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检索条件"任意字段=23rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2022"

共 102 条记录，以下是51-60 订阅

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Design and Implementation of Monitoring System for New Energy data Call Based on Blockchain 23

Design and Implementation of Monitoring System for New Energ...

引用

23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liu, Ziang Huang, Yuanyuan Zhou, Qiang Zhang, Yingli Zhou, Xiaokang Zhou, Rui School of Information Science and Engineering Lanzhou University Lanzhou China State Grid Gansu Electric Power Research Institute Lanzhou China Shiga University Faculty of Data Science Hikone Japan Riken Center for Advanced Intelligence Project Tokyo Japan

ISBN: (纸本)9781665494571

With the energy revolution starting the third transformation to new energy, new energy has become essential for human energy. In order to better organize and utilize new energy, more and more research is committed to predicting the power generation capacity of new energy through the power generation data collected by intelligent systems to make better use of new energy. However, with the increase of new energy digital platforms and supporting infrastructure improvement, the distribution and management of relevant data also begin to face significant challenges. New energy data faces a substantial potential threat in distributing and using data to various digital platforms. Based on this, this paper designs and implements a new energy data call monitoring system based on blockchain to ensure the security of new energy data by tracking data operation information. © 2021 IEEE.

关键词： Smart cities Consensus algorithm Machine learning Blockchains Safety Recording intelligent systems

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Steel Rebar Yield-Point Prediction Model for automated Quality Inspection in Tempcore Process 23

Steel Rebar Yield-Point Prediction Model for Automated Quali...

引用

23rd international conference on Control, Automation and Systems, ICCAS 2023

作者： Kim, Yejin Kim, Young-Keun Handong Global University Department of Mechanical and Control Engineering Pohang37554 Korea Republic of

ISBN: (纸本)9788993215274

This paper conducts fundamental research to apply several widely used data-driven models to automate the quality inspection of Tempcore-processed steel rebar and design a prediction model for rebar mechanical properties by utilizing an optimal model. The steel rebar mechanical property prediction model has the potential to replace the conventional destructive test, thereby resulting in a reduction in test time and human resources and mitigating the risk of defective products. Subsequently conducting a comparative analysis of various machine learning-based regression models, the random forest (RF) model was chosen since it best predicted the mechanical properties of steel rebar. The performance of the RF model in predicting the yield point of rebar was determined to be 0.98 coefficient of determination, with a root mean square error (RMSE) of 12.59 [MPa]. Furthermore, the inside of the model was analyzed using the Local interpretable model-agnostic explanations method, with the aim of evaluating the rationality of the decision-making process of the model and assessing its reliability. © 2023 ICROS.

关键词： Forecasting

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Federated learning With Dynamic Staleness Correction for Privacy Protection in Vehicular Networks 23

Federated Learning With Dynamic Staleness Correction for Pri...

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作者： Liu, Jiajia Wu, Hao Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing China School of Electronics and Information Engineering Beijing Jiaotong University Beijing China Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing China

ISBN: (纸本)9781665494571

Edge intelligence combines mobile edge computing(MEC) with artificial intelligence(AI), which has great potential for improving the security and efficiency of data-driven intelligent transportation systems (ITS) and emerging Internet Of Vehicles (IOV) services. However, when big data and AI empower ITS, the information security issue in vehicular networks needs to be paid more attention. In order to protect the privacy and security of vehicular training data, we propose a privacy protection federated learning scheme with staleness asynchronous update to overcome the differences caused by vehicle heterogeneity. Moreover, unlike the traditional weighted average on the server side only by the number of samples, our solution introduces dynamic time weights according to the calculation and communication capabilities of different vehicles, to make full use of the previously trained local model. The proposed scheme is able to reduce traffic load of the network and improve learning performance while enhancing security and privacy. Experiment results demonstrate that in terms of communication cost and model accuracy, the performance of the proposed asynchronous federated learning is better than the benchmark algorithm, and it also achieves good performance under NIID data. © 2021 IEEE.

关键词： Privacy data privacy Costs Smart cities Training data Telecommunication traffic Collaborative work

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Experts' View on Challenges and Needs for Fairness in Artificial Intelligence for Education 23rd

Experts' View on Challenges and Needs for Fairness in Artifi...

