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RaiiRED: a Node-RED-Based Framework for modeling Train control Management Systems

RaiIRED: a Node-RED-Based Framework for Modeling Train Contr...

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IEEE International conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Alessandra Rizzardi Raffaele Della Corte Jesús F. Cevallos M. Vittorio Orbinato Simona De Vivo Sabrina Sicari Domenico Cotroneo Alberto Coen-Porisini Dipartimento di Scienze Teoriche e Applicate Universita degli Studi dell'Insubria Varese VA Italy Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione Universita degli Studi di Napoli Federico II Naples NA Italy

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

The modeling and simulation of Internet of Things (IoT) and Industrial IoT (iioT) systems allow practitioners to obtain valuable insights into the system's behavior before their actual deployment in the field. Early designing permits the analysis of the interactions among the involved entities, evaluating the effects of modifications, and understanding the impact of failures on the system. In particular, this is exacerbated in the context of IoT/iioT, which is characterized by multiple and heterogeneous subsystems, different processing levels, and communication protocols. In such a direction, recent innovations in IT devices have enabled the rail industry to gather information from Train control and Monitoring Systems (TCMS) to check conditions constantly and prevent issues, thus improving relia-bility and safety and, in some cases, leading to cost-saving by optimizing maintenance resources. In such a scenario, this paper presents RailRED, a framework for simulating and prototyping a TCMS based on the Node-RED tool. In RaiiRED, the main TCMS subsystems are modeled using Node-RED flows, while the subsystem interconnections are performed through a low footprint and encrypted gateway based on the MQTT protocol. The proposal can also generate diagnostic data that mimic the behavior of a real-world TCMS. RailRED communication latency and its ability to generate diagnostic data have been analyzed, with the latter evaluated by using clusters of diagnostic events collected from a real-world TCMS running on a high-speed train.

关键词： Wireless communication Rails Technological innovation Protocols control systems Rail transportation Safety Cryptography Monitoring Industrial Internet of Things

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Research analysis and Optimization Suggestions on Smooth Transportation of Baggage Handling System

Research Analysis and Optimization Suggestions on Smooth Tra...

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Computer Simulation and modeling, Information Security (CSMIS), International conference on

作者： Xiang Gao Lina Tang Bin Wu The Civil Aviation Chengdu Logistics Technology Co. Ltd. The Second Research Institute of Civil Aviation Administration of China Chengdu China Chongqing Jiangbei International Airport Expansion Command Chongqing China

ISBN: (数字)9798350343724

ISBN: (纸本)9798350343731

This paper conducted a questionnaire survey on the use of baggage handling system in 42 terminals of 37 airports in China, and divided the stages affecting the smoothness of baggage handling system into 4 parts: Baggage acceptance, Baggage transfer process, Baggage transfer terminal, and system operation control. And based on the results of the questionnaire survey, field research was conducted on the baggage handling systems of Chengdu Tianfu Airport (T2), Chongqing Jiangbei Airport (T2A, T2B, T3A) and Guiyang Longdongbao Airport (T3), which are representative ones. Through the analysis of the research data, the key stick and jam points and causes that affect the smooth conveyance of the baggage handling system are clarified, and the suggested measures to solve the stick and jam baggage are given from various perspectives such as optimization of design methods, manufacturing and installation control. The suggested measures show the way for the quality improvement of China's airport baggage handling system and also provide relevant reference for the design, manufacture and installation of baggage handling system.

关键词： Surveys Technological innovation Systems operation Transportation process control Information security Airports

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Research on modeling Method of New Energy Generation about Randomness

Research on Modeling Method of New Energy Generation about R...

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IEEE conference on Energy Internet and Energy System Integration (EI2)

作者： Li Chen Ming Yang Zewei Wang Peng Li Chao Wang Xiyang Guan Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China New energy power forecast Changji University Changji China

It's significance to establish a rational model of the randomness of new energy power for the analysis and control of new energy power system. One way to realize the probability prediction of new energy power generation is to model and quantify the probability distribution of prediction error based on the deterministic prediction results. Based on Itô process model, this paper describes the stochastic characteristics of new energy power in the form of stochastic differential equation. Firstly, the model based method is used to construct Itô process parameters under the condition of given statistical model, and the probability distribution of prediction object is obtained by superimposing the probability distribution of prediction error on the deterministic prediction results. Secondly, the method based on historical data is used to estimate the Itô process parameters with the maximum likelihood value given the historical data. Finally, a numerical example proves that Itô process is practical in describing the randomness of new energy sources.

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Leveraging Machine Learning and Threshold-Free Cluster Enhancement to Unravel Perception of Emotion and Implied Movement

Leveraging Machine Learning and Threshold-Free Cluster Enhan...

