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7th International Conference on Sustainable Information engineering and Technology, SIET 2022

作者： Kurniawan, Wijaya Hangos, Katalin M. Marton, Lorinc Faculty of Computer Science University of Brawijaya Malang Indonesia Dept. of Electrical Engineering and Information Systems University of Pannonia Veszprem Hungary Institute for Computer Science and Control Budapest Hungary Dept. of Electrical Engineering Sapientia Hungarian University of Transylvania Targu Mures Romania

ISBN: (纸本)9781450397117

In this paper, the problem of fault estimation and localization in the connecting dynamic elements of distributed heating and cooling systems are treated. The fault represents the physical parameter change related to the heat transfer between the system and the external environment. First, based on the bi-linear dynamic state space model of the system in the presence of a fault, structural observability analysis using Signed Directed Graph (SDG) has been performed to investigate the sensor placement problem. Then, a nonlinear observer with a parameter adaptation algorithm was proposed for fault estimation. The simulation results show that it can successfully detect and estimate the fault. Fault localization along the length of the element has also been attempted, but it has been found that the localization cannot be performed using practically changeable input variables. Frequency domain analysis is presented to discuss this phenomenon. © 2022 ACM.

关键词： Parameter estimation

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Navigating Green Transportation in Urban Agglomerations: A Refined Approach for Carbon Emissions Assessment 24

Navigating Green Transportation in Urban Agglomerations: A R...

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

作者： Chen, Boyou Wang, Yuanqing Abolarin, Emmanuel Zhang, Shuangyan Liu, Xihao Guo, Hanlin Ren, Baolong Dept. of Built Environment Bartlett School of Planning Univ. College London London United Kingdom Dept. of Transportation Engineering College of Transportation Engineering Chang’an Univ. Xi’an China Dept. of Industrial and Manufacturing Systems Engineering College of Engineering and Computer Science Univ. of Michigan–Dearborn Dearborn United States School of Civil and Transportation Engineering Beijing Univ. of Civil Engineering and Architecture Beijing China Sino-French Engineer School Beihang Univ. Beijing China Dept. of Instrument Science and Technology College of Mechanical Engineering Nanjing Univ. of Science and Technology Nanjing China Sino-Danish College Univ. of Chinese Academy of Sciences Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Rapid urbanization has expanded urban agglomerations, escalating carbon emissions from increased transportation needs. These agglomerations have distinct multi-modal transportation systems. Analyzing their carbon footprints and eco-friendly transportation evolution is essential for sustainability. However, research in this domain is limited. This research refines emissions estimation methods, introducing models tailored for urban agglomerations. An evaluation framework for green transportation detailing a computational method for future policies is proposed. The research highlights primary transportation structures and energy consumption. Using a "bottom-up" approach, carbon estimation models for inter- and intra-city transportation are drafted. Assessment indicators are redefined by merging the DPSIR model with urban metrics, forming five main modules with 19 indicators. Using the Regression Random Forest method and focusing on the Yangtze River Delta in China, the link between annual emissions and these indicators is examined, providing a theoretical analysis approach for general cases. © ASCE.

关键词： Carbon sequestration

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A Web Based Four-Tier Architecture using Reduced Feature Based Neural Network Approach for Prediction of Student Performance 2

A Web Based Four-Tier Architecture using Reduced Feature Bas...

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2nd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2021

作者： Hossen, Anwar Alamgir, Rakib Bin Alam, Arman Ul Siddika, Fatema Hossain, Shah Fahad Arman, Md. Shohel Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Jagannath University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665415767

Enhancing student's performance is a significant part of developing quality education in any educational institute. It is very difficult to get promising student performance without student categorization according to their academic performance as there are different standardized students. In this paper, our aim is to determine the performance of the students. For this purpose, a survey has been conducted on students in our university in order to collect data and to analyze and predict the student category based on their performance. Apart from this, another purpose of this study is to examine the effect of the reduced features on the classification model using state-of-art machine learning algorithms. Here, we propose a workflow of web-based four-tier architecture for the student performance prediction that will define the student's category in order to help them exactly pinpoint their learning capabilities. Hence, we used multiple supervised learning-based machine learning techniques for the prediction of student performance. Each of the student category categorized by considering on the top features. The analysis results indicate that we got the highest performance that is 88.00% by using the Artificial Neural Network (ANN) among the classifiers by showing its superiority to the existing model. © 2021 IEEE.

关键词： Students

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The YOLOv8 Edge: Harnessing Custom Datasets for Superior Real-Time Detection 18

The YOLOv8 Edge: Harnessing Custom Datasets for Superior Rea...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ahmed, Tafreed Maaz, Ahmad Mahmood, Danyaal Abideen, Zain Ul Arshad, Usama Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time video streams has the potential to revolutionize various fields and has been the subject of extensive research. Although there have been advances in object detection, there is still a gap in the research for real-time detection of custom objects with high accuracy and speed. This research addresses this gap by training a YOLOv8 detector on a custom dataset of objects and evaluating its performance on real-time video streams which is by far the latest model and thus is faster and more accurate. Our experimental results demonstrate that our custom-trained YOLOv8 detector achieves high accuracy and real-time performance on a custom dataset of objects. The detector achieved an overall mAP50 of 0.864 and a mAP50-95 of 0.758, with individual class results ranging from 0.47 to 0.995. These findings show that custom training data and YOLOv8 are effective in real-time object detection, which has practical applications in various fields. The significance of the results and our contribution lies in demonstrating the effectiveness of custom training data for improving object detection accuracy and speed using YOLO, which has implications for a wide range of real-world applications. © 2023 IEEE.

