检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Chaudhuri, Abhra Mancini, Massimiliano Akata, Zeynep Dutta, Anjan The Department of Computer Science The University of Exeter United Kingdom The Cluster of Excellence Machine Learning The University of Tübingen Germany MPI for Informatics Germany MPI for Intelligent Systems Germany The Institute for People-Centred AI The University of Surrey United Kingdom

Fine-grained categories that largely share the same set of parts cannot be discriminated based on part information alone, as they mostly differ in the way the local parts relate to the overall global structure of the object. We propose Relational Proxies, a novel approach that leverages the relational information between the global and local views of an object for encoding its semantic label. Starting with a rigorous formalization of the notion of distinguishability between fine-grained categories, we prove the necessary and sufficient conditions that a model must satisfy in order to learn the underlying decision boundaries in the fine-grained setting. We design Relational Proxies based on our theoretical findings and evaluate it on seven challenging fine-grained benchmark datasets and achieve state-of-the-art results on all of them, surpassing the performance of all existing works with a margin exceeding 4% in some cases. We also experimentally validate our theory on fine-grained distinguishability and obtain consistent results across multiple benchmarks. Implementation is available at https://***/abhrac/relational-proxies. © 2022, CC BY.

关键词： Discriminators

来源：评论

学校读者我要写书评

暂无评论

Predicting Phosphoglycerylation with Transformer Features and Deep Learning

Predicting Phosphoglycerylation with Transformer Features an...

引用

computer science and Data Engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Abel Chandra Alok Sharma Iman Dehzangi Tatsuhiko Tsunoda Abdul Sattar Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Department of Computer Science Rutgers University Camden NJ USA Center for Computational and Integrative Biology Rutgers University Camden USA Department of Biological Sciences Laboratory for Medical Science Mathematics School of Science The University of Tokyo Tokyo Japan Department of Computational Biology and Medical Sciences Laboratory for Medical Science Mathematics Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan

Understanding protein sequences can advance treatments for various diseases. However experimentally obtaining this information is laborious, time-consuming, and expensive. Traditional machine learning techniques, like support vector machine, random forest and logistic regression, offer potential to fast-track this process but are sometimes limited by data complexity. Deep learning algorithms, in contrast, tend to yield higher performance. In this study, we employed a convolutional neural network to predict protein phosphoglycerylation. Features were extracted from pre-trained transformer models and compared with conventional features, such as evolutionary information and physicochemical/biochemical properties. Our results indicate significant performance improvements across all feature types, with the combination of transformerbased features and the convolutional neural network being especially effective. This methodology holds potential for other protein property prediction tasks. Our software and datasets used in this study are publicly available at https://***/abelavit/***.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Cooperative Awareness in Cognitive Wireless Networks for Jamming Control

Cooperative Awareness in Cognitive Wireless Networks for Jam...

引用

IEEE World Forum on Internet of Things (WF-IoT)

作者： André Mendes Diego Brandão Miguel Diaz-Cacho Research Centre in Digitalization and Intelligent Robotics (CeDRI) Instituto Politécnico de Bragança Bragança Portugal Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC) Instituto Politécnico de Bragança Bragança Portugal Computer Science Depart. CEFET/RJ Rio de Janeiro Brazil Department of Systems Engineering and Automatics Universidade de Vigo Spain

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

In cognitive wireless networks, opportunistic network devices can be programmed to take advantage of licence holders' idle times and dynamically adjust their operating parameters to improve transmissions. For this to be successful, these idle periods must be reliably detected. To support this requirement, this work proposes a cooperative detection mechanism for the signals transmitted by the licenced system through the use of positioning-enabled devices. Such devices can provide information to guide and control opportunistic network devices to limit interference to the licenced system. Simulations show that keeping interference within specification limits makes it possible to maintain opportunistic network communications in an ad hoc scenario with quality.

关键词： Wireless networks Power control Interference Ad hoc networks Reliability Internet of Things Jamming

来源：评论

学校读者我要写书评

暂无评论

A Development of Time-Varying Weight Model Predictive Control for Autonomous Vehicles

A Development of Time-Varying Weight Model Predictive Contro...

