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Advanced Deep learning Methods for Comprehensive Quality Inspection and Fault Detection in Casting Processes 2

Advanced Deep Learning Methods for Comprehensive Quality Ins...

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2nd international conference on Advancement in Computation and Computer Technologies, InCACCT 2024

作者： Sharma, Gunjan Anand, Vatsala Chauhan, Rahul Pokhariya, Hemant Singh Gupta, Sheifali Sunil, G. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to be University Computer Science and Engineering Uttarakhand Dehradun India SR University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350371314

Encouraging the production of casting products while cutting down on inspection expenses and time commitments is made possible through automated surface fault inspection. As a key element of contemporary modern manufacturing, intelligent systems with image classification capabilities are frequently used in visual inspection. In comparison to traditional machine learning methods, Convolutional Neural Networks have demonstrated notable improvements in image categorization tasks. The purpose of this research is to investigate the use of transfer learning techniques for the recognition and classification of casting defects on the surface using the Resnet152V2-based Convolutional Neural Network architecture. To demonstrate the model's adaptability in heterogeneous casting, a range of image-processing techniques were first used to casting datasets for data enhancement. The lightweight ResNet152V2 model that has already been trained is then modified, and its hyperparameters are adjusted to maximize its performance. The effort produces a scalable, lightweight, and adaptive model that is perfect for edge devices with limited resources. The model has been assessed using different performance matrix. The accuracy of the model is 99.65%, precision achieved is 99.95%, recall is 99%, based on statistical parameters. These findings demonstrate that a CNN framework based on the ResNet150V2 architecture can be effectively used for the identification of casting surface defects of impellers used in pumps in manufacturing sectors, despite its uncomplicated architecture. © 2024 IEEE.

关键词： Inspection

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Self-adaptive reconstruction of spacecraft irregular sampling telemetry data based on neural ordinary differential equation 22

Self-adaptive reconstruction of spacecraft irregular samplin...

引用

22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Wang, Yingqi Song, Yuchen Meng, Shengwei Liu, Datong The School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China Zhengzhou Research Institute Harbin Institute of Technology Zhengzhou450046 China

ISBN: (纸本)9798331527471

Spacecraft telemetry data is crucial for assessing the health of a spacecraft, but it often contains missing or redundant information that affects data quality. To ensure accurate status assessments, it is necessary to research methods for reconstructing telemetry data. Since spacecraft are subject to many short-term disturbances, the number of missing points or redundancy in the data on a short time scale is highly random, so it is difficult to reconstruct the data using traditional deep learning models (such as recurrent neural networks). Therefore, this paper proposes a self-adaptive reconstruction method of spacecraft telemetry data based on a neural ordinary differential equation (NODE). First, this paper uses a time-continuous model based on NODE to better learn the long-term continuity of data. On this basis, self-adaptive reconstruction windows are constructed on the time scale for irregular sampling telemetry data (with only one missing condition in each window). Afterward, the NODE model is used to adaptively reconstruct the data. Finally, a real spacecraft telemetry dataset is used to verify the method proposed in this paper. © 2024 IEEE.

关键词： Telemetering equipment

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Multi-Spectral Entropy Constrained Neural Compression of Solar Imagery 22

Multi-Spectral Entropy Constrained Neural Compression of Sol...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Zafari, Ali Khoshkhahtinat, Atefeh Mehta, Piyush M. Nasrabadi, Nasser M. Thompson, Barbara J. Kirk, Michael S. F. Da Silva, Daniel West Virginia University Department of Computer Science & Electrical Engineering WV United States West Virginia University Department of Mechanical & Aerospace Engineering WV United States Nasa Goddard Space Flight Center MD United States

ISBN: (纸本)9798350345346

Missions studying the dynamic behaviour of the Sun are defined to capture multi-spectral images of the sun and transmit them to the ground station in a daily basis. To make transmission efficient and feasible, image compression systems need to be exploited. Recently successful end-to-end optimized neural network-based image compression systems have shown great potential to be used in an ad-hoc manner. In this work we have proposed a transformer-based multi-spectral neural image compressor to efficiently capture redundancies both intra/inter-wavelength. To unleash the locality of window-based self attention mechanism, we propose an inter-window aggregated token multi head self attention. Additionally to make the neural compressor autoencoder shift invariant, a randomly shifted window attention mechanism is used which makes the transformer blocks insensitive to translations in their input domain. We demonstrate that the proposed approach not only outperforms the conventional compression algorithms but also it is able to better decorrelates images along the multiple wavelengths compared to single spectral compression. © 2023 IEEE.

