International Journal of Advanced Network, Monitoring and Controls最新文献

A Target Recognition Method of Small Sample Based on RCS Data 基于 RCS 数据的小样本目标识别方法

International Journal of Advanced Network, Monitoring and Controls Pub Date : 2024-01-01 DOI: 10.2478/ijanmc-2024-0001

Ruocheng Ma, Haoyang Liu, Jun Yu, Zhi-yi Hu

{"title":"A Target Recognition Method of Small Sample Based on RCS Data","authors":"Ruocheng Ma, Haoyang Liu, Jun Yu, Zhi-yi Hu","doi":"10.2478/ijanmc-2024-0001","DOIUrl":"https://doi.org/10.2478/ijanmc-2024-0001","url":null,"abstract":"\u0000 During the training of target recognition models based on Radar Cross Section (RCS) data, a persistent challenge arises in sampling due to the inherent difficulty in acquiring a sufficient number of samples. This scarcity of data poses a significant impediment to the effective training of models, resulting in diminished accuracy in target recognition. To address this issue, this article proposes a target classification method based on RCS data under small sample conditions. The approach adopts the fundamental concept of Model-Agnostic Meta-Learning (MAML) to train the target recognition model, enhancing the structure of MAML model. An hourglass-shaped convolution layer is introduced to the input layer, with an additional convolution layer preceding the output layer, and a switch to a central loss function. To substantiate the efficacy of the improved MAML model, comprehensive comparative analyses are conducted with benchmark models, including MAML, ResNet 18-layers, Long Short-Term Memory (LSTM), among others. Experimental results conclusively demonstrate the superior performance of the refined MAML model in target recognition under conditions of limited samples, attaining an average prediction accuracy of 85.62%. This signifies a noteworthy 5-percentage-point improvement compared to the baseline model prior to the introduced enhancements.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"15 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140522594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic Landing Control of Aircraft Based on Cognitive Load Theory and DDPG 基于认知负荷理论和 DDPG 的飞机自动着陆控制

International Journal of Advanced Network, Monitoring and Controls Pub Date : 2024-01-01 DOI: 10.2478/ijanmc-2024-0007

Chao Wang, Changyuan Wang

{"title":"Automatic Landing Control of Aircraft Based on Cognitive Load Theory and DDPG","authors":"Chao Wang, Changyuan Wang","doi":"10.2478/ijanmc-2024-0007","DOIUrl":"https://doi.org/10.2478/ijanmc-2024-0007","url":null,"abstract":"\u0000 The keypoint of autonomous driving technology is the accurate instructions maked by desicision-makers based on the perception information. Human plays an important role in the decision-makers. The cognitive load is usually used to quantify the impact of human-computer interaction during flighting. In this paper, we proposed a innovate automatic landing control method based on the cognitive load theory and Deep Deterministic Policy Gradient. Different to the traditional algorithm which heavily relays on an accurate model, the reinforcement learning algorithm is used to design the control strategy in the proposed method. And an improved DDPG algorithm is proposed based on the impact of cognitive load, to improve the training efficiency of the DDPG algorithm and reduce the correlation between data. And construct a human-machine reinforcement learning model. The final position, mean square error of pitch angle, and standard deviation of the aircraft gradually decrease with the number of iterations and tend to 0, indicating that the aircraft is gradually stabilizing its landing. The experimental results demonstrate that the proposed model can greatly improve the longitudinal stability of the aircraft.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"30 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140518237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Securing Operating Systems (OS): A Comprehensive Approach to Security with Best Practices and Techniques 确保操作系统 (OS) 安全：采用最佳实践和技术的全面安全方法

International Journal of Advanced Network, Monitoring and Controls Pub Date : 2024-01-01 DOI: 10.2478/ijanmc-2024-0010

Zarif Bin Akhtar

{"title":"Securing Operating Systems (OS): A Comprehensive Approach to Security with Best Practices and Techniques","authors":"Zarif Bin Akhtar","doi":"10.2478/ijanmc-2024-0010","DOIUrl":"https://doi.org/10.2478/ijanmc-2024-0010","url":null,"abstract":"\u0000 Operating system (OS) security is paramount in ensuring the integrity, confidentiality, and availability of computer systems and data. This research manuscript presents a comprehensive investigation into the multifaceted domain of OS security, aiming to enhance understanding, identify challenges, and propose effective solutions. The research methodology integrates diverse approaches, including an extensive exploration for available knowledge process mechanics, empirical data collection, case studies investigations, experimental analysis, comparative studies, qualitative analysis, synthesis, and interpretation. Through various experimental perspectives, theoretical foundations, historical developments, and current trends in OS security are also explored. Empirical data collection involves gathering insights from publicly available reports, security advisories, case studies, and expert interviews to capture real-world perspectives and experiences. Case studies illustrate practical implications of security strategies, while experimental analysis evaluates the efficacy of security measures in controlled environments. Comparative studies and qualitative analysis provide insights into strengths, limitations, and emerging trends in OS security. The synthesis and interpretation of the findings offer actionable insights for improving OS security practices, policy recommendations, and providing towards future research directions. This research contributes to advancing knowledge in OS security and informs the development of effective strategies to safeguard computer systems against evolving threats and vulnerabilities.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"43 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140519367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Simulation Approximate Solution Strategy for Complex Kinematic Models 复杂运动学模型模拟近似解法研究

