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Chinese Automation Congress (CAC)

作者： Hongyu Long Xiao Yin Chao Liu Yuansen Xu Tian Tian Fan Ye Key Laboratory of Big Data Intelligent Computing School of Automation/School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China School of Automation/School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China Economic and Technical Research Institute of State Grid Chongqing Electric Power Company Chongqing China

In response to the unique topographical challenges posed by mountainous environments, this paper investigates a probabilistic model of electric vehicle charging load, taking into account the unique traffic behavior and regional characteristics of mountainous cities. Regarding the traffic behavior, first, a traffic road network model for mountainous cities is proposed. Next, the travel chain theory is combined with the road chain to consider the functional partition of the city, which can better describe the travel patterns and charging behavior of users. Based on the analysis of traffic behavior, by exploring the influences of ambient temperature, road grade, and the undulating nature of mountainous roads, an improved Floyd shortest path algorithm is used for path decision-making, leading to the development of an EV power consumption model specifically tailored for mountainous cities. Based on this model, the prediction of EV load in mountainous cities is subsequently conducted. Overall, this study proposes a more realistic EV load prediction model by integrating traffic behavior, regional characteristics and user travel patterns, and the simulation results demonstrate the adaptability as well as the effectiveness of the method for application in mountainous cities.

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AdaFuse: Adaptive Medical Image Fusion Based on Spatial-Frequential Cross Attention

arXiv

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arXiv 2023年

作者： Gu, Xianming Wang, Lihui Deng, Zeyu Cao, Ying Huang, Xingyu Zhu, Yue-Min Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 Lyon69621 France

Multi-modal medical image fusion is essential for the precise clinical diagnosis and surgical navigation since it can merge the complementary information in multi-modalities into a single image. The quality of the fused image depends on the extracted single modality features as well as the fusion rules for multi-modal information. Existing deep learning-based fusion methods can fully exploit the semantic features of each modality, they cannot distinguish the effective low and high frequency information of each modality and fuse them adaptively. To address this issue, we propose AdaFuse, in which multimodal image information is fused adaptively through frequency-guided attention mechanism based on Fourier transform. Specifically, we propose the cross-attention fusion (CAF) block, which adaptively fuses features of two modalities in the spatial and frequency domains by exchanging key and query values, and then calculates the cross-attention scores between the spatial and frequency features to further guide the spatial-frequential information fusion. The CAF block enhances the high-frequency features of the different modalities so that the details in the fused images can be retained. Moreover, we design a novel loss function composed of structure loss and content loss to preserve both low and high frequency information. Extensive comparison experiments on several datasets demonstrate that the proposed method outperforms state-of-the-art methods in terms of both visual quality and quantitative metrics. The ablation experiments also validate the effectiveness of the proposed loss and fusion strategy. © 2023, CC BY.

关键词： Deep learning

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Edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers

Cleaner Engineering and Technology

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Cleaner Engineering and Technology 2025年 27卷

作者： Ma, Shuaiyin Cao, Yichun Liu, Yang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an 710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an 710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an 710121 China Shaanxi Union Research Centre of University and Enterprise for 5G+ Industrial Internet Communication Terminal Technology Xi'an University of Posts and Telecommunications Xi'an 710121 China Department of Management and Engineering Linköping University Linköping SE-581 83 Sweden Industrial Engineering and Management University of Oulu Oulu 90570 Finland

Within the context of cleaner production, enhancing energy efficiency and sustainability has emerged as a central focus in supercomputing center development. To address the challenges in predicting energy consumption, this study proposes an architecture of edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers. This architecture combines time-series generative adversarial network (TimeGAN) for data augmentation with the neural basis expansion analysis for time series (N-BEATS) for prediction, providing a robust solution for accurate energy consumption prediction. TimeGAN enhances the training dataset by generating high-quality synthetic time-series data, effectively mitigating issues of data sparsity and imbalance. N-BEATS, with its modular architecture and strong adaptability to temporal data, ensures precise predictions by capturing both global trends and local variations in energy usage. Experimental results demonstrate the effectiveness of the proposed architecture, exhibiting superior performance across key metrics when compared to traditional models. Specifically, the RMSE of TimeGAN-N-BEATS reduced by more than 8 %, the MSE decreased by over 18 %, and the R² reached 97.31 %, outperforming baseline models such as long short-term memory and gated recurrent unit, and their attention-enhanced variants. This study highlights the potential of integrating generative and predictive models to optimize energy efficiency in liquid cooling systems, offering valuable insights for sustainable supercomputing operations. © 2025 The Authors

关键词： Edge computing Liquid cooling system N-BEATS Supercomputing centers TimeGAN

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Multi-scale Contrastive Learning for Gastroenteroscopy Classification

Multi-scale Contrastive Learning for Gastroenteroscopy Class...

