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1. Application and Optimization of Intelligent Traffic Signal Control and Networked Autonomous Vehicles in Mixed Traffic Environments NSTL国家科技图书文献中心

Ruqi Chen | Mengxiao Wu ... - 《Workshop on Electronics Communication Engineering (WECE 2024)》 - Workshop on Electronics Communication Engineering - 2025, - 1355309.1～1355309.9 - 共9页

摘要：This paper investigates the application and | optimization of intelligent traffic signal control systems | and networked autonomous vehicles in mixed-traffic | environments. By integrating advanced control algorithms and | communication technologies, the study aims to enhance traffic

关键词： Intelligent traffic signal control | Networked autonomous vehicles | Mixed traffic environments | Traffic efficiency | Traffic safety | Deep q-Network

2. Research and Application of Multi-Scenario Propagation Model in Network Twinning Simulation NSTL国家科技图书文献中心

Xuemin Huang | Zhongqiu Xiang ... - 《Workshop on Electronics Communication Engineering (WECE 2024)》 - Workshop on Electronics Communication Engineering - 2025, - 1355305.1～1355305.4 - 共4页

摘要：With the rapid development of wireless | networks and the diversification of application scenarios | , the traditional single transmission model has been | difficult to meet the needs of different terrain | , environment and urban characteristics. In this paper, a

关键词： Scenario propagation model | Twinning simulation | AP clustering

3. Stacking Ensemble Models with Integrated Classifier for Network Anomaly-Based Intrusion Detection NSTL国家科技图书文献中心

Eva-Liisa Kafita | Tatsuya Yamazaki - 《Workshop on Electronics Communication Engineering (WECE 2024)》 - Workshop on Electronics Communication Engineering - 2025, - 1355307.1～1355307.12 - 共12页

摘要：As digital transformation and technology | rapidly advances, protecting organization's networks | from cyberattacks is crucial. Improving traditional | Intrusion Detection Systems (IDSs) is essential due to the | increasing sophistication of cyberattacks on vital and

关键词： Stacking ensemble | Anomaly intrusion detection | Feature selection | Cybersecurity

4. Extended Multi-scale Feature Fusion and Balanced Generative Adversarial Network for Image Inpainting under Limited Data NSTL国家科技图书文献中心

TING XIAO | MINQIAN SUN ... - 《Workshop on Electronics Communication Engineering (WECE 2024)》 - Workshop on Electronics Communication Engineering - 2025, - 135530E.1～135530E.10 - 共10页

摘要：Image inpainting has made remarkable progress | in recent years due to the availability of | sufficient training data. However, when the training data | is insufficient, i.e., image inpainting in few-shot | regimes, the performance of existing image inpainting

关键词： Image inpainting | Limited data | Multi-scale feature fusion | Generative adversarial networks (GANs)

5. An Improved CycleGAN Model for Low-light or Exposure-light Images Preprocessing and Its Application NSTL国家科技图书文献中心

Jinxiang Chen | Yanguang Sun ... - 《Workshop on Electronics Communication Engineering (WECE 2024)》 - Workshop on Electronics Communication Engineering - 2025, - 135530C.1～135530C.6 - 共6页

摘要：Image preprocessing model plays an improvement | role in accurately detecting objects in low-light or | exposure-light images, because it can provide clear | images for image detection models. An improved CycleGAN | (ICGAN) model is presented to enhance low-light or

关键词： Low-light or exposure-light image | Image enhancement | Attention mechanism | Surface defects | Hot rolled steel strips

6. An Efficient and Lightweight Image Detection Model Based on YOLOv8 and Its Application NSTL国家科技图书文献中心

Jinxiang Chen | Junbo Zhao ... - 《Workshop on Electronics Communication Engineering (WECE 2024)》 - Workshop on Electronics Communication Engineering - 2025, - 135530D.1～135530D.9 - 共9页

摘要：An efficient and lightweight image detection | model based on YOLOv8 is presented, and it is applied | to detect the surface defects on steel strips in | this paper. Under the framework of YOLOv8, a multi | -scale detail enhancement mechanism is firstly inserted

关键词： LSDECD | YOLOv8 | Surface defect detection | Deep learning

7. ELS-YOLO: A Lightweight Algorithm for Strip Steel Surface Defect Detection NSTL国家科技图书文献中心

Hongwei Lu | Huihui Zhou - 《Workshop on Electronics Communication Engineering (WECE 2024)》 - Workshop on Electronics Communication Engineering - 2025, - 135530A.1～135530A.11 - 共11页

摘要：To address the issues of defect leakage | , inaccurate detection, and the high computational expense | linked to the YOLOv10n model in the context of strip | steel defect identification, we introduce an improved | defect detection algorithm called ELS-YOLO. Firstly, we

关键词： YOLOv10n | Strip steel surface defect detection | Efficient multi-scale perception network | Lightweight | Large separable kernel attention

8. Design of Ultra-Wideband Vivaldi Antenna for Electromagnetic Environment Detection NSTL国家科技图书文献中心

Shaoqi Zuo | Junli Lu ... - 《Workshop on Electronics Communication Engineering (WECE 2024)》 - Workshop on Electronics Communication Engineering - 2025, - 1355303.1～1355303.6 - 共6页

