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Journal of Applied Science and engineering 2024年第6期27卷 2643-2653页

作者： An, Xiaoyuan Zhao, Qi Li, Xiaoli School of Urban Construction Changchun University of Architecture and Civil Engineering Jilin Changchun130012 China School of Energy and Power Changchun Institute of Technology Jilin Changchun130012 China

The importance of producing clean energy without any pollutants, including carbon dioxide and nitrogen oxides, has increased due to climate changes caused by global warming. Wind turbine can produce clean energy. To use a wind turbine, the feasibility of using a wind turbine in that area should be checked. In this research, energy, exergy, economic as well as exergy destruction analyses were conducted for the feasibility of using the Nordex wind turbine in Manjiil city. According to the meteorological organization report, the number of winds in Manjiil city was obtained, and the average speed, average power, energy efficiency, exergy efficiency, exergy destruction and economic cost were calculated using existing equations. According to the outcomes of this research, the average power produced, energy efficiency and exergy destruction are high in the months with high average wind speed, and these months have lower exergy efficiency and economic cost. The cost of electricity generated in October is 0.0194 S/kWh, which is the lowest. The cost of electricity generated in December is 0.0292$/kWh, which is the highest. Also, the effects of cut speed, rated speed and failure speed on wind turbine performance were investigated. As the nominal speed increases, exergy destruction, production entropy and economic cost increase and energy efficiency and exergy efficiency decrease. © The Author(’s).

关键词： Wind turbines

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Single photon scattering with the giant and small atom interplay in a one-dimensional coupled resonator waveguide

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Communications in Theoretical Physics 2025年第2期77卷 64-69页

作者： Xi Tian Xiaojun Zhang Wei Zhao Jingcheng Hou Zhihai Wang Tian Tian School of Materials Science and Engineering Changchun UniversityChangchun 130022China Center for Quantum Sciences and School of Physics Northeast Normal UniversityChangchun 130024China Institute for Interdisciplinary Quantum Information Technology Jilin Engineering Normal UniversityChangchun 130052China

We study single photon scattering in a one-dimensional coupled resonator waveguide,which is dressed by a small and a giant artificial atom ***,we have set the small atom to be a neighbor to one leg of the giant atom,and the giant atom couples to the waveguide via two distant *** the small and giant atoms are both resonant with the bare resonator in the waveguide,we observe the perfect reflection of the resonant incident *** the other hand,when the small atom is detuned from the giant atom,the single photon reflection is characterized by a wide window and Fano line *** hope our work will pave the way for the potential application of small and giant atom hybrid systems in the study of photonic control in the lowdimensional waveguide structure.

关键词： giant atom single-photon scattering fano shape

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Autonomous embodied navigation task generation from natural language dialogues

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Science China(Information Sciences) 2025年第5期68卷 119-132页

作者： Haifeng XU Yongchang LI Lumeng MA Chunwen LI Yanzhi DONG Xiaohu YUAN Huaping LIU Department of Automation Tsinghua University School of Physics and Electronic Information Yantai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Computer Science and Technology Tsinghua University

Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks often require multiple instructions and prolonged monitoring, which can be time-consuming and demanding for users. Despite this, there is limited research on enabling robots to autonomously generate tasks based on real-life scenarios. Advanced intelligence necessitates robots to autonomously observe and analyze their environment and then generate tasks autonomously to fulfill human requirements without explicit commands. To address this gap, we propose the autonomous generation of navigation tasks using natural language dialogues. Specifically, a robot autonomously generates tasks by analyzing dialogues involving multiple persons in a real office environment to facilitate the completion of item transportation between various *** propose the leveraging of a large language model(LLM) through chain-of-thought prompting to generate a navigation sequence for a robot from dialogues. We also construct a benchmark dataset consisting of 625 multiperson dialogues using the generation capability of LLMs. Evaluation results and real-world experiments in an office building demonstrate the effectiveness of the proposed method.

