检索结果-内蒙古大学图书馆

Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Mina Khalid Muhammad Hamza Zulfiqar Muhammad Qasim Mehmood Rifaqat Hussain Akram Alomainy Department of Biomedical Engineering University of Engineering and Technology Lahore Pakistan Electrical Engineering Department Micro Nano Lab Information Technology University (ITU) of the Punjab Lahore Pakistan Antenna and Electromagnetics Research Group School of Electronic Engineering and Computer Science Queen Mary University of London London UK

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

Electronic waste (e-waste) production is increasing day by day, polluting the environment and causing life-threatening scenarios. Non-rechargeable batteries are a major source of e-waste because of the increasing demand for battery-powered devices. Here, we report the utilization of abandoned batteries in recycling to develop graphite-based conductive paste for textile-based flexible strain sensors. A modified interdigitated electrode (MIDE) structure is developed on fabric using a facile and solvent-free fabrication process. The strain sensors are utilized in wearable applications to monitor the movement of body joints at different angles. The sensors efficiently detect the stain applied by human wrist, knee, and elbow joints with high sensitivity GF > 350. The proposed facile paste from e-waste is a promising candidate for developing green electronics for wearable applications.

关键词： Wrist Electronic waste Capacitive sensors Recycling Batteries Biomedical monitoring Wearable devices

来源：评论

学校读者我要写书评

暂无评论

Efficient Visual Recognition:A Survey on Recent Advances and Brain-inspired Methodologies

引用

Machine Intelligence research 2022年第5期19卷 366-411页

作者： Yang Wu Ding-Heng Wang Xiao-Tong Lu Fan Yang Man Yao Wei-Sheng Dong Jian-Bo Shi Guo-Qi Li Applied Research Center Laboratory Tencent Platform and Content Group Shenzhen 518057China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi′an Jiaotong UniversityXian710049China School of Artificial Intelligence Xidian UniversityXi'an 710071China Division of Information Science Nara Institute of Science and TechnologyNara 6300192Japan Peng Cheng Laboratory Shenzhen 518000China Department of Computer and Information Science University of PennsylvaniaPhiladelphia PA19104-6389USA Institute of Automation Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China

Visual recognition is currently one of the most important and active research areas in computer vision,pattern recognition,and even the general field of artificial *** has great fundamental importance and strong industrial needs,particularly the modern deep neural networks(DNNs)and some brain-inspired methodologies,have largely boosted the recognition performance on many concrete tasks,with the help of large amounts of training data and new powerful computation *** recognition accuracy is usually the first concern for new progresses,efficiency is actually rather important and sometimes critical for both academic research and industrial ***,insightful views on the opportunities and challenges of efficiency are also highly required for the entire *** general surveys on the efficiency issue have been done from various perspectives,as far as we are aware,scarcely any of them focused on visual recognition systematically,and thus it is unclear which progresses are applicable to it and what else should be *** this survey,we present the review of recent advances with our suggestions on the new possible directions towards improving the efficiency of DNN-related and brain-inspired visual recognition approaches,including efficient network compression and dynamic brain-inspired *** investigate not only from the model but also from the data point of view(which is not the case in existing surveys)and focus on four typical data types(images,video,points,and events).This survey attempts to provide a systematic summary via a comprehensive survey that can serve as a valuable reference and inspire both researchers and practitioners working on visual recognition problems.

关键词： Visual recognition deep neural networks(DNNS) brain-inspired methodologies network compression dynamic inference survey

来源：评论

学校读者我要写书评

暂无评论

Unleashing the Dynamic Strategies in the Era of Change - A Spotlight on the ISPRS Student Consortium's Global Contributions

Unleashing the Dynamic Strategies in the Era of Change - A S...

引用

2024 Symposium on Beyond the Canopy: Technologies and Applications of Remote Sensing

作者： Yadav, Yogender Thapa, Laxmi Pucino, Nicolas Kushwaha, Sunni Kanta Prasad Lagahit, Miguel Luis Rivera Okolie, Chukwuma John Politecnico di Torino Italy School of Built Environment University of New South Wales Sydney Australia Department of Operations and Information Management Aston University Birmingham United Kingdom The Australian National University Canberra Australia Geomatics Group Department of Civil Engineering Indian Institute of Technology Uttarakhand Roorkee India Department of Architecture and Building Engineering Tokyo Institute of Technology Japan Tokyo Tech Academy for Super Smart Society Tokyo Institute of Technology Japan Data Platform Research Team National Institute of Advanced Industrial Science and Technology Japan Department of Surveying & Geoinformatics University of Lagos Nigeria Abuja Nigeria

