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25th International Conference on computer and Information technology, ICCIT 2022

作者： Ibne Joha, Md. Shafiul Islam, Md. Ahamed, Shamim Khulna University of Engineering & Technology Dept. of Electrical and Electronic Engineering Khulna9203 Bangladesh Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna9203 Bangladesh

ISBN: (纸本)9798350346022

Internet connectivity has become an essential aspect of the 21st century. Internet of Things (IoT) has embedded devices, including various sensors, software, and appliances. IoT-based intelligent home automation system offers the automated monitoring, operation, and control of home appliances via the internet. This paper presents an IoT-based, intelligent control and protection system for home appliances using NodeMCU and Blynk app. System control and operation via the app are independent of the Wi-Fi network's nature. It eliminates additional coding to connect the system to any Wi-Fi network rather than home Wi-Fi. Moreover, it automatically detects and protects home appliances from damage against overheating, overloading, gas leakage, and fire hazards. Furthermore, the dimmer circuit offers fan speed and light intensity control. The system also provides a time-scheduling option and voice command control via Google Assistant. Above all, it saves energy and enhances human comfort. © 2022 IEEE.

关键词： Domestic appliances

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Multi-Positive Sample Quantum Contrastive Learning for Human Activity Recognition

Multi-Positive Sample Quantum Contrastive Learning for Human...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ren, Yanhui Wang, Di An, Lingling Mao, Shiwen Wang, Xuyu Xidian University Guangzhou Institute of Technology Guangzhou China Xidian University School of Computer Science and Technology Xi'an China Auburn University Department of Electrical and Computer Engineering AuburnAL United States Florida International University Knight Foundation School of Computing & Information Sciences MiamiFL United States

ISBN: (纸本)9798350351255

Human activity recognition (HAR) based on wearable devices has become an active research direction in the field of ubiquitous computing, and has a wide range of Internet of Things (IoT) applications. Unfortunately, it is challenging to obtain large amounts of labeled sensing data, and manual annotation is time-consuming and labor-intensive, making it impossible for the extensive deployment of HAR systems. Consequently, self-supervised learning has emerged to address this challenge by training on unlabeled data. However, traditional contrastive learning fails to simulate more sample diversity problems caused by environmental heterogeneity and sensor heterogeneity. In this paper, we propose a multi-positive sample quantum contrastive learning (MPSQCL) framework. By increasing the positive samples for contrastive learning and leveraging the advantages of quantum machine learning (QML) techniques, the richer features of input samples are extracted to improve the robustness and generalization of the model. Moreover, we design a new contrastive loss function to adapt to multiple positive sample contrastive learning scenarios. Finally, we validate the effectiveness of the proposed framework on several publicly available HAR datasets. © 2024 IEEE.

关键词： Contrastive Learning

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Enhancing Disease Prediction through Random Forests in Healthcare Analytics 7

Enhancing Disease Prediction through Random Forests in Healt...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kukreti, Sanjeev Al-Attabi, Kassem Chandrashekar, Rakesh Rani, K. Pushpa Badhoutiya, Arti Boob, Nandini Shirish Srivastava, Abhinav Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gla University Department of Electrical Engineering Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Fs University Shikohabad Firozabad India

ISBN: (纸本)9798350350067

This research investigates the application of Random Forest algorithms to enhance disease prediction within healthcare analytics. Using large healthcare datasets, the research compares Random Forests to other machine learning algorithms, such as Neural Networks and Support Vector Machines, and assesses their ability to forecast different illnesses. According to the findings, Random Forests have the ability to reliably forecast diseases since they provide greater accuracy, precision, recall, and F1-score. In addition, healthcare providers may benefit greatly from the model's feature significance metrics, which help them understand which aspects have the most impact on illness outcomes. This study emphasises the revolutionary power of Random Forests to improve healthcare diagnoses, personalised treatment regimens, and patient care overall. © 2024 IEEE.

关键词： Disease Prediction Healthcare Analytics Machine Learning Predictive Performance Random Forests

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Stress Matrix-Based Formation Control of Multi-Agent Systems with Discrete-Time Communication 12

Stress Matrix-Based Formation Control of Multi-Agent Systems...

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2024 12th International Conference on Systems and Control, ICSC 2024

作者： Onuoha, Okechi Ubochi, Brendan Chijioke Kurawa, Suleiman Tang, Zezhi Wu, Changshun Dong, Yi University of Hertfordshire United Kingdom Federal University of Technology The Department of Electrical and Electronic Engineering Akure Nigeria University of Manchester The Department of Electrical Engineering United Kingdom University of Sheffield The Department of Automatic Control and Systems Engineering United Kingdom University Grenoble Alpes Grenoble38000 France The School of Electronics & Computer Science University of Southampton United Kingdom

ISBN: (纸本)9798331531812

This study addresses the affine formation maneuver control of cooperative multi-agent systems (MAS) having periodic inter-agent communication for both static and dynamic leader cases. Here, we focus on the leader-follower MASs. The primary aim of the control system is to steer the entire collection of agents to produce required patterns (geometric) along with any required maneuver through the direct control of only few a selected agents referred to as leaders. Most of the existing works are constrained to either the individual agents communicate with each other in continuous-time or the sample-data scenario where the leaders are stationary or have constant acceleration or velocities. Here, we consider the scenarios where the velocities of the leaders can be time-varying or constant. Here, different cases are addressed and some control laws are proposed. Conditions are established to help guarantee the overall stability of the systems. A simulation study is employed for the illustration of our proposed laws. © 2024 IEEE.

