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LLM-Project: automated engineering Task Planning via Generative AI and WBS Integration 14

LLM-Project: Automated Engineering Task Planning via Generat...

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14th IEEE international conference on Cyber Technology in Automation, Control, and intelligent Systems, CYBER 2024

作者： Zhen, Yue Bi, Sheng Tang, Shuo Lu, Xing-Tong Pan, Wei-Qin Shi, Hai-Peng Chen, Zi-Rui Fang, Yi-Shu Wang, Xin-Meng South China University of Technology School of Computer Science and Engineering Guang Zhou510006 China University of Washington Human Centered Design and Engineering Seattle98195 United States Northeastern University Khoury College of Computer Sciences Boston02115 United States

ISBN: (纸本)9798331506056

We propose and implement a task planning system that integrates a series of validated robotic instructions, deep learning models, and demonstration cases, into a larger structure using the Work Breakdown Structure (WBS) which is commonly used in engineering project management. By organizing these small-scale solutions in a sequence based on temporal and resource dependencies, we use simulations and evaluation functions to select the optimal structure as the Standard Operating Procedure (SOP). these SOPs are then used to train a generative AI, enabling it to mimic human experts in generating high-level structures according to real-world needs and perform minor parameter generalizations to address issues involving temporal dependencies, spatial relationships, and resource allocation. the generated high-level structures are gradually transformed into low-level operations to execute actual tasks. the system records the execution results for automatic or manual analysis, and the feedback is used to continuously improve the generative AI's output. Our work lays the foundation for further research on the innovative application of combining generative AI with WBS data in robotic task planning. https://***/NOMIzy/LLM-Project. © 2024 IEEE.

关键词： Resource allocation

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Application Research of intelligent Machinery Design Based on CAD System 14th

Application Research of Intelligent Machinery Design Based o...

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14th international conference on Frontier Computing, FC 2024

作者： Wang, Yinghui Guangdong Innovative Technical College Dongguan523960 China

ISBN: (纸本)9789819627974

With the development of artificial intelligence (AI) and machine learning (ML) technologies, CAD systems have evolved from simple drawing tools to complex design and analysis platforms. the system is now able to leverage AI algorithms to optimize the design process and assist designers through pattern recognition, automated design processes, and parameter optimization. Today's intelligent CAD systems can automatically identify design requirements and constraints to provide more accurate design solutions. Moreover, the intelligent CAD system can also carry out complex simulation and analysis, and the application research of intelligent mechanical design based on CAD system shows the great potential of this field, which not only improves the design quality and efficiency, but also opens new possibilities for the future development of the mechanical design industry. According to the experimental data, the success rate of intelligent mechanical design, the adaptation rate of material selection, the accuracy rate of manufacturing process and the cost control scheme of the CAD system combined with manual audit are all above 99.88%, and the error rate is below 0.12%. Moreover, with the continuous progress of technology, the application of intelligent CAD system in the field of mechanical design will be more extensive and in-depth in the near future. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Application Design Artificial Intelligence CAD Technology Cost Control Mechanical Design

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A Deep Multi-Task learning Network for Activity Recognition and User Identification Using Smartphone Sensors 16

A Deep Multi-Task Learning Network for Activity Recognition ...

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16th international conference on ENTERprise Information Systems, CENTERIS 2024 - 12th international conference on Project MANagement, ProjMAN 2024 - 14th international conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand

Human activity recognition (HAR) and user identification utilizing smartphone sensors are crucial in domains such as wellness tracking, tailored services, and security. Conventional approaches handle these activities as distinct entities, leading to less-than-ideal performance and restricted applicability. this research presents a novel deep multi-Task learning network that simultaneously performs activity recognition and user identification using smartphone sensor data. By leveraging shared representations and task-specific information, the network enhances accuracy. It combines a shared convolutional neural network (CNN) with bidirectional long short-Term memory (BiLSTM) layers to extract features from raw sensor data. the CNN identifies patterns, while the BiLSTM captures sequential dependencies and contextual information. these features are processed by fully connected layers for both activity recognition and user identification. A multi-Task loss function is proposed to optimize both tasks based on their complexity and importance. the approach is tested on the UCI-HAR dataset, containing diverse activities from multiple users. Experimental results show that the proposed CNN-BiLSTM model achieves 97.64% accuracy in activity recognition and 82.59% in user identification, surpassing current state-of-the-Art methods. this demonstrates the effectiveness of the multi-Task learning framework and the advantage of using the shared CNN-BiLSTM architecture for improving both tasks. © 2025 the Author(s).

