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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

共 37040 条记录，以下是4621-4630 订阅

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Development the Reinforcement Learning Model for Sources Identification of H2S Industrial Emissions

Development the Reinforcement Learning Model for Sources Ide...

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international conference on Industrial engineering, Applications and manufacturing (ICIEAM)

作者： Aleksey Kychkin Igor Chernitsin Olga Vikentyeva Centre of Data and Artificial Intelligence Engineering National Research University Higher School of Economics Perm Russia

ISBN: (数字)9798331512194

ISBN: (纸本)9798331512200

Industry 4.0 concept focuses on sustainability problem that requires to control air emissions, especially for harmful substances like H2S, and reduction their impact on nature by using environmental monitoring and sources identification systems. This task requires solving inverse problem of dispersion models, which should establish complex mathematical dependences between the sensor data, the location and emission rate on a chimney in dynamics. In our research we propose the reinforcement learning (RL) model for H2S sources identification on given example of real-life data. The search algorithm is based on the Q-Learning that uses dispersion simulations on industrial emissions within different rates, meteorological data and landscape specifics for training. Python library and visualization tool as the industrial software application has been developed, with the help of which it is possible to analyze the possible H2S sources on large industries.

关键词： Training Q-learning Software algorithms systems architecture Companies data models Mathematical models Software Sustainable development Dispersion

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Sustainable Intelligent Information System for Tourism Industry 8

Sustainable Intelligent Information System for Tourism Indus...

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8th IEEE international conference for Convergence in Technology, I2CT 2023

作者： Kumar, Anurag Goyal, Himanshu Rai Sharma, Sachin Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350334012

The technological advancements of the modern period have made the tourist sector one of the most significant and influential on a global scale. Artificial intelligence, one of the most cutting-edge technologies, is presently utilized in a number of different businesses. The combination of these factors led to the development of "Intelligent Tourism,"which may be considered as the evolution of modern tourism. Intelligent systems are conscious of their surroundings and gain knowledge from the activities they take to accomplish particular objectives. They are more frequently utilized to support decision-making, work processes, and information gathering for tourism. Intelligent information systems should be effective enough to deliver accurate, accurate, and timely information to the appropriate person in order to achieve the goals of intelligent tourism. It's crucial to give tourists the information they need in order to understand how intelligent tourism behaves. The past ten years have seen a significant amount of study activity in the area of next-generation intelligent tourism, but our findings show that none of the research studies conducted in the past included a thorough literature assessment in this sector. The methodologies and core ideas that are most frequently applied in the field of intelligent tourism are identified in this article, and they are presented in depth together with the relevant papers. We succeeded in achieving this by critically assessing a significant number of latest research papers. This study identifies the following ideas as crucial ones: intelligent information system, big data, enhanced reality, social media, real-time information sharing, context awareness, cultural heritage, recommender systems, and user modelling. Important topics related to sustainable intelligent tourism are examined concurrently in order to lay the groundwork for future studies on the tourism arena. © 2023 IEEE.

关键词： Recommender systems

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Description Method and Failure Ontology for Utilizing Maintenance Logs with FMEA in Failure Cause Inference of manufacturing systems

Description Method and Failure Ontology for Utilizing Mainte...

引用

IEEE/ASME (AIM) international conference on Advanced Intelligent Mechatronics

作者： Takuma Fujiu Toshinori Yasui Sho Okazaki Kohei Kaminishi Jun Ota Department of Precision Engineering School of Engineering The University of Tokyo Tokyo Japan DENSO CORPORATION Aichi Japan Research into Artifacts Center for Engineering School of Engineering The University of Tokyo Tokyo Japan

