Journal of Electronic Research and Application

Design and Simulation of a Miniaturized Printed Wideband Antenna with a Gradual Radiator and Defected Ground

Qi Wang — 2026-07-07

To improve the integration capability and impedance matching of printed antennas for wideband wireless terminals, a miniaturized planar antenna with a gradual radiator and a defected partial ground is designed and verified by full-wave simulation. The antenna is implemented on an FR4 substrate of 31 mm x 18 mm x 1 mm and fed by a 50 Ohm microstrip line. A gradual, chamfered radiator with a top arc and T-shaped loading is combined with a partial ground containing a central slot and tapered edge, so that the forward and return current paths can be tuned simultaneously. The Driven Modal solver in Ansys HFSS is used to evaluate the reflection coefficient, field, and current distributions, and far-field radiation. The simulated S11 curve exhibits resonances near 3.3 GHz, 4.4-4.6 GHz, 7.7-8.0 GHz, and 13.0 GHz; the deepest resonance reaches about -35 dB, and the typical peak GainTotal is about 3.2 dBi. A parameter sweep of La from 0.6 mm to 0.8 mm shows little influence on the main resonances. The results indicate that the proposed structure provides a compact and feasible basis for multi-band or wideband terminal antennas.

Finite-Element Modeling and Analytical Validation of a Coaxial Cylindrical Capacitor Using Maxwell 2D

Qi Wang — 2026-07-07

To improve the reliability of capacitance prediction for compact coaxial components, a cylindrical capacitor with copper conductors and an air dielectric is modeled and verified by Ansys Maxwell 2D electrostatic simulation. The inner conductor radius is 0.6 mm, the outer conductor inner radius is 1.0 mm, and the outer conductor wall thickness is 0.2 mm. A 1 V potential is assigned to the inner conductor, while the outer conductor is grounded. Based on Gauss’s law, the analytical per-unit-length capacitance is calculated to be 108.9 pF/m. The finite-element model yields 107.4 pF/m, corresponding to a relative error of 1.38%. The simulated electric-field distribution is radially symmetric and reaches its maximum near the inner conductor, which agrees with the theoretical relation E(r) ∝ 1/r. The results show that Maxwell 2D can accurately reproduce the electrostatic behavior of coaxial capacitors and provide an efficient workflow for capacitance extraction, field visualization, and preliminary structural optimization.

Structural and Control System Design of an Automatic Cooking Robot

Juanjuan Huang — 2026-07-07

With the rapid development of electronics, household kitchen appliances are gradually becoming intelligent. To improve the well‑being of office workers and address challenges such as limited time for cooking rice, this paper proposes the structure and control system of a cooking robot. The system uses an STM32 microcontroller as its core and integrates IoT and other new technologies to enable interaction among the smartphone, cloud, and the cooking robot. It supports remote control and monitoring via a mobile app, and on‑device monitoring of the operating status or the cooking curve through a touch screen. Experimental tests show that the robot can automatically complete a series of operations, including rice metering, rice washing, and rice cooking, making the system more user‑friendly and intelligent.

Architectural Design of RT-DETR-L for PCB Surface Defect Detection: A Systematic Comparison of Attention Mechanisms, Backbone Replacement, and Cross-Dataset Generalization

Tao Huang — 2026-07-07

Based on RT-DETR-L, this paper systematically compares five attention mechanisms (SE, CBAM, CA, ECA, and EMA) at the P3/P4/P5 outputs of the feature-pyramid neck under identical training conditions, and evaluates FasterNet backbone replacement and a P2 small-object detection head as complementary improvements. Experiments reveal a performance gap of up to 4.29 percentage points (CA: 93.04% to EMA: 97.33% in mAP₅₀), indicating that the choice of attention mechanism has a substantial impact on RT-DETR-type PCB detectors. EMA achieves the best mAP₅₀ (97.33%) and the highest mAP_50:95 (56.45%); ECA offers a competitive trade-off without increasing GFLOPs (96.69%); CA performs worst (93.04%), a 3.34 pp drop below the baseline, tentatively attributed to an architectural conflict with the AIFI encoder. FasterNet backbone replacement trades accuracy for efficiency (31% fewer parameters, 40% lower GFLOPs); and, when trained from scratch on the second dataset DeepPCB, the EMA variant again yields the largest gain (mAP₅₀89.33%, 4.83 pp over the baseline), showing that the improvement is not specific to a single dataset.

