Journal of Electronic Research and Application

Optimization of Laminating Angles for Skirt Panels of EMUs Front Using Composite Materials Based on the Cheetah Optimizer

2025-10-15T09:35:38+08:00

With the development of composite materials, their lightweight and high-strength characteristics have caused more widespread use from aerospace applications to automotive and rail transportation sectors, significantly reducing the energy consumption during the operation of EMUs (Electric Multiple Units). This study aims to explore the application of composite materials in the lightweight design of EMU front skirts and proposes a design method based on three-dimensional Hashin failure criteria and the Cheetah Optimizer (CO) to achieve maximum lightweight efficiency. The UMAT subroutine was developed based on the three-dimensional Hashin failure criteria to calculate failure parameters, which were used as design parameters in the CO. The model calculations and result extraction were implemented in MATLAB, and the Cheetah Optimizer iteratively determined the optimal laminating angle design that minimized the overall failure factor. After 100 iterations, ensuring structural integrity, the optimized design reduced the weight of the skirt panel by 60% compared to the original aluminum alloy structure, achieving significant lightweight benefits. This study provides foundational data for the lightweight design of EMUs.

Application and Prospects of SDN Technology in Modern Network Management

2025-10-15T09:35:36+08:00

With the rapid development of information technology, the scale of the network is expanding, and the complexity is increasing day by day. The traditional network management is facing great challenges. The emergence of software-defined network (SDN) technology has brought revolutionary changes to modern network management. This paper aims to discuss the application and prospects of SDN technology in modern network management. Firstly, the basic principle and architecture of SDN are introduced, including the separation of control plane and data plane, centralized control and open programmable interface. Then, it analyzes the advantages of SDN technology in network management, such as simplifying network configuration, improving network flexibility, optimizing network resource utilization, and realizing fast fault recovery. The application examples of SDN in data center networks and WAN optimization management are analyzed. This paper also discusses the development status and trend of SDN in enterprise networks, including the integration of technologies such as cloud computing, big data, and artificial intelligence, the construction of an intelligent and automated network management platform, the improvement of network management efficiency and quality, and the openness and interoperability of network equipment. Finally, the advantages and challenges of SDN technology are summarized, and its future development direction is provided.

Construction of a Virtual Twin Testing Framework for Safety of the Intended Functionality in Intelligent Connected Vehicles

2025-10-15T09:35:34+08:00

This study aims to construct a virtual twin testing framework for the safety of the intended functionality of intelligent connected vehicles to address the safety requirements of intelligent driving and transportation systems. The research methods include the construction of a theoretical model of safety for intelligent connected vehicles based on the concept of virtual twins, the correlation study between key concepts and functional safety, and the application research of virtual twin technology in the safety testing of intelligent connected vehicles. The results reveal that the virtual twin testing framework can effectively enhance the functional safety of intelligent connected vehicles, reduce development costs, and shorten the product launch cycle. The conclusion suggests that this framework provides strong support for the healthy development of the intelligent connected vehicle industry and has a positive impact on the safety and efficiency of intelligent transportation systems.

Exploration of the Evolution of LiDAR Technology

2025-10-15T09:35:32+08:00

Since its inception in the 1960s, light detection and ranging (LiDAR) technology has demonstrated great potential in various fields such as autonomous driving, robot navigation, and environmental monitoring due to its high precision, high resolution, and strong anti-interference capability. This paper reviews the development history, technical principles, application fields, and future development trends of LiDAR technology. It introduces the technical applications of LiDAR technology in autonomous driving, robot navigation, and environmental monitoring, and explores the development direction of SLAM algorithms in multi-sensor fusion and real-time map construction, providing a reference basis for the development and research of LiDAR.

Research on Teaching Reform Strategies of Python Programming Course Based on Artificial Intelligence Technology

2025-10-15T09:35:30+08:00

As one of the core courses for computer-related majors, the Python programming course has become increasingly important in the era of artificial intelligence. It aims to help students develop good computer thinking and improve their abilities in programming and data analysis. The application of artificial intelligence technology in the teaching of Python programming courses is of great significance for optimizing the allocation of teaching resources, enriching students’ learning experience, and significantly improving teaching quality. Based on this, this paper first briefly expounds on the importance of applying artificial intelligence technology in the teaching of Python programming courses. On this basis, it focuses on exploring effective strategies for the teaching reform of Python programming courses based on artificial intelligence technology, hoping to provide new ideas for the teaching of Python programming courses and contribute to cultivating more Python programming talents with artificial intelligence literacy.