ISPRS Student Consortium (ISPRS SC) has played a significant role in bringing students and young researchers closer to the field of remote sensing and photogrammetry in the evolving landscape of geospatial sciences. Innovative strategies and events have been employed by the ISPRS SC to address contemporary challenges and harness emerging opportunities in the field of photogrammetry and remote sensing. Through an analysis of the global contributions of ISPRS SC, ranging from research initiatives to the online webinar series, this paper underscores the consortium's commitment to fostering collaboration, innovation, and knowledge exchange among young scholars and professionals worldwide. The paper highlights key thematic areas addressed by ISPRS SC such as advancements in remote sensing technologies, geospatial data analysis, and applications in diverse domains including environmental monitoring, agriculture, urban planning, and disaster management. Moreover, in this paper, we emphasize the role of the consortium in nurturing future leaders in geospatial sciences through ISPRS SC mentorship programs, networking opportunities, and capacity-building initiatives. By highlighting the initiatives led by the ISPRS Student Consortium, this paper underscores the key role of youth-driven innovation for positive change and shaping the future of geospatial sciences on a global scale. © Author(s) 2024.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Wind speed prediction and insight for generalized predictive modeling framework: a comparative study for different artificial intelligence models

引用

Neural Computing and Applications 2024年第23期36卷 14119-14150页

作者： Bhagat, Suraj Kumar Tiyasha, Tiyasha Shather, A.H. Jamei, Mehdi Kumar, Adarsh Al-Khafaji, Zainab Goliatt, Leonardo Shafik, Shafik S. Alawi, Omer A. Yaseen, Zaher Mundher Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Engineering Technology Al Kitab University Altun Kopru Kirkuk00964 Iraq Faculty of Civil Engineering and Architecture Shahid Chamran University of Ahvaz Ahvaz Iran Canadian Centre for Climate Change and Adaptation University of Prince Edward Island St PetersPE Canada Building and Construction Techniques Engineering Department College of Engineering and Engineering Techniques Al-Mustaqbal University Hillah Babylon51001 Iraq Department of Computational and Applied Mechanics Federal University of Juiz de Fora Juiz de Fora Brazil Experimental Nuclear Radiation Research Group Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq Johor Bahru Skudai81310 Malaysia Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India Dept of Environmental Science Gitam School of Science GITAM University Visakhapatnam India

Wind speed (WS) has played a vital role in local urban and sub-urban weather, agriculture, and ecosystem. Several meteorological parameters are influencing WS such as relative humidity (at 2 m, %), surface pressure (kPa), maximum temperature (at 2 m, °C), minimum temperature (at 2 m, °C), average temperature (at 2 m, °C), and all sky insolation incident on a horizontal surface (kW-h/m2/day). The current research was conducted to predict WS at different locations at Vietnam using the feasibility of computer aid models (i.e., multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost) and random forest generator (Ranger)). Pearson correlation (PC) was investigated to select the high significant predictors to predict the WS at 10 m high. All inputs (maximum number, 6) are chosen by the PC approach for PhuongNinh, DaNang, and HaNoi;and for minimum number of inputs i.e four, are selected for PhuongHung, CanTho, and SaPa city;that exhibit the relationship with WS, citywise. The sequence selection of input parameters differed in each station as per the PC analysis. Based on the statistical evaluation and graphical presentation, MARS model attained the best prediction results, followed by XGBoost and Ranger. MARS predictive model remains at the top performance among others based on 95% confidence interval. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

information Theoretic Weighted Fuzzy Clustering Ensemble

引用

computers, Materials & Continua 2021年第4期67卷 369-392页

作者： Yixuan Wang Liping Yuan Harish Garg Ali Bagherinia Hamïd Parvïn Kim-Hung Pho Zulkei Mansor Department of Electronic Information Engineering School of Information EngineeringWuhan University of TechnologyWuhanChina School of Information Engineering Wuhan University of TechnologyWuhanChina School of Information Engineering Wuhan Huaxia University of TechnologyWuhanChina School of Mathematics Thapar Institute of Engineering and TechnologyDeemed UniversityPatialaPunjabIndia Department of Computer Science Dehdasht BranchIslamic Azad UniversityDehdashtIran Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science Nourabad Mamasani BranchIslamic Azad UniversityMamasaniIran Fractional Calculus Optimization and Algebra Research GroupFaculty of Mathematics and StatisticsTon Duc Thang UniversityHo Chi Minh CityVietnam Fakulti Teknologi dan Sains Maklumat Universiti Kebangsaan Malaysia 43600 UKM BangiSelangorMalaysia

