIUrl":"https://doi.org/10.1155/2024/3443028","url":null,"abstract":"Face recognition is one of the important elements that can be used for securing the facilities, emotion recognition, sentiment exploration, fraud analysis, and traffic pattern analysis. Intelligent face recognition has yielded excellent accuracy in a controlled environment whereas vice versa in an uncontrolled environment. However, conventional methods can no longer satisfy the demand at present due to their low recognition accuracy and restrictions on many occasions. This study proposed an optimal deep learning-based face recognition system that improves the security of the model developed in the IoT cloud environment. Initially, the dataset of images was gathered from the public repository. The captured images are explored using image processing techniques like image preprocessing employing the Gaussian filter technique for removing the noise and smoothing the image. The histogram of oriented gradients (HOGs) is used for the image segmentation. The processed images are preserved at the cloud service layer. Extract features were linked to facial activities using the spatial-temporal interest point (STIP). On the other hand, the extracted feature vectors are investigated using galactic swarm optimization (GSO) techniques that give optimized feature vectors. The necessary features are selected using the gray level co-occurrence matrix (GLCM), which separates the statistical texture features. The GSO output is fed into the deep convolutional neural network (DCNN) that effectively trains the captured face images. This will allow the effectiveness of the GSO-CNN technique to be assessed in terms of recognition accuracy, recall, precision, and error rate.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122489","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}
Due to its superior insulating qualities, SF6 gas is extensively used in the power sector. However, because of its poor environmental protection properties, finding ecologically acceptable insulating gas has become a critical challenge in the power sector in the context of pursuing green electricity. This work simulates the arc-quenching performance of a gas mixture of CF3I and CO2, which is thought to be a workable substitute for SF6 gas. The COMSOL software is used to build a two-dimensional model of a single-pipe arc-quenching chamber based on the concepts of magnetohydrodynamics (MHD) theory. The lightning impulse current is made by applying electrical stimulation to pure CO2 gas, gas mixtures with 10% CF3I and 90% CO2, and gas mixtures with 30% CF3I and 70% CO2 in the single-pipe arc-quenching chamber. During the first stage of arc formation, the results show that CF3I/CO2 gas mixtures with 10% and 30% CF3I have lower electrical conductivity than pure CO2 gas. An 8/20 μs lightning impulse current waveform with a magnitude of 4 kA is used for this observation. The highest airflow velocity for pure CO2 is 1744 m/s, but the mixture of 10%/90% CF3I/CO2 has a maximum airflow velocity of 1593 m/s. The 30%/70% CF3I/CO2 mixture has the highest maximum airflow velocity at 1840 m/s. Airflow velocity increases and the overpressure in the arc-quenching chamber is prolonged when there is a greater concentration of CF3I gas in the gas mixture. Consequently, these factors greatly reduce the duration of the arc-extinguishing time. The arc-quenching chamber’s overpressure is extended when the amount of CF3I gas in the gas mixture is increased, which increases the velocity of the airflow. As a result, these factors significantly decrease the duration of the arc-extinguishing time.
由于具有优异的绝缘性能，SF6 气体被广泛应用于电力行业。然而，由于 SF6 气体的环保性能较差，在追求绿色电力的背景下，寻找生态上可接受的绝缘气体已成为电力行业面临的严峻挑战。本研究模拟了 CF3I 和 CO2 混合气体的熄弧性能，这种混合气体被认为是 SF6 气体的可行替代品。根据磁流体力学（MHD）理论的概念，利用 COMSOL 软件建立了单管熄弧室的二维模型。通过对单管熄弧室中的纯 CO2 气体、含 10% CF3I 和 90% CO2 的混合气体以及含 30% CF3I 和 70% CO2 的混合气体施加电刺激，产生雷电脉冲电流。结果表明，在电弧形成的第一阶段，含 10% 和 30% CF3I 的 CF3I/CO2 混合气体的导电率低于纯 CO2 气体。该观测采用了幅值为 4 kA 的 8/20 μs 雷电脉冲电流波形。纯 CO2 的最高气流速度为 1744 m/s，但 10%/90% CF3I/CO2 混合物的最高气流速度为 1593 m/s。30%/70% CF3I/CO2 混合物的最高气流速度为 1840 米/秒。当混合气体中的 CF3I 浓度较高时，气流速度会增加，熄弧室中的过压时间也会延长。因此，这些因素大大缩短了灭弧时间。当混合气体中的 CF3I 气体量增加时，熄弧室的过压时间也会延长，从而增加气流速度。因此，这些因素都会大大缩短熄弧时间。