International Journal of Advanced Network, Monitoring and Controls Pub Date : 2024-01-01 DOI: 10.2478/ijanmc-2024-0006

WenJing Qu, Zhongsheng Wang

{"title":"Research on Simulation Approximate Solution Strategy for Complex Kinematic Models","authors":"WenJing Qu, Zhongsheng Wang","doi":"10.2478/ijanmc-2024-0006","DOIUrl":"https://doi.org/10.2478/ijanmc-2024-0006","url":null,"abstract":"\u0000 In order to meet the needs of military, road construction, multimedia industry and other aspects, UAVs are gradually given more functions. As the basic function of UAV applications, the fixed-point delivery problem model has higher and higher accuracy requirements. However, in the actual scene, the UAV delivery problem is affected by the interaction of various factors such as flight height, air resistance, and dive angle, which makes it difficult to achieve high stability and high hit accuracy. This paper will analyze the complex motion model based on the fixed-point delivery of explosives by UAV, study the relationship between the stability of UAV delivery and the hit accuracy, and analyze the influence of relevant parameters on the problem by using modeling. In this paper, a multivariate nonlinear continuous time change model is proposed, and a continuous time slice discretization idea operation model is introduced to approximate the time slice splitting inside the UAV launch motion. Secondly, the design quantified evaluation index reaction the initial velocity of the explosive, the launch Angle, the height off the ground and other parameters to analyze the model. Finally, the best scheduling strategy is obtained and verified by using the idea of variable traversal and trial- and-error simulation. The experimental results show that the variation of UAV flying height, speed, depression and other interference factors is consistent with the prediction of score and hit accuracy, according to the environment setting of this model, when the UAV is 300 meters above the ground and 290 meters away from the target horizontal position, the delivery speed is 250m/s, and the pitch angle is about 27°, the fixed-point delivery of explosives is the best strategy.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"31 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140518625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lightweight Low-Altitude UAV Object Detection Based on Improved YOLOv5s 基于改进型 YOLOv5s 的轻量级低空无人机物体探测技术

International Journal of Advanced Network, Monitoring and Controls Pub Date : 2024-01-01 DOI: 10.2478/ijanmc-2024-0009

Haokai Zeng, Jing Li, Liping Qu

{"title":"Lightweight Low-Altitude UAV Object Detection Based on Improved YOLOv5s","authors":"Haokai Zeng, Jing Li, Liping Qu","doi":"10.2478/ijanmc-2024-0009","DOIUrl":"https://doi.org/10.2478/ijanmc-2024-0009","url":null,"abstract":"\u0000 In the context of rapid developments in drone technology, the significance of recognizing and detecting low-altitude unmanned aerial vehicles (UAVs) has grown. Although conventional algorithmic enhancements have increased the detection rate of low-altitude UAV targets, they tend to neglect the intricate nature and computational demands of the algorithms. This paper introduces ATD-YOLO, an enhanced target detection model based on the YOLOv5s architecture, aimed at tackling this issue. Firstly, a realistic low-altitude UAV dataset is fashioned by amalgamating various publicly available datasets. Secondly, a C3F module grounded in FasterNet, incorporating Partial Convolution (PConv), is introduced to decrease model parameters while upholding detection accuracy. Furthermore, the backbone network incorporates an Efficient Multi-Scale Attention (EMA) module to extract essential image information while filtering out irrelevant details, facilitating adaptive feature fusion. Additionally, the universal upsampling operator CARAFE (Content-aware reassembly of features) is utilized instead of nearest-neighbor upsampling. This enhancement boosts the performance of the feature pyramid network by expanding the receptive field for data feature fusion. Lastly, the Slim-Neck network is introduced to fine-tune the feature fusion network, thereby reducing the model’s floating-point calculations and parameters. Experimental findings demonstrate that the improved ATD-YOLO model achieves an accuracy of 92.8%, with a 31.4% decrease in parameters and a 28.7% decrease in floating-point calculations compared to the original model. The detection speed reaches 75.37 frames per second (FPS). These experiments affirm that the proposed enhancement method meets the deployment requirements for low computational power while maintaining high precision.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"15 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140522010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Personalized Recommendation Multi-Objective Optimization Model Based on Deep Learning 基于深度学习的个性化推荐多目标优化模型

International Journal of Advanced Network, Monitoring and Controls Pub Date : 2024-01-01 DOI: 10.2478/ijanmc-2024-0005