Distributed Flexible Job Shop Scheduling Based on an Improved Genetic Algorithm

Wenwen Chen — 2026-07-23

Aiming at the multi-dimensional and highly complex decision-making characteristics of distributed flexible job shop scheduling, and the local convergence and slow convergence speed of the traditional genetic algorithm, an improved genetic algorithm- the dynamic hierarchical genetic algorithm is proposed. The algorithm enhances the global search ability and local optimization ability of the traditional genetic algorithm through a dynamic hierarchical structure, a multi-neighborhood local search strategy, and adaptive adjustment of the crossover and mutation rates. The simulation results show that after 10 independent runs on several improved Kacem (MK) series test cases, the algorithm has a lower minimum average maximum completion time than the traditional genetic algorithm. Especially in the complex cases of MK03 and MK09, not only is the completion time reduced by 24%, but also the number of iterations is reduced. This fully verifies the ability of the algorithm to improve scheduling efficiency and stability in distributed flexible manufacturing workshops. The completion time of MK09 is reduced by about 24%, and the number of iterations is also reduced. This fully verifies the ability of the algorithm to improve the scheduling efficiency and stability in the distributed flexible manufacturing workshop scheduling problem.

CVaR-Based Optimal Scheduling of an Integrated Energy System with Battery Storage, Hydrogen Storage, and Demand Response

Delong Yan — 2026-07-23

High renewable penetration improves the low-carbon performance of integrated energy systems, but it also increases scheduling uncertainty and renewable curtailment risk. This paper proposes a CVaR-based optimal scheduling model for an electric-heat-hydrogen integrated energy system with battery energy storage, hydrogen storage, and demand response. The proposed model minimizes a weighted objective that combines expected operating cost and tail-risk cost, while considering electricity purchase and sale, gas consumption, carbon emissions, battery degradation, hydrogen conversion, demand response compensation, and renewable curtailment penalty. Wind power, photovoltaic generation, and electric load uncertainty are represented by multiple scenarios, and the same scenario set is used for all comparative cases to ensure fairness. Five operation schemes are studied, including no storage, battery energy storage only, hydrogen storage only, battery-hydrogen storage, and the proposed battery-hydrogen-demand response scheme. The numerical results show that the proposed scheme achieves the lowest weighted objective, expected cost, and CVaR cost. Compared with the no-storage case, the proposed scheme reduces the weighted objective from 12337.22 to 9677.39, increases renewable utilization from 92.3% to 99.8%, and reduces expected carbon emissions from 4771.88 to 2994.13. These results indicate that coordinated scheduling of battery storage, hydrogen storage, and demand response can improve economic performance, reduce operational risk, and enhance renewable energy accommodation in high-renewable integrated energy systems.

Analysis of the Application of AI Visual Inspection Technology in Precision Mechanical Manufacturing Automation

Tianwei Zheng — 2026-07-23

In recent years, driven by the Industry 5.0 wave, precision mechanical manufacturing is accelerating its evolution toward ultra-precision, intelligence, and unmanned operation, posing unprecedented, stringent requirements for precision control, efficiency improvement, and cost optimization in the manufacturing process. As a core carrier of in-depth integration of artificial intelligence and industrial automation, AI visual inspection technology, with its advantages of non-contact inspection, real-time response, and adaptive learning, has gradually replaced traditional manual inspection and conventional machine visual inspection. It has become a key technical support for addressing pain points such as micro-defect identification, adaptation to complex working conditions, and full-process quality control in precision mechanical manufacturing. In this regard, this paper first analyzes the application scenarios of AI visual inspection technology in precision mechanical manufacturing automation, and then expounds the development trends of its applications in this field, to provide a reference for relevant researchers.

