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Automated Domain Modeling with Large Language Models: A Comparative Study 26

Automated Domain Modeling with Large Language Models: A Comp...

引用

26th ACM/IEEE international conference on Model Driven engineering Languages and Systems, MODELS 2023

作者： Chen, Kua Yang, Yujing Chen, Boqi Hernandez Lopez, Jose Antonio Mussbacher, Gunter Varro, Daniel McGill University Electrical and Computer Engineering Montreal Canada University of Murcia DIS Murcia Spain Linköping University Linköping Sweden McGill University Montreal Canada

ISBN: (纸本)9798350324808

Domain modeling is an essential part of software engineering and serves as a way to represent and understand the concepts and relationships in a problem domain. Typically, software engineers interpret the problem description written in natural language and manually translate it into a domain model. Domain modeling can be time-consuming and highly depends on the expertise of software engineers. Recently, Large Language Models (LLMs) have exhibited remarkable ability in language understanding, generation, and reasoning. In this paper, we conduct a comprehensive, comparative study of using LLMs for fully automated domain modeling. We assess two powerful LLMs, GPT3.5 and GPT4, employing various prompt engineering techniques on a data set containing ten diverse domain modeling examples with reference solutions created by modeling experts. Our findings reveal that while LLMs demonstrate impressive domain understanding capabilities, they are still impractical for full automation, with the top-performing LLM achieving F1 scores of 0.76 for class generation, 0.61 for attribute generation, and 0.34 for relationship generation. Moreover, the F1 score is characterized by higher precision and lower recall;thus, domain elements retrieved by LLMs are often reliable, but there are many missing elements. Furthermore, modeling best practices are rarely followed in auto-generated domain models. Our data set and evaluation provide a valuable baseline for future research in automated LLM-based domain modeling. © 2023 IEEE.

关键词： automation

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Radioactive Metal Detection in Dustbin using Metaheuristic with Augmented Reality for Enhanced Safety and Security 6

Radioactive Metal Detection in Dustbin using Metaheuristic w...

引用

6th international conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Preethi, C. Hammed, S. Shahul Haripriya, K. Pavalarajan, S. Saran, V. S.Shree Meikannan, R. Pranesh Raj Psna College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798350394702

Enabling everyday objects to collect, exchange, and analyze data, the Internet of Things (IoT) is a technology paradigm that has the potential to revolutionize industries through data-driven decision-making, automation, and efficiency. IoT's fundamental principles and applications are succinctly summarized in this abstract, with emphasis on its role as a catalyst for the Fourth Industrial Revolution. Its ubiquity in various domains, such as smart homes and industrial automation, highlights its importance in shaping our interconnected world. With this in mind, we are encouraged to explore the vast opportunities and challenges presented by the IoT ecosystem. In industrial, medical, and research environments utilizing radioactive materials, ensuring radiation safety and security is essential. An important measure in preventing harm to staff and surroundings is identifying and segregating radioactive metal objects. Introducing an innovative camera-based approach for augmenting the detection of such objects is the aim of this *** order to develop a reliable and real-time detection system, our suggested Radioactive Metal Detection Bin (RMDB) integrates cutting-edge camera systems with powerful image processing *** acquire pictures of items moving across the detecting region, the RMDB system places high-resolution cameras strategically. These cameras have specific sensors and filters that are made to find gamma radiation coming from radioactive metals. © 2023 IEEE.

关键词： automation

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Equivalent Transformations of a Cube-like Passive Linear One-port Networks with Equal Impedances in their Branches 11

Equivalent Transformations of a Cube-like Passive Linear One...

引用

11th international Scientific conference on computer Science, COMSCI 2023

作者： Filipova-Petrakieva, Simona Technical University of Sofia Faculty of Automation Dept. Fundamentals of Electrical Engineering Sofia Bulgaria

ISBN: (纸本)9798350325256

In the present paper equivalent transformations of cube-like passive linear one-port networks supplied by alternating-current (AC) sources are suggested. The initial analyzed one-port is represented in the 3D space as a cube, resp. a parallelepiped. In each arm of the cube equal passive elements are connected in series so that the resulting impedance contains both real and imaginary parts, named active and reactive parts. Since all impedances are either inductive or capacitive it is not possible the phenomenon resonance to take *** on the well-known transformations of passive linear one-ports networks of series and parallel type, a representation of the cube-like one-port with the only one impedance connected between the supplied nodes is proposed. One possible case of DC power supply and three possible cases of AC power supply between 3 possible pairs of nodes of the analyzed passive linear one-port network are considered. © 2023 IEEE.

关键词： Electric impedance measurement

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Efficient Assembly Line Operations through Machine Vision-Enabled Assembly Part Classification 2

Efficient Assembly Line Operations through Machine Vision-En...