引用

Annual IEEE Symposium on Computer-Based Medical Systems

作者： Dan Li Xuechen Li Zhibin Peng Wenting Chen Linlin Shen Guangyao Wu Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology ShenZhen University Shenzhen China City University of Hong Kong Hong Kong SAR China Shenzhen Institute of Artificial Intelligence & Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University General Hospital

In gastroenteroscopy image analysis, numerous CADs demonstrate that deep learning aids doctors' diagnosis. The shapes and sizes of the lesions are varied. And in the clinic, the dataset appears to be data imbalanced. However, existing methods directly classify by texture and ignore lesions with various shapes and sizes. To address the issue above, we propose a deep neural network, which consists of multi-scale feature extraction, contrastive feature learning and a multi-scale feature fusion module. We train the contrastive feature learning module and multi-scale feature fusion module simultaneously to alleviate the issue of data distribution differences. Thus, the proposed network can better identify various categories. Extensive experiments on the Hyper Kvasir dataset show that the proposed Hybrid-M2CL outperforms the benchmark proposed by the dataset with 5.0% Macro Precision, 3.3% Macro Recall, 3.4% Macro F1-score, 3.3% Micro Precision, 3.6% MCC. In addition, it outperforms the SOTA by 1.1% Macro F1-score, 2.6% MCC, and 2.0% B-ACC.

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A Method of Searching for Optimal Coalition Structure for Solving Resource Scheduling Problem of Overall Load Balancing in Edge Computing Environments 4

A Method of Searching for Optimal Coalition Structure for So...

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2020 4th International Workshop on Advanced Algorithms and Control Engineering, IWAACE 2020

作者： Yanan, Hu Chunsheng, Li Kejia, Zhang School of Computer and Information Technology Northeast Petroleum University Daqing163318 China Heilongjiang Provincial Key Laboratory of Oil Big Data and Intelligent Analysis Daqing163318 China

The cooperative computing resource scheduling problem in the distributed computing environment is usually transformed into the problem about how to find the optimal coalition structure. However, affected by factors such as high node density and large number of concurrent tasks in edge computing environments, it is difficult to formulate resource scheduling schemes for overall load balancing in a large strategic space by traditional methods. In order to solve this problem, we proposed a method of searching for optimal coalition structure based on discrete particle swarm optimization (DPSO). Firstly, we converted the resource scheduling problem of edge computing into an optimization problem model, constructed a new structure of coalition structure to express the resource scheduling scheme, and gave key elements such as optimization objectives, decision variables, and constraint conditions. Secondly, we improved DPSO and designed M-ary discrete particle swarm optimization (MDPSO): improved particle state determination method for adapting to the new expression of the coalition structure, and proposed a power series-based inertia weight computing method to improve the optimization speed and the optimal solution quality. Finally, we simulated real application scenarios and designed comparative experiments on the optimization speed, the optimal solution quality, stability, etc., which verifies that the method proposed in this paper has obvious advantages in solving the resource scheduling problem of overall load balancing in edge computing. © Published under licence by IOP Publishing Ltd.

关键词： Structural optimization

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Identifying outliers in astronomical images with unsupervised machine learning

arXiv

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arXiv 2022年

作者： Han, Yang Zou, Zhiqiang Li, Nan Chen, Yanli School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Jiangsu Nanjing China Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Astronomical outliers, such as unusual, rare or unknown types of astronomical objects or phenomena, constantly lead to the discovery of genuinely unforeseen knowledge in astronomy. More unpredictable outliers will be uncovered in principle with the increment of the coverage and quality of upcoming survey data. However, it is a severe challenge to mine rare and unexpected targets from enormous data with human inspection due to a significant workload. Supervised learning is also unsuitable for this purpose since designing proper training sets for unanticipated signals is unworkable. Motivated by these challenges, we adopt unsupervised machine learning approaches to identify outliers in the data of galaxy images to explore the paths for detecting astronomical outliers. For comparison, we construct three methods, which are built upon the k-nearest neighbors (KNN), Convolutional Auto-Encoder (CAE) + KNN, and CAE + KNN + Attention Mechanism (attCAE KNN) separately. Testing sets are created based on the Galaxy Zoo image data published online to evaluate the performance of the above methods. Results show that attCAE KNN achieves the best recall (78%), which is 53% higher than the classical KNN method and 22% higher than CAE+KNN. The efficiency of attCAE KNN (10 minutes) is also superior to KNN (4 hours) and equal to CAE+KNN (10 minutes) for accomplishing the same task. Thus, we believe it is feasible to detect astronomical outliers in the data of galaxy images in an unsupervised manner. Next, we will apply attCAE KNN to available survey datasets to assess its applicability and reliability. Copyright © 2022, The Authors. All rights reserved.

