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Chinese Control and Decision Conference, CCDC

作者： Wei He Hongyan Ma Yingda Zhang Shuai Wang Jiaming Dou Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China National Virtual Simulation Experimental Center for Smart City Education Beijing China

ISBN: (纸本)9781665478977

Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner state of a battery cell, which cannot be directly measured. In order to improve the estimation accuracy of SOC, this paper develops a SOC estimation model for a lithium-ion battery using a Particle Swarm Optimization-Extreme Learning Machine(PSO-ELM) algorithm. The PSO is applied to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal ELM model. The inputs to the PSO-ELM model are the battery voltage, current, and temperature, and the output is the actual SOC values. The performance of the proposed model is compared with BP neural network and ELM models and verified based on the mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and SOC error. The results demonstrate that the PSO-ELM model offers higher accuracy and lower SOC error rate than ELM and BP neural network models.

关键词： Lithium-ion batteries Temperature measurement Temperature distribution Neurons Estimation Particle measurements Battery charge measurement

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CUE: Compound Uniform Encoding for Writer Retrieval

CUE: Compound Uniform Encoding for Writer Retrieval

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Jiakai Luo Hongwei Lu Xin Nie Shenghao Liu Xianjun Deng Chenlu Zhu Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Hubei Chutian Expressway Digital Technology Co. Ltd. China

Writer retrieval is crucial in document forensics and historical document analysis. However, due to the difference in syntactic structure between Chinese and other languages, the existing methods may not be directly applied to Chinese writer retrieval. Previous work on Chinese writer retrieval does not overcome the performance degradation problem when the number of samples grows. In this paper, we propose a novel compound uniform encoding algorithm (CUE) for Chinese writer retrieval, which mainly consists of a combined feature extraction module (CFE) and a prototype substitution module (PS). The CFE module combines two complementary features from image filter response and character contour. It counts local symmetries and edge co-occurrence pairs. PS module substitutes the outliers with the class prototypes to alleviate the influence of the outliers. Finally, the weighted Chi-square distance is applied to measure the similarity between writer and text. To verify the superiority of our proposed method, experiments are conducted on four public datasets and our built dataset. The results validate that CUE outperforms the state-of-the-art algorithms on mAP metric.

关键词： Weight measurement Measurement Text analysis Image edge detection Prototypes Syntactics Feature extraction

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FAGC: Free Space Fragmentation Aware GC Scheme based on Observations of Energy Consumption

FAGC: Free Space Fragmentation Aware GC Scheme based on Obse...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Lihua Yang Zhipeng Tan Fang Wang Yang Xiao Wei Zhang Biao He Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology College of Science and Technology National University of Defense Technology China Huawei Technology Co. Ltd China

Smartphones are everyday necessities with limited power supply. Charging a smartphone twice a day or more affects user experience. Flash friendly file system (F2FS) is a widely-used log-structured file system for smartphones. Free space fragmentation of F2FS consists of invalid blocks mainly causing performance degradation. F2FS reclaims invalid blocks by garbage collection (GC). We explore the energy consumption of GC and the effect of GC on reducing free space fragments. We observe the energy consumption of one background GC is large but its effect on reducing free space fragments is limited. These motivate us to improve the energy efficiency of GC. We reassess how much free space is a free space fragment based on data analysis, use the free space fragmentation factor to measure the degree of free space fragmentation quickly. We suggest the free space fragmentation aware GC scheme (FAGC) that optimizes the selection for victim segments and the migration for valid blocks. Experiments on real platform show that FAGC reduces GC count by 82.68% and 74.51% respectively than traditional F2FS and the latest GC optimization of F2FS, ATGC. FAGC reduces the energy consumption by 164.37 J and 100.64 J compared to traditional F2FS and ATGC respectively for a synthetic benchmark.

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Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels

arXiv

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arXiv 2022年

作者： Ma, Zhongchen Li, Lisha Mao, Qirong Chen, Songcan The School of Computer Science and Communications Engineering Jiangsu University Zhenjiang212013 China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China Nanjing211106 China

Contrastive learning (CL) has shown impressive advances in image representation learning in whichever supervised multi-class classification or unsupervised learning. However, these CL methods fail to be directly adapted to multi-label image classification due to the difficulty in defining the positive and negative instances to contrast a given anchor image in multi-label scenario, let the label missing one alone, implying that borrowing a commonly-used way from contrastive multi-class learning to define them will incur a lot of false negative instances unfavorable for learning. In this paper, with the introduction of a label correction mechanism to identify missing labels, we first elegantly generate positives and negatives for individual semantic labels of an anchor image, then define a unique contrastive loss for multilabel image classification with missing labels (CLML), the loss is able to accurately bring images close to their true positive images and false negative images, far away from their true negative images. Different from existing multi-label CL losses, CLML also preserves low-rank global and local label dependencies in the latent representation space where such dependencies have been shown to be helpful in dealing with missing labels. To the best of our knowledge, this is the first general multi-label CL loss in the missing-label scenario and thus can seamlessly be paired with those losses of any existing multi-label learning methods just via a single hyperparameter. The proposed strategy has been shown to improve the classification performance of the Resnet101 model by margins of 1.2%, 1.6%, and 1.3% respectively on three standard datasets, MSCOCO, VOC, and NUS-WIDE. Code is available at https://***/chuangua/ContrastiveLossMLML. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning systems

