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14th International Conference on Social Robotics, ICSR 2022

作者： Chandra, Shruti Loucks, Torrey Castaneda, Gerardo Chavez Dautenhahn, Kerstin Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada Faculty of Rehabilitation Medicine University of Alberta Edmonton Canada Department of Communication Sciences and Disorders Jacksonville University JacksonvilleFL United States System Design Engineering Department University of Waterloo 200 University Ave WaterlooONN2L3G1 Canada

ISBN: (纸本)9783031246692

This research aims to explore the feasibility of integrating social robots as a tool for speech-language pathologists in stuttering intervention. This research followed an exploratory, human-centred approach leveraging the Double Diamond Design Process Model. In this study, we employed co-design tools, including a survey, one-to-one semi-structured expert interviews and a focus group to understand challenges frequently reported by speech-language pathologists and discuss opportunities for social robots to face some of these challenges. The qualitative results of our study revealed five major themes, pointing towards several avenues for social robots to aid speech-language pathologists and their clients over the course of a stuttering intervention program. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine design

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Performance of Building-Integrated Photovoltaic and Addressing the Challenges of Color, Safety, and Light-Emitting Diode Integration

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Physica Status Solidi (A) Applications and Materials Science 2025年第10期222卷

作者： Mohammed, Shurouq Abdulqadir Yousuf, Hasnain Jony, Jaljalalul Abedin Khokhar, Muhammad Quddamah Rahman, Rafi Ur Aida, Maha Nur Alamgeer Park, Sangheon Yi, Junsin Department of Electrical and Computer Engineering Sungkyunkwan University Gyeonggi-Do Suwon16419 Korea Republic of Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Gyeonggi-Do Suwon16419 Korea Republic of Department of Future Energy Engineering Sungkyunkwan University Gyeonggi-Do Suwon16419 Korea Republic of College of Information and Communication Engineering Sungkyunkwan University Gyeonggi-Do Suwon16419 Korea Republic of

Building-integrated photovoltaics (BIPVs) provide a dual-purpose solution, functioning as both energy-generating systems and integral building materials. BIPV adoption has been limited due to a lack of color integration, preventing the panels from being used in building designs. However, efforts to incorporate color into the BIPV layers have been made, but they often reduce panel efficiency. BIPV implementation faces several challenges that hinder widespread adoption, such as fire safety concerns and the integration of color aesthetics and light-emitting diode technology. This article explores these challenges and recent advancements that enhance the aesthetic versatility and efficiency of BIPV systems, including the use of quantum dots, Bragg reflectors, and silver-alloyed copper indium gallium selenide cells. The study also highlights critical considerations for optimizing BIPV systems, such as energy performance and fire safety. PVsyst 7.2 simulation results for a 10 kW BIPV system installed in South Korea demonstrate effective strategies for mitigating efficiency losses through investigating the orientation of angles. Results showcase a high-performance ratio of 0.891, a high production of 14 147 kWh year−1 at a tilt angle of 30° while facing south, and a peak energy of almost 1450 kWh injected into the grid under varying conditions. © 2025 Wiley-VCH GmbH.

关键词： building-integrated photovoltaics colors efficiencies light-emitting diodes technologies

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Based on Internet of Things Tracking of Hand Hygiene and Practices 2nd

Based on Internet of Things Tracking of Hand Hygiene and Pra...

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2nd International Conference on Mechanical and Energy Technologies , ICMET 2021

作者： Jayadeva, Sujay Mugaloremutt Singh, Krishanveer Karnan, Lokanayaki Franklin Telfer, L. Bharti, Anupama Dhiman, Viney Department of Health System Management Studies JSS Academy of Higher Education and Research Karnataka Mysuru India GLA University Mathura India Department of Computer Science St. Francis de Sales College Bengaluru India Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Sociology and Social Work Himachal Pradesh University Himachal Pradesh Shimla India Department of Cardiology PGIMER Chandigarh India

