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Evidence Theory and Fuzzy Discriminations in Dependable and Resilient Interactive MADM. Application in Emergency Psychiatric Diagnostics

Evidence Theory and Fuzzy Discriminations in Dependable and ...

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IEEE International Conference on Dependable systems, Services and Technologies (DESSERT)

作者： Gia Sirbiladze Janusz Kacprzyk Bidzina Midodashvili Bezhan Ghvaberidze Jose Antonio Lozano Tinatin Davitashvili Department of Computer Sciences Iv. Javakhishvili Tbilsi State University Tbilisi Georgia Intelligent Systems Laboratory Systems Research Institute Polish Academy of Sciences Warsawa Poland Department of Computer Science and Artificial Intelligence University of the Basque Country Donostia-San Sebastian Spain

Today, much attention is paid to the engineering of dependable and resilient intelligent systems and models. This direction includes multi-attribute decision-making (MADM) models and systems in emergency situations. A new interactive MADM approach based on evidence theory and fuzzy discrimination analysis is developed. MADM’s utilities in the discrimination q-rung orthopair fuzzy values in the Dempster-Shaper’s believe structure environment are transformed. New aggregation operators - extensions of Dempster’s extremal expectations under discrimination q-rung orthopair fuzzy information are constructed. MADM’s alternatives by new constructed operators are ranked. For the illustration of obtained results, a numerical example is created from Emergency Psychiatric Diagnostics (EPD).

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Automated Food Intake Monitoring System to Prevent Malnutrition Using the Tiago Robot Camera*

Automated Food Intake Monitoring System to Prevent Malnutrit...

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Data science and Engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Fotios S. Konstantakopoulos Daphni K. Plati Labrina A. Pliakou Francesco Scotto Di Luzio Nevio Luigi Tagliamonte Loredana Zollo Manolis Tsiknakis Dimitrios I. Fotiadis Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department University of Ioannina Biomedical Research Institute FORTH Ioannina GR 45110 Greece Research Unit of Advanced Robotics and Human-Centred Technologies Campus Bio-Medico University Rome Italy Institute of Computer Science FORTH Heraklion Crete GR 70013 Greece

The management of a daily diet is a significant concern among individuals in modern culture. The utilization of dietary assessment systems has significantly contributed to the efficient management of malnutrition and dietary habits over a period of time. In order to determine the nutritional value of the food that individuals consume, this study introduces a novel food monitoring system and its architecture. By creating a dataset of food images in a tray and using image processing, machine learning, and computer vision techniques, the nutrients and calories consumed by a patient are calculated. The proposed system achieves a 95.6% Intersection over Union score in the segmentation task, 92.3% top-1 accuracy in the classification task, and 4.6 g mean weight absolute error in the weight estimation *** Relevance: The proposed system has the ability to calculate the nutritional composition, encompassing calories, proteins, fats, and carbs, of food consumed by a patient, making it suitable for nutritional monitoring applications and healthcare systems that aim to monitor malnutrition in hospital patients.

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Fuzzy System of Wind Turbine Defect Image Processing

Fuzzy System of Wind Turbine Defect Image Processing

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International Conference on Advanced computer Information Technologies (ACIT)

作者： Lesia Dubchak Anatoliy Sachenko Carsten Wolff Nadia Vasylkiv Department of Computer Engineerin West Ukrainian National University Ternopil Ukraine Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Department of Tele-Informatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland Dortmund University of Applied Science and Arts Dortmund Germany Department of Information Computer Systems and Control West Ukrainian National University Ternopil Ukraine

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

Wind turbines are an important component of the global strategy for the transition to renewable energy sources and the fight against climate change. Their implementation contributes to sustainable development and to improving the ecological situation of the planet. Detecting and elimination of defects helps reduce the wear of the components and prevent premature failure. This can significantly extend the life of wind turbines, which is economically beneficial for owners and operators. The use of UAVs allows for minimizing the risks for personnel, as inspection and diagnosis of defects can be carried out without the need to lift people to great heights or stay in dangerous areas. This is especially important for turbines located in places of difficult access. The images taken by the UAV contain many extraneous objects and fuzzy data. Fuzzy logic allows you to create models for pattern recognition and object classification in images, taking into account uncertainty and variation in the data. This increases the accuracy and reliability of pattern recognition systems. This article proposes an image processing system to detect wind turbine defects, with a fuzzy system module that selects images from a video stream for further processing.