In order to improve performance and robustness of clustering,it is proposed to generate and aggregate a number of primary clusters via clustering ensemble *** clustering ensemble approaches attempt to improve the performance of fuzzy clustering ***,in these approaches,cluster(or clustering)reliability has not paid much attention *** cluster(or clustering)reliability makes these approaches weak in dealing with low-quality base clustering *** this paper,we have utilized cluster unreliability estimation and local weighting strategy to propose a new fuzzy clustering ensemble method which has introduced Reliability Based weighted co-association matrix Fuzzy C-Means(RBFCM),Reliability Based Graph Partitioning(RBGP)and Reliability Based Hyper Clustering(RBHC)as three new fuzzy clustering consensus *** fuzzy clustering ensemble approach works based on fuzzy cluster unreliability *** unreliability is estimated according to an entropic criterion using the cluster labels in the entire *** do so,the new metric is dened to estimate the fuzzy cluster unreliability;then,the reliability value of any cluster is determined using a Reliability Driven Cluster Indicator(RDCI).The time complexities of RBHC and RBGP are linearly proportional with thnumber of data *** and robustness of the proposed method are experimentally evaluated for some benchmark *** experimental results demonstrate efciency and suitability of the proposed method.

关键词： Fuzzy clustering ensemble cluster unreliability consensus function

来源：评论

学校读者我要写书评

暂无评论

A Toolbox of Feminist Wonder: Theories and methods that can make a difference 26

A Toolbox of Feminist Wonder: Theories and methods that can ...

引用

26th ACM Conference on computer-Supported Cooperative Work and Social Computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

来源：评论

学校读者我要写书评

暂无评论

Enhancing IoT Security: Federated Learning with GANs for Effective Attack Detection

Enhancing IoT Security: Federated Learning with GANs for Eff...

引用

International Bhurban Conference on Applied Sciences & technology, IBCAST

作者： Bakhtawar Saeed Sobia Arshad Sanay Muhammad Umar Saeed Muhammad Awais Azam Computer Engineering Department Deep Packet Inspection Lab (DPI) National Center of Cyber Security (NCCS) UET Taxila Technology Innovation Research Group School of Information Technology Whitecliffe NZ

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

The rapid proliferation of the Internet of Things (IoT) has given rise to security challenges, necessitating the real-time detection and mitigation of cyberattacks. Federated Learning (FL) is promising because it enables collaborative global model training in IoT for attack detection while maintaining data privacy. However, FL-enabled systems for IoT face challenges related to class imbalance, inconsistent data distributions, and increased training time. This paper presents a novel FL-enabled approach for IoT attack detection that effectively resolves class imbalance by utilizing Generative Adversarial Networks (GANs). The proposed approach also handles non-iid data distributions through the implementation of Vertical FL. Additionally, ordinal encoding techniques are employed to enhance detection accuracy. The effectiveness of the proposed approach is extensively evaluated on the ToN-IoT and N-BaIoT datasets, the DAEs and MLPs both deep learning (DL) algorithms consistently models achieve high precision, recall, and F1 score on ToN-IoT (MLPs: 99.39%, DAEs: 99.62%) and N-BaIoT datasets (DAEs: 93.66%, MLPs: 94.63 %), demonstrating its capability in improving attack detection accuracy and mitigating the limitations of FL-enabled systems in IoT scenarios.

关键词： Training Accuracy Federated learning Prevention and mitigation Generative adversarial networks Real-time systems Encoding Internet of Things Security Faces

来源：评论

学校读者我要写书评

暂无评论

Improving Botnet Detection with a Generative Adversarial Network-Based Technique

Improving Botnet Detection with a Generative Adversarial Net...

