Ebook: Electronics, Communications and Networks

The Conference on Electronics, Communications and Networks (CECNet) is an annual conference focused on electronics technology, communication engineering, wireless communications and computer engineering. Following the success of CECNet 2023 in Macao (China), CECNet 2024 is held face-to-face in Matsue, Japan, from 5 to 8 November 2024. The CECNet conference series has now completed its fourteenth edition, this 2024 conference being the second time as a live conference following three years in online mode due to the global Covid pandemic. Contributions fall into three main categories: 1) Electronics Technology and VLSI, 2) Internet Technology and Signal Processing and 3) Information Communication and Communication Networks. The term Very Large Scale Integration (VLSI) dates from the end of the 1970s, prior to the appearance of Metal Oxide Semiconductor (MOS) chips. Ultra Large Scale Integration (ULSI) was coined ten years later, and relates to a design which may contain as many as a million transistors. Giant Large Scale Integration (GLSI) was established in this century about fifteen years ago and uses more than a billion transistors per integrated circuit. There are a couple more acronyms that may also be found in research, including XLSI (eXtreme Large Scale Integration) and SLSI (Super Large Scale Integration) which refer to a similar level as ULSI and GLSI.

The conference included four keynote speakers: Professor Zhongxiang Shen, Yangtze Delta Region Academy of Beijing Institute of Technology (Jiaxing), China (h-index of 62 and about 12,000 citations according to Google Scholar as of October 2024); Professor Qin Zhang, Institute of Nuclear and New Energy Technology and Department of Computer Science and Technology, Tsinghua University, China. Professor Qin Zhang has a long experience in universities all over the world and is an emeritus member of the China Association for Science and Technology; Professor Larbi Talbi, Department of Computer Science and Engineering (DCSE), University of Quebec, Canada; and Professor Fusaomi Nagata, Department of Mechanical Engineering, Faculty of Engineering, Sanyo-Onoda City University, Japan.

All papers were exhaustively reviewed by program committee members and peer-reviewers, taking into account the breadth and depth of the research topics that fall within the scope of CECNet. From 280 submissions, the 74 most promising and FAIA-mainstream-relevant contributions presenting innovative original ideas or results of general significance supported by clear and rigorous reasoning, and compelling new light in evidence as well as method, were included in this book. We would like to thank the keynote speakers and authors for their efforts in preparing a contribution for this leading international conference. Moreover, we are very grateful to all those, especially the program committee members and reviewers, who devoted time to the evaluation of papers. It is a great honor to continue with the publication of these proceedings in the prestigious series Frontiers in Artificial Intelligence and Applications (FAIA) from IOS Press, and our particular thanks also go to FAIA series editors for once more supporting this conference. Finally, we hope you enjoy your visit to Matsue (Shimane), which is located between the Shinji and Nakaumi lakes on the coast of Japan, and that you also discover a few treasures, such as Matsue Castle or Kounkaku Palace, and learn a few words of Japanese if this is your first visit to this beautiful city.

Antonio J. Tallón-Ballesteros

unmapped: uri https://orcid.org/0000-0002-9699-1894

Department of Electronic, Computer Systems and Automation Engineering, University of Huelva (Spain), Huelva city, Spain

The detection of partial discharge is crucial for the safe operation of the power grid. Current live detection technologies are ineffective at detecting intermittent partial discharge signals, and online monitoring systems are not widely adopted due to high costs. This paper presents the development of a flexible deployment long-term partial discharge monitoring device that enables rapid deployment, full-station coverage, continuous data collection, and remote control capabilities. The device consists of digital sensors, a digital frequency synchronization unit, a power supply and communication unit, and an analysis and diagnostic backend. It receives, analyzes, and diagnoses partial discharge signals, and uses an integrated 4G communication module to display monitoring data and diagnostic results via the cloud.

We propose a microwave sensor on the basis of complementary circular spiral resonator (CCSR) for substrate determination and thickness identification in this paper. The developed sensor consists of a microstrip transmission line and CCSR. By taking advantage of High Frequency Structure Simulator (HFSS), the designed device operates at 2.9 GHz with high Q factor. Through simulation, a strong electric field is generated besides CCSR, making this region very susceptible to the transformation in nearby dielectric substrate. When different substrates to be tested are put on sensing area, resonance frequencies change greatly. The designed device can also be used for thickness determination of a particular substrate. Two parabolic functions are calculated and fitted to evaluate relative permittivity of unknown material to be tested and the thickness of FR4 substrate. The sensor has many advantages, such as low cost, easy operation, high sensitivity and robustness, making it popular in substrates dielectric characterization.

