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Optimization of Memory March X Test Algorithm Based on Nuclear Safety Level DCS System Platform 1

9th International Conference on Energy engineering and Environmental engineering, ICEEEE 2022

作者： Bai, Yongfei Wu, Zhiqiang Liu, Jie Jiang, Wei Zhong, Ke Han, Wenxing Chen, Zhi School of Computer Science University of South China Hengyang421200 China Nuclear Power Institute of China Chengdu610000 China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hunan Hengyang China

ISBN: (数字)9783031302336

ISBN: (纸本)9783031302329

At present, the testing algorithm of nuclear power safety level DCS system for memory is GALPAT algorithm. This algorithm has a high time complexity and low fault coverage for memory. During the testing process, additional storage space is required, and the security of additional memory cannot be guaranteed. Based on the memory fault testing theory, this paper analyzes and studies the March X algorithm. It is found that the algorithm can not meet the coverage requirements for memory faults when applied to the nuclear power safety level DCS system. A March X+ algorithm is proposed. The algorithm can meet the index requirements of the nuclear power safety level DCS system for memory testing algorithms, and the fault detection rate reaches more than 95%, And it innovatively proposes to save the data to the register during the detection process, which does not require additional memory and improves the security. To verify the feasibility of the algorithm designed in this paper, the algorithm test time and functionality were verified in the main control PBIST module under a nuclear power safety level DCS system platform. The results show that the proposed test algorithm has certain advantages in test time and memory fault coverage. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nuclear energy

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Performance Verification of State Feedback H∞ State Molecular Controller Via Chemical Reaction Networks 5

Performance Verification of State Feedback H∞ State Molecul...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Yao, Jingwang Wang, Xing'An Lv, Hui Li, Yang Zhang, Wujie Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Dalian University College of Environmental and Chemical Engineering Dalian116622 China Dalian Chivy Biotechnology Co. Ltd Dalian116023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China

ISBN: (纸本)9798350325294

An important goal in synthetic biology is in the design and implementation of control strategies embedded in biochemical reaction systems to enable control of the process at the molecular level. The problem of set-point tracking and perturbation disturbances in the biochemical reaction process can be solved by a state feedback control strategy and a more stable and better dynamic performance of synthetic controllers. First, it is shown how a set of CRNs can provide state feedback to the biomolecular implementation of the H∞ controller. Then, to facilitate the modularity of biochemical process design, CRNs of trigonometric functions and repetitive sequence step signals are defined for the first time as input signals to the controller. Finally, numerical simulation experiments are designed to verify the effectiveness of the whole scheme from several perspectives, such as setpoint tracking and external perturbations. The results show that the state feedback H∞ molecular controller outperforms the conventional linear controller because it has a faster tracking response and better perturbation rejection performance. © 2023 IEEE.

关键词： Controllers

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Domain Adaptation Random Vector Functional Link for White Blood Cell Classification

Domain Adaptation Random Vector Functional Link for White Bl...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qin Wang Xi-Zhao Wang Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

There's growing recognition of how machine learning can revolutionize the precision and swiftness of clinical diagnoses by improving the classification of white blood cells. However, a machine learning model specialized in white blood cell classification demands access to a large trove of well-annotated data. This poses a challenge, as in the medical field, annotating data comprehensively comes with a high cost in terms of both money and time. To circumvent this, domain adaptation (DA) techniques are employed to leverage similar cell images from related domains, thereby boosting the model's performance in white blood cell classification in specific contexts. Among the myriad DA approaches available for this application, including TCA-based and deep learning methods, all typically suffer from lengthy training durations. Addressing this challenge, we introduce a novel DA method inspired by the random vector functional link (RVFL) network, a type of neural network characterized by its random weights. Our proposed method, named Domain Adaptation Random Vector Functional Link (DA-RVFL), capitalizes on the efficiency of RVFL networks to enhance white blood cell classification. This innovative approach shows some insights for future research in the efficient and effective classification of white blood cells.

关键词： White blood cells Training Deep learning Adaptation models Neural networks Focusing Vectors Data models Medical diagnosis Medical diagnostic imaging

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CMANet: A CNN-Mamba aggregation network for face super-resolution

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Pattern Recognition 2025年 168卷

作者： Wu, Ziyi Lu, Tao Zhang, Yanduo Chai, Xiaoyu Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China Computer School Hubei University of Arts and Science Xiangyang China National Engineering Research Center for Multimedia Software Wuhan University Wuhan China

