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The next milestone in clinical trial of a measurement system developed to diagnose NAFLD 21

The next milestone in clinical trial of a measurement system...

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21st ieee International symposium on computational intelligence and Informatics

作者： Gyorfi, Nina Vereczkei, Andras Karadi, Zoltan Nagy, Karoly Kalmar Vizvari, Zoltan Sari, Zoltan Odry, Peter Gal, Adrian Robert Toth, Attila Univ Pecs Szentagothai Res Ctr Inst Physiol Med Sch Pecs Hungary Univ Pecs Clin Ctr Dept Surg Pecs Hungary Univ Pecs Med Sch Inst Physiol Pecs Hungary Univ Pecs Fac Engn & Informat Technol Pecs Hungary Univ Dunaujvaros Dept Comp Syst & Control Engn Inst Informat Technol Dunaujvaros Hungary Univ Pecs Dept Med Biol Pecs Hungary Univ Pecs Pecs Cent Electron Microscop Lab Pecs Hungary

ISBN: (纸本)9781665426848

Our publication presents the next important milestone in development of method aimed at the non-destructive diagnosis of Non-alcoholic Fatty Liver Disease (NAFLD). In clinical trials, a specific group of patients (with liver and other carcinomas) were studied. In their case, liver histological examinations are always carried out, which has given us the opportunity to compare bioimpedance spectrum (BIS) data with biopsy results. In our publication, we present the relevant measurement methodology after that we highlight some typical examples that describe the most common pathologies and their associated BIS origins.

关键词： Non-alcoholic Fatty Liver Disease bioimpedance spectrum measurement clinical study new diagnostic method

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Leveraging Ensemble Structures to Elucidate the Impact of Factors That Influence the Quality of Ultra-High Performance Concrete

Leveraging Ensemble Structures to Elucidate the Impact of Fa...

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ieee symposium Series on computational intelligence (SSCI)

作者： Farzad Rezazadeh P. Axel Dürrbaum Gregor Zimmermann Andreas Kroll Department of Measurement & Control University of Kassel Kassel Germany German Technologies & Engineering Concepts G.tecz Engineering GmbH Kassel Germany

Concrete is an essential material ubiquitously employed in construction. Yet, deciphering the factors that influence its quality is a formidable challenge due to partially understood physical relationships, the high dimensionality of the data, and its limited availability. This study introduces an ensemble framework designed to address these challenges. It uses a combination of individual methods within an ensemble configuration to identify the critical features that determine concrete quality. Within this framework, diverse base methods are harmonized using an average-based technique, leading to a robust final verdict. After selecting the potential influencing factors, 50 experiments are conducted using the Taguchi Orthogonal Array (L-50) to generate the data points. The proposed ensemble learning framework underscores the substantial impact of storage conditions during the curing time on the final quality of concrete.

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Real-Time Neural control for Discrete Nonlinear Systems Under Unknown Input and State Disturbances

Real-Time Neural Control for Discrete Nonlinear Systems Unde...

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ieee symposium Series on computational intelligence (SSCI)

作者： Alma Y. Alanis Jesus G. Alvarez Oscar D. Sanchez Pavel Zuniga Gustavo Munoz-Gomez CUCEI University of Guadalajara Guadalajara Mexico Tecnologico Nacional de Mexico Nochistlan Mexico

This paper presents the design of an intelligent controller for uncertain discrete-time nonlinear systems. The proposed controller is resilient to external unknown disturbances as well as state and input uncertainties, even though the model of the system is considered unknown. An intelligent controller is designed for an unknown discrete-time nonlinear system, this controller is model-free and it is based on sensor measurements and therefore including unknown system dynamics, actuator nonlinearities, measurement errors, noise, uncertainties, external disturbances and other phenomena associated to real-world applications. Then, using the neural model, a backstepping controller is designed to ensure a resilient performance. Finally, realtime results are included to demonstrate the effectiveness of the proposed approach using a three-phase induction motor.

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Hybrid Attention-based Transformer for Long-range Document Classification

Hybrid Attention-based Transformer for Long-range Document C...

