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An Ensemble Learning Approach for Wind Power Forecasting 3

An Ensemble Learning Approach for Wind Power Forecasting

引用

3rd International Conference on Electronic Information Engineering and Computer, EIECT 2023

作者： Huang, Yuesheng Chen, Sida Wu, Qiling Xiao, Jiapeng Ren, Yongqi Wang, Leijun Chen, Rongjun Li, Jiawen Guangdong Polytechnic Normal University School of Computer Science Guangzhou China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350357707

With the growing global energy demand and requirement for environmental protection, renewable energy is attracting attention as a vital development direction. Particularly, wind power is rapidly developing as a clean and sustainable form of energy. Nonetheless, wind speed uncertainty and system dynamics make wind power prediction and optimization complex and challenging. Accurate wind power prediction is essential to improve system reliability and performance. In this regard, traditional forecasting methods have limitations in some cases. In this study, based on XGBoost and decision tree with Bagging methods in ensemble learning, wind power data is predicted. The results indicated that better performances than the traditional linear regression and ordinary decision tree models are achieved, revealing the efficiency of the proposed approach. © 2023 IEEE.

关键词： Decision trees

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Two-sided Online Stable Task Assignment with Incomplete Lists and Ties in Spatial Crowdsourcing 19

Two-sided Online Stable Task Assignment with Incomplete List...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Huang, Weiyi Li, Peng Li, Bo Liu, Jing Nie, Lei Bao, Haizhou Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350346558

For the application of task assignment in crowdsourcing, previous research has focused on offline version or one-sided online version. Some of them draw extra attention to stability in task assignment. Our work extends a one-sided task assignment and its stable generalization. In particular, we present a two-sided online stable task assignment (TS-OSTA) problem in which any vertex from two sides can maintain an incomplete neighbor list and ties. We first give the fundamental definitions of task assignment in spatial crowdsourcing and stable matching. Then, we analyze and present the matching condition and formulate the TS-OSTA problem. As a warm-up, we investigate and analyze two classical baseline algorithms Greedy and Random Threshold. Then we present a bipartite ranking algorithm that maintains the efficiency of Greedy and effectiveness of Random Threshold. Through extensive experiments, we examine the performance of our algorithm in size, memory, and time. The results show that our algorithm performs better than the baselines. © 2022 IEEE.

关键词： Crowdsourcing

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Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling 17th

Learning from Noisy Labels with Coarse-to-Fine Sample Cred...

引用

17th European Conference on Computer Vision, ECCV 2022

作者： Zhang, Boshen Li, Yuxi Tu, Yuanpeng Peng, Jinlong Wang, Yabiao Wu, Cunlin Xiao, Yang Zhao, Cairong YouTu Lab Shenzhen China Tongji University Shanghai China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031250620

Training deep neural network (DNN) with noisy labels is practically challenging since inaccurate labels severely degrade the generalization ability of DNN. Previous efforts tend to handle part or full data in a unified denoising flow via identifying noisy data with a coarse small-loss criterion to mitigate the interference from noisy labels, ignoring the fact that the difficulties of noisy samples are different, thus a rigid and unified data selection pipeline cannot tackle this problem well. In this paper, we first propose a coarse-to-fine robust learning method called CREMA, to handle noisy data in a divide-and-conquer manner. In coarse-level, clean and noisy sets are firstly separated in terms of credibility in a statistical sense. Since it is practically impossible to categorize all noisy samples correctly, we further process them in a fine-grained manner via modeling the credibility of each sample. Specifically, for the clean set, we deliberately design a memory-based modulation scheme to dynamically adjust the contribution of each sample in terms of its historical credibility sequence during training, thus alleviating the effect from noisy samples incorrectly grouped into the clean set. Meanwhile, for samples categorized into the noisy set, a selective label update strategy is proposed to correct noisy labels while mitigating the problem of correction error. Extensive experiments are conducted on benchmarks of different modality, including image classification (CIFAR, Clothing1M etc.) and text recognition (IMDB), with either synthetic or natural semantic noises, demonstrating the superiority and generality of CREMA. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Research on low energy task allocation and scheduling algorithm based on imprecise heterogeneous multi-core technology

Research on low energy task allocation and scheduling algori...