Correlation Analysis Between Investor Sentiment and Stock Price Fluctuations Based on Large Language Models

2025-10-15T09:35:28+08:00

The efficient market hypothesis in traditional financial theory struggles to explain the short-term irrational fluctuations in the A-share market, where investor sentiment fluctuations often serve as the core driver of abnormal stock price movements. Traditional sentiment measurement methods suffer from limitations such as lag, high misjudgment rates, and the inability to distinguish confounding factors. To more accurately explore the dynamic correlation between investor sentiment and stock price fluctuations, this paper proposes a sentiment analysis framework based on large language models (LLMs). By constructing continuous sentiment scoring factors and integrating them with a long short-term memory (LSTM) deep learning model, we analyze the correlation between investor sentiment and stock price fluctuations. Empirical results indicate that sentiment factors based on large language models can generate an annualized excess return of 9.3% in the CSI 500 index domain. The LSTM stock price prediction model incorporating sentiment features achieves a mean absolute percentage error (MAPE) as low as 2.72%, significantly outperforming traditional models. Through this analysis, we aim to provide quantitative references for optimizing investment decisions and preventing market risks.

Effects of Manifold Structures on Velocity Distribution of V- and A-Type Microchannel Plates

2025-10-15T09:35:26+08:00

Flow velocity uniformity of the microchannel plate is a major factor affecting the performance of microchannel devices. In order to improve the velocity distribution uniformity of the microchannel plate, we designed two new microchannel structures: V-type and A-type. The effects of various structural parameters of the manifolds on the velocity distribution are reported. The V-type and A-type microchannel plates had a more uniform velocity distribution compared to the Z-type microchannel plate. The final result showed that it is beneficial for the V-type microchannel plate to obtain a more uniform velocity distribution when the manifold structure parameters are X_in = -1, X_out = 0, Y_in = 10, Y_out = 6, H_in = 4, H_out = 1, and R = 0.5.

Fluorescent Temperature Characteristics of CaMoO4:5%Tb3+ Based on Variable Temperature Excitations

2025-10-15T09:35:24+08:00

This study focuses on the fluorescent thermometric properties of CaMoO4:5%Tb³⁺ under different temperature excitations. At the detection wavelength of 544 nm, with the temperature varying from 293 K to 563 K, there is a broadband absorption peak in the range of 250 nm to 350 nm. The results indicate that this phenomenon is caused by the superposition of the 4f-5d transition of Tb³⁺ and the O^2--Mo⁶⁺ charge transfer. It is considered that as the temperature rises, the luminescent intensity of the material shows an obvious continuous decreasing trend, which reflects a significant luminescent thermal quenching trend; thus, this quenching belongs to the “strong coupling” type. Based on the excitation spectrum results, two excitation wavelengths, 312 nm and 338 nm, were specifically selected to excite the samples, which correspond to the top of the charge transfer band, the redshift intersection of the charge transfer band, and the edge of the charge transfer band at 293 K, respectively.

Exploring 3D Model Rendering Techniques for Cultural Relics Based on 3D Gaussian Splatting

2025-10-21T18:07:22+08:00

With the widespread application of 3D visualization in digital exhibition halls and virtual reality, achieving efficient rendering and high-fidelity presentation has become a key challenge. This study proposes a hybrid point cloud generation method that combines traditional sampling with 3D Gaussian splatting, aiming to address the issues of rendering delay and missing details in existing 3D displays. By improving the OBJ model parsing process and incorporating an adaptive area-weighted sampling algorithm, we achieve adaptive point cloud generation based on triangle density. Innovatively, we advance the ellipsoidal parameter estimation process of 3D Gaussian splatting to the point cloud generation stage. By establishing a mathematical relationship between the covariance matrix and local curvature, the generated point cloud naturally exhibits Gaussian distribution characteristics. Experimental results show that, compared to traditional methods, our approach reduces point cloud data by 38% while maintaining equivalent visual quality at a 4096×4096 texture resolution. By introducing mipmap texture optimization strategies and a GPU-accelerated rasterization pipeline, stable rendering at 60 frames per second is achieved in a WebGL environment. Additionally, we quantize and compress the spherical harmonic function parameters specific to 3D Gaussian splatting, reducing network transmission bandwidth to 52% of the original data. This study provides a new technical pathway for fields requiring high-precision display, such as the digitization of cultural heritage.