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CRNN-Refined Spatiotemporal Transformer for Dynamic MRI reconstruction

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Computers in Biology and Medicine 2024年 182卷 109133-109133页

作者： Wang, Bin Lian, Yusheng Xiong, Xingchuang Han, Hongbin Liu, Zilong Center for Metrology Scientific Data National Institute of Metrology Beijing100029 China Key Laboratory of Metrology Digitalization and Digital Metrology State Administration for Market Regulation Beijing100029 China School of Printing and Packaging Engineering Beijing Institute of Graphic Communication Beijing102600 China Department of Radiology Peking University Third Hospital. Institute of Medical Technology Peking University Health Science Center. Beijing Key Laboratory of Magnetic Resonance Imaging Devices and Technology Peking University Third Hospital Beijing100191 China

Magnetic Resonance Imaging (MRI) plays a pivotal role in modern clinical practice, providing detailed anatomical visualization with exceptional spatial resolution and soft tissue contrast. Dynamic MRI, aiming to capture both spatial and temporal characteristics, faces challenges related to prolonged acquisition times and susceptibility to motion artifacts. Balancing spatial and temporal resolutions becomes crucial in real-world clinical scenarios. In the realm of dynamic MRI reconstruction, while Convolutional Recurrent Neural Networks (CRNNs) struggle with long-range dependencies, CRNNs require extensive iterations, impacting efficiency. Transformers, known for their effectiveness in high-dimensional imaging, are underexplored in dynamic MRI reconstruction. Additionally, prevailing algorithms fall short of achieving superior results in demanding generative reconstructions at high acceleration rates. This research proposes a novel approach for dynamic MRI reconstruction, named CRNN-Refined Spatiotemporal Transformer Network (CST-Net). The spatiotemporal Transformer initiates reconstruction, modeling temporal and spatial correlations, followed by refinement using the CRNN. This integration mitigates inaccuracies caused by damaged frames and reduces CRNN iterations, enhancing computational efficiency without compromising reconstruction quality. Our study compares the performance of the proposed CST-Net at 6 × and 12 × undersampling rates, showcasing its superiority over existing algorithms. Particularly, in challenging 25× generative reconstructions, the CST-Net outperforms current methods. The comparison includes experiments under both radial and Cartesian undersampling patterns. In conclusion, CST-Net successfully addresses the limitations inherent in existing generative reconstruction algorithms, thereby paving the way for further exploration and optimization of Transformer-based approaches in dynamic MRI reconstruction. Code and datasets can be available: https://

关键词： Recurrent neural networks

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Spatial public goods games on any population structure

arXiv

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arXiv 2024年

作者： Wang, Chaoqian Su, Qi Department of Computational and Data Sciences George Mason University FairfaxVA22030 United States Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

Understanding the emergence of cooperation in spatially structured populations has advanced significantly in the context of pairwise games, but the fundamental theory of group-based public goods games (PGGs) remains less explored. Here, we provide theoretical conditions under which cooperation thrive in spatial PGGs on any population structure, which are accurate under weak selection. We find that PGGs can support cooperation across all kinds of model details and on almost all network structures in contrast to pairwise games. For example, a class of networks that would otherwise fail to produce cooperation, such as star graphs, are particularly conducive to cooperation in spatial PGGs. This fundamental advantage of spatial PGGs derives from reciprocity through second-order interactions, allowing local structures such as the clustering coefficient to play positive roles. We also verify the robustness of spatial PGGs on empirical networks where pairwise games cannot support cooperation, which implies that PGGs could be a universal interaction mode in real-world systems. © 2024, CC BY.

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Research on Quantitative Trading Investment Strategy Based on LSTM and Dynamic Programming

Research on Quantitative Trading Investment Strategy Based o...

