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An asymmetrically substituted dithieno[3,2-b:2',3'-d]pyrrole organic small-molecule hole-transporting material for high-performance perovskite solar cells

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Chinese Journal of Chemical Engineering 2022年第5期35卷 51-57页

作者： Jingwen Jia Yue Zhang Liangsheng Duan Quanping Wu Yu Chen Song Xue Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin 300384China Tianjin Key Laboratory of Organic Solar Cells and Photochemical Conversion Department of Applied ChemistryTianjin University of TechnologyTianjin 300384China

Hole-transporting materials play a vital role in terms of the performance of perovskite solar cells(PSCs).The dithieno[3,2-b:2’,3’-d]pyrrole(DTP),an S,N-heterocyclic building block,has been proved to be desirable for molecular design of hole-transporting materials in *** developed an asymmetrically substituted DTP small-molecule(JW12)and a reference compound(JW11).The asymmetrical structure of JW12 leads to different absorption properties and electron *** device in a planar n-i-p architecture using JW12 shows a much higher PCE(18.07%)than that based on JW11(15.46%),which is also better than the device based on spiro-OMe TAD(17.47%).We hope our research can provide a new perspective in molecular design of organic HTMs for perovskite solar cells.

关键词： Hole-transporting materials DTP Perovskite Photovoltaic performance

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A theoretical and deep learning hybrid model for predicting surface roughness of diamond-turned polycrystalline materials

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International Journal of Extreme Manufacturing 2023年第3期5卷 620-644页

作者： Chunlei He Jiwang Yan Shuqi Wang Shuo Zhang Guang Chen Chengzu Ren Tianjin Key Laboratory of Equipment Design and Manufacturing Technology Department of Mechanical EngineeringTianjin UniversityTianjin 30054People’s Republic of China Department of Mechanical Engineering Keio UniversityYokohama 223-8522Japan Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System School of Mechanical EngineeringTianjin University of TechnologyTianjin 300384People’s Republic of China School of Mechanical Engineering Yanshan UniversityQinhuangdao 066004People’s Republic of China

Polycrystalline materials are extensively employed in *** surface roughness significantly affects the working *** defects,particularly grain boundaries,have a great impact on the achieved surface roughness of polycrystalline ***,it is difficult to establish a purely theoretical model for surface roughness with consideration of the grain boundary effect using conventional analytical *** this work,a theoretical and deep learning hybrid model for predicting the surface roughness of diamond-turned polycrystalline materials is *** kinematic–dynamic roughness component in relation to the tool profile duplication effect,work material plastic side flow,relative vibration between the diamond tool and workpiece,etc,is theoretically *** material-defect roughness component is modeled with a cascade forward neural *** the neural network,the ratio of maximum undeformed chip thickness to cutting edge radius RT S,work material properties(misorientation angle θ_(g) and grain size d_(g)),and spindle rotation speed n s are configured as input *** material-defect roughness component is set as the output *** validate the developed model,polycrystalline copper with a gradient distribution of grains prepared by friction stir processing is machined with various processing parameters and different diamond *** with the previously developed model,obvious improvement in the prediction accuracy is observed with this hybrid prediction *** on this model,the influences of different factors on the surface roughness of polycrystalline materials are *** influencing mechanism of the misorientation angle and grain size is quantitatively *** fracture modes,including transcrystalline and intercrystalline fractures at different RTS values,are ***,optimal processing parameters are obtained with a simulated annealing *** experiments are performed with

关键词： diamond turning material-defect roughness component polycrystalline copper neural network simulated annealing algorithm

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Welding Seam Image Dust Removal Algorithm Based on Fusion of Dual-scale Dark Channel and Bright Channel

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2021年第2期48卷 1-8页

作者： Zheng, Qingchun Zhang, Zhidong Liu, Jingna Zhu, Peihao Ma, Wenpeng Professor in the Key Laboratory of Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin300384 China Postgraduate student in the Key Laboratory of Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin300384 China A Lecturer in the Key Laboratory of Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin300384 China An Assistant Professor in the Key Laboratory of Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin300384 China

The dust removal technology of welding seam image has important practical significance for promoting the development of weld instance segmentation and target tracking. In this paper, we propose a welding seam image dust removal algorithm based on fusion of dual-scale dark channel and bright channel, which is to solve the problem that the color distortion and halo effect of dark channel prior image restoration algorithm in weld depth of field sudden change area and bright region. Firstly, the results of the minimum filtering of two scale windows are adaptively weighted and fused to obtain the fused dark channel. Secondly, the dark channel prior and the bright channel prior fusion strategy is adopted to obtain the fused atmospheric light to make the rough transmittance estimation more accurate. Finally, the weighted guided filtering is combined with up-sampling and down-sampling to optimize the rough transmittance, and the weld image is restored by combining the atmospheric scattering model. The experimental results indicate that our algorithm can restore the color and edge details of welding seam images and effectively suppress the halo effect. The objective comparison with several typical algorithms proves the feasibility and superiority of the proposed algorithm. © 2021. All Rights Reserved.