High-Frequency Stable Wireless Amplitude Modulation System Based on a Pierce Circuit

2025-10-15T09:35:22+08:00

This paper designs a high-frequency stable wireless amplitude modulation (AM) system based on a Pierce circuit. The system utilizes an oscillator and comparator to generate a 20 kHz square wave with an adjustable duty cycle, combined with a 41 MHz carrier wave produced by a passive crystal oscillator Pierce circuit. A 100% modulation index amplitude modulation is achieved through the AD835 multiplier. The modulated signal is amplified by a power amplifier circuit and transmitted wirelessly via the transmitter antenna. Upon reception, the signal undergoes two-stage high-frequency amplification before passing through a Schottky diode envelope detector. The NE5532 shaping circuit then restores the square wave. Experimental results demonstrate reliable 11-meter transmission with carrier frequency deviation < 0.75% and demodulation error < 1%.

Research on Quality Assurance and Testing Strategies of Quality Engineers in Software Product Development

2025-10-15T09:35:19+08:00

Quality engineers play a key role in software product development, covering various stages such as requirements analysis, design, coding, testing, and delivery. Its responsibilities include formulating quality standards, writing test cases, conducting functional and performance tests, and optimizing the product based on feedback. In government procurement projects, quality evaluation focuses on process compliance, security, and functional compatibility. KPI evaluation trees are commonly used for quantitative assessment, and a dynamic adjustment mechanism for indicators needs to be established to cope with complex demands. In addition, risk-driven testing and agile development should be combined to set up quality access control to ensure that each iteration version meets expectations. The multi-dimensional quality assurance and verification scoring mechanism can effectively enhance product reliability and reduce project risks.

Research on Engineering Technological Innovation and Risk Management Strategies in Electric Power Construction

2025-10-15T09:35:18+08:00

This article focuses on electric power engineering and expounds the development characteristics and applications of new electric power engineering technologies, including technologies such as smart grids and digital design platforms. It explores the identification and classification of risk elements in electric power engineering and analyzes the deficiencies of traditional risk assessment methods. It introduces the applications of new technologies such as intelligent sensor networks in risk management, proposes a dual-driven model of technology and management, and looks forward to the application prospects of artificial intelligence and blockchain technologies.

Data Elements and Trustworthy Circulation: A Clearing and Settlement Architecture for Element Market Transactions Integrating Privacy Computing and Smart Contracts

2025-10-15T09:35:16+08:00

This article explores the characteristics of data resources from the perspective of production factors, analyzes the demand for trustworthy circulation technology, designs a fusion architecture and related solutions, including multi-party data intersection calculation, distributed machine learning, etc. It also compares performance differences, conducts formal verification, points out the value and limitations of architecture innovation, and looks forward to future opportunities.

Structural Optimization and Innovative Practice in the Mechanical Design of Amusement Equipment

2025-10-15T09:35:15+08:00

Materials mechanics and structural dynamics provide theoretical support for the structural optimization of amusement facilities. The design code system guides the design process, covering aspects such as strength and fatigue life. This paper introduces optimization methods like standardized module interfaces and variable density methods, as well as topics related to finite element simulation, reliability enhancement, innovative practices, and their significance.

Remote Diagnosis and Analysis of Rail Vehicle Status Based on Train Control Management System Data

2025-10-21T18:12:40+08:00

This article focuses on the remote diagnosis and analysis of rail vehicle status based on the data of the Train Control Management System (TCMS). It first expounds on the importance of train diagnostic analysis and designs a unified TCMS data frame transmission format. Subsequently, a remote data transmission link using 4G signals and data processing methods is introduced. The advantages of remote diagnosis are analyzed, and common methods such as correlation analysis, fault diagnosis, and fault prediction are explained in detail. Then, challenges such as data security and the balance between diagnostic accuracy and real-time performance are discussed, along with development prospects in technological innovation, algorithm optimization, and application promotion. This research provides ideas for remote analysis and diagnosis based on TCMS data, contributing to the safe and efficient operation of rail vehicles.