关键词： Continuous time systems

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A low-cost buoyant force measurement device for monitoring sugar concentration in water solution

A low-cost buoyant force measurement device for monitoring s...

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electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Luka Kaučić Ivana Kovačević Ivan Aleksi Tomislav Keser Josip Zidar Snježana Rimac-Drlje Department of Computer Engineering and Automation Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Communications Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

Monitoring sugar concentration during fermentation is crucial for producing high-quality alcoholic beverages. Traditional methods for measuring sugar concentration can be costly and time-consuming, especially for small-scale producers. In this study, we developed a low-cost buoyant force measurement device for monitoring sugar concentration in water solutions. The device consists of a buoyant object fully submerged in the solution and connected to a load-cell sensor. As the sugar concentration in the liquid increases, the buoyant force on the object increases, and the load-cell measures this force. Proposed device is calibrated using solutions of known sugar concentrations and its high accuracy and precision is presented. The device is tested in controlled environment to ensure accurate tracking changes in sugar concentration over time. Proposed device can be a valuable tool for small-scale producers looking to optimize their fermentation processes while minimizing costs.

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Novel Design of a Wideband Low-Noise Amplifier with Adjustable Gain for Underwater Applications

Novel Design of a Wideband Low-Noise Amplifier with Adjustab...

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Frontiers of Information technology (FIT)

作者： Saqib Ali Khurram Shehzad Umar Hamid Khurram Ali Junaid Arshad Ahmed Nazir School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Pakistan

ISBN: (数字)9798331510503

ISBN: (纸本)9798331510510

Low-noise amplifiers (LNAs) are designed to amplify only the desired signal while minimizing noise, making them essential for applications requiring precise signal detection, processing in low SNR and high-sensitivity environments. This paper presents a novel LNA design aimed at enhancing signal reception in underwater imaging and communication applications. The proposed design achieves a wideband amplifier with specific parameters: a variable gain range of 40 dB to 80 dB, a broad 30 kHz bandwidth centered at 20 kHz, and a low input-referred voltage noise density of 0.1 μV/√ Hz. The proposed design employs a two-stage amplification approach, first stage with a fixed 40 dB gain and a variable gain in the second stage, reaching up to 80 dB. An initial filter stage reduces out-of-band noise to improve signal quality. This paper also provides a comprehensive analysis of the selection of low-noise op-amps, passive components, filter topologies, and preamplifier configurations. Extensive simulations confirm the effectiveness of the proposed design in significantly enhancing signal reception for underwater applications.

关键词： Low-noise amplifiers Sensitivity Passive filters Voltage Preamplifiers Topology Gain Wideband Signal detection Signal to noise ratio

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Self-Supervise Reinforcement Learning Method for Vacant Parking Space Detection Based on Task Consistency and Corrupted Rewards

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IEEE Transactions on Intelligent Transportation Systems 2024年第2期25卷 1346-1363页

作者： Nguyen, Manh-Hung Chao, Tzu-Yin Hsiao, Ching-Chun Li, Yung-Hui Huang, Ching-Chun Hcm University of Technology and Education Faculty of Electrical Electronic Engineering Ho Chi Minh City700000 Viet Nam National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300093 Taiwan Hon Hai Research Institute Ai Research Center New Taipei City114699 Taiwan

This paper proposes a novel task-consistency learning method that enables us to train a vacant space detection network (target task) based on the logic consistency with the semantic outcomes from a flow-based motion behavior classifier (source task) in a parking lot. Note that the source task can introduce false detection during task-consistency learning, which implies noisy rewards or supervision. The target network can be trained in a reinforcement learning setting by appropriately designing the reward mechanism upon semantic consistency. We also introduce a novel symmetric constraint to detect corrupted samples and reduce the effect of noisy rewards. Unlike conventional corrupted learning methods that use only training losses to identify corrupted samples, our symmetric constraint also explores the relationship among training samples to improve performance. Compared with conventional supervised detection methods, the main contribution of our work is the ability to learn a vacant space detector via semantic consistency rather than supervised labels. The dynamic learning property allows the proposed detector to be easily deployed and updated in various lots without heavy human loads. Experiments demonstrate that our noisy task consistency mechanism can be successfully applied to train a vacant space detector from scratch. © 2000-2011 IEEE.

关键词： Semantics

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An Enhanced Control Strategy for Standalone Brushless Doubly Fed Induction Generator Under Unbalanced Load Using Reactive Power Loop 1

An Enhanced Control Strategy for Standalone Brushless Doubly...