关键词： inter-window token aggregation multi-spectral neural image compression shift invariant self-attention

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Estimating Link Confidence for Human-in-the-loop Table Annotation 22

Estimating Link Confidence for Human-in-the-loop Table Annot...

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22nd IEEE/WIC international conference on Web Intelligence and intelligent Agent Technology (WI-IAT)

作者： Avogadro, Roberto Ciavotta, Michele De Paoli, Flavio Palmonari, Matteo Roman, Dumitru Univ Milano Bicocca DISCo Milan Italy SINTEF Oslo Norway

ISBN: (纸本)9798350309188

Annotating tables with high-quality semantic labels and linking cells in these tables to entities in reference knowledge graphs (KGs) serve several downstream applications such as knowledge graph construction, data enrichment, data analytics, and so on. There is an increasing interest in combining algorithms for the automatic annotation of tables with interactive applications that let users control and improve the annotations and use them for data enrichment. In this paper, we propose a supervised entity linking network that behaves as a re-ranker and score normalizer of scores returned by off-the-shelves entity retrieval systems. By combining these refined scores with additional signals of reliability of the ranking obtained thereof, we can estimate a confidence score, which tells how reliable is the decision to link a cell to the top-ranked candidate entity. The confidence score is used to decide whether to establish a link or not and to prioritize the cells to be revised by the users to improve the annotation quality. Experiments suggest that the proposed approach provides an effective approach to identifying most critical decisions and supporting link revision within a human-in-the-loop data enrichment paradigm.

关键词： Semantic Table Interpretation Entity Linking Entity Reconciliation Human in the Loop Machine learning

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Enhancing V2V Communication through Adaptive Clustering and intelligent Routing based on Vehicle Attributes and Behavior 22

Enhancing V2V Communication through Adaptive Clustering and ...

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22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Feng, Keyi Zheng, Tao Thar, Kyi Gidlund, Mikael Guizani, Mohsen School of Electronic and Information Engineering Beijng Jiaotong University China Department of Computer and Electrical Engineering Mid Sweden University Sweden Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331527471

With the rapid development of Internet of Vehicles (IoV) technology, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication have become an important part of intelligent transportation systems (ITS). In V2V communication, clustering is essential to optimize network efficiency and reliability. Conventional V2V clustering prioritizes the physical distance between vehicles, neglecting key attributes information of the vehicles (i.e., computational capacity, energy consumption) and behavior like relative motion states between vehicles. Thus, this paper proposes a clustering algorithm based on vehicle attributes and behavioral characteristics (C-VABC) that utilizes the Fuzzy C-Means (FCM) algorithm. Once clusters are established, intelligent data routing decisions between clusters is facilitated by a Deep Q-Network (DQN) based algorithm. This algorithm selects neighboring vehicular Cluster Heads with similar mobility states and higher computational power as the next hop vehicles for packet forwarding. The experimental results demonstrate that the proposed clustering algorithm possesses strong adaptability and reliability, while enhancing the communication quality and efficiency in vehicle networking situations. This provides invaluable support for intelligent transportation systems, as well as intelligent and autonomous driving systems. © 2024 IEEE.

关键词： DQN FCM Software-defined Connected Vehicle V2V

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Aspect-aware Multi-criteria Recommendation Model with Aspect Representation learning 22

Aspect-aware Multi-criteria Recommendation Model with Aspect...

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22nd IEEE/WIC international conference on Web Intelligence and intelligent Agent Technology (WI-IAT)

作者： Hasan, Emrul Ding, Chen Toronto Metropolitan Univ Dept Comp Sci Toronto ON Canada

ISBN: (纸本)9798350309188

Multi-criteria recommendation system refers to a recommendation system that takes multi-criteria or multiaspect ratings into consideration when learning user preferences. The core of the multi-criteria recommendation system is predicting the criteria ratings leading to overall rating estimation. The existing approaches for multi-criteria recommendation system rely on either 1) using collaborative filtering to predict the criteria ratings based on historical criteria ratings, or 2) extracting latent aspects and predicting ratings from the review. The latter approach ignores the explicit ratings and the inferred ratings from the review may not be accurate. In the former approach, historical criteria ratings can be scarce (users may only provide ratings to a few criteria), which could affect the prediction accuracy. In this work, we introduce a novel multi-criteria recommendation model that predicts the criteria ratings from the review and then uses an aggregation function to estimate the overall rating. Multi-criteria ratings are predicted by fine-tuning BERT (Bidirectional Encoder Representations from Transformer) with an added aspect representation layer. Finally, the overall ratings are computed using a deep neural network-based aggregation function. The performance of our proposed model is evaluated with three different datasets including Tripadvisor, RateBeer, and BeerAdvocate. Our method outperforms several competitive baselines.

