检索结果-内蒙古大学图书馆

13th International Conference on Biomimetic and Biohybrid systems, Living Machines 2024

作者： Pham, Huy Jackson, Clayton B. Armstead, Benjamin Nourse, William R. P. Andrada, Emanuel Quinn, Roger D. Department of Mechanical and Aerospace Engineering Case Western Reserve University ClevelandOH44106 United States Institute of Zoology and Evolutionary Research Friedrich-Schiller University Jena Germany Department of Electrical Computer and Systems Engineering Case Western Reserve University ClevelandOH44106 United States

ISBN: (纸本)9783031725968

This paper presents the design of robotic rat forelimbs, scaled to 2.5 times the size of a female Sprague-Dawley rat. Based on a previous rat hindlimb model, significant enhancements were implemented to facilitate manufacturing, robustness, improve overall performance, and enhance biological accuracy. The two front legs are each equipped with five motors to enable sagittal rotations of the scapula, elbow, and wrist joints, along with scapula abduction and adduction. The leg segment dimensions are based on a scanned rat bones model, but the shapes are modified for 3D printing and assembly. Parts are printed using micro carbon fiber filled nylon, strengthened by various amounts of continuously inlaid carbon fiber. Revolute joints are driven by motors, and a pulley-belt transmission system is incorporated to move wrist and elbow motors higher up in order to reduce the inertia of the leg. Furthermore, the foot structure comprises two segments that are passively controlled by two torsion springs to enhance flexibility and facilitate a more naturalistic movement. Additionally, a new electrical board is integrated to enhance operational smoothness and align with the control scheme. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Machine design

来源：评论

学校读者我要写书评

暂无评论

Enhanced Student Stress Prediction in Higher Education Using a TriBranch CNN Framework 3

Enhanced Student Stress Prediction in Higher Education Using...

引用

3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Amudha, K. Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Mullana Ambala India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Machine learning has evolved into a powerful tool for stress prediction, allowing for proactive support of at-risk youngsters. In order to better anticipate college students' stress levels, this study presents an enhanced TriBranch CNN framework. In order to provide the best possible performance, the technique includes data preparation, feature extraction, and model training. The dimensionality and redundancy are reduced using Principal Component Analysis (PCA). After being trained and tested against other models, the Fully Conditional Temporal Bayesian Network (Fully CTBN) outperformed them all with an accuracy of 93.29%, beating both traditional CNN and FCN approaches. These findings demonstrate that the Fully CTBN approach is useful for identifying students who may be struggling academically and providing schools with the information they need to implement data-driven interventions that boost student well-being and performance. © 2025 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Deployment of neural networks for WLAN thumbprint indoor positioning system

引用

International Journal of Information Technology (Singapore) 2025年 1-7页

作者： Kushwaha, Ajay Agarwal, Trapty Singh, Manpreet MP, Sunil Sharma, Amit Jain, Romil Bhuvaneswari, P. Computer Science and Engineering Rungta College of Engineering and Technology Chhatisgarh Bhilai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Centre of Research Impact and Outcome Chitkara University Punjab India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Jain (Deemed-to-be University) Karnataka Bangalore India School of Computer Applications Lovely Professional University Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Chandigarh India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Chennai India

With the application and development of related technologies based on user location information, location-based services have become essential for daily lives. Despite this fact, Wireless Local area networks (WLAN’s) often face time complexities in streamlining and collection of data. Thus, the given paper introduces a deep-learning model based on convolutional neural network (CNN) to predict user’s indoor location from the received wireless signal strength fingerprint maps. The aim is to enhance WLAN indoor localization by streamlining the data collection process. The proposed indoor positioning model leverages a region of interest (ROI) extraction technique to simulate the wireless received signal strength intensity in WLAN’s. By incorporating this advanced technique, the model aims to improve the accuracy and efficiency of WLAN localization. To optimize and evaluate the model’s performance in real-world scenarios, experiments are conducted using simulated datasets obtained from the publicly available database such as UCI repository. The results show that the proposed model attains the highest validation accuracy (97.11%) and training accuracy (97.34%) as compared to the existing deep learning algorithms used for indoor localization. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： CNN Deep learning Indoor localization Region of interest (ROI) Signal strength WLAN

