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Electrical, Electronic and Computing engineering (IcETRAN), International Conference on

作者： Aleksandra Stojnev Ilic Dragan Stojanovic dept. Computer Science Faculty of Electronic engineering University of Nis Nis Serbia

ISBN: (数字)9798350386998

ISBN: (纸本)9798350387001

Recent developments in sensor technology have enabled real-time data acquisition, high-frequency and multimodal data capturing thus underlying the need for monitoring physical or operational conditions in various aspects of the data. High dimensionality and volume of the data poses significant statistical challenges creating a need for accurate concept drift detection and prompt responses, a task that can be performed either by an algorithm or by human expert. Visual analytics plays a crucial role in concept drift detection by enabling analysts to visually explore, analyze, and interpret complex data streams, facilitating real-time monitoring, decision-making, and communication with stakeholders. This paper examines the usability of radar graphs for concept drift exploration, as well as their usage in other steps of analysis. For the purpose of demonstration, a pipeline for real-time health data analysis is presented and steps are visualized using radar graphs.

关键词： Data analysis Visual analytics Concept drift Pipelines Decision making Radar detection Data visualization

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Peephole Optimization for Quantum Approximate Synthesis

arXiv

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arXiv 2024年

作者： Clark, Joseph Thapliyal, Himanshu Dept. of Electrical Engineering and Computer Science University of Tennessee Knoxville United States

Peephole optimization of quantum circuits provides a method of leveraging standard circuit synthesis approaches into scalable quantum circuit optimization. One application of this technique partitions an entire circuit into a series of peepholes and produces multiple approximations of each partitioned subcircuit. A single approximation of each subcircuit is then selected to form optimized result circuits. We propose a series of improvements to the final phase of this architecture, which include the addition of error awareness and a better method of approximating the correctness of the result. We evaluated these proposed improvements on a set of benchmark circuits using the IBMQ FakeWashington simulator. The results demonstrate that our best-performing method provides an average reduction in Total Variational Distance (TVD) and Jensen-Shannon Divergence (JSD) of 18.2% and 15.8%, respectively, compared with the Qiskit optimizer. This also constitutes an improvement in TVD of 11.4% and JSD of 9.0% over existing solutions. © 2024, CC BY.

关键词： Quantum electronics

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Mental Health and Stress Prediction Using NLP and Transformer-Based Techniques

Mental Health and Stress Prediction Using NLP and Transforme...

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IEEE Symposium on Wireless Technology and Applications (ISWTA)

作者： Abhishek Pandey Sanjay Kumar Dept. of Computer Science and Engineering Delhi Technological University New Delhi India

ISBN: (数字)9798350351354

ISBN: (纸本)9798350351361

The integration of Natural Language Processing (NLP) and machine learning technologies into the field of mental health and stress prediction represents a significant advancement in the early detection and intervention of mental health issues. This paper explores the application of various NLP and transformer-based techniques, such as TF-IDF, n-grams, BERT, RoBERTa, and ALBERT, to analyze textual data from diverse sources such as social media platforms towards mental health and stress prediction. The developed methodology involves comprehensive data preprocessing, innovative feature extraction, and the application of deep learning models to understand the refinement of language indicative of mental health states. By comparing the effectiveness of different models and approaches, this paper aims to establish a robust framework for accurately predicting mental health conditions, thereby contributing to the broader goal of improving mental health care through technology. The simulation performed on a real-life dataset obtained from Reddit reveals that RoBERTa-based techniques perform better than the other methods.

关键词： Deep learning Social networking (online) Data preprocessing Mental health Bidirectional control Predictive models Transformers

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A software framework of roadside units for traffic condition perception and broadcast 22

A software framework of roadside units for traffic condition...

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2022 Conference on Research in Adaptive and Convergent Systems, RACS 2022

作者： Wang, Shao-Hua Yiu, Wei-Liang Tu, Chia-Heng Chang, Da-Wei Lee, Wei-Hsun Dept. of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Dept. of Transportation and Communication Management Science National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781450393980

While vehicle-to-everything technology has been proposed to improve road traffic efficiency and safety, it would suffer from the low coverage during an early stage of vehicle-to-everything deployment. The infrastructure-based solutions have been developed to tackle the problem by gathering traffic status on a roadside unit and broadcasting the detected information to nearby connected vehicles. In this work, we aim to augment such an infrastructure-centric design by proposing a multiple applications software framework to broadcast the sensed traffic conditions to connected vehicles in the vicinity. In addition, we propose a software module to improve the handling of multiple applications for the roadside unit. We have evaluated the proposed software in indoor tests, which shows that the framework can significantly improve traffic safety while conforming to the performance specifications of the standards defined by the Society of Automotive Engineers. © 2022 ACM.