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作者： Jiayuan Mao Kexin Wang Wei Zhao Ningning Liu Youyu Wang Wei Fan College of Science Chongqing University of Technology Chongqing Key Laboratory of Public Big Data Security Technology College of materials science and Engineering Hebei University of Science And Technology IT service center Heibei Open University College of Computer Science and Engineering Chongqing University of Technology

As financial markets matured,more standardized and quantitative research *** this paper,we model the historical price returns of *** and BTC over a five-year trading period from September 11,2016,to September 10,2021,and estimate the total investment returns as of September 10,***,we build a variety of price-prediction models to fit the image to the data to get the most accurate image of the actual price *** was found that the LSTM neural networkbased multi-interval segmentation price prediction model,which has the best prediction effect by comparison with the actual ***,it was used as the core prediction model for this ***,we use the planning model to find the optimal investment plan by using the difference between the price change of gold and bitcoin trading in the next three days as the decision variable,the highest Sharpe ratio on the third day as the objective *** the same time,the variance of the forecast value in the next three days is used as the risk characterization,and different weights are assigned to the risk and return as the objective function to characterize the investment strategy under different investment *** order to accelerate the convergence of the planning model,we added a particle swarm algorithm for *** the end,we obtained the results for the specific daily investment scenarios for aggressive investors,intermediate investors,and steady investors.

关键词： quantitative trading LSTM neural network Sharpe ratio Dynamic Programming

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A Secure Authentication Framework to Guarantee the Traceability of Avatars in Metaverse

arXiv

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arXiv 2022年

作者： Yang, Kedi Zhang, Zhenyong Tian, Youliang Ma, Jianfeng The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China The Institute of Cryptography & Data Security GuiZhou University Guiyang550025 China The School of Cyber Engineering Xidian University Xi’an710126 China

Metaverse is a vast virtual environment parallel to the physical world in which users enjoy a variety of services acting as an avatar. To build a secure living habitat, it’s vital to ensure the virtual-physical traceability that tracking a malicious player in the physical world via his avatars in virtual space. In this paper, we propose a two-factor authentication framework based on chameleon signature and biometric-based authentication. First, aiming at disguise in virtual space, we propose a chameleon collision signature algorithm to achieve the verifiability of the avatar’s virtual identity. Second, facing at impersonation in physical world, we construct an avatar’s identity model based on the player’s biometric template and the chameleon key to realize the verifiability of the avatar’s physical identity. Finally, we design two decentralized authentication protocols based on the avatar’s identity model to ensure the consistency of the avatar’s virtual and physical identities. Security analysis indicates that the proposed authentication framework guarantees the consistency and traceability of avatar’s identity. Simulation experiments show that the framework not only completes the decentralized authentication between avatars but also achieves the virtual-physical tracking. Copyright © 2022, The Authors. All rights reserved.

关键词： Authentication

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Design of Offline Analysis System for Remote Sensing data Service Based on Hive

Design of Offline Analysis System for Remote Sensing Data Se...

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Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Hongkuo Zhang Jiaxiang Zhang Meng Tian Baojun Qiao School of Computer and Information Engineering Heanan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9781665406314

With the wide application of remote sensing data service platform, the platform accumulates more and more data, and the remote sensing users' demand for remote sensing data service becomes more and more diverse. Therefore, how to deal with remote sensing service data, make statistics and analyze its potential value, and meet the diverse needs of remote sensing users becomes particularly important. Based on big data technology, this paper firstly collects and stores remote sensing user behavior data and remote sensing service platform business data, then uses Hive to build offline data warehouse, performs dimensional modeling and hierarchical design of the data, and finally obtains relevant index data values by statistical analysis. Through the statistical analysis of remote sensing service data can not only provide data support for active service mode, but also provide support for the decision of remote sensing application department managers.

关键词： Process design Analytical models Statistical analysis data warehouses data models Indexes Intelligent systems

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A Unified Channel Estimation Framework for Stationary and Non-Stationary Fading Environments

A Unified Channel Estimation Framework for Stationary and No...

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作者： Shi, Qi Liu, Yangyu Zhang, Shunqing Xu, Shugong Lau, Vincent K. N. Key Laboratory of Specialty Fiber Optics and Optical Access Networks School of Information and Communication Engineering Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China The Hong Kong University of Science and Technology Hong Kong Hong Kong

Channel estimation is crucial to modern wireless systems and becomes more and more challenging with the growth of user throughput in sub-6 GHz multiple input multiple output configuration. Plenty of literature spends great efforts in improving the estimation accuracy, while the interpolation schemes are overlooked. To deal with this challenge, we exploit the super-resolution image recovery scheme to model the non-linear interpolation mechanisms. Moreover, in order to extend the estimation scheme into the non-stationary environment which is especially attractive in the coming 6G, we utilize the recurrent network structure to approximate the non-linear channel statistic correlation to model the non-stationary behavior which is difficult to accomplish in the theoretical way. To make it more practical, we offline generate numerical channel coefficients according to the statistical channel models to train the neural networks and directly apply them in different environments. As shown in this paper, the proposed unified super-resolution based channel estimation scheme can outperform the conventional approaches in both stationary and non-stationary scenarios, which we believe can significantly change the current channel estimation method in the near future. © 1972-2012 IEEE.

关键词： Channel estimation

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