关键词： proactive robot robot navigation service robot large language model

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Constructing main/side chain dual-cation poly(mequitazine-terphenyl piperidinium)anion exchange membranes for high-performance fuel cells

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Materials Reports(Energy) 2025年第2期5卷 68-76,I0002页

作者： Shiyao Sun Jialin Zhao Yijia Lei Jingyi Wu Jian Gao Na Li Jiayao Yang Jiahao Lu Liying Yin Zhe Wang School of Chemistry and Life Sciences Changchun University of TechnologyChangchun130012China School of Chemical Engineering Changchun University of TechnologyChangchun130012China Advanced Institute of Materials Science Changchun University of TechnologyChangchun130012China Key Laboratory of Advanced Functional Polymer Membrane Materials of Jilin Province Changchun130012China

Anion exchange membranes(AEMs)combining high hydroxide conductivity and alkali-resistant stability have become a major challenge for the long-term development of anion exchange membrane fuel cells(AEMFCs).Here,we designed a series of poly(mequitazine-terphenyl piperidinium)(QPMTP-X)AEMs with dual-functionalized quaternary ammonium cations by introducing a certain proportion of large steric hindrance mequitazine(MEQ)molecular building unit into the poly(aryl piperidinium)***-X retains the excellent mechanical properties of the poly(aryl piperidinium),while also combining the alkaline stability and high ionic conductivity exhibited by MEQ with flexible quinuclidinium side chains,achieving an overall improvement of membrane ***,QPMTP-30 exhibits an ultra-high conductivity of up to 206.83 mS cm^(-1)and excellent alkaline stability(over 95%conductivity is maintained after 1000 h of conditioning in 2 M NaOH at 80℃).In fuel cell performance test,QPMTP-30 achieves a peak power density(PPD)of 974.5 mW cm^(-2)and operates stably at 80℃for more than 60 h(0.1 A cm^(-2)).Incorporating large steric hindrance building blocks and multi-cations into the poly(aryl piperidinium)backbone not only synergizes the development of highperformance AEMs but also opens up new ideas for the structural design of future AEMs.

关键词： Anion exchange membranes Mequitazine Dual cations High efficiency ion transport Fuel cells

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Fluorine-free,robust and self-healing superhydrophobic surfaces with anticorrosion and antibacterial performances

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Journal of Materials Science & technology 2024年第19期186卷 231-243页

作者： Wenliang Zhang Shuyi Li Dongsong Wei Zaihang Zheng Zhiwu Han Yan Liu Key Laboratory of Bionic Engineering(Ministry of Education) Jilin UniversityChangchun130022China Weihai Institute for Bionics-Jilin University Weihai264402China School of Chemical Engineering Changchun University of TechnologyChangchun130012China

Superhydrophobic surfaces with water-repelling ability have important applications, such as self-cleaning, antibacterial and corrosion protection. However, the using of harmful fluorinated materials and its poor mechanochemical stability limit its practical application. Herein, a fluorine-free, robust and self-healing superhydrophobic surface is prepared through a two-step method of laser processing and spraying coating for anticorrosion and antibacterial applications. Laser processing is used to construct periodic micron-sized pillars for obtaining strong interface bonding between coating and substrate by mechanical interlocking effect, and as an ‘armor’, preventing the removal of the coating. The coating consists of epoxy resin (EP), hexadecyltrimethoxysilane (HDTMS) and γ-aminopropyltriethoxysilane treated Cu2O (KH550-Cu2O). The superhydrophobic surface can withstand various mechanical durability tests, such as multiple sandpaper abrasion and tape peeling cycles. It exhibits excellent corrosion inhibition efficiency (ηp > 99 %) on Mg alloy, Tinplate and Al alloy, which results from superhydrophobicity and organic coating. The superhydrophobicity endows surface with excellent antibacterial adhesion performance in a static liquid environment. The bactericidal activity of KH550-Cu2O can effectively inactivate the bacteria in contact with the surface and the free bacteria, providing excellent antibacterial ability in a dynamic liquid environment. It still exhibits good anticorrosion and antibacterial abilities after multiple mechanical abrasion cycles due to the outstanding mechanical durability. Moreover, it exhibits outstanding self-healing ability to plasma etching and oil contamination, self-cleaning ability under air and oil conditions, and chemical stability against acids and alkalis solution. All the above excellent performances promote its application in a wider range of fields.

关键词： Superhydrophobicity Self-healing Durability and stability Antibacterial adhesion and bactericidal activity Anticorrosion

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An image segmentation fusion algorithm based on density peak clustering and Markov random field

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Multimedia Tools and Applications 2024年第37期83卷 85331-85355页

作者： Feng, Yuncong Liu, Wanru Zhang, Xiaoli Zhu, Xiaoyan College of Computer Science and Engineering Changchun University of Technology Jilin Changchun130012 China Artificial Intelligence Research Institute Changchun University of Technology Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China College of Computer Science and Technology Jilin University Jilin Changchun130012 China

