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Predicting Collaboration Requests in Dynamic Construction Environments for Smooth Worker-Robot Collaboration

Predicting Collaboration Requests in Dynamic Construction En...

2024 International Conference on Construction and Real Estate Management: ESG Development in the Construction Industry, ICCREM 2024

作者： Pan, Zaolin Yu, Yantao Dept. of Civil and Environmental Engineering Hong Kong Univ. of Science and Technology Hong Kong

ISBN: (数字)9780784485910

ISBN: (纸本)9780784485910

Worker-Robot Collaboration (WRC) holds great potential in boosting productivity and safety in construction by leveraging the strengths of humans and robots. One crucial aspect of WRC is the ability of assistive robots to anticipate collaboration requests, enabling them to proactively support their human counterparts. However, dynamic construction environments present unique challenges due to constantly changing workplaces, worker interactions, and intentions. To address this challenge, our study focuses on determining when workers require assistance in such dynamic environments. We propose a vision-based approach, employing YOWOv2-based action detection for real-time prediction of workers' locations and their assistance intentions. We evaluate the effectiveness of our method through experiments conducted on a scaffold assembly task, showcasing its efficiency (52 FPS) and accuracy (0.92 frame mAP). These findings contribute to the development of robot assistive policies tailored to dynamic workplaces, paving the way for smooth WRC in construction. © ASCE.

关键词： Scaffolds

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A New Three-phase Multi-mode AC/DC LLC Converter with Output-controlled Active Rectifier (with V2G and G2V functions) For Fast DC Charging Application 14

A New Three-phase Multi-mode AC/DC LLC Converter with Output...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Xia, Xiaoyi Lam, John York University Dept. of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798331516116

This paper proposes a new three-phase multi-mode AC/DC LLC resonant converter with an output-controlled active rectifier for electric vehicle (EV) fast DC charging applications. In the proposed approach, two low-frequency bidirectional switches at the primary side in each phase of the converter allow vehicle-to-grid (V2G, i.e. DC/AC mode) and grid-to-vehicle (G2V, i.e. AC/DC mode) modes. When the proposed converter operates in AC/DC mode, each phase consists of an integrated bridgeless boost power factor corrector and an LLC resonant converter at the primary side. Output voltage regulation is managed by switches on the high frequency transformer's secondary side output rectifier in each phase, with soft-switching operation achieved in all the switches. When the proposed three-phase converter operates in DC/AC mode, each phase consists of a half-bridge DC/DC resonant converter cascaded with a half-bridge grid-side inverter. The operation of the proposed converter is explained in this paper. Results from a 20kW, 480VLLrms/350Vdc, 130kHz design, and a hardware test on a 130kHz, 250V-output proof-of-concept prototype are presented to validate the functionality of the proposed converter. © 2025 IEEE.

关键词： Zero voltage switching

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Integrating Temporal Analysis with Hybrid Machine Learning and Deep Learning Models for Enhanced Air Quality Prediction 19

Integrating Temporal Analysis with Hybrid Machine Learning a...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Omer, Muhammad Ali, Sardar Jaffar Raza, Syed M. Le, Duc-Tai Choo, Hyunseung Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of James Watt School of Engineering University of Glasgow Glasgow United Kingdom Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

Air pollution remains a critical issue, adversely affecting public health and the environment. In this study, we utilize the Air Quality index dataset from Kaggle to analyze temporal and seasonal variations of key pollutants, specifically Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Benzene (C6H6 ). Building upon this analysis, we predict Absolute Humidity (AH), a vital meteorological factor influencing pollutant dispersion, using Machine Learning (ML) and Deep Learning (DL) techniques. Three ML techniques, Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR), and three DL techniques, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are employed for predicting AH. The results indicate that while the RF model achieved the lowest Mean Absolute Error (MAE) among ML methods (0.02), the CNN model, despite having an MAE of 0.04, demonstrated statistical superiority through paired t-tests and Wilcoxon signed-rank tests (p © 2025 IEEE.

关键词： Convolutional neural networks

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Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification

Bidirectional Multi-Step Domain Generalization for Visible-I...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Alehdaghi, Mahdi Shamsolmoali, Pourya Cruz, Rafael M. O. Granger, Eric Livia Ills Dept. of Systems Engineering Ets Montreal Canada University of York Dept. of Computer Science United Kingdom

ISBN: (纸本)9798331510831

A key challenge in visible-infrared person re-identification (V-I ReID) is training a backbone model capable of effectively addressing the significant discrepancies across modalities. State-of-the-art methods that generate a single intermediate bridging domain are often less effective, as this generated domain may not adequately capture sufficient common discriminant information. This paper introduces Bidirectional Multi-step Domain Generalization (BMDG), a novel approach for unifying feature representations across diverse modalities. BMDG creates multiple virtual intermediate domains by learning and aligning body part features extracted from both I and V modalities. In particular, our method aims to minimize the cross-modal gap in two steps. First, BMDG aligns modalities in the feature space by learning shared and modality-invariant body part prototypes from V and I images. Then, it generalizes the feature representation by applying bidirectional multi-step learning, which progressively refines feature representations in each step and incorporates more prototypes from both modalities. Based on these prototypes, multiple bridging steps enhance the feature representation. Experiments11Our code is available at: ***/BMDG conducted on V-I ReID datasets indicate that our BMDG approach can outperform state-of-the-art part-based and intermediate generation methods, and can be integrated into other part-based methods to enhance their V-I ReID performance. © 2025 IEEE.

