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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Sibilio, Pasquale Conte, Federica Paci, Paola Sapienza University of Rome Department of Computer Control and Management Engineering Rome Italy National Research Council Institute for Systems Analysis and Computer Science 'Antonio Ruberti' Rome Italy Karolinska Institutet Stockholm17177 Sweden

ISBN: (纸本)9798350386226

Chronic obstructive pulmonary disease (COPD) is a lung disease causing hundred thousand of death each year worldwide and defined as a respiratory and airflow impairment majorly due to large and small airways dysfunctions. COPD could be considered as a syndrome that includes disease axes of variable pathological and clinical conditions. Despite recent advances, the comprehension of the molecular mechanisms responsible for COPD disease spectrum is far to be reached. Moreover, we should also consider continuous smoking exposure, which could lead to variability in the disease mechanisms and progression. Network-based multi-omics data integration can help to study the association between molecular determinants on diverse biomolecular layers in a disease context. In a previous study, we leveraged lung RNA-seq and DNA-methylation data of a COPD-control cohort to build a correlation-based integrated network (called coupled network), that helped to unveil genes involved in immune and inflammatory modulation of COPD. Therefore, in our previous study we highlighted the most important genes of the coupled network and inspected their single contribution to disease-related pathways. In this study we aim to overcome this limitation by performing a pathway activity analysis by considering the expression and DNA methylation profiles of coupled network genes. Moreover, we exploit this analysis to study the possible contribution of coupled network genes to the differential disease progression between current and former smokers patients. © 2024 IEEE.

关键词： Lung cancer

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Development of a Modular Automation Framework for Data-Driven Modeling and Optimization of Coating Formulations 28

Development of a Modular Automation Framework for Data-Drive...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Polke, Dominik Surjana, Alvin Diepers, Florian Ahle, Elmar Söffker, Dirk University of Applied Sciences Niederrhein Electrical Engineering and Computer Science Krefeld Germany University of Duisburg-Essen Dynamics and Control Duisburg Germany

ISBN: (纸本)9798350339918

Chemistry 4.0 is the new era of chemical process industry, where digitalization, modularization, sustainability, and circular economy play key roles. A growing interest in the use of process data with the aim of gaining a better understanding of the production process and optimizing products can be observed. In chemical industry, data-driven models are used when the production process is too complex to be described by chemical laws. For generating data-driven models of industrial processes, many manual and time-consuming steps have to be carried out. This leads to delay in information acquisition and product optimization. Another key element to realize a modular automation framework is standardization, e.g., the Module Type Package (MTP). In this paper, a modular automation framework to optimize coating formulations with data-driven modeling in industrial environment is presented. Therefore, the presented framework automates the acquisition of data via modular and standardized interfaces in a process control system (PCS), data preparation for data-driven modeling, model training, and deployment of the model to propose new coating formulations. The framework shown in this paper is applied to optimize the scratch resistance of a coating using Gaussian processes (GPs) as data-driven modeling method. For the novel automation of machine learning driven design of experiments (DoE), the Bayesian optimization is used. © 2023 IEEE.

关键词： Automation

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Energy Efficient IoT Air Quality Monitoring System 24

Energy Efficient IoT Air Quality Monitoring System

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24th International Conference on control Systems and computer science, CSCS 2023

作者： Petrica, Silvian-Marian Fagarasan, Ioana Arghira, Nicoleta Stamatescu, Iulia Neculoiu, Giorgian Flangea, Oana University Politehnica of Bucharest Faculty of Automatic Control and Computer Science Romania Technical University of Civil Engineering of Bucharest Faculty of Hidrotechnics Romania

ISBN: (纸本)9798350313390

Air pollution is one of the major environmental issues discussed lately due to its influence on human health. Particular attention is paid to air quality monitoring today and most developed societies have implemented weather and air quality monitoring stations in various locations inside or outside cities to monitor and reduce the effects of poor air quality. Modern standards and sensors have started to be used in recent years to monitor air quality. Conventional air quality monitoring systems, on the other hand, are not able to provide real-time air quality data with acceptable spatial and temporal resolutions at a low cost, so the challenge is to achieve low-cost and energy-independent solutions. Using modern technologies, e.g. Internet of-things (IoT), an original approach is proposed for building an air quality monitoring system considering low cost equipment and efficient energy autonomous approach. Smart cities have successfully implemented air quality monitoring systems that combine both hardware and software. The results presented in the paper show the viability of the proposed solution, exposing the designed solution and different ways of presenting air quality monitoring. © 2023 IEEE.