引用

23rd international conference on Artificial Intelligence in Education (AIED)

作者： Fenu, Gianni Galici, Roberta Marras, Mirko Univ Cagliari Cagliari Italy

ISBN: (纸本)9783031116445;9783031116438

In recent years, there has been a stimulating discussion on how artificial intelligence (AI) can support the science and engineering of intelligent educational applications. Many studies in the field are proposing actionable data mining pipelines and machine-learning models driven by learning-related data. The potential of these pipelines and models to amplify unfairness for certain categories of students is however receiving increasing attention. If AI applications are to have a positive impact on education, it is crucial that their design considers fairness at every step. Through anonymous surveys and interviews with experts (researchers and practitioners) who have published their research at top-tier educational conferences in the last year, we conducted the first expert-driven systematic investigation on the challenges and needs for addressing fairness throughout the development of educational systems based on AI. We identified common and diverging views about the challenges and the needs faced by educational technologies experts in practice, that lead the community to have a clear understanding on the main questions raising doubts in this topic. Based on these findings, we highlighted directions that will facilitate the ongoing research towards fairer AI for education.

关键词： Education Fairness data mining Machine learning

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Cherry detection algorithm based on improved YOLOv5s network 23

Cherry detection algorithm based on improved YOLOv5s network

引用

作者： Gai, Rongli Li, Mengke Chen, Na School of Information Engineering Dalian University Dalian China

ISBN: (纸本)9781665494571

In order to realize the automatic fruit picking by robot in orchard environment, cherry target detection is carried out by studying the algorithm based on deep learning, so as to identify cherry and carry out picking operation. However, most of the existing methods are aimed at relatively sparse fruits and cannot solve the detection problem of small and dense fruits. In this paper, a YOLOv5s-cherry model based on YOLOv5s was proposed for cherry detection. By adjusting the CSP structure of BackBone and Neck, deepening the depth and width of the network, improving the ability of network feature extraction, feature fusion and learning ability of network feature extraction, so as to improve the detection ability of small and dense cherry fruit. The model is trained on the self-made cherry maturity classification data set. The trained YOLOv5s cherry network is used to extract the feature and location information of cherry images with different maturity, so as to realize the classification and detection of cherry. We tested the performance of the model, compared YOLOv5s-cherry with YOLOv5s and YOLOv4, and used F1 value as the evaluation value. The test results show that the F1 of YOLOv5s cherry is 0.08 higher than that of YOLOv4 algorithm and 0.03 higher than that of YOLOv5s algorithm. The improved YOLOv5s cherry model provides better detection performance for small and dense cherry detection, which is conducive to intelligent picking. © 2021 IEEE.

关键词： Training Solid modeling Smart cities Object detection Feature extraction data models Robustness

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Digital Twin Mobility Profiling: A Spatio-Temporal Graph learning Approach 23

Digital Twin Mobility Profiling: A Spatio-Temporal Graph Lea...

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作者： Chen, Xin Hou, Mingliang Tang, Tao Kaur, Achhardeep Xia, Feng School of Software Dalian University of Technology Dalian116620 China School of Engineering It and Physical Sciences Federation University Australia BallaratVIC3353 Australia

ISBN: (纸本)9781665494571

With the arrival of the big data era, mobility profiling has become a viable method of utilizing enormous amounts of mobility data to create an intelligent transportation system. Mobility profiling can extract potential patterns in urban traffic from mobility data and is critical for a variety of traffic-related applications. However, due to the high level of complexity and the huge amount of data, mobility profiling faces huge challenges. Digital Twin (DT) technology paves the way for cost-effective and performance-optimised management by digitally creating a virtual representation of the network to simulate its behaviour. In order to capture the complex spatio-temporal features in traffic scenario, we construct alignment diagrams to assist in completing the spatio-temporal correlation representation and design dilated alignment convolution network (DACN) to learn the fine-grained correlations, i.e., spatio-temporal interactions. We propose a digital twin mobility profiling (DTMP) framework to learn node profiles on a mobility network DT model. Extensive experiments have been conducted upon three real-world datasets. Experimental results demonstrate the effectiveness of DTMP. © 2021 IEEE.

关键词： Correlation Convolution Smart cities Predictive models Logic gates Digital twins data mining

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Semantic Segmentation of Remote Sensing Image Based on Two-time Augmentation and Atrous Convolution 23

Semantic Segmentation of Remote Sensing Image Based on Two-t...

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作者： Yang, Ruiqi Zhang, Yue Cheng, Haiyan Zhao, Yili Dai, Qinling Chen, Nan College of Big Data and Intelligent Engineering South Forestry University Yunnan Kunming China South Forestry University Faculty of Forestry Yunnan Kunming China Art and Design College South Forestry University Yunnan Kunming China

ISBN: (纸本)9781665494571

Semantic segmentation is a hot topic in remote sensing image research. With the development of deep learning, semantic segmentation based on full convolutional neural network has greatly improved the segmentation accuracy. In order to solve the problem of large information content and uneven samples of RS images, there is still a lot of room for improvement in classification accuracy. In this paper, the main model used is renet-101 network as the main framework, adding atrous convolution to the network to obtain more semantic information. The model is evaluated on the Postdam dataset using a combination of secondary augmented image and ResNET-101 with Atrous Convolution. The results show that: The proposed method achieved 76.43% MIOU on the Postdam dataset, which is 14.82% higher than that of the method without secondary preprocessing and atrous convolution. © 2021 IEEE.