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2nd international conference on Recent Trends in Microelectronics, automation, Computing, and Communications Systems, ICMACC 2024

作者： Kaki, Ramesh Mushtaq, Faisel Deshmukh, Sandip Ray, Tathagata Parimi, Chandu Tandon, Praveen Kumar Jha, Pramod BITS Pilani Hyderabad Campus Department of Mechanical Engineering Hyderabad India BITS Pilani Hyderabad Campus Department of Computer Science & Information Systems Hyderabad India BITS Pilani Hyderabad Campus Department of Civil Engineering Hyderabad India Centre for Advanced Systems DRDO Hyderabad India

ISBN: (纸本)9798350366570

Assembly line complexity presents significant challenges in integrating various systems, subsystems, and mechanical components. Manual inspection of assembled parts often leads to quality assurance issues, potentially resulting in production downtime, cost overruns, and product failures. This research proposes a novel automated machine vision system to address these challenges by identifying and classifying mechanical parts' spatial dimensions in assembly lines. The study compared the results of deep learning techniques. Specifically, Convolutional Neural Networks and ResNet50 are trained on a high-performance computing infrastructure for robust component identification. Our approach achieves a remarkable 99.68% training accuracy, significantly outperforming existing methods. The developed model is deployed for real-time prediction of components in assembly lines, offering a versatile solution applicable across various manufacturing sectors. A key innovation of this study is integrating the deep learning model with a user-friendly Graphical User Interface (GUI), enhancing operator efficiency and reducing human error. This research contributes to the field of smart manufacturing by demonstrating the feasibility and effectiveness of AI-driven quality control in complex assembly processes, potentially transforming industrial inspection methodologies. © 2024 IEEE.

关键词： Graphical user interfaces

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Optimal Transition Trajectory Search for Tilt-Rotor Aircraft Based on Ant Colony Algorithm 2

Optimal Transition Trajectory Search for Tilt-Rotor Aircraft...

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2nd international conference on Artificial Intelligence and computer Information Technology, AICIT 2023

作者： Zhang, Zhiqi Tian, Jia Chen, Hongzheng Sheng, Shouzhao College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350302950

In view of the design of the transition trajectory of rotary rotor aircraft, although the traditional conventional two-dimensional transition corridor planning and transition trajectory design are simple and convenient to calculate, due to the limitations of the method, the matching relationship between the forward flight speed and the short capsule in the transition is only considered, while ignoring the Angle of attack information. The transition corridor through dynamic balancing calculation, evaluates the flight status points according to the defined integrated performance index function, and uses the ant colony algorithm, which is the optimal transition path of the tilt rotor craft under the integrated performance index function. This paper will consider the safety attitude index, power index and control quantity index, use the ant colony algorithm to analyze the optimal transition path, and take XV-15 as the simulation object to conduct the transition flight simulation. The simulation experiment results show that the designed 3D transition corridor and transition trajectory can complete the tilt transition while ensuring the performance indexes, and the transition effect is good. © 2023 IEEE.

关键词： Trajectories

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IOT & Machine Learning Models to Develop The Architecture For Assessment Of Security Threats.

IOT & Machine Learning Models to Develop The Architecture Fo...

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2023 international conference on New Frontiers in Communication, automation, Management and Security, ICCAMS 2023

作者： Alotibi, Mohammed Masaad Rajeyyagari, Sivaram Al-Omari, Omaia Mohammed Alymani, Mofadal Planning and Digital Transformation Ministry of Health Riyadh Saudi Arabia College of Computing and Information Technology Department of Computer Engineering Shaqra Saudi Arabia College of Computing and Information Technology Department of Information Systems Shaqra Saudi Arabia

ISBN: (纸本)9798350317060

The Internet of Things (IoT) and other recent advancements in communication and information technology have profoundly impacted and enhanced people's quality of life. IoT systems are vulnerable to previously unanticipated cyber threats such as denial of service, jamming, phishing, obfuscations eavesdropping, spoofing and invasions because of the widespread availability and rising demand for connected devices. The emerging cyber-physical security problem is difficult to avoid and guard against using conventional methods. Securing IoT systems calls for robust, dynamic, and current security mechanisms. When addressing emerging security concerns in cyber-physical systems (CPS), machine learning (ML) technology is often regarded as the most cutting- edge and promising option. This literature review explains how IoTs are built, looks into the many threats they face, and discusses the most up-to-date efforts being made to ensure their security using machine learning. In addition, it covers some of the research obstacles that may arise in the future while trying to implement security measures in IoT infrastructure. © 2023 IEEE.

关键词： Internet of things

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Competing Texture Directions and Continuous Surface Types For 3D Palmprint Recognition 3

Competing Texture Directions and Continuous Surface Types Fo...

引用

3rd international conference on Machine Learning and computer Application, ICMLCA 2022

作者： Lin, Sen Shang, Peng School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China

ISBN: (纸本)9781510664814

Three dimensional(3D) palmprint recognition is a prospective biometric recognition technology, and texture direction is one of its main features. Firstly, the texture direction extracted by the Shape Index(SI) is susceptible to noise interference, and to solve this problem, the noise is eliminated by convolution operation, meanwhile the texture direction is extracted using competitive coding. Secondly, the traditional Surface Type(ST) tends to ignore the small curvature feature information, while the palmprint adjacent points have the same curvature, so a Continuous Surface Type(CST) is proposed to obtain surface consistency. Finally, the method of Collaborative Representation(CR) is used to combine features to complete palmprint recognition in classification recognition. The results of the experiment on the 3D palmprint library of the Hong Kong Polytechnic University show that the average recognition rate obtained can reach 99.92%, and the average recognition time is 0.032s compared with other classification methods. The proposed method can improve the recognition accuracy of 3D palmprint while maintaining low recognition time, and the feature extraction ability and classification recognition effect are of practical value. © 2023 SPIE.