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IEEE EMBS International conference on Information Technology Applications in Biomedicine (ITAB)

作者： Shyamal Y. Dharia Mahdis Hojjati Sergio G. Camorlinga Stephen D. Smith Amy S. Desroches Applied Computer Science University of Winnipeg Winnipeg Canada Psychology University of Winnipeg Winnipeg Canada

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Understanding the neural mechanisms underlying emotional processing is critical for advancements in emotional neuroscience. This study explores the relationship between emotion and motion perception using Event-Related Potentials (ERPs) in a structured experimental setup. We incorporate subjective intensity ratings to enrich the data by capturing the subjective experiences of participants in response to emotional stimuli with implied motion and no motion. Thirty university students participated in the study, where EEG data was collected and analyzed using threshold-free cluster enhancement (TFCE) for time-domain analysis and Repeated Measures ANOVA for frequency-domain analysis. Furthermore, we developed a multimodal deep learning model to predict subjective intensity levels from EEG-derived features. This model leverages statistical, spectral, and auto covariance features, integrated through a transformer encoder layer, to enhance predictive capability. Our findings contribute to a deeper understanding of emotional processing in the brain and highlight the importance of incorporating subjective measures in neuroscience research.

关键词： Deep learning Electric potential Neuroscience process control Predictive models Brain modeling Transformers Electroencephalography Time-domain analysis analysis of variance

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Input Variable Relation and Importance Learning for Industrial process Soft Sensing with Spatial-Temporal Graph Neural Networks

Input Variable Relation and Importance Learning for Industri...

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第43届中国控制会议

作者： Mei Tang Yu Qi Siyuan Tang Zeyu Yang Le Yao School of Mathematics Hangzhou Normal University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In soft sensor modeling, spatial correlations among variables within industrial processes are often overlooked by conventional data-driven methods, leading to a missed opportunity to enhance model accuracy and *** paper addresses this gap by proposing a variable structure learning graph convolutional network(VSL-STGCN)model that leverages the spatial correlations of variables for improved forecasting. The model employs an adjacency matrix self-learning approach, which does not rely on prior knowledge and learns the graph structure through end-to-end gradient descent, making it suitable for the complex and dynamic nature of industrial processes. Moreover, through utilizing feature importance analysis method based on Shapley value, researchers can gain insights into the decision-making process, leading to increased trust in the model predictions. The integration of Shapley values into the GNN model provides a robust interpretability framework, allowing for the quantification of each variable contribution to the model output. This enhances the transparency and credibility for soft sensing model, particularly for key indicators in industrial processes, and facilitates targeted monitoring and control in real-world applications.

关键词： Graph neural network structure learning interpretable modeling soft sensor process monitoring

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9th International Work-conference on Bioinformatics and Biomedical Engineering, IWBBIO 2022

9th International Work-Conference on Bioinformatics and Biom...

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9th International Work-conference on Bioinformatics and Biomedical Engineering, IWBBIO 2022

ISBN: (纸本)9783031077036

The proceedings contain 75 papers. The special focus in this conference is on Bioinformatics and Biomedical Engineering. The topics include: Automated TTC Image-Based analysis of Mouse Brain Lesions;PET-Neuroimaging and Neuropsychological Study for Early Cognitive Impairment in Parkinson’s Disease;architecture and Calibration of a Multi-channel Electrical Impedance Myographer;advanced Incremental Attribute Learning Clustering Algorithm for Medical and Healthcare Applications;Assessment of Inflammation in Non-calcified Artery Plaques with Dynamic 18F-FDG-PET/CT: CT Alone, Does-It Detect the Vulnerable Plaque?;Comparative analysis of the Spatial Structure Chloroplasts and Cyanobacteria Photosynthetic Systems I and ii Genes;Unsupervised Classification of Some Bacteria with 16S RNA Genes;modern Approaches to Cancer Treatment;a Service for Flexible Management and analysis of Heterogeneous Clinical data;linear Predictive modeling for Immune Metabolites Related to Other Metabolites;reconfigurable Arduino Shield for Biosignal Acquisition;smart Watch for Smart Health Monitoring: A Literature Review;data Quality Enhancement for Machine Learning on Wearable ECGs;Measurable Difference Between Malignant and Benign Tumor of the Thyroid Gland Recognizable Using Echogenicity Index in Ultrasound B-MODE Imaging: An Experimental Blind Study;initial Prototype of Low-Cost Stool Monitoring System for Early Detection of Diseases;Cerebral Activation in Subjects with Developmental Coordination Disorder: A Pilot Study with PET Imaging;on the Use of Explainable Artificial Intelligence for the Differential Diagnosis of Pigmented Skin Lesions;estimating Frontal Body Landmarks from Thermal Sensors Using Residual Neural Networks;NMF for Quality control of Multi-modal Retinal Images for Diagnosis of Diabetes Mellitus and Diabetic Retinopathy;radiomic-Based Lung Nodule Classification in Low-Dose Computed Tomography;modelling of Arbitrary Shaped Channels and Obstacles by Distance Function;Segmentati

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Distillation-Based Model Compression Framework for Swin Transformer

Distillation-Based Model Compression Framework for Swin Tran...