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Mohammad Reza Chalak Qazani Houshyar Asadi Arian Shajari Zoran Najdovski Chee Peng Lim Saeid Nahavandi Department of the Computer Science and Information Systems Murray State University Murray Murray KY United States Institute for Intelligent System Research and Innovation Deakin University Geelong VIC Australia Swinburne University of Technology Hawthorn VIC Australia Harvard Paulson School of Engineering and Applied Sciences Harvard University Allston MA USA

Autonomous vehicles, commonly known as self-driving cars, are rapidly gaining popularity due to their numerous advantages, such as reducing traffic, pollution, and emissions while increasing safety, convenience, and transportation connectivity. In order to accurately track the motion signal, these vehicles are now utilising advanced control techniques, such as model predictive control (MPC). However, the efficiency of MPCs heavily relies on properly tuning their weights. The primary function of the MPC is to recalculate the optimal values for the vehicle control commands, such as desired speed, steering angle, etc., while considering the dynamic model of the autonomous vehicle. The existing linear MPC models cannot reach higher efficiency because of using fixed weights without considering the error. This paper introduces a novel approach for developing an MPC model with a time-varying weights algorithm for autonomous vehicles. The study aims to minimise motion tracking errors such as lateral position and yaw angle errors. Relevant MPC weights are calculated online using fuzzy logic-based units considering the lateral position and yaw angle errors. The proposed linear time-varying MPC was designed and developed using MATLAB software, resulting in improved motion tracking performance with 31.62% and 20.89% reduction of the root means square error of lateral position and yaw angle.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comparison of two newly suggested power, refrigeration, and hydrogen production, for moving towards sustainability schemes using improved solar-powered evolutionary algorithm optimization

引用

Chemosphere 2023年第1期336卷 139160页

作者： Hai, Tao Abd El-Salam, Nasser M. Kh, Teeba Ismail Chaturvedi, Rishabh El-Shafai, Walid Farhang, Babak School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province Guizhou Duyun558000 China Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Natural Science Department Community College King Saud University Riyadh12642 Saudi Arabia Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Mechanical Engineering Institute of Engineering & Technology GLA University UP Mathura281001 India Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Energy Technology Aalborg University Denmark

In the third millennium, developing countries will confront significant environmental problems such as ozone depletion, global warming, the shortage of fossil resources, and greenhouse gas emissions. This research looked at a multigenerational system that can generate clean hydrogen, fresh water, electricity, heat, and cooling. The system's components include Rankine and Brayton cycles, an Organic Rankine Cycle (ORC), flash desalination, an Alkaline electrolyzer, and a solar heliostat. The proposed process has been compared for two different start-up modes with a combustion chamber and solar heliostat to compare renewable and fossil fuel sources. This research evaluated various characteristics, including turbine pressure, system efficiency, solar radiation, and isentropic efficiency. The energy and exergy efficiency of the proposed system were obtained at around 78.93% and 47.56%, respectively. Exergy study revealed that heat exchangers and alkaline electrolyzers had the greatest exergy destruction rates, at 78.93% and 47.56%, respectively. The suggested system produces 0.04663 kg/s of hydrogen. Results indicate that at the best operational conditions, the exergetic efficiency, power, and hydrogen generation of 56%, 6000 kW, and 1.28 kg/s is reached, respectively. Also, With a 15% improvement in the Brayton cycle's isentropic efficacy, the quantity of hydrogen produced increases from 0.040 kg/s to 0.0520 kg/s. © 2023 Elsevier Ltd

关键词： Solar energy

来源：评论

学校读者我要写书评

暂无评论

Enhancing UAV Autonomous Navigation in Indoor Environments Using Reinforcement Learning and Convolutional Neural Networks

Enhancing UAV Autonomous Navigation in Indoor Environments U...

