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Frontiers in Energy 2019年第3期13卷 597-602页

作者： Lijun WU Yu zhang Bingjiang LI Pengxiang WANG Lishuang FAN Naiqing zhang Kening SUN State Key Laboratory of Urban Water Resource and Environment School of Chemistry and Chemical EngineeringHarbin Institute of TechnologyHarbin 150001China School of Chemistry and Chemical Engineering Academy of Fundamental and Interdisciplinary SciencesHarbin Institute of TechnologyHarbin 150001China

VS4 has gained more and more attention for its high theoretical capacity (449 mAh/g with 3e^- transfer) in lithium ion batteries (LIBs). Herein, a layered structure VS4 anchored in graphene aerogels is prepared and first reported as cathode material for LIBs. VS4@GAs composite exhibits an exceptional high initial reversible capacity (511 mAh/g), an excellent high-rate capability (191 mAh/g at the 5 C), and an excellent cyclic stability (239 mAh/g after 15 cycles).

关键词： VS4 graphene aerogels cathode lithium storage

Integrated strategy for real-time wind power fluctuation mitigation and energy storage system control

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Global Energy Interconnection 2024年第1期7卷 71-81页

作者： Yu zhang Yongkang zhang Tiezhou Wu Hubei University of Technology Hubei Key Laboratory of Solar Energy Efficient Utilization and Energy Storage Operation ControlHubeiWuhan 430068P.R.China

To address the impact of wind-power fluctuations on the stability of power systems,we propose a comprehensive approach that integrates multiple strategies and methods to enhance the efficiency and reliability of a system.First,we employ a strategy that restricts long-and short-term power output deviations to smoothen wind power fluctuations in real time.Second,we adopt the sliding window instantaneous complete ensemble empirical mode decomposition with adaptive noise(SW-ICEEMDAN)strategy to achieve real-time decomposition of the energy storage power,facilitating internal power distribution within the hybrid energy storage system.Finally,we introduce a rule-based multi-fuzzy control strategy for the secondary adjustment of the initial power allocation commands for different energy storage components.Through simulation validation,we demonstrate that the proposed comprehensive control strategy can smoothen wind power fluctuations in real time and decompose energy storage power.Compared with traditional empirical mode decomposition(EMD),ensemble empirical mode decomposition(EEMD),and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)decomposition strategies,the configuration of the energy storage system under the SW-ICEEMDAN control strategy is more optimal.Additionally,the state-of-charge of energy storage components fluctuates within a reasonable range,enhancing the stability of the power system and ensuring the secure operation of the energy storage system.

关键词： SW-ICEEMDAN HESS Real-time smoothing Rule-based multi-fuzzy control SoC

Estimation of state of health based on charging characteristics and back-propagation neural networks with improved atom search optimization algorithm

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Global Energy Interconnection 2023年第2期6卷 228-237页

作者： Yu zhang yuhang zhang Tiezhou Wu Hubei University of Technology Hubei Key Laboratory of Solar Energy Efficient Utilization and Energy Storage Operation ControlHubeiWuhan 430068P.R.China

With the rapid development of new energy technologies, lithium batteries are widely used in the field of energy storage systems and electric vehicles. The accurate prediction for the state of health(SOH) has an important role in maintaining a safe and stable operation of lithium-ion batteries. To address the problems of uncertain battery discharge conditions and low SOH estimation accuracy in practical applications, this paper proposes a SOH estimation method based on constant-current battery charging section characteristics with a back-propagation neural network with an improved atom search optimization algorithm. A temperature characteristic, equal-time temperature variation(Dt_DT), is proposed by analyzing the temperature data of the battery charging section with the incremental capacity(IC) characteristics obtained from an IC analysis as an input to the data-driven prediction model. Testing and analysis of the proposed prediction model are carried out using publicly available datasets. Experimental results show that the maximum error of SOH estimation results for the proposed method in this paper is below 1.5%.

关键词： State of health Lithium-ion battery Dt_DT Improved atom search optimization algorithm

Overexpression of ACE2 ameliorates Aβ-induced blood–brain barrier damage and angiogenesis by inhibiting NF-κB/VEGF/VEGFR2 pathway

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Animal Models and Experimental Medicine 2023年第3期6卷 237-244页

作者： Xueling zhang Yu zhang Ling zhang Chuan Qin Institute of Laboratory Animal Sciences CAMS&PUMCBeijingChina National Human Diseases Animal Model Resource Center BeijingChina NHC Key Laboratory of Human Disease Comparative Medicine BeijingChina Changping National Laboratory(CPNL) BeijingChina

