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Analyses of cell-to-cell communication combining a heterogeneous deep ensemble framework and scoring approaches from single-cell RNA sequencing data

Analyses of cell-to-cell communication combining a heterogen...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Peng, Lihong Yuan, Ruya Han, Chendi Tan, Jingwei Wang, Zhao Chen, Min Chen, Xing Hunan University of Technology School of Computer Science Zhuzhou China China University of Mining and Technology School of Information and Control Engineering Xuzhou China Hunan Institute of Technology School of Computer Science Hengyang China Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China

ISBN: (纸本)9781665468190

Cell-to-cell communication (CCC) plays essential roles in multicellular organisms. the identification of CCC between cancer cells themselves and one between cancer cells and normal cells in tumor microenvironment contributes to the understanding of carcinogenesis, cancer development and metastasis. CCC is usually mediated by Ligand-Receptor Interactions (LRIs). In this manuscript, we developed an LRI-mediated CCC estimation framework (LRI-EnABCLG) by incorporating LRI collection, prediction and filtering, CCC inference and visualization. First, four LRI datasets were collected. Second, LRIs were predicted by a heterogeneous deep ensemble model. Third, LRIs were filtered by combining single-cell sequencing (scRNA-seq) data. Fourth, CCC was inferred by combining the filtered LRIs and scRNA-seq data. Finally, the proposed CCC prediction framework was applied to CCC analysis in colorectal tumor tissues. Our proposed LRI-EnABCLG model obtained better LRI prediction performance. Case study demonstrated that fibroblasts was more likely to communicate with colorectal cancer cells, which was in accord with the results from iTALK (a classical CCC analysis pipeline). We anticipate that this work can contribute to diagnosis and treatment of cancers. © 2022 IEEE.

关键词： Cancer cells

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Switching control for Identification Deception in Cyber-Physical Systems

Switching Control for Identification Deception in Cyber-Phys...

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IEEE Conference on Decision and control

作者： Christos Mavridis Aris Kanellopoulos Henrik Sandberg Karl Henrik Johansson Division of Decision and Control School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We investigate the problem of deceiving a malicious agent employing an identification method to estimate the closed-loop dynamics of a cyber-physical system. In particular, we propose a moving target defense mechanism that utilizes stochastic switching between linear closed-loop dynamics to drive a linear system identification process of a potential adversary to sub-optimal solutions with non-vanishing error. We provide a statistical analysis of the induced identification error and show that it is not possible for any linear system identification method to reconstruct the average dynamics of a stochastic switched linear system. Finally, we utilize the theory of Markov jump linear systems to guarantee asymptotic stability of the switching system, and formulate the switching control problem as an optimization problem that guarantees stability while taking into account the trade-off between security and switching effort. Simulation results showcase the efficacy of the proposed approach in inducing identification error for the adversary using minimal switching.

关键词： Linear systems Asymptotic stability Switching systems Statistical analysis Simulation Switches Cyber-physical systems control systems Stability analysis Security

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Concentration Diagnosis in Soft Sensing Based on Bayesian T-Distribution Mixture Regression 9

Concentration Diagnosis in Soft Sensing Based on Bayesian T-...

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9th International Conference on Fuzzy Systems and Data Mining, FSDM 2023

作者： Xia, Changhai Peng, Zhiping Cui, Delong Li, Qirui Sun, Lihui School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China Jiangmen Polytechnic College Jiangmen City529090 China School of Computer Science Guangdong University of Petrochemical Technology Maoming City525000 China

ISBN: (纸本)9781643684703

This study presents a cutting-edge soft sensing approach for coke-making diagnostics, aimed at tackling the challenges posed by multifaceted, nonlinear, non-Gaussian, and noisy operational data prevalent in coke-making ovens. Our proposed method leverages a Bayesian t-distributed mixed regression model, effectively capturing the intricate nature of multivariate, nonlinear, and non-Gaussian data. The utilization of the t-distribution ensures the model's resilience to interference, with model parameter estimation achieved within a Bayesian framework. Conducting simulation experiments and real industrial experiments, as well as comparative analysis with PLSR, GMR, and GPR models, we demonstrate the model's good robustness, excellent prediction accuracy, and robustness, further confirming its potential application in coking diagnosis. © 2023 The authors and IOS Press.