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

In the realm of manufacturing systems, inferences of failure causes have been performed mainly depending on the reuse of Failure Mode and Effect Analysis (FMEA). However, achieving inference with the same level of quality as experts has been challenging. The objective of this study is to improve the quality of inference by combining maintenance logs and FMEA in the inference of failure causes of manufacturing systems. There are two challenges in using maintenance logs for inference. First, it is difficult to extract causal relationships among failures because the format and quantity of maintenance logs are not consistent. Second, the hierarchy of failures described in each item of maintenance logs is not fixed, making it difficult to search for similar failures by using the items. To address these challenges, we propose a description method for maintenance logs that describes causal relationships among failures and those between failures and functions. We also introduce a failure ontology that represents the hierarchy of failures and conditions impaired by failures based on expert knowledge of manufacturing systems. For the two assumed failures, the inferences derived from maintenance logs and FMEA using the proposed method show better quality than the inferences using FMEA alone. The recall calculated from the failure cause candidates enumerated by experts increased by 3.7 and 5.0 times, respectively.

关键词： Mechatronics Ontologies Maintenance High frequency manufacturing systems

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Toward Digital Twin: Leveraging Pre-training Approaches for Multivariate Time Series Forecasting 6

Toward Digital Twin: Leveraging Pre-training Approaches for ...

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6th IEEE international conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Yan, Leon Zhang, Zhenwei Wang, Xin Zhang, Yu Gu, Yuantao Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798350399059

Time series forecasting has been an active research area, particularly in the context of Digital Twin (DT) systems. Despite the excellent results yielded by pre-trained models in Natural Language Processing (NLP) and Computer Vision (CV), only a few studies have researched pre-training strategies for time series forecasting networks within DT systems. Recent studies demonstrate that transfer learning across multiple time series datasets does not always provide promising results, making self-supervised pre-training directly on the downstream dataset a temporarily considered optimal solution. To the best of our knowledge, the only general pre-training task in the time series field is the Masked Autoencoder. However, this approach may lead to redundant representations for downstream forecasting within DT systems. Therefore, we propose three pre-training tasks specially designed for time series forecasting within DT frameworks: Inverse Forecasting (IF), Coarser Forecasting (CF), and Anomaly Forecasting (AF). These tasks are respectively designed to capture bidirectional dependency, reduce noise, and augment data, all crucial aspects in enhancing the predictive capabilities of DT systems. By integrating the three tasks, we obtain a composite pre-training task which generally improves the forecasting results of time series models within DT systems across multiple datasets. This work contributes to the ongoing efforts to improve the accuracy and efficiency of DT systems, paving the way for more robust and reliable digital representations of physical systems. © 2023 IEEE.

关键词： Time series

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A Validation Approach for the Lipschitz Constant for Neural Networks 24

A Validation Approach for the Lipschitz Constant for Neural ...

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24th IEEE international conference on High Performance Computing and Communications, 8th IEEE international conference on data Science and systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zuo, Zhiwei Cai, Qianqian Ji, Jungang Tang, Zhongan College of Computer Science and Electronic Engineering National Supercomputering Center in Changsha Hunan University Changsha China Szzt Electronics Co. Ltd Shenzhen China Third Surveying and Mapping Institute of Hunan Province Changsha China

ISBN: (纸本)9798350319934

Robustness is consistently the most crucial point for High Performance Computing(HPC), machine learning, or large-scale machine learning facilitated by HPC. It is important to ensure a neural network is robust before exploring it in a safety-critical domain. Throughout the literature, we find that for non-differentiable activation functions such as ReLU, the correctness of some robust analysis approaches is still not guaranteed. To address this issue, our method relies on Clark's generalized gradients to derive local Lipschitz constants and interval arithmetic to effectively numerically validate existing methods for which the correctness of the results cannot be guaranteed. On this basis, extensive experiments are conducted to compare the Lipschitz constant results produced by our interval algorithm with previous methods for different parameter settings. We also perform convergence and error analysis on the experimental findings. The work done in this paper provides a reasonable basis for subsequent research on optimization algorithms. © 2022 IEEE.