引用

International Symposium on intelligent systems and Informatics (SISY)

作者： Homayoun Safarpour Motealegh Mahalegi Amirfarhad Farhadi György Molnár Eniko Nagy Obuda University Institute of Mechatronics and Vehicle Engineering Budapest Hungary Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Apaczai Csere Janos Faculty of Humanities Education and Social Sciences Kando Kalman Faculty of Electrical Engineering Budapest Obuda University Hungary Szechenyi University Gyor Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Obuda University Budapest Hungary

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

In recent years, the number of unmanned aerial vehicle (UAV) applications has increased. However, navigating them indoors is still tricky because no GPS signals are available, and the obstacles constantly change. This study exploits the collaboration between convolutional neural networks (CNNs) and reinforcement learning (RL) to overcome these challenges, improving the level of independence of UAVs in indoor settings. We present a new technique known as potential-based reward shaping, which directly incorporates expert knowledge into the reinforcement learning framework. This technique efficiently addresses the problem of sparse rewards that often hinder learning in conventional RL configurations. In addition, we have created a reflective reinforcement learning agent that systematically assesses its actions to prioritize those that result in the most substantial enhancements. We have extensively evaluated the performance of our method with the High-Definition Indoor Navigation (HDIN) dataset to show that our method continuously outperformed current non-introspective RL techniques in terms of decision-making speed and navigation accuracy in this evaluation. The results show that this new CNN-RL combination is feasible and suggests a scalable method for complex indoor navigation problems.

关键词： Indoor navigation Refining Reinforcement learning Autonomous aerial vehicles Indoor environment Convolutional neural networks intelligent systems Informatics Autonomous robots Global Positioning System

来源：评论

学校读者我要写书评

暂无评论

Teleoperation of Humanoid Robots: A Survey

arXiv

引用

arXiv 2023年

作者： Darvish, Kourosh Penco, Luigi Ramos, Joao Cisneros, Rafael Pratt, Jerry Yoshida, Eiichi Ivaldi, Serena Pucci, Daniele Artificial and Mechanical Intelligence Center for Robotics and Intelligent Systems Istituto Italiano di Tecnologia Genoa Italy Florida Institute for Human and Machine Cognition 40 S Alcaniz St PensacolaFL32502 United States Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign United States Department of Information Technology and Human Factors Tsukuba Japan Inria Loria Université de Lorraine CNRS Nancy France Department of Applied Electronics Faculty of Advanced Engineering Tokyo University of Science Japan

Teleoperation of humanoid robots enables the integration of the cognitive skills and domain expertise of humans with the physical capabilities of humanoid robots. The operational versatility of humanoid robots makes them the ideal platform for a wide range of applications when teleoperating in a remote environment. However, the complexity of humanoid robots imposes challenges for teleoperation, particularly in unstructured dynamic environments with limited communication. Many advancements have been achieved in the last decades in this area, but a comprehensive overview is still missing. This survey paper gives an extensive overview of humanoid robot teleoperation, presenting the general architecture of a teleoperation system and analyzing the different components. We also discuss different aspects of the topic, including technological and methodological advances, as well as potential applications. A web-based version of the paper can be found at https://***/. © 2023, CC BY.

关键词： Remote control

来源：评论

学校读者我要写书评

暂无评论

Evaluation Metrics for Collaborative Fault Detection and Diagnosis in Cyber-Physical systems

Evaluation Metrics for Collaborative Fault Detection and Dia...

引用

International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Luis Piardi André Oliveira Pedro Costa Paulo Leitão Research Center in Digitalization and Intelligent Robotics (CeDRI) Instituto Politécnico de Bragança Bragança Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal Universidade Tecnológica Federal do Paraná (UTFPR) Curitiba-PR Brazil Technology and Science (INESC TEC) Institute for Systems and Computer Engineering Porto Portugal Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC) Instituto Politécnico de Bragança Bragança Portugal