来源：评论

学校读者我要写书评

暂无评论

Joint Encryption and Optimization for 6G MEC-Enabled IoT Networks

引用

IEEE Access 2025年 13卷 79757-79770页

作者： Ahmed, Manzoor Ullah Khan, Wali Alrayes, Fatma S. Said, Yahia Al-Sharafi, Ali M. Kim, Mi-Hye Dashdondov, Khongorzul Ullah, Inam Hubei Engineering University School of Computer and Information Science Institute for AI Industrial Technology Research Xiaogan432000 China Luxembourg City1855 Luxembourg Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems P.O. Box 84428 Riyadh11671 Saudi Arabia Northern Border University Center for Scientific Research and Entrepreneurship Arar73213 Saudi Arabia University of Bisha College of Computing and Information Technology Department of Computer Science and Artificial Intelligence Bisha67714 Saudi Arabia Chungbuk National University Department of Computer Engineering Chungbuk Cheongju-si28644 Korea Republic of Gachon University College of IT Convergence Department of Computer Engineering Seongnam13120 Korea Republic of Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of

With the advent of advancements in future sixth-generation (6G) communication systems, Internet of Things (IoT) devices, characterized by their limited computational and communication capacities, have become integral in our lives. These devices are deployed extensively to gather vast amounts of data in real-time applications. However, their restricted battery life and computational resources present significant challenges in meeting the requirements of advanced communication systems. Mobile Edge Computing (MEC) has emerged as a promising solution to these challenges within the IoT realm in recent years. Despite its potential, securing MEC infrastructure in the context of IoT remains an open task. This study explores the operational dynamics of a secured IoT-enabled MEC infrastructure, focusing on providing real-time, on-demand, secure computational resources to low-powered IoT devices. It outlines a joint optimization problem to maximize computational throughput, minimize device energy consumption, reduce computational latency, and mitigate security overhead. An optimization algorithm is introduced to address these challenges by jointly allocating resources, thereby optimizing throughput, conserving energy, and meeting latency benchmarks through dynamic system adaptation. The effectiveness of the proposed model and algorithm is demonstrated through comparisons with relevant benchmark schemes, highlighting its efficiency in various scenarios. This work showcases the potential of advancements in encryption to deliver scalable security solutions with reduced resource consumption as the number of devices increases. © 2013 IEEE.

关键词： Mobile telecommunication systems

来源：评论

学校读者我要写书评

暂无评论

Durable Ag-Coated Micro-Crack Velcro Electrode for Hi-Fi Bioelectric Signals in Hairy Areas 38

Durable Ag-Coated Micro-Crack Velcro Electrode for Hi-Fi Bio...

引用

38th IEEE International Conference on Micro Electro Mechanical systems, MEMS 2025

作者： Guo, Jun Fu, Kang Zhang, Zimo Bai, Ruiyu Wang, Xuanqi Xue, Kai Chen, Huazhen Li, Le Hu, Huijing Wang, Minghao Chang, Honglong Ji, Bowen Northwestern Polytechnical University Unmanned System Research Institute China Northwestern Polytechnical University National Key Laboratory of Unmanned Aerial Vehicle Technology Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology China Northwestern Polytechnical University Ministry of Education Key Laboratory of Micro/Nano Systems for Aerospace School of Mechanical Engineering China Northwestern Polytechnical University Institute of Medical Research China Hangzhou Dianzi University Moe Engineering Research Center of Smart Microsensors and Microsystems College of Electronics and Information China

ISBN: (纸本)9798331508890

This paper reports a conductive Velcro electrode with micro-cracks for the first time. The laser-induced micro-cracks increase the surface area of Velcro hooks and facilitate the adhesion of more silver particles. A cluster of Nylon-Ag hook are durable with repeated mechanical compression and multiple washing. After 100 times of compression, the plastic deformation is less than 23 μm ( © 2025 IEEE.

关键词： Hooks

来源：评论

学校读者我要写书评

暂无评论

Machine Learning in Higher Education using RF and SVM for Pedagogical Enhancement 3

Machine Learning in Higher Education using RF and SVM for Pe...