关键词： Fuzzy logic Renewable energy sources Costs Accuracy Uncertainty Image processing Streaming media Wind turbines Pattern recognition Fuzzy systems

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Classification of Wind Turbine Defects based on the SqueezeNet Neural Network

Classification of Wind Turbine Defects based on the SqueezeN...

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International computer sciences and Information Technologies (CSIT)

作者： Lesia Dubchak Anatoliy Sachenko Carsten Wolff Nadiia Vasylkiv Zenovii Bernas Department of Computer Engineering West Ukrainian National University Ternopil Ukraine Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Department of Tele-Informatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland Dortmund University of Applied Science and Arts Dortmund Germany Department of Information Computer Systems and Control West Ukrainian National University Ternopil Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

The article presents a study on classifying wind turbine defects using the SqueezeNet neural network. Wind turbines are critical for renewable energy, but defects such as corrosion, erosion, and cracks can significantly reduce their efficiency. The study proposes using image classification techniques with neural networks, particularly SqueezeNet, to automate defect detection. The compact nature of SqueezeNet makes it suitable for real-time applications with limited computing resources. Through training and testing, the network achieved an accuracy of 89%, demonstrating its potential to improve wind turbine maintenance and reduce operational costs.

关键词： Training Accuracy Costs Neural networks Real-time systems Wind turbines Maintenance Defect detection Testing Image classification

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The Smart Hospital: Data and AI Challenges

The Smart Hospital: Data and AI Challenges

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Data science and Engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Daphni K. Plati Fotios S. Konstantakopoulos Theofanis Kalatzis Dimitrios Manousos Thomas Kassiotis Francesco Scotto Di Luzio Nevio Luigi Tagliamonte Loredana Zollo Manolis Tsiknakis Dimitrios I. Fotiadis Materials Science and Engineering Department Unit of Medical Technology and Intelligent Information Systems University of Ioannina Biomedical Research Institute FORTH Ioannina GR 45110 Greece Institute of Computer Science FORTH Heraklion Crete GR 70013 Greece Research Unit of Advanced Robotics and Human-Centred Technologies Campus Bio-Medico University Rome Italy

Healthcare systems generally constitute a combination of healthcare facilities and providers that collaborate in order to offer a complete range of healthcare services. As the primary providers of healthcare, hospitals hold an essential place in the healthcare system. This study introduces three novel automated Artificial Intelligence (AI) systems by using the robotic platform of the TIAGo robot: (i) an image-based system that monitors the food consumption of the patients and provides information regarding their energy intake to prevent malnutrition; (ii) a rehabilitation system that monitors patients as they perform prescribed exercises to prevent loss of mobility, and (iii) a monitoring system for the correctness of the oxygen therapy in terms of the proper positioning of the oxygen mask to prevent hypoxia complications. A well-designed observational clinical study is planned to evaluate and validate the utility and effectiveness of the proposed AI systems in improving patient stay in the hospital. For the abovementioned systems, three novel datasets were *** Relevance: Through the implementation of AI systems, the proposed research intends to provide specialists with an extra tool to aid in the monitoring of hospitalized patients, thus helping to prepare the way for the Smart Hospital.

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A multiplexed FBG based sensor platform for flow and temperature measurements in the Baltic Sea

A multiplexed FBG based sensor platform for flow and tempera...