Research on the Controllable Growth Mechanism and Structural Characterization Methods of Single-Walled Carbon Nanotubes

Jingchun Zhang — 2026-07-23

As a typical one-dimensional carbon nanomaterial, single-walled carbon nanotubes (SWCNTs) possess a unique electronic structure, excellent mechanical properties, and outstanding thermal and electrical conductivity, showing important application value in cutting-edge fields such as nanoelectronic devices and composite materials. Structure determines properties, which is the core foundation for the application of SWCNTs. Their physicochemical properties are significantly affected by microstructural parameters such as diameter and chirality. Therefore, in-depth analysis of the controllable growth mechanism and research on precise structural characterization methods are of great significance for promoting the large-scale application of SWCNTs.

Research on Position-Sensorless Control of PMSM Based on ELO and NLESO-PLL

Wei Liu — 2026-07-23

In the context of the position and speed information of the rotor being difficult to directly obtain in the position sensorless control of permanent magnet synchronous motors, this paper establishes a PMSM position sensorless vector control system model and designs a speed and position estimation structure consisting of an extended Luenberger observer and NLESO-PLL. This method first uses the extended Luenberger observer to estimate the motor’s back electromotive force, and then estimates its components in the α and β coordinate systems from the stator voltage and current signals. Then, the estimated back electromotive force is input into NLESO-PLL, and the rotor position, angular velocity, and mechanical speed are estimated through nonlinear error feedback and the expansion state observation loop. Compared with the traditional PLL, NLESO-PLL can enhance dynamic tracking capability and anti-disturbance performance under conditions of sudden speed changes and load disturbances. Using MATLAB/Simulink, a PMSM position sensorless vector control simulation model is built, and speed step, load disturbance, and position estimation experiments are conducted under different operating conditions. The simulation results show that the proposed method can better track the actual speed and rotor position in the medium-to-high-speed operating range and maintain good estimation stability under speed changes and load disturbances, providing a feasible solution for position-sensorless control of PMSM at medium and high speeds.

Research on Multi-feature Fusion Method for Software Defect Prediction Based on Industrial Internet of Things

Jinpeng Xu — 2026-07-23

With the rapid development of Industrial Internet of Things technology, the scale of its software is expanding, the complexity of the system continues to rise, and the problem of software defects has become increasingly prominent. Software defect prediction technology can locate potential defects in advance and improve software reliability. However, most of the traditional software defect prediction methods rely on a single code metric feature, which makes it difficult to fully characterize the complex characteristics of Industrial Internet of Things software in the semantic information, program structure, and software evolution process, resulting in limited prediction performance. In view of the above problems, this paper focuses on the research of multi-feature fusion in software defect prediction in Industrial Internet of Things scenarios, focusing on the analysis of the role of different types of features in defect prediction and their fusion mechanism. Firstly, the features of code metrics, semantic features, and structure features involved in Industrial Internet of Things software defect prediction are analyzed. Secondly, the influence of different feature fusion methods on prediction performance is studied, including feature concatenation, weighted feature concatenation, attention fusion, and gating fusion. The research results have a certain reference value for Industrial Internet of Things software quality assurance and intelligent defect analysis.

Research on the Key Role of Data Assets in Operation and Maintenance Retrospective Analysis Under Rail Transit Network Security Control

Jianyong Huang — 2026-07-23

This paper takes the national railway ticketing system as the research object, and studies the role of data assets in the operation and maintenance retrospective analysis under the network security control situation. Comprehensively sort out the overall scheme of network security operation and maintenance of the railway passenger ticket system, and focus on the significance of data assets with asset accounts as the core in operation and maintenance management, including important links such as asset modeling, status monitoring, log correlation, and fault tracing. Based on this premise, this paper studies the data collection, correlation analysis, and retrospective analysis technology for security operation and maintenance, and explains the supporting significance of the data asset entity model and relationship model to improve the efficiency of fault location and security event analysis. Research and summarize the practical experience of data asset management, operation, and maintenance, and provide a reference for data asset management of other major information infrastructures in network security operation and maintenance.