摘要：An ultra-wideband Vivaldi antenna for | electromagnetic detection is proposed in this paper. The | proposed antenna loads a resistance structure between the | rectangular cavities of the Vivaldi antenna and sets a via | between the feed line and the radiation patch. While

关键词： Electromagnetic detection | Vivaldi | Vivaldi antenna | Ultra-wideband | Resistor between the open cavity | Via between the feeder and the patch

9. Cooperative Control Algorithms for Traffic Congestion Mitigation and Accident Prevention in Multi-Agent Environments NSTL国家科技图书文献中心

Xishi Chen | Yufei Ke ... - 《Workshop on Electronics Communication Engineering (WECE 2024)》 - Workshop on Electronics Communication Engineering - 2025, - 1355308.1～1355308.9 - 共9页

摘要：As urban landscapes expand and the vehicular | population surges, the resultant traffic congestion and | accident rates present formidable challenges. Traditional | traffic management, reliant on manual intervention | , rudimentary surveillance, and static signage, is ill

关键词： Cooperative adaptive cruise control (CACC) | Multi-Agent reinforcement learning (MARL) | SUMO | Multi-Agent environments

10. Speed Sensor Interference Source Identification Based on GWO-CFDP Algorithm NSTL国家科技图书文献中心

Song Gang | Wan Ying Li ... - 《Workshop on Electronics Communication Engineering (WECE 2024)》 - Workshop on Electronics Communication Engineering - 2025, - 1355302.1～1355302.13 - 共13页

摘要：In order to accurately identify the sources of | interference affecting the TCU speed sensors during train | operation, an improved Density Peak Fast Clustering (CFDP | ) algorithm based on Grey Wolf Optimizer (GWO) was proposed | . Initially, the actual interference signals of the train

摘要：This paper investigates the application and | optimization of intelligent traffic signal control systems | and networked autonomous vehicles in mixed-traffic | environments. By integrating advanced control algorithms and | communication technologies, the study aims to enhance traffic

关键词： Intelligent traffic signal control | Networked autonomous vehicles | Mixed traffic environments | Traffic efficiency | Traffic safety | Deep q-Network

摘要：With the rapid development of wireless | networks and the diversification of application scenarios | , the traditional single transmission model has been | difficult to meet the needs of different terrain | , environment and urban characteristics. In this paper, a

关键词： Scenario propagation model | Twinning simulation | AP clustering

摘要：As digital transformation and technology | rapidly advances, protecting organization's networks | from cyberattacks is crucial. Improving traditional | Intrusion Detection Systems (IDSs) is essential due to the | increasing sophistication of cyberattacks on vital and

关键词： Stacking ensemble | Anomaly intrusion detection | Feature selection | Cybersecurity

摘要：Image inpainting has made remarkable progress | in recent years due to the availability of | sufficient training data. However, when the training data | is insufficient, i.e., image inpainting in few-shot | regimes, the performance of existing image inpainting

关键词： Image inpainting | Limited data | Multi-scale feature fusion | Generative adversarial networks (GANs)

摘要：Image preprocessing model plays an improvement | role in accurately detecting objects in low-light or | exposure-light images, because it can provide clear | images for image detection models. An improved CycleGAN | (ICGAN) model is presented to enhance low-light or

关键词： Low-light or exposure-light image | Image enhancement | Attention mechanism | Surface defects | Hot rolled steel strips

摘要：An efficient and lightweight image detection | model based on YOLOv8 is presented, and it is applied | to detect the surface defects on steel strips in | this paper. Under the framework of YOLOv8, a multi | -scale detail enhancement mechanism is firstly inserted

关键词： LSDECD | YOLOv8 | Surface defect detection | Deep learning

摘要：To address the issues of defect leakage | , inaccurate detection, and the high computational expense | linked to the YOLOv10n model in the context of strip | steel defect identification, we introduce an improved | defect detection algorithm called ELS-YOLO. Firstly, we

关键词： YOLOv10n | Strip steel surface defect detection | Efficient multi-scale perception network | Lightweight | Large separable kernel attention

摘要：An ultra-wideband Vivaldi antenna for | electromagnetic detection is proposed in this paper. The | proposed antenna loads a resistance structure between the | rectangular cavities of the Vivaldi antenna and sets a via | between the feed line and the radiation patch. While

关键词： Electromagnetic detection | Vivaldi | Vivaldi antenna | Ultra-wideband | Resistor between the open cavity | Via between the feeder and the patch

摘要：As urban landscapes expand and the vehicular | population surges, the resultant traffic congestion and | accident rates present formidable challenges. Traditional | traffic management, reliant on manual intervention | , rudimentary surveillance, and static signage, is ill

关键词： Cooperative adaptive cruise control (CACC) | Multi-Agent reinforcement learning (MARL) | SUMO | Multi-Agent environments

摘要：In order to accurately identify the sources of | interference affecting the TCU speed sensors during train | operation, an improved Density Peak Fast Clustering (CFDP | ) algorithm based on Grey Wolf Optimizer (GWO) was proposed | . Initially, the actual interference signals of the train

关键词： Speed sensors | Peak density clustering | Grey wolf algorithm | Sparse self-coding | Kernel principal component analysis

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