引用

3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Anand, Taruna Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Ambala India University Department of Humanities and Social Sciences Dehradun India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

AI's revolutionary potential in higher education is examined in this proposal, including how it could revolutionize teaching, learning, administration, and research. Adaptive learning platforms, intelligent tutoring systems, and virtual learning assistants may change how students learn by improving engagement, providing quick feedback, and enabling tailored learning. The suggested process includes preprocessing, feature extraction, and model training. Preprocessing involves de-anonymizing, tokenizing, and removing punctuation, spaces, and numbers before analysis. The linear transformation method PCA extracts features. Training the systems uses the RF -SVM approach, which combines the advantages of RF and SVM. The suggested RF -SVM model beats two RF and SVM models with an average accuracy of 90.39 *** improves resource use, strategic decision-making, and research capacity in university management. The RF-SVM model's success in academia highlights the practicality of AI-driven solutions. © 2025 IEEE.

关键词： Teaching

来源：评论

学校读者我要写书评

暂无评论

Maize leaf disease multiclass classification and recognition for sustainable agriculture using multi preceptive deep learning model

引用

Discover Sustainability 2025年第1期6卷 1-18页

作者： Gautam, Vinay Quraishi, Aadam AlGhamdi, Azzah Kaur, Gaganpreet Alghayadh, Faisal Byeon, Haewon Bhatt, Mohammed Wasim CUIET Chitkara University Punjab Rajpura 140401 India M.D. Research Intervention Treatment Institute Texas Houston United States Computer Information Systems Department College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh Saudi Arabia Convergence Department Korea University of Technology and Education Cheonan South Korea Model Institute of Engineering and Technology J&K Jammu India

Agriculture sector is faced with perennial challenges that threaten both its productivity and sustainability. Among the greatest threats to cereal crops is disease, especially on cereal grains such as maize. Maize is an important grain that has grown globally, yet it often falls prey to maize leaf disease, a destructive and prevalent disorder. The consequences of these diseases go beyond individual farmers;reduced yields destabilize supply chains, market stability, and global efforts towards creating sustainable food systems. The prevalence of leaf diseases adversely affects crop productivity, which directly impacts the objective of sustainable agriculture. In order to address this problem, technology, more specifically artificial intelligence, has been a game-changer. Adopting cutting-edge research for maize disease detection not only raises diagnostic accuracy but also supports sustainable agricultural practices. This approach encourages effective input use, supports food security, minimizes environmental degradation, and provides farmers with accurate tools, all of which contribute to long-term agricultural resilience and sustainability. However, the incorporation of AI in farming is confronted by a number of challenges. Numerous hindrances hinder precise detection and categorization of maize leaf diseases via artificial intelligence methods. In tackling the limitations, the current study introduces an AI-based approach. It applied Multi-scaled Xception pre-trained models to extract deep features from images. The models were fine-tuned with varying weights for advancing the feature extraction so as to enhance the likelihood of correct visual classification. In addition to its strong accuracy, the research provides a formal and strict mathematical formulation, and different optimization methods further establish the effectiveness of the model. Additionally, the examination of fusion operators helps to improve the interpretability of the model. The expected model w

关键词： CCA Deep learning Feature extraction Feature Fusion Xception

来源：评论

学校读者我要写书评

暂无评论

research on the structure of a cloud field distribution measurement system in a high-speed icing wind tunnel

Research on the structure of a cloud field distribution meas...

引用

2024 International Conference on Optoelectronic Materials and Devices, ICOMD 2024

作者： Zhang, Quan Qing, Chao Zuo, Chenglin Hu, Jiahui Li, Yan School of Computer Science and Software Engineering Southwest Petroleum University Chengdu610500 China Low Speed Aerodynamics Institute China Aerodynamics Research and Development Center Sichuan Mianyang621000 China Key Laboratory of Icing and Anti/De-Icing China Aerodynamics Research and Development Center Mianyang621000 China Intelligent Oil and Gas Laboratory Southwest Petroleum University Chengdu610500 China