The safety and reliability of power transmission lines are the cornerstone of the stable operation of the national economy. However, in recent years, with the frequent occurrence of extreme weather events and geological disasters, the damage suffered by transmission lines is increasing, which poses a serious challenge to the stable operation of the power system. In order to promote the progress and development of the theory of disaster-resistant design of transmission lines, this paper discusses in depth the disaster cases that have occurred, analyses in detail their causes and damage modes, and comprehensively evaluates the various methods and measures currently adopted to ensure the safe operation of transmission lines. Through the systematic summary and conclusion of these issues, it aims to uncover design flaws. Based on this, targeted suggestions are proposed to enhance transmission lines’ resistance to external loads.

Job stability is an important indicator for assessing the consistency and reliability of jobs under different conditions. This study proposes a job stability algorithm model based on the KMO(Kaiser-Meyer-Olkin) test to assess the appropriateness of using factor analysis on data and factor analysis method, aiming to evaluate the stability level of jobs by analyzing various factors within the job. The model collects data related to the stability status of jobs, assesses the suitability of the data through the KMO test to determine if it is suitable for factor analysis, and uses an appropriate factor extraction method to extract key factors. Factor validation is then conducted, and reliable indicators of the factors, such as Cronbach’s alpha coefficient, and validity indicators, such as the proportion of variance explained, are calculated. Based on the extracted factors and their loading, scores or indices for job stability are calculated, and an appropriate method is selected to compute the comprehensive assessment of job stability. This algorithm model provides an effective method for evaluating job stability. By extracting and analyzing key factors within jobs, it is possible to objectively assess the consistency and reliability of jobs. The application of this model can help improve job stability and provide guidance for optimization and improvement in the job execution process.

This article introduces a control strategy designed to manage the intricate dynamics, external disturbances, and model uncertainties of flexible robotic arms. The approach is termed adaptive fuzzy compensation-based sliding mode control for robotic arms. Its key features include integrating sliding mode control and vague approximation, as well as utilizing a fuzzy system for adaptive approximation of unknown components in the robotic arm model. Through Matlab/Simulink simulations, the authors validate the stability of this method in control systems. They demonstrate its effectiveness in reducing fuzzy gains while achieving the desired dynamic performance indicators.

Vectorization of floor plans (VFP) is an object detection task that involves the localization and recognition of different structural primitives in 2D floor plans(FP). The output of VFP can be further processed for the purpose of plan reconstruction, 3D reconstruction and automatic furniture layout. So far how to make the existing 2D floor plans vectorized faces the problem of recognition inaccuracy and inefficiency. This paper proposed a floor plan recognition algorithm based on key points, which is meaningful and useful. First, the algorithm identifies the effective subject of the FP with the help of the object detection algorithm; then, it builds a deep backbone network to identify the key points and semantic information of the marked plane elements; finally, the algorithm utilizes the post-processing algorithm to optimize and retrieve vectorized data information. Compared with existing methods, the algorithm adopted in this paper enhances the support for the recognition of elements such as sloping walls and bay windows, and effectively improves the recognition accuracy.

The refractive index of seawater is one of the important optical properties of the air-sea interface, which is of great significance to the study of ocean optics, ocean dynamics, and air-sea interaction. The refractive index of seawater refers to the ratio of the propagation speed of light waves in a vacuum to that in seawater. In this paper, the refractive index of seawater is measured quickly and accurately by a spectrophotometer combined with the double triangular prism method. By comparing with the measured data of the Abbe refractometer, the measured data of this experimental method are accurate and reliable, which can be extended to the rapid measurement of the refractive index of other liquids by simple instruments. The double-prism method for rapid measurement of seawater refractive index is to use the existing simple equipment to measure the liquid refractive index. It provides accurate optical parameters for investigating the optical properties of the ocean or the propagation law of light in the sea and provides accurate optical parameters for studying marine optics.