{"title":"Simulation Analysis of Arc-Quenching Performance of Eco-Friendly Insulating Gas Mixture of CF3I and CO2 under Impulse Arc","authors":"Dong Wu, Wengui Chen, Zelin Ji","doi":"10.1155/2024/8604095","DOIUrl":"https://doi.org/10.1155/2024/8604095","url":null,"abstract":"Due to its superior insulating qualities, SF6 gas is extensively used in the power sector. However, because of its poor environmental protection properties, finding ecologically acceptable insulating gas has become a critical challenge in the power sector in the context of pursuing green electricity. This work simulates the arc-quenching performance of a gas mixture of CF3I and CO2, which is thought to be a workable substitute for SF6 gas. The COMSOL software is used to build a two-dimensional model of a single-pipe arc-quenching chamber based on the concepts of magnetohydrodynamics (MHD) theory. The lightning impulse current is made by applying electrical stimulation to pure CO2 gas, gas mixtures with 10% CF3I and 90% CO2, and gas mixtures with 30% CF3I and 70% CO2 in the single-pipe arc-quenching chamber. During the first stage of arc formation, the results show that CF3I/CO2 gas mixtures with 10% and 30% CF3I have lower electrical conductivity than pure CO2 gas. An 8/20 μs lightning impulse current waveform with a magnitude of 4 kA is used for this observation. The highest airflow velocity for pure CO2 is 1744 m/s, but the mixture of 10%/90% CF3I/CO2 has a maximum airflow velocity of 1593 m/s. The 30%/70% CF3I/CO2 mixture has the highest maximum airflow velocity at 1840 m/s. Airflow velocity increases and the overpressure in the arc-quenching chamber is prolonged when there is a greater concentration of CF3I gas in the gas mixture. Consequently, these factors greatly reduce the duration of the arc-extinguishing time. The arc-quenching chamber’s overpressure is extended when the amount of CF3I gas in the gas mixture is increased, which increases the velocity of the airflow. As a result, these factors significantly decrease the duration of the arc-extinguishing time.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141125545","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}
The cloudification of telecommunication network functions with 5G is a novelty that offers higher performance than that of previous generations. However, these virtual network functions (VNFs) are exposed to internet threats when hosted in the cloud, resulting in new security challenges. Another fact is that many VNFs vendors with different security policies will be implied in 5G deployment, creating a heterogeneous 5G network. The authorities also require data privacy enhancement in 5G deployment and there is the fact that mobile operators need to inspect data for malicious traffic detection. In this situation, how can network traffic inspections be conducted effectively without infringing on data privacy? This study addresses this gap by proposing a novel state-of-the-art hybrid deep neural network that combines a convolutional neural network (CNN) stacked to bidirectional long short-term memory (BiLSTM) and unidirectional long short-term memory (LSTM) for the deep inspection of network flow for malicious traffic detection. The approach utilizes federated learning (FL) to facilitate multiple VNFs vendors to collaboratively train the proposed model without sharing VNFs’ raw data, which can mitigate the risk of data privacy violation. The proposed framework incorporates transport layer security (TLS) encryption to prevent data tempering or man-in-the-middle attacks between VNFs. The framework was validated through simulation using open-access benchmark datasets (InSDN and CICIDS2017). They achieved 99.99% and 99.58% accuracy and 0.048% and 0.617% false-positive rates for the InSDN and CICIDS2017 datasets, respectively, for FL. This study demonstrates the potential of hybrid deep learning-based FL for heterogeneous 5G network VNFs security monitoring.