Exploring the Development Model of UAVs Empowered by the Low-Altitude Economy

2025-10-15T09:35:13+08:00

The “14th Five-Year Plan” and the Long-Range Objectives Through the Year 2035 propose to strengthen the construction of strategic emerging industrial clusters, promote the deep integration of the internet, big data, artificial intelligence, blockchain technology, etc. with the real economy, facilitate the development of advanced manufacturing, and consider unmanned aerial vehicles (UAVs) as an important breakthrough, providing significant opportunities for the development of the UAV industry. Therefore, this article takes the current status of the UAV industry development as a starting point, analyzes the exploration and practice of the UAV development model based on the low-altitude economy, and discusses strategic suggestions to promote the development of UAVs empowered by the low-altitude economy. Through analysis, this article aims to provide theoretical references and practical guidance for promoting the sustainable development of the UAV industry under the wave of the low-altitude economy.

Design of a Pressure Sensor Array System Based on Minecraft

2025-10-15T09:35:11+08:00

Multimodal information sensing becomes increasingly critical under the rapid development of automation and information technology. With the ability to provide high-density and high-sensitivity pressure detection, pressure sensor arrays have been applied to a variety of fields, including intelligent robotics, medical monitoring, and industrial automation. This study proposes a pressure sensor array system based on the Minecraft game platform. The simulation and testing of the pressure sensor arrays system have been conducted using redstone circuits and pressure plates in Minecraft to simulate real-world piezoelectric pressure sensor arrays. A series of experiments verified the feasibility and effectiveness of the system.

A Review of AI-Driven Optimization Technologies for Distributed Photovoltaic Power Generation Systems

2025-10-15T09:35:09+08:00

The rapid development of artificial intelligence (AI) technology, particularly breakthroughs in branches such as deep learning, reinforcement learning, and federated learning, has provided powerful technical tools for addressing these core bottlenecks. This paper provides a systematic review of the research background, technological evolution, core systems, key challenges, and future directions of AI technology in the field of distributed photovoltaic power generation system optimization. At the same time, this paper analyzes the current technical bottlenecks and cutting-edge response strategies. Finally, it explores fusion innovation directions such as quantum-classical hybrid algorithms and neural symbolic systems, as well as business model expansion paths such as carbon finance integration and community energy autonomy.

Research on Anti-UAV Technology in Urban Environments

2025-10-21T18:07:20+08:00

With the rapid development of drone technology, drones are increasingly used in urban environments, but they also bring many security risks, such as illegal reconnaissance, smuggling, and terrorist attacks. Therefore, it is of great significance to study the anti-UAV technology in the urban environment. This paper analyzes the advantages and disadvantages of existing technologies and their applicability in the urban environment from the aspects of UAV detection, identification, and countermeasures, and discusses the future development trend of anti-UAV technology, aiming to provide a reference for urban safety protection.

Design and Application of a Drone-Based AI Inspection System for Longan Pests and Diseases

2025-10-21T18:07:18+08:00

Aiming at the problem that longan trees in Guangdong Province have long been affected by pests and diseases, and to address issues such as low efficiency, high cost, and limited coverage in longan pest and disease inspection, this paper designs a drone-based AI inspection system for longan pests and diseases. The system uses drones as a platform to collect images of longan orchards, which are transmitted in real time via 4G/5G networks. Meanwhile, it integrates an AI algorithm model for AI early warning and prescription suggestions. In practical applications, the system can quickly locate the areas where pests and diseases occur, identify longan pests and diseases, and provide fruit farmers with a basis for timely prevention and control. It significantly enhances the timeliness and accuracy of longan pest and disease control, and offers strong technical support for the precise management of the longan industry.

A Collaborative Approach to Distributed Heterogeneous Process Engines for Cross-Organizations

2025-10-21T18:07:53+08:00

In today’s complex and rapidly changing business environment, the traditional single-organization service model can no longer meet the needs of multi-organization collaborative processing. Based on existing business process engine technologies, this paper proposes a distributed heterogeneous process engine collaboration method for cross-organizational scenarios. The core of this method lies in achieving unified access and management of heterogeneous engines through a business process model adapter and a common operation interface. The key technologies include: Meta-Process Control Architecture, where the central engine (meta-process scheduler) decomposes the original process into fine-grained sub-processes and schedules their execution in a unified order, ensuring consistency with the original process logic; Process Model Adapter, which addresses the BPMN2.0 model differences among heterogeneous engines such as Flowable and Activiti through a matching-and-replacement mechanism, providing a unified process model standard for different engines; Common Operation Interface, which encapsulates the REST APIs of heterogeneous engines and offers a single, standardized interface for process deployment, instance management, and status synchronization. This method integrates multiple techniques to address API differences, process model incompatibilities, and execution order consistency issues among heterogeneous engines, delivering a unified, flexible, and scalable solution for cross-organizational process collaboration.

