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SolarDesign:An online photovoltaic device simulation and design platform

Chinese Physics B 2025年第1期34卷 135-141页

作者： Wei E.I.Sha Xiaoyu Wang Wenchao Chen Yuhao Fu Lijun Zhang Liang Tian Minshen Lin Shudi Jiao Ting Xu Tiange Sun Dongxue Liu College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China State Key Laboratory of Integrated Optoelectronics Key Laboratory of Automobile Materials of MOEKey Laboratory of Material Simulation Methods&Software of MOESchool of Materials Science and EngineeringJilin UniversityChangchun 130012China ZJU-UIUC Institute International CampusZhejiang UniversityHaining 314400China Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchun 130012China College of Electrical Engineering Zhejiang UniversityHangzhou 310027China Science and Technology Research Institute China Three Gorges CorporationBeijing 101199China

Solar Design(https://***/)is an online photovoltaic device simulation and design platform that provides engineering modeling analysis for crystalline silicon solar cells,as well as emerging high-efficiency solar cells such as organic,perovskite,and tandem *** platform offers user-updatable libraries of basic photovoltaic materials and devices,device-level multi-physics simulations involving optical–electrical–thermal interactions,and circuit-level compact model simulations based on detailed balance *** internationally advanced numerical methods,the platform accurately,rapidly,and efficiently solves optical absorption,electrical transport,and compact circuit *** achieves multi-level photovoltaic simulation technology from“materials to devices to circuits”with fully independent intellectual property *** to commercial softwares,the platform achieves high accuracy and improves speed by more than an order of ***,it can simulate unique electrical transport processes in emerging solar cells,such as quantum tunneling,exciton dissociation,and ion migration.

关键词： photovoltaic device simulation silicon solar cells organic and perovskite solar cells multi-physics and circuit simulation

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Incorporating metapath interaction on heterogeneous information network for social recommendation

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Frontiers of Computer Science 2024年第1期18卷 33-48页

作者： Yanbin JIANG Huifang MA Xiaohui ZHANG Zhixin LI Liang CHANG College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal UniversityGuilin 541001China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Heterogeneous information network(HIN)has recently been widely adopted to describe complex graph structure in recommendation systems,proving its effectiveness in modeling complex graph *** existing HIN-based recommendation studies have achieved great success by performing message propagation between connected nodes on the defined metapaths,they have the following major *** works mainly convert heterogeneous graphs into homogeneous graphs via defining metapaths,which are not expressive enough to capture more complicated dependency relationships involved on the ***,the heterogeneous information is more likely to be provided by item attributes while social relations between users are not adequately *** tackle these limitations,we propose a novel social recommendation model MPISR,which models MetaPath Interaction for Social Recommendation on heterogeneous information ***,our model first learns the initial node representation through a pretraining module,and then identifies potential social friends and item relations based on their similarity to construct a unified *** then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different *** experiments on five real datasets demonstrate the effectiveness of our method.

关键词： heterogeneous information network social reco-mmender system metapath interaction attention mechanism

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RobFL: Robust Federated Learning via Feature Center Separation and Malicious Center Detection 40

RobFL: Robust Federated Learning via Feature Center Separati...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhou, Ting Liu, Ning Song, Bo Lv, Hongtao Guo, Deke Liu, Lei National University of Defense Technology Science and Technology Laboratory on Information Systems Engineering Changsha China School of Software Shandong University Jinan China Shandong Research Institute of Industrial Technology Jinan China

ISBN: (纸本)9798350317152

In recent years, the integration of federated learning and deep learning technologies has become increasingly prevalent in privacy-preserved scenarios, such as smart health applications and automatic financial support. However, the inherent robustness issue in deep learning poses potential risks to federated learning systems when subjected to various attack methods. These attacks can inflict damage during the training and testing phases, perturbing models and inputs. To enhance the robustness of existing federated learning systems, we propose a novel framework called RobFL. This framework incorporates a unique feature learning module -feature center separation learning -that is specifically designed to increase the margins between different classes in the feature space, thereby augmenting the difficulty of attacks employing imperceptible perturbations on inputs. Furthermore, we design a malicious center detection method to detect malicious clients and mitigate their adverse impact. Extensive experiments substantiate the robustness of our proposed framework, RobFL, demonstrating its resilience against both evasion attacks and poisoning attacks. © 2024 IEEE.

