Journal of Electrical and Computer Engineering

In order to overcome the poor observability of yaw measurement for foot-mounted inertial measurement unit (IMU), an integrated IMU+Compass scheme for self-contained pedestrian navigation is presented. In this mode, the compass measurement is used to provide the accurate yaw to improve the accuracy of the attitude transformation matrix for the foot-mounted IMU solution. And then, when the person is in a stance phase during walk, a unbiased finite impulse response (UFIR) filter based on the self-contained pedestrian navigation scheme is investigated, which just needs the state vector size <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msup><mml:mrow><mml:mi>M</mml:mi></mml:mrow><mml:mrow><mml:mi>U</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math> and the filtering horizon size <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msup><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mi>U</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math>, while ignoring the noise statistics compared with the Kalman filter (KF). Finally, a real test has been done to verify the performance of the proposed self-contained pedestrian navigation using the IMU and compass measurements via UFIR filter. The test results show that the proposed filter has robust performance compared with the KF.

Meng Hou

Yuan Xu

Xiao Liu

Robust Self-Contained Pedestrian Navigation by Fusing the IMU and Compass Measurements via UFIR Filtering

Ensuring compliance with safety regulations regarding wearing is essential for the safety and security of those working on substation construction sites. However, relying on supervisors to monitor workers in real time on the work site or through remote surveillance videos is both unreasonable and inefficient. A deep learning network approach named FFA-YOLOv7 is presented in this study that utilizes an improved version of YOLOv7 to detect violations of worker wearing in real time during power construction site surveillance. In YOLOv7, the feature pyramid network (FPN) of the neck stage is constructed through continuous upsampling and skip connections for feature fusion, after continuous downsampling of the backbone. However, this process can result in the loss of precise shallow position information. To tackle this issue, we have introduced a novel feature fusion pathway to the FPN architecture, enabling each layer not only to fuse feature maps from the same level during the downsampling course but also to fuse feature maps from shallower levels. This approach combines precise positional information from shallow layers with rich semantic information from deep layers. Additionally, we utilized attention after feature fusion in each layer to optimize the feature map fusion effect and achieve better detection accuracy performance. In order to conduct comparative experiments, we trained six variations of the YOLO model as detectors using a dataset gathered from realistic construction sites. The experimental results indicate that our proposed FFA-YOLOv7 attained a detection precision of 95.92% and a recall rate of 97.13%, demonstrating a high level of accuracy and a low rate of missed detections. These outcomes effectively satisfy the requirements for robust and accurate detection of real-world power construction violations.

Rong Chang

Bingzhen Zhang

Qianxin Zhu

Shan Zhao

Kai Yan

Yang Yang

FFA-YOLOv7: Improved YOLOv7 Based on Feature Fusion and Attention Mechanism for Wearing Violation Detection in Substation Construction Safety

FFA-YOLOv7：基于特征融合与注意力机制改进的YOLOv7用于变电站施工安全中的着装违规检测

With the development of mobile platform, such as smart cellphone and pad, the E-Learning model has been rapidly developed. However, due to the low completion rate for E-Learning platform, it is very necessary to analyze the behavior characteristics of online learners to intelligently adjust online education strategy and enhance the quality of learning. In this paper, we analyzed the relation indicators of E-Learning to build the student profile and gave countermeasures. Adopting the similarity computation and Jaccard coefficient algorithm, we designed a system model to clean and dig into the educational data and also the students’ learning attitude and the duration of learning behavior to establish student profile. According to the E-Learning resources and learner behaviors, we also present the intelligent guide model to guide both E-Learning platform and learners to improve learning things. The study on student profile can help the E-Learning platform to meet and guide the students’ learning behavior deeply and also to provide personalized learning situation and promote the optimization of the E-Learning.

Kun Liang

Yiying Zhang

Yeshen He

Yilin Zhou

Wei Tan

Xiaoxia Li

Online Behavior Analysis-Based Student Profile for Intelligent E-Learning

基于在线行为分析的学生智能电子学习档案

The prediction of consumers’ electricity consumption is an important demand‐side energy management technique for adaptable dynamic tariff pricing. In this regard, historical consumption patterns play an important role in accurately predicting the future energy consumption. In this paper, we propose a novel system based on machine learning (ML) algorithms to accurately predict not only the future energy consumption based on historical data and various environmental factors but also energy‐saving recommendations for the consumers. The proposed system is evaluated on real‐world energy consumption data, and the results show that random forest (RF) more accurately predicts energy consumption with 98% R‐squared value and 0.51 root mean square error, whereas decision tree (DT) is best fit for personalized energy recommendations resulting in 85% R‐squared and 0.14 root mean square error. The implementation of this system will greatly assist utility companies in making precise future energy consumption forecasts to formulate demand‐side energy management policies which are cost‐effective for both consumers and utility companies.