During the processing of aquatic products, the detection of water content is a very important indicator. In this paper, by studying and analyzing the changes of water content in different aquatic products processing by low field nuclear magnetic resonance technology, it is found that it can provide information on the kinetic behavior of water molecules, the content and distribution of components, the storage process and the preservation method. Therefore, low-field nuclear magnetic resonance technology has broad application prospects in the field of aquatic product processing and research, and can provide important scientific basis for the quality control and improvement of aquatic products.

In order to study the phenomenon of a circular disc with a hole at its center being able to float on the water surface under vertical inflow, this article conducted experimental measurements and analysis. We tested discs with diameters of 15 cm and 20 cm and center hole diameters of 0.5 cm, and compared the flow velocity and flow rate ranges of discs with and without a concave center hole. The results showed that discs with a concave center hole had a larger range of flow velocity and flow rate than those without, and as the diameter of the disc increased, so did the flow velocity and flow rate. We also measured the pressure difference between the upper and lower surfaces of the disc through experiments and found that the larger the diameter of the disc, the greater the pressure difference between the upper and lower surfaces. In addition, we found that the additional pressure force generated by the water leap was very small and could be ignored. Studying these thin plate phenomena is of great significance for fields such as aviation, navigation, and civil engineering.

An effective location recommendation function in smart building management systems can optimize space utilization and enhance user service experience. Despite recent advances in Large Language Models (LLMs) for NLP-based recommender systems, smart building systems often lack communication and coordination with other devices, resulting in subpar interactivity and serviceability. To address these challenges, this paper proposes a multi-modal recommendation system for utilizing and sharing open spaces in smart buildings. The system includes a “vision-based recommendation module” that uses visual Language Models and real-time surveillance images to identify locations based on user-requested keywords. The “knowledge-based recommendation module” utilizes knowledge graph technology to match user requirements with historical feedback data, improving semantic matching and optimizing user experience. The system combines the outputs from both modules using decision fusion technology to provide final location recommendations. Simulation results demonstrate that the proposed system can effectively understand user intentions and provide satisfactory location recommendations. The multi-modal approach outperforms individual recommendation methods.

Hot dip plating is a widely used metal surface treatment process, which can provide protection and enhancement of metal surfaces and is suitable for electric power fields. The surface of hot-dip Galfan coatings was prepared with lanthanum salt conversion film. A comprehensive investigation was conducted using various characterization techniques, including scanning electron microscopy with energy dispersive spectroscopy (SEM/EDS), X-ray photoelectron spectroscopy (XPS), and atomic force microscopy (AFM). Additionally, the corrosion resistance of the film was studied through neutral salt spray tests, electrochemical polarization analysis, and electrochemical impedance spectroscopy, and the optimal film-forming time was determined. Based on the above research, it can be concluded that lanthanum salt conversion films significantly enhance the corrosion resistance of hot-dip Galfan coatings, particularly in transmission and transformation engineering. This contributes to further improving the performance and stability of the coatings.

To improve the accuracy of power load forecasting, a short-term load forecasting method based on Principal Component Analysis (PCA), XGBoost (eXtreme boosting system), and Long Short Term Memory (LSTM) using neural networks is proposed. By studying the variation patterns of residential electricity load, a feature set is constructed using date factors, climate factors, and daily load factors as inputs. Firstly, the sample dataset is divided into a load dataset and an influencing factor dataset composed of date and climate factors for data preprocessing; Then, the PCA principal component analysis method is used to extract features with significant impact, and a strong correlation feature vector is formed with the load data. The results are input into XGBoost and LSTM networks for mathematical fusion; Finally, using the electricity load data of residents in a certain area of Nanjing, the parameters of each group of network models were trained and the load prediction values were output. The results were compared and verified with the Convolutional Neural Networks (CNN) model, LSTM network model, the Gated Recurrent Neural Network (GRU) model and XGBoost LSTM network model, respectively. The results showed that the method can effectively reduce the error between the predicted values and the true values.