关键词： Deep learning Image segmentation Convolution Semantics Feature extraction Indexes Convolutional neural networks

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Contract Theory Based Incentive Mechanism for Clustered Federated learning 23

Contract Theory Based Incentive Mechanism for Clustered Fede...

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23rd IEEE international conference on Communication Technology, ICCT 2023

作者： Wang, Siyang Zhao, Haitao Xia, Wenchao Wen, Wanli Wang, Bin Zhu, Hongbo The Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China The Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China The School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China The College of Electrical Engineering Zhejiang University Hangzhou310058 China

ISBN: (纸本)9798350325959

Clustered Federated learning (CFL) can be applied to Internet of Things (IoT) scenarios such as intelligent transportation and healthcare, which can effectively solve the problem of heterogeneous data distribution. However, users may be reluctant to contribute their computation and communication resources to perform learning tasks if the CFL server does not give them proper incentives. In this paper, we would like to address the above issue. Specifically, we design a set of optimal contracts for clusters which satisfy the constraints of individual rationality and incentive compatibility. The proposed contract theory based incentive mechanism not only effectively motivates every cluster, but also overcome the information asymmetry problem to maximize the utility of the CFL server. Finally, simulation results validate the effectiveness of the designed contracts. © 2023 IEEE.

关键词： Clustered federated learning contract theory incentive mechanism

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Domain Adversarial Training for Aspect-Based Sentiment Analysis 23rd

Domain Adversarial Training for Aspect-Based Sentiment Analy...

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23rd international conference on Web Information Systems engineering (WISE)

作者： Knoester, Joris Frasincar, Flavius Trusca, Maria Mihaela Erasmus Univ Burgemeester Oudlaan 50 NL-3062 PA Rotterdam Netherlands Bucharest Univ Econ Studies Bucharest 010374 Romania

ISBN: (纸本)9783031208904;9783031208911

The continuously expanding digital possibilities, increasing number of social media platforms, and growing interest of companies in online marketing increase the importance of Aspect-Based Sentiment Analysis (ABSA). ABSA focuses on predicting the sentiment of an aspect in a text. In an ideal scenario, we would have labeled data for every existing domain, but acquiring annotated training data is costly. Transfer learning resolves this issue by building models that can be employed in different domains. The proposed work extends the state-of-the-art LCR-Rot-hop++ model for ABSA with the methodology of Domain Adversarial Training (DAT) in order to create a deep learning adaptable cross-domain structure, called the DAT-LCR-Rot-hop++. The major advantage of the DAT-LCR-Rot-hop++ is the fact that it does not require any labeled target domain data. The results are obtained for six different domain combinations with testing accuracies ranging from 37% up until 77%, showing both the limitations and benefits of this approach. Once DAT is able to find the similarities between domains, it produces good results, but if the domains are too distant, it is not capable of generating domain-invariant features.

关键词： Aspect-Based Sentiment Classification Transfer learning Adversarial Training

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Adaptive Network Formation and Trajectory Optimization for Multi-UAV-Assisted Wireless data Offloading 23

Adaptive Network Formation and Trajectory Optimization for M...

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作者： Wang, Meng Long, Yusi Gong, Shimin Xu, Jing School of Intelligent Systems Engineering Sun Yat-sen University China School of Electronic Information and Communications Huazhong University of Science and Technology China

ISBN: (纸本)9781665494571

In this paper, we employ multiple UAVs coordinated by a cellular base station (BS) to help the IoT users to offload their sensing data to the BS. To minimize the overall energy consumption and transmission delay, we allow different UAVs to optimize their trajectories and cooperate in data transmission via multi-hop relaying. This implies complicated spatial and temporal coupling between the UAVs' trajectories optimization and network formation. To solve this problem, we propose a two-step algorithm to iterate between adaptive network formation and trajectory optimization. Firstly, in order to balance the UAVs' energy consumption and the queue size, we adopt a heuristic algorithm to update adaptive network formation. Then, once we update the network formation, the UAVs' trajectories are further optimized by using the multi-agent deep reinforcement learning algorithm. Our simulation results show that the joint trajectory optimization and network formation can better exploit the cooperation among multiple UAVs in data offloading, compared with the conventional non-cooperative cases. © 2021 IEEE.

关键词： Wireless communication Wireless sensor networks Energy consumption Adaptive systems Smart cities Heuristic algorithms Simulation

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