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ieee International Conference on Fuzzy Systems (FUZZ-ieee) / ieee World Congress on computational intelligence (ieee WCCI) / International Joint Conference on Neural Networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Qin, Ruyu Huang, Min Liu, Jiawei Miao, Qinghai Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Anhui Peoples R China

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Transformer with the self-attention mechanism, which allows fully-connected contextual encoding over input tokens, has achieved outstanding performances in various NLP tasks, but it suffers from quadratic complexity with the input sequence length. Long-range contexts are often tackled by Transformer in chunks using a sliding window to avoid GPU memory overflow. However, how to achieve considerable performance on downstream tasks under the premise of modeling sequences as long as possible with limited GPU resources is still a problem to be explored. To address this issue, we propose a new framework using hybrid attention-based Transformer to capture long-range contextual features. More specifically, we make a combination of three types of attention, i.e. sliding window local attention, clustering-based long-range attention and specific global attention. Experiments comparing the performance of our model with mainstream efficient improved Transformers are conducted on document classification task in public datasets IMDB and CAIL-Long. Experimental results demonstrate that the proposed approach can outperform the state-of-the-art models on the adopted datasets, which verifies the effectiveness of our model on long-range document classification task.

关键词： Transformer clustering attention legal document document classification

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Data-driven robust adaptive control for wastewater treatment process 37

Data-driven robust adaptive control for wastewater treatment...

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37th Youth Academic Annual Conference of Chinese Association of automation, YAC 2022

作者： Cao, Yaxin Qie, Qiuyue Wang, Gongming Qufu Normal University Library Management Centre Qufu273165 China Army Aviation Institute of the PLA Research Center of the UAV System Beijing101123 China Beijing University of Technology Beijing Institute of Artificial Intelligence Beijing100124 China

ISBN: (纸本)9781665465366

Because of highly complexity and large-scale operation, wastewater treatment process (WWTP) is considered as a robust adaptive control problem. This paper proposes a data-driven robust adaptive control with deep learning (DRAC-DL) for WWTP to improve the operational performance. In ths DRACDL framework, a robust controller is firstly designed to construct the closed-loop control scheme. Meanwhile, an adaptive deep belief network (ADBN) is developed based on the self-incremental learning strategy to approximate the ideal control law. The main advantage of DRAC-DL lies in its improved robustness and low computational burden, which benefit from Lyapunov-based closed-loop controller and efficient ADBN approximator. Finally, the effectiveness of DRAC-DL is demonstrated on the benchmark simulation model No.1 (BSMI) of WWTP. © 2022 ieee.

关键词： Wastewater treatment

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Research on gesture recognition method based on PSO optimized LSTM for surface muscle signals 6

Research on gesture recognition method based on PSO optimize...

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6th Asia symposium on Image Processing, ASIP 2024

作者： Dai, Jianyi Lu, Guangda Qin, Zhuanpin Guo, Xiaoqian Liu, Xinlin Yin, Qianhan Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Hexi District No. 1310 Tianjin300222 China

ISBN: (纸本)9798350376159

(sEMG) Surface EMG signals can quickly and accurately reflect the movement intention of human body, gesture recognition by sEMG has been widely studied in the field of human-robot interaction in rehabilitation robots, in order to improve the accuracy of gesture recognition. A gesture recognition method based on PSO optimised LSTM for surface muscle signals is investigated. Firstly, the S_Dbw method is used to evaluate the clustering validity of the time domain features of the surface EMG signals, and the feature (MAV)combination of absolute mean +(RMS)root mean square (RMS)+ (VAR) variance is selected as the feature space and input to the model of the depth algorithm. Secondly, in order to solve the problem of cumbersome parameter debugging and low accuracy of (LSTM)Long Short-Term Memory neural network (LSTM), Particle Swarm Optimization-Long Short-Term Memory PSO-LSTM algorithm is proposed and combined with convolutional neural network (convolutional neural network, CNN) and Long Short-Term Memory LSTM. The experimental results show that the PSO-LSTM model has an average recognition accuracy of 94.1%, which is 8.3% and 4.7% higher than the CNN and LSTM, and the average training time of the LSTM model is reduced by 35.52s compared to the LSTM. Therefore, it is advantageous to use PSO-LSTM model in gesture sEMG signal recognition and classification. © 2024 ieee.

关键词： Convolutional neural networks

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Configuration Honeypots with an Emphasis on Logging of the Attacks and Redundancy 20

Configuration Honeypots with an Emphasis on Logging of the A...

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20th ieee Jubilee World symposium on Applied Machine intelligence and Informatics, SAMI 2022

作者： Palsa, Jakub Hurtuk, Jan Chovancova, Eva Havira, Marek Technical University of Košice Department of Computer and Informatics Košice Slovakia

ISBN: (纸本)9781665497046

The number of devices connected to a computer network has increased in recent years. This is also evidenced by the increased number of attacks and viruses by which attackers try to control devices in their favor. By using honeypots in the network infrastructure, we can attract such attackers to a fake system and obtain valuable information from them that we can use to improve network security. This diploma thesis deals with the issue of honeypot configuration in order to obtain information about attackers as well as ensuring system redundancy. We performed the redundancy using the Heartbeat system, which offers us cluster infrastructure services. Honeypot Honeyd was used to interact with low-level attackers on which we configured virtual devices. Honeypot Cowrie served us to interact with mid-level attackers. Subsequently, we verified the functionality of our solution using simulations in a computer network. We compared the amount of information obtained and based on it decided to deploy the Cowrie honeypot to a public IP address. The information was expressed by the behavioral manifestation of ELK Stakc in the extension of Kiban into the graphs and diagrams that we analyzed. © 2022 ieee.