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2023 European Workshop on Optical Fibre Sensors, EWOFS 2023

作者： Dzipalski, A. Morton, J.A.S. Papachristou, N. Maier, R.R.J. MacPherson, W.N. Ristolainen, A. Kruusmaa, M. Reilent, E. Wolf, B.J. Pirih, P. van Netten, S.M. Shuhova, I. Lips, U. McFarlane, N. Macleod, R. Hendry, M. Sheehy, J. Almoghayer, M. Rojas, N. Davies, G. Hakim, A. Ng, A. Heriot-Watt University Institute of Photonics and Quantum Sciences Edinburgh United Kingdom Tallinn Institute of Technology Centre for Bio Robotics Tallinn Estonia University of Groningen Computer Science and Artificial Intelligence Groningen Netherlands Tallinn Institute of technology Department of Marine Systems Tallinn Estonia Hydrobond Engineering Aberdeen Energy Park Aberdeen United Kingdom Aquatera Ltd The Charles Coulson Building Stromness United Kingdom Orcades Marine Warness Park Kirkwall United Kingdom

ISBN: (纸本)9781510665002

In this paper a multiplexed multi-parameter marinized sensory array is described. This was deployed on the continental slope off the Keri Island marine observatory in the Gulf of Finland (Estonia). The sensor array is made up of 4 measurement stations which are connected in series. Across these measurement stations, a total of 16 temperature sensors, 4 attitude sensors (each consisting of 3 individual fiber sensors) and 16 flow sensors were successfully deployed. They were addressed over 1.1 km via 20 single-mode (SMF-28e) optical fibers contained in a single marine compliant ruggedized umbilical. The bio-inspired fiber optic flow sensors are designed to mimic the behavior of the superficial neuromasts naturally found as part of the lateral line sensory organ present in fish. The sensor is composed of optical fibers inscribed with Fiber Bragg Gratings glued together in a polymer matrix which are then encapsulated in a polyurethane shell. The sensors response has been tested in DC flows in a tow tank and have demonstrated the ability of measuring flow speed from 0.05 ms-1 to 2.5 ms-1. The main aim of the deployment was to demonstrate the capabilities of fiber sensor technology for long-term oceanographic applications. The measurement period described lasted over two months and the sensor system performed well in comparison with data was gathered from commercial instrumentation available. © 2023 SPIE.

关键词： Fiber Bragg gratings

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AceMiner: Accelerating Graph Pattern Matching using PIM with Optimized Cache System

AceMiner: Accelerating Graph Pattern Matching using PIM with...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Liang Yan Xiaoyang Lu Xiaoming Chen Sheng Xu Xingqi Zou Yinhe Han Xian-He Sun Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing Department of Compute Science Illinois Institute of Technology Chicago IL School of Computer and Information Anhui Normal University Wuhu

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

Graph pattern matching (GPM), a critical algorithm for discovering specific patterns within complex structures, is becoming increasingly important in the data-driven world. GPM applications are memory-bound and can be accelerated by memory-centric computing systems, such as processing-in-memory (PIM). However, there are three primary challenges when it comes to accelerating GPM applications with PIM: (1) difficulty in utilizing locality, (2) heavy data movement, and (3) heavy comparison overhead due to pruning. To address these challenges, we propose AceMiner, a framework to accelerate GPM applications with a software and hardware co-design per-spective using PIM. In AceMiner, we embed hybridCache, a novel in-DRAM cache system with lower access latency and optimized replacement policy, to leverage the potential locality and reduce data movement in PIM. Additionally, we introduce a comparison unit to address the huge pruning overhead. Experimental results show that AceMiner outperforms the state-of-the-art, achieving speedups of 40.2% and 13.3% over NDMiner and DIMMining respectively, with less energy consumption and design overhead.

关键词： Energy consumption Random access memory computer architecture Minimization Software Hardware Pattern matching

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Vibration Suppression of Non-Deformable Metal Strip for Robot Assisted Assembly Operation

Vibration Suppression of Non-Deformable Metal Strip for Robo...

