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SSRN 2024年

作者： Wang, Zihao Liang, Cuiyuan Yang, Yuanhe Sun, Jiaxue Zhang, Xuelin Wang, Yu Jiang, Ying Liu, Yan Qi, Dianpeng MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage National and Local Joint Engineering Laboratory for Synthesis Transformation and Separation of Extreme Environmental Nutrients School of Chemistry and Chemical Engineering Harbin Institute of Technology Harbin150001 China Key Laboratory of Science and Engineering for the Multi-modal Prevention and Control of Major Chronic Diseases Ministry of Industry and Information Technology Zhengzhou Research Institute Harbin Institute of Technology Zhengzhou450000 China School of Astronautics Harbin Institute of Technology Harbin150001 China Department of Electromechanical Engineering Centre for Artificial Intelligence and Robotics University of Macau 999078 China

The flexible strain sensor is a crucial component of the wearable technology, offering considerable potential for monitoring physiological signals. Notably, strain sensors based on nanomaterial thin films have gained much attention from researchers due to their excellent performance and ease of preparation. Nevertheless, challenges remain, such as the rapid expansion of cracks in rigid conductive films under strain, which greatly reduces the working range of the sensors. Conversely, soft conductive films characterized by small cracks are much less sensitive. This study introduces a novel conductive strategy centered on the double-layered microcracks of gold/PPy (Polypyrrole) composite films. The as-prepared strain sensor exhibits ultra-high sensitivity with a gauge factor (GF) of approximately 3.604 × 107, an expansive working range spanning from 0% to 60%, high strain resolution at 0.1% and commendable cycling stability exceeding 1000 cycles. The crack formation and sensing mechanisms were thoroughly investigated, elucidating the key role of the double-layered microcracks in enhancing sensing performance. Ultimately, the practicality of the developed sensor for human health monitoring and human-computer interaction was demonstrated by the accurate detection of human motion. © 2024, The Authors. All rights reserved.

关键词： Thin films

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Variable Frequency Phase-Shift Modulation Technique for Single Stage Dual-Active-Bridge AC-DC Converter

Variable Frequency Phase-Shift Modulation Technique for Sing...

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International Conference on Power Electronics (ICPE)

作者： Dongxin Guo Panbao Wang Chunguang Ren Josep M. Guerrero School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Shanxi Key Lab. of Power System Operation and Control Taiyuan University of Technology Taiyuan China Department of Energy Technology Aalborg University Aalborg Denmark

This article proposes a variable frequency phase-shift modulation technique for single stage dual-active-bridge (DAB) AC-DC converter. This isolated single-stage single-phase converter consists of a DAB and a full bridge (FB) inverter. The proposed strategy applies a composite control strategy combining frequency modulation and extended phase shift (EPS) method to overcome the drawback of the non-linear power control of traditional phase-shift control scheme, which significantly reduces the complexity of the controller algorithm. Moreover, the synchronous inverter control applied to the back-end FB inverter is designed, which can realize the energy conversions in a single-stage, and a high frequency (HF) filter capacitor can replace the electrolytic capacitor at the output side DC-link. Thus, the safety of system operation improves and the power density of the system increases. Finally, simulation results confirm the validity and practical feasibility of the proposed control strategy.

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Generalized Predictive Analysis of Reactions in Paper Devices Via Graph Neural Networks

SSRN

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SSRN 2024年

作者： Sun, Hao Pan, Yihan Dong, Hui Liu, Canfeng Yang, Jintian Tao, Yihui Jia, Yuan School of Mechanical Engineering and Automation Fuzhou University Fuzhou China School of Mechatronics Engineering Harbin Institute of Technology Harbin China Fuzhou University China Department of Automation Control and System Engineering University of Sheffield Sheffield United Kingdom Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China