关键词： Anomaly detection

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Fast resistivity imaging of transient electromagnetic using an extreme learning machine

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Soft Computing 2025年第2期29卷 1121-1131页

作者： Li, Ruiyou Zhang, Yong Li, Guang Li, Ruiheng Hu, Jia Li, Min School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics NanChang330013 China School of Geophysics and Measurement Control Technology East China University of Technology NanChang330013 China School of Information Engineering Hubei University of Economics Wuhan430205 China The State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400044 China

The transient electromagnetic method (TEM) is widely used in geophysical exploration. In TEM data interpretation, nonlinear inversion plays an important role. However, traditional TEM nonlinear inversion adopts the OCCAM imaging method, which merely presents the approximate shape of the stratum model, with poor inversion accuracy and much iteration time. To solve the above problems, a novel nonlinear inversion approach based on extreme learning machine (ELM) is proposed in this paper. The ELM is required to establish the input–output mapping relationship of the inversion network only once through the analytical method, which is different from the traditional neural network method that demands iterative gradient learning and is prone to fall into the local optimum. Moreover, the ELM inversion network by randomly assigning the hidden layer parameters is capable of mapping the observed TEM data and quickly producing resistivity images, which avoids time-consuming iteration and inversion calculations. The presented approach is applied to both synthetic and field examples. The results show that compared with the traditional nonlinear inversion algorithms (BP and OCCAM), the proposed method achieves better inversion accuracy and significantly reduces the calculation time, which verifies the effectiveness of the ELM algorithm for TEM data interpretation. Additionally, the research provides a new method and technology for TEM data inversion. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Electromagnetic transients

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Reliable image classification based on evidence and GCN

Reliable image classification based on evidence and GCN

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2024 International Conference on Electronics, Electrical and Information engineering, ICEEIE 2024

作者： He, Zhonghai Xu, Xiang Lian, Zhuming Luo, Yu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Intelligent Terminal Key Laboratory of Sichuan Province Yibin China

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

With good classification performance, digital medical image methods based on deep learning have received more and more attention. However, the problem of these methods is that the black-box characteristic of deep learning makes it difficult to explain the model and prediction results. In order to improve the interpretability and reliability of medical image classification tasks, this paper designs a network structure that introduces eliminating uncertainty in GCN feedback by using evidence theory and subjective logic. The diagnosis experiments of COVID-19 images show that the model can effectively improve the recognition accuracy of COVID-19 patients, and has good reliability. © 2024 SPIE.

关键词： COVID-19

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Feature Model Configuration Reuse Scheme for Self-Adaptive Systems

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Computers, Materials & Continua 2022年第4期71卷 1249-1262页

作者： Sumaya Alkubaisi Said Ghoul Oguz Ata Electrical and Computer Engineering Department Altinbas UniversityIstanbul34218Turkey Faculty of Information Technology Research Laboratory on Bio-Inspired Software EngineeringPhiladelphia UniversityAmman26160Jordan

Most large-scale systems including self-adaptive systems utilize feature models(FMs)to represent their complex architectures and benefit from the reuse of commonalities and variability ***-adaptive systems(SASs)are capable of reconfiguring themselves during the run time to satisfy the scenarios of the requisite ***,reconfiguration of SASs corresponding to each adaptation of the system requires significant computational time and *** process of configuration reuse can be a better alternative to some contexts to reduce computational time,effort and ***,systems’complexity can be reduced while the development process of systems by reusing elements or *** are considered one of the new ways of reuse process that are able to introduce new opportunities for the reuse process beyond the conventional system *** current FM-based modelling techniques represent,manage,and reuse elementary features to model SASs concepts,modeling and reusing configurations have not yet been *** this context,this study presents an extension to FMs by introducing and managing configuration features and their reuse *** results demonstrate that reusing configuration features reduces the effort and time required by a reconfiguration process during the run time to meet the required scenario according to the current context.