The Design and Application of a Mobile Sound Source Localization System

2025-10-21T18:07:50+08:00

The mobile sound source localization system is a technology that can track and locate mobile sound sources in real time and has broad application prospects in many fields. This article first provides an overview of the mobile sound source localization system, introducing its concept and composition, as well as its design and application significance. It elaborates on the importance of the mobile sound source localization system from multiple aspects, such as safety, production, and daily life, and deeply explores its design and application strategies. The problems faced by the mobile sound source localization system and its future development direction were pointed out.

An Image Manipulation Localization Method Based on Dual-Branch Hybrid Convolution

2025-10-21T18:07:47+08:00

In existing image manipulation localization methods, the receptive field of standard convolution is limited, and during feature transfer, it is easy to lose high-frequency information about traces of manipulation. In addition, during feature fusion, the use of fixed sampling kernels makes it difficult to focus on local changes in features, leading to limited localization accuracy. This paper proposes an image manipulation localization method based on dual-branch hybrid convolution. First, a dual-branch hybrid convolution module is designed to expand the receptive field of the model to enhance the feature extraction ability of contextual semantic information, while also enabling the model to focus more on the high-frequency detail features of manipulation traces while localizing the manipulated area. Second, a multi-scale content-aware feature fusion module is used to dynamically generate adaptive sampling kernels for each position in the feature map, enabling the model to focus more on the details of local features while locating the manipulated area. Experimental results on multiple datasets show that this method not only effectively improves the accuracy of image manipulation localization but also enhances the robustness of the model.

Synthesis and Application of Zero-Dimensional Metal Oxide Composites in Energy Chemistry

2025-10-21T18:07:16+08:00

Against the backdrop of increasingly prominent global energy shortages and environmental issues, the development of efficient energy conversion and storage technologies has become crucial. Zero-dimensional (0D) metal oxide composites exhibit significant application value in the field of energy chemistry due to their unique properties, such as quantum size effect and high specific surface area. From a broad perspective, this paper reviews the main synthesis methods of these composites, including sol-gel method, hydrothermal/solvothermal method, precipitation method, and template method, while analyzing the characteristics of each method. It further discusses their applications in photocatalytic hydrogen production, fuel cells, lithium-ion batteries, and supercapacitors. Additionally, the current challenges, such as material dispersibility and interface bonding, are pointed out, and future development directions are prospected, aiming to provide references for related research.

SW-YOLO: Lightweight Attitude Estimation Algorithm Based on Weighted Convolution and Star Network

2025-10-21T18:07:43+08:00

This paper proposes SW-YOLO (StarNet Weighted-Conv YOLO), a lightweight human pose estimation network for edge devices. Current mainstream pose estimation algorithms are computationally inefficient and have poor feature capture capabilities for complex poses and occlusion scenarios. This work introduces a lightweight backbone architecture that integrates WConv (Weighted Convolution) and StarNet modules to address these issues. Leveraging StarNet’s superior capabilities in multi-level feature fusion and long-range dependency modeling, this architecture enhances the model’s spatial perception of human joint structures and contextual information integration. These improvements significantly enhance robustness in complex scenarios involving occlusion and deformation. Additionally, the introduction of WConv convolution operations, based on weight recalibration and receptive field optimization, dynamically adjusts feature importance during convolution. This reduces redundant computations while maintaining or enhancing feature representation capabilities at an extremely low computational cost. Consequently, SW-YOLO substantially reduces model complexity and inference latency while preserving high accuracy, significantly outperforming existing lightweight networks.

Lightweight Multi-Object Detection for Construction Sites Based on YOLO-World

2025-10-21T18:07:40+08:00

Addressing the current issues in construction site detection algorithms—such as missed detections, false positives, and high model complexity—caused by occlusions and scale variations in dense environments. This paper proposes a lightweight multi-object detection model for construction sites based on YOLO-World, named the LCS-YOLO model, to achieve a balance between detection efficiency and accuracy. We propose the RGNet (Re-parameterization GhostNet) module, which integrates re-parameterized convolutions and a multi-branch architecture. This approach addresses the issue of information redundancy in intermediate feature maps while enhancing feature extraction and gradient flow capabilities. Combined with the adaptive downsampling module ADown (Adaptive Downsampling), it better captures image features and achieves spatial compression, reducing model complexity while enhancing interaction between images and text. Experiments demonstrate that the LCS-YOLO model outperforms other comparison models in overall performance, achieving a balance between accuracy and efficiency.