Zepeng Yang, Pin Lu, Pingping Liu

{"title":"Personalized Recommendation Multi-Objective Optimization Model Based on Deep Learning","authors":"Zepeng Yang, Pin Lu, Pingping Liu","doi":"10.2478/ijanmc-2024-0005","DOIUrl":"https://doi.org/10.2478/ijanmc-2024-0005","url":null,"abstract":"\u0000 Recommended in this paper, because the existing single objective experience is poor, and the recommended model in a large difference of targets under the complex relationship of joint optimization and the conflict caused by faults, this paper proposes a personalized recommendation based on the deep learning multi-objective optimization algorithm, the estimated probability of users on the individual behavior as a model to study target, Multiple objectives are integrated into a model for learning. Firstly, the embedding layer is used to change the feature vectors, so that the bottom layer of the model shares the same feature embedding. Secondly, the factorization machine and deep learning are used to construct high-low order feature interaction. Then, the gating network and multilevel expert network constructed by a fully connected neural network are used to learn the characteristic relationship of user behavior. Finally, make connections between goals. Through experiments on public and real datasets, The results show that the multi-objective model proposed in this paper has better co-optimization performance and increases the AUC value by 0.1% compared with advanced personalized recommendation models such as MMoE and ESMM, to achieve the ultimate goal of increasing the prediction accuracy and improving user satisfaction.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140526602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Face Recognition System Based on Capsule Networks 基于胶囊网络的人脸识别系统

International Journal of Advanced Network, Monitoring and Controls Pub Date : 2024-01-01 DOI: 10.2478/ijanmc-2024-0003

JiangRong Shi, Li Zhao

引用次数: 0

The Time-Sensitive Networking Scheduling Algorithm Based on Q-learning 基于 Q-learning 的时间敏感型网络调度算法

International Journal of Advanced Network, Monitoring and Controls Pub Date : 2024-01-01 DOI: 10.2478/ijanmc-2024-0008

Jiayi Zhao, Jing Cheng

{"title":"The Time-Sensitive Networking Scheduling Algorithm Based on Q-learning","authors":"Jiayi Zhao, Jing Cheng","doi":"10.2478/ijanmc-2024-0008","DOIUrl":"https://doi.org/10.2478/ijanmc-2024-0008","url":null,"abstract":"\u0000 Time-Sensitive Networking (TSN) occupies a vital position in modern communication domains, with the 802.1Qbv standard being an important network technology designed to meet real-time requirements. This standard requires network traffic to be transmitted within strict time windows, presenting challenges in network planning, necessitating efficient resource allocation and scheduling strategies. This paper addresses the 802.1Qbv planning problem through the introduction of reinforcement learning algorithms, offering an automated and intelligent solution. We have designed a reinforcement learning agent capable of perceiving network status, learning optimal resource allocation strategies, and dynamically adjusting in real-time environments. Through simulation and experimentation, we have validated the effectiveness of our proposed method, comparing it with traditional planning approaches. The contribution of this study lies in introducing a novel solution to the 802.1Qbv planning problem for time-sensitive networks, enhancing network resource utilization and performance. This approach offers strong support for the development and enhancement of TSN-like networks, holding significant importance for meeting the growing demands of real-time applications.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"17 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140527232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Modified Energy Enhancement in WSN Using the Shortest Path Transmission Technique 利用最短路径传输技术增强 WSN 中的修正能量

International Journal of Advanced Network, Monitoring and Controls Pub Date : 2024-01-01 DOI: 10.2478/ijanmc-2024-0004

Ajaegbu Chigozirim, Adediran Oluwaseyi

引用次数: 0

Indoor Robot SLAM with Multi-Sensor Fusion 利用多传感器融合实现室内机器人 SLAM

International Journal of Advanced Network, Monitoring and Controls Pub Date : 2024-01-01 DOI: 10.2478/ijanmc-2024-0002

Jionglin He, Jiaxiang Fang, Shuping Xu, Dingzhe Yang

{"title":"Indoor Robot SLAM with Multi-Sensor Fusion","authors":"Jionglin He, Jiaxiang Fang, Shuping Xu, Dingzhe Yang","doi":"10.2478/ijanmc-2024-0002","DOIUrl":"https://doi.org/10.2478/ijanmc-2024-0002","url":null,"abstract":"\u0000 In order to solve the problem of large positioning error and incomplete mapping of SLAM based on two-dimensional lidar in indoor environment, a multi-sensor fusion SLAM algorithm for indoor robots was proposed. Aiming at the mismatch problem of the traditional ICP algorithm in the front end of the lidar SLAM, the algorithm adopts the PL-ICP algorithm that is more suitable for the indoor environment, and uses the extended Kalman filter to fuse the wheel odometer and IMU to provide the initial motion estimation value. Then, during the mapping phase, the pseudo 2D laser data converted from the 3D point cloud data obtained by the depth camera is fused with the data obtained from the 2D lidar to compensate for the lack of vertical field of view in the 2D lidar mapping. The final experimental results show that the fusion odometer data has improved the positioning accuracy by at least 33% compared to a single wheeled odometer, providing a higher initial iteration value for the PL-ICP algorithm. At the same time, fusion mapping compensates for the shortcomings of a single two-dimensional lidar mapping, and constructs an environmental map with more complete environmental information.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"18 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140518763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0