关键词： Aspects Criteria Aspect-Representation learning Multi-criteria Recommendation System Aggregation function

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Support learning Vovinam Exercises Based on Computer Vision 22nd

Support Learning Vovinam Exercises Based on Computer Vision

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22nd international conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Pham Son Tung Thai Thanh Do Pham Hong Giang Phan Duy Hung FPT Univ Hanoi Vietnam

ISBN: (纸本)9783031425073;9783031425080

Computer vision has many applications which has attracted many researchers, especially with the problems of recognizing actions, postures, movements. This study offers a method to support students to perform correct postures duringmartial arts practice. We have collected and labeled data themovements of a traditional Vietnamese martial art called Vovinam. The deep learning architecture is based on the ST-GCN model with an additional processing stage for the input. On our dataset, adding the input processing stage to the recognition model has yielded much better results than applying the original model. The final accuracy is 99.23%, showing that the model has the potential to be applied in practice.

关键词： Computer Vision ST-GCN Martial Art Vovinam

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Personalized Adaptive Cruise Control Based on Passenger’s Subjective Risk Evaluation and Model Predictive Control 22

Personalized Adaptive Cruise Control Based on Passenger’s S...

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22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Zhang, Chenshuo Yan, Yongjun Wu, Jinxiang Zhang, Tingyu Wang, Hongliang Pi, Dawei School of Mechanical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331527471

In this study, a personalized adaptive cruise control system founded on driving risk field (DRF) is proposed, which aims to make the control system more consistent with the driver’s driving habits and reduce discomfort. Firstly, founded on the collected driver’s operation data, the common characteristics of different drivers’ operation behavior under different conditions are analyzed, and the spatial-temporal coupling DRF model under the event of car-following is constructed. Secondly, the data processing and clustering analysis of the High D natural driving data set are carried out to obtain the individual driving preferences of aggressive, normal and conservative drivers, and the DRF is calibrated based on the clustering results. Finally, based on the calibrated DRF, an intelligent vehicle adaptive cruise controller with minimum driving risk is designed, and compared with the traditional optimal velocity model (OVM), the advantages of the constructed DRF in personalized cruise following are verified. © 2024 IEEE.

关键词： Model predictive control

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HOPE: Human-Centric Off-Policy Evaluation for E-learning and Healthcare 22

HOPE: Human-Centric Off-Policy Evaluation for E-Learning and...

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22nd international conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Gao, Ge Ju, Song Ausin, Markel Sanz Chi, Min Department of Computer Science North Carolina State University RaleighNC United States

Reinforcement learning (RL) has been extensively researched for enhancing human-environment interactions in various human-centric tasks, including e-learning and healthcare. Since deploying and evaluating policies online are high-stakes in such tasks, off-policy evaluation (OPE) is crucial for inducing effective policies. In human-centric environments, however, OPE is challenging because the underlying state is often unobservable, while only aggregate rewards can be observed (students' test scores or whether a patient is released from the hospital eventually). In this work, we propose a human-centric OPE (HOPE) to handle partial observability and aggregated rewards in such environments. Specifically, we reconstruct immediate rewards from the aggregated rewards considering partial observability to estimate expected total returns. We provide a theoretical bound for the proposed method, and we have conducted extensive experiments in real-world human-centric tasks, including sepsis treatments and an intelligent tutoring system. Our approach reliably predicts the returns of different policies and outperforms state-of-the-art benchmarks using both standard validation methods and human-centric significance tests. © 2023 international Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Reinforcement learning

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Reinforcement learning and Mechanism Design for Routing of Connected and Autonomous Vehicles 22

Reinforcement Learning and Mechanism Design for Routing of C...

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22nd international conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Koohy, Behrad Citizen-Centric Artificial Intelligence Systems University of Southampton Southampton United Kingdom

The data provided by Connected and Autonomous Vehicles (CAVs) is a powerful tool, providing insight into user incentives and preferences, and combined with existing road data sources, provides a number of new research avenues for intelligent traffic systems. In this paper, we propose the use of Reinforcement learning (RL) for adaptive pricing of travel systems such as trains, buses and toll-road, in simulations which consider multiple transport providers and traffic management systems, known as the multi-market pricing problem. We also propose two research directions for this problem, the use of incentives when user preferences are included and development of detection and prevention of unintentional collusion between RL pricing agents. © 2023 international Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Vehicle routing

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