Currently, most face super-resolution (FSR) methods rely on convolutional neural networks (CNNs) or Transformers, both of which have inherent limitations: CNNs suffer from a limited receptive field, while Transformers introduce quadratic computational complexity due to self-attention. In recent years, Mamba has effectively addressed these challenges using the state space model (SSM), excelling in global modeling. However, due to the highly structured nature of face images, relying solely on global modeling is insufficient for high-quality reconstruction. It is crucial to preserve global structural information while enhancing local texture details. While Mamba excels in global modeling, it lacks the ability to effectively extract local details. To eliminate this issue, we propose the CNN-Mamba Aggregation Network (CMANet) for FSR, which consists of two branches: a CNN branch and a Mamba branch. The CNN branch is a traditional end-to-end FSR network, in which the proposed Multi-Scale Residual Block (MSRB) extracts multi-scale local details. The Mamba branch, the core of the network, is built upon a UNet architecture. Its central component is a carefully designed Dual Scan Mamba Block (DSMB), which combines the Dual Scan Mamba Module (DSMM) and Feature Refinement Feed-Forward Network (FR-FFN). The DSMM enhances the global modeling capabilities of the SSM in both plane and channel scanning directions, while the FR-FFN refines the expressiveness and adaptability of the features. Finally, CMANet reconstructs high-fidelity face images by aggregating features from both branches. Extensive experimental results show that our model significantly outperforms many advanced methods in both objective metrics and subjective image quality. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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Survey of Multimedia Information Mining Techniques Based on Deep Learning in the Context of Crime Domain 20

Survey of Multimedia Information Mining Techniques Based on ...

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20th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Yuandan, Deng Bo, Chen Weiqin, Chen Bohang, Sun Juntao, Zeng Yanling, Mao Sichuan Provincial Key Laboratory of Intelligent Terminal Jointly Built by Hall and City Yi Bin644000 China University of Electronic Science and Technology of China Yi Bin644000 China School of Information and Software Engineering University of Electronic Science and Technology of China Cheng Du61000 China

ISBN: (纸本)9798350318982

The rapid expansion of multimedia data has made multimedia data mining a crucial area of research in the field of crime. In the realm of crime, the significance of multimedia data mining lies in its ability to reveal hidden patterns, correlations, and knowledge within criminal activities, providing powerful support for crime investigation and prevention. For instance, through similarity search, multimedia data relevant to criminal suspects can be swiftly identified. By identifying associations and connections between criminal organizations, criminal networks can be discovered. Through entity resolution and classification, individuals, locations, and items involved in criminal events can be automatically identified. The automatic feature learning and large-scale data processing capabilities of deep learning make it closely intertwined with data mining. Therefore, this paper summarizes the applications of deep learning-based multimedia data mining techniques in the field of crime since 2018, along with the compilation of relevant crime datasets and evaluation metrics. Additionally, it outlines the challenges present in this domain and provides an outlook on future developments. © 2023 IEEE.

关键词： Crime

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MC-PDARTS: Multi-Cell Progressive Differentiable Architecture Search

MC-PDARTS: Multi-Cell Progressive Differentiable Architectur...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Qun Liu Yi Cai Junying Chen School of Software Engineering South China University of Technology Key Laboratory of Big Data and Intelligent Robot (SCUT) Ministry of Education Guangzhou China

Recently, network architecture search has been practiced in more and more tasks, but its high cost makes it difficult for small research teams or individual researchers to follow. Differentiable architecture search (DARTS) algorithm provides an easier available solution in searching effective network architectures. Although DARTS algorithm has greatly reduced the search cost as compared with other evolution based or reinforcement learning based methods, there is still a long way to go before it is widely used, since gradient based methods consume a lot of GPU memory and its search space lacks diversity. Encouraged by the idea of progressive differentiable architecture search (P-DARTS), we propose a multi-cell progressive differentiable architecture search (MC-PDARTS) algorithm, which allows cells at different levels to learn different structures, and progressively searches the best structure for the corresponding level in each search stage. The proposed algorithm greatly enlarges the search space of the search network while reducing the memory consumption and search time in the search process, and alleviates the problem of performance collapse caused by too many skip connections which often occur in previous gradient based methods. Compared with the previous algorithms, the proposed MC-PDARTS algorithm achieves state-of-the-art performance on CIFAR-10 and CIFAR-100 datasets with around 4-hour search on a single Nvidia GTX1080Ti GPU. In order to reduce the performance loss in the transfer process, MC-PDARTS searches directly on a large target dataset (ImageNet) without proxy tasks, achieving state-of-the-art performance.

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Security and privacy with opacity-based state observation for finite state machine

Security and privacy with opacity-based state observation fo...

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作者： Zhang, Zhipeng Xia, Chengyi Chen, Shengyong Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin China

Topics on security and privacy are very fundamental and important in the Internet of Things (IoT). In this paper, we explore the impact of state observation on the opacity of IoT, which is modeled by Moore-type finite state machine. Firstly, to characterize the intrusion estimator under the event-state observation, a multi-linear algebraic expression is proposed through the Boolean semi-tensor product (STP), and on the basis of the novel representation, the validation criterion is given by some matrix manipulations. Subsequently, considering that the input information may not be measured due to the physical shielding of the system against external intruders, we continue to discuss the opacity with state observation by a permutation structure matrix. Meanwhile, the verification of opacity with event-state observation is generalized to the one with only state observation. Finally, we offer an example to illustrate the effectiveness of the newly proposed method. Taken together, the current results are helpful to provide a novel theoretical approach for investigating the privacy-preserving problem in the area of IoT and complex cyber-physical networks. © 2021 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd.