Persimmon Fruit Quality Grading Detection Based on an Improved YOLOv8s Lightweight Model

2025-10-21T18:07:37+08:00

Addressing challenges in accurately detecting persimmon fruit quality in orchards—such as reliance on manual grading, low efficiency, severe foliage obstruction, and subtle differences between quality grades—this paper proposes a lightweight persimmon detection model based on an improved YOLOv8s architecture. First, the Conv layer in the backbone network is replaced with an ADown module to reduce model complexity. Second, MSFAN is introduced in the Neck layer to fully extract texture features from persimmon images, highlighting differences between quality grades. Finally, the Wise-IoU loss function mitigates the impact of low-quality sample data on grading accuracy. The improved model accurately identifies and separates persimmons of varying quality, effectively addressing quality grading detection in complex backgrounds. This provides a viable technical approach for achieving persimmon quality grading detection.

Gated Attention-Enhanced Informer

2025-10-21T18:07:35+08:00

The Informer model leverages its innovative ProbSparse self-attention mechanism to demonstrate significant performance advantages in long-sequence time-series forecasting tasks. However, when confronted with time-series data exhibiting multi-scale characteristics and substantial noise, the model’s attention mechanism reveals inherent limitations. Specifically, the model is susceptible to interference from local noise or irrelevant patterns, leading to diminished focus on globally critical information and consequently impairing forecasting accuracy. To address this challenge, this study proposes an enhanced architecture that integrates a Gated Attention mechanism into the original Informer framework. This mechanism employs learnable gating functions to dynamically and selectively impose differentiated weighting on crucial temporal segments and discriminative feature dimensions within the input sequence. This adaptive weighting strategy is designed to effectively suppress noise interference while amplifying the capture of core dynamic patterns. Consequently, it substantially strengthens the model’s capability to represent complex temporal dynamics and ultimately elevates its predictive performance.

Analysis on BRICS Cybersecurity, New E-Commerce Platforms, and Digital Sovereignty: A Case Study of China and Pakistan

2025-10-21T18:07:32+08:00

Taking the cooperation between China and Pakistan as an example, this paper expounds on the current situation, governance concept, obstacles to cooperation, and differentiated policies of Western countries in the areas of cybersecurity, the role of new e-commerce platforms, and digital sovereignty of BRICS countries. It aims to promote inter-governmental cooperation through civil dialogue and lead information technology cooperation among developing countries through the BRICS mechanism, as well as to collaborate to establish guidelines for global cybersecurity, new e-commerce platforms, and digital sovereignty.

IMLMA: An Intelligent Algorithm for Model Lifecycle Management with Automated Retraining, Versioning, and Monitoring

2025-10-21T18:07:14+08:00

With the rapid adoption of artificial intelligence (AI) in domains such as power, transportation, and finance, the number of machine learning and deep learning models has grown exponentially. However, challenges such as delayed retraining, inconsistent version management, insufficient drift monitoring, and limited data security still hinder efficient and reliable model operations. To address these issues, this paper proposes the Intelligent Model Lifecycle Management Algorithm (IMLMA). The algorithm employs a dual-trigger mechanism based on both data volume thresholds and time intervals to automate retraining, and applies Bayesian optimization for adaptive hyperparameter tuning to improve performance. A multi-metric replacement strategy, incorporating MSE, MAE, and R², ensures that new models replace existing ones only when performance improvements are guaranteed. A versioning and traceability database supports comparison and visualization, while real-time monitoring with stability analysis enables early warnings of latency and drift. Finally, hash-based integrity checks secure both model files and datasets. Experimental validation in a power metering operation scenario demonstrates that IMLMA reduces model update delays, enhances predictive accuracy and stability, and maintains low latency under high concurrency. This work provides a practical, reusable, and scalable solution for intelligent model lifecycle management, with broad applicability to complex systems such as smart grids.

Innovative Application of Automatic Test Equipment in the Control Board Testing of Household Appliances

2025-10-21T18:07:29+08:00

This article introduces the composition and working principle of home appliance control board automation testing equipment, elaborates on the importance of key technical indicators, explains the integrated design of functional modules, signal processing modules, and data analysis modules, and covers aspects such as the application of machine learning algorithms and the establishment of fault waveform databases. Finally, it looks forward to the development of intelligent testing systems and emphasizes the importance of building a standardized testing system.