关键词： Convolutional neural networks

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Development Trends of Big data and Artificial Intelligence in Virtual Simulation Technology 14th

Development Trends of Big Data and Artificial Intelligence i...

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14th international conference on Frontier Computing, FC 2024

作者： Yang, Yidong Wang, Lanfeng Wei, Yuhua Guangzhou Huali Science and Technology Vocational College Guangzhou China

ISBN: (纸本)9789819627974

With the development of information technology, big data technology and artificial intelligence have become important directions for informationization in the world today. Big data technology can help people better process, manage, and utilize massive amounts of data, while artificial intelligence can make computers more intelligent, achieving automated and intelligent processing and decision-making. Combining the two can achieve more powerful information processing and application capabilities, with broad application prospects. the application of artificial intelligence in virtual simulation technology has also played a crucial role. Artificial intelligence technology can simulate human intelligent behavior and provide more intelligent support for virtual simulation technology. through artificial intelligence algorithms, virtual simulation can be more automated and intelligent for simulation and prediction, improving the efficiency and accuracy of virtual simulation. In addition, artificial intelligence can also help virtual simulation technology achieve autonomous learning and optimization, continuously improving its performance and functionality. Based on the above issues, this article explores a virtual gesture interaction perception simulation model based on CNN (Convolutional Neural Networks) algorithm, extracts gesture datasets using artificial intelligence algorithms, and estimates gesture coordinates. the experimental results indicate that the sample sizes are 100, 200, and 300. the time for virtual gesture interaction perception based on CNN algorithm is 7.39ms, 10.87ms, and 14.67ms, which are 4.37ms, 5.61ms, and 7.07ms less than RNN (Recurrent Neural Network) algorithm, respectively. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Virtual environments

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IoT Enabled Secure Irrigation Management using Machine learning Techniques 14

IoT Enabled Secure Irrigation Management using Machine Learn...

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14th international conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Akshatha, Y. Gupta, Srishti Mahesh, Teli Saloni Poornima, Dr A.S. Siddaganga Institute of Technology Computer Science and Engineering Tumkur India

IoT has emerged as one of the technologies that is more vulnerable to assaults due to the rapidly expanding demand and easy connectivity of smart devices and networks. Internet of things is a key component of smart computing (IoT). the Internet of things (IoT) is a network of physical devices or things that can communicate and exchange data. A wireless sensor network is a collection of many sensor nodes that communicate wirelessly. It is anticipated that WSN will be included into the "Internet of things," where sensor nodes join the internet on a dynamic basis and use it for communication and task completion. Adopting smart technologies in agriculture contributes to an increase in crop quality and yield. IoT offers automated systems to assist farmers because they are unable to be present in the agricultural fields around-the-clock and because they lack the knowledge necessary to use various equipment to evaluate environmental conditions and keep an eye on activity there. the automated systems run without human interference and alert the farmers to make the proper decisions if they encounter any issues during farming. intelligent irrigation is a part of safe and smart farming. this application is being considered as follows: With sensors placed all over the ground that can detect temperature, humidity, and soil moisture content, a sizable farming area that is IoT enabled. If an attacker introduces erroneous temperature, humidity, or soil moisture readings into this smart irrigation scenario, it could lead to overwatering or underwatering, which would harm the farm's crop. © Grenze Scientific Society, 2023.

关键词： Support vector machines

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Cooperative learning Strategy for Human Behavior Prediction Using Multi-Modal data 14

Cooperative Learning Strategy for Human Behavior Prediction ...

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14th international conference on Information and Communication Technology Convergence, ICTC 2023

作者： Lee, Doyun Lee, Hoon Pukyong National University Department of Intelligent Robot Engineering Busan Korea Republic of Unist Department of Electrical Engineering Ulsan Korea Republic of

ISBN: (纸本)9798350313277

Understanding human behaviors leads to fully-automated systems in the near future. this paper investigates a deep learning solution that forecasts human activity patterns based on sensing signals measured by internet-of-things devices. Practical limitations on these small-form-factor sensors request remote deep learning services at a distant edge computing server. therefore, we need to involve impairments in sensor-server communication phases, such as random packet loss, resource constraint, and propagation noise, in the design of the remote learning architecture. To address these challenges, we propose a collaborative learning strategy among the sensors and server. Each sensor is equipped with its own encoding neural network that compresses high-dimensional sensing signals to communication messages. these are forwarded to the server through imperfect backhaul channels. then, a classifier at the server infers desired labels. A joint training mechanism of the encoders and classifier is developed along with the channel impairment. By doing so, we can obtain a robust prediction model for arbitrary communication noises. Numerical results demonstrate the viability of the proposed methods. © 2023 IEEE.