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2020 IEEE International Conference on Emerging Trends in Communication, Control and Computing, ICONC3 2020

作者： Jalendra, Chetan Rout, B.K. Center for Robotics and Intelligent Systems Birla Institute of Technology and Science Department of Mechanical Engineering Pilani India

ISBN: (纸本)9781728114200

Current work presents a vibration suppression strategies of a Non-Deformable Metal Strip which is induced by rapid action of an industrial robot. In this case an external controller is designed as outer controller without any interruption in the robot internal controller to suppress the residual vibration in NDMS. The proposed controller is simulated in MATLAB/Simulink environment and designed in Python IDLE to validate the controller. The external controller is a closed-loop feedback Proportional Integral Derivative (PID) controller designed especially for an industrial robot that does not have control over acceleration. The robustness of the proposed controller is tested experimentally through the vibration control of a NDMS which is supposed to perform peg in hole assembly operation in various operating conditions. The designed controller suppresses vibration in less than 5 seconds and the stability time is reduced by 95% for in a Peg-in-hole assembly task. © 2020 IEEE.

关键词： Three term control systems

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Pseudo-label Assisted Optimization of Multi-branch Network for Cross-domain Person Re-identification

Pseudo-label Assisted Optimization of Multi-branch Network f...

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IEEE International Conference on Mechatronics and Automation

作者： Zhengyang Wang Shuxiang Guo Xue Shang Xiufen Ye College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China Department of Electrical and Computer Engineering National University of Singapore Singapore School of Life science Beijing Institute of Technology Beijing China

Person re-identification plays an important role in the field of robot intelligence perception and public safety. Its main task is to identify person targets under cross-camera. However, the domain diversity between different datasets poses a clear challenge for adapting a model trained on one dataset to another. Currently, person re-identification methods based on domain adaptive learning and pseudo-label have made good progress on this problem. Unfortunately, inferior pseudo-labels and source domain noise affect the performance. In order to improve the quality of generated pseudo-labels and enhance the feature representation capability of the model, we propose a pseudo-label assisted optimization of a multi-branch person re-identification method. The multi-branch network is able to extract and represent more effective global and local features, and the generated pseudo-labels are optimized by using cosine similarity and DBSCAN clustering on the feature vectors, thus improving the consistency of the supervised information to enhance the cross-domain recognition performance. We also use a loss function combining cross-entropy loss and triplet loss to make the best feature learning. Experiments show that our method performs well in the Market-to-Duke and Duke-to-Market cross-domain recognition tasks.

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Early Prediction of Cryptocurrency Price Decline: A Deep Learning Approach

Early Prediction of Cryptocurrency Price Decline: A Deep Lea...

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International Conference on computer and Information Technology (ICCIT)

作者： Mst Shapna Akter Hossain Shahriar Md Abdur Rahman Muhammad Rahman Alfredo Cuzzocrea Department of Intelligent Systems and Robotics University of West Florida USA Center for Cybersecurity University of West Florida USA Department of Mathematics Jahangirnagar University Savar Dhaka Bangladesh Department of Computer Science Clayton State University USA iDEA Lab University of Calabria Rende Italy

In recent times, the cryptocurrency market has emerged as one of the fastest-growing financial markets worldwide. It is, however, known for its high volatility and illiquidity compared to traditional markets such as equities, foreign exchange, and commodities. This inherent risk creates uncertainty among investors. The aim of this research is to forecast the level of risk in the cryptocurrency market. To assist cryptocurrency investors in navigating these challenges, we propose an approach that involves calculating the risk factor based on existing parameters. We employed various machine learning algorithms, including CNN, LSTM, BiLSTM, and GRU, to predict the risk factor in twenty elements of the cryptocurrency market. Through extensive experimentation, we developed a new model that outperformed existing models, achieving the highest Root Mean Square Error (RMSE) value of 1.3229 and the lowest RMSE value of 0.0089. Furthermore, we tested the generalization ability of our proposed model on a new dataset, different from the one used for training. Even with this new dataset, our model displayed robust performance. In contrast, the other existing models achieved higher RMSE values, with the highest being 14.5092 and the lowest 0.02769. By adopting our approach, investors can trade more confidently in complex and challenging financial assets such as Bitcoin, Ethereum, and Dogecoin. Our proposed model demonstrates superior performance and generalization capabilities, providing valuable insights for participants in the cryptocurrency market.

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