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Exploring the Role of Big Data Analytics on Engineering Costs 3

Exploring the Role of Big Data Analytics on Engineering Cost...

引用

2023 3rd international conference on Big Data, Artificial intelligence and Risk Management, ICBAR 2023

作者： Han, Ruiqi School of Humanities Shanghai Institute of Technology Shanghai Fengxian201400 China

ISBN: (纸本)9798400716478

Big data analytics has had a significant impact on various industries as a technology that is still in its infancy. The engineering sector is no exception, and big data analytics is becoming increasingly important in improving engineering costs' accuracy and cost control. This paper will explore the impact of big data analytics on engineering costs, its application in the field of engineering costs, and the benefits it brings to improving the accuracy of engineering costs, and comprehensively examine the specific applications of big data technology in the engineering cost management process for precise management. © 2023 ACM.

关键词： Big data

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Path Planning Methods for UAVs: A Survey 24

Path Planning Methods for UAVs: A Survey

引用

3rd international conference on Computer, Artificial intelligence and Control Engineering, CAICE 2024

作者： Chen, Xi Tang, Jun Ruan, Yirun Zhan, Jianjun National University of Defense Technology Changsha China

ISBN: (纸本)9798400716942

After years of development, low-cost Unmanned Aerial Vehicles (UAVs) play a pyramidally important role in applications of miscellaneous fields by virtues of their excellent mobility and safety. With the increasing difficulty of practical applications, UAV technology research is evolving towards the direction of multi-UAV collaboration. As the primary problem of multi-UAV cooperative mission, path planning attracts more and more researchers' attention. It is followed by diversified multi-UAV path planning algorithms. This paper therefore provides a review of this issue and proposes a novel systematic classification of path planning algorithms. According to this classification, this paper mainly summarizes the typical path planning methods and their corresponding application or development on multiple UAVs. The performance of each algorithm is also discussed. Finally, this paper prospects the future development trend of multi-UAV path planning. © 2024 ACM.

关键词： Motion planning

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Deep Learning-based Plant Species Identification using VGG16 and Transfer Learning: Enhancing Accuracy in Biodiversity and Agricultural applications 3

Deep Learning-based Plant Species Identification using VGG16...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kiran, K. Kaur, Arpanpreet School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

In this paper, the method of deep learning is applied for plant species identification, with a special emphasis on Convolutional Neural Network VGG16 for feature extraction and classification. Identification of plants is important in matters concerning biological diversity, agriculture as well as management and utilization of resources but the conventional methods used in identification are time consuming and sometimes inaccurate. Machine learning specifically deep learning is a more suitable replacement by offering a fast and accurate identity recognition. The research work works with a Kaggle dataset comprising of 24881 images of four plant species categorized into twenty-two with the objectives of increasing efficiency of the classification. We used transfer learning with weights from the ImageNet data set and obtained accuracy greater than 90% on the test data set, which shows the model’s ability to identify plant species based on leaf structure. To counter overfitting and enhance model generality, data augmentation methods were employed where the input images were rotated by 30, 45 and 60 degrees, and flipped vertically. The performances suggest that deep learning models, such as VGG 16, yield improved recognition for plant classification than conventional methods provide higher reliability. This research makes a valuable input toward a number of applications related to automated systems for agriculture and conservation, discussing possible uses of AI technique for better penetration of plant species, enhancing conservation goals, and future advancements in the agrarian field. Future research will leverage on these discoveries to make improvements on classification frameworks. © 2025 IEEE.

关键词： Plant diseases

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Parallel Self-assembly for Modular robots Using Deep Reinforcement Learning 17th

Parallel Self-assembly for Modular Robots Using Deep Reinfor...

引用

17th international conference on Intelligent robotics and applications

作者： Mao, Yanbo Yao, Meibao Jilin Univ Sch Artificial Intelligence Intelligent Robot Lab IRL Changchun 130012 Peoples R China

ISBN: (纸本)9789819607976;9789819607983

Modular robots can adapt to different tasks by assembling into various configurations. Parallely executing the self-assembly process can significantly enhance its efficiency. However, the implementation of parallel assembly presents challenges, as it requires not only accurate mapping of modules between configurations, but also simultaneous control of the robot's motion. A hierarchical framework is proposed for the parallel self-assembly of modular robots, capable of accommodating varying numbers of robots and assembling them into different target configurations. The framework comprises a mapping algorithm to determine the reconfiguration scheme between the initial and target configurations, as well as a transferable motion model based on SAC (Soft Actor-Critic). The mapping algorithm computes the optimal module position assignments, while the motion model provides an efficient motion strategy for the modular robots. We conducted simulations and experiments on the Webots platform to verify the proposed framework, and the experimental results show that it produces a high-quality parallel self-assembly scheme.