YOLO11-SPMB: Application of an Improved Instance Segmentation Network for Tire Defect Detection

Haixu You — 2026-07-23

Tire quality is vital for vehicle safety, but finding surface defects automatically is hard. Current systems often fail to spot tiny flaws hidden inside complex tread patterns. Also, because defects vary in size and blend into the dark rubber, standard models usually output broken or messy segmentation masks. To solve this, we built YOLO11-SPMB, a new instance segmentation network based on YOLO11. We designed it specifically for tire inspection. First, we added a Multi-Scale Adaptive Feature Fusion (MS-AFF) module to the network. It filters out the repeating noise from normal treads so tiny defects can stand out. Second, we created a Boundary Enhancement and Imbalance Calibration (BE-IC) strategy. It uses a boundary Dice loss to keep the mask edges smooth and whole. At the same time, it adjusts class weights to help the model catch rare but major damage, fixing the data imbalance problem. Tests on our custom dataset show the model works very well. YOLO11-SPMB achieves a mAP@0.5:0.95 of 0.702, a mIoU of 0.782, and a recall of 0.928. It also runs at 60 frames per second (FPS), making it fast and accurate enough for real factory production lines.

Structure- and Frequency-aware Domain Adaptation for SAR Raft Aquaculture Extraction

Yijie Wu — 2026-07-23

Marine aquaculture is an important part of the marine economy in China. Synthetic aperture radar (SAR) images can work at all times, which makes them suitable for large-scale monitoring of aquaculture areas. However, SAR images from different regions often have obvious differences in background scattering, aquaculture structures, and imaging conditions. These differences cause serious domain shift problems and reduce the segmentation performance of existing methods in cross-region tasks. To solve this problem, this paper proposes a structure- and frequency-aware domain adaptation method (SFDA) for raft aquaculture extraction from SAR images. The method includes a directional structure alignment (DSA) module and a frequency domain alignment (FDA) module. The DSA module aligns the directional structure features between the source and target domains, while the FDA module aligns texture and scattering features in the frequency domain. Experiments on the Gaofen-3 SAR dataset from Jiangsu province show that the proposed method is effective for cross-region raft aquaculture extraction.

Exploration of Digital Twin-Driven Heterogeneous Terminal Linkage and Intelligent Control Technology in the Internet of Things

Tao Zhang — 2026-07-23

Digital twin technology provides a new theoretical path for solving the difficulties of heterogeneous terminal interconnection and low control efficiency in the Internet of Things. Starting from the endogenous logic of empowering IoT terminals with digital twins, this article analyzes the mirror mapping and closed-loop interaction mechanism between physical entities and virtual models, achieving a paradigm leap from data synchronization to semantic collaboration. On this basis, a digital twin collaboration architecture for heterogeneous terminal linkage was constructed, and core issues such as unified twin model construction, virtual real dynamic association maintenance, twin inter collaboration triggering, and cross protocol communication decoupling were discussed. Then focus on the key technologies of intelligent management and control based on digital twins, including real-time data stream processing and edge computing integration, terminal state autonomous awareness and exception diagnosis, policy adaptive generation under the centralized-decentralized hybrid architecture, and the management and control evolution path from rule-driven to target-driven. The research provides a systematic theoretical framework for intelligent linkage and efficient control of large-scale heterogeneous IoT terminals.

Design and Simulation of Wireless Charging Circuit for Electric Vehicles

Zijian Lu — 2026-07-23

In the 21st century, wireless charging technology has developed rapidly, and realizing wireless charging for electric vehicles is a key focus of future charging work. Building upon previous research, this paper studies and designs a wireless charging circuit based on the principle of electromagnetic induction, which includes a transmitter and receiver, a bridge rectifier, and an RLC series resonant circuit. A set of feasible key parameters was analyzed, and some optimization schemes were proposed. The feasibility and safety of the circuit were verified through simulation software testing. The circuit was found to be relatively stable and applicable to charging most practical electric vehicles. This could provide a new solution for wireless charging of electric vehicles. It also has the potential to contribute to the efficient and convenient development of electric vehicle charging and is worthy of further research.