关键词： Object detection

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Effectiveness of Performance Visualizations for Declarative Model Transformations

Effectiveness of Performance Visualizations for Declarative ...

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IEEE International Workshop on Visualizing software for Understanding and Analysis (VISSOFT)

作者： Raffaela Groner Matthias Tichy Dept. of Computer Science and Engineering Chalmers | University of Gothenburg Gothenburg Sweden Institute of Software Engineering and Programming Languages Ulm University Ulm Germany

ISBN: (数字)9798331528485

ISBN: (纸本)9798331528492

Several profilers for general-purpose languages like Java offer visualizations to support users in understanding the execution of a program and identifying the causes of a performance issue. Unfortunately, these performance visualizations are difficult to reuse in profilers for declarative model transformations since they cannot display transformation-specific information. For example, a profiler for a declarative model transformation language must provide information on the traversal of the input model since it impacts the performance but is hidden from the developer. Moreover, the respective visualization must scale for input models that consist of several thousand model elements. Hence, we developed performance visualizations for the declarative model transformation language Henshin that provide insights into the transformation execution. Subsequently, we performed a mixed methods study with 18 Henshin novices to evaluate the effectiveness of our visualizations. In our study, the participants tried to improve the execution performance of four transformations by performing a root cause analysis using our visualizations. The results of our study show that depending on the task, between 16 and 18 participants understood the execution of a transformation correctly based on our visualizations. Moreover, between 12 and 18 participants proposed effective optimizations using our visualizations.

关键词： Visualization Root cause analysis Java Color software Encoding Data mining Optimization

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Pursuit-Evasion on a Sphere and When It Can Be Considered Flat 63

Pursuit-Evasion on a Sphere and When It Can Be Considered Fl...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Milutinovic, Dejan Moll, Alexander Von Manyam, Satyanarayana G. Casbeer, David W. Weintraub, Isaac E. Pachter, Meir Electrical and Computer Engineering Dept. University of California Santa Cruz Santa CruzCA United States Control Science Center Aerospace Systems Directorate Air Force Research Laboratory DaytonOH45435 United States Dcs Corporation DaytonOH United States Air Force Institute of Technology Wright-Patterson Afb Dept. of Electrical Engineering OH United States

ISBN: (纸本)9798350316339

In planar pursuit-evasion differential games considering a faster pursuer and slower evader, the interception points resulting from equilibrium strategies lie on the Apollonius circle. This property is instrumental for leveraging geometric approaches for solving multiple pursuit-evasion scenarios in the plane. Here, we study a pursuit-evasion differential game on a sphere and generalize the planar Apollonius set to the spherical domain. We find that the interception point from the equilibrium strategies can leave the Apollonius set boundary and present a condition to keep the intercept point on the boundary. This condition allows for generalizing planar pursuitevasion strategies to the sphere. © 2024 IEEE.

关键词： Spheres

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RFSNU: Reliable Forwarding Strategy in NDN based UWSN

RFSNU: Reliable Forwarding Strategy in NDN based UWSN

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Ahmed Khalid Rana Asif Rehman Byung-Seo Kim Department of Computer Science National University of Computer & Emerging Sciences Pakistan Dept. of Software and Communications Engineering Hongik University Sejong-si South Korea

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Underwater wireless sensor network (UWSN) is a communication paradigm that works in underwater environment such as in oceans, rivers, lakes, and seas. Multiple sensors are deployed in underwater that collect information according to user demand and forwards the data towards the Underwater Autonomous Vehicle (UAV) that send the data towards the ship or user. UWSN is based on Internet Protocol (IP). There are several limitations in existing IP based architecture such as data redundancy, location dependency, mobility. Therefore, there is need for a new network that enhance the network efficiently. Named Data Network (NDN) is a new communication paradigm that provides several built-in benefits such as in-network caching, mobility, avoid data duplication, and fast content retrieval time. In case of NDN based UWSN (NUWSN), network traffic increases due to bandwidth and large propagation delay. Due to dynamic nature of water the ratio of traffic load, packet duplication, packet retransmission increases. When delay is large then more packets can loss and more number of packet retrans-mission, and data duplication occurs. Therefore, there is need for efficient forwarding mechanism in NUWSN that minimize data duplication and packet retransmission. In this paper, we proposed a forwarding mechanism named as Reliable Forwarding Strategy in NDN based UWSN (RFSNU). RFSNU improves the forwarding process in underwater environment using dept. based forwarding. Our scheme improves packets retransmission rate, and packet duplication that are main problems in NUWSN.

