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检索条件"任意字段=8th IFIP International Conference on Engineering for Human-Computer Interaction"

共 2051 条记录，以下是341-350 订阅

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An intelligent interactive system based on LLM to combine CAD API development with FEA

An intelligent interactive system based on LLM to combine CA...

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2024 8th international conference on Electrical, Mechanical and computer engineering (ICEMCE)

作者： Mengqiang Yu Yujie Sheng Zhongxu Wan Shuyu Yang Muping Sun Hongxia Cai School of Mechatronic Engineering and automation Shanghai University Shanghai China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai China School of Communication & Information Engineering Shanghai University Shanghai China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

this paper presents an intelligent interactive system based on large language model (LLM). It aims to simplify the operation flow of CAD and FEA software and improve the efficiency of design and simulation analysis. the system uses C# and Python to develop CAD Secondarily and access LLM API locally. Build a professional database and implement the interaction between CAD and LLM. the natural language dialogue between the user and the CAD software is realized, and the design guidance and optimization suggestions are provided. Taking the design of the end mechanism of fruit picking robot as an example, the application effect of this system is demonstrated. the results of the case show that the system can effectively improve the efficiency of design and simulation analysis, and improve the design quality, and has a broad application prospect.

关键词： Electric potential Databases Terminology Interactive systems Large language models Software Natural language processing Robots Optimization Python

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A Study on an Intelligent Elevator Control interaction System Integrating Palmprint Recognition and Gesture Recognition 4th

A Study on an Intelligent Elevator Control Interaction Syste...

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4th international conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2024

作者： Liu, Jiayu Jia, Wei Zhang, Jing Computer and Information Engineering College HFUT He Fei China

ISBN: (纸本)9789819653171

the imperative pursuit of computer vision technology research lies in developing a more accurate, intelligent, swift, and secure gesture imaging recognition methodology. In this context, we have devised an advanced intelligent elevator control interaction system that seamlessly integrates gesture and palmprint recognition. the system employs the Multi-Feature Robust Alignment Technique (MFRAT) to extract distinctive palmprint features, utilizing point-pair regions for palmprint matching. Gesture recognition is achieved through a sophisticated hybrid modeling approach, incorporating mediapipe and Resnet34. Subsequently, a dynamic frequency localization method is employed to translate and analyze specific elevator gesture commands, effectively executing user instructions and ensuring precise floor navigation. Experimental results underscore the system’s proficiency, with a recognition rate of up to 95.5% for single-frame gesture images and 92.84% for combined gesture images. By harnessing the diversity of gestures and the unique characteristics of palmprints, the system employs non-contact gesture imaging to facilitate seamless, efficient, and secure human-machine interaction between users and elevators, showcasing promising applications across various domains. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gesture recognition

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MSFDTA: Predicting drug target affinity based on multi-scale features and adaptive fusion

MSFDTA: Predicting drug target affinity based on multi-scale...

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2024 8th international conference on Electrical, Mechanical and computer engineering (ICEMCE)

作者： Wanhua Huang Xuechong Tian Ying Su Xiaoyi Lv School of Computer Science and Technology Xinjiang University Shuimogou Urumqi Xinjiang China School of Software Xinjiang University Shaybak District Urumqi Xinjiang China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

Accurate prediction of drug target affinity (DTA) is crucial for drug development and drug repurposing. However, most deep learning-based methods base their predictions only on the molecular structure information of the drug or target or the network information of its interactions. this may neglect the specificity of different scale features thus reducing the effectiveness of the experiment. In this study, we propose a multiscale approach that combines the molecular structure scale and interaction network scale of drugs and targets to capture feature information, and then obtain composite features through a feature adaptive fusion module. Our experimental results on two benchmark datasets show that the proposed MSFDTA outperforms the state-of-the-art methods, and the significant improvement of DTA prediction performance by combining multiscale feature information is demonstrated by ablation experiments.

关键词： Drugs Learning systems Adaptive systems Accuracy Correlation Benchmark testing

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Face Gesture Based Virtual Mouse Using Mediapipe

Face Gesture Based Virtual Mouse Using Mediapipe

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international conference for Convergence of Technology (I2CT)

作者： Akula Kumar Raja Chidakash Sugandhi Gorantla Nymish Nama Sai Havish Manazhy Rashmi Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Amritapuri India

A disabled person’s life is always dependent on someone else who needs aid with mobility or any other task. Individuals with disabilities may face challenges when using computers. the most common way of interacting with computers is using a mouse and keyboard. It is difficult for people with physical disabilities to use them. Facial movements are one of the best possible actions by physically disabled individuals, By recognizing and responding to these movements, it is possible for them to operate the computer using only their facial expressions. Face recognition is a contemporary approach to interaction between humans and computers (HCI) i.e., the proposed system can easily control the computers by using face gesture recognition. It can be a viable replacement for traditional HCI tools in the future. this research outlines the techniques utilized in the design, implementation, and evaluation of the experiments conducted and presents the results obtained.