关键词： Machine learning

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Enhanced knowledge Extraction using Cutting-Edge Rough and Soft Graph Techniques

Enhanced Knowledge Extraction using Cutting-Edge Rough and S...

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Emerging systems and Intelligent Computing (ESIC), international conference on

作者： Surendra Nath Bhagat Premansu Sekhar Rath Anirban Mitra Department of Computer Science and Engineering GIET University Gunupur India Department of Computer Science and Engineering Amity University Kolkatta India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Addressing uncertainties in data is crucial for real-world applications, including social networks, where relationships often lack clear definition. Soft and rough graph theories allow for nuanced modeling of such data by extending traditional graph structures with parameters that account for uncertainty. Social networks exhibit complex, often ambiguous relationships that traditional graphs cannot fully represent, especially in dynamic, imprecise settings. This study aims to explore soft and rough graphs as effective tools for extracting knowledge from social networks by modeling vague or partially known relationships. Our rough graph analysis identified strong, definite communication channel between employees sharing dual expertise and different weaker channels based on single expertise, suggesting potential collaboration points. In soft graph analysis, two subgroups were detected around shared interests in Sports and Music, identifying central and bridging users within the network. Soft and rough graphs enhance knowledge extraction from social networks, enabling insights into communities, influencers, and communication channels that reflect the uncertain and dynamic nature of social interactions, which can also be used in data science to evaluate ambiguous or incomplete datasets, find hidden patterns, and enhance decision-making in fields like network optimization, fraud detection, and recommendation systems. This approach is shown to be beneficial in structuring and understanding intricate social data patterns.

关键词： knowledge engineering Uncertainty Social networking (online) Computational modeling Decision making Rough sets Communication channels Graph theory data mining Sports

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Intelligent Approach Integrating Multiagent systems and Case-Based Reasoning in Brain-Computer Interface

Intelligent Approach Integrating Multiagent Systems and Case...

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2023 international conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Malik, Kiran Dhatterwal, Jagjit Singh Kaswan, Kuldeep Singh Gupta, Manish Thakur, Jyoti Mriem Department of Computer Science & Engineering Haryana Rohtak India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation AP Guntur India School of Computer Science & Engineering Galgotias University Greater UP Noida India Lovely Professional University Iqac Punjab India Gl Bajaj Institute of Technology and Management Department of Information Technology UP Greater Noida India

ISBN: (纸本)9798350357769

In this research paper investigates the integration of Multiagent systems (MAS) and Case-Based Reasoning (CBR) within Brain-Computer Interfaces (BCls). The aim is to enhance decision-making and adaptation in BCls, addressing limitations of standalone approaches. The problem statement revolves around the need for improved BCI performance and adaptability. Methodology involves designing a hybrid MAS-CBR framework and evaluating its performance against traditional BCI systems. MAS enables collaborative decision-making among agents, while CBR facilitates learning from past cases. Integration is achieved by using MAS for agent coordination and CBR for case adaptation. Key findings indicate that the hybrid approach yields improved decision accuracy and adaptation compared to individual systems. The collaborative nature of MAS and the knowledge reuse of CBR synergize effectively, resulting in enhanced BCI performance. This integration showcases the potential to advance BCls beyond their current limitations. Implications of this research are significant, as it offers a novel approach to overcome BCI challenges. The hybrid MAS-CBR framework can be further refined to optimize real-time decision-making and adaptability in various application domains. Ultimately, this research contributes to the evolution of BCls by leveraging the strengths of both MAS and CBR, leading to more efficient and robust brain-computer interaction paradigms. © 2023 IEEE.

关键词： Brain Computer Interface Case Base Reasoning Coordination Multi-agent System Simulations

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Mitigate the Negative TL using Adaptive Thresholding for Fault Diagnosis

Mitigate the Negative TL using Adaptive Thresholding for Fau...