ISBN: (纸本)9789811901072

The study explores the tracking and practices essentiality in hospitals and health facilities, because related infections, such as higher risks and acceleration of illness, can have significant effects on health care. Human observers tracking and Practices have problems of their own, such as loss of observation concentration, accumulation of human mistake and expert deformation. In this investigation, the results of two internet-based techniques for controlling the hand hygiene of medical personnel during patient visits were carried out and evaluated. As base stations and smart phones, we employed ESP modules as mobile nodes and calculated the range to be located in the patient's room using Bluetooth RSSI values. We analyzed the RSSI in the proximity solution from a sensor host measured on different ESP nodes/module and used the premise that the sensor host is nearest to the ESP node which offers the maximum RSSI values. We utilized the RSSI value to measure the location between mobile nodes and every ESP node in the trilateration method and we applied the trilateration technique to identify the mobile node inside the room. Our tests have shown that 20% of the solution based on proximity was erroneous, whilst the 8% of the solution based on trilateration was erroneous. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smartphones

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A Comprehensive Study on Drones and Big Data for Supply Chain Optimization Using a Novel Approach

A Comprehensive Study on Drones and Big Data for Supply Chai...

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Advanced Computing and Emerging Technologies (ACET), International Conference on

作者： Dankan Gowda V D Palanikkumar Suganthi. N A.S. Malleswari Sanjog Thapa Rama Chaithanya Tanguturi Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore Tamilnadu India Department of Electrical and Electronics Engineering Dayananda Sagar College of Engineering Bangalore Karnataka India Department of Computer Science & Engineering Aditya College of Engineering and Technology Surampalem Andhra Pradesh India Department of Information System and Analysis Lamar University Beaumont Texas USA School of Business Woxsen University Hyderabad India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

This research paper aims to provide qualitative and quantitative insights regarding the application of drones and big data techniques in the SCM field through a new scientific method. The proposed method exploits the opportunities of drones for real-time data capturing and big data technologies to enrich decision making in supply chain. The study is pertinent to modern tendencies in supply chain systems and proves the efficiency of the new method through the precise calculations and contrastive analysis. The following are some of the findings that suggest efficiency, cost decrease, and outstanding performance of the supply chain. The studies being offered in this research will prove beneficial for practitioners and researchers who endeavor to advance the concepts and practice of logistics and supply chain management.

关键词： Costs Supply chain management Supply chains Demand forecasting Big Data Real-time systems Security Reliability Drones Resilience

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Evolutionary computing in energy systems integration: Techniques and applications

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Energy Reports 2025年 13卷 6450-6478页

作者： Vivek Saxena Saibal Manna Saurabh Kumar Rajput Bhupender Sharma Praveen Kumar Sourav Diwania Varun Gupta Mohammed H. Alsharif Mun-Kyeom Kim Department of Electrical & Computer Engineering ABES Engineering College Ghaziabad Uttar Pradesh India Centre for Internet of Things Madhav Institute of Technology and Science Gwalior India Department of Electrical & Electronics Engineering KIET Group of Institutions Ghaziabad India Department of Electronics & Communication Engineering National Institute of Technology Sikkim India Department of AI Convergence Electronic Engineering Sejong University Seoul 05006 Republic of Korea School of Energy System Engineering Chung-Ang University 84 Heukseok-ro Dongjak-gu Seoul 06974 Republic of Korea

In the foreseeable future, escalating energy production and consumption will rapidly deplete finite fossil fuel resources and cause significant ecological damage. Despite substantial progress in developing renewable energy and power generation technologies over the past decade, carbon dioxide emissions continue to surge at an alarming rate. This underscores the necessity of a holistic approach to energy generation, one that considers the entire power sector and its interconnected factors. The concept of energy systems integration highlights the potential synergies among diverse energy systems, offering significant benefits for both consumers and producers. Trigeneration and cogeneration, notable established technologies, can simultaneously produce multiple forms of energy framework. Integrated Energy systems (IESs) show great promise in reducing energy consumption, diversifying fuel sources, and providing cleaner energy alternatives. Optimizing these systems requires a variety of strategies tailored to specific objectives. This study presents different optimization approaches aimed at enhancing system performance, both with and without constraints, each characterized by distinct methodologies and comprehensive statistical analyses. Optimization techniques in this domain are broadly categorized as constrained and unconstrained, each offering unique advantages and applications. Furthermore, the research delves into the expanding field of evolutionary computing and its potential applications in optimizing IESs. These applications include combined heat and power (CHP), combined cooling, heating, and power (CCHP), and the integration of renewable energy sources. Traditional optimization methods are gradually being supplanted by evolutionary computing techniques due to their effectiveness in addressing complex optimization challenges. Modern heuristic algorithms play a crucial role in optimizing specific components of energy systems. Unlike Particle Swarm Optimization (PS