关键词： Modular robot Self-assembly Deep reinforcement learning

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Chat Generative Pretrained Transformer (ChatGPT) for Data Analysis 3

Chat Generative Pretrained Transformer (ChatGPT) for Data An...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Niranjanamurthy, M. Kantharaju, V. Satish, Sirisha Umashankar, Nidhi Raviram, V. BMS Institute of Technology and Management Dept. of AI&ML Bangalore India BMS Institute of Technology and Management Dept. of ISE Bangalore India Sri Siddhartha Institute of Technology Department of CS&E Tumakuru India

ISBN: (纸本)9798350317022

OpenAI created and published ChatGPT, an AI chatbot, in November 2022. It was developed using supervised and reinforcement learning methods and is based on OpenAI's GPT-3.5 and GPT -4 families of big language models. The creation of Artificial intelligence has started a new era in the field of computer science. Although it was first established in 1950, by, now known as the father of artificial intelligence, John McCarthy, and before that by Alan Turning who explored its mathematical possibilities, technology is generally ranked using a sigmoid curve, wherein you have a slow take off as the technology is invented, followed by an explosion of growth depending on how useful it is. That results in improvement in the technology as people find more real world practical applications for it, and then it reaches a stagnation point where you can't improvise the tech anymore. We believe that Artificial intelligence would be the start of a new sigmoid curve. Artificial intelligence has now reached a new peak after the maturation of ChatGPT. Currently, AI has a gradual growth rate of 21 % per annum, creating a new market in this highly competitive industry. This paper represented the ChatGPT features, paradigm and data analysis process. © 2023 IEEE.

关键词： ChatGPT Cons data analysis Pros Reinforcement Learning Stages of ChatGPT

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Performance Improvement of EV Charger Using AI Based Controllers 3

Performance Improvement of EV Charger Using AI Based Control...

引用

3rd international conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Divya, S. Latha, P.G. School of Engineering Cochin University of Science and Technology Division of Electrical Engineering Kochi India

ISBN: (纸本)9798350349207

The rapidly growing demand of electric vehicle(EV) has a detrimental impact on power quality of the utility distribution grid. The choice of EV battery charger design is crucial in reducing power quality issues. Conventional diode bridge rectifier(DBR) based converters introduce significant harmonics which also adversely affects the battery life. A DBR interfaced with a zeta converter can reduce the power supply distortions and improve the input power factor during battery charging. PI controllers are comprehensively applied in battery chargers;nevertheless, their performance tends to suffer under system dynamics. The diversity of electric vehicles (EVs) and the dynamic changes in battery operating conditions introduce numerous uncertainties, making an artificial intelligence (AI) based controller a preferable choice for EV charging applications. The performance of an isolated zeta converter with PI, fuzzy logic and Artificial Neural Network(ANN) based controllers is tested in MATLAB/Simulink environment under different operating conditions. Analysis of the results clearly indicates that AI based controllers are not only effective in reducing harmonics but also in ensuring improved performance under both steady-state and dynamic conditions. © 2024 IEEE.

关键词： Controllers

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The use of the Emotiv Epoc Flex kit in applications involving artificial intelligence 3

The use of the Emotiv Epoc Flex kit in applications involvin...

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3rd international Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2023

作者： Pawuś, Dawid Paszkiel, Szczepan University of Technology Prószkowska 76 Street Opole45-758 Poland Faculty of Electrical Engineering Automatic Control and Informatics Poland

This article presents a wide, proprietary range of Emotiv Epoc Flex Gel headset applications for EEG signal measurement. It is about its use in systems involving artificial intelligence, such as artificial neural networks and expert systems. The constantly developing field of biomedical engineering as well as newer and more advanced BCI (brain-computer interface) systems require their designers to constantly develop and search for various innovative methods used in their creation. In response to practical requirements and the possibility of using the system in real conditions, the authors propose an advanced solution using EEG signal analysis (electroencephalography). An AI-based approach to designing the BCI system was used for advanced signal analysis. The article contains a detailed description of two applications based on artificial intelligence using EEG signals. The first one, for controlling a mobile robot using mental commands. The second one, on the other hand, for controlling a mobile robot with verification in the form of an EMG signal. This article provides a comprehensive overview of the integration of the Emotiv Epoc Flex Kit with proprietary AI systems and its significant impact on the field. © 2023 CEUR-WS. All rights reserved.

