检索结果-内蒙古大学图书馆

Personalized Sports Health Recommendation System Assisted by q-learning algorithm

INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION 2025年第4期41卷 1889-1901页

作者： Yang, Yang Zhao, Yuanji Chengdu Sport Univ Coll Phys Educ Chengdu Peoples R China Wuhan Sports Univ Sch Phys Educ Wuhan Peoples R China

In response to the current problem of single sports plan and lack of long-term motivation in recommendation systems, a more intelligent personalized sports health recommendation system was designed by introducing q-learning (quality learning) algorithm. Firstly, user sports health data was collected, and the user model was constructed to track user sport preferences and historical behavior. Secondly, the sports environment was defined, including different types of sports activities, venues, and weather. Then, the reward function was formulated to reward and punish users based on their sports activities and goals, in order to maximize long-term health benefits. Finally, the q-learning algorithm was implemented to continuously iteratively learn and optimize user recommendation models to provide the best personalized sports recommendations. For personalized accuracy, indicators such as precision, recall, F1 value, MAE (Mean Absolute Error), and RMSE (Root Mean Square Error) were used to evaluate, while the system's participation in sports, user satisfaction, long-term incentive effects, and overall health improvement were collected. The results showed that the average precision of the recommendation system on 10 different datasets was 88%, and the average AUC (Area Under Curve) was 96%, which was 6.7% higher than the SVD (Singular Value Decomposition) algorithm. The user's sports persistence rate was improved by 25%, and the health score was improved by about 13.3%. These data not only reflect the superior performance of the recommendation system but also highlight its positive impact on long-term user motivation and overall health levels. The results indicate that the proposed personalized exercise health recommendation system, assisted by the q-learning algorithm, has significantly improved accuracy. Moreover, it offers users more intelligent and personalized exercise suggestions, effectively increasing long-term participation in physical activities and overall healt

关键词： Sports health system q-learning algorithm personalized recommendations user modeling long-term motivation

来源：评论

学校读者我要写书评

暂无评论

Integrated information system based on q-learning algorithm and multi-objective particle swarm optimization with molecular fuzzy-based decision-making for corporate environmental investments

引用

INFORMATION SCIENCES 2025年 698卷

作者： Dincer, Hasan Yuksel, Serhat Olaru, Gabriela Oana Eti, Serkan Istanbul Medipol Univ Sch Business Istanbul Turkiye Istanbul Medipol Univ Sch Commun Istanbul Turkiye Istanbul Medipol Univ IMU Vocat Sch Istanbul Turkiye Khazar Univ Res Ctr Sustainable Econ Dev Baku Azerbaijan

Determining the most important criteria is a great necessity to increase the environmental performance of renewable energy projects. This situation helps to reach cost efficiency and effective use of limited resources. However, there are very few studies in the literature where priority analysis is made for these factors. This condition indicates an important gap in the literature on this subject. Accordingly, this study aims to identify the most convenient investment strategies to increase the environmental performance of renewable energy projects. First, the balanced expert dataset is generated via q-learning algorithm and multi-objective particle swarm optimization. In the following step, selected criteria are weighted with molecular fuzzy cognitive maps approach. The final stage consists of ranking the alternatives with fuzzy molecular ranking (MORAN). The contribution of this study to the literature is to define the most appropriate investment strategies to increase the environmental performance of these projects by generating a new decision-making model. Integrating molecular geometry into decision-making processes increases both the originality and effectiveness of the model. Thus, it is possible to calculate the degrees with a more scientific infrastructure. Integrating q-learning and swarm optimization techniques into the decision-making model also increases the superiority of the model. In this context, the optimization technique is used to obtain the importance weights of the experts. The findings denote that energy capacity expansion plays the most important role in improving the environmental performance of businesses because it has the greatest weight (0.2563). Return on investment is another factor that plays an important role in increasing this performance with the weight of 0.2559. Geothermal and hydropower are found as the most successful renewable energy types regarding environmental performance improvement since they have the highest aggregated v

关键词： q-learning algorithm Molecular fuzzy sets MORAN Swarm optimization Environmental performance

来源：评论

学校读者我要写书评

暂无评论

q-learning algorithm Enabled Topology Control Scheme in Power Line Communication Networks 4

Q-Learning Algorithm Enabled Topology Control Scheme in Powe...