关键词： ACO Ant Colony Optimization AI Artificial Intelligence CAES Compressed Air Energy Storage CGAM Constrained Generalized Additive Model CHPED Combined Heat and Power Economic Dispatch CO2 Carbon di oxide DH District Heating DHC District Heating and Cooling EA Evolutionary Algorithm ED Economic Dispatch FC Fuel Cell GSHP Ground Source Heat Pump GSM Golden Section methods HJM Hooke and Jeeves method HT-PEMFC High-Temperature Proton Exchange Membrane Fuel Cell LCOE Levelized Cost of Electricity LP Linear Programming MAS Multiagent system MG Microgrid MPPT Maximum Power Point Tracking NLP Non-linear Programming ORC Organic Rankine Cycle QP Quadratic Programming SIC Specific Investment Cost UC Unit Commitment Artificial intelligence Integrated energy Optimization Renewable energy

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Classification of chili plant growth using deep learning 10

Classification of chili plant growth using deep learning

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10th IEEE International Conference on system engineering and Technology, ICSET 2020

作者： Aldabbagh, A.D.A. Hairu, C. Hanafi, M. Department of Computer and Communication System Engineering Faculty of Engineering Universiti Putra Malaysia Malaysia

ISBN: (纸本)9781728199108

Chili is among top grown crops in Malaysia. Nevertheless, chili plant growth monitoring in Malaysia is still performed manually by human labor, which consumed lots of time, energy and the plant growth can only be monitored at site. Hence, this paper discussed the potential of deep learning algorithm in classifying chili plant growth images from a small dataset. The experiment is performed on 256 chili plant images that under various conditions, where the images were captured using a 12-megapixel and f/1.8 aperture camera. Experimented using ResNet-101 and ResNet-50 of Mask R-CNN models with 75% of the dataset for training and 25% for testing, the results showed that both models were able to detect the correct age of chili plants with accuracy of 96% for Mask R-CNN ResNe-50 which is lower than Mask R-CNN ResNe-101 by 1%. © 2020 IEEE

关键词： Classification (of information)

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Feature Map Guided Strategy for Robust Multi-Resolution Image Dehazing

Feature Map Guided Strategy for Robust Multi-Resolution Imag...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Avishek Kumar Nagendra Kumar Akhilesh Kumar Mishra Gouransh Pathak Amit Chatterjee Centre for Sensors Instrumentation and Cyber-Physical System Engineering IIT Delhi New Delhi India Department of Electronics and Communication Engineering NIT Jamshedpur Jamshedpur India Department of Electrical and Electronics Engineering IILM Univeristy Greater Noida India Department of Computer Science and Engineering PIEMR-Indore Indore India School of Computing DIT University Dehradun India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Presence of haze in digital images poses significant challenges in computer vision applications, adversely impacting tasks like image segmentation and object detection. To tackle this issue, this paper introduces a fusion-based dehazing strategy by integrating color conversion, adaptive gamma correction, and parameter estimation. Through multi resolution fusion of chromatic and brightness maps, the algorithm efficiently removes haze, surpassing conventional single-image approaches while maintaining computational efficacy. From comparative analysis it is evident that, the proposed strategy is highly effective in terms of improving image quality for diverse applications.