关键词： Brain computer interface

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Parallel intelligence-enabled IoV Information Management System based on Blockchain 3

Parallel Intelligence-enabled IoV Information Management Sys...

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3rd IEEE international conference on Digital Twins and Parallel intelligence, DTPI 2023

作者： Han, Shuangshuang Bai, Yongqiang Chen, Yueyun Tellambura, Chintha University of Science and Technology Beijing School of Computer & Communication Engineering Beijing China University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798350318470

The current Internet of Vehicles (IoV) data are facing challenges such as data silos, security and privacy concerns, data quality issues, and collaboration barriers. This paper proposes an IoV Information Management System (IIMS) based on parallel intelligence and blockchain to address these challenges. By leveraging the decentralized, immutable, and traceable nature of blockchain, combined with the incentive mechanisms and collaborative models of decentralized autonomous organizations (DAOs), a secure and trustworthy data sharing platform is established for collaborative analysis, business cooperation, and innovative applications of IoV data. The proposed approach utilizes the decentralized nature and data backup capabilities of blockchain to mitigate the single point of failure issues associated with centralized servers. Additionally, the anonymity, immutability, and traceability features of blockchain ensure the privacy, security, and integrity of data sharing. Moreover, the incentive mechanisms and DAO solutions within the blockchain promote data sharing among vehicles while ensuring data quality. © 2023 IEEE.

关键词： Blockchain

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3rd international conference on Artificial intelligence in Electronics Engineering, AIEE 2022

3rd International Conference on Artificial Intelligence in E...

引用

3rd international conference on Artificial intelligence in Electronics Engineering, AIEE 2022

ISBN: (纸本)9781450395489

The proceedings contain 23 papers. The topics discussed include: sentiment analysis with deep learning models: a comparative study on a decade of Sinhala language Facebook data;LSTM-based modelling for coagulant dosage prediction in wastewater treatment plant;analysis on the realization of autonomous driving technology and its development prospect;design and application of virtual production command service in power distribution network based on artificial intelligence;research on the application of concurrent design method to develop smart vehicles;artificial intelligence and the evolution of global value chains in the context of intelligent manufacturing;indoor scene classification algorithm based on an object vector for robot applications;research on anomaly detection method of online monitoring data of dissolved gas in transformer oil;development of verification device for interval velocity measurement system based on GNSS;and research on the application of fall detection technology based on the security field of the smart care for the elderly.

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Using LiDAR Data as Image for AI to Recognize Objects in the Mobile robot Operational Environment 3rd

Using LiDAR Data as Image for AI to Recognize Objects in the...

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3rd international conference on Optimization, Learning Algorithms and applications (OL2A)

作者： Nowakowski, Marek Kurylo, Jakub Braun, Joao Berger, Guido S. Mendes, Joao Lima, Jose Mil Inst Armoured & Automot Technol Okuniewska 1 PL-05070 Sulejowek Poland Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia Braganca Portugal INESC Technol & Sci Porto Portugal Inst Politecn Braganca Lab Sustainabil & Technol Mt Reg SusTEC Braganca Portugal Univ Tras os Montes & Alto Douro UTAD Sch Sci & Technol Dept Engn P-5000801 Vila Real Portugal Bialystok Tech Univ 45A Wiejska St PL-15351 Bialystok Poland

ISBN: (纸本)9783031530357;9783031530364

Nowadays, there has been a growing interest in the use of mobile robots for various applications, where the analysis of the operational environment is a crucial component to conduct our special tasks or missions. The main aim of this work was to implement artificial intelligence (AI) for object detection and distance estimation navigating the developed unmanned platform in unknown environments. Conventional approaches are based on vision systems analysis using neural networks for object detection, classification, and distance estimation. Unfortunately, in the case of precise operation, the used algorithms do not provide accurate data required by platforms operators as well as autonomy subsystems. To overcome this limitation, the authors propose a novel approach using the spatial data from laser scanners supplementing the acquisition of precise information about the detected object distance in the operational environment. In this article, we introduced the application of pretrained neural network models, typically used for vision systems, in analysing flat distributions of LiDAR point cloud surfaces. To achieve our goal, we have developed software that fuses detection algorithm (based on YOLO network) to detect objects and estimate their distances using the MiDaS depth model. Initially, the accuracy of distance estimation was evaluated through video stream testing in various scenarios. Furthermore, we have incorporated data from a laser scanner into the software, enabling precise distance measurements of the detected objects. The paper provides discussion on conducted experiments, obtained results, and implementation to improve performance of the described modular mobile platform.

关键词： Convolutional Neural Network Depth Estimation Point Clouds

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