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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Chang, Yuchen Zong, Mengya Dang, Yutian Wang, Kaiping Dept. of Computer Science Xi’an Univ. of Architecture and Technology Xi’an China Dept. of Architecture Xi’an Univ. of Architecture and Technology Xi’an China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Transport hubs play a major role in the national transport network. Larger and deeper subway stations attract a considerable influx of passengers, particularly during peak hours. Therefore, long-term pedestrian flow prediction is important for subway station management. This paper proposes an end-to-end learning framework based on the Graph Convolutional Network and the Attention Mechanism. In the first phase, key functional facilities within the subway station are abstracted into a graph structure. Subsequently, pedestrian flow data for graph nodes are obtained using Anylogic pedestrian simulation. In the second phase, a Graph Convolutional Network is employed to extract spatial features among nodes to capture the spatial dependencies of each node. The temporal attention operation is utilized to capture long-term temporal dependencies among nodes. This aids in the rational allocation of subway personnel and reducing congestion. Results demonstrate the validity and robustness of our model for pedestrian prediction. © ASCE.

关键词： Subway stations

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Predicting Crowd Movement Trajectories: A Graph Network-Based Approach Informed by Simulation Data 24

Predicting Crowd Movement Trajectories: A Graph Network-Base...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Zong, Mengya Chang, Yuchen Dang, Yutian Wang, Kaiping Dept. of Computer Science Xi’an Univ. of Architecture and Technology Xi’an China Dept. of Architecture Xi’an Univ. of Architecture and Technology Xi’an China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

As-built assessment of transport hubs, based on large-scale simulation and passenger big data, has been an active area of research recently. Exploring the direction of crowd flow at future moments within a building is crucial for the rational assessment of the layout within the building. To tackle this problem, this paper proposes a two-stage framework for predicting passenger trajectories. In the first stage, our graph LSTM models the passenger’s trajectory sequence with the aim of capturing the temporal and spatial correlations in the passenger’s historical trajectory. In the second stage, different attentional weights are assigned to the previously embedded information to capture the interaction between pedestrians and the surrounding crowd. Finally, an LSTM decodes the information encoded for the passenger, predicting multiple plausible future trajectories. Experiments show that our model achieves excellent performance in the metrics ADE and FDE compared to the existing baseline of pedestrian trajectory prediction models. © ASCE.

关键词： Prediction models

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Autonomous Social Amenity, Pothole Detection and Reporting 4

Autonomous Social Amenity, Pothole Detection and Reporting

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4th International Multidisciplinary Information technology and Engineering Conference, IMITEC 2024

作者： Mosela, David Olaifa, Moses Owolawi, Pius Malele, Vusi Tshwane University of Technology Dept. Computer Systems Engineering Tshwane South Africa Tshwane University of Technology Dept. of Computer Systems Engineering Tshwane South Africa

ISBN: (纸本)9798350387988

Challenges faced by traffic management in many developing nations across the globe is a result of the poor quality of road infrastructure. Inadequate and defective road infrastructure are major contributors to this effect. A key element that poses significant risk to road safety and that leads to increased road management cost is the increasing number of potholes and inefficient detection and reporting approach. This study provides an in-dept. exploration of the latest developments in pothole detection systems and examines various methodologies and technologies used in these systems, emphasizing the importance of real-time monitoring for proactive road maintenance. In addition, we proposed a system that addresses the persistent challenge of road infrastructure degradation by adopting cutting-edge technologies, including computer vision, machine learning, and sensor networks (such as ultrasonic and GPIO systems) that are used to identify and assess potholes on road surfaces. To achieve the aim of the study, the system adopts the use of RasberryPi 4 Model B for pothole detection and YOLOv4 for image processing. © 2024 IEEE.