关键词： Image reconstruction

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Impulsive Control of Heterogeneous Multi-agent Systems with Saturation Constraint and Time Delay

Impulsive Control of Heterogeneous Multi-agent Systems with ...

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2024 China Automation Congress, CAC 2024

作者： Jiao, Ranran Li, Bo Chen, Xiangyong Jiang, Xiaowei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China

ISBN: (纸本)9798350368604

This paper addresses the problem of leader-following consensus in heterogeneous multi-agent systems (HMAS) with input saturation and communication delay. Given the practical differences between leader and follower dimensions, we design virtual leaders to address this problem by allowing actual leaders and followers to communicate with virtual leaders separately, thus enabling indirect communication between actual leaders and followers. Additionally, the control protocol proposed in this paper takes into account the effects of input saturation and time delay that exist in real communication. At last, a simulation example is presented to demonstrate the effectiveness of the designed control protocol. © 2024 IEEE.

关键词： Multi agent systems

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BContext2Name: Naming Functions in Stripped Binaries with Multi-Label Learning and Neural Networks 10

BContext2Name: Naming Functions in Stripped Binaries with Mu...

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10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Xia, Bing Ge, Yunxiang Yang, Ruinan Yin, Jiabin Pang, Jianmin Tang, Chongjun Dept.ZhongYuan University of Technology Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Dept.ZhongYuan University of Technology Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou China Dept.ZhongYuan University of Technology Zhengzhou China Dept.Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350312461

Conducting binary function naming helps reverse engineers understand the internal workings of the code and perform malicious code analysis without accessing the source code. However, the loss of debugging information poses the challenge of insufficient high-level semantic information description for stripping binary code function naming. Meanwhile, the existing binary function naming scheme has one function label for only one sample. The long-tail effect of function labels for a single sample makes the machine learning-based prediction models face the challenge. To obtain a function correlation label and improve the propensity score of uncommon tail labels, we propose a multi-label learning-based binary function naming model BContext2Name. This model automatically generates relevant labels for binary function naming by function context information with the help of PfastreXML model. The experimental results show that BContext2Name can enrich function labels and alleviate the long-tail effect that exists for a single sample class. To obtain high-level semantics of binary functions, we align pseudocode and basic blocks based on disassembly and decompilation, identify concrete or abstract values of API parameters by variable tracking, and construct API-enhanced control flow graphs. Finally, a seq2seq neural network translation model with attention mechanism is constructed between function multi-label learning and enhanced control flow graphs. Experiments on the dataset reveal that the F1 values of the BContext2Name model improve by 3.55% and 15.23% over the state-of-the-art XFL and Nero, respectively. This indicates that function multi-label learning can provide accurate labels for binary functions and can help reverse analysts understand the inner working mechanism of binary code. Code and data for this evaluation are available at https://***/CSecurityZhongYuan/BContext2Name. © 2023 IEEE.

关键词： Graphic methods

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Hierarchically Micro/Nanostructured Current Collectors Induced by Ultrafast Femtosecond Laser Strategy for High-Performance Lithium-ion Batteries

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Energy & Environmental Materials 2022年第3期5卷 969-976页

作者： Yaya Wang Zexu Zhao Jiang Zhong Tao Wang Lei Wang Hanjiao Xu Jinhui Cao Jinhao Li Guanhua Zhang Huilong Fei Jian Zhu State Key Laboratory for Chemo/Biosensing and Chemometrics College of Chemistry and Chemical EngineeringHunan Key Laboratory of TwoDimensional MaterialsEngineering Research Center of Advanced CatalysisMinistry of EducationHunan UniversityChangsha 410082China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan Provincial Key Laboratory of Intelligent Laser ManufacturingHunan UniversityChangsha 410082China College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China

Commercial Cu and Al current collectors for lithium-ion batteries(LIBs)possess high electrical conductivity,suitable chemical and electrochemical ***,the relatively flat surface of traditional current collectors causes weak bonding strength and poor electrochemical contact between current collectors and electrode materials,resulting in potential detachment of active materials and rapid capacity degradation during extended ***,we report an ultrafast femtosecond laser strategy to manufacture hierarchical micro/nanostructures on commercial Al and Cu foils as current collectors for high-performance *** hierarchically micro/nanostructured current collectors(HMNCCs)with high surface area and roughness offer strong adhesion to active materials,fast electronic delivery of entire electrodes,significantly improving reversible capacities and cyclic stability of HMNCCs based ***,LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2)(NCM523)cathode with Al HMNCC generated a high reversible capacity after 200 cycles(25%higher than that of cathode with Al CC).Besides,graphite anode with Cu HMNCC also maintained prominent reversible capacity even after 600 ***,the full cell assembled by graphite anode with Cu HMNCC and NCM523 cathode with Al HMNCC achieved high reversible capacity and remarkable cycling stability under industrial-grade mass *** study provides promising candidate for achieving high-performance LIBs current collectors.

关键词： currentcollectors femtosecondlaserstrategy hierarchical micro/nanostructures high rate performance lithium-ion battery

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Development and Implementation of a Wisdom Course Utilizing the "teaching-Innovation-Competition" Model within the Virtual Teaching and Research Section of Mechatronic Engineering Major in China 24

Development and Implementation of a Wisdom Course Utilizing ...