Image segmentation is a crucial task in the field of computer vision. Markov random fields (MRF) based image segmentation method can effectively capture intricate relationships among pixels. However, MRF typically requires an initial labeling field, and the number of classifications needs to be manually selected. To tackle these issues, we propose a novel medical image segmentation algorithm based on density peak clustering (DPC) and Markov random fields. Firstly, we improve DPC to make it applicable to grayscale images, named GIDPC. In the GIDPC method, local gray density and gray bias are defined to enable the automatic determination of the number of classifications. Then, GIDPC and MRF are combined to achieve image segmentation. Furthermore, a segmentation fusion method is employed to enhance the accuracy of image segmentation. We conduct comparison experiments on the whole brain atlas image library. Our proposed algorithm achieves high average values in uniformity measure, accuracy, precision and sensitivity, respectively. Experimental results demonstrate that the proposed algorithm outperforms other image segmentation methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image segmentation

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A Fault Diagnosis Method for Rolling Bearings Based on Improved Snake Swarm Optimization and Support Vector Machine

A Fault Diagnosis Method for Rolling Bearings Based on Impro...

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2024 China Automation Congress, CAC 2024

作者： Chen, P. Siyu Chang, Xiaopeng Zhang, Xiyu Zhang, Bangcheng Gao, Zhi School of Mechatronic Engineering Changchun University of Technology Changchun China School of Mechanical and Aerospace Engineering Jilin University Changchun China School of Mechatronic Engineering Changchun University of Technology Changchun Institute of Technology Changchun China

ISBN: (纸本)9798350368604

Support vector machine (SVM) is widely used in rolling bearing fault diagnosis because of its excellent performance in minor sample problems. However, SVM performance is highly dependent on the choice of hyperparameters, such as the regularization parameter C and the kernel parameter y, and different combinations of hyperparameters can lead to significant differences in model performance. This paper proposes a fault diagnosis classification model that optimizes support vector machines with an improved snake swarm optimization algorithm (SOA). By introducing adaptive speed update, dynamic weight adjustment, and diversity maintenance strategies, the improved SOA significantly enhances the global search capability and convergence speed and effectively avoids the risk of falling into local optimal. Compared with the traditional hyperparameter optimization method, this method makes up for the inherent defects of SVM. It significantly improves the accuracy and efficiency of fault diagnosis, providing an effective and efficient solution for rolling bearing fault diagnosis. © 2024 IEEE.

关键词： Roller bearings

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Research on Machining and Simulation Optimization System of Automobile Steering Knuckle Based on Advanced Algorithm 6th

Research on Machining and Simulation Optimization System of ...

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6th International Conference on Cyber Security Intelligence and Analytics, CSIA 2024

作者： Chang, Xiaopeng Chen, Siyu Zhang, Xiyu Zhang, Bangcheng Yu, Bo School of Mechanical and Electrical Engineering Changchun University of Technology Changchun130012 China School of Mechanical and Electrical Engineering Changchun Institute of Technology Changchun130012 China

ISBN: (纸本)9783031882869

With the rapid development of automobile industry, automobile steering knuckle is one of the most important parts on automobile steering bridge, therefore, the design of its high-efficiency production process has become the inevitable choice for the development of automobile steering knuckle. In view of the characteristics of automobile steering knuckle, such as complex structure, many machining parts, high machining requirements, difficult positioning and clamping, and large production batch, combined with the actual machining process of automobile steering knuckle, including production line layout and position arrangement, production rhythm and production capacity status quo, discussed and analyzed the production line of our car steering knuckle specific process problems. According to the structure characteristics of automobile steering knuckle, the machining technology scheme is worked out, and the machining positioning datum, part clamping scheme and balance analysis are given. Based on the Quest platform, the simulation test of maximizing the machining efficiency and optimizing the man-hour configuration is carried out, and the simulation results are analyzed and evaluated, the validity of the research is proved. Finally, the optimal process plan of the automobile steering knuckle is given, which is helpful to the reference design of the machining process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Automobile Industry Optimization Design Processing technology Simulation Analysis Steering Knuckle

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Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2253-2269页

作者： Kui Jiang Ruoxi Wang Yi Xiao Junjun Jiang Xin Xu Tao Lu IEEE the School of Computer Science and Technology Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology the School of Artificial Intelligence Jianghan University the School of Geodesy and Geomatics Wuhan University the Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology the School of Computer Science and Engineering and also with the Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology

Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network(PerTeRNet). It contains two subnetworks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery,we develop a novel perturbation-guided texture enhancement module(PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://***/kuijiang94/PerTeRNet.

关键词： Association learning attention mechanism image enhancement perturbation modeling

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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