关键词： Vehicle to Everything

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Modular Design of Solid-State Transformer with High Flexibility

Modular Design of Solid-State Transformer with High Flexibil...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Zhuxuan Ma Hui Cao Yue Zhao Dept. of Electrical Engineering and Computer Science University of Arkansas Fayetteville AR USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper presents a modular solid state transformer (SST) design incorporating dual active bridge and cascaded H-Bridge topologies to enhance electric vehicle (EV) charging infrastructure. By using modular design concept for the power cell hardware, the proposed SST can be suitable for industrial usage with low manufacturing cost, and low maintenance complexity. With various combinations of the power cells, the whole SST converter has high flexibility for the power scale and circuit topology. This guarantees the fast installation and high transportability of the SST, suitable for the high-demanded EV charging applications. The design detail of the busbar used for the SST has been presented, which shows how to reduce the stray inductance of the busbar while keeping its high current conduction ability and high insulation. To validate the high current conduction ability of the busbar, finite element analysis is conducted to assess busbar current density and temperature. Finally, an advanced control strategy is introduced to effectively reduce DC link ripple, eliminating the need for bulky components.

关键词： Inductance Circuit topology Bridge circuits Transportation Thermal conductivity Electric vehicle charging Topology Thermal stresses Stress Power transformer insulation

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Exploring the Relationship between Rent and Flat Prices Through Random Forest and Grid Search

Exploring the Relationship between Rent and Flat Prices Thro...

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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Ghosh, Joyita Maji, Kalyan Mzili, Toufik Roy, Priyanka Chakraborty, Monalisa Gupta, Subir W.B Haldia India India Chouaib Doukkali University Dept of Computer Science El Jadida Morocco Dr. B. C. Roy Engineering College Dept. of It WB Durgapur India Dr. B. C. Roy Engineering College Dept. of Cse WB Durgapur India

ISBN: (纸本)9798350365306

This paper explores the critical relationship between flat rents and prices, an important metric reflecting the state of the housing market and its underlying economic factors. The study introduces predictive models based on Random Forest algorithms, refined through Grid Search optimisation, to provide accurate market forecasts. Such predictive capabilities are crucial for policymakers, investors, and developers, who require reliable data for strategic decision-making and policy formulation. The research methodology employs Random Forest for its robust handling of complex datasets, with Grid Search utilised to calibrate the model parameters for enhanced precision. The resulting models are rigorously evaluated, achieving a training accuracy of 0.948 and testing accuracies of 0.745 for price prediction and 0.885 for rent prediction, indicating predictive solid performance. Low Log Loss metrics further validate the models' reliability. The study identifies the model's strengths and areas for refinement through detailed visualisations - accuracy comparisons, correlation heatmaps, and residual distribution plots. The residual plots, in particular, indicate a better generalisation capability in rent forecasting compared to flat price predictions, which exhibit minor overfitting. The findings provide actionable insights, demonstrating the efficacy of Random Forest and Grid Search in capturing the complex dynamics of flat rents and prices. The success of these models indicates their potential for broader application across different regions and market conditions, suggesting a valuable asset for stakeholders in achieving a balanced and fair housing market. This paper aims to contribute to the literature by offering a novel, data-driven perspective on market analysis and economic forecasting within the real estate sector © 2024 IEEE.

关键词： Prediction models

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MAD: A Meta-Learning Approach to Detect Advanced Persistent Threats using Provenance Data in Industrial IoT

MAD: A Meta-Learning Approach to Detect Advanced Persistent ...

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computer Security Applications Conference Workshops (ACSAC Workshops), Annual

作者： Bikash Saha Nanda Rani Sandeep Kumar Shukla Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India

ISBN: (数字)9798331532819

ISBN: (纸本)9798331532826

The increasing reliance on Industrial Internet of Things (IIoT) systems in critical infrastructure has made these environments prime targets for stealthy and prolonged Advanced Persistent Threats (APTs) that evade traditional security systems. This study introduces MAD (Meta-learning based APT Detection), a novel framework designed to detect APTs in IIoT environments by leveraging provenance data. This data provides a detailed view of system-level interactions, capturing subtle and persistent malicious behaviors. We evaluate MAD on the CICAPT-IIoT dataset, which simulates real-world APT scenarios with severe class imbalance due to the "low and slow" nature of APT attacks. MAD includes two variants: MAD v1, utilizing a multi-layer perceptron (MLP), and MAD v2, employing Model-Agnostic Meta-Learning (MAML) to improve detection of rare attack behaviors. MAD v1 achieves F1-score of 0.91 and outperforms the existing baselines. MAD v2 achieves F1-score of 0.95 surpassess existing baselines as well as MAD v1, which demonstrates its enhanced capability to detect rare APT activities despite the dominance of benign samples. This work combines provenance-based detection with meta-learning to address class imbalance and enhance APT detection in IIoT, demonstrating MAD’s potential in tackling emerging IIoT security challenges.

