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Deep-Reinforcement-learning-Based Resource Allocation for Energy Harvesting D2D Communication 4

Deep-Reinforcement-Learning-Based Resource Allocation for En...

引用

4th International conference on Electronic Communication and Artificial Intelligence, ICECAI 2023

作者： Qi, Yuan Geng, Shuqin Beijing University of Technology Beijing China

ISBN: (纸本)9798350314496

In this paper, we studied the spectrum resource allocation for the D2D communication under cellular networks. Based on the current situation that energy harvesting is rarely considered in reinforcement learning optimization schemes, this paper combined with energy harvesting to solve the energy consumption problem of communication devices. Specifically, this paper proposes an intelligent allocation scheme for spectrum resources based on the Double DQN algorithm that can realize energy harvesting. the simulation results show that the scheme of this paper obtains superior performance than the traditional DQN scheme and the random allocation scheme. Finally, based on the scheme proposed in this paper, the energy harvesting power that conforms to the energy efficiency performance is presented, and then the overall scheme design is completed. © 2023 IEEE.

关键词： Energy harvesting

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D2D Resource Allocation optimization Algorithm Based on Deep Reinforcement learning 4

D2D Resource Allocation Optimization Algorithm Based on Deep...

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4th International conference on Electronic Materials and Information Engineering (EMIE)

作者： Zheng, Kaijin Wang, Yijun Zhuang, Dawei Zhou, Jialu Changchun Univ Sci & Technol Sch Elect & Informat Engn Changchun Peoples R China

ISBN: (纸本)9798350390230;9798350390223

In the cellular network framework, the resource allocation problem for Device-to-Device (D2D) communication technology is particularly critical. the core issue is how to efficiently allocate channel resources to D2D users while minimizing interference to cellular users (CU), ensuring the overall performance and Quality of Service (QoS) of the network. To address this challenge, this letter has proposed a resource allocation strategy based on deep reinforcement learning algorithms, aimed at dynamically adjusting channel allocation and transmission power in D2D communication. Firstly, an in-depth study and analysis of the conflicts and interference issues between D2D communication and cellular users in the cellular network environment regarding channel reuse are conducted. By quantitatively analyzing the impact of signal interference on communication quality, key factors requiring optimization are identified. Secondly, Double-Actor Critic (D-AC) algorithm is developed for dynamically allocating communication channels and adjusting transmission power. this algorithm iteratively optimizes D2D channel reuse and power control strategies, aiming to balance the efficient use of channel resources with the minimization of signal interference. thirdly, the D-AC algorithm is implemented and evaluated for its effectiveness in enhancing system throughput, reducing collision probabilities, and maintaining quality of service standards. By comparing with existing resource management schemes, the advantages of the D-AC algorithm are demonstrated.

关键词： Device to Device (D2D) Deep Reinforcement learning (DRL) power control resource allocation

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Optimizing intelligent Hotel Room Resource Allocation Using Deep learning Techniques 4

Optimizing Intelligent Hotel Room Resource Allocation Using ...

引用

4th International conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Gao, Sheng Yang, Yulin Li, Jiaqi Wu, Yaxuan Zhang, Jingbao Wang, Danzhi Beijing University of Posts and Telecommunications Century College Beijing China

ISBN: (纸本)9798331531225

In light of the accelerated advancement of intelligent hospitality establishments, the effective distribution of room resources has emerged as a pivotal research area, with the objective of enhancing operational efficacy and customer satisfaction. To address this issue, this paper proposes a novel room resource allocation optimisation model based on deep learning, which builds upon the existing Transformer architecture. In particular, an enhanced Transformer model has been devised, which incorporates the time embedding module into the network architecture in order to capture the temporal dynamic characteristics of customer booking behaviour. Furthermore, the ability to identify key features has been enhanced by introducing a weighted self-attention mechanism. Furthermore, the model output layer incorporates a multi-objective optimisation strategy, which serves to balance the relationship between customer satisfaction and hotel revenue. the experimental results demonstrate that the proposed model exhibits superior performance in terms of room utilisation, resource allocation efficiency and customer satisfaction when compared to the traditional method. © 2024 IEEE.

关键词： Hotels

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Machine learning Driven Problem Detection for Production optimization - Well Cease to Flow Prediction 4

Machine Learning Driven Problem Detection for Production Opt...