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2023 IEEE international conference on Omni-Layer Intelligent systems, COINS 2023

作者： Mp, Pavan Kumar Chen, Kun-Chih Jimmy National Sun-Yat Sen University Department of Computer Science and Engineering Taiwan Institute of Electronics National Yang Ming Chiao Tung University Taiwan

ISBN: (纸本)9798350346473

The fourth industrial revolution has created a data-centric ecosystem where the implementation of Prognostics and Health Management (PHM) technology is crucial to support contemporary industrial systems. To enhance performance in fault diagnosis and health assessment of mechanical equipment, Deep Learning (DL) has been integrated into PHM. However, DL models encounter several challenges in PHM, such as the requirement for large amounts of labeled data and a lack of generalizability. TL (TL) has emerged as a promising technique to overcome these limitations. Fine-Tuning, a commonly used approach to the inductive transfer of deep models, assumes that the source and target tasks are related and that pre-Trained parameters from the source task are likely to be close to the optimal parameters for the target task. Nevertheless, when the amount of training data on the target domain is limited, fine-Tuning can lead to negative transfer and catastrophic forgetting. To overcome these issues, we propose a novel regularization approach to selectively modulate the features of normalized inputs based on their distance from the mini-batch mean during fine-Tuning. Our approach aims to prevent the negative transfer of pre-Trained knowledge that is irrelevant to the target task and mitigate catastrophic forgetting. Furthermore, our approach yields a 0.9-5% increase in accuracy under the same environment and 2.8-6.2% under different environmental conditions, compared to other state-of-The-Art regularization-based methods. © 2023 IEEE.

关键词： Failure analysis

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A GAN Based data Augmentation Method for Road Pothole Detection 2

A GAN Based Data Augmentation Method for Road Pothole Detect...

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2nd international conference on Networking systems of AI, INSAI 2022

作者： Wang, Lu School of Information and Communication Engineering Liaoning Equipment Manufacturing Vocational and Technical College Shenyang110161 China School of Information and Communication Engineering Liaoning Open University Shenyang110034 China

ISBN: (纸本)9781665491785

The detection accuracy of road potholes is not high due to the small number of positive samples. In order to expand the training dataset of the detection network and improve its detection accuracy, this paper proposes a road pothole data augmentation method combining generative adversarial network and image fusion technology. In this method, the clear forged pothole images with different morphometry are generated separately through SinGAN network, and the pothole image and road image are synthesized by Poisson image fusion. A mask image generation method for Poisson image fusion is also presented to further improve the edge smoothness of the fused part. The results of experiments have shown that the image samples generated by this method can significantly improve the accuracy of the road pothole detection network, and verified the effectiveness of this method. © 2022 IEEE.

关键词： Image fusion

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Personalized Navigation Recommendation for E-book Page Jump

Personalized Navigation Recommendation for E-book Page Jump

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2024 Joint of international conference on Learning Analytics and knowledge Workshops, LAK-WS 2024

作者： Ma, Boxuan Chen, Li Lu, Min Kyushu University Faculty of Art and Science Fukuoka Japan Kyushu University Faculty of Information Science and Electrical Engineering Fukuoka Japan Akita University Faculty of Engineering Science Akita Japan

As the utilization of digital learning materials continues to rise in higher education, the accumulated operational log data provide a unique opportunity to analyze student reading behaviors. Previous works on reading behaviors for e-books have identified jump-back as frequent student behavior, which refers to students returning to previous pages to reflect on them during the reading. However, the lack of navigation in e-book systems makes finding the right page at once challenging. Students usually need to try several times to find the correct page, which indicates the strong demand for personalized navigation recommendations. This work aims to help the student alleviate this problem by recommending the right page for a jump-back. Specifically, we propose a model for personalized navigation recommendations based on neural networks. A two-phase experiment is conducted to evaluate the proposed model, and the experimental result on real-world datasets validates the feasibility and effectiveness of the proposed method. © 2024 CEUR-WS. All rights reserved.

关键词： Navigation

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