检索结果-内蒙古大学图书馆

Gas Source Localization in Outdoor Environment Using Deep Learning: Effects of Data Length Reduction and Wind Data Smoothing

引用

IEEJ Transactions on Sensors and Micromachines 2023年第11期143卷 357-364页

作者： Zhao, Gao-Ju Sakaue, Motoki Matsukura, Haruka Ishida, Hiroshi Graduate School of Bio-Applications and Systems Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Koganei Tokyo184-8588 Japan Graduate School of Informatics and Engineering University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo182-8585 Japan

The aim of this research project is to attain accurate gas-source localization in outdoor environments with large wind fluctuations. For this purpose, we propose to use a long short-term memory deep-learning framework to time-series data collected by a sensor network consisting of multiple gas sensors and an anemometer. This paper describes impacts of the length of time-series data and smoothing of wind data provided to a deep neural network model. We have collected three datasets by placing 30 semiconductor gas sensors and one ultrasonic anemometer in an outdoor field in different seasons. We have found that the success rate of gas-source location estimation can be effectively increased by removing high frequency fluctuations in the time-series data of the wind velocity vector by taking moving average before applying the data to the neural network. By adjusting the data length provided to the neural network and smoothing the wind data, the success rate of gas-source location estimation has been increased from 82.5% to 86.7%. A success rate of 78.8% has been obtained even when half of the gas sensors have been removed. © 2023 The Institute of Electrical Engineers of Japan.

关键词： Gases

来源：评论

学校读者我要写书评

暂无评论

Integrating Textual and Financial Time Series Data for Enhanced Forecasting∗ 16

Integrating Textual and Financial Time Series Data for Enhan...

引用

16th IIAI International Congress on Advanced Applied informatics, IIAI-AAI 2024

作者： Mou, Shangyang Xue, Qiang Zhang, Wenting Kinkyo, Takuji Hamori, Shigeyuki Chen, Jinhui Takiguchi, Tetsuya Ariki, Yasuo Graduate School of System Informatic Kobe University Kobe Japan Graduate School of Economics Kobe University Kobe Japan Socio-Informatics Wakayama University Faculty of Systems Engineering Wakayama Japan

ISBN: (纸本)9798350377903

Time series forecasting is crucial in various real-world dynamic systems, with machine learning techniques increasingly applied for this purpose. Despite extensive research, there is still a lack of specialized deep learning-based forecasting models in the financial domain. Financial time series, such as stock prices and gold prices, present significant predictive challenges due to their complex short-term and long-term temporal dependencies. Furthermore, they are influenced by underlying fundamentals often reflected in diverse news sources. Current financial time series prediction methods typically concentrate on numerical data, often overlooking the potential insights offered by textual information. Even when considering textual data, the prevailing approach is to utilize sentiment information extracted from news sources as features. This paper introduces an innovative methodology that integrates relevant financial news content directly with corresponding time series data, aiming to explore the synergistic effects of numerical and textual information to enhance the accuracy of financial time series forecasting. Our experimental results demonstrate a substantial improvement in predictive accuracy when considering related textual data, offering a promising avenue for more nuanced and comprehensive economic market analysis. © 2024 IEEE.

关键词： Decentralized finance

来源：评论

学校读者我要写书评

暂无评论

Evaluating Non-Expert Stakeholder Interaction with Artificial Intelligence on Energy Urban Domain Using VIRTSI: The Case of ChatGPT 15

Evaluating Non-Expert Stakeholder Interaction with Artificia...

引用

15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Tsihrintzis, George A. Sarmas, Elissaios Marinakis, Vangelis Panagoulias, Dimitrios Tsihrintzi, Evangelia-Aikaterini Virvou, Maria University of Piraeus Department of Informatics Piraeus Greece School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Greece National and Kapodistrian University of Athens Department of Informatics and Telecommunicatios Athens Greece

ISBN: (纸本)9798350368833

This paper investigates the interaction of non-expert stakeholders with Artificial Intelligence (AI) in the energy urban domain, using the VIRTSI model and focusing on the capabilities of ChatGPT. VIRTSI (Variability and Impact of Reciprocal Trust States towards Intelligent systems), is a rigorous computational model for human-AI Interaction that simulates human trust states, spanning from overtrust to distrust, through user modelling and quantifies the efficiency of the interaction in VIRTSI-adapted confusion matrices. The research employed an 16-question survey, evaluating the accuracy and usefulness of ChatGPT's responses regarding energy consumption, cost-effective solutions, and renewable energy production for residential buildings in Greece. Each answer was assessed by human stakeholders who were non-expert in the energy Urban Domain (e.g house owners, building managers, etc.), who either accepted or rejected the responses based on validation processes. The analysis highlighted key aspects such as repetition, specification, and objections in the interaction with ChatGPT, offering insights into the effectiveness of AI in supporting energy-related decisions. The findings reveal that while AI can provide valuable information, user validation and expert consultation are critical for practical implementation. This highlights both the potential and limitations of integrating AI tools like ChatGPT in enhancing non-expert stakeholder engagement and decision-making in urban energy management, emphasizing the ongoing need for human expert involvement. © 2024 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

A DATA DRIVEN TOOL TO SUPPORT DESIGN TEAM COMPOSITION MEASURING SKILLS DIVERSITY 24

A DATA DRIVEN TOOL TO SUPPORT DESIGN TEAM COMPOSITION MEASUR...