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1st IEEE IAS Global Conference on Emerging Technologies, GlobConET 2022

作者： Elbabo Mohammed, Omer Mohammed Liu, Yi Xu, Wei Islam, Md. Rabiul University of Sinnar Faculty of Engineering Department of Electrical Engineering Sinnar Sudan Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Wuhan China University of Wollongong School of Electrical Computer and Telecommunications Engineering Wollongong Australia

ISBN: (数字)9781665443579

ISBN: (纸本)9781665443579

Without brushes and slip rings, the brushless double-fed induction generator (BDFIG) can operate more stably and reliably, so it is prospective to be one of important machines for standalone systems in the next few years. The stability of amplitude of voltage and frequency of power winding (PW) is major aim of control in off-grid system. Also, the standalone system is oversensitive to abnormal running situations, especially with unbalanced conditions. The unbalanced loads would lead to harmful phenomena, such as the unbalanced impact of current and voltage of PW. To handle this issue, the reactive power loop is presented to minimize voltage unbalance of PW through creating control winding (CW) compensation voltage via converter of machine side (MSC). The simulation is executed to test the performance of presented method. © 2022 IEEE.

关键词： Reactive power

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CdS/Ti-Si-O Composite Photoanode for Photoelectrochemical Hydrogen Generation

CdS/Ti-Si-O Composite Photoanode for Photoelectrochemical Hy...

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10th Symposium on Dielectrics for Nanosystems: Materials science, Processing, Reliability, and Manufacturing, held at the 245th ECS Meeting

作者： Feng, Weilong Ding, Dongyan Chen, Zhi SJTU-Zhongmu Joint Research Center for Materials and Technology of Intelligent and Connected Vehicles Institute of Electronic Materials and Technology School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical & Computer Engineering Center for Nanoscale Science & Engineering University of Kentucky LexingtonKY40506 United States

ISBN: (纸本)9781623326784

Advancements in clean energy drive socio-economic development and foster a sustainable future by restructuring energy systems and promoting low-carbon development. In this paper, we presented a comprehensive investigation on the design and performance characterization of CdS/Ti-Si-O composite photoanodes for photoelectrochemical (PEC) hydrogen generation. Silicon-doped TiO2 nanotube arrays were fabricated through anodization of Ti-5Si alloy, followed by the deposition of CdS using the SILAR (successive ionic layer adsorption and reaction) process, resulting in nanostructured Ti-Si-O photoanodes modified with CdS. The composite photoanodes were characterized in terms of their composition, microstructure, optical properties and photoelectrochemical hydrogen production performance. The composite photoanodes revealed a notable enhancement in PEC hydrogen generation properties with a photocurrent density of 7.86 mA/cm2,which was 19.15 times and 9.59 times to those of the undoped TiO2 photoanode and Ti-Si-O photoanode, respectively. The Si-doping mechanism of the photoanodes was elucidated through Density Functional Theory (DFT) calculations. The synergistic combination of Si-doping and CdS modification represents a novel and promising approach for the design of efficient nanostructured photoanodes. © The Electrochemical Society.

关键词： Cadmium sulfide

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Conceptualizing Suicidal Behavior: Utilizing Explanations of Predicted Outcomes to Analyze Longitudinal Social Media Data 22

Conceptualizing Suicidal Behavior: Utilizing Explanations of...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Nguyen, Van Minh Nur, Nasheen Stern, William Mercer, Thomas Sen, Chiradeep Bhattacharyya, Siddhartha Tumbiolo, Victor Goh, Seng Jhing Florida Institute of Technology Mathematical Sciences MelbourneFL32901 United States Florida Institute of Technology Electrical Engineering and Computer Science MelbourneFL32901 United States Florida Institute of Technology Mechanical and Civil Engineering MelbourneFL32901 United States

ISBN: (纸本)9798350345346

The COVID-19 pandemic has escalated mental health crises worldwide, with social isolation and economic instability contributing to a rise in suicidal behavior. Suicide can result from social factors such as shame, abuse, abandonment, and mental health conditions like depression, Post-Traumatic Stress Disorder (PTSD), Attention-Deficit/Hyperactivity Disorder (ADHD), anxiety disorders, and bipolar disorders. As these conditions develop, signs of suicidal ideation may manifest in social media interactions. Analyzing social media data using artificial intelligence (AI) techniques can help identify patterns of suicidal behavior, providing invaluable insights for suicide prevention agencies, professionals, and broader community awareness initiatives. Machine learning algorithms for this purpose require large volumes of accurately labeled data. Previous research has not fully explored the potential of incorporating explanations in analyzing and labeling longitudinal social media data. In this study, we employed a model explanation method, Layer Integrated Gradients, on top of a fine-tuned state-of-the-art language model, to assign each token from Reddit users' posts an attribution score for predicting suicidal ideation. By extracting and analyzing attributions of tokens from the data, we propose a methodology for preliminary screening of social media posts for suicidal ideation without using large language models during inference. © 2023 IEEE.

关键词： Large Language Model Layer Integrated Gradients Natural Language Processing Suicidal Ideation TF-IDF Token Attribution

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