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Future Machine Learning and data Science (FMLDS), IEEE International conference on

作者： Mazen Amria Aziz M. Qaroush Mohammad Jubran Alaa Zuhd Ahmad Khatib Department of Electrical and Computer Engineering Birzeit University Ramallah Palestine

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391220

Vision Transformers (ViTs) have gained significant attention in computer vision due to their exceptional model capa-bilities. However, most ViT models suffer from high complexity, with a large number of parameters that demand considerable memory and inference time, limiting their applicability on resource-constrained devices. To address this issue, we propose a distillation-based framework for compressing large models for smaller datasets. The framework leverages fine-tuning and knowledge distillation to accelerate the training process of compressed models. To evaluate its effectiveness, two compressed Swin Transformer models, Swin-N and Swin-M, were introduced and tested on the CIFAR-100 dataset. Experimental results demonstrate that when trained using the proposed framework, both Swin-N and Swin-M exhibit significant improvements in accuracy compared to their counterparts trained from scratch, with Swin-N achieving an 18.89% increase and Swin-M showing a 20.10% improvement. Additionally, Swin-M closely approximates the accuracy of the Swin- T teacher model, further validating the effectiveness of the framework.

关键词： Training Computer vision Accuracy Limiting Computational modeling process control Performance gain Transformers Harmonic analysis Optimization

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Towards Efficient Generation of a Multi-Domain Engineering Graph with Common Concepts 26

Towards Efficient Generation of a Multi-Domain Engineering G...

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26th IEEE International conference on Emerging Technologies and Factory Automation (ETFA)

作者： Rinker, Felix Meixner, Kristof Waltersdorfer, Laura Winkler, Dietmar Lueder, Arndt Biffl, Stefan Christian Doppler Lab Secur & Qual Improvement Pr Vienna Austria Sem Sys Res Lab Vienna Austria TU Wien Inst Informat Sys Engn Vienna Austria Ctr Digital Prod Vienna Austria OVGU Inst Ergon Mfg Sys & Automat Magdeburg Germany

ISBN: (纸本)9781728129891

Industry 4.0 envisions adaptive production systems, i.e., Cyber-Physical Production Systems (CPPSs), to manufacture products from a product line. Product-process-Resource modeling represents the essential aspects of a CPPS. However, due to discipline-specific models, e.g., mechanical, electrical, and automation models, it is often unclear how to integrate the proprietary data into an integrated model due to missing common understanding. This paper investigates (i) how to integrate local engineering views with Common Concepts (CCs) and using them as a defined taxonomy for modeling a network of engineering concepts;(ii) how to build an engineering network graph for visualisation and analysis considering discipline-specific needs. We motivate a method to support CPPS engineering organisations to integrate their heterogeneous data using CCs. This builds the basis for defining multi-domain engineering graphs for visualisation and analysis aspects. In this paper, we present a research agenda discussing open issues and expected results.

关键词： Adaptation models Production systems Taxonomy Knowledge based systems data visualization Inspection data models

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Efficiency analysis of Tandem Substring Search Methods

Efficiency Analysis of Tandem Substring Search Methods

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International Carpathian control conference (ICCC)

作者： László Kovács Dávid Polonkai Department of Information Technology University of Miskolc Miskolc Hungary

Strings and lists play a fundamental role in data structures. Considering string operations, one of the key functions is the search for matching parts within a given host string. The location of repeating substring pattern is a special type of general substring search. This task is of outstanding importance, among other in biology (examining genes problem) or in process modeling (searching for cycles). This article presents a specific alternative method for tandem substring search which unlike the traditional, direct pattern search methods presents, is based on the application of neural networks. The performed comparative analyzes carried out show that a neural network-based methods is a good alternative, especially in the case of approximate searches.

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A Granger causality analysis method based on GRBF network

A Granger causality analysis method based on GRBF network

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data Driven control and Learning Systems (DDCLS)

作者： Huang Chen Jian-Guo Wang Pangbin Ding Xiang-Yun Ye Yuan Yao He-Lin Chen Shanghai Key Lab of Power Station Automation Technology School of Mechatronical Engineering and Automation Shanghai University Shanghai China Department of Chemical Engineering National Tsing-Hua University Hsin-Chu Taiwan Ironmaking Plant Baoshan Iron & Steel Co. Ltd. Shanghai China

Accurate and efficient fault root cause diagnosis is an effective means to prevent major accidents in industrial systems. Due to the difficulty of modeling complex systems, Granger causal analysis is widely used. Root cause diagnosis in the shortest possible time after a fault occurs can improve the accuracy of diagnostic results. Due to the strong nonlinear relationship in the short observation data, this paper introduces Generalized Radial Basis Function(GRBF) neural network of the nonlinear dimensionality reduction method into the Granger causal model to realize the root cause diagnosis of Granger faults based on the nonlinear short observation data. The effectiveness of the proposed method is verified by numerical simulation and fault diagnosis experimental study of Tennessee Eastman,(TE) chemical process. The results show that the proposed method improves the processing ability of Granger causal analysis for nonlinear causality, and can use a small amount of the fault data to complete accurate fault root cause diagnosis.

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