The Application of Artificial Intelligence Technology in Assisting R&D Project Initiation

2025-10-21T18:07:12+08:00

This paper reviews the latest advancements in artificial intelligence-assisted R&D project initiation, aiming to provide intelligent solutions for R&D management. It thoroughly examines the value of artificial intelligence technologies in four core areas: intelligent requirement analysis, technical feasibility assessment, market prospect forecasting, and automated risk identification. Furthermore, it proposes three forward-looking trends—enhanced intelligence, the establishment of industry standards, and deeper human-machine collaboration. These insights are expected to improve project approval success rates and shorten initiation timelines, driving a paradigm shift in R&D management from experience-based to data-driven decision-making. The review highlights how artificial intelligence, through machine learning, natural language processing, and data mining, effectively addresses chronic challenges in traditional initiation processes such as inefficiency, delayed decisions, and resource misallocation. It also identifies critical hurdles, including data quality, model interpretability, and organizational transformation, offering a vital reference framework for the future of intelligent R&D development.

Quantum-Secure OTN Framework Integrating QKDPQC Technologies

2025-10-21T18:07:27+08:00

The Optical Transport Network (OTN) is a protocol for sending network messaging over optical fiber networks. Intelligent optical networks provide an ideal solution for high-bandwidth services. Currently, data encryption schemes for OTN typically rely on mathematical problems such as elliptic curve cryptography or discrete logarithms, which are vulnerable to attacks by quantum computers. This paper investigates a quantum-secure OTN Framework that integrates Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC) technologies, enabling OTN leased lines to resist quantum attacks. This framework can provide users with highly secure quantum-encrypted OTN leased lines services.

CW-HRNet: Constrained Deformable Sampling and Wavelet-Guided Enhancement for Lightweight Crack Segmentation

2025-10-21T18:07:25+08:00

This paper presents CW-HRNet, a high-resolution, lightweight crack segmentation network designed to address challenges in complex scenes with slender, deformable, and blurred crack structures. The model incorporates two key modules: Constrained Deformable Convolution (CDC), which stabilizes geometric alignment by applying a tanh limiter and learnable scaling factor to the predicted offsets, and the Wavelet Frequency Enhancement Module (WFEM), which decomposes features using Haar wavelets to preserve low-frequency structures while enhancing high-frequency boundaries and textures. Evaluations on the CrackSeg9k benchmark demonstrate CW-HRNet’s superior performance, achieving 82.39% mIoU with only 7.49M parameters and 10.34 GFLOPs, outperforming HrSegNet-B48 by 1.83% in segmentation accuracy with minimal complexity overhead. The model also shows strong cross-dataset generalization, achieving 60.01% mIoU and 66.22% F1 on Asphalt3k without fine-tuning. These results highlight CW-HRNet’s favorable accuracy-efficiency trade-off for real-world crack segmentation tasks.

The Design and Implementation of an Intelligent Guide Dog Robot Based on Multimodal Perception

2025-10-21T18:07:10+08:00

Aiming at the problems of traditional guide devices such as single environmental perception and poor terrain adaptability, this paper proposes an intelligent guide system based on a quadruped robot platform. Data fusion between millimeter-wave radar (with an accuracy of ± 0.1°) and an RGB-D camera is achieved through multi-sensor spatiotemporal registration technology, and a dataset suitable for guide dog robots is constructed. For the application scenario of edge-end guide dog robots, a lightweight CA-YOLOv11 target detection model integrated with an attention mechanism is innovatively adopted, achieving a comprehensive recognition accuracy of 95.8% in complex scenarios, which is 2.2% higher than that of the benchmark YOLOv11 network. The system supports navigation on complex terrains such as stairs (25 cm steps) and slopes (35° gradient), and the response time to sudden disturbances is shortened to 100 ms. Actual tests show that the navigation success rate reaches 95% in eight types of scenarios, the user satisfaction score is 4.8/5.0, and the cost is 50% lower than that of traditional guide dogs.