Microfluidic technology facilitates high-throughput generation of time series data for biological and medical studies. Deep learning enables accurate, predictive analysis and proactive decision-making based on autonomous recognition of intricate pattern hidden in series. In this work, we first devised a paper-based microfluidic system for portable nucleic acid amplification test with economic energy consumption. Then, we employed Graph Neural Network (GNN), distinguished by its non-Euclidean data structure tailored for deep learning, with spatio-temporal attention mechanism to perform near-sensor predictive analysis of the on-chip reaction. Our findings demonstrated that the novel GNN model can provide accurate predictions of positive outcomes at the early stages of the reaction using less than one-third of the total reaction time. Then, the deep learning model trained by on-chip data was subsequently applied to more than 900 clinical plots. Generalization of the GNN model was successfully validated across different detection methods, diverse types of datasets and time series with variable length. Accuracy, sensitivity and specificity of the predictive approach were 96.5%, 94.3% and 99.0% by utilizing the early half of reaction information. Finally, we compared the GNN model with various deep learning models. Despite differences in the prediction of negative samples among various models were minute, GNN obviously offered overall superior performance. This work ignites a cutting-edge application of deep learning in point-of-care and near-sensor tests. By harnessing the power of body area networks and edge/fog computing, our approach unlocks promising possibilities in diverse fields like healthcare and instrument science. © 2024, The Authors. All rights reserved.

关键词： Energy utilization

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Role of biochar surface characteristics in the adsorption of aromatic compounds:Pore structure and functional groups

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Chinese Chemical Letters 2021年第10期32卷 2939-2946页

作者： Xue-Fei Tan Shi-Shu Zhu Ru-Peng Wang Yi-Di Chen Pau-Loke Show Feng-Fa Zhang Shih-Hsin Ho College of Materials and Chemical Engineering Heilongjiang Institute of TechnologyHarbin 150050China State Key Laboratory of Urban Water Resource and Environment School of EnvironmentHarbin Institute of TechnologyHarbin 150090China School of Environmental Science and Engineering Sun Yat-sen UniversityGuangzhou 510275China Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology Sun Yat-sen UniversityGuangzhou 510275China State Key Laboratory of Urban Water Resource and Environment School of Civil and Environmental EngineeringHarbinShenzhen 518055China Institute of Technology(Shenzhen) Department of Chemical and Environmental Engineering Faculty of Science and EngineeringUniversity of Nottingham Malaysia43500 SemenyihSelangorMalaysia

Biochar(BC)are widely used as highly efficient adsorbents to alleviate aromatics-based contaminants due to their ease of preparation,wide availability,and high *** surface properties of BCs usually vary greatly due to their complex chemical constituents and different preparation processes and are reflected in the values of parameters such as the specific surface area(SSA),pore volume/size,and surface functional groups(SFGs).The effects of SSA and pore volume/size on the adsorption of aromatics have been widely ***,the corresponding mechanisms of BC SFGs towards aromatics adsorption remains unclear as the compositions of the SFGs are usually complex and hard to *** address in this gap in the literature,this review introduces a new perspective on the adsorption mechanisms of *** collecting previously-reported results,the parameters log P(logarithm of the Kow),polar surface area,and the positive/negative charges were carefully calculated using Chem Draw3D,which allowed the hydrophobicity/hydrophilicity properties,electron donor-acceptor interactions,Hbonding,and electrostatic interactions between SFGs and aromatics-based contaminates to be inferred *** predictions were consistent with the reported results and showed that tailor-made BCs can be designed according to the molecular weights,chemical structures,and polarities of the target ***,this review provides new insight into predicting the physicochemical properties of BCs through revealing the relationship between SFGs and adsorbates,which may provide useful guidance for the preparing of highly-efficient,functional BCs for the adsorption of aromatics.

关键词： Biochar Surface characteristics Aromatics Adsorption Surface functional groups

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Smart palm-size optofluidic hematology analyzer for automated imaging-based leukocyte concentration detection

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Opto-Electronic Science 2023年第12期2卷 1-8页

作者： Deer Su Xiangyu Li Weida Gao Qiuhua Wei Haoyu Li Changliang Guo Weisong Zhao Innovation Photonics and Imaging Center School of Instrumentation Science and EngineeringHarbin Institute of TechnologyHarbin 150080China Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150081China Department of Neurosurgery The Second Affiliated Hospital of Harbin Medical UniversityHarbin 150086China Institute of Optical Measurement and Intellectualization Harbin Institute of TechnologyHarbin 150080China Beijing Institute of Collaborative Innovation Beijing 100094China State Key Laboratory of Membrane Biology Beijing Key Laboratory of Cardiometabolic Molecular MedicineInstitute of Molecular MedicineNational Biomedical Imaging CenterSchool of Future TechnologyPeking UniversityBeijing 100871China