引用

4th International Conference on Smart Power and Internet Energy Systems (SPIES)

作者： Liu, Lin Zheng, Libin Wang, Yusi State Grid Dalian Elect Power Supply Co Dalian Liaoning Peoples R China Beijing SmartChip Microelect Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9781665489577

The topology control technology is of great importance in the Power Line Communications (PLC) networks for smart power grid. Due to the weak topology controlling ability of current large-scale PLC networks, the throughput of current PLC networks is limited seriously and fail to support more real-time services. To deal with this problem, this paper proposes a q-learning algorithm enabled topology control scheme of PLC networks. This robust topology control scheme introduces the q-learning algorithm into the CSMA/CA protocol and adopts the Markov process to build the networking model of PLC system. Through period on-line learning by proposed algorithm, the robust topology can be established in PLC networks. Test results show that the proposed approach can improve topology control ability for real-time services by smart grid IoT (SG-IoT) systems, in terms of both packet loss rate and time delay performance with great feasibility and efficiency.

关键词： Topology control scheme q-learning algorithm power line communications smart grid IoT

来源：评论

学校读者我要写书评

暂无评论

Compatibility and Performance Improvement of the WPT Systems Based on q-learning algorithm

引用

IEEE TRANSACTIONS ON POWER ELECTRONICS 2024年第8期39卷 10582-10593页

作者： Liu, Xu Chao, Jie Rong, Cancan Liao, Zhijuan Xia, Chenyang China Univ Min & Technol Sch Elect Engn Xuzhou 221116 Peoples R China

Different kinds of compensation topologies are widely used in wireless power transfer (WPT) systems, resulting in loads with different compensation topologies hardly obtaining the same power from the same power transmitter. In addition, the load power and system efficiency are hard to hold constant when the mutual inductance or load resistance varies, and these two variation parameters are both hard to be accurately identified. These severely limit the practical popularization of the WPT technology. Therefore, a new strategy to improve the compatibility and performance of the WPT system based on the q-learning algorithm and switch-controlled-capacitor (SCC) is proposed in this article. The q-learning algorithm is used to train an intelligent offline database by monitoring the voltage and current in the primary side of the system to obtain an optimized compensation capacitor on the primary side without knowing the mutual inductance and load resistance. A 1-kW prototype is established and the experimental results demonstrate that the load side can receive desired power when the load changes from one kind of compensation topology to another, or the load resistance and mutual inductance vary together. Meanwhile, the coil-to-coil efficiency can also be effectively maintained.

关键词： Topology Transmitters Resistance q-learning Inductance Mathematical models Coils Closed-loop control compatibility performance optimization q-learning algorithm wireless power transfer (WPT)

来源：评论

学校读者我要写书评

暂无评论

Generation a shooting on the walking for soccer simulation 3D league using q-learning algorithm

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第6期14卷 6947-6957页

作者： Li, Yun Song, Yibin Rezaeipanah, Amin Yantai Inst Technol Sch Informat Engn Yantai 264005 Shandong Peoples R China Univ Rahjuyan Danesh Borazjan Dept Comp Engn Bandar Bushehr Iran

RoboCup is one of the greatest human endeavors to operationalize research in the field of robotics and artificial intelligence. RoboCup soccer simulation 3D (RoboCup3D) competition provide a great opportunity to work with humanoid robots without the need for hardware. One of the most important goals of the teams participating in RoboCup3D league is the ability to increase the number of shoots. The reason for this importance is that superiority over the opponent requires a powerful and precise shoot. The methods introduced for shooting so far are mostly based on Inverse Kinematics (IK) and point analysis. The assumption of these methods is that the positions of the robot and the ball is fixed. However, this is not always the case for shooting. In this paper, a shooting strategy is presented for situations where the robot is walking. Here, a curved path is designed to movement the robot towards the ball so that the robot will eventually have an optimal position to shoot. In general, the vision preceptor in RoboCup3D has noise. Hence, robot movement parameters such as speed and angle are more precisely adjusted by the q-learning algorithm. Finally, when the robot is in the optimal position relative to the ball and the goal, the IK module is applied to the shooting strategy. Simulations and experiments prove the superiority of the proposed algorithm compared to most teams in RoboCupSoccer and Iran's Open RoboCup3D leagues.