关键词： Wireless communication Wireless sensor networks Protocols Redundancy Telecommunication traffic Sensors Rivers

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An Efficient GPU-based Implementation for Noise Robust Sound Source Localization

arXiv

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arXiv 2025年

作者： Lin, Zirui Takigahira, Masayuki Terakado, Naoya Gulzar, Haris Busto, Monikka Roslianna Eda, Takeharu Itoyama, Katsutoshi Nakadai, Kazuhiro Amano, Hideharu Dept. of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Honda Research Institute Japan Co. Ltd. Saitama Japan NTT Software Innovation Center Tokyo Japan Dept. of Information and Computer Science Keio University Kanagawa Japan

Robot audition, encompassing Sound Source Localization (SSL), Sound Source Separation (SSS), and Automatic Speech Recognition (ASR), enables robots and smart devices to acquire auditory capabilities similar to human hearing. Despite their wide applicability, processing multi-channel audio signals from microphone arrays in SSL involves computationally intensive matrix operations, which can hinder efficient deployment on Central Processing Units (CPUs), particularly in embedded systems with limited CPU resources. This paper introduces a GPU-based implementation of SSL for robot audition, utilizing the Generalized Singular Value Decomposition-based Multiple Signal Classification (GSVD-MUSIC), a noise-robust algorithm, within the HARK platform, an open-source software suite. For a 60-channel microphone array, the proposed implementation achieves significant performance improvements. On the Jetson AGX Orin, an embedded device powered by an NVIDIA GPU and ARM®Cortex™-A78AE v8.2 64-bit CPUs, we observe speedups of 4645.1× for GSVD calculations and 8.8× for the SSL module, while speedups of 2223.4× for GSVD calculation and 8.95× for the entire SSL module on a server configured with an NVIDIA A100 GPU and AMD EPYC™ 7352 CPUs, making real-time processing feasible for large-scale microphone arrays and providing ample capacity for real-time processing of potential subsequent machine learning or deep learning tasks. © 2025, CC BY-NC-ND.

关键词： Acoustic generators

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A Task Scheduler for Mobile Edge Computing Using Priority-based Reinforcement Learning

A Task Scheduler for Mobile Edge Computing Using Priority-ba...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Amin Avan Farnaz Kheiri Qusay H. Mahmoud Akramul Azim Masoud Makrehchi Shahryar Rahnamayan Dept. of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Dept. of Engineering Brock University St. Catharines ON Canada

Edge computing offers cloud-like services closer to users and IoT devices, providing high speed and accessibility for network users. Edge computing, often called Mobile Edge Computing (MEC), is a distributed paradigm that utilizes heterogeneous computational and storage resources with well-provisioned capabilities rather than relying on the ample resources of the cloud. In addition, edge users usually refer to portable and mobile devices that connect to and disconnect from the network at will. Therefore, scheduling tasks at the appropriate time and allocating the right resources can be modeled as a multi-objective optimization problem in MEC. Moreover, each task has specific requirements, further adding to the complexity of the optimization problem. In this study, we formulate the scheduling problem as a Markov Decision Process (MDP) to schedule the tasks. The learning time of the task scheduler is minimized when it faces new users and edge servers. Subsequently, we employ the Q-learning (QL) algorithm from the Reinforcement Learning (RL) paradigm to address the optimization problem and effectively adapt the proposed scheduler to the dynamic nature of MEC. Accordingly, we designed the valid state space, action space, and reward function with appropriate conditions and proper rewards for the proposed QL-based technique. We conducted comprehensive experiments to validate the results of the proposed solution, taking into account the inherent randomness of the QL-based technique. The experimental results demonstrate that the proposed technique achieves the lowest learning time compared to Deep learning-based and Deep RL-based approaches. Furthermore, on average, the proposed technique obtains a 72% faster runtime compared to previous works, using 58% fewer computation cycles and 50% less memory. These improvements make the proposed approach an efficient and lightweight task scheduler for MEC.

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Using Deep Learning dept. Maps to Improve Monocular SLAM

Using Deep Learning Depth Maps to Improve Monocular SLAM

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Simon Hollows Richard Green Dept. Mechatronics Engineering University of Canterbury Christchurch New Zealand Dept. Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

This paper proposes the use of a neural network to generate dept. maps from 2D frames, offering an alternative to dept. cameras. The goal is to address the accuracy limitations of monocular SLAM by gaining more accurate dept. data through deep learning. This approach combines the simplicity and cost-effectiveness of a single camera setup with the enhanced accuracy of RGB-D ***-SLAM running with RGB-D input was used as the SLAM framework for this method. This allowed for accurate comparisons between dept. sensor dept. maps in test datasets and the generated dept. maps. The MiDaS neural network was used to generate estimated dept. maps. These were then scaled to create consistency over the entire test sequence. The TUM RGB-D dataset was used for *** generated dept. maps were able to compare with the TUM RGB-D data in accuracy. On one dataset, the TUM dept. maps had a root mean square error (RMSE) or 0.24 m while the generated dept. maps had a RMSE of 0.18 m. In general, the proposed method lacks consistency and the lack of good fusion between dept. maps causes this approach to fail in slow-moving scenes.

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