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4th EAGE Digitalization conference and Exhibition

作者： Gönczi, D. Reddicharla, N. Ramarao Rachapudi Venkata, S. Toumi, O.T. Abdalla, R.A. Nikolaev, D. Datagration Solutions Inc. United States ADNOC Onshore United Arab Emirates

ISBN: (纸本)9789462824850

Early detection of the onset of a well ceasing to flow was always a challenging task. Liquid loading or well cease to flow problem is the inability of a well to remove liquids that are produced from the wellbore. the produced liquid accumulates in the well creating a static column of liquid, therefore creating a back pressure against formation pressure while resulting production decline until the well ceases production. the proposed solution exhibits the capacity to discern prospective opportunities with greater efficiency compared to the conventional multidisciplinary team assessment, achieving this outcome within a matter of hours as opposed to the protracted durations of weeks or months. the utilisation of automation and machine learning problem detection significantly expedites the reassessment and identification of optimal intervention candidates, thereby mitigating the risk of failure and consequently enhancing the overall economic success rate. the entirety of the workflow processes is automated, undergoing updates upon the availability of new datasets in accordance with a predetermined scheduling system. through this comprehensive approach, the solution harmonizes domain knowledge and human expertise, resulting in a substantial reduction in both time and cost associated with the evaluation of intervention candidates. © 4th EAGE Digitalization conference and Exhibition 2024.

关键词： Stoping

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optimization of UAV Coverage Efficiency Based on Improved Random Wandering Algorithm 4

Optimization of UAV Coverage Efficiency Based on Improved Ra...

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4th International conference on Mechatronics Technology and intelligent Manufacturing, ICMTIM 2023

作者： Yu, Yinhui Minnan Institute of Technology College of Information Management Jilin Changchun China

ISBN: (纸本)9798350314434

With the rapid deployment of Unmanned Aerial Vehicle (UAV) and the terrain-independent benefits of UAVs make them important in aiding communication. the current research related to UAV-assisted communication stays at the single UAV stage, and the cooperative operation of multiple UAVs has a great advantage in information perception and mission accomplishment efficiency compared with a single UAV. However, multiple UAVs are faced with the problems of coverage efficiency and communication delay, and quality that are difficult to be achieved at the same time. therefore, an optimization scheme is proposed to address the problem of communication delay reduction caused by the increase of area coverage efficiency when multiple UAV operate cooperatively. the scheme can maximize the coverage efficiency while ensuring that the communication delay satisfies the constraints through an improved random wandering algorithm with the introduction of goal orientation. the experimental results show that the improved random wandering algorithm can coordinate the number of iterations and the optimal solution space size, and can determine the optimal value of step λ to achieve the best balance of UAV coverage efficiency and communication quality and time delay. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Efficient Mobile Deployment of Lightweight CNN-Transformer Model for Crop Disease Diagnosis in Smart Agriculture 4

Efficient Mobile Deployment of Lightweight CNN-Transformer M...

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4th Asia conference on Information Engineering, ACIE 2024

作者： Lv, Zirong China University of Geosciences Hubei Key Lab. of Adv. Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation Wuhan China

ISBN: (纸本)9798350371925

Crop disease diagnosis is of great significance for crop yield and agricultural production. Deep learning methods have become a major research direction for solving crop disease diagnosis. However, recent studies have shown that deploying complex neural network models on mobile devices poses a significant challenge to mobile hardware performance. To address this issue, this paper proposes a lightweight network model based on the Transformer and deploys it on mobile devices. Compared to other lightweight network models, our approach significantly reduces computational complexity using Transformer modules and exhibits a particular advantage in recognition accuracy. It achieves a recognition rate of 97.76% for ten categories of tomato diseases, while the model size is only 3.86 MB. Furthermore, we deploy the model on embedded devices and apply it to tomato crop recognition, enabling farmers to monitor crop conditions in real-time and realizing smart agriculture. © 2024 IEEE.

关键词： Deep learning

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Enhancing CAN-FD Security with an Ensemble learning Approach for Intrusion Detection 4

Enhancing CAN-FD Security with an Ensemble Learning Approach...

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4th International conference on intelligent Technologies, CONIT 2024

作者： Sreelekshmi, M.S. Aji, S. University of Kerala Departement of Computer Science Kerala India

ISBN: (纸本)9798350349900

As the integration of advanced technologies in vehicular systems continues to rise, the risk of cyber threats to in-vehicle networks is also becoming a critical concern. Intrusion detection systems (IDS) play a crucial role in safeguarding these networks against unauthorized access and malicious activities. Traditional IDS approaches often face challenges in effectively detecting cyber threats due to the technological advancements in automotive environments. this study proposes an ensemble learning-based intrusion detection model tailored for CAN-FD (Controller Area Network - Flexible Datarate) networks. the model combines the strength and computational simplicity of multiple classification algorithms, including Random Forest (RF), Decision Trees (DT), and k-Nearest Neighbors (KNN), to create a robust and adaptive detection system. through comprehensive experimentation and evaluation with the benchmark CAN-FD intrusion dataset, the proposed ensemble model demonstrates the reliability of the model compared to individual classifiers. this research contributes to in-vehicle security by providing an effective and scalable approach to intrusion detection in CAN-FD networks. © 2024 IEEE.