Background:Pathological angiogenesis and blood–brain barrier damage may play an important role in Alzheimer's disease(AD).ACE2 is mainly expressed on the surface of endothelial cells in brain.Recent studies have shown that the expression of ACE2 in AD is reduced,but its role in AD is still unclear.Method:We induced AD damage in endothelial cells using Aβ25-35 and overexpressed ACE2 in bEend.3 cells through lentiviral transfection.We detected the effect of Aβ25-35 on cell viability using the CCK-8 assay and examined the effect of overexpressing ACE2 on angiogenesis using an angiogenesis assay.We used western blot and cell immunofluorescence to detect changes in the expression of the VEGF/VEGFR2 pathway,tight junction protein,and NF-κB pathway.Results:Aβ25-35 treatment significantly decreased the expression of ACE2 and reduced cell viability.ACE2 overexpression(1)reduced the number of branches and junctions in tube formation,(2)inhibited the activation of the VEGF/VEGFR2 pathway induced by Aβ25-35,(3)increased the expression of TJPs,including ZO-1 and claudin-5,and(4)restored Aβ25-35-induced activation of the NF-κB pathway.Conclusion:Overexpression of ACE2 can improve pathological angiogenesis and blood–brain barrier damage in AD models in vitro by inhibiting NF-κB/VEGF/VEGFR2 pathway activity.ACE2 may therefore represent a therapeutic target for endothelial cell dysfunction in AD.

关键词： ACE2 Alzheimer's disease angiogenesis blood-brain barrier VEGF

A Privacy-preserving Algorithm for AC Microgrid Cyber-physical System Against False Data Injection Attacks

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Journal of Modern Power Systems and Clean Energy 2023年第5期11卷 1646-1658页

作者： Jun Yang Yu zhang College of Information Science and Engineering Northeastern UniversityShenyangChina Liaoning Provincial Transportation Planning and Design Institute Co. Ltd.ShenyangChina

A new privacy-preserving algorithm based on the Paillier cryptosystem including a new cooperative control strategy is proposed in this paper, which can resist the false data injection(FDI) attack based on the finite-time control theory and the data encryption strategy. Compared with the existing algorithms, the proposed privacy-preserving algorithm avoids the direct transmission of the ciphertext of frequency data in communication links while avoiding complex iterations and communications. It builds a secure data transmission environment that can ensure data security in the AC microgrid cyber-physical system(CPS). This algorithm provides effective protection for AC microgrid CPS in different cases of FDI attacks. At the same time, it can completely eliminate the adverse effects caused by the FDI attack. Finally, the effectiveness, security, and advantages of this algorithm are verified in the improved IEEE 34-node test microgrid system with six distributed generators(DGs) in different cases of FDI attacks.

关键词： AC microgrid cyber-physical system(CPS) distributed cooperative control false data injection(FDI)attack Paillier cryptosystem

Recommendation System with Biclustering

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Big Data Mining and Analytics 2022年第4期5卷 282-293页

作者： Jianjun Sun Yu zhang School of Computer Science Northwestern Polytechnical UniversityXi’an 710072China Ningbo Institute of Northwestern Polytechnical University Ningbo 315103China

The massive growth of online commercial data has raised the request for an automatic recommender system to benefit both users and merchants.One of the most frequently used recommendation methods is collaborative filtering,but its accuracy is limited by the sparsity of the rating dataset.Most existing collaborative filtering methods consider all features when calculating user/item similarity and ignore much local information.In collaborative filtering,selecting neighbors and determining users’similarities are the most important parts.For the selection of better neighbors,this study proposes a novel biclustering method based on modified fuzzy adaptive resonance theory.To reflect the similarity between users,a new measure that considers the effect of the number of users’common items is proposed.Specifically,the proposed novel biclustering method is first adopted to obtain local similarity and local prediction.Second,item-based collaborative filtering is used to generate global predictions.Finally,the two resultant predictions are fused to obtain a final one.Experiment results demonstrate that the proposed method outperforms state-of-the-art models in terms of several aspects on three benchmark datasets.

关键词： Recommendation System(RS) Collaborative Filtering(CF) local pattern biclustering similarity measure

Dendritic Cell Algorithm with Bayesian Optimization Hyperband for Signal Fusion

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Computers, Materials & Continua 2023年第8期76卷 2317-2336页

作者： Dan zhang Yu zhang Yiwen Liang School of Computer Science Wuhan UniversityWuhan430072China GNSS Research Center Wuhan UniversityWuhan430079China

The dendritic cell algorithm(DCA)is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune system.Too many parameters increase complexity and lead to plenty of criticism in the signal fusion procedure of DCA.The loss function of DCA is ambiguous due to its complexity.To reduce the uncertainty,several researchers simplified the algorithm program;some introduced gradient descent to optimize parameters;some utilized searching methods to find the optimal parameter combination.However,these studies are either time-consuming or need to be revised in the case of non-convex functions.To overcome the problems,this study models the parameter optimization into a black-box optimization problem without knowing the information about its loss function.This study hybridizes bayesian optimization hyperband(BOHB)with DCA to propose a novel DCA version,BHDCA,for accomplishing parameter optimization in the signal fusion process.The BHDCA utilizes the bayesian optimization(BO)of BOHB to find promising parameter configurations and applies the hyperband of BOHB to allocate the suitable budget for each potential configuration.The experimental results show that the proposed algorithm has significant advantages over the otherDCAexpansion algorithms in terms of signal fusion.