引用

International Bhurban Conference on Applied Sciences & technology, IBCAST

作者： Shehla Gul Sobia Arshad Sanay Muhammad Umar Saeed Adeel Akram Bakhtawar Saeed Muhammad Awais Azam Computer Engineering Department Deep Packet Inspection Lab (DPI) National Center of Cyber Security (NCCS) UET Taxila Technology Innovation Research Group School of Information Technology Whitecliffe NZ

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

The number of internet users has increased rapidly, leading to significant concern for individuals about cyber attacks. These attacks have evolved and have become more complex, posing a challenge to their detection and prevention. Botnets are high-risk cyber-attacks that are constantly emerging and changing with time. Many studies have suggested using Machine Learning (ML) techniques to detect and classify botnet a ttack s . However, traditional algorithms often miss many attacks or generate false alarms in large-scale network environments with high bandwidth. The scarcity of data about attacks results in imbalanced training sets that can impact the analysis performance of these methods. We propose a Generative Adversarial Networks (GANs) based oversampling technique to address this problem. We use Wasserstein GANs (WGANs) to simulate botnet attacks and create synthetic attack instances that mimic the high-dimensional distribution of botnet attacks. The experiments have been performed on the CICIDS-2017 dataset. After addressing the issue of imbalanced class, we employed several ML classifiers - Random Forest (RF), K-Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Gradient Boosting Decision Trees (GBDT) to detect botnet attacks. We attained the highest accuracy of 99.9% for the GBDT classifier. The outcomes have shown that the suggested method can successfully address the issue of class imbalance, enhance ML models' detection performance, and outperform state-of-the-art techniques.

关键词： Support vector machines Training Machine learning algorithms Botnet Prevention and mitigation Nearest neighbor methods Generative adversarial networks Cyberattack Random forests Classification tree analysis

来源：评论

学校读者我要写书评

暂无评论

BEI-DETR: A Multimodal Remote Sensing Object Detection via Integrating EEG and Eye Movement

BEI-DETR: A Multimodal Remote Sensing Object Detection via I...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Junfeng Huang Mengyue Zhang Min Wang Gang Wang Meixia Qu Bin Jiang Qiang Guo Shenzhen Research Institute of Shandong University The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China School of Mechanical Electrical and Information Engineering Shandong University Weihai China School of Computer Science of Technology Shandong University of Finance and Economics Jinan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Recently, the DEtection TRansformer (DETR) and its variants have achieved success in object detection, but they still require manual intervention in complex scenes. Object detection methods cannot entirely replace human capabilities. Leveraging human perception to assist remote sensing object detection offers a new technical approach. In the process of manual verification, the most prominent signals reflecting human identification of specific targets are electroencephalogram (EEG) and eye movement. To our best knowledge, there is currently no publicly available dataset for this task. Therefore, we collect two types of human perception information while observing remote sensing images, forming a trimodal dataset including brain signals, eye movements, and remote sensing images. Upon this, we propose a multimodal object detection method, termed as BEI-DETR (Brain-Eye-Image DETR). Specifically, we decouple the classification and localization tasks in the object detection, using EEG for classification and eye movement information for localization. Subsequently, we propose a feature matching fusion module (FMFM) to fuse multimodal features and then put them into the decoder and prediction heads to obtain the final detection results. Comparative and ablation experiments demonstrate the satisfactory performance of BEI-DETR, indicating that our model can effectively learn human perceptual information for the remote sensing object detection task. The trimodal dataset and source code are publicly available at https://***/Hickey-Curry/BEI-DETR and https://***/Hickey-Curry/BEIDataset.

关键词： Location awareness Tracking Source coding Object detection Manuals Feature extraction Brain modeling Transformers Electroencephalography Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Dynamic Task Sequence Allocation Based on Natural Language Instruction

Dynamic Task Sequence Allocation Based on Natural Language I...

引用

Automation, Robotics and computer engineering (ICARCE), International Conference on

作者： Yongchang Li Jianhang Feng Zhigang Tao Lumeng Ma Yanzhi Dong Xiaohu Yuan School of Physics and Electronic Information Yantai University Yantai China Key Lab of Information System Requirement Nanjing Research Institute of Electronics Engineering Nanjing China The 28th Research Institute of China Elecchonics Technology Group Corporation Nanjing China School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

In recent years, with the development of technology, robots have become more prominent in the public eye. Service robots are now being used in some smart hotels to decrease manual transportation costs. However, these service robots operate according to set instructions, and humans usually apply fixed operating instructions to control the robots. Whether in daily life or at work, humans are more likely to use natural language to communicate and give task instructions. Moreover, in the field of multi-robotics, most of the existing researches on multi-task assignment have been conducted in simulation environments on a list of pre-set tasks, and seldom update the task sequences in real time in conjunction with the real environmental information and time. In this paper, we propose a method for assigning dynamic navigation task sequences based on natural language instruction and verify its effectiveness in a real scenario.

关键词： Service robots Navigation Human-robot interaction Transportation Transforms Oral communication Real-time systems Natural language processing Resource management Robots

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：