引用

2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Gu, Haonan Wu, Jing Hu, Wei Ma, Tianao Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science and Technology Wuhan China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

In heterogeneous embedded systems, we try to find a new non real-time scheduling algorithm, which can not only meet the reliability requirements, but also achieve low energy consumption. 'Resource reservation time deterministic cyclic scheduling (RRTDCS)' algorithm is a kind of scheduling for offline cache pre-allocation. This scheduling algorithm combines time reservation and priority strategy. The algorithm can dynamically adjust according to different task sets. It can also adjust its own scheduling process to adapt to different scheduling environments. The periodic cyclic strategy enables low power consumption in task allocation. In the experimental part, we compared the algorithm proposed in this paper with the current mainstream real-time scheduling scheme. Experimental results show that RRTDCS algorithm has better performance. © 2022 IEEE.

关键词： Scheduling

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Low Energy Consumption and Time Deterministic Energy-saving Workflow Task Scheduling Algorithm based on DVFS 9

Low Energy Consumption and Time Deterministic Energy-saving ...

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9th IEEE International Conference on Cyber Security and Cloud Computing and 8th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Liu, Xun Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science and Technology Wuhan China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

With the development of streaming media, soft real-time system in today's life could participate in the use of more extensive areas. The use frequency was also increasing. Consequently, modern processors were equipped with software control mechanisms such as DVFS (Dynamic Voltage Frequency Scaling) to allow operating systems to meet required performance while reducing power consumption. Therefore, we propose a task scheduling algorithm combined DVFS technology and time deterministic cyclic scheduling to achieve energy saving effect. First, the algorithm needed to minimize the preemption between tasks to reduce latency, so we created a buffer to save periodic tasks to avoid preemption. Second, to reduce the computational cost of the scheduling scheme, a scheduling template were designed to perform tasks. In this paper, the scheduling of periodic tasks, task scheduling would be designed when the task scheduling template would be fixed length. Besides, this algorithm supported that task could adopt appropriate voltage and frequency through DVFS technology in idle time under the condition of satisfying task dependence. Experimental analysis showed that the proposed algorithm could effectively reduce the system energy consumption while ensuring the completion of the task. © 2022 IEEE.

关键词： Scheduling

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Teaching Reform and Practice for the Course of Web Application Development technology Under the Background of Online Education 17th

Teaching Reform and Practice for the Course of Web Applicati...

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17th International Conference on Computer Science and Education, ICCSE 2022

作者： Dong, Yi Xiaoning, Peng Xian, Zhang Chunqiao, Mi Yiwen, Liu Taiguo, Qu School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819924455

With the advent of knowledge economy, online education relies on the Internet and mobile terminals, it breaks the limitation of time and space of traditional education. Online education has gradually becoming a new way for people to learn. Under the special background of COVID-19 since 2020, online education has been well applied. The course of Web Application Development technology is not only a course which is the core course in software development for computer majors, but also is a course with strong practicality and application. This paper analyzes the problems existing in the online teaching of the courses of web course under the background of online education, and presents a new scheme of the teaching reform measures for the course according to the characteristics of online education and the course of Web Application Development technology which is well applied by the author’s own practices. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Improving energy consumption forecasting using DLnet based on periodic modeling

Improving energy consumption forecasting using DLnet based o...

引用

2022 International Conference on Networks, Communications and Information technology, NCIT 2022

作者： Liao, XueChao Huang, Xiang School of Computer Science and Technology Wuhan University of Science and Technology Hubei China Key Laboratory of Intelligent Information Processing and Real-Time Industrial Systems Hubei China

ISBN: (纸本)9783800760190

Improving the prediction accuracy of energy consumption in office buildings is necessary to achieve high energy efficiency in smart buildings. The existing forecasting methods rarely analyze the periodic characteristics of energy consumption independently. In this paper, a short-term office building energy consumption prediction model (DLnet) is proposed to address the problem of inefficiency in the utilization of periodic energy consumption ***, the period component of the energy consumption data is decomposed using STL, and the optimal period of the energy consumption data is searched for by a grid search algorithm, and then the Periodic block is constructed based on the optimal period;Secondly, the Time-series block data is constructed according to the data shape of the Periodic block;then the Time-series block data and the Periodic block data are trained and learned using LSTM;Finally, the prediction results of the Time-series block data and the Periodic block data are fused by linear *** four prediction accuracy indicators of the proposed model have been demonstrated to be 7%, 21%, 25%,and 26% higher than those of the LSTM model. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Office buildings

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A Multitask Learning Approach for Chinese National Instruments Recognition and Timbre Space Regression 9th

A Multitask Learning Approach for Chinese National Instrumen...

