Electronics

Du, Shenhui;Liu, Yang;Wang, Yao;Li, Ying;Yan, Zhibang

doi: 10.3390/electronics12204188pmid: N/A

This paper designs an improved sliding mode observer (ISMO) compound control scheme combined with a disturbance observer to solve the chattering and anti-disturbance problems of the traditional sliding mode observer (SMO) for permanent magnet synchronous motor (PMSM) in a sensorless control system. First, the sign function is replaced by an exponential type input function, and the fuzzy control rules are developed to automatically regulate the boundary layer control coefficient of the exponential input function, thereby changing the convergence characteristics of the exponential input function and improving the system observation accuracy. Then, the integral sliding mode surface and the quadratic radical term function of the square of the state variable are introduced to reduce system chattering. The proposed ISMO is proved using Lyapunov’s law to guarantee the whole system is stable. Based on the exponential input function and the integral sliding surface, an improved sliding mode disturbance observer (ISMDO) is constructed as a feed-forward compensator, which can optimize the dynamic performance of the improved observation system and ensure the strong robustness of the system by compensating the q-axis current. Finally, MATLAB/Simulink simulation and experimental platform verification have been carried out, which confirms the feasibility of the proposed composite control scheme.

Zhou, Pengwei;Xie, Zhiqiang;Zhou, Wei;Tan, Zhenjiang

doi: 10.3390/electronics12204189pmid: N/A

In the process of the integrated scheduling of multi-variety and small-batch complex products, the process structure and attribute characteristics are often ignored, which affects the overall scheduling effect. Aiming at solving this problem, a heuristic integrated scheduling algorithm (HIS-IDA) based on the improved Dijkstra algorithm is proposed. The algorithm takes the processing time of the process itself as the path value of the preceding and the following adjacent processes. Firstly, the improved Dijkstra algorithm prioritized the scheduling of the process sequence with long longitudinal paths and realized the “longitudinal optimization” of the integrated scheduling. Secondly, the layer priority strategy is used to shorten the interval time of process processing and realize the “horizontal optimization” of integrated scheduling. On the basis of “vertical and horizontal optimization”, the idle time of the equipment is further reduced by using the process priority strategy of the leaf node, and the “idle optimization” of the integrated scheduling is realized, so as to optimize the overall effect of the integrated scheduling. The effectiveness and superiority of the algorithm are proved using comparison analysis.

Yu, Qianqian;Zhu, Guangzhou

doi: 10.3390/electronics12204190pmid: N/A

The Hakka cardigan stands as a quintessential representation of traditional Hakka attire, embodying not only the rich cultural heritage of a nation but also serving as a global cultural treasure. In this academic paper, we focus on a representative model to showcase the development of an autonomous 3D scanning system founded on an offline point cloud generation algorithm. Through a meticulous process involving the emulation of clothing pattern restoration, we employ a diverse array of software tools including Photoshop, Autodesk Maya, and CORELDRAW, harnessing graphic and image processing techniques to seamlessly transition from two-dimensional pattern restoration to a three-dimensional realm. This study revolves around the establishment of an autonomous 3D scanning system centered on a representative model, leveraging an offline point cloud generation algorithm. We incorporate the La-place mesh deformation algorithm to execute conformal transformations on neighboring vertices of motion vertices, while delving into the fundamental methodologies behind digital restoration and the three-dimensional virtual presentation of Hakka cardigans. Our experiments culminate in the measurement of six three-dimensional clothing pieces, revealing absolute deviation between the model and the actual clothing. Furthermore, when we compare the automatic measurements from 200 3D scanned human bodies with their manually obtained counterparts, the displayed measurement error hovers at approximately 0.5 cm. This research endeavor charts an expedited pathway to achieve digital restoration and three-dimensional virtual representation of Hakka cardigans. It not only offers a novel perspective for the digital revitalization of traditional clothing but also serves as a valuable augmentation to contemporary methods of preserving traditional clothing.

Hou, Boyuan;Zhang, Kailong;Gong, Zu;Li, Qiugang;Zhou, Junle;Zhang, Jiahao;de La Fortelle, Arnaud

doi: 10.3390/electronics12204191pmid: N/A

With the rapid development of emerging information technology and its increasing integration with transportation systems, the Intelligent Transportation System (ITS) is entering a new phase, called Cooperative ITS (C-ITS). It offers promising solutions to numerous challenges in traditional transportation systems, among which the Vehicle Routing Problem (VRP) is a significant concern addressed in this work. Considering the varying urgency levels of different vehicles and their different traveling constraints in the Service-oriented Cooperative ITS (SoC-ITS) framework studied in our previous research, the Service-oriented Cooperative Vehicle Routing Problem (SoC-VRP) is firstly analyzed, in which cooperative planning and vehicle urgency degrees are two vital factors. After examining the characteristics of both VRP and SoC-VRP, a Deep Reinforcement Learning (DRL)-based prioritized route planning mechanism is proposed. Specifically, we establish a deep reinforcement learning model with Rainbow DQN and devise a prioritized successive decision-making route planning method for SoC-ITS, where vehicle urgency degrees are mapped to three priorities: High for emergency vehicles, Medium for shuttle buses, and Low for the rest. All proposed models and methods are implemented, trained using various scenarios on typical road networks, and verified with SUMO-based scenes. Experimental results demonstrate the effectiveness of this hybrid prioritized route planning mechanism.