关键词： Coke

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Application of Deep Learning Models Based on Chaos Modeling in Power Internet of Things Forecasting Tasks 10th

Application of Deep Learning Models Based on Chaos Modeling ...

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10th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2024

作者： Miao, Jiahao Li, Zhixin Long, Yang School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China School of Computer Technology and Engineering Changchun Institute of Technology Changchun130012 China

ISBN: (纸本)9789819787425

Deep learning has provided a reliable technological foundation for time series prediction tasks. In the field of time series prediction, deep learning models are widely used in the Power Internet of Things due to their powerful fitting capabilities. However, efficiently capturing the relationships between time data remains a challenge. To overcome this challenge, this paper proposes a time series prediction model called the CTN-former based on chaos theory and a self-attention mechanism. CTN-former can detect chaos in time series through a carefully designed chaotic theory network (CTN), derive the relationships between time data, and perform phase space reconstruction. Additionally, to improve efficiency, the model includes a master-slave self-attention module to reduce quadratic complexity. Finally, experiments were conducted on four large-scale public datasets from three different domains. The results demonstrate that compared to traditional time series prediction models, the CTN-former achieves a relative improvement rate of 12.7% in time series prediction tasks, and the CTN-former achieves a relative improvement rate of 12.7% in time series prediction tasks and provides a novel solution for time series prediction by recovering the motion trajectory between chaotic time series, thereby enhancing the data utilization efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Prediction models

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Secure Recursive State Estimation of Networked Systems Against Eavesdropping: A Partial-Encryption-Decryption Method

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IEEE Transactions on Automatic control 2024年第6期70卷 3681-3694页

作者： Zou, Lei Wang, Zidong Shen, Bo Dong, Hongli Donghua University College of Information Science and Technology Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education Shanghai201620 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom Northeast Petroleum University Artificial Intelligence Energy Research Institute Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Daqing163318 China

This paper addresses the problem of secure recursive state estimation for a networked linear system, which may be vulnerable to interception of transmitted measurement data by eavesdroppers. To effectively protect information security, an encryption-decryption-based communication scheme can be used, but encrypting all the measurement data from sensors can result in significant computational costs. To address this issue, a partial-encryption-decryption (PED) mechanism is proposed to enhance information security with relatively low computational costs. In this mechanism, only part of the transmitted measurement signals are encrypted, and the remaining signals are transmitted directly to the estimator. A Jordan-canonical-form-based approach is developed to select the appropriate parameter for the PED mechanism, and recursive formulas for the state estimator are designed based on the principle of minimum mean squared error. Sufficient conditions are derived to guarantee the ultimate boundedness of the estimation error variance matrix. Finally, the proposed PED-based recursive state estimation scheme is evaluated through two simulation examples to demonstrate its effectiveness. © 1963-2012 IEEE.

关键词： Mean square error

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Fault Diagnostic Opportunities for Robot Servo Motors Using the Physics of Failure Analysis 14

Fault Diagnostic Opportunities for Robot Servo Motors Using ...

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14th International Conference on Reliability, Maintainability and Safety, ICRMS 2023

作者： Zhang, Bo Liang, Shan Wang, Kai Key Laboratory of Networked Control Systems Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China College of Computer Science and Technology University of Chemical Technology Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350329988

Joint robots are widely used in various industries, such as aviation, aerospace, and automotive manufacturing. The performance degradation of servo motors can significantly affect the overall performance of the robots because of the core component of joint robots, and therefore, it is critical to detect and address servo motor failures promptly. In this study, we analyze some common causes and mechanisms of performance degradation in robot servo motors using the physics of failure analysis. We also discuss strategies for detecting and diagnosing faults using these mechanisms before the servo motors fail completely. © 2023 IEEE.