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9th Conference on Sound and Music technology, CSMT 2021

作者： Xu, Shenyang Jiang, Yiliang Li, Zijin Sun, Xiaoheng Li, Wei Central Conservatory of Music Beijing100031 China School of Computer Science and Technology Fudan University Shanghai200438 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789811947025

Musical instrument recognition is an essential task in the domain of music information retrieval. So far, most existing research are focused on western instruments. In this research, we turn to Chinese national instruments recognition. First, a dataset containing 30 Chinese national instruments is created. Then, a well-designed end-to-end Convolutional Recurrent Neural Network is proposed. Moreover, we combine instrument recognition with instrument timbre space regression using a multitask learning approach to improve performances of both tasks. We conduct experiments in instrument recognition and timbre space regression to evaluate our model and multitask learning approach. Experimental results show that our proposed model outperforms previous algorithms, and the multitask approach can further improve the results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Music

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Adaptive Spatial Modeling and Multi-Scale Attention Aggregation for Semantic Segmentation of Remote Sensing Images

Adaptive Spatial Modeling and Multi-Scale Attention Aggregat...

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Signal, Information and Data processing (ICSIDP), IEEE International Conference on

作者： Wenying Yang Yuchuan Zhang Yuxuan Jin Yupei Wang Liang Chen National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Chongqing Innovation Center Chongqing China School of Artificial Intelligence Jianghan University Wuhan China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Chongqing Innovation Center Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

As remote sensing technology advances and high-resolution sensors are deployed, the analysis of high-resolution remote sensing images encounters challenging issues, such as intra-class variability, and inter-class similarity, due to the unobvious appearance and complicated background. The high-resolution remote sensing images pose critical issues for precise semantic segmentation due to these fundamental issues. To this end, we propose an adaptive hierarchical aggregation network. Different from the previous approach of modeling semantic interdependencies solely within a single feature map in the channel dimension, we introduce multi-scale channel-wise feature aggregation to capture global contextual dependencies. Meanwhile, we design an adaptive detail-aware module to model long-range spatial dependencies, adaptively extracting effective details from different levels as compensation. Additionally, this module utilizes depth-wise separable convolution to selectively filter out irrelevant spatial details from lower-level feature maps. The outputs of the two modules are fused layer by layer to achieve refined feature representation, thereby generating precise pixel-level segmentation results. Our experiments are conducted on the Vaihingen dataset and the Potsdam dataset. The experimental results demonstrate that the proposed algorithm surpasses several existing state-of-the-art approaches.

关键词： Image sensors Adaptation models Adaptive systems Semantic segmentation Semantics Filtering algorithms Feature extraction Data processing Sensors Remote sensing

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Mining Alarm Association Rules in Optical Transmission Networks Using a Statistical Approach 24

Mining Alarm Association Rules in Optical Transmission Netwo...

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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： Deng, Miao Li, Peng Liu, Yu Zhu, Rongjing Zhang, Yujie School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350319934

Due to the interconnection between devices in the optical transmission network (OTN), when one device fails and generates alarm information, a large number of other invalid alarms are generated in OTN. How to mining association rules in alarm data is an important problem. In this paper, we investigate the association rules mining problem based on a statistical method. First, we remove the chattering alarm in preprocess to reduce data redundancy. Second, we propose a statistics-based association rule mining algorithm. We use a sliding time window to scan the alarm data, and we exploit the matrix to record the corresponding results. According to the matrix, we calculate the support and confidence of association rules. Compared with other traditional association rule mining algorithms, the proposed algorithm does not need to set the minimum support artificially, and the obtained association rules have strong causality. Through extensive experiments on syn-thetic and real OTN alarm datasets, we verify the effectiveness of our proposed algorithm and the correctness of the obtained association rules. © 2022 IEEE.

关键词： Association rules

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