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3rd International Conference on Artificial Intelligence and Education, ICAIE 2024

作者： Chen, Wei Ma, Wenpeng Hu, Wenhua Liu, Fulong Zhang, Xiaotao Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China Tianjin University of Technology Tianjin China

ISBN: (纸本)9798400712692

A new teaching model of teaching-innovation-competition is presented and a wisdom course based on this model is developed for mechatronics engineering majors, mainly to deal with several existing problems such as limited experimentation and lack of innovative inspiration in Chinese universities. This study elaborates the course objectives and structure of curriculum system, including digitalized teaching content, project-driven teaching means, innovation platform, web-based resource support, team competition and diversified evaluation method. Through a teaching case study within mechatronic engineering major, the teaching mechanism and achievements are demonstrated. The study results indicate that the proposed teaching mode based on the virtual teaching and research section, significantly improves students' innovation ability and the teaching effects, compared to traditional teaching methods. This teaching mode can also be extended to other majors for reference. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Teaching

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Disease Phenotype Classification Model Based on Multi-channel Deep Supervised Bi-LSTM

Disease Phenotype Classification Model Based on Multi-channe...

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International Conference on Computer Engineering and Applications (ICCEA)

作者： Decong Wang Changyuan Zou Ziqi Wei Zhaoqian Zhong Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China CAS Key Laboratory of Molecular Imaging Institute of Automation Beijing China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Phenotypic classification of diseases refers to the process of categorizing diseases into different classes based on their manifestations or characteristics, aiding doctors in diagnosing diseases and developing treatment plans. Current classification methods are challenged by issues such as strong subjectivity, high instability, and low data utilization. To conduct phenotypic classification of diseases more effectively, this paper proposed a disease phenotypic classification model based on multi-channel deep supervised Bi-LSTM. The proposed model integrates structured and unstructured data for classification, uses the multi-channel module to integrate temporal information from multi-variable time sequence data, and utilizes Bi-LSTM module to capture long-term dependencies in single-channel sequence data. Additionally, a deep supervision module is used to accelerate model convergence and improve generalization capability. The experimental results on the MIMIC-III dataset indicate that the proposed model achieves higher classification accuracy compared to various baseline models, with a smaller parameter size and shorter training time, thereby demonstrating the effectiveness of the method presented in this paper.

关键词： Training Phenotypes Runtime Computational modeling MIMICs Data models Computational efficiency

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K-MEANS CLUSTERING BASED SPARSE CROSS-ENTROPY MINIMIZATION ALGORITHM FOR DOA ESTIMATION

K-MEANS CLUSTERING BASED SPARSE CROSS-ENTROPY MINIMIZATION A...

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IET International Radar Conference 2023, IRC 2023

作者： Guo, Qiang Jiang, Hanyu Xiang, Jianhong Zhong, Yu College of Information and Communication Harbin Engineering University Harbin China Key Laboratory of Advanced Ship Communication and Information Technology Harbin Engineering University Harbin China Agile and Intelligent Computing Key Laboratory Chengdu China

ISBN: (纸本)9781839539954

Aiming at the problem that traditional greedy class compressed sensing algorithms are influenced by noise in the estimation of direction of arrival (DOA), this paper proposes the K-Means Clustering based Sparse Cross-Entropy Minimization (KMCSCEM) algorithm. First the algorithm selects atoms by minimizing the sparse cross-entropy to improve the accuracy of atom selection at the low signal-to-noise ratio (SNR). Subsequently, K-mean clustering is added in each iteration to reduce the range of atom selection, which further improves the estimation accuracy. Finally, the speed of convergence is improved by introducing an orthogonal least squares (OLS) criterion. Simulation experiments show that the proposed algorithm has an exact estimation ratio close to 90% when the SNR is less than -5 dB. © The Institution of Engineering & Technology 2023.

关键词： Signal to noise ratio

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Wireless power transmission system with CC and CV output based on hybrid LCC-S/P topology 5

Wireless power transmission system with CC and CV output bas...

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5th International Conference on Electronics and Communication, Network and Computer Technology, ECNCT 2023

作者： Li, Kun Gong, Zheng Meng, Jiaheng Liu, Qing Tianjin University of Technology Tianjin Key Laboratory of Film Electronic and Communication Device Tianjin China Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin China

ISBN: (纸本)9798350328660

Wireless Power Transfer (WPT) technology provides a convenient and efficient power supply solution for mobile devices. The utilization of constant current (CC) and constant voltage (CV) control in WPT systems proves valuable for charging battery applications. This paper presents a novel WPT system with CC and CV output characteristics, employing a switching topology. Specifically, the LCC-S and LCC-P topologies are applied and rigorously simulated. The proposed MCR-WPT system's output characteristics in both CC and CV modes are meticulously validated using Saber software. The simulation results distinctly demonstrate the system's capability to achieve CC and CV outputs within a varying load resistance range of 5Ω to 30Ω, thus conclusively establishing the efficacy of the topology switching technology. © 2023 IEEE.

关键词： Topology

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