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Computer Modeling in Engineering & Sciences 2015年第3期104卷 227-249页

作者： Z.Karakaya B.Baranoglu B.Çetin A.Yazici Computer engng.dept. Atilim UniversityTurkey Manufacturing engng.dept. Atilim UniversityTurkey Microfluidics & Lab-on-a-chip Research Group Mech.Engng.Dept.Bilkent UniversityTurkey software engng.dept. Atilim UniversityTurkey

A new formulation for tracking multiple particles in slow viscous flow for microfluidic applications is *** method employs the manipulation of the boundary element matrices so that finally a system of equations is obtained relating the rigid body velocities of the particle to the forces applied on the *** formulation is specially designed for particle trajectory tracking and involves successive matrix multiplications for which SMP(Symmetric multiprocessing)parallelisation is *** is observed that present formulation offers an efficient numerical model to be used for particle tracking and can easily be extended for multiphysics simulations in which several physics involved.

关键词： Boundary Element Method particle tracking Stoke's flow parallel computing

A New Encryption Mechanism Supporting the Update of Encrypted Data for Secure and Efficient Collaboration in the Cloud Environment

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 813-834页

作者： Chanhyeong Cho Byeori Kim Haehyun Cho Taek-Young Youn Department of Cyber Security Dankook UniversityYongin16890Republic of Korea Department of software Soongsil UniversitySeoul07027Republic of Korea

With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly ***,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient *** paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative *** bundle includes updated information,allowing only the updated portions to be reencrypted when changes *** encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates *** proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext *** utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those *** approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data *** validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size *** show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size ***,our security evaluation confirms that this method provides robust protection against both passive and active attacks.

关键词： Cloud collaboration mode of operation data update efficiency

Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 909-931页

作者： Adel Binbusayyis Mohemmed Sha Department of software Engineering College of Computer Engineering and Sciences in Al-Kharj Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid *** the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the *** also possesses a better impact on averting overloading and permitting effective energy *** though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized *** overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning *** accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data ***,the pre-processed data are taken for training and *** that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in *** PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed *** hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on *** results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

关键词： Smart Grid machine learning particle swarm optimization XGBoost dynamic inertia weight update

EXPERIMENTAL INVESTIGATION INTO PLASTIC DAMAGE OF MICROVOIDS WITH INTERACTION

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Acta Mechanica Sinica 1997年第3期13卷 280-288页

作者：方竞倪受庸杨宏伟白树林戴福隆 dept.of Mechanics & engng.Science Peking UniversityBeijing 100871China dept.of engng.Mechanics Tsinghua UniversityBeijing 100084China

A mechanism of plastic how localization in ductile matter near microvoids is studied. The voids with the size-scale of micromillimeter exist in sheet specimens under tensile loading, and the plastic strain held around voids is obtained by digital image processing of deformed grids. The size growth of the microvoids, the spacing change of the neighboring voids, and the development of shear bands in the ligament between the voids, are presented by experimental results accompanied with the plastic strain distribution, that gives good interpretation to the process of void growth and coalescence with the how localization in the ligaments.

关键词： void damage flow localization plastic strain field void interaction

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Adaptive-basis decomposition-based low-rank network for efficient non-uniform motion deblurring

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Optoelectronics Letters 2025年第1期21卷 43-50页

作者： CHEN Lei XIONG Qingbo ZHANG Wei LI Runde School of software Henan University School of Applied Technology China University of Labor Relations