关键词： Servomotors

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FUSE: a federated learning and U-shape split learning-based electricity theft detection framework

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Science China(Information Sciences) 2024年第4期67卷 339-340页

作者： Xuan LI Naiyu WANG Liehuang ZHU Shuai YUAN Zhitao GUAN School of Control and Computer Engineering North China Electric Power University School of Cyberspace Science and Technology Beijing Institute of Technology Department of Finance Operationsand Information Systems(FOIS)Brock University

In recent years, the data-driven electricity theft detection methods integrated with edge cloud computing [1, 2] have not only demonstrated superior detection accuracy but also improved efficiency, making them viable alternatives to indoor inspections. Energy service providers(ESPs) typically manage regions by dividing them into various transformer districts(TDs). The detection of electricity theft in a particular region is performed by the associated TD,

关键词： Sensitive data

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BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural Networks for Medical Image Classification 5th

BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural ...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Ghosh, Triyas Chakraborty, Soham Kaplun, Dmitrii Gulvanskii, Vyacheslav Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9789819638628

In this paper, we have proposed a novel model, called Bonferroni Mean Operator-aided Fusion of Neural Networks (BFuse-Net). Here, we have taken advantage of the capabilities of four deep learning models as the base learners and then applied a customized attention method to prioritize different fine-grained features by passing their outputs via four parallel pipelines. Next, we have employed a novel aggregation method, a modified Bonferroni Mean operator, to merge the decision scores acquired from every pipeline, prioritizing their individual contributions above their interactions with others in the decision scores. Three medical image datasets— LC25000 (colon cancer), Oral Squamous Cell Carcinoma, and Malaria Cell Images have been used for experimentation. The model yields state-of-the-art outcomes for the three datasets. The source codes and additional results can be found at GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Adversarial Training with Maximal Coding Rate Reduction

Adversarial Training with Maximal Coding Rate Reduction

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Asilomar Conference on Signals, Systems & computers

作者： Hsiang-Yu Chu Hongbo Zhao Markus Flierl Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Deep convolutional networks can solve various complex tasks in the field of image processing. However, adversarial attacks have been shown to have the ability of fooling deep learning models. Adversarial training is one commonly used strategy to improve the robustness of deep learning models against adversarial examples, which is performed by incorporating adversarial examples into the training process. Traditionally, during this process, cross-entropy loss is used as the loss function. In order to improve the robustness of deep learning models against adversarial examples, we propose in this paper two new methods of adversarial training by applying the principle of Maximal Coding Rate Reduction (MCR 2 ). We evaluate the performance of different adversarial training methods by comparing the clean accuracy and adversarial accuracy. It is shown that adversarial training with the MCR 2 loss function yields a more robust network than the traditional adversarial training method. In our experiments, adversarial accuracies are improved by up to 10%. The two loss functions are discussed by using a model.

关键词： Training Deep learning Hands Accuracy Image coding Computational modeling Robustness Data models Encoding Convolutional neural networks

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An AI-Driven Framework for Personalized Diet Generation and Nutrition Suggestions Using Machine Learning, computer Vision and NLP

An AI-Driven Framework for Personalized Diet Generation and ...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Jyoti Kanjalkar Pramod Kanjalkar Raj Khanke Rushikesh Mane Kunal Kharat Kanad Kolhe Department of Computer Science & Engineering (AIML) Vishwakarma Institute of Technology Pune India Department of Instrumentation & Control Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

To counteract unhealthy eating habits among the younger generation, a novel and economical AI-powered framework has been developed to serve as a virtual nutritionist and nutrition counsellor. The system uses machine learning, computer vision, and natural language processing to provide personalized nutrition plans via an easy-to-use online application. It generates tailored meal plans that consider user preferences, allergies, and meal times by calculating Body Mass Index (BMI) based on user input. In addition to blog links and tailored training regimens (enhanced by the LLM3 model trained on a Kaggle gym dataset), a feedback mechanism allows for continuous optimization. Real-time food classification uses VGG16 and ResNet deep learning models to detect damaged or harmful foods and send out alerts for unsuitable foods. Additionally, integration with Google Maps and KNN helps users locate nearby dietitians, fostering better access to professional guidance. These comprehensive improvements significantly contribute to maintaining a good diet.

关键词： Photography computer vision Wearable Health Monitoring Systems Visualization Web services Real-time systems Natural language processing User experience Satellite images Allergies

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