引用

24th International Conference on engineering Design, ICED 2023

作者： Chiarello, Filippo Spada, Irene Barandoni, Simone Giordano, Vito Fantoni, Gualtiero School of Engineering Department of Energy Systems Land and Construction Engineering University of Pisa Italy School of Engineering Department of Civil and Industrial Engineering University of Pisa Italy School of Engineering Department of Engineering Informatics University of Pisa Italy Department of Informatics University of Pisa Italy B4DS - Business Engineering for Data Science Lab University of Pisa Italy

Team composition in Project Based Learning is the first task for the class and has a great impact on the learning experience. Anyway, little space is dedicated in literature about team composition, considering their personal inclinations towards design tasks. For these reasons we propose a tool that aims to map the design skills of students to optimise team composition. The tool is based on a questionnaire grounded in the design theory and aims at measuring the willingness of students at performing certain design tasks. The results of the questionnaires are analysed using Principal Component Analysis to normalise each students' answers to the whole class, and to show the distribution of students in the space of engineering design skills. We present the design process of the tool, and a first experimentation on two classes of master's degree students in Management engineering and Data Science, testing the tool on a total of 72 students. The results are promising and demonstrate the robusteness of the questionnaire and of the analytical method. Also, we propose next steps for our research activity, calling for other researchers to test our method in different contexts. © The Author(s), 2023. Published by Cambridge University Press.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Large Language Models and Adversarial Reinforcement Learning to Automate PLCs Programming: A Preliminary Investigation 9

Large Language Models and Adversarial Reinforcement Learning...

引用

9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Boudribila, Abderrahmane Chadi, Mohamed-Amine Tajer, Abdelouahed Boulghasoul, Zakaria Cadi Ayyad University National School of Applied Sciences Systems Engineering Applications Laboratory Marrakech Morocco Cadi Ayyad University Faculty of Sciences Semlalia Informatics Systems Engineering Laboratory Marrakech Morocco

ISBN: (纸本)9798350311402

The manual programming of Programmable Logic Controllers (PLCs) is a time-consuming and error-prone task, particularly for complex systems. Researchers have proposed various techniques, such as the modular, decentralized, hierarchical, and distributed approaches, which all fall under the Supervisory Control Theory (SCT) whose main goal is to generate a supervisor that ensures that the controller's behavior satisfies the specifications. While these techniques have demonstrated efficiency in systems of low complexity, high-complexity systems and probabilistic ones remain a major challenge. In this paper, we propose a novel pipeline that relies solely on Artificial Intelligence (AI)-techniques, namely, Reinforcement Learning (RL) and Natural Language Processing (NLP) techniques, including Named Entity Recognition (NER) and Large Language Models (LLMs). As a preliminary result, we demonstrate the potential of the proposed pipeline using the recent ChatGPT model by making a sample dataset in this context and generating Structured Text code from specifications presented in a natural language format. Finally, we outline future directions toward a fully automated AI-based PLC programming tool. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Radar Probing Optimization for Joint Beamforming and UAV Trajectory Design in UAV-Enabled Integrated Sensing and Communication

引用

IEEE Transactions on Communications 2024年第6期73卷 4469-4485页

作者： Liu, Yaxi Mao, Wencan He, Boxin Huangfu, Wei Huang, Tianyao Zhang, Haijun Long, Keping University Of Science And Technology Beijing Beijing Advanced Innovation Center For Materials Genome Engineering Beijing Engineering And Technology Research Center For Convergence Networks And Ubiquitous Services School Of Computer And Communication Engineering Beijing100083 China University Of Science And Technology Shunde Innovation School Beijing China National Institute Of Informatics Information Systems Architecture Science Research Division Tokyo101-8430 Japan

Unmanned aerial vehicle (UAV)-enabled massive multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) is an emerging platform to perform communication and sensing efficiently and flexibly. However, the existing works barely consider the radar probing tasks and neglect the benefits of the dedicated sensing signal. In this paper, we focus on joint optimizations in radar probing tasks, and a novel indicator is introduced, namely radar probing error. Two optimizations in radar probing tasks are established: i) joint transmit beamforming design for large-scale regional radar probing and communication task;ii) joint transmit beamforming and UAV trajectory design for communication enhancement and radar probing task. For the former task, we adopt both communication and novel sensing precoders to further support the MIMO radar. A semidefinite relaxation is utilized to relax the original non-convex problem, which is proven to be tight. For the latter task, we adopt block coordinate descent to alternately optimize the precoders and UAV trajectory where the fractional programming approach and successive convex approximation are further adopted. Experiment results testify the validation of the proposed methods for radar probing tasks in UAV-enabled MIMO ISAC. Moreover, results show the fundamental trade-off between the dual functions and reveal the effectiveness of the introduced sensing precoder. © 1972-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Decision Prediction in Turkish Legal Texts 11

Deep Learning-Based Decision Prediction in Turkish Legal Tex...