关键词： RoboCup3D Soccer simulation 3D league Shooting strategy q-learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Designing of on line intrusion detection system using rough set theory and q-learning algorithm

引用

NEUROCOMPUTING 2013年 111卷 161-168页

作者： Sengupta, Nandita Sen, Jaydeep Sil, Jaya Saha, Moumita Univ Coll Bahrain Informat Technol Program Manama Bahrain Indian Inst Technol Kanpur IITK Dept Comp Sci & Engn Kanpur Uttar Pradesh India Bengal Engn & Sci Univ Shibpur Dept Comp Sci & Technol Howrah 711103 W Bengal India Indian Inst Technol Dept Comp Sci & Engn Kharagpur 721302 W Bengal India

Development of an efficient real time intrusion detection system (IDS) has been proposed in the paper by integrating q-learning algorithm and rough set theory (RST). The objective of the work is to achieve maximum classification accuracy while detecting intrusions by classifying NSL-KDD network traffic data either 'normal' or 'anomaly'. Since RST processes discrete data only, by applying cut operation attributes in training data are discretized. Using indiscernibility concept of RST, reduced attribute sets, called reducts are obtained and among the reducts a single reduct is chosen which provides highest classification accuracy. However, for the test data the same reduct would not provide highest classification accuracy due to change of discretized attribute values. Therefore, to overcome the problem discretization and feature selection processes are dealt in a comprehensive and systematic way in the paper using machine learning approach. The q-learning algorithm has been modified to learn optimum cut value for different attributes so that corresponding reduct produces maximum classification accuracy while classifying network traffic data. Since, not all attributes but reduct only take part to detect intrusions, the proposed algorithm is faster than q-learning and reduces complexity of the IDS. Classification accuracy with 98% success rate has been obtained using real time data, which demonstrates superior performance compared to other classifiers. (C) 2013 Elsevier B.V. All rights reserved,

关键词： q-learning algorithm Rough set theory Discretization Intrusion detection system

来源：评论

学校读者我要写书评

暂无评论

Safeguarding multiuser communication using full-duplex jamming and q-learning algorithm

引用

IET COMMUNICATIONS 2018年第15期12卷 1805-1811页

作者： qiu, Xiaoying Jiang, Ting Wang, Ning Beijing Univ Posts & Telecommun Minist Educ Key Lab Universal Wireless Commun Beijing Peoples R China

In this study, the authors consider secure communications in multiuser wireless networks where full-duplex (FD) jamming operates to enhance physical layer security. The considered multiuser system is equipped with FD legitimate receivers in contrast to conventional frameworks where a half-duplex (HD) receiver is at hand. This study investigates an alternative solution in which the authors take advantage of FD capability of the receivers to send jamming signals against the eavesdropper. Under these assumptions, the impact of self-interference and channel interference on physical layer security is investigated. They derive the expressions of secrecy outage probability and ergodic secrecy rate in the proposed system. Two FD jammer selection schemes are proposed to further improve the security. In addition, they apply a reinforcement learning technology, called q-learning, to model the interaction between the source and multiple jamming users. The preliminary results show that the application of FD jamming and user selection scheme leads to a significant improvement in the wireless network security.