{"title":"Balancing Data Privacy and 5G VNFs Security Monitoring: Federated Learning with CNN + BiLSTM + LSTM Model","authors":"Abdoul-Aziz Maiga, Edwin Ataro, Stanley Githinji","doi":"10.1155/2024/5134326","DOIUrl":"https://doi.org/10.1155/2024/5134326","url":null,"abstract":"The cloudification of telecommunication network functions with 5G is a novelty that offers higher performance than that of previous generations. However, these virtual network functions (VNFs) are exposed to internet threats when hosted in the cloud, resulting in new security challenges. Another fact is that many VNFs vendors with different security policies will be implied in 5G deployment, creating a heterogeneous 5G network. The authorities also require data privacy enhancement in 5G deployment and there is the fact that mobile operators need to inspect data for malicious traffic detection. In this situation, how can network traffic inspections be conducted effectively without infringing on data privacy? This study addresses this gap by proposing a novel state-of-the-art hybrid deep neural network that combines a convolutional neural network (CNN) stacked to bidirectional long short-term memory (BiLSTM) and unidirectional long short-term memory (LSTM) for the deep inspection of network flow for malicious traffic detection. The approach utilizes federated learning (FL) to facilitate multiple VNFs vendors to collaboratively train the proposed model without sharing VNFs’ raw data, which can mitigate the risk of data privacy violation. The proposed framework incorporates transport layer security (TLS) encryption to prevent data tempering or man-in-the-middle attacks between VNFs. The framework was validated through simulation using open-access benchmark datasets (InSDN and CICIDS2017). They achieved 99.99% and 99.58% accuracy and 0.048% and 0.617% false-positive rates for the InSDN and CICIDS2017 datasets, respectively, for FL. This study demonstrates the potential of hybrid deep learning-based FL for heterogeneous 5G network VNFs security monitoring.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140363839","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}
The object of research is the fundamental and technical indicators of companies after the release of the earnings report. This study attempts to address the issue of understanding the impact of fundamental and technical analysis indicator dynamics on profits and loss news releases. This research provides an in-depth analysis of stock price forecasting models, focusing on the influence of earning report seasons as catalysts for stock price growth. The study explores the relationship between key financial indicators, including earnings per share (EPS), revenue, and the maximum price observed in the 52-week period of the previous year (MaxW52). A trading algorithm is developed based on the adaptive neurofuzzy inference system (ANFIS). Through a comprehensive analysis of the neural network’s training sample, it is concluded that abnormally large negative indicators have a profound impact on traders’ emotional reactions. This results leads to a hypothesis for further research, suggesting that report indicators may be processed by computational algorithms, potentially including artificial intelligence (AI). Consequently, the emergence of emotional trading robots managed by investment funds becomes a crucial area for investigation. Understanding the behavior of these algorithms enables proactive decision-making, allowing traders to leverage their knowledge and sell-purchased securities to these algorithms before their transactions occur. The implications of this research shed light on the evolving landscape of trading strategies and the role of emotionality in financial markets.
{"title":"Enhancing Analytical Precision in Company Earnings Reports through Neurofuzzy System Development: A Comprehensive Investigation","authors":"Bakhyt Matkarimov, A. Barlybayev, Didar Karimov","doi":"10.1155/2024/8515203","DOIUrl":"https://doi.org/10.1155/2024/8515203","url":null,"abstract":"The object of research is the fundamental and technical indicators of companies after the release of the earnings report. This study attempts to address the issue of understanding the impact of fundamental and technical analysis indicator dynamics on profits and loss news releases. This research provides an in-depth analysis of stock price forecasting models, focusing on the influence of earning report seasons as catalysts for stock price growth. The study explores the relationship between key financial indicators, including earnings per share (EPS), revenue, and the maximum price observed in the 52-week period of the previous year (MaxW52). A trading algorithm is developed based on the adaptive neurofuzzy inference system (ANFIS). Through a comprehensive analysis of the neural network’s training sample, it is concluded that abnormally large negative indicators have a profound impact on traders’ emotional reactions. This results leads to a hypothesis for further research, suggesting that report indicators may be processed by computational algorithms, potentially including artificial intelligence (AI). Consequently, the emergence of emotional trading robots managed by investment funds becomes a crucial area for investigation. Understanding the behavior of these algorithms enables proactive decision-making, allowing traders to leverage their knowledge and sell-purchased securities to these algorithms before their transactions occur. The implications of this research shed light on the evolving landscape of trading strategies and the role of emotionality in financial markets.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140231377","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}
S. Elouaham, A. Dliou, W. Jenkal, M. Louzazni, H. Zougagh, S. Dlimi
The electrocardiogram (ECG) is a diagnostic tool that provides insights into the heart’s electrical activity and overall health. However, internal and external noises complicate accurate heart issue diagnosis. Noise in the ECG signal distorts and introduces artifacts, making it difficult to detect subtle abnormalities. To ensure an accurate evaluation, noise-free ECG signals are crucial. This study introduces the empirical wavelet transform (EWT), a contemporary denoising method. EWT decomposes the signal into frequency components, allowing detailed analysis by constructing a customized wavelet basis. Researchers and practitioners can enhance signal analysis by separating the desired components from unwanted noise. The EWT approach effectively eliminates noise while maintaining signal information. The study applies DWT-ADTF, FST, Kalman, Liouville–Weyl fractional compound integral filter LW, Weiner, and EWT denoising methods to two ECG databases from MIT-BIH, which encompass a wide range of cardiac signals and noise levels. The comparative analysis highlights EWT’s strengths through improved signal quality and objective performance metrics. This adaptive transform proves promising for denoising ECG signals and facilitating accurate analysis in clinical and research settings.