ISBN: (数字)9781510689046

ISBN: (纸本)9781510689039

This study addresses the need for cloud uniformity measurement in icing wind tunnel experiments by designing and developing a laser extinction tomography system for circular icing wind tunnels. The system is aimed at enhancing the accuracy of wind tunnel experiments simulating aircraft icing behavior, providing precise data for research on aircraft icing and anti-icing technologies. The system consists of 24 radiation sources and 24 detectors, uniformly distributed around a circular ring with a diameter of 800 mm, enabling real-time monitoring and analysis of cloud field uniformity. Using COMSOL multiphysics simulation software, the propagation paths of the laser through the cloud field were simulated, optimizing the arrangement of radiation sources and detectors to improve measurement accuracy. By integrating the Algebraic Reconstruction Technique (ART) with regularization methods, the image reconstruction algorithm enhances both precision and stability, effectively capturing the spatial distribution of the cloud field. Experimental results demonstrate that the system accurately reflects the cloud field distribution, with the error ratio controlled within acceptable limits, validating the system’s accuracy and reliability. This system provides a high-precision measurement platform for aircraft icing research and anti-icing technology experiments. © 2025 SPIE.

关键词： Tomography

来源：评论

学校读者我要写书评

暂无评论

Oracle-guided Dynamic User Preference Modeling for Sequential Recommendation 25

Oracle-guided Dynamic User Preference Modeling for Sequentia...

引用

18th ACM International Conference on Web Search and Data Mining, WSDM 2025

作者： Xia, Jiafeng Li, Dongsheng Gu, Hansu Lu, Tun Zhang, Peng Shang, Li Gu, Ning Fudan University Shanghai China Microsoft Research Asia Shanghai China Amazon Seattle United States School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China Fudan Institute on Aging MOE Laboratory for National Development and Intelligent Governance Shanghai Institute of Intelligent Electronics & Systems Fudan University Shanghai China

ISBN: (纸本)9798400713293

Sequential recommendation methods can capture dynamic user preferences from user historical interactions to achieve better performance. However, most existing methods only use past information extracted from user historical interactions to train the models, leading to the deviations of user preference modeling. Besides past information, future information is also available during training, which contains the "oracle" user preferences in the future and will be beneficial to model dynamic user preferences. Therefore, we propose an oracle-guided dynamic user preference modeling method for sequential recommendation (Oracle4Rec), which leverages future information to guide model training on past information, aiming to learn "forward-looking" models. Specifically, Oracle4Rec first extracts past and future information through two separate encoders, then learns a forward-looking model through an oracle-guiding module which minimizes the discrepancy between past and future information. We also tailor a two-phase model training strategy to make the guiding more effective. Extensive experiments demonstrate that Oracle4Rec is superior to state-of-the-art sequential methods. Further experiments show that Oracle4Rec can be leveraged as a generic module in other sequential recommendation methods to improve their performance with a considerable margin. © 2025 Copyright held by the owner/author(s).

关键词： dynamic user preference modeling

来源：评论

学校读者我要写书评

暂无评论

Risks and Challenges Associated with the Chain of Custody of Cryptographic Assets in Law Enforcement Operations 6th

Risks and Challenges Associated with the Chain of Custod...

引用

6th International Congress on Blockchain and Applications, BLOCKCHAIN 2024

作者： Abad, Jesús Martín Guardeño, David Arroyo Casino, Fran Universidad Autónoma de Madrid Madrid Spain Madrid Spain Department of Computer Engineering and Mathematics Rovira i Virgili University Tarragona Spain Information Management Systems Institute of Athena Research Center Artemidos 6 Marousi151 25 Greece

ISBN: (纸本)9783031819278

The emergence of cryptocurrencies represents a radical change in the transmission of value in a disintermediated manner, which, in many cases, means that they are beyond the control of regulators and law enforcement bodies. This circumstance, together with the growing training in the use of crypto-asset management resources, has led to an increase in their popularity, and it is becoming increasingly common to find these crypto-assets in criminal operations. From a law enforcement perspective, the use of crypto-currency poses a challenge in terms of the procedure for investigating crimes involving the use of crypto-assets. Indeed, in addition to the problem of tracking and attribution, there is the complex issue of interception, custody and, if the investigation concludes that there is no criminal activity, the refund of the cryptographic asset to its owner. This paper analyses the challenges facing law enforcement investigation of criminal events involving cryptocurrencies that have been intercepted. In particular, it addresses the difficulties in achieving adequate protection to the chain of custody of crypto-assets, all by the inalienable criterion of the presumption of innocence that must govern all criminal proceedings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cybersecurity

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：