Strategic Analysis of Dynamic Detection and Tracking Methods for Autonomous Vehicles

Chenhe Kang — 2026-07-23

To solve the problem of perception failure due to vehicle occlusion in urban areas, this paper will review and analyze many dynamic detection and tracking methods proposed in recent years in detail. The main idea of the first representative study is to use laser beams and geometric figures for rigid matching. They are very effective in open areas, but because there is no information on the surface, the algorithm cannot work when there is occlusion. Earlier, researchers put forward a soft-assignment method by building a “four-rectangle measurement model”, and it was found that probabilistic modeling is better than binary matching; now, a laser ray does not need to hit an object, and the matching probability of local sparse point clouds can be calculated. A good way to improve the efficiency of tracking is the scaled sequence algorithm. The first problem of this algorithm is that it involves a high-dimensional state search. The detection rate of severe occlusion in the KITTI dataset for the new model is higher than that of traditional methods. As shown in the above case, although this method is relatively stable in the face of missing values, it has some defects; that is, it is too sensitive to the initial prior and may be trapped in a local optimum under difficult conditions. Due to the shortcomings of the previous models, such as fixed dimensions and an inability to address all kinds of traffic flexibly, it is hoped that future research will be based on multi-scale adaptive geometric models to achieve all-weather sensing and all-conditions operation. Vehicle occlusion, Likelihood Field Model, Scaling sequence algorithm, self-motion compensation.

Structural Design and Optimization of Tesla Valve Based on Bezier Curves

Mingtao Gong — 2026-07-23

To reduce forward vortex losses caused by discontinuous curvature in conventional polyline Tesla valves, this study introduces Bezier curves into Tesla valve design to improve unidirectional flow control through curvature continuity. Eight differentiated Bezier-curve configurations (ta-th) are designed, covering concave, convex, and baseline combinations of Curve 1 (inlet to bifurcation midsection) and Curve 2 (bifurcation midsection to outlet). The channel section is set to 3 mm × 2 mm with a bifurcation angle of 20°. A CFD model is established using the standard k-ω turbulence model, with a velocity inlet of 5 m/s and a pressure outlet of 0 Pa gauge to analyze forward and reverse pressure drops, diodicity, and the distributions (pressure, velocity, and turbulent kinetic energy). The results show that the tc structure (concave Curve 1 and convex Curve 2) achieves the best unidirectional flow performance, with a diodicity of 1.700183, a reverse pressure drop of 16035.53 Pa, and a forward pressure drop within 9400–9700 Pa. A concave Curve 1 improves forward flow uniformity and reduces energy loss, whereas a convex Curve 2 strengthens the contraction-expansion effect in reverse flow and enhances blocking capacity. This study confirms that Bezier curves address the structural deficiencies of conventional Tesla valves, establishing links among quantitative geometry, flow, and performance and parametric design rules. This fills a research gap in continuous-curvature structures and provides high-performance passive fluid control for EV thermal management, flame safety, wastewater treatment, as well as theoretical guidance for other passive fluidic devices.

A Brief Analysis of the Development of the Wire and Cable Industry and Quality Test Technology in Shaanxi Province in the Age of Artificial Intelligence

Beini An — 2026-07-23

As the “industrial blood vessel” of the national economy, wire and cable products underpin the safety of infrastructure and power systems. The rise of artificial intelligence (AI) is driving the transformation of cable quality testing from manual operation to intelligent mode. As a major energy base in China, Shaanxi Province has formed a distinctive cable industrial cluster, yet faces prominent problems such as low intelligent quality test level and insufficient high-end supply. This paper sorts out the development status of Shaanxi’s wire and cable industry, analyzes the bottlenecks of traditional quality test technology, and elaborates the enabling value of AI from four dimensions: defect identification, non-destructive testing, whole-process quality control, and intelligent supervision. On this basis, it summarizes the practical challenges of AI-industry integration, and proposes targeted development paths including building a provincial AI quality test service center, promoting industry-university-research collaboration, upgrading industrial clusters, formulating local standards and constructing an intelligent supervision system, so as to provide references for the high-quality development of Shaanxi’s cable industry.