A critical function of flow cytometry is to count the concentration of blood cells,which helps in the diagnosis of certain ***,the bulky nature of commercial flow cytometers makes such tests only available in hospitals or laboratories,hindering the spread of point-of-care testing(POCT),especially in underdeveloped ***,we propose a smart Palm-size Optofluidic Hematology Analyzer based on a miniature fluorescence microscope and a microfluidic platform to lighten the device to improve its *** gadget has a dimension of 35×30×80 mm and a mass of 39 g,less than 5%of the weight of commercially available flow ***,automatic leukocyte concentration detection has been realized through the integration of image processing and leukocyte counting *** compared the leukocyte concentration measurement between our approach and a hemocytometer using the Passing-Bablok analysis and achieved a correlation coefficient of *** Bland-Altman analysis,we obtained the relationship between their differences and mean measurement values and established 95%limits of agreement,ranging from−0.93×10^(3)to 0.94×10^(3)cells/μ*** anticipate that this device can be used widely for monitoring and treating diseases such as HIV and tumors beyond hospitals.

关键词： hematology analyzer miniature fluorescence microscope microfluidics leukocyte concentration

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Harmonic Analysis and ZVS Implementation in Double-Side LCC WPT System

Harmonic Analysis and ZVS Implementation in Double-Side LCC ...

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2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021

作者： Liu, Yuxin Gao, Fei Liu, Hongchen Li, Huang Shanghai Jiao Tong University Key Laboratory of Control of Power Transmission and Conversion Ministry of Education Shanghai China Harbin Institute of Technology Department of Electrical Engineering and Automation Harbin China

ISBN: (纸本)9781665414395

This paper proposed an analysis method of higher harmonic components of the double-side LCC compensation circuit in WPT system. Input impedance of the WPT system is given with the different order of the harmonic. The harmonic components are taken advantage of to realize ZVS for MOSFETs. A two-coil configuration is built and self-inductance is calculated in this paper. Simulation results shows that the input impedance of the higher harmonic components are inductive. Input current is analyzed in both time domain and frequency domain. The higher harmonic components of the input current is approximately inductive, and the natural ZVS characteristic is verified. A 150W prototype of the WPT system with double-side LCC topology has been setup. The efficiency of the system is up to 82.5%. © 2021 IEEE.

关键词： Zero voltage switching

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Determination of Key Components and Failure Mechanism Analysis of Smart Electricity Meter 3

Determination of Key Components and Failure Mechanism Analys...

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3rd International Conference on System Reliability and Safety engineering, SRSE 2021

作者： Li, Wenwen Yuan, Ruiming Guo, Jiao Li, Haoxiang Ye, Xuerong State Grid Jibei Electric Power Co. Ltd. Quality Control Department Beijing China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China

ISBN: (纸本)9781665401609

Smart electricity meter is an important computing terminal in the construction of ubiquitous power Internet of Things, and its reliable life is uneven. The identification of key components of smart electricity meters plays a key role in improving the reliable life of smart electricity meters. This paper presents a method to determine key components of smart electricity meters based on simulation. According to the working principle of smart electricity meters, the key units of smart electricity meters are determined and the simulation model of key units is built. Through the analysis of simulation software, the key components affecting the output characteristics of smart electricity meters are determined, and the failure mechanism of these key components is analyzed combined with the actual working environment of smart electricity meters. This paper provides a reference for the design, production and reliability improvement of smart electricity meters. © 2021 IEEE.

关键词： Reliability analysis

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Economic Dispatch Strategy for Virtual Power Plants Based on Distributed Multi-Agent Algorithms

Economic Dispatch Strategy for Virtual Power Plants Based on...

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Energy engineering and Power Systems (EEPS), International Conference on

作者： Dong Jiang Chenxia Wang Yali Xue Department of Energy and Power Engineering State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China College of New Energy Harbin Institute of Technology at Weihai Weihai China Department of Energy and Power Engineering State Key Laboratory of Power System Operation and Control Tsinghua Shanxi Clean Energy Research Institute Tsinghua University Beijing China

ISBN: (数字)9798350375138

ISBN: (纸本)9798350375145

This paper proposes an economic dispatch strategy for virtual power plants(VPP) based on distributed multi-agent algorithms to optimize resource allocation and maximize the VPP economy, in response to the volatility and uncertainty associated with a significant amount of renewable energy. An economic dispatch model for VPP is established in which the coefficients of the cost function are time-varying to account for the effect of the renewable energy output variation. Then a distributed multi-agent algorithm based on graph theory and the Lagrange multiplier method is developed to facilitate communication among individual units and minimize the total operational cost of the system. The effectiveness of the proposed algorithm is validated through numerical examples, which demonstrate its capability to optimize resource allocation, achieve economic dispatch for the VPP, and exhibit a rapid rate of convergence.