关键词： Performance evaluation Heart beat computational modeling Redundancy Network security Systems simulation IP networks

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Automating Damage Recovery in a Legged Robot

Automating Damage Recovery in a Legged Robot

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computational intelligence in Artificial Life and Cooperative Intelligent Systems (ALIFE-CIS), ieee symposium on

作者： Alexandros Pouroullis David Blore Michael Scott Julius Smith Sindiso Mkhatshwa Geoff Nitschke Department of Computer Science University of Cape Town South Africa Centre for the Advanced Study of Collective Behaviour University of Konstanz Germany

ISBN: (数字)9798331508371

ISBN: (纸本)9798331508388

Autonomous robots are increasingly used in remote and hazardous environments, where automated recovery given damage to sensory-actuator systems would be extremely beneficial. Such robots must therefore have controllers that continue to function effectively given unexpected hardware malfunctions and damage. We evaluate various controller types (oscillatorstyle central pattern generators and artificial neural networks), for producing adaptable gait behaviors. These controller types are run for hexapod robot gait control in concert with the Intelligent Trial and Error (IT&E) and Map-Elites algorithm to maintain behavioral diversity. Specifically, we investigate the impact of behavior map-size in MAP-Elites (the first phase of the IT&E algorithm), in company with various controller types for multiple leg failures scenarios using a simulated hexapod robot. Results support previous work demonstrating a trade-off between adapted gait speed and controller adaptability across leg-damage scenarios, where map-size is crucial for generating behavioral diversity required for adaptation.

关键词： Legged locomotion Artificial neural networks Learning (artificial intelligence) Robot sensing systems Encoding Generators Hardware Maintenance Intelligent systems Robots

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Offline and Online Energy Simulation using Virtual Commissioning Models with Extended Dynamical Behavior

Offline and Online Energy Simulation using Virtual Commissio...

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ieee/SICE International symposium on System Integration

作者： Darius Deubert Denis Pfeifer Christian Scheifele Jörg Fehr Alexander Verl Bosch Rexroth AG 97816 Lohr am Main Germany and Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart Germany Denis Pfeifer is with ISG Industrielle Steuerungstechnik GmbH 70563 Stuttgart and Institute of Engineering and Computational Mechanics University of Stuttgart Stuttgart Germany ISG Industrielle Steuerungstechnik GmbH Stuttgart Institute of Engineering and Computational Mechanics University of Stuttgart Stuttgart Germany Alexander Verl is with Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart Germany

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Considering energy consumption and dynamical behavior within the simulation of manufacturing systems emerges as an important objective for their development, commissioning, and operation. Currently, these aspects are often neglected in many fields including virtual commissioning. This work presents an approach to extend highly detailed virtual commissioning models of components from industrial automation by dynamics models for the calculation of energy consumption in an offline simulation and for reuse in an online simulation. To validate the presented approach, an exemplary offline virtual commissioning simulation of a pick-and-place robot application is implemented and subsequently transferred to an online simulation. The validation shows a good accuracy of the simulated energy consumption, enabling virtual commissioning engineers to analyze and optimize energy consumption already in the design phase of manufacturing systems. Additionally, the dynamical behavior was well reflected in the online simulation, contributing to the capability of simulation in the operation phase.

关键词： Energy consumption automation Accuracy Service robots System integration Manufacturing systems

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Wind System control at Time-Varying Wind Speeds Using the Perturb and Observe Method

Wind System Control at Time-Varying Wind Speeds Using the Pe...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Florinel Butaru Mihaela Codruta Ancuti Geza Mihai Erdodi Ciprian Sorandaru Sorin Musuroi Razvan Ancuti Department of Electrical Engineering University POLITEHNICA Timisoara Timisoara Romania University Aurel Vlaicu Arad Romania

In this paper, the operation of the wind turbine (WT) at its maximum power point (MPP) is analyzed, using the classical perturb and observe method (POM). The wind system, consisting of the wind turbine (WT) and the electric generator, is currently operating in the energetically optimal zone using the perturb and observe method (POM) at constant wind speeds over time. This research demonstrates the inefficiency of the perturb and observe method (POM) at time-varying wind speeds using extended simulation findings based on experimental data from the wind turbines region in Dobrogea and using mathematical models.

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