关键词： Metalearning Deep learning Adaptation models Adaptive systems Conferences Threat assessment Real-time systems Critical infrastructure computer security Industrial Internet of Things

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Dirt Identification and Removal based on Non-Contact Infrared Temperature Sensor on Solar Panel

Dirt Identification and Removal based on Non-Contact Infrare...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Rezmah Jemima Rupin Imran Hasan Rownak Jahan Md. Rakibul Haque Ontor Md. Motaharul Islam Dept. of Computer Science Engineering East West University Dhaka Bangladesh Dept. of Computer Science Engineering United International University Dhaka Bangladesh

Temperature sensing is a technique to detect dirt on a solar-panel-surface that can influence its surface temperature and can reduce its performance, which necessitates monitoring to optimize solar panel output generation, reduce unnecessary costs, and perform efficient cleaning. Temperature sensing can achieve faster and more accurate detection of dirt without using any robotic cameras or manual observation. In this study, a method is proposed to detect dirt on a solar panel with the help of an MLX90614 sensor, a DHT22 sensor connecting with Arduino UNO components. DC motors and gear motors are being used for the removal of accumulated dirt. The temperature of the solar panel surface is measured by an MLX90614 sensor using infrared radiation, while the environment's humidity and temperature are measured by a DHT22 sensor. The system implies measuring the solar panel’s temperature and comparing it to the ambient temperature. This system aims at improving previously proposed dirt detection methods using this approach and suggests a motor-driven dirt removal technique.

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Machine Learning based Anxiety Detection using Physiological Signals and Context Features

Machine Learning based Anxiety Detection using Physiological...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Arushi Jain Rajeev Kumar Dept. of Computer Science and Engineering National Institute of Technology Hamirpur Hamirpur India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

Anxiety is a common mental disorder that affects millions of people worldwide. Anxiety can be detected using physiological signals such as heart rate, skin conductance, blood pressure, and respiration. Machine learning is the ability to learn from data and forecast future events. Physiological signals can be analyzed and classified into different anxiety levels using machine learning. This paper aims to present the contribution of context features in detection of anxiety using physiological signals via machine learning. It is an imbalanced multiclassification problem using ECG and EDA signals with context features collected from 15 healthy individuals. These signals of sampling rate 700 Hz collected using RespiBAN from the Wearable Stress and Affect Detection (WESAD) dataset are used here with the 6-STAI questionnaire and the context collected about the subject during the experiment. Scores for each subject are calculated from this questionnaire which categorized subjects into different levels of anxiety. Multiple features are extracted from these signals and scaling is done on the extracted features. With the help of different data balancing techniques and different machine learning algorithms, this study will classify anxiety into three classes, namely, low, moderate and high. Classification of anxiety with fusion of multimodal physiological signals gave better results as compared to single modal physiological signals with an accuracy of 89.8% with ECG signal, 85.9% with EDA signal and 96.7% with ECG and EDA both using Gradient Tree Boosting algorithm with LOOCV after random oversampling. While, the best results are achieved when multimodal physiological signals are fused with context features, it gives the accuracy of 97.3% with Gradient Tree Boosting and LOOCV algorithm.

关键词： Machine learning algorithms Anxiety disorders Human factors Electrocardiography Feature extraction Boosting Physiology

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Adaptive and Responsive Traffic Signal control Using Reinforcement Learning and Fog Computing

Adaptive and Responsive Traffic Signal Control Using Reinfor...

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IEEE Cloud Summit

作者： Chengyu Tang Sanjeev Baskiyar Dept. of Computer Science and Software Engineering Auburn University Auburn Alabama USA

ISBN: (数字)9798350370065

ISBN: (纸本)9798350370072

As urban traffic becomes increasingly complex, conventional actuated traffic signal control methods are showing their limitations in mitigating congestion, and Deep Reinforcement Learning (DRL) is considered a promising solution. Existing studies mainly focus on using distributed agents, each intersection being controlled by a separate agent that can communicate with its neighbors to facilitate local coordination, which subsequently increases network complexity and usage. This article presents a DRL-based intelligent traffic signal control framework that leverages the fog computing paradigm. Traffic signals are divided into clusters, each controlled by a shared DRL agent in a fog node based on the collective information of the regional traffic situation. We have conducted simulation experiments in multiple scenarios, and the experimental results show that the proposed method yields lower average waiting times compared to existing methods.

关键词： Deep reinforcement learning Complexity theory Edge computing

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