关键词： human behavior prediction Lifelog dataset multi-modal features

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Cooperative learning Strategy for Human Behavior Prediction Using Multi-Modal data

Cooperative Learning Strategy for Human Behavior Prediction ...

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international conference on Information and Communication Technology Convergence (ICTC)

作者： Doyun Lee Hoon Lee Department of Intelligent Robot Engineering Pukyong National University Busan Korea Department of Electrical Engineering UNIST Ulsan South Korea

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Face Emotion Recognition Using Deep Neural Network (DNN)

Face Emotion Recognition Using Deep Neural Network (DNN)

引用

international conference on Communication Systems and Network Technologies (CSNT)

作者： A Rajendra Babu Mohebbanaaz S. Chandrika G. Rajeswari U. Vidya Pavani Dept of ECE Ravindra College of Engineering for Women Kurnool Andhra Pradesh India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Facial expression recognition is an intelligent human-computer interaction technology that gives a great sense of communication of the expression of our emotions, understanding, and intent between ourselves. Many researchers have attempted to categorize facial expressions, and it has been proven that there are seven universal basic emotions: happiness, sadness, anger, fear, surprise, disgust, and neutrality. Real-time emotion detection is achievable using a webcam. the paper discusses an architecture based on CNN for emotion detection. this model was trained on the FER-2013 dataset after performing some pre-processing steps such as scaling images and color mode correction. A multi-layer CNN model is designed for the classification of emotions as happy, sad, angry, fearful, or surprised, as well as disgusted and neutral. In addition to this, it focuses on real-time analysis to present solutions based on emotions detected, such as automatically playing videos to address sad or angry or fearful emotions. Our developed algorithm attained an highest accuracy of 97.8%. the developed algorithms are very robust, effective and helps in automated seizure detection.

关键词： Human computer interaction Emotion recognition Accuracy Machine learning algorithms Face recognition Signal processing algorithms Artificial neural networks Real-time systems data models Classification algorithms

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iTB-test: An intelligent Image-enabled Diagnostic System for In Vitro Screening of Infectious Diseases 14

iTB-test: An Intelligent Image-enabled Diagnostic System for...

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14th international conference on Software, Knowledge, Information Management and Applications, SKIMA 2022

作者： Tania, Marzia Hoque Kaiser, M. Shamim Shabut, Antesar M. Abu-Hassan, Kamal Mahmud, Mufti Hossain, M.A. Institute of Biomedical Engineering University of Oxford Department of Engineering Science Oxford United Kingdom Institute of Information Technology Jahangirnagar University Savar Bangladesh Computing and Digital Industries Leeds Trinity University Faculty of Business Leeds United Kingdom School of Computing and Engineering University of West London London United Kingdom Nottingham Trent University Department of Computer Science Nottingham United Kingdom Cambodia University of Technology and Science Phnom Penh Cambodia

ISBN: (纸本)9781665493345

this paper performs an investigation into the development of an intelligent image-based automatic in vitro diagnostic system for infectious diseases using personal devices. the proposed framework of the image-based diagnostic system is demonstrated using the case study of Tuberculosis (TB)-specific antibody detection. the developed system, denoted as the iTB-test, is an intelligent bio-sensing system, comprised of a plasmonic Enzyme-Linked Immunosorbent Assay based colourimetric test in combination with an artificial intelligence-enabled image-based system. the presented system can separate the region of interest with 99.62% accuracy using clustering-based hybrid image processing algorithms, whereas the classification accuracy of antibody detection using a supervised machine learning technique is 100% based on the experiments conducted for the case study. © 2022 IEEE.

关键词： Chemical detection

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LLM-Project: automated engineering Task Planning via Generative AI and WBS Integration

LLM-Project: Automated Engineering Task Planning via Generat...

引用

IEEE international conference on Cyber Technology in Automation, Control, and intelligent Systems

作者： Yue Zhen Sheng Bi Shuo Tang Xing-tong Lu Wei-qin Pan Hai-peng Shi Zi-rui Chen Yi-shu Fang Xin-meng Wang South China University of Technology School of Computer Science and Engineering Guang Zhou Human centered design and engineering University of Washington Seattle Northeastern University Khoury College of Computer Sciences Boston

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

关键词： Automation Generative AI Service robots Robot sensing systems Encoding Real-time systems Planning Resource management Reliability Standards

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