关键词： Energy efficiency

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Decision System for Ensuring Thermal Comfort of Occupants in a Smart Building 24

Decision System for Ensuring Thermal Comfort of Occupants in...

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24th International Conference on control Systems and computer science, CSCS 2023

作者： Boicu, Mihaela-Gabriela Stamatescu, Grigore Fudulu, Cosmin-Florin Dobrea, Marius-Alexandru Vasluianu, Mihaela Neculoiu, Giorgian University Politehnica of Bucharest Faculty of Automatic Control and Computer Science Romania Technical University of Civil Engineering of Bucharest Faculty of Hidrotechnics Romania

ISBN: (纸本)9798350313390

This paper presents the development of a system that uses fuzzy logic in order to evaluate the thermal comfort conditions expressed by means of a score by the occupants of an office room The inputs of the system are represented by three datasets relevant to the study: the air temperature, the positioning of the occupant and the level of activity carried out by the occupant. Based on the comfort score obtained, the HVAC system located at the room level will maintain a comfortable environment. The proposed solution also contributes to the minimization of energy consumption in buildings. In order to collect the data sets necessary for the study, we used the *** module as a wearable device. © 2023 IEEE.

关键词： Energy efficiency

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Wind disturbance compensated path-following control for fixed-wing UAVs in arbitrarily strong winds

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Chinese Journal of Aeronautics 2024年第2期37卷 431-445页

作者： Hao LU Lan GAO Yunda YAN Mingzhe HOU Chenliang WANG Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Science and Technology Hangzhou Normal UniversityHangzhou 311121China Department of Computer Science University College LondonLondon WC1E 6BTUnited Kingdom Center for Control Theory and Guidance Technology Harbin Institute of TechnologyHarbin 150001China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

Wind is the primary challenge for low-speed fixed-wing unmanned aerial vehicles to follow a predefined flight *** cope with various wind conditions,this paper proposes a wind disturbance compensated path following control strategy where the wind disturbance estimate is incorporated with the nominal guiding vector field to provide the desired airspeed direction for the *** the control input vector for the outer-loop kinematic subsystem needs to satisfy a magnitude constraint,a scaling mechanism is introduced to tune the proportions of the compensation and nominal ***,an optimization problem is formulated to pursue a maximum wind compensation in strong winds,which can be solved analytically to yield two scaling factors.A cascaded inner-loop tracking controller is also designed to fulfill the outer-loop wind disturbance compensated guiding vector ***-fidelity simulation results under sensor noises and realistic winds demonstrate that the proposed path following algorithm is less sensitive to sensor noises,achieves promising accuracy in normal winds,and mitigates the deviation from a desired path in wild winds.

关键词： Path following Guiding vector field Wind disturbance Disturbance rejection Fixed wings

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A Generalized Current Balancing control for Series-Capacitor Buck Converter with Interleaved Phase Angle 24

A Generalized Current Balancing Control for Series-Capacitor...