关键词： k-nearest neighbors

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A Traversal Multi-Target Path Planning Method for Multi-Unmanned Surface Vessels in Space-Varying Ocean Current

SSRN

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SSRN 2023年

作者： Yu, Jiabin Chen, Zhihao Zhao, Zhiyao Yao, Peng Xu, Jiping Artificial Intelligence Beijing Technology and Business University Beijing100048 China China and Beijing Laboratory for Intelligent Environmental Protection Beijing Technology and Business University Beijing100048 China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing100048 China College of Engineering Ocean University of China Qingdao266100 China

The multi-unmanned surface vessels (USVs) cooperation is widely used. Multi-target traversing is an important aspect on the research on the multi-USVs cooperation in space-varying ocean current environment. Therefore, this paper proposes a traversal multi-target path planning method for multi-unmanned surface vessels in space-varying ocean current. The proposed algorithm is divided into two steps: First, a task allocation method for multi-USVs traveling multi-target based on improved Hungary algorithm is proposed for multi-target task allocation. The improved Hungary algorithm is used to allocate tasks of multi-target. Second, to solve the path planning problem, a virtual field RRT* (VF-RRT*) algorithm is proposed. The virtual field sampling algorithm and the ocean current constraint function are introduced in the RRT* algorithm. In order to verify the effectiveness of the algorithm, three groups of simulation experiments are implemented. The results show that the proposed algorithm has good effectiveness in path planning and task allocation. © 2023, The Authors. All rights reserved.

关键词： Motion planning

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A big data framework for short-term load forecasting using heterogenous data

A big data framework for short-term load forecasting using h...

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2020 International Conference on Virtual Reality and intelligent Systems, ICVRIS 2020

作者： Zhao, Haibo Li, Xiangjun Ren, Yancheng Yan'An University School of Physics and Electronic Information Shaanxi Yan'an716000 China Shaanxi Key Laboratory of Intelligent Processing of Big Energy Data Shaanxi Yan'an716000 China

ISBN: (纸本)9781728196367

In order to forecast Short-Term Load Forecast (STLF) for individual electric customers and then intelligently and flexibly utilize renewable energy, a big data framework is proposed that collects the data from smart meters and weather forecasting, pre-processes and loads it into a NoSQL database that is capable to store and further process large volumes of heterogeneous data. Then, a Long Short-Term Memory (LSTM) recurrent neural network is designed and implemented to determine the load profiles and forecast the electricity consumption for residential community for the next 24 hours. The proposed framework is tested with a publicly available smart meter dataset of a residential community, of which LSTM's performance is compared with two benchmark algorithms in terms of Root Mean Square Errors (RMSE) and Mean Absolute Percentage Errors (MAPE). © 2020 IEEE.

关键词： big data

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Recent advances in enhancing dielectric properties of CCTO materials via core-shell design strategies

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Journal of Alloys and Compounds 2025年 1036卷

作者： Yurun Du Yang Liu Yanning Yang Liang Yang School of Physics and Electronic Information Yan’an University Yan’an 716000 China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan’an 716000 China

The escalating global energy demand has rendered the development of advanced energy storage systems an imperative scientific challenge. Dielectric materials optimization serves as a critical pathway for achieving enhanced energy storage performance. Calcium copper titanate (CaCu 3 Ti 4 O 12 , CCTO) has garnered substantial research interest owing to its colossal dielectric permittivity and remarkable thermal/frequency stability. Nevertheless, the elevated dielectric loss tangent (tan δ) significantly constrains its technological implementation. The core-shell structural provides a viable strategy for dielectric loss mitigation in CCTO systems. This review systematically examines core-shell structural synthesis methodologies and the impact of structural optimization on the dielectric properties of CCTO. The internal barrier layer capacitor (IBLC) mechanism is elucidated through interfacial polarization analysis, along with a detailed analysis of the key role played by the selection and structural regulation of shell materials in enhancing performance. Finally, the potential applications of CCTO core-shell structures in electronic devices are explored, providing valuable insights for researchers in the field of dielectric materials.

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Distributed Image Retrieval Base on LSH Indexing on Spark 1st

Distributed Image Retrieval Base on LSH Indexing on Spark

引用

1st International Conference on big data and Security, ICBDS 2019

作者： Hou, Zelei Huang, Chao Wu, Jiagao Liu, Linfeng School of Computer Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing NanjingJiangsu210023 China

ISBN: (纸本)9789811575297

With the advent of the era of big data, how to process massive image, video and other multimedia data timely and accurately has become a new challenge in related fields. Aiming at the computational bottleneck and inefficiency of traditional image content retrieval system, in this paper, a distributed image retrieval framework base on Location Sensitive Hash (LSH) indexing is proposed on Spark, which combines with the distributed storage and computing characteristics of Spark big data platform. Then, distributed K-means based Bag of Visual Word (BoVW) algorithm and LSH algorithm are proposed to build LSH index vectors on Spark in parallel. The experiment shows that the retrieval time of our framework can be reduced significantly, and both retrieval recall and precision are high compared with the traditional retrieval method. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Indexing (of information)

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