关键词： computers human computer interaction Tracking Face recognition Nose Mouth Keyboards

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Towards Control Rooms as human-Centered Pervasive Computing Environments 18th

Towards Control Rooms as Human-Centered Pervasive Computing ...

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18th ifip TC 13 international conference on human-computer interaction (INTERACT)

作者： Flegel, Nadine Poehler, Jonas Van Laerhoven, Kristof Mentler, Tilo Trier Univ Appl Sci D-54293 Trier Germany Univ Siegen Adolf Reichwein Str 2a D-57076 Siegen Germany

ISBN: (纸本)9783030983888;9783030983871

State-of-the-art control rooms are equipped with a variety of input and output devices in terms of single-user workstations, shared public screens, and multimodal alarm systems. However, operators are bound to and sitting at their respective workstations for the most part of their shifts. therefore, cooperation efforts are hampered, and physical activity is limited for several hours. Incorporating mobile devices, wearables and sensor technologies could improve on the current mode of operation but must be considered a paradigm shift from control rooms as a collection of technically networked but stationary workstations to control rooms as pervasive computing environments being aware of people and processes. However, based on the reviewed literature, systematic approaches to this paradigm shift taking usability and user experience into account are rare. In this work, we describe a root concept for control rooms as human-centered pervasive computing environments and introduce a framework for developing a wearable assistant as one of the central and novel components. Furthermore, we describe design challenges from a socio-technical perspective based on 9 expert interviews important for further research on pervasive computing environments in safety-critical domains.

关键词： Control room Pervasive computing environment Wearable assistant Scalable interaction design Usability User experience

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Research on high precision real-time digital twin model

Research on high precision real-time digital twin model

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2024 8th international conference on Electrical, Mechanical and computer engineering (ICEMCE)

作者： Zhangzhi Tao Yulou Li CCCC SECOND HARBOR ENGINEERING CO. LTD NO.5 BRANCH Wuhan Hubei China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

With the development of digital twin technology, high-precision digital twin technology is becoming increasingly urgent in production practice. this article proposes a high-precision digital twin model construction method for electromechanical actuators based on practical production needs. Firstly, a twin mechanism model was constructed from the aspects of geometry, physics, behavior, and rules. the time-varying transfer function of the electromechanical actuator was established, and the parameters were identified and updated through particle swarm optimization to achieve digital mapping. Secondly, a twin data model was constructed, taking historical data of relevant features as input, and a BP neural network-based input voltage prediction model was constructed. Finally, the accuracy and feasibility of the twin mechanism model were verified through simulation, and the introduced digital twin technology provides a new method for real-time interaction and adjustment between the theoretical model of electromechanical actuators and actual data.

关键词： Actuators Computational modeling Transfer functions Voltage Production Predictive models Data models Real-time systems Digital twins Physics

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Emotion Recognition Using CNN

Emotion Recognition Using CNN

引用

international conference on Trends in Electronics and Informatics (ICEI)

作者： Ranjani M Rahul Nalla Mahisha S Sandhia G K Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department Of Computing Technologies SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

In this research paper, we assess the efficacy of different Convolutional Neural Network (CNN) models, which are powerful tools for solving the image recognition problem. More specifically, we will concentrate on how to use deep learning along with OpenCV and Python to identify and recognize human facial expressions. this study explores how effectively combining OpenCV libraries in Python with deep learning can be achieved for human face detection and the analysis of their emotional states. the proposed framework is intended to recognize human faces and classify their emotions in real time, making it suitable for many platforms such as computers, smartphones, and various software applications. human-machine interaction depends on emotion recognition, as it helps AI systems understand and react to human emotions. this paper discusses advances and methods for emotion recognition, including speech, hand and body gestures, and facial expressions. the main objective of this study is to suggest a suitable real-time emotion recognition strategy based on CNN-based deep learning models that will improve the accuracy and applicability of emotion recognition in different environments.