In this study, we present a unified sparsity-driven framework that significantly enhances motion deblurring performance by integrating two key components: a custom-designed dataset and a low-rank module(LRM). This framework leverages the inherent sparsity of per-pixel blur kernels to bolster both deblurring accuracy and model interpretability. Firstly, we propose an adaptive-basis decomposition-based deblurring(ADD) approach, which constructs a tailored training dataset to enhance the generalization capacity of the deblurring network. The ADD framework adaptively decomposes motion blur into sparse basis elements, effectively addressing the intricacies associated with non-uniform blurs. Secondly, an LRM is proposed to improve the interpretability of deblurring models as a plug-and-play module, primarily designed to identify and harness the intrinsic sparse features in sharp images. A series of ablation studies have been conducted to substantiate the synergistic advantages of combining the proposed ADD with the LRM for overall improvement in deblurring efficacy. Subsequently, we empirically demonstrate through rigorous experimentation that incorporating the LRM into an existing Uformer network leads to substantial enhancement in reconstruction performance. This integration yields a sparsity-guided low-rank network(SGLRN). Operating under the overarching principle of sparsity, SGLRN consistently outperforms state-of-the-art methods across multiple standard deblurring benchmarks. Comprehensive experimental results, assessed through quantitative metrics and qualitative visual evaluations, provide compelling evidence of its effectiveness. The overall deblurring results are available at Google Drive.

关键词： Image enhancement

Traffic safety helmet wear detection based on improved YOLOv5 network

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Optoelectronics Letters 2025年第1期21卷 35-42页

作者： GUI Dongdong SUN Bo College of software Southeast University Quanzhou Equipment Research Centre Haixi Institute Chinese Academy of Sciences

Aiming at the problem that the current traffic safety helmet detection model can't balance the accuracy of detection with the size of the model and the poor generalization of the model, a method based on improving you only look once version 5(YOLOv5) is proposed. By incorporating the lightweight Ghost Net module into the YOLOv5 backbone network, we effectively reduce the model size. The addition of the receptive fields block(RFB) module enhances feature extraction and improves the feature acquisition capability of the lightweight model. Subsequently, the high-performance lightweight convolution, GSConv, is integrated into the neck structure for further model size compression. Moreover, the baseline model's loss function is substituted with efficient insertion over union(EIoU), accelerating network convergence and enhancing detection precision. Experimental results corroborate the effectiveness of this improved algorithm in real-world traffic scenarios.

关键词： Traffic control

ACTIVE CONTROL OF FLEXIBLE BEAMS SUBJECTED TO MULTI-DISTURBANCE USING ONE CONTROLLER

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Acta Mechanica Sinica 1997年第3期13卷 273-279页

作者：程伟王大钧 Department of Mechanics & engng.Science Peking UniversityBeijing 100871China

The active control of vibration for a beam subjected to multi-disturbances is investigated based on wave propagating suppression. In this control system, there are the same numbers of the sensors, the signal inputting to the controller and the disturbances, but there is only one controller. It is a local control system, the system parameters depend only on the characteristics of the structure bounded by the sensors and the controller, and we need not take into account. the boundary conditions and the properties of structures outside of this held. The system is efficient when a structure vibrates in middle and high frequency regions. Some control design rules are developed from the calculation results.

关键词： vibration active control wave propagation power flow flexural structure wave suppression

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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India Department of AI and software Engineering Kangwon National UniversitySamcheok25913Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

A MICROMECHANICS CONSTITUTIVE MODEL FOR PURE DILATANT MARTENSITIC TRANSFORMATION OF ZrO2 CONTAINING CERAMICS

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Acta Mechanica Sinica 1990年第2期6卷 141-150页

作者： Sun Qingping Yu Shouwen Hwang Kehchih (dept.of engng.Mech.,Tsinghua University,Beijing,China) 1. dept. of engng. Mech. Tsinghua University Beijing China

A new micromechanics constitutive model for pure dilatant transformation plasticity of structure ceramics is proposed in this *** on the thermodynamics,micromechanics and microscale t→ m trans- formation mechanism analysis of the TZP and PSZ ZrO2-containing ceramics,an analytic expressions of the Helmholtz and complementary free energy of the constitutive element for the case of pure dilatant transforma- tion is derived for the first time in a self-consistent *** the analysis of energy dissipation in the for- ward and reverse transformations,the mieromechanics constitutive law is derived in the framework of Hill-Rice’s internal variable constitutive theory.

关键词： constitutive element microscopic and macroscopic quantities internal variables free energy thermodynamic force pure dilatant transformation inclusions and matrix

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Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(Information Sciences) 2025年第1期 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of software Technology Zhejiang University School of software Engineering Sun Yat-sen University School of software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

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