关键词： Dendritic cell algorithm signal fusion parameter optimization bayesian optimization hyperband

Autophagy and autophagy-related molecules in neurodegenerative diseases

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Animal Models and Experimental Medicine 2023年第1期6卷 10-17页

作者： Changsong Dou Yu zhang Ling zhang Chuan Qin NHC Key Laboratory of Human Disease Comparative Medicine Key Laboratory of Human Diseases Animal ModelInstitute of Laboratory Animal SciencesComparative Medicine CenterPeking Union Medical College(PUMC)Chinese Academy of Medical Sciences(CAMS)BeijingChina Beijing Engineering Research Center for Experimental Animal Models of Human Critical Diseases Beijing Comparative Medicine CenterPeking Union Medical College(PUMC)Chinese Academy of Medical Sciences(CAMS)BeijingChina

Autophagy is one of the degradation pathways to remove proteins or damaged or-ganelles in cells that plays an important role in neuroprotection.Different stages of autophagy are regulated by autophagy-related genes,and many molecules such as transcription factor EB(TFEB)are involved.The complete autophagy process plays an important role in maintaining the dynamic balance of autophagy and is crucial to the homeostasis of intracellular substance and energy metabolism.Autophagy balance is disrupted in neurodegenerative diseases,accounting for a variety of degeneration dis-orders.These impairments can be alleviated or treated by the regulation of autophagy through molecules such as TFEB.

关键词： autophagy mitophagy neurodegenerative TFEB

Bidirectional Transformer with absolute-position aware relative position encoding for encoding sentences

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Frontiers of Computer Science 2023年第1期17卷 63-71页

作者： Le QI Yu zhang Ting LIU School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China

Transformers have been widely studied in many natural language processing (NLP) tasks, which can capture the dependency from the whole sentence with a high parallelizability thanks to the multi-head attention and the position-wise feed-forward network. However, the above two components of transformers are position-independent, which causes transformers to be weak in modeling sentence structures. Existing studies commonly utilized positional encoding or mask strategies for capturing the structural information of sentences. In this paper, we aim at strengthening the ability of transformers on modeling the linear structure of sentences from three aspects, containing the absolute position of tokens, the relative distance, and the direction between tokens. We propose a novel bidirectional Transformer with absolute-position aware relative position encoding (BiAR-Transformer) that combines the positional encoding and the mask strategy together. We model the relative distance between tokens along with the absolute position of tokens by a novel absolute-position aware relative position encoding. Meanwhile, we apply a bidirectional mask strategy for modeling the direction between tokens. Experimental results on the natural language inference, paraphrase identification, sentiment classification and machine translation tasks show that BiAR-Transformer achieves superior performance than other strong baselines.

关键词： Transformer relative position encoding bidirectional mask strategy sentence encoder

YOLO-CORE: Contour Regression for Efficient Instance Segmentation

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Machine Intelligence Research 2023年第5期20卷 716-728页

作者： Haoliang Liu Wei Xiong Yu zhang School of Computer Science and Engineering and the Key Laboratory of Computer Network and Information Integration(Ministry of Education) Southeast UniversityNanjing 211189China

Instance segmentation has drawn mounting attention due to its significant utility.However,high computational costs have been widely acknowledged in this domain,as the instance mask is generally achieved by pixel-level labeling.In this paper,we present a conceptually efficient contour regression network based on the you only look once(YOLO)architecture named YOLO-CORE for instance segmentation.The mask of the instance is efficiently acquired by explicit and direct contour regression using our designed multiorder constraint consisting of a polar distance loss and a sector loss.Our proposed YOLO-CORE yields impressive segmentation performance in terms of both accuracy and speed.It achieves 57.9%AP@0.5 with 47 FPS(frames per second)on the semantic boundaries dataset(SBD)and 51.1%AP@0.5 with 46 FPS on the COCO dataset.The superior performance achieved by our method with explicit contour regression suggests a new technique line in the YOLO-based image understanding field.Moreover,our instance segmentation design can be flexibly integrated into existing deep detectors with negligible computation cost(65.86 BFLOPs(billion float operations per second)to 66.15 BFLOPs with the YOLOv3 detector).

关键词： Computer vision instance segmentation object shape prediction contour regression polar distance.