A Vessel Segmentation Method Based on Prior-Guided False Positive Suppression Gating

Jintao Zhang — 2026-07-23

Vessel segmentation is a fundamental task in medical image analysis and plays an important role in computer-aided diagnosis, lesion localization, vascular morphology analysis, and subsequent three-dimensional reconstruction. However, blood vessels usually exhibit elongated shapes, complex branching patterns, significant scale variations, and locally low contrast. Under challenging conditions such as complex backgrounds, noise interference, and blurred boundaries, tiny vessels are prone to missed detection, while background textures and spurious edges are easily misclassified as vessels, resulting in increased false positives. To address these issues, this paper proposes a prior-guided vessel segmentation method with false positive suppression. The proposed method adopts an encoder-decoder architecture as the backbone and introduces a Vessel False Positive Suppression Gate (VFPSGate) into the skip connections during the decoding stage. By integrating vessel region priors and edge priors, the shallow features are recalibrated in a suppression-oriented manner, thereby reducing the interference of background noise and non-vascular high responses on segmentation results. In addition, a Vessel False Positive Suppression Loss (VFPSLoss) is designed to impose extra constraints on abnormally high responses in background regions that are not supported by the priors, thus enhancing the model’s targeted suppression ability against false positives at the optimization level. Experimental results on the DCA dataset demonstrate that the proposed method achieves competitive performance, with IoU, DSC, ACC, and SEN reaching 66.49%, 79.72%, 97.88%, and 85.16%, respectively. Overall, the proposed method can more effectively distinguish real vessels from pseudo-vessels, providing a feasible solution for vessel segmentation under complex background conditions.

Research on Real-Time Intelligent Rendering Method for Immersive Interactive Virtual Reality Scenes

Wenzhen Wang — 2026-07-23

Virtual reality scenes oriented towards immersive interaction have strict requirements for real-time rendering, such as high frame rate, low latency, and visual consistency. Traditional rendering methods are difficult to ensure both real-time performance and immersive experience under limited computing resources. This article systematically explores the key technologies and implementation paths of intelligent rendering methods: firstly, it analyzes the rendering pressure bottleneck, multi view continuity, and visual dual constraints caused by immersive interaction; Furthermore, an intelligent rendering mechanism based on gaze point prediction, scene semantic modeling, and closed-loop resource allocation will be constructed; On this basis, adaptive strategies such as behavior driven detail level adjustment, dynamic and static differentiation processing, spatiotemporal consistency, and load coordination are proposed; Finally, the evaluation indicators, stability analysis, and scalability verification of real-time performance and immersion are discussed. This article provides a system framework for the theoretical construction and engineering implementation of intelligent rendering methods.

Virtual Simulation Modeling and Validation of Traffic Measurement Equipment Based on Multi-Physical Field Coupling

Lin Zhou — 2026-07-23

As the smart transportation system continues to evolve, the precise and stable operation of traffic measurement equipment directly determines the overall effectiveness of traffic data monitoring and system management. Traditional field tests are limited by specific operational scenarios, narrow coverage of driving conditions, high equipment wear and maintenance costs, and fail to meet the rigorous performance verification requirements under complex environments. This study integrates theories from mechanics, thermodynamics, and electromagnetism to establish a virtual simulation framework for traffic measurement equipment, enabling accurate replication of real-world operating conditions, conducting performance simulations, and facilitating continuous model refinement. This approach overcomes the limitations inherent in single-physical-field simulations. The outcomes provide robust digital support for equipment performance testing, structural optimization, and condition-specific calibration, thereby advancing the development and modernization of measurement systems in smart transportation applications.