Natasha Nigar

Muhammad Kashif Shahzad

Hafiz Muhammad Faisal

Sunday Adeola Ajagbe

Matthew O. Adigun

Improving Demand‐Side Energy Management With Energy Advisor Using Machine Learning

利用机器学习改进能源顾问的需求侧能源管理

The electronic throttle valve (ETV) system is one of the essential parts of the automobile because it controls the amount of airflow into the engine’s combustion system. In fact, there are challenges in ETV control due to several nonsmooth nonlinearities, such as backlash, stick‐slip friction, and a discontinuous nonlinear spring. This paper proposes eight control schemes; the first two are the conventional proportional‐integral‐derivative (PID) controller and the fractional‐order PID (FOPID) controller, while the second two schemes are modified versions of conventional PID and FOPID using the integral of the arc tan function of the error rather than the error in the integral action. However, all these control schemes above have been studied with and without considering the technique of manipulating the windup problem or antiwindup. A metaheuristic optimization technique, namely, the grey wolf optimization (GWO) algorithm, is introduced for optimizing the controllers’ parameters while minimizing the integral of the cube time square error (IT^3SE) cost function. A comparison study between these control schemes and other controllers has been conducted based on numerical simulation. The computer simulation demonstrates that the performance of the arc tan FOPID (AT‐FOPID) controller is superior to that of the other optimal control schemes and other controllers, as follows: best value of performance criterion IT^3SE, best exponential transmission of transient response, least overshoot, least rise time, least settling time, low control effort, least oscillations of the control signal, and smooth phase trajectory from the initial state to the target state without circling about the target. Finally, the most important feature of this scheme is its superior robustness compared to others.

Mohamed Jasim Mohamed

Luay Thamir Rasheed

Design of Nonlinear PID and FOPID Controllers for Electronic Throttle Valve Plate’s Position

电子节气门阀片位置的非线性PID与FOPID控制器设计

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%.

Akindele S. Afolabi

Olubunmi A. Akinola

Network Intrusion Detection Using Knapsack Optimization, Mutual Information Gain, and Machine Learning

使用背包优化、互信息增益和机器学习的网络入侵检测

Acoustic emission (AE) signals produced by different types of rocks have different characteristics of information. Determining the brittle mineral content of rock according to the acoustic emission characteristics of rock is helpful to understand the mechanical behavior of rock in field monitoring. This article constructs a deep learning algorithm model to identify acoustic emission signals released from rock fractures with different brittle mineral contents. In response to the interference characteristics of acoustic emission signal data, a multiscale one-dimensional convolutional neural network embedded with efficient channel attention (ECA) module was incorporated into the model, and multiscale convolutional kernels were used to extract features of different levels of precision. In the latter half of the model, the BLSTM network was incorporated to extract time series-related features, local spatial uncorrelated features, and weak periodic pattern features from the acoustic emission signal data. To solve the problem that the recognition accuracy of minority samples decreases, this study replaces ReLU activation function with SELU. The results show that the multiscale 1DCNN-BLSTM model embedded in ECA module has a good antinoise performance, and the recognition accuracy can reach over 90%. The discovery of this work provides a new idea for exploring the mechanism of rock mass instability.

Weihua Wang

Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTM

基于多尺度1DCNN-BLSTM的岩石断裂声发射信号识别算法

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 (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msub><mml:mrow><mml:mi>K</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi></mml:mrow></mml:msub></mml:math>) can be electronically controlled by adjusting the external bias current (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:msub><mml:mrow><mml:mi>I</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub></mml:math>). 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.

Nuttapon Seechaiya

Winai Jaikla

Amornchai Chaichana

Phamorn Silapan

Piya Supavarasuwat

Peerawut Suwanjan

Electronically Tunable Grounded and Floating Capacitance Multipliers Using a Single Active Element

使用单个有源元件的接地和浮地电容倍增器的电子可调谐性

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.

Rana H. Al-Abboodi

Ayad A. Al-Ani

A Novel Technique for Facial Recognition Based on the GSO-CNN Deep Learning Algorithm

基于GSO-CNN深度学习算法的新型人脸识别技术

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.

Dong Wu

Wengui Chen

Zelin Ji

Simulation Analysis of Arc-Quenching Performance of Eco-Friendly Insulating Gas Mixture of CF3I and CO2 under Impulse Arc

冲击电弧下CF3I与CO2环保绝缘气体混合物的灭弧性能仿真分析

短名	J. Electr. Comput. Eng.
Journal Impact	1.35
国际分区	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q4)

ISSN	2090-0147, 2090-0155
h-index	37

涉及主题	计算机科学工程类数学物理人工智能电气工程量子力学操作系统电信计算机网络算法程序设计语言生物统计经济计算机视觉热力学电子工程机器学习哲学功率（物理）心理学化学管理地理
出版信息	出版商: Hindawi Publishing Corporation，出版周期: ，期刊类型: journal，开源期刊: 是
基本数据	创刊年份: 2010，原创研究文献占比: 98.75%，自引率:16.70%， Gold OA占比: 100.00%
期刊费用	$950详情
平均审稿周期	网友分享经验：19 Weeks

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