关键词： Multi-task learning

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Real-Time Traffic Prediction And Optimization using Machine Learning 3

Real-Time Traffic Prediction And Optimization using Machine ...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vineetha, Kundrapu Tharun Reddy, M. Jaffer Ali, Md. Prathima, Narisetty Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The Real-Time Traffic Prediction and Optimization System may be described as enriched, spacious, algorithm-oriented, and designed to help contribute to the anti-symptomatic deprievement of increasingly congested urban traffic streams. The system with the help of integrating real time data of numerous sources like GPS tracker, traffic sensor as well as weather API, the system using advanced machine learning algorithm in order to predict traffic accurately and optimize routes based on it. This predictive capability then greatly improves the flow of vehicles, the time taken, and the occurrence of choke points. The structure of the system is fully based in the cloud, which makes it easy to scale and very compatible with other systems to amalgamate a vast array of data inputs with high dependability. Fully customizable and intuitive control panel gives live view of traffic, offers actionable stats, and instant control mechanisms to traffic operators to make correct decisions quickly. Congestion in urban areas has an influence on time taken to cover a certain distance, fuel consumption and emission to the atmosphere. On the basis of the above analysis, this work extends a Real-Time Traffic Prediction and Optimization System (RTPOPS) using machine learning approach to predict the traffic density and to further optimize traffic signals and routes. The used system demonstrated successful management and reduction of vehicle density by 30%, and increasing speed limits by 18% and proved that Intelligent Urban Mobility has the potential to be achieved. © 2025 IEEE.

关键词： Traffic congestion

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Experimental Study for Temperature History Effects on Swelling Pressure, Free Swelling Ratio, and Methylene Blue Adsorption Volume of Bentonites

Experimental Study for Temperature History Effects on Swelli...

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Geo-EnvironMeet 2025: Geoenvironmental Contamination and Waste Containment

作者： Ruan, Kunlin Komine, Hideo Wang, Hailong Ito, Daichi Dept. of Civil and Environmental Engineering School of Creative Science and Engineering Waseda Univ. Shinjuku-ku Tokyo Japan Faculty of Science and Engineering Waseda Univ. Shinjuku-ku Tokyo Japan Dept. of Civil Engineering Univ. of Tokyo Waseda Univ. Bunkyo-ku Tokyo Japan

ISBN: (数字)9780784485699

Granular sodium-type bentonite: MX-80 and powder calcium-type bentonite: Kunibond were cured in oven at 130°C, 200°C, and 300°C for 30, 60, 120, 240, 480, and 1,000 days to give them different temperature histories. Then, cured bentonites were sent to swelling pressure and free swelling ratio tests for assessing the temperature history effect on swelling characteristics of bentonites. Additionally, methylene blue adsorption tests were conducted to examine the temperature history influences on montmorillonite amount of bentonites. Results from testing observations are: (1) for MX-80, equilibrium swelling pressure, free swelling ratio, and methylene blue adsorption volume were not apparently affected by the temperature history in the range of applied conditions and (2) for Kunibond, equilibrium swelling pressure, free swelling ratio, and methylene blue adsorption volume of Kunibond bentonite reduced as curing duration increased in the same curing temperature. At the same curing durations, equilibrium swelling pressure, free swelling ratio, and methylene blue adsorption volume of Kunibond bentonite decreased with growing curing temperature. © ASCE.

关键词： Curing

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LeXNet++: Layer-wise eXplainable ResUNet++ framework for segmentation of colorectal polyp cancer images

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Neural Computing and Applications 2025年第1期37卷 213-229页

作者： Das, Surajit Khan, Soumya Suvra Sengupta, Diganta Debashis, De Dept. of Information Technology Meghnad Saha Institute of Technology Behind Urbana Complex Near Ruby General Hospital Anandapur Rd Uchhepota Kolkata700150 India Dept. of Computer Science & Engineering Meghnad Saha Institute of Technology Behind Urbana Complex Near Ruby General Hospital Anandapur Rd Uchhepota Kolkata700150 India Dept. of Computer Science & Engineering Heritage Institute of Technology Chowbaga Road West Bengal Anandapur Kolkata700107 India Dept. of Computer Science & Engineering Maulana Abul Kalam Azad University of Technology West Bengal NH-12 Simhat Haringhata West Bengal Nadia741249 India