The multi-source partial discharge PRPD pattern can realize the pattern recognition of multi-source partial discharge types through the target detection algorithm training and identification of shape features. However, when the characteristics of different discharge pattern overlap, the small target is easily blocked by the large target, resulting in false detection and missed detection. Therefore, this paper proposes a multi-source partial discharge PRPD pattern identification algorithm with optimized non-maximum suppression. The Soft-NMS algorithm was introduced to solve the missed detection caused by overlapping targets; GIoU was used to replace the traditional IoU to calculate the similarity between targets and the loss function was optimized; the YOLOv7 network model was further built to identify the PRPD pattern of four typical discharges shape features. After cross-validation between simulation experiments and charged field data, the results prove that the average detection accuracy of the algorithm can reach 98.2% in simulation experiments and 88.4% in field experiments, effectively reducing the false detection rate and successfully identifying the characteristics of multi-source local discharge PRPD pattern when the targets overlap.

Transformer faults have now become a major cause of power system failures, with transformers being more susceptible to malfunctions in adverse weather conditions. This article takes the example of a fault in the on-load tap changer switch of a transformer caused by a localized overvoltage short circuit during severe thunderstorm weather. The author elaborates on three types of lightning-induced overvoltage, combining fault analysis with extensive on-site experimental data and comparisons with standard values to convincingly identify the fault causes. The article briefly discusses five types of overvoltage in neutral point ungrounded systems and, in conjunction with one of the transformer’s crucial components, the on-load tap changer switch, provides a concise analysis of four main fault types associated with OLTC. Finally, the author traces the root cause of the fault, summarizes the findings, and presents effective recommendations to prevent similar incidents in the future.

In the process of digital transformation in power enterprises, a number of new business systems based on microservices architecture have gradually been constructed. However, traditional passive monitoring methods lack the ability to face microservice architecture systems. At the same time, objective problems such as complexity of invocation logic, difficulty in fault localization, and lack of monitoring perspectives have become prominent. This article proposes a multidimensional monitoring method based on microservice architecture for business systems, extending the monitoring perspective to the business layer. So it could achieve comprehensive capture of the operation of the business system, and enabling timely detection of anomalies from the business layer. Meanwhile, it horizontally constructs invocation links at various levels of the system, vertically connects system resources at each level. Consequently breaking through the bottleneck of the disconnection between business and system, and improving the efficiency and accuracy of anomaly tracing greatly. Based on this design concept, it meets the different needs of various entities such as business, components, and management for system operation and maintenance, effectively improving the refinement and intelligence level of operation and maintenance monitoring. On this basis, relying on unified monitoring indicators, alarm systems, and visual large screen capabilities, it assists in locating key nodes where faults occur. Build an enterprise level operation monitoring platform, form a comprehensive and comprehensive monitoring mode, collaborate to ensure stable business operation, and improve the intelligence level of enterprise operation management.

With the continuous development and promotion of IoT technology and products, problems such as “multiple, miscellaneous, and chaotic” have become prominent, which cannot meet the safety control needs of power grid infrastructure construction sites. The article proposes a cloud-edge collaborative IoT framework based on edge IoT agents. Firstly, by designing multimodal communication components and adopting standardized communication interfaces and protocols, standardized access to IoT devices and standardized collection and management of perception data were achieved. Secondly, a mechanism for cloud-edge collaboration was designed based on the IoT model on the IoT management platform. This framework effectively supports the application of power grid infrastructure business and enhances the safety management capability of construction sites. The article concentrates on on-site construction personnel and presents typical business application scenarios. The practicality and significance of the research results presented in the article are evident from the application situation.

This experiment mainly studies the interference diffraction phenomenon of two kinds of complex structures. One is to use the hexagonal structure of the watch strap to carry out the interference diffraction experiment of light. By changing the distance between the light source and the hexagon, and the position of the receiving screen to observe the diffraction phenomenon, it is found that the hexagonal interference diffraction phenomenon is obvious, and the distance is an important parameter affecting the interference phenomenon. At the same time, we set up a set of instruments that conform to Kirchhoff diffraction. Using Kirchhoff theory, the relative intensity diffraction formula of diffraction of complex structure-hexagonal-like holes on the screen is derived, and the instrument is used to simulate different incident angles. Different diffraction patterns find out the internal laws between them.