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Cyber-physical systems (CPS) rapidly expand within industrial contexts in a new era of digitalization, processing power, and inter-device communication capabilities. These advancements integrate technologies such as the Internet of Things (IoT), artificial intelligence (AI), and cloud and edge computing, granting processes and operations a high degree of autonomy. In addition, these interconnections foster collective intelligence arising from information exchange and collaboration between components, often outperforming individual capabilities. This collective intelligence manifests in fault detection and diagnosis (FDD) tasks within CPS, as it significantly improves the flexibility, performance, and scalability. However, the inherent complexity of CPS poses challenges in determining the best configuration of the collaboration parameters, such as when and how to collaborate, wherein incorrect adjustments may lead to decision errors and compromise the system's performance. With this in mind, this paper proposes seven metrics to evaluate collaboration performance for fault detection and diagnosis in multi-agent systems (MAS)-based CPS, evaluating when the collaboration is beneficial or when the collaboration parameters need to be adjusted. The experiments focus on collaborative fault detection in temperature and humidity sensors within warehouse racks, where the proposed evaluation metrics point out the impact of collaboration on the detection task, as well as possible actions to be adopted to improve the agent's performance.

关键词： Measurement Temperature sensors Fault detection System performance Collaboration Cyber-physical systems Collective intelligence Sensors Internet of Things Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

A Deployment Optimization for Wireless Sensor Networks Based on Stacked Auto Encoder and Probabilistic Neural Network

引用

Digital Communications and Networks 2024年

作者： Hui Feng Chen Xu Bo Jin Min Zhang School of Intelligent Engineering Technology Jiangsu Vocational College of Finance and Economics Huai'an 223003 China School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai 200235 China Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra 3030-290 Portugal

In the field of consumer electronics, Wireless Sensor Networks (WSNs) can be used to build large-scale IoT systems and achieve intelligent data collection and processing. This paper proposes a deep neural network model SAE-PNN by Stacked Auto Encoder (SAE) and Probabilistic Neural Network (PNN) in a stack manner. A comprehensive training algorithm of SAE-PNN based on a group of orthogonal function bases is established by introducing a time-varying input and connection weight function, based on the conventional algorithm of unsupervised layer-by-layer initialization and gradient descent of deep neural network. Our proposed SAE-PNN model is used to predict the distance from unknown nodes to known nodes. Based on the collaborative work of nodes and network coverage, an optimal set of working nodes is established to reduce the energy loss of network nodes. A balance relationship between network coverage and energy consumption has been established to solve the problem of optimizing the balance in location coverage and the contradiction between individual nodes and the overall performance of the network. The experimental results show that the proposed method can improve the coverage of the monitored area and the perceived quality of service, and uses a distributed node synchronous scheduling mechanism to reduce the overall energy consumption of the network.

关键词： Wireless sensor networks Stacked auto encoder Probabilistic neural network Deep neural network Deployment strategy Node energy consumption

来源：评论

学校读者我要写书评

暂无评论

A Channel Selection Method for Motor Imagery EEG Based on Fisher Score of OVR-CSP

A Channel Selection Method for Motor Imagery EEG Based on Fi...

引用

International Winter Workshop on Brain-computer Interface (BCI)

作者： Wei Mu Junkongshuai Wang Lu Wang Pengchao Wang Jiaguan Han Lan Niu Jianxiong Bin Lusheng Liu Jing Zhang Jie Jia Lihua Zhang Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI & Robotics Laboratory for Neural Interface and Brain Computer Interface Institute of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

EEG is widely used in the field of brain computer interfaces. It has the advantages of being non-invasive, easy acquisition, and so on. However, multi-channel EEG signals will bring noise interference and redundant information while improving the resolution, and channel selection can reduce noise signals to obtain more real signals. Therefore, appropriate channel selection is very necessary for BCI system application. This paper proposes a Fisher score calculation method based on OVR-CSP features for channel selection and uses a transformer to classify. The experimental results show that the proposed method achieves 85.54% accuracy in selecting 16.55 channels on the public dataset BCI IV IIa.

关键词： Electrodes Interference Transformers Feature extraction Electroencephalography Brain-computer interfaces Physiology

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：