关键词： Highway administration

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BIOPHYSICAL SIGNAL PROCESSING FOR AUTOMATIC ANXIETY CLASSIFICATION IN A VIRTUAL REALITY EXPOSURE THERAPY SYSTEM

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and computer Science 2024年第2期86卷 73-84页

作者： Jinga, Nicolae Petrescu, Cătălin-Dumitru Mitruț, Oana Moldoveanu, Alin Moldoveanu, Florica Petrescu, Livia Dept. of Computer Science National University of Science and Technology POLITEHNICA Bucharest Romania Dept. of Automatic Control and Systems Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Faculty of Biology University of Bucharest Romania

This paper presents a signal processing framework for automatic anxiety level classification in a virtual reality exposure therapy system. Two types of biophysical data (heart rate and electrodermal activity) were recorded, pre-processed and passed through a feature extraction procedure that provided input for the real-time anxiety level classification algorithm. We showcase the mathematical and engineering techniques behind the procedures and conclude with the challenges encountered in our research and future development ideas. The proposed method provides a good estimate of the level of anxiety while having at the same time a reduced level of complexity, allowing implementation on equipment with limited computing resources. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Classification (of information)

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Assessing Interaction Mechanics in Extended Reality for Control Engineering Applications 32

Assessing Interaction Mechanics in Extended Reality for Cont...

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32nd International Conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： Remenyi, Armin Alsaleh, Saleh Tepljakov, Aleksei Tallinn University of Technology Dept. of Software Science Tallinn Estonia Tallinn University of Technology Dept. of Computer Systems Tallinn Estonia

ISBN: (纸本)9789532901382

This research paper delves into the evaluation of human-machine interactions within the realm of Extended Reality (XR) applications, focusing on the integration of Digital Twins (DT) for control systems. The research explores multiple XR interaction methods and assesses their efficacy in enhancing user experience and operational efficiency in industrial settings, specifically in control systems. The study employs advanced tools such as the Microsoft HoloLens 2 headset, Unreal Engine 5, and the Mixed Reality Toolkit (MRTK) to develop and test XR interaction methods. Data collection and analysis involves usability testing using the System Usability Scale (SUS) and the NASA Task Load Index (NASA-TLX) to measure cognitive, physical and temporal demands on users. The findings highlight the most effective XR interaction strategies, providing valuable insight for future research and practical implementations in intelligent manufacturing environments. © 2024 University of Split, FESB.

关键词： NASA

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Intelligent Doctor Chatbot 4

Intelligent Doctor Chatbot

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4th International Multidisciplinary Information technology and Engineering Conference, IMITEC 2024

作者： Khoza, Mahlori Olaifa, Moses Owolawi, Pius Malele, Vusi Tshwane University of Technology Dept. of Computer Systems Engineering Tshwane South Africa

ISBN: (纸本)9798350387988

This study proposes a cutting edge Intelligent Doctor Chatbot that improves healthcare delivery and provides patients with easily accessible and customized health related information through the use of Natural language processing (NLP) and artificial intelligence (AI) technologies. These technologies are used by the chatbot to provide accurate medical advice based on symptom analysis and general health guidance in an interactive manner. The doctor chatbot addresses the growing demand for reliable healthcare information and assistance, particularly in situations where access to medical professionals is limited. By leveraging its advanced algorithms and medical knowledge base, the chatbot provides users with quick and accurate answers to their health-related questions. A key feature of the doctor chatbot is that users describe their symptoms and receive preliminary assessments on potential causes and suggested courses of action. It allows people to take an active role in their healthcare management and preventive measures. © 2024 IEEE.

关键词： Diagnosis

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Applying LSTM for River's Water Level Forecasting as Potential Flood Indication: A Case Study in Greece 15

Applying LSTM for River's Water Level Forecasting as Potenti...

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15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Tziolas, Theodoros Papageorgiou, Elpiniki Papageorgiou, Konstantinos Zardas, Georgios Lekidis, Alexios Panagiotis, Papazoglou University of Thessaly Dept. of Energy Systems Gaiopolis Campus Larisa41500 Greece University of Thessaly Dept. of Electrical and Computer Engineering Volos Greece National and Kapodistrian University of Athens Dept. of Architecture and Computer and Communication Simulation Systems Chalkida Greece

ISBN: (纸本)9798350368833

Floods can have significant life-threatening and economic impacts on human societies. Flood forecasting with data-driven methods is a crucial aspect of disaster risk management, providing valuable insights into potential flood events and allowing for timely mitigation measures. This work illustrates the application of a Long Short-Term Memory (LSTM) neural network for short-term forecasting of a river's water level. The water treatment data used for training the LSTM models were gathered from Sperchios river, located in central Greece. Historical time-series data in a half-hour interval of the river's water level, water velocity, and area precipitation were utilized for the development of the proposed scheme. The developed model is deployed in the backend of a web application for half-hour ahead predictions of the river's water level that are visualized in the frontend. The post-analysis of the deployed forecasting model indicates that it consistently delivers forecasts with minimal error and demonstrates high accuracy in recent flood events. © 2024 IEEE.