Byeon, Yeong-Hyeon;Kwak, Keun-Chang

doi: 10.3390/electronics12204192pmid: N/A

This study focuses on personal identification using bidirectional long short-term memory (LSTM) with efficient features from electromyogram (EMG) biomedical signals. Personal identification is performed by comparing and analyzing features that can be stably identified and are not significantly affected by noise. For this purpose, 13 efficient features, such as enhanced wavelength, zero crossing, and mean absolute value, were obtained from EMG signals. These features were extracted from segmented signals of a specific length. Then, the bidirectional LSTM was trained on the selected features as sequential data. The features were ranked based on their classification performance. Finally, the most effective features were selected, and the selected features were connected to achieve an improved classification rate. Two public EMG datasets were used to evaluate the proposed model. The first database was acquired from eight-channel Myo bands and was composed of EMG signals from 10 varying motions of 50 individuals. The total numbers of segments for the training and test sets were 30,000 and 20,000, respectively. The second dataset consisted of ten arm motions acquired from 40 individuals. A performance comparison of the dataset revealed that the proposed method exhibited good performance and efficiency compared to other well-known methods.

Tang, Chunju;Liu, Yanping

doi: 10.3390/electronics12204193pmid: N/A

Due to the increasing demand of high data rate, spectrum scarcity is a key problem for providing unprecedented capacity in diversified applications for future wireless networks. Therefore, the efficiently shared use of unlicensed bands is one of the promising solutions for addressing the spectrum scarcity issue. We study decentralized machine learning approaches using the paradigm of integrated sensing and communication (ISAC) for the shared use of unlicensed millimeter-wave bands. We first present a 5G–WiFi fusion protocol stack for sharing unlicensed millimeter-wave bands, and then design an ISAC-based access protocol and an ISAC based coexistence protocol integrated with decentralized learning function to achieve the efficiently shared use of unlicensed bands. Using the coexistence protocol, we propose promising decentralized machine learning approaches to share unlicensed millimeter-wave bands. Finally, simulations are provided to verify the performance of the proposed scheme, where the results have shown that the proposed scheme greatly reduces the search space of the solution and effectively protects the communication performance of the WiFi system compared to traditional schemes, which indicates that simultaneous transmissions of 5G-U and WiFi at the 60 GHz band are feasible under the proposed scheme.

Fan, Daoerji;Sun, Yuxin;Wang, Zhixin;Peng, Yanjun

doi: 10.3390/electronics12204194pmid: N/A

Mongolian online handwriting recognition is a complex task due to the script’s intricate characters and extensive vocabulary. This study proposes a novel approach by integrating a pre-trained language model into the sequence-to-sequence(Seq2Seq) + attention mechanisms(AM) model to enhance recognition accuracy. Three fusion models, including former, latter, and complete fusion, are introduced, showing substantial improvements over the baseline model. The complete fusion model, combined with synchronized language model parameters, achieved the best results, significantly reducing character and word error rates. This research presents a promising solution for accurate Mongolian online handwriting recognition, offering practical applications in preserving and utilizing the Mongolian script.

Uddin, Md Nazim;Islam, Md Khadimul;Ortiz, Michael;Alwan, Elias A.

doi: 10.3390/electronics12204195pmid: N/A

This paper presents an intelligent design method for a corner-truncated microstrip patch antenna (CTMPA) operating at 32 GHz using various well-known machine learning (ML) techniques. Our objectives are to obtain a gain of >5 dBic across a 10% bandwidth, an axial ratio (AR) of <3 dB, and a return loss of <−10 dB. First, a dataset of 715 full-wave simulated samples is analyzed with four distinct antenna characteristics (viz. features), along with the related computed |S11|, gain, and AR. Using mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and R2 score, 12 ML regression models were examined to compare the training data with the new predicted values. Next, the model that best satisfies our objectives was chosen. Results showed that the artificial neural network (ANN) followed by k-nearest neighbor (KNN) regression produced the lowest error compared to all tested ML models. The design parameters that achieved our intended objectives were computed using the predicted results. The predicted design was validated using a full-wave simulation and a prototype measurement.

Liu, Weidong;Li, Mingjie;Wang, Tong;Jin, Mengzhe;Fang, Qingyuan

doi: 10.3390/electronics12204196pmid: N/A

Positioning systems based on the Time Difference of Arrival (TDOA) often struggle to accurately estimate time delays for partial discharge signals in complex electromagnetic environments. To address this, we introduce an improved joint-weighted generalized cross-correlation (GCC) time delay estimation algorithm. Traditional GCC time delay estimations, when reliant on conventional weighted functions, significantly underperform in situations characterized by low signal–noise ratios (SNRs). Our proposed improved joint weighted function combines the advantages of both phase transform (PHAT) and correlation transform (SCOT) weightings, resulting in a composite function. Compared to the conventional weighted GCC, the improved joint weighted GCC displays a more distinct peak in cross-correlation functions and ensures more robust delay estimations, especially under low SNR conditions. Experimental results indicate that the improved joint-weighted GCC algorithm achieves an error margin below 1 ns in challenging outdoor electromagnetic scenarios, demonstrating its aptitude for detecting and localizing partial discharge signals in practical engineering applications.

Li, Yongtao;Li, Hanyan;Feng, Jinjun

doi: 10.3390/electronics12204197pmid: N/A

In this work, a Spindt cold cathode electron gun with a PPM (periodic permanent magnet) focusing system for a terahertz TWT (traveling wave tube) was designed and simulated based on the Pierce electron gun structure. More specifically, a new 3D (three dimensional) emission model was used, where the cathode radius of the electron gun was 1 mm and the cathode current was 30 mA, with an emitting half angle of about 28°. It was demonstrated that the electron beam was well focused with an electron beam radius of 0.3 mm and a filling ratio of 0.5 when the maximum value of the PPM field along with the axis was 0.122T. According to the simulation results, a planar cold cathode electron gun was developed. Measurements demonstrated that the I/V characteristics of the cold cathode gun were consistent with that of a cold cathode, revealing that the electrons emitted from the cathode are not intercepted when passing through the electron gun.