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共 34966 条记录，以下是51-60 订阅

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IoT based Fish Farming Management 3

IoT based Fish Farming Management

引用

3rd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kanishka, R. Saravanan, V. Dinesh, S. Rajasekaran, N. K.S.Rangasamy College of Technology Tiruchengode Namakkal637215 India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Traditional fish farming often relies on labor-intensive and inefficient management practices. This paper presents an IoT-based system designed to enhance fish farming through real-time monitoring, automation, and environmental control. Each pond operates as a wireless sensor network integrating microcontrollers, sensors, actuators, and wireless modules to continuously monitor water quality parameters like temperature, pH, dissolved oxygen, and ammonia levels. The system employs fuzzy logic controllers to optimize water quality and environmental conditions based on sensor readings. Data is transmitted to a centralized platform for analysis, enabling automated adjustments to feeding schedules and water conditions, improving fish growth, reducing waste, and minimizing environmental impact. Farmers can remotely access real-time updates and control systems via mobile or web applications. Predictive analytics and machine learning further anticipate potential issues, allowing proactive measures. The system demonstrates accuracy, reliability, and profitability, supporting sustainable and modern aquaculture practices. © 2025 ieee.

关键词： Dissolved oxygen sensors

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Assessment and Management of Security Risks in Accounting Information systems from a Lifecycle Perspective

Assessment and Management of Security Risks in Accounting In...

引用

2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Gong, Lijiahui Business School Wuhan Huaxia Institute of Technology Wuhan430223 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

As the carrier of information assets, the importance of accounting information systems is increasing with the development of informatization. Meanwhile, information systems are facing more security threats. How to effectively ensure the security of information systems and prevent information assets from being stolen or damaged has become an inevitable challenge. Therefore, conducting research on the security issues of information systems is of great significance. In response to the data security risks in accounting information systems, this article first establishes a risk indicator system and proposes an information system risk assessment model based on adaptive fuzzy neural networks, which scores the risk indicator attributes during the assessment process. In addition, we propose a fine-grained weight adaptive adjustment algorithm in the model, which provides a quantitative method for the risk indicators of accounting information systems. The feasibility and applicability of the model have been verified through simulation testing. © 2025 SPIE.

关键词： Information security Risk assessment Network security Computer security Computing systems Fuzzy logic Situational awareness sensors Neural networks systems modeling Prototyping

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A Recurrent Neural Network Based Bicriteria Repetitive Motion Collision Avoidance Scheme for Motion Planning of Dual Redundant Manipulators 13

A Recurrent Neural Network Based Bicriteria Repetitive Motio...

引用

13th international conference on intelligent control and Information Processing, ICICIP 2025

作者： Chen, Shirui Gao, Huanli Hu, Shaolin Zhang, Zhijun The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China The School of Automation Guangdong University of Petrochemical Technology Maoming Guangzhou525000 China The Guangzhou Key Laboratory of Brain-Computer Interaction and Applications Guangzhou China The Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Guangzhou510555 China The Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China The College of Computer Science and Engineering Jishou University Jishou416000 China Guangzhou510335 China The Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi University of Technology Hanzhong723001 China The School of Information Science and Engineering Changsha Normal University Changsha410100 China The School of Automation Science and Engineering Maoming525000 China The Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China

ISBN: (纸本)9798331516147

To achieve low joint-angle drift and avoid mutual collision between dual redundant manipulators (DRMs) when they are doing collaboration works, a recurrent neural network based bicriteria repetitive motion collision avoidance (BRMCA) scheme is proposed for motion planning of DRMs. By designing a combined optimization objective encompassing both repetitive motion criterion and collision avoidance criterion, the BRMCA scheme has larger feasible region and less constraint contradiction issues compared with traditional inequality constraints based method. Furthermore, by incorporating velocity-level kinematic model and physical limits of DRMs into the scheme, a time varying quadratic programming (TVQP) problem is formulated. The linear variational inequality-based primal–dual neural network (LVI-PDNN) is used to solve the proposed scheme due to the high accuracy and low computation complexity. Finally, through comparison simulations of tracking Circle-shape and C-shape trajectories between BRMCA scheme and minimum velocity norm (MVN) scheme, it exhibits that the proposed BRMCA scheme can achieve low joint-angle drift, collision avoidance and high tracking accuracy when DRMs are doing cooperative tasks. © 2025 ieee.

关键词： Recurrent neural networks

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Research on the Fusion of Autonomous Navigation Algorithms and Environmental Perception in intelligent Ship technology 5

Research on the Fusion of Autonomous Navigation Algorithms a...