关键词： Image quality computers Image segmentation computer vision Image resolution Parameter estimation Image color analysis Digital images Object detection Computational efficiency

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Utilizing Artificial Neural Networks and Multivariate Patient Data for Anemia Detection using WEKA based Approach for Diagnosis

Utilizing Artificial Neural Networks and Multivariate Patien...

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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： Prof Neeraj Bhargava Pramod Singh Rathore Parul Vaishnav Manish Rai Professor School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India M.Tech Research Scholar MDS University Ajmer Department of Computer Science and Engineering (AI& ML) Manipal University Jaipur India

This research study describes a method that uses neural networks to figure out if a woman has anemia. An artificial neural network is a type of computer network that is based on the real neural networks that make up the structure of the human brain. In this paper, the WEKA tool is used for the classification process carried out using an ANN structure, which is referred to as one of the data mining approaches and is based on multiple perceptions. The network has seven inputs (RBC, HCT, MCV, MCH, MCHC, RDW, and WBC) and an output (decision). Simulations on 567 patients demonstrate that the Artificial Neural Network (ANN) accurately diagnose the disease. This study investigates the utilization of Artificial Neural Network (ANN)-supervised machine learning techniques for predicting anemia by using the data from Kaggle.

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Efficient IOT based COVID patient healthcare control system using arduino with mobile app

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Measurement: Sensors 2024年 33卷

作者： sudha, K. Mercy Theresa, M. Vinayagam, P. Suganthi, R. Sripriya, T. uthayakumar, G.S. Computer Science and Business System R. M. D Engineering College India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur India Department of ECE Saveetha Engineering College India Department of ECE Panimalar Engineering College India Department of Electronics and Communication Engineering Jeppiaar Institute of Technology Tamilnadu Chennai India Department of ECE St.Joseph's Institute of Technology India

Healthcare monitoring frameworks emerged as one of the most essential frameworks and innovations established over the last decade. As a result of failing to provide adequate clinical attention to patients at the appropriate time, many people are facing the possibility of an untimely death. Ultimately, the goal was to develop an IoT-based integrated healthcare monitoring framework that could be relied upon by healthcare professionals to screen their patients, whether they were in the hospital or at home, to ensure that they were being well-cared for. Sensors, an information acquisition unit, a microcontroller (e.g., Arduino), and a product modification were used to construct a mobile phone-based remote healthcare monitoring framework that may provide continuous web-based data about a patient's physiological states (i.e., JAVA). The framework checks, displays, and stores the patient's temperature, heart rate, and EEG data before sending it out on the specialist's portable device with the application. In this way, an IoT-based patient monitoring framework may effectively monitor a patient's health state and save their life on time. © 2024

关键词： Data acquisition

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Towards improving detection performance for malware with a correntropy-based deep learning method

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Digital communications and Networks 2021年第4期7卷 570-579页

作者： Xiong Luo Jianyuan Li Weiping Wang Yang Gao Wenbing Zhao School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Beijing Intelligent Logistics System Collaborative Innovation Center Beijing101149China China Information Technology Security Evaluation Center Beijing100085China Department of Electrical Engineering and Computer Science Cleveland State UniversityClevelandOH44115USA

With the rapid development of Internet of Things(IoT)technologies,the detection and analysis of malware have become a matter of concern in the industrial application of Cyber-Physical system(CPS)that provides various services using the IoT ***,many advanced machine learning methods such as deep learning are popular in the research of malware detection and analysis,and some achievements have been made so ***,there are also some *** example,considering the noise and outliers in the existing datasets of malware,some methods are not robust ***,the accuracy of malware classification still needs to be *** at this issue,we propose a novel method that combines the correntropy and the deep learning *** our proposed method for malware detection and analysis,given the success of the mixture correntropy as an effective similarity measure in addressing complex datasets with noise,it is therefore incorporated into a popular deep learning model,i.e.,Convolutional Neural Network(CNN),to reconstruct its loss function,with the purpose of further detecting the features of *** present the detailed design process of our ***,the proposed method is tested both on a real-world malware dataset and a popular benchmark dataset to verify its learning performance.

关键词： Malware detection Mixture correntropy Deep learning Convolutional neural network(CNN)

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