Colorectal polyps are benign lesions that develop in the colon and can progress to cancer if left untreated. Clinical observations from medical images are often preferred over computational results due to the lack of trust in the machine learning models, thereby posing serious challenge for the explainability of the results. In order to computationally diagnose colorectal polyps from cancerous images and explain the results, we propose a Layer-wise eXplainable ResUNet++ (LeXNet++) framework for segmentation of the cancerous images, followed by layer-wise explanation of the results. We utilize a publicly accessible dataset that contains of 612 raw images with a resolution of 256×256×3 and an additional 612 clinically annotated and labeled images with a resolution of 256×256×1, which includes the infected region. The LeXNet++ framework comprises of three components—encoder, decoder and the bridge. The encoder and the decoder components each comprise of four layers. Each of the four layers in the encoder and the decoder comprises of 14 and 11 internal sub-layers, respectively. Among the sub-layers of the encoder and the decoder, there are three 3×3 convolutional layers with an additional 3×3 convolution-transpose layer in the decoder. The output of each of the sub-layers has been explained through heatmap generation after each iteration which have been further explained. The encoder and the decoder are connected by the bridge which comprises of three sub-layers. The results obtained from these three sub-layers have also been explained to inculcate trust in the findings. In this study, we have used three models to segment the images, namely UNet, ResUNet, and proposed LeXNet++. LeXNet++ exhibited the best result among the three models in terms of performance;hence, only LeXNet++ was explained layer-wise. Apart from explanation of the results fetched in this study, the performance of the proposed explainable model has been observed to be 2% greater than the existing poly

关键词： Image segmentation

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Enhanced Image Captioning Using CNN and BLIP Models

Enhanced Image Captioning Using CNN and BLIP Models

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Tejesh, Vambara Supriya, M. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331509828

This research focuses on generating image captions using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. As deep learning advances, the availability of large datasets and increased computing power make it more feasible to build models capable of creating captions for images. This paper uses CNN and RNN models in Python to achieve this. Image captioning combines image recognition and Natural Language Processing (NLP) to interpret image context and express it in English, drawing on core computer vision principles. This study reviews important concepts in image captioning, including the applications of Keras, NumPy, and Jupyter notebook for development. This paper also explores the use of the Flickr dataset and CNN for image classification. The BLEU score of the proposed models is found to be close to 60%. On further enhancement to the model, it could be achieved to get a better score. © 2025 IEEE.

关键词： Convolutional neural networks

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Adaptive Weather Routing for Advanced Ship Navigation: A Comprehensive Review 3

Adaptive Weather Routing for Advanced Ship Navigation: A Com...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Venkatesan, Iyyappan Dharanish, M.B. Muthumari Lakshmi, S. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

Maritime navigation offers a comprehensive approach to optimising ship navigation, in such a way that guarantees the safety and effectiveness of the journey through multiple techniques, like the adaptive weather routing technique, which uses the predictive model and the current weather conditions for efficient navigation. The review mainly emphasizes the basic principles and computational simulation for sustainable navigation in advancing the route defining algorithm. Discusses about the conventional route defining algorithms and dynamic route defining algorithm by adjusting for wind, waves, and currents. A comparison analysis shows how well different algorithms work in various weather conditions, and case studies show how they are used in the actual world, while new trends are being discussed, major problems also concern the need for accurate climate predictions, high-speed data processing, and an efficient response plan. The Advancements in artificial intelligence and composite path forecasting also address issues related to major needs, such as accurate weather forecasts, high-speed data processing, and effective reaction tactics. Through this insights of this review provides on future directions for adaptive weather routing research and development that the sustainable growth of intelligent naval navigation is supported. © 2025 IEEE.

关键词： Weather forecasting

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Artificial Intelligence to Enhance the Security of Internet of Things: A Comprehensive Review 3

Artificial Intelligence to Enhance the Security of Internet ...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dheekshaa, V.M. Harsha, V. Sindhu, M. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

The Internet of Things is the term we call when devices are connected to the network and work together to provide a better experience for users, facilitate better decision-making, and improve operations. As with any invention, this one also has its disadvantages. IoT devices have complex structures and low processing capacities. There are serious security risks in designing IoT like poor authentication, data leakage, and DDoS attacks. In this review paper, how AI can secure IoT is discussed. A few AI-driven strategies that help mask all these weaknesses are also discussed regarding intrusion detection systems (IDS), behavioural analytics, anomaly detection, and real-time threat monitoring. It presents a four-layered IoT architecture consisting of perception, network, support, and application layers. The use of the IoT systems will become more flexible and efficient in data security and collection, secure and encrypted communication, and predictive security analytics. AI will play the same role in improving the threat perception, real-time identification, and response, as this will make it indispensable to ensure the IoT environment security. This work emphasizes the urgent need for solid AI-powered, multilayered adaptive security frameworks to match the evolving demands of cybersecurity. © 2025 IEEE.

关键词： Network intrusion

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