A Binary Vulnerability Similarity Detection Model Based on Deep Graph Matching

2025-10-21T18:07:05+08:00

To enhance network security, this study employs a deep graph matching model for vulnerability similarity detection. The model utilizes a Word Embedding layer to vectorize data words, an Image Embedding layer to vectorize data graphs, and an LSTM layer to extract the associations between word and graph vectors. A Dropout layer is applied to randomly deactivate neurons in the LSTM layer, while a Softmax layer maps the LSTM analysis results. Finally, a fully connected layer outputs the detection results with a dimension of 1. Experimental results demonstrate that the AUC of the deep graph matching vulnerability similarity detection model is 0.9721, indicating good stability. The similarity scores for vulnerabilities such as memory leaks, buffer overflows, and targeted attacks are close to 1, showing significant similarity. In contrast, the similarity scores for vulnerabilities like out-of-bounds memory access and logical design flaws are less than 0.4, indicating good similarity detection performance. The model’s evaluation metrics are all above 97%, with high detection accuracy, which is beneficial for improving network security.

An Intelligent Control Method Based on the Artificial Neural Network Model

2025-10-21T18:07:03+08:00

The topology structure of the artificial neural network is an intelligent control model, which is used for the intelligent vehicle control system and household sweeping robot. When setting the intelligent control system, the connection point of each network is regarded as a neuron in the nervous system, and each connection point has input and output functions. Only when the input of nodes reaches a certain threshold can the output function of nodes be stimulated. Using the networking mode of the artificial neural network model, the mobile node can output in multiple directions. If the input direction of a certain path is the same as that of other nodes, it can choose to avoid and choose another path. The weighted value of each path between nodes is different, which means that the influence of the front node on the current node varies. The control method based on the artificial neural network model can be applied to vehicle control, household sweeping robots, and other fields, and a relatively optimized scheme can be obtained from the aspect of time and energy consumption.

Research on Optimization Strategies for Signal Integrity of High-Speed Digital Circuits in Electronic Information Engineering

2025-10-21T18:07:01+08:00

With the rapid development of electronic information engineering, high-speed digital circuits have been increasingly widely applied in various fields. In high-speed digital circuits, signal integrity is prone to interference from various external factors, leading to issues such as signal distortion or degradation of system performance. Based on this, this paper conducts research on the optimization strategies for signal integrity of high-speed digital circuits in electronic information engineering. It deeply analyzes the importance of high-speed digital circuits, elaborates on the challenges they face and the specific manifestations of signal integrity issues, and proposes a series of optimization strategies in electronic information engineering. The aim is to improve the signal integrity of high-speed digital circuits and provide theoretical support and practical guidance for the development of related fields.

Research on the Intelligent Evaluation of University Bursary Based on Blockchain

2025-10-21T18:06:59+08:00

Aiming at the problems of easy falsification of information and inaccurate evaluation results in the existing university bursary evaluation, a bursary evaluation model (XGBoost Model based on Blockchain, XMB) combining machine learning and blockchain was designed. The relevant basic data of the bursary evaluation is stored on the chain to solve the problem of easy falsification of data in the evaluation process. At the same time, the evaluation results of the student bursary are uploaded to the chain to realize the traceability of historical data. In addition, the improved XGBoost algorithm is used to intelligently analyze and evaluate the basic data of students, and objectively give the student a bursary grade, which realizes the intelligence and scientific nature of the evaluation process and ensures the accuracy of the evaluation results. The experimental results prove that the model proposed in this paper has an accuracy rate of about 6% higher than that of the traditional XGBoost model, which has higher evaluation accuracy, throughput, and time efficiency. The method proposed in this paper is suitable for the evaluation of scholarships and bursaries in the student management system of colleges and universities.

Research on Low-Energy Information Transmission Based on Wireless Network

2025-10-21T18:06:57+08:00

In this paper, the topological structure of the vehicle wireless network M2M (Machine to Machine) is used as the experimental research model, and four kinds of light coefficients are set as factors affecting the experimental results, namely, light intensity factor ∈ and α, to represent the light intensity coefficient and influence factor. The remaining energy consumption of mobile terminal equipment was measured respectively, the distance parameter from device to device, the maximum transmission energy consumption, and the correlation coefficient between environmental parameters and energy consumption parameters was analyzed. This paper discusses the impact of different topological structures on the environment, energy saving and emission reduction in the relatively flat terrain area, based on the planning scheme of parking area within the coverage range of base station signal, the transmission capability of vehicles as mobile device nodes within the coverage range of base station signal, and the signal coverage range of base station under different light intensity. As the distance between the base station and the vehicle mobile device node changes, the maximum transmission energy consumption of the mobile device node is obtained. Based on the above factors, the optimal performance optimization parking scheme and the optimal energy consumption optimization transmission scheme are obtained.