关键词： Palmprint recognition

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Attention-Based Grayscale Image Colorization 20

Attention-Based Grayscale Image Colorization

引用

20th IEEE international conference on Mechatronics and automation, ICMA 2023

作者： Sun, Lei Ju, Dongjie Ke, Shi School of Electrical Engineering and Automation Binshui Xidao Extension 391 Tianjin300384 China Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Complicated Industry Systems Binshui Xidao Extension 391 Tianjin300384 China

ISBN: (纸本)9798350320831

Image colorization has always been a hot topic in computer vision. Since the emergence of deep learning and its excellent performance in many image-processing tasks, image colorization methods based on convolutional neural networks (CNN) emerge in an endless stream, including automatic colorization methods and A coloring method based on a reference image. In the coloring method based on the reference image, an existing problem is that an algorithm for calculating the similarity between the grayscale image and the reference image needs to be designed to map the color blocks in the reference image to the grayscale image. And we use the attention mechanism to spread color implicitly, and propose an attention-guided image colorization network, which extracts features and corresponding color information from the reference image through a reference encoding sub-network, and simultaneously passes a target encoding sub-network The network extracts grayscale features from grayscale images, uses attention to calculate the correlation between the two types of features, and then restores the features and reconstructs colors through the decoding network. Our method can quickly capture the color of the reference feature, accurately transfer it to the target feature, and effectively solve the problem of small target loss during downsampling. By validating on multiple datasets, the results show that our method is effective, outperforming multiple state-of-the-art image colorization algorithms. © 2023 IEEE.

关键词： Color

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Path Planning Design for Autonomous Underwater Vehicles in 3D Environments Avoiding Static Obstacles 9

Path Planning Design for Autonomous Underwater Vehicles in 3...

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9th international conference on Control, Instrumentation and automation, ICCIA 2023

作者： Norouzi, Soheil Akhbari, Amirhossein Ghavifekr, Amir A. University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9798350366013

Efficient path planning is a major challenge in the field of autonomous underwater vehicles (AUVs), particularly in complex three-dimensional (3D) environments with static obstacles. This paper presents a novel approach for path planning design of these systems that utilizing the modified Grey Wolf Optimization (GWO) algorithm to enable AUVs to navigate through intricate underwater landscapes while avoiding stationary obstacles. To represent obstacles in the underwater environment, the Spherical Method is employed which transforms these impediments into 3D models within the simulation space, relying on their central coordinates. The factors such as the faster path planning time, path safety, and angle change are considered in order to find an appropriate path. Through the utilization of 3D environmental data and obstacle detection techniques, the algorithm facilitates real-time decision-making for the AUV, ensuring dynamic adaptation to the changing underwater landscape. Simulation results will be provided to validate the superior performance of the GWO-based path planning method compared to conventional approaches. Also, the algorithm's effectiveness is demonstrated in simulated 3D environments with diverse obstacle configurations. © 2023 IEEE.

关键词： Autonomous underwater vehicles

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A Transformer Network Based on Taylor Expansion for Generating Phase Contrast Images of Adherent Cells

A Transformer Network Based on Taylor Expansion for Generati...

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2024 IEEE international conference on Real-Time Computing and Robotics, RCAR 2024

作者： Guo, Yongquan Zheng, Jintu Ding, Yi Hu, Ying Cao, Yi Wang, Zenan University of Jinan Institute of Information Science and Engineering Jinan250024 China Institute of Advanced Integration Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9798350372601

The use of automation technology is essential in stem cell research and large-scale preparation to address core technological challenges. The efficiency and accuracy of the observation phase in the large-scale preparation process of stem cells are decisive factors that affect production quality and efficiency. However, existing observation technologies such as phase contrast imaging and traditional computer vision-based methods are inefficient and require manual correction. Therefore, developing a technology that can quickly and efficiently observe the stem cell growth states during the automation process is necessary. A new study introduces a multi-scale Transformer network based on Taylor expansion (TTNet) that aims to monitor stem cell growth states accurately and in real-time during the automation process. This network utilizes a Taylor series expansion to provide an approximation of the conventional softmax attention mechanism and corrects errors in the Taylor expansion through an attention refinement module. Additionally, the network incorporates multi-scale patch embedding technology, achieving effective feature embedding through overlapping deformable convolutions of different scales. By optimizing and compressing the model, this study significantly improves the generation speed of the model without sacrificing the quality of generation. Experimental results demonstrate that TTNet has significant advantages in comprehensive performance over existing generative models in the task of generating bright-field images of stem cells. It achieves the shortest generation time while ensuring the quality of generation. © 2024 IEEE.

关键词： Stem cells

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