关键词： Economics Renewable energy sources Uncertainty Virtual power plants Cost function Graph theory Scheduling Power systems Resource management Convergence

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Machine learning algorithms to predict stroke in China based on causal inference of time series analysis

arXiv

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arXiv 2025年

作者： Zheng, Qizhi Zhao, Ayang Wang, Xinzhu Bai, Yanhong Wang, Zikun Wang, Xiuying Zeng, Xianzhang Dong, Guanghui College of Computer and Control Engineering Northeast Forestry University No.26 Hexing Road Xiangfang District Harbin150040 China Department of Anesthesiology Chongqing University Cancer Hospital No.181 Hanyu Road Shapingba District Chongqing400030 China Harbin Institute of Technology Harbin150001 China

Importance: Identifying and managing high-risk populations for stroke in a targeted manner is a key area of preventive healthcare. Objective: To assess machine learning (ML) models and causal inference of time series analysis for predicting stroke clinically meaningful model. Design: This is a retrospective cohort study and data is from China Health and Retirement Longitudinal Study (CHARLS) assessed 11,789 adults in China from 2011 to 2018. Data analysis was performed from June 1 to December 1, 2024. Setting: CHARLS adopts a multi-stage probability sampling method, covering samples from 28 provinces, and collects data every two years through computer-aided personal interviews (CAPI). Participants: This study employed a combination of Vector Autoregression (VAR) model and Graph Neural Networks (GNN) to systematically construct dynamic causal inference. Multiple classic classification algorithms were compared, including Random Forest, Logistic Regression, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gradient Boosting, and Multi Layer Perceptron (MLP). The SMOTE algorithm was used to undersample a small number of samples and employed Stratified K-fold Cross Validation. Main Outcome(s) and Measure(s): AUC (Area Under the Curve), Accuracy, Precision, Recall, F1 Score, and Matthews Correlation Coefficient (MCC). Results: This study included a total of 11,789 participants, including 6,334 females (53.73%) and 5,455 males (46.27%), with an average age of 65 years. Introduction of dynamic causal inference features has significantly improved the performance of almost all models. The area under the ROC curve of each model ranged from 0.78 to 0.83, indicating significant difference (P © 2025, CC BY.

关键词： Graph neural networks

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Graph Convolutional Network with Neural Inductive Matrix Completion for Predicting Disease-Related LncRNA Genes

Graph Convolutional Network with Neural Inductive Matrix Com...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiang Yang Shuxin Zhang Suyu Dong Long Xu Weihe Dong Xiaokun Li Pengzhong Sun Feng Jiang Xianyu Zhang Gongning Luo College of Computer and Control Engineering Northeast Forestry University Harbin China School of Medicine and Health Harbin Institute of Technology Harbin China School of Computer Science and Technology Heilongjiang University Harbin China School of Computer Science and Technology Harbin Institute of Technology Harbin China Department of Breast Surgery Harbin Medical University Cancer Hospital Harbin China Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

Numerous researches emphasized that long non-coding RNA (lncRNA) plays a vital factor in various biological processes, and its mismatched expression and dysfunction are tightly linked with the occurrence of human diseases. Thus, computational models were designed to identify lncRNA-disease interactions by merging heterogeneous biological data. However, most of them neglected the intrinsic structure of multi-source information, which limits the performance for potential lncRNA-disease association prediction. Here, GCN-NIMC is introduced to alleviate the dilemma for disease-associated lncRNA genes identification based on the graph convolutional network with neural inductive matrix. This method builds a feature matrix with multi-source heterogeneous data and then learn the various information contained in the feature matrix for the sake of acquiring better feature expressions of the lncRNA-disease interactions. Experimental results on 10-repeated 5-fold cross-validation demonstrated that our proposed GCN-NIMC is superior to existing cutting-edge methods for identifying disease-related lncRNA genes. Furthermore, case studies confirmed our computational method as a practical tool with clinical benefits to develop the therapeutic schedule at lncRNA-level.

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