引用

11th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Solmaz, Seher Dursun, Mahir Graduate School Of Informatics Gazi University Dept of Information Systems Ankara Turkey Gazi University Faculty of Technology Dept. of Electrical and Electronics Engineering Ankara Turkey

ISBN: (纸本)9798350362541

Since text recognition studies in the literature are generally carried out in English, tokenization processes of Turkish legal texts in the Ural-Altaic language family cannot be used much. In this study, we tokenize Turkish legal texts and perform decision prediction using a deep learning Convolutional Neural Network (CNN) model. Artificial intelligence (AI), which is developing at a rapid pace, has become applicable to almost all fields of study and has started to be used effectively. Developments in AI have affected the field of law as well as other fields of study. The use and spread of AI in the field of law has increased significantly in recent years. One of the most important factors in this increase is that there is a huge amount of data in the field of law. It has become a need to analyze this data and transform it into meaningful and usable information. This paper aims to make decision prediction using artificial intelligence in judicial applications. In the study, using CNN, a neural network-based deep learning model, decision prediction was made with the data published by the Ombudsman's Office on the institution's website, which is free from publicly accessible personal data. The model was trained on the applicant information and the type of decision in the decision text. An F1 score of 0.63 was achieved for decision prediction. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence in physiological characteristics recognition for internet of things authentication

引用

Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

来源：评论

学校读者我要写书评

暂无评论

ZKP Protocols for Usowan,Herugolf,and Five Cells

引用

Tsinghua Science and Technology 2024年第6期29卷 1651-1666页

作者： Daiki Miyahara Léo Robert Pascal Lafourcade Takaaki Mizuki Graduate School of Informatics and Engineering The University of Electro-CommunicationsTokyo 1828585Japan MIS Lab University of Picardy Jules VerneAmiens 80000France Laboratory of Computer Science Modelling and Systems OptimisationUniversity of Clermont AuvergneClermont-Ferrand 63178France Cyberscience Center Tohoku UniversitySendai 9808578Japan

A Zero-Knowledge Proof (ZKP) protocol allows a participant to prove the knowledge of some secret without revealing any information about it. While such protocols are typically executed by computers, there exists a line of research proposing physical instances of ZKP protocols. Up to now, many card-based ZKP protocols for pen-and-pencil puzzles, like Sudoku, have been designed. Those games, mostly edited by Nikoli, have simple rules, yet designing them in card-based ZKP protocols is non-trivial. In this work, we propose a card-based ZKP protocol for Usowan, a Nikoli game. In Usowan, for each room of a puzzle instance, there is exactly one piece of false information. The goal of the game is to detect this wrong data amongst the correct data and also to satisfy the other rules. Designing a card-based ZKP protocol to deal with the property of detecting a liar has never been done. In some sense, we propose a physical ZKP for hiding of a liar. This work extends a previous paper appearing in Ref. [1]. In this extension, we propose two other protocols, for Herugolf and Five Cells. The puzzles are specifically chosen because each of those three puzzles shares a common constraint, connectivity. However, showing the connected configuration cannot be done with generic approach and brings new construction to the existing connectivity ZKP protocol. Indeed, in Herugolf, the connectivity is handled with a given length of cell which is decremental (i.e., the length of each connected cell decreases by one at each step). For Five Cells, there is an additional step in the setup allowing to encode all the information needed to ensure a valid ZKP protocol.

关键词： Zero-Knowledge Proof(ZKP)protocol playing cards card-based cryptography physical assumptions Usowan Herugolf FiveCells

来源：评论

学校读者我要写书评

暂无评论

Development of Virtual Space for Individualized and Optimal Learning 16

Development of Virtual Space for Individualized and Optimal ...

引用

16th IIAI International Congress on Advanced Applied informatics, IIAI-AAI 2024

作者： Maruko, Keiya Shengkai, Lin Mouri, Kousuke Okamoto, Masaru Matsubara, Yukihiro Hiroshima City University Graduate School of Information Sciences and Technology Department of Intelligent Engineering Hiroshima Japan Nagoya University Graduate School of Informatics Sciences and Technology Department of Intelligent Systems Nagoya Japan

ISBN: (纸本)9798350377903

This research proposal describes the development of a virtual space platform aimed at transforming learning and collaboration in the post-corona era. This research uses technology and the Unity platform to construct a space where learning data can be collected and where students attend lectures that are close to reality. © 2024 IEEE.

关键词： Virtual environments

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：