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24th IEEE Workshop on control and Modeling for Power Electronics, COMPEL 2023

作者： Li, Chung-Yi Nien, Chin-Fu Lin, Li Chen, Hung-Chi Chang Gung University Department of Electronics Engineering Taiwan Chang Gung University Department of Computer Science and Information Engineering Taiwan National Yang Ming Chiao Tung University Institution of Electrical Control Engineering Department of Electrical and Computer Engineering Taiwan

ISBN: (纸本)9798350316186

In addition to increasing the output current, an interleaved buck converter can significantly reduce the current ripple at the output. However, the bottleneck of the interleaved buck converter application is the unbalanced inductor current. In order to achieve the current balance in the entire section of the duty cycle, this paper proposes a control architecture with interleaved alignment, using the adjustment of the duty cycle and phase offset, to analyze and realize a high output current and current balanced in three-arm series capacitor interleaved buck converters (SCIBCs) in this work. By analyzing and modeling the SCIBC and using a single-loop control architecture that feedbacks the output voltage, the proposed current-balancing method balances all inductors' current within the full duty cycle without any current sensor. In addition, the small-signal transfer function of the control signal to the output voltage and design of the controller parameters are derived according to the specification, and the SCIBC with the proposed current-balancing control with PI controller of the alignment of interleaved phase angle is constructed. The high output current and current-balancing characteristics of the proposed circuit control architecture are validated in the simulation tool SIMPLIS. © 2023 IEEE.

关键词： controllers

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A Study of Data Augmentation for Imbalance Problem in Network Traffic 3

A Study of Data Augmentation for Imbalance Problem in Networ...

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3rd International Conference on Blockchain Technology and Information Security, ICBCTIS 2023

作者： Wang, Xu Sun, Shimin Han, Li School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tiangong University Tianjin China School of Computer Science and Engineering Tianjin University of Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

ISBN: (纸本)9798350340402

The complexity and diversity of smart grid systems increase the likelihood of anomalies in communications between devices in the system, and how these anomalies are detected is critical to the security of the grid system. However, the distribution of traffic types in the grid is often highly unbalanced, with the total amount of normal traffic data often far outweighing the anomalous data. This imbalance compromises the effectiveness of the detection model and poses a significant threat to the overall security of the grid This paper improves on traditional conditional generation adversarial neural networks and designs a new approach to overcome this obstacle and enhance the security of smart grid systems. In the generator part, the spatial features of the data are captured by convolutional neural networks, and the temporal features of the data are captured by gated recurrent units. By integrating these two components into the generative network we can generate synthetic data with both temporal and spatial features, thus enhancing the minority class samples. By incorporating the augmented minority data into the training process, the proposed approach enables the detection model to understand the distribution of features of the minority anomaly samples more accurately. This improved learning accuracy translates into improved model performance, resulting in higher detection rates for Previously under-represented anomalies. In addition, our approach effectively addresses the inherent bias towards traffic types with higher flows typically observed in asymmetric datasets, thus providing a more equitable and comprehensive security solution for smart grid systems. © 2023 IEEE.

关键词： Anomaly detection

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Futures Price Forecasting Based on LSTM Model Using Public Opinion Sentiment Analysis 42

Futures Price Forecasting Based on LSTM Model Using Public O...

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42nd Chinese control Conference, CCC 2023

作者： Shi, Panfeng Song, Shifan Lang, Qi Liu, Xiaodong School of Control Science and Engineering Dalian University of Technology Dalian116024 China School of Computer Science and Information Technology Northeast Normal University Changchun130117 China

ISBN: (纸本)9789887581543

As the economy continues to develop and the futures market grows, more and more attention is being paid to the subject of futures price forecasting. The factors influencing the futures market are complex, and the existing forecasting models tend to ignore the acquisition of information other than market information. Therefore, this paper proposes a futures price forecasting model based on sentiment analysis of online public opinions. Several futures products are selected for training and validation of the model, and the Chinese posts in the futures bar are used as input for public opinion, which is processed by the investor sentiment analysis fusion module to obtain sentiment values as a priori information for the task. Three groups of control experiments are set up to verify the validity of the proposed model and to compare the effect of sentiment feature fusion. Finally, the experimental results show that the proposed model incorporating public opinion gets better results in the selected futures product price prediction task. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Time series analysis

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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning 27

Video2Reward: Generating Reward Function from Videos for Leg...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

ISBN: (纸本)9781643685489

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. © 2024 The Authors.

关键词： Robots

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