The torque multiplier, as a device that generates extra-large torque, is widely used in industries such as aviation, aerospace, and shipbuilding. The multiplier amplifies and transmits input torque via an internal mechanical gear set, that the mechanical wear and increased assembly clearance caused by long-term use will have a certain impact on the accuracy of its output torque value. The development of an on-site calibration device, which measurement range is 200–5000 Nm, and can calibrate coaxial or non-coaxial torque multipliers is introduced in this paper. The device with small volume is easy to move. It is also equipped with intelligent instruments, which can achieve loading and unloading control, automatic recognition of sensors, automatic collection and calculation of output input torque ratio.

This paper presents a hybrid buck-boost DC-DC converter structure to address the limitations of single-output and low conversion ratios (CR) in traditional energy harvesting systems. To reduce the CR of the inductive boost and enhance the total CR of the converter while improving efficiency, the proposed converter cascades a boost circuit with a reconfigurable switched capacitor (SC), and also includes a supercapacitor. The advantage of this architecture is the ability to use low-voltage low-threshold transistors with lower parasitic capacitance and on-resistance, which achieve high-voltage output while reducing switching and conduction losses. The supercapacitor can store energy under a light load and provide additional energy replenishment in buck mode under heavy load. The reconfigurable SC circuit can enable a 2V or 3V output by configuring the switch array. Adaptive switching frequency control was used to achieve a balance between load capability and switching loss. The proposed DC-DC converter is designed with a 0.18 µm CMOS process and has maximum conversion efficiencies of 91.83%, 89.42%, and 88.09% for the inductive boost stage, 2V SC output stage, and 3V SC output stage, respectively. The output voltage ripple is less than 20 mV at 1V output and less than 30 mV at 2V output and 3V output. The input voltage of the converter is as low as 20 mV. The converter features multi-output, high-conversion-ratio, and enhanced efficiency, which are suitable for energy-harvesting applications.

This proposal presents a high gain array antenna of double-ridged horn for X-band radar applications. The antenna array consists of 5184 radiating elements divided into 1296 subarrays, each subarray has 4 horn elements fed by a 4-port waveguide power divider in horizontal plane. The subarray is integrated with calibration signal to improve the system accuracy. The measurement results of 72×72 horn array in a bandwidth of 7% matching with the simulation results indicate an effective design approach: the design antenna has achieved a good active return loss (S₁₁) less than –10 dB when beamforming upto ±55⁰, a peak gain of 42.7 dBi, a pencil beam with half-power beamwidth (HPBW) less than 1.4⁰ in both E and H plane in the operating bandwidth.

The distribution network is directly connected to the user side, making its position critical. Demand-side response enables users to actively participate in distribution network scheduling, which plays a vital role in ensuring both the security and economic operation of the network. Therefore, incorporating demand-side response for optimal scheduling in new energy distribution networks is crucial. This paper focuses on the following aspects: First, the components and advantages of new energy active distribution networks are examined, and a detailed model of incentive-based demand-side response is developed. Then, an optimal dispatch model is established with economic efficiency as the primary objective, solved using an improved particle swarm optimization algorithm, and tested with the IEEE 33-node distribution network standard. Finally, data analysis reveals that with the participation of demand-side response, the new energy distribution network operates with enhanced economic efficiency.

The use of unmanned aerial vehicles (UAVs) in logistics is becoming increasingly popular because it brings many benefits, such as increased operational efficiency, reduced process costs, and shortened delivery times. Therefore, many companies decide to implement them. However, this also involves certain challenges, such as the need to comply with legal regulations, ensure flight safety, limited range and payload of the aerial vehicles, and the necessity of integrating with existing logistics systems. As a result, the implementation of such solutions is most often undertaken by large enterprises. The aim of this article was to demonstrate that even in small companies, with a modest financial outlay, it is possible to use drones and achieve tangible results in this area. This assumption became the genesis of this article. Additionally, it presents the potential of unmanned aerial vehicles and showcases the possibilities of their application in logistics.

In this study, we developed an “MR (Mixed Reality) work support system” that can visually display information from the construction supervisor to worker with the aim of quickly and accurately communicating complex instructions to workers that were difficult to convey. The comparative experiment with two conventional instruction methods was performed to verify the effectiveness of the proposed system. The results confirmed that the proposed system decreased working time, and the proposed system was also effective in reducing workers’ mental burden.