关键词： Rivers

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LightNN: An Optimized Deep Learning Framework for Enhanced Fruit Classification

LightNN: An Optimized Deep Learning Framework for Enhanced F...

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2024 IEEE International Conference on Computing, Applications and systems, COMPAS 2024

作者： Afroj, Maharin Jahan, Israt Islam, Md Polash Saifur Rahman, Md. Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Stockholm University Dept. of Computer and Systems Sciences Stockholm Sweden

ISBN: (纸本)9798331529765

The crucial field of fruit classification in computer vision and deep learning brings about breakthroughs that have broad applications in the retail and agricultural industries. By automating the identification and sorting of various fruit types, these technologies enhance efficiency, accuracy, and scalability, benefiting processes from farm production to market distribution. The integration of sophisticated algorithms and neural networks not only improves the speed and reliability of fruit classification but also offers the potential for real-time quality control and inventory management, revolutionizing traditional methods and paving the way for more innovative, data-driven approaches in these sectors. This study introduces LightNN, a novel and effective paradigm with a lightweight architecture that tackles the complex problems related to computational efficiency. Combining a small classifier with a simplified feature extractor, LightNN achieves the ideal balance between simplicity and accuracy. The feature extractor is capable of extracting hierarchical features from RGB images by using convolutional layers with ReLU activations and max-pooling procedures. The flattened output is then processed by the classifier, which consists of fully connected layers with ReLU activations and dropout regularisation. Remarkably, LightNN, with just 298,835 parameters, manages to get a respectable 96.37% test accuracy in fruit categorization tasks. The results highlight LightNN's promise as an effective and efficient fruit classifying method, particularly in situations with constrained computational resources. © 2024 IEEE.

关键词： Fruits

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Empowering Low-Resource Language Translation: Methodologies for Bhojpuri-Hindi and Marathi-Hindi ASR and MT 21

Empowering Low-Resource Language Translation: Methodologies ...

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21st International Conference on Spoken Language Translation, IWSLT 2024

作者： Anand, Harpreet Singh Dash, Amulya Ratna Sharma, Yashvardhan Dept. of Computer Science and Information Systems Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9798891761414

This paper presents the methodologies implemented for the Automatic Speech Recognition and Machine Translation for the language pairs Bhojpuri-Hindi and Marathi-Hindi for the Dialectal and Low-Resource shared task proposed by The International Conference on Spoken Language Translation (IWSLT) for 2024. The implemented method uses the transcriptions generated through a fine-tuned Whisper models(for Marathi-Hindi) and vakyansh-wav2vec model (for Bhojpuri-Hindi) and generates the translations using fine-tuned NLLB(No Language Left Behind) Models for both the tasks. The selection of more accurate translation is done through sentence-embeddings generated using the MuRIL(Multilingual Representations for Indian Languages)(Khanuja et al., 2021) model for the Marathi-Hindi task. ©2024 Association for Computational Linguistics.

关键词： Machine translation

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Enhancing Train Transportation in Sri Lanka: A Smart IOT Based Multi-Subsystem Approach Using MQTT

Enhancing Train Transportation in Sri Lanka: A Smart IOT Bas...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Balasingham, Dhanushka Samarathunga, Sadeesha Bandara, Anuththara Arachchige, Gayantha Godakanda Gamage, Narmada Wijayaraja, Jaliya L. Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Sri Lanka

ISBN: (纸本)9798350350821

This research proposes a system as a solution for the challenges faced by Sri Lanka's historic railway system, such as scheduling delays, overcrowding, manual ticketing, and management inefficiencies. It proposes a multi-subsystem approach, incorporating GPS tracking, RFID-based e-ticketing, seat reservation, and vision-based people counting. The GPS-based real-time train tracking system performs accurately within 24 meters, with the MQTT protocol showing twice the speed of the HTTP-based system. All subsystems use the MQTT protocol to enhance efficiency, reliability, and passenger experience. The study's data and methodology demonstrate the effectiveness of these innovations in improving scheduling, passenger flow, and overall system performance, offering promising solutions for modernizing Sri Lanka's railway infrastructure. © 2024 IEEE.

关键词： Global positioning system

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