引用

5th ieee international conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Zhang, Zhijie Zong, Yonggang Binzhou Polytechnic Ocean Department Binzhou China

ISBN: (纸本)9798331533694

The research focus of this paper is on the organic combination of autonomous navigation algorithm and environmental perception technology, with the aim of enhancing the autonomous navigation capability of intelligent ships in complex marine environments. Autonomous navigation relies on the close coordination of multiple perception systems and algorithms, among which the simultaneous localization and mapping (SLAM) algorithm is particularly critical. The SLAM algorithm generates high-precision environmental maps in real time and provides accurate ship positioning by fusing multi-sensor information. This paper proposes and designs an autonomous navigation system that integrates environmental perception and SLAM technology. Its system architecture includes sensor data processing, path planning and control decision modules. The effectiveness of the proposed algorithm is verified through a series of model simulations, and experimental evaluation is carried out in multiple test scenarios. The simulation results show that compared with traditional navigation methods, the algorithm significantly improves the navigation accuracy and safety of ships in complex marine environments, and the positioning error rate is reduced by more than 15%. The research results of this paper provide a solid theoretical basis and technical support for the future research and development of autonomous navigation systems for intelligent ships. © 2025 ieee.

关键词： Marine navigation

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Blind Spots Detection in Autonomous Robots using Data Fusion and Deep Learning 22

Blind Spots Detection in Autonomous Robots using Data Fusion...

引用

22nd international Learning and technology conference, L and T 2025

作者： Dwedar, Mohamed Bayram, Fatih Jesser, Alexander Heilbronn University of Applied Science Intelligent Cyber Physical Systems Heilbronn Germany

ISBN: (纸本)9798331532215

The adoption of humanoid robots has surged in recent years, particularly in addressing operational limitations. This study introduces a methodology for detecting blind spots and anomalies in robot motion, using the Nao Robot as a case *** approach integrates multisensor data with deep learning techniques validated on a proprietary dataset covering diverse real-world scenarios, including dynamic non-visible obstacles. High-frequency sensor sampling (T0 = 0.2 s) ensures accurate data, while DBSCAN clustering and ANOVA F-value-based feature selection identify critical features like obstacle distances and motion parameters for collision *** Learning with Sigmoid and ReLU activations, the ADAM optimizer, and Sparse Categorical Crossentropy loss achieved a 95% F1 score and 99% AUC-ROC, demonstrating robust performance in dynamic environments also in real life environment. © 2025 ieee.

关键词： Anthropomorphic robots

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intelligent Production and Quality control technology based on Computer technology and Internet of Things technology 1

Intelligent Production and Quality Control Technology based ...

引用

1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Li, Yaping Business School Ludong University Shandong Yantai China

ISBN: (纸本)9798331529246

The traditional manufacturing process seriously limits the improvement of production efficiency and product quality due to incomplete data collection and lagging information feedback. This paper uses genetic algorithms to construct an intelligent production scheduling model in the workshop, optimize production scheduling, and help enterprises shorten production cycles. At the same time, the Stacking algorithm is used for product quality prediction, and the deviation of a single model is effectively evaluated and corrected by integrating multiple models, thereby improving the prediction accuracy. Experiments show that the workshop production scheduling model based on genetic algorithm has a resource utilization rate of 96.14% when processing 1,000 production tasks;using the model constructed by Stacking, the average accuracy rate of product defect rate prediction is 94.5%.The method proposed in this article can quickly find the optimal or approximately optimal production scheduling scheme, and significantly improve production efficiency. © 2025 ieee.

关键词： Smart manufacturing

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AEGIS: Adaptive Ensemble-based Guard for Intrusion systems 3

AEGIS: Adaptive Ensemble-based Guard for Intrusion Systems

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kumar, Niharika Prasanna Rajgoli, Sarah Rukhmuddin R V Institute of Technology and Management Dept of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798331515911

The evolving sophistication of cyber threats demands an adaptive, ensemble-based approach to Intrusion Detection systems (IDS). This paper introduces AEGIS, an advanced framework designed to enhance real-time threat detection through a multi-layered ensemble architecture. AEGIS incorporates novel features like adaptive thresholding, temporal-aware feature selection, and concept drift monitoring to maintain robust performance amidst dynamic and polymorphic attacks. With a combination of Random Forest classifiers and gradient boosting, AEGIS achieves 99.97% detection accuracy, while minimizing false positives. The system's resilience is further fortified by adversarial testing, time-based cross-validation, and ensemble diversity management, ensuring high adaptability to evolving network behaviors. Our results demonstrate that AEGIS not only surpasses traditional IDS frameworks in detection accuracy but also introduces significant advancements in real-time anomaly detection, scalability, and computational efficiency. The proposed framework provides a pathway for the development of future-proof IDS architectures capable of defending against increasingly sophisticated threats. © 2025 ieee.

关键词： Network intrusion

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Improved Multiview Graph Convolutional Network for Threat Detection in Internet of Things-based Smart Farming systems 1

Improved Multiview Graph Convolutional Network for Threat De...