关键词： Network intrusion

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Study and Development of intelligent KPI Management System for Discrete Manufacturing Enterprises in Industry 4.0 4

Study and Development of Intelligent KPI Management System f...

引用

4th International conference on Mechatronics Technology and intelligent Manufacturing, ICMTIM 2023

作者： Gu, Mingrui Luo, Liang Li, Pengzhong Tongji University School of Mechanical Engineering No. 4800 Caoan Highway Jiading District Shanghai China Tongji University Sino-German College of Postgraduate Studies No. 4800 Caoan Highway Jiading District Shanghai China

ISBN: (纸本)9798350314434

Key performance indicators (KPIs) are essential for manufacturing management and production optimization in discrete manufacturing enterprises. In the context of Industry 4.0, KPI management in discrete manufacturing enterprises struggles in obtaining large amounts of heterogeneous data, low transmission efficiency and poor real-time KPI evaluation. Towards the above-mentioned problems, this paper investigates the definition and characteristics of intelligent KPI management system in discrete manufacturing enterprises and designs a distributed application architecture of the system based on OPC UA protocol. On this basis, the system node functions and the production site deployment of the nodes are designed according to the production organization structure of the enterprises, to achieve accurate KPI real-time evaluation. Moreover, intelligent KPI optimization support is designed, which provides intelligent KPI optimization recommendation based on KPI optimization experience and optimization knowledge, as well as optimization task management for real-time tracking of optimization tasks. Finally, we adopt the Spring Boot framework to develop the cloud-intelligent KPI management system, which provides enterprise managers with a visual approach to digitally and intelligently manage KPIs. © 2023 IEEE.

关键词： Industry 4.0

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An Industrial Internet of things (IIoT) platform for intelligent wind turbine gearbox fault detection with automatic annotation 24

An Industrial Internet of Things (IIoT) platform for intelli...

引用

4th International conference on Internet of things and Machine learning

作者： Fu, Xiao Han, Junyi Cao, Kuo Shanghai Lixin Univ Accounting & Finance Sch Informat Management Shanghai Peoples R China Yingke Zhushu Network Technol Co Ltd Zhuzhou Peoples R China Golden Data Ltd London England

ISBN: (纸本)9798400710353

Machine learning-based fault detection plays a critical role in improving the operational reliability and efficiency of wind turbines. However, existing high-performance methods typically rely on large, manually annotated training datasets, which are not readily available in the wind industry. With the fourth industrial revolution, the Industrial Internet of things (IIoT) concept that guides the operation of the real industry through the simulation, can provide easier and reliable maintenance. therefore, this paper proposes an IIoT platform for real-time health monitoring of wind turbine gearboxes, through which a kurtosis-based automatic annotation approach to identify normal and abnormal measurements to characterize the health condition of each individual gearbox is developed. the dataset containing 22,074 measurements collected from four damaged wind turbines is employed, and the proposed method produces annotations comparable to those provided by independent domain experts. In this way, virtual-real fusion is applied to improve the integration of artificial intelligence in manufacturing, which allows a more effective condition monitoring system in the current era of Internet of Everything.

关键词： Industrial internet of things (IIoT) Fault detection Automatic annotation Wind turbine Gearbox

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Exploration of the Application of Reinforcement learning Algorithms in Network Security Situation Awareness 4

Exploration of the Application of Reinforcement Learning Alg...

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4th IEEE International conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Yang, Hao Bao, Jian Lv, Wanting Gao, Yang Chen, Wei Zhao, Xuejiao State Grid XinJiang Information & Telecommunication Company Xinjiang Urumqi China

ISBN: (纸本)9798350352931

this article discusses the key technical challenges of accurate perception, real-time response and new threat identification in the field of network security situation awareness, and studies a network security situation awareness method based on reinforcement learning. Network security situation awareness is facing a surge in data volume and the continuous evolution of new threats, and existing methods have shortcomings in these aspects. this article introduces in detail the technical solutions including data collection, feature engineering, reinforcement learning model design and security policy optimization, aiming to improve the ability to identify and respond to complex network threats. the research results show that the proposed reinforcement learning model achieves 95.2 % accuracy and 94.6% recall in accurate perception, an average response time of 0.23 seconds and a recovery time of 0.35 seconds in real-time response capability, and an 85% tracing success rate and a 5% mis-tracing rate in attack tracing capability, which are better than traditional methods. the conclusion summarizes the application potential of reinforcement learning algorithms in network security situation awareness, points out the limitations of the research and future research directions, and provides new ideas and methods for network security research. © 2024 IEEE.

关键词： network security situation awareness network threat detection real time monitoring reinforcement learning security strategy optimization

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