{"title":"Empirical Wavelet Transform Based ECG Signal Filtering Method","authors":"S. Elouaham, A. Dliou, W. Jenkal, M. Louzazni, H. Zougagh, S. Dlimi","doi":"10.1155/2024/9050909","DOIUrl":"https://doi.org/10.1155/2024/9050909","url":null,"abstract":"The electrocardiogram (ECG) is a diagnostic tool that provides insights into the heart’s electrical activity and overall health. However, internal and external noises complicate accurate heart issue diagnosis. Noise in the ECG signal distorts and introduces artifacts, making it difficult to detect subtle abnormalities. To ensure an accurate evaluation, noise-free ECG signals are crucial. This study introduces the empirical wavelet transform (EWT), a contemporary denoising method. EWT decomposes the signal into frequency components, allowing detailed analysis by constructing a customized wavelet basis. Researchers and practitioners can enhance signal analysis by separating the desired components from unwanted noise. The EWT approach effectively eliminates noise while maintaining signal information. The study applies DWT-ADTF, FST, Kalman, Liouville–Weyl fractional compound integral filter LW, Weiner, and EWT denoising methods to two ECG databases from MIT-BIH, which encompass a wide range of cardiac signals and noise levels. The comparative analysis highlights EWT’s strengths through improved signal quality and objective performance metrics. This adaptive transform proves promising for denoising ECG signals and facilitating accurate analysis in clinical and research settings.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140250572","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}
The distributed generation (DG) units’ penetrations in power systems are becoming more prevalent. The majority of recent studies are now focusing on how to best position and size PV-DG units to further improve grid performance. In actuality, and as a result of ideal design requirements, the size and position of the PV are chosen and executed, and no luxury for a change. In this work, the PV-DG unit sizing and location were determined and placed beforehand. Also, load change is a fact and is to be highly considered in the grid. Studying the grid performance and how to enhance it under these conditions is the main objective of this study. This examination was executed using an IEEE 15 bus system in a MATLAB environment. Distribution lines were proposed to connect the PV-DG from its restricted location to the required bus. The purpose of this study is therefore to evaluate the grid’s performance with various actual loads on each bus while connecting a PV-DG unit through a distribution line while taking the available transfer capacity (ATC) of the network into account to find the optimally connected bus. The results said that the optimally connected bus is changed by changing the load which is not doable on land. The results obtained indicate that breaking up PV-DG units into smaller units in the same location and connecting them to every bus was the best option for improving grid performance.
{"title":"A Novel Technique for High-Performance Grid Integrated with Restricted Placement of PV-DG considering Load Change","authors":"A. Soliman, Safaa M. Emara, Amir Y. Hassan","doi":"10.1155/2024/5395272","DOIUrl":"https://doi.org/10.1155/2024/5395272","url":null,"abstract":"The distributed generation (DG) units’ penetrations in power systems are becoming more prevalent. The majority of recent studies are now focusing on how to best position and size PV-DG units to further improve grid performance. In actuality, and as a result of ideal design requirements, the size and position of the PV are chosen and executed, and no luxury for a change. In this work, the PV-DG unit sizing and location were determined and placed beforehand. Also, load change is a fact and is to be highly considered in the grid. Studying the grid performance and how to enhance it under these conditions is the main objective of this study. This examination was executed using an IEEE 15 bus system in a MATLAB environment. Distribution lines were proposed to connect the PV-DG from its restricted location to the required bus. The purpose of this study is therefore to evaluate the grid’s performance with various actual loads on each bus while connecting a PV-DG unit through a distribution line while taking the available transfer capacity (ATC) of the network into account to find the optimally connected bus. The results said that the optimally connected bus is changed by changing the load which is not doable on land. The results obtained indicate that breaking up PV-DG units into smaller units in the same location and connecting them to every bus was the best option for improving grid performance.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139800977","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}
The distributed generation (DG) units’ penetrations in power systems are becoming more prevalent. The majority of recent studies are now focusing on how to best position and size PV-DG units to further improve grid performance. In actuality, and as a result of ideal design requirements, the size and position of the PV are chosen and executed, and no luxury for a change. In this work, the PV-DG unit sizing and location were determined and placed beforehand. Also, load change is a fact and is to be highly considered in the grid. Studying the grid performance and how to enhance it under these conditions is the main objective of this study. This examination was executed using an IEEE 15 bus system in a MATLAB environment. Distribution lines were proposed to connect the PV-DG from its restricted location to the required bus. The purpose of this study is therefore to evaluate the grid’s performance with various actual loads on each bus while connecting a PV-DG unit through a distribution line while taking the available transfer capacity (ATC) of the network into account to find the optimally connected bus. The results said that the optimally connected bus is changed by changing the load which is not doable on land. The results obtained indicate that breaking up PV-DG units into smaller units in the same location and connecting them to every bus was the best option for improving grid performance.