关键词： jamming telecommunication security telecommunication network reliability wireless channels learning (artificial intelligence) cooperative communication radio networks probability MIMO communication jamming signals physical layer security secrecy outage probability ergodic secrecy rate FD jammer selection schemes reinforcement learning technology source multiple jamming users user selection scheme wireless network security safeguarding multiuser communication full-duplex jamming q-learning algorithm secure communications wireless networks considered multiuser system FD legitimate receivers conventional frameworks half-duplex receiver alternative solution FD capability

来源：评论

学校读者我要写书评

暂无评论

Multi-AGV route planning in automated warehouse system based on shortest-time q-learning algorithm

引用

ASIAN JOURNAL OF CONTROL 2024年第2期26卷 683-702页

作者： Zhang, Zheng Chen, Juan Zhao, Wenbing Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing Peoples R China Goldwind Sci & Technol Co Ltd R&D Ctr Beijing Peoples R China Cleveland State Univ Dept Elect Engn & Comp Sci Cleveland OH 44115 USA

Route planning for automated guided vehicles (AGVs) is one of the key factors that affects work efficiency of automated storage and retrieval systems (AS/RSes). Route planning plays an important role in the operation of AGVs. Since the characteristic of AS/RSes is chessboard-like, the environment is more complex than traditional route planning environments because the number of nodes is large, more than one shortest route exists between two nodes, and the routes with the shortest distance may not be the most energy-saving routes. Although the traditional route planning algorithms such as the classical q-learning algorithm can work well in AGV route planning, it also has some limitations. This paper proposes a novel multi-AGV route planning approach to solving the AGV route planning problem in the chessboard-like warehouse, which can improve the route planning efficiency greatly. First, by combining adjacency matrix and reward matrix, we propose a low-dimensional adjacency-reward matrix for route planning. This algorithm improves the efficiency of classical q-learning algorithms and accelerates dynamic route planning significantly. We further improve the algorithm by considering the travel directions to minimize the number of turns in the route and additionally by considering whether the AGV is loaded or not and plan routes accordingly. Finally, we propose a multi-AGV online collision-free route planning algorithm based on these considerations for dynamic route planning for multi-AGVs operating in a large-scale warehouse. The proposed algorithms are validated with several case studies.

关键词： AGV automated warehouse collision-free q-learning algorithm route planning shortest time

来源：评论

学校读者我要写书评

暂无评论

Horizontal trajectory control of stratospheric airships in wind field using q-learning algorithm

引用

AEROSPACE SCIENCE AND TECHNOLOGY 2020年 106卷 106100-106100页

作者： Yang, Xiaowei Yang, Xixiang Deng, Xiaolong Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China

This paper proposes an adaptive horizontal trajectory control method for stratospheric airships in uncertain wind field using q-learning algorithm. Firstly, horizontal trajectory control of the airships is decomposed into the target tracking, and the observation model of airships is constructed. Then, the Markov decision process (MDP) model of airships is established, in which the action strategy is determined by the wind direction, and a cerebellar model articulation controller (CMAC) neural network is designed to optimize the action strategy for each state. Finally, numerical simulations demonstrate that the proposed control method performs well stability and intelligent decision-making ability in the process of horizontal trajectory control for stratospheric airships. (C) 2020 Elsevier Masson SAS. All rights reserved.

关键词： Stratospheric airships Horizontal trajectory control q-learning algorithm Wind field CMAC neural network

来源：评论

学校读者我要写书评

暂无评论

A modified q-learning algorithm to solve cognitive radio jamming attack

引用

INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS 2018年第1期10卷 41-51页

作者： Slimeni, Feten Scheers, Bart Chtourou, Zied Le Nir, Vincent Attia, Rabah Mil Acad Tunisia VRIT Lab Nabeul Tunisia RMA CISS Dept Brussels Belgium EPT Univ Carthage SERCOM Lab Marsa Tunisia

Since the jamming attack is one of the most severe threats in cognitive radio networks, we study how q-learning can be used to pro-actively avoid jammed channels. However, q-learning needs a long training period to learn the behaviour of the jammer. We take advantage of wideband spectrum sensing to speed up the learning process and we take advantage of the already learned information to minimise the number of collisions with the jammer. The learned anti-jamming strategy depends on the elected reward strategy which reflects the preferences of the cognitive radio. We start with a reward strategy based on the avoidance of the jammed channels, then we propose an amelioration to minimise the number of frequency switches The effectiveness of our proposal is evaluated in the presence of different jamming strategies and compared to the original q-learning algorithm. We compare also the anti-jamming strategies related to the two proposed reward strategies.

关键词： cognitive radio network jamming attack q-learning algorithm Markov decision process MDP

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：