关键词： Deep learning human computer interaction Emotion recognition Accuracy Face recognition Computational modeling Real-time systems Convolutional neural networks Artificial intelligence Python

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Research on Load Forecasting Based on IPSO-BILSTM-GRU Model 8

Research on Load Forecasting Based on IPSO-BILSTM-GRU Model

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8th IEEE international conference on computer and Communications, ICCC 2022

作者： Li, Xueting Wang, Hongliang Xiu, Pengfei Li, Yongfeng Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Prov. Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (纸本)9781665450515

High accuracy of resource load prediction can provide basis for real-time task scheduling, thus reducing energy consumption. Aiming at the problems that the CPU and memory load changes in cloud computing platform cannot be accurately predicted and it is difficult to adjust the parameters of common timing prediction algorithms, a combined prediction model of bidirectional long short-term memory network (BILSTM) and gated recurrent unit (GRU) based on improved particle swarm optimization (IPSO), IPSO-BILSTM-GRU, was proposed. Our model combines the characteristics of high prediction accuracy of BILSTM network and short prediction time of GRU network. the IPSO algorithm is used to optimize the model parameters. In the experiment, the combined values of CPU and memory indexes of Google data center are selected as input data and compared with many typical time series prediction algorithms. Experimental results show that the MAPE (mean absolute percentage error) of IPSO-BILSTM-GRU model is reduced by 1.0% to 4.4%, and the training time is shortened by more than 11%. the experimental results verify that the method has higher accuracy and strong applicability in time series prediction, which provides an important scientific basis for elastic capacity expansion using load prediction results. © 2022 IEEE.

关键词： Cloud computing

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Path Planning for human-Machine interaction in Complex Environments: Application Study of the DP-APF Algorithm 14th

Path Planning for Human-Machine Interaction in Complex Envir...

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14th international conference on computer engineering and Networks, CENet 2024

作者： Liu, Xiang Meng, Hao Yue, Xian Huo, Zhilin Liu, Siqu Zhang, Yi Duan, Yiyang Cai, Daomeng Beihang University No. 37 Xueyuan Road Haidian District Beijing China Systems Engineering Research Institute China State Shipbuilding Corporation No. 1 Fengxian East Road Haidian District Beijing China School of Physics University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9789819642281

To overcome the issues encountered by traditional Artificial Potential Field (APF) algorithms in handling complex obstacle environments, such as local minima, unreachable targets, and path tortuosity, this paper proposes an improved path planning method that combines APF with the Douglas-Peucker algorithm. Recognizing the limitations of traditional path planning methods in addressing the uncertainties and flexibility of human behavior in human-machine interaction systems, this study introduces adaptive step length strategies and path simplification techniques to enhance real-time responsiveness and computational efficiency. By dynamically adjusting the step length during path planning and utilizing the Douglas-Peucker algorithm to reduce the number of nodes in the path, this method significantly optimizes the path generation process. Additionally, the use of cubic B-spline curve smoothing techniques further enhances the smoothness and applicability of the path. Simulation results validate the effectiveness of the improved algorithm in complex environments, demonstrating a significant increase in path planning accuracy and efficiency. the number of nodes was reduced by approximately 86.2%, greatly decreasing computational load and memory consumption. this approach is particularly suitable for simulation systems requiring high dynamic interaction. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Splines

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AI-Powered Innovations to Enhance Kidney Disease Detection and Patient interaction

AI-Powered Innovations to Enhance Kidney Disease Detection a...

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8th international conference on Inventive Computation Technologies, ICICT 2025

作者： Sri Vanna Mathi, S. Vasudevan, V. Dhanasekaran, S. Department of Computer Applications Technology Kalasalingam Academy of Research and Education Tamil Nadu Krishnankovil India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankovil India Department of Information Kalasalingam Academy of Research and Education Tamil Nadu Krishnankovil India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Kidney disease is a serious worldwide health concern that can be improved by early detection and efficient monitoring. Convolutional neural networks (CNNs), a recent development in artificial intelligence (AI), have greatly improved the precision of kidney disease segmentation and classification in medical imaging. Furthermore, conversational AI has become a useful tool for supporting patient interaction and early diagnosis. 40 research studies on CNN-based renal illness classification, semantic segmentation methods, and AI-driven chatbots for kidney disease detection are thoroughly examined in this review paper. We emphasize the advantages, disadvantages, and performance indicators of different datasets, preprocessing strategies, feature extraction approaches, and deep learning architectures. According to the results, CNN-based models such as U-Net, ResNet, and Vision Transformers have segmented kidney structures with remarkable accuracy, and hybrid AI chatbots that combine machine learning and natural language processing show promise in supporting clinical decision-making. But there are still issues like model generalizability, dataset constraints, and ethical issues. In addition to pointing out current research gaps, this study offers suggestions for future paths, such as better explainability of AI models, multimodal data integration, and improved chatbot interactions for individualized healthcare. © 2025 IEEE.

关键词： Clinical Conversational AI Convolutional Neural Networks Deep Learning Diagnosis human Kidney Disease Medical Imaging Patients

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