{"title":"A Novel Technique for High-Performance Grid Integrated with Restricted Placement of PV-DG considering Load Change","authors":"A. Soliman, Safaa M. Emara, Amir Y. Hassan","doi":"10.1155/2024/5395272","DOIUrl":"https://doi.org/10.1155/2024/5395272","url":null,"abstract":"The distributed generation (DG) units’ penetrations in power systems are becoming more prevalent. The majority of recent studies are now focusing on how to best position and size PV-DG units to further improve grid performance. In actuality, and as a result of ideal design requirements, the size and position of the PV are chosen and executed, and no luxury for a change. In this work, the PV-DG unit sizing and location were determined and placed beforehand. Also, load change is a fact and is to be highly considered in the grid. Studying the grid performance and how to enhance it under these conditions is the main objective of this study. This examination was executed using an IEEE 15 bus system in a MATLAB environment. Distribution lines were proposed to connect the PV-DG from its restricted location to the required bus. The purpose of this study is therefore to evaluate the grid’s performance with various actual loads on each bus while connecting a PV-DG unit through a distribution line while taking the available transfer capacity (ATC) of the network into account to find the optimally connected bus. The results said that the optimally connected bus is changed by changing the load which is not doable on land. The results obtained indicate that breaking up PV-DG units into smaller units in the same location and connecting them to every bus was the best option for improving grid performance.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139860944","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}
As a commonly used mode of transportation in people’s daily lives, the normal operation of railway transportation is crucial. The track circuit, as a key component of the railway transportation system, is prone to malfunctions due to environmental factors. However, the current method of inspecting track circuit faults still relies on the experience of on-site personnel. In order to improve the efficiency and accuracy of fault diagnosis, we propose to establish an intelligent fault diagnosis system. Considering that the fault data are a one-dimensional time series, this paper presents a fault diagnosis method based on the UNet-LSTM network (ULN). The LSTM network is established on the basis of fault data and used for ZPW-2000A track circuit fault diagnosis. However, the use of a single LSTM network has a high error rate in the common fault diagnosis of track circuits. Therefore, this paper proposes a feature extraction method based on the UNet network. This method is used to extract the features of the original data and then input them into the LSTM network for fault diagnosis. Through experiments with on-site fault data, it has been verified that this method can accurately classify seven common track circuit faults. Finally, the superiority of the method is verified by comparing it with other commonly used fault classification methods.
{"title":"Track Circuits Fault Diagnosis Method Based on the UNet-LSTM Network (ULN)","authors":"Weijie Tao, Xiaowei Li, Zheng Li","doi":"10.1155/2024/1547428","DOIUrl":"https://doi.org/10.1155/2024/1547428","url":null,"abstract":"As a commonly used mode of transportation in people’s daily lives, the normal operation of railway transportation is crucial. The track circuit, as a key component of the railway transportation system, is prone to malfunctions due to environmental factors. However, the current method of inspecting track circuit faults still relies on the experience of on-site personnel. In order to improve the efficiency and accuracy of fault diagnosis, we propose to establish an intelligent fault diagnosis system. Considering that the fault data are a one-dimensional time series, this paper presents a fault diagnosis method based on the UNet-LSTM network (ULN). The LSTM network is established on the basis of fault data and used for ZPW-2000A track circuit fault diagnosis. However, the use of a single LSTM network has a high error rate in the common fault diagnosis of track circuits. Therefore, this paper proposes a feature extraction method based on the UNet network. This method is used to extract the features of the original data and then input them into the LSTM network for fault diagnosis. Through experiments with on-site fault data, it has been verified that this method can accurately classify seven common track circuit faults. Finally, the superiority of the method is verified by comparing it with other commonly used fault classification methods.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139867888","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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