关键词： Self-adaptive system feature model system reuse configuration management variability modeling

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Distinguishing Sensitive and Insensitive Options for the Winograd Schema Challenge 28th

Distinguishing Sensitive and Insensitive Options for the ...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Li, Dong Wang, Pancheng He, Liangliang Pang, Kunyuan Li, Shasha Tang, Jintao Wang, Ting College of Computer Science and Technology National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China

ISBN: (纸本)9783031306747

The Winograd Schema Challenge (WSC) is a popular benchmark for commonsense reasoning. Each WSC instance has a component that corresponds to the mention of the correct answer option of the two options in the context. We observe that the answers of many instances are insensitive to the options. In this paper, based on this observation, we propose an approach based on fine-tuning the pre-trained language model for WSC by distinguishing sensitive and insensitive options. First, we split WSC instances into option-sensitive and insensitive categories, and use option expanding and option masking strategies to weaken the options so that the model does not pay attention to options when they are insensitive during fine-tuning. Second, we treat the two categories as intermediate-task of each other, and use transfer learning to improve the performance. We fine-tune BERT-Large and T5-XXL with our approach on WINOGRANDE, a new dataset of WSC, and the experiment shows our method outperforms baselines by a large margin, achieving state-of-the-art, which indicates the effectiveness of our instance-distinguishing strategy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Large dataset

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Generative Positive-Unlabeled Classification for Hunting Small Open Reading Frames

Generative Positive-Unlabeled Classification for Hunting Sma...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Jie Liao, Wenbin Wen, Du Li, Jianqiang Wang, Fangzhong Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Tianjin University Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology China

ISBN: (纸本)9798350337488

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model's robustness. © 2023 IEEE.

关键词： Class Imbalance Data Generation Gene Annotation Positive-Unlabeled Learning

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NL2CTL: Automatic Generation of Formal Requirements Specifications via Large Language Models 1

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25th International Conference on Formal engineering Methods, ICFEM 2024

作者： Zhao, Mengyan Tao, Ran Huang, Yanhong Shi, Jianqi Qin, Shengchao Yang, Yang National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai China Hardware/Software Co-Design Technology and Application Engineering Research Center East China Normal University Shanghai China Guangzhou Institute of Technology Xidian University Xi’an China ICTT and ISN Laboratory Xidian University Xi’an China Software Engineering Institute East China Normal University Shanghai China

ISBN: (数字)9789819606177

ISBN: (纸本)9789819606160

Reducing the gap between natural language requirements and precise formal specifications is a critical task in requirements engineering. In recent years, requirement engineering is becoming increasingly complex alongside the growing intricacy of system engineering. Most requirements are expressed in natural language, which can be incomplete and ambiguous. However, formal languages with strict semantics can accurately represent certain temporal logic properties and allow for automated verification and analysis. This often limits the application of verification techniques, as writing formal specifications is a manual, error-prone, and time-consuming task. To address this, this paper proposes a framework that leverages Large Language Models (LLMs) to achieve automated conversion of natural language requirements to Computation Tree Logic (CTL). To address the issue of dataset scarcity, we leveraged the interactive and generative capabilities of LLMs. By constructing a random generation algorithm and utilizing prompt engineering, we generated an NL-CTL dataset using LLMs. The generated dataset was then used to fine-tune the T5-Large model, enhancing its generative capacity. To improve generalization, this paper proposes the use of the GPT-3.5 Atomic Proposition (AP) Recognition method, which eliminates the constraints of using the framework across different domains. A series of experimental evaluations showed that the fine-tuned LLM achieved an accuracy of 46.4%, whereas the LLM with few-shot learning using only prompt engineering achieved only 2% accuracy, demonstrating the feasibility of this approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Semantics

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Symplectic solutions for orthotropic micropolar plane stress problem

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Acta Mechanica Sinica 2025年第1期41卷 106-127页

作者： Long Chen Zhaofei Tang Qiong Wu Qiang Gao State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDepartment of Engineering MechanicsFaculty of Vehicle Engineering and MechanicsDalian University of TechnologyDalian 116024China

The symplectic approach was utilized to derive solutions to the orthotropic micropolar plane stress *** Hamiltonian canonical equation was first obtained by applying Legendre’s transformation and the Hamiltonian mixed energy variational ***,by using the method of separation of variables,the eigenproblem of the corresponding homogeneous Hamiltonian canonical equation was ***,the corresponding eigensolutions for three kinds of homogeneous boundary conditions were *** to the adjoint symplectic orthogonality of the eigensolutions and expansion theorems,the solutions to this plane stress problem were expressed as a series expansion of these *** numerical results for the orthotropic micropolar plane stress problem under various boundary conditions were presented and validated using the finite element method,which confirmed the convergence and accuracy of the proposed *** also investigated the relationship between the size-dependent behaviour and material parameters using the proposed ***,this approach was applied to analyze lattice structures under an equivalent micropolar continuum approximation.

关键词： Symplectic approach Micropolar Orthotropic materials Size effect Analytical solution

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