关键词： Internet of things

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Compact and Accurate Adaptive Width Radial Basis Function Neural Network with Discriminative Features for Thyroid Gland Segmentation

Compact and Accurate Adaptive Width Radial Basis Function Ne...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Haijun You Junying Chen School of Software Engineering South China University of Technology Key Laboratory of Big Data and Intelligent Robot (SCUT) Ministry of Education Guangzhou China

The goal of this work is to develop a fast, compact, accurate, and robust neural network for automatic thyroid gland segmentation in medical ultrasound images when the computational resources are limited, such as portable intelligent medical diagnostic devices or robots. An adaptive width radial basis function neural network model with discriminative features is proposed in this work. The model combines the local characteristics and global context of the entire feature space, which improves the performance of the automatic thyroid gland segmentation, and enhances the data fitting capability and robustness of the neural network. A new feature selection method combining the measurements of area under the curve values and Pearson correlation coefficients is proposed to select six discriminative and low correlated features from a set of handcrafted features, which helps to construct a compact yet accurate radial basis function neural network. The proposed method is experimented on thyroid ultrasound images using five-fold cross validation. The proposed method achieves an average accuracy of 0.9770 and an average IoU of 0.7841, achieving competitive segmentation performance compared to convolutional neural networks, with thousands of times faster training speed on a CPU.

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Predictive ISAC Beamforming Using LoRA-Transformer LSTM Integrated with GNN in Ultra-Dense D2D mmWave Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Haochen Bi, Jia Li, Bohan Xiang, Luping Yang, Xin Zhang, Zhaolin Wang, Ling Northwestern Polytechnical University School of Electronics and Information Xi'an710019 China Rutherford Appleton Laboratory Scientific Computing Department STFC DidcotOX11 0QX United Kingdom Ocean University of China Faculty of Information Science and Engineering Qingdao266100 China Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China School of Intelligent Software and Engineering Suzhou China

Effective beamforming is crucial in ultra-dense device-to-device (D2D) mmWave networks to reduce interference and optimize communication. However, current methods often struggle to balance high data rates with minimal signaling overhead, especially with imperfect channel state information (CSI). This paper introduces a machine learning-assisted beamforming design tailored for integrated sensing and communication (ISAC), combining an efficient Low-Rank Adaptation Blocked LSTM Transformer with Message Passing Graph Neural Network that is called as LoRA_BLT-MPGNN. The LoRA_BLT component provides a key advantage by efficiently capturing both temporal and spatial patterns through fixed-weight updates during training and low-rank approximation for fine-tuning, significantly reducing computational complexity without sacrificing accuracy. Using historical channel data, the method implicitly captures channel characteristics and directly predicts the beamforming matrix for the subsequent time slot, minimizing signaling overhead while maximizing the achievable sum rate. Our research and simulation results show that the proposed predictive beamforming design maintains robust sensing performance and achieves a sum rate closely approaching the upper bound achievable with perfect CSI. Additionally, the incorporation of MPGNNs enables efficient modeling and learning of complex inter-node relationships, making the approach adaptable to networks of varying sizes and densities. Simulations further demonstrate that the LoRA_BLT-MPGNN model can be generalized across different network densities and coverage areas, reducing computational complexity by approximately 32× in terms of Tensor Floating Point Operations (TFLOPs) compared to conventional methods while maintaining high performance. © 2025 IEEE.

关键词： Beamforming

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Data Augmentation for Vulnerability Detection Based on Code Refactoring and Mixup

Data Augmentation for Vulnerability Detection Based on Code ...

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IEEE International Conference on software Reliability engineering Workshops (ISSRE Wksp)

作者： Ying Xing Jiaqi Huang Guilong Wang Yehao You Bin Yang Xiaofeng Li Yixing Luo School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China School of Artifcial Intelligence Beijing University of Posts and Telecommunications Beijing China Yunnan Key Laboratory of Software Engineering Yunnan China China Unicom Research Institute Beijing China Software Development Center Beijing Institute of Control Engineering Beijing China

ISBN: (数字)9798350367041

ISBN: (纸本)9798350367058

In recent years, the number of software vulnerabilities has gradually increased, posing a threat to the security of software, and identifying whether there are vulnerabilities in software is crucial for assuring its quality. With the development of deep learning, vulnerability detection based on deep learning has achieved good results. However, real-world software vulnerability datasets often suffer from class imbalance and a lack of labeled data, limiting the performance of deep learning models. Therefore, this paper proposes a data augmentation method to alleviate these two problems. Firstly, we treats samples with vulnerabilities in the dataset as code, and generates more samples with vulnerabilities through code refactoring to alleviate class imbalance issues. Secondly, we use the mixup method to perform linear interpolation on the embedding vectors which represent codes, and add the interpolated vectors into the training set, thereby increasing the amount of labeled training data. To evaluate our method, we conducted experiments on three projects of the Promise dataset. The experimental results show that our data augmentation method can effectively improve the effect of vulnerability detection.

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