引用

1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Prakash, S.P. Mohamadabbas, Haydeer Meghana, A. Adam Smith, G. Ghamya, K. Bannari Amman Institute of Technology Department of Electronics and Communication Engineering Sathyamangalam India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electrical and Electronics Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Civil Engineering Mamallapuram India Tirupati India

ISBN: (纸本)9798331529246

Over past few years, the agriculture industry has experienced a significant transformation in farming by adapting advance technologies like Internet of Things (IoT), which leads to a modern era of smart farming systems. These systems use smart technologies such as IoT devices and sensors for optimize farming. However, these systems possess various cyber threats such as data breaches, device tampering and unauthorized access which complicates the system functionality. Therefore, this research proposes Improved Multiview Graph Convolutional Network (IMGCN) for threat detection. Initially, data is collected from Neuro Fuzzy Bot (NF-Bot-IoT) and preprocessed by using Synthetic Minority Over-sampling Technique (SMOTE) which handles class imbalance issues. Further, feature selection is performed using Recursive Feature Elimination (RFE) which reduces the impact of noisy or irrelevant features. Finally, threat detection in smart farming systems is done by using proposed IMGCN which detects threats and protects data. The proposed IMGCN achieved better results in terms of accuracy (93.25%), precision (92.23%) as well as F1-Score (91.78%) when compared with existing Multi-Layer Perceptron (MLP). © 2025 ieee.

关键词： Smart agriculture

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Enhancing Cloud Infrastructure Security with GraphSAGE-Based Intrusion Detection systems 1

Enhancing Cloud Infrastructure Security with GraphSAGE-Based...

引用

1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Kumar, G. Nanda Kishor Syed, Shakir Mogal, Amit Karbhari Abid, Tahera Perada, Anvesh Suneetha, Sampathirao University of South Florida United States Malla Reddy University Department of Computer Science and Engineering Hyderabad India Bargersville IN United States Mvp Samaj's Cmcs College Department of Computer Science and Application Nashik India Nawab Shah Alam Khan College of Engineering and Technology Department of Information Technology Hyderabad India Drexel University Department of Electrical and Computer Engineering Philadelphia United States Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798331529246

Cloud computing provides scalable and adaptable infrastructure however is significantly susceptible to cyber-attacks. This work presents a hybrid methodology that integrates the GOA and GA for feature selection, alongside the Residual GraphSAGE model for intrusion detection. The hybrid GOA-GA method enhances feature selection by optimizing the balance between exploration and exploitation, mitigating overfitting, and augmenting classification performance. The Residual GraphSAGE model alleviates over smoothing while effectively capturing node and edge interactions for precise intrusion classification. Assessment on a benchmark dataset reveals exceptional performance, attaining an accuracy of 91.74%, surpassing established models such as GCN and ResNet. This study emphasizes the need of hybrid optimization and sophisticated graph-based learning in improving cloud infrastructure security. © 2025 ieee.

关键词： Network intrusion

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Application of Smart Terminals in the Power Industrial Internet

Application of Smart Terminals in the Power Industrial Inter...

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international Hydropower Development conference, IHDC 2024

作者： Fuqiang, Huang Zhengpu, Chen Jiangtao, Xiao Zhaoan, Jin Jun, Chen Hai, Yang Xiluodu Hydropower Station China Yangtze Power Co. Ltd. Yunnan Province Yongshan County657300 China State Grid International Development Co. Ltd. Hong Kong Shanghai Liqian Power Technology Co. Ltd. Shenzhen China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China

ISBN: (纸本)9789819791835

The Power Industrial Internet serves as an essential platform for the digital transformation of the electric power industry, during its implementation, the integration of Information technology (IT) and Operational technology (OT) in power production and maintenance has posed significant challenges, necessitating the development of intelligent equipment based on cyber-Physical systems (CPS) to bridge this gap. Microcomputer platforms are well-established and reliable systems capable of running various complex algorithms. However, traditional microcomputer-based protection devices face limitations such as firmware programming, limited types of signal sampling, and insufficient data transmission capabilities. In the current era of digital transformation, there is an urgent need for intelligent devices and smart terminals that can overcome these shortcomings through cloud-edge collaboration to assist the digital transformation of the power industry, continuously creating effective applications that enhance quality and efficiency, and fostering new productive forces. This article starts from the strategic orientation of industrial development, the problem orientation of digital transformation, and the demand orientation for quality improvement and efficiency enhancement. It focuses on the key issues in the advancement of the industrial internet, proposes solutions based on microcomputer platforms, and explores new directions in the development of intelligent and digital transformation in the field of relay protection specialization. © The Author(s) 2025.

关键词： Cloud-Edge Collaboration cyber-Physical systems Industrial Internet Microcomputer Platform Power Industry Smart Terminal

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