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Optimizing 3D Flight Paths for Multiple UAVs with Connectivity Management in Urban Delivery Systems 19

Optimizing 3D Flight Paths for Multiple UAVs with Connectivi...

引用

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

作者： Lee, Yeongmin Syahran, Raihan Muhammad Spangenberger, Mael Yang, Huigyu Oh, Jiyong Choo, Hyunseung Dept. of Artificial Intelligence Sungkyunkwan University Suwon Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon Korea Republic of Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Informatics Télécom SudParis Évry France

ISBN: (纸本)9798331507817

The dynamic deployment of aerial vehicles in urban delivery scenarios demands precise route planning, reliable data links, and efficient use of network infrastructure. Although prior efforts have explored various aspects of unmanned navigation or communication optimization, existing approaches often overlook the combined impact of three-dimensional path selection and robust connectivity maintenance. To address this gap, this paper proposes a deep reinforcement learning framework employing Independent Proximal Policy Optimization (IPPO) to steer the flight paths of unmanned vehicles while curtailing needless interactions with base stations (BSs). By incorporating communication-specific metrics, Reference Signal Received Power (RSRP), the proposed system adapts to urban conditions and upholds resilient network links. Comprehensive performance evaluations reveal the potential of the framework to reduce excessive BS connections by up to 82.24%, preserve RSRP levels above -80 dBm, and balance handover frequencies across flight trajectories. These findings affirm the scalability and effectiveness of the method for achieving efficient aerial navigation and consistent communication in urban delivery services. © 2025 IEEE.

关键词： Deep reinforcement learning

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Dual-Branch Neural Networks for Predicting Shared Bikes 17

Dual-Branch Neural Networks for Predicting Shared Bikes

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Yang, Huigyu Raza, Syed M. Le, Duc-Tai Kim, Dongsoo S. Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Indiana Uiversity-Purdue University Dept. of Electrical and Computer Engineering Indianapolis United States Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665453486

Bike sharing is efficient urban mobility method as a user determines the rental period upon his/her demand. A dock-based sharing system designates places (i.e., bike docks or stations) where a user rents or returns a bike. Due to regionally and temporally erratic nature of urban mobility demand, bike imbalance among stations inevitably occurs and a service operator rebalances bikes to prevent service impairment. The precise prediction of imbalance for each station is essential to optimize the rebalancing schedule. This paper presents Dual-Branch Neural Networks (DBNNs) employing Long Short-Term Memory (LSTM) to classify the imbalance level of each station along with the prediction of bike counts. To address the dual objectives, the model trains by backpropagating both classification and prediction losses to the dual output branches individually, and jointly updates the trainable parameters of LSTM layers. The results showcase that the proposed model predicts bike counts within 8% average error for next 24 hours, and achieves 94% accuracy of classification for the same time period. © 2023 IEEE.

关键词： Forecasting

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Dynamic Blockchain Reconfiguration: Balancing the Trilemma Trade-off Using Digital Twins 27

Dynamic Blockchain Reconfiguration: Balancing the Trilemma T...

引用

27th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2023

作者： Diamantopoulos, Georgios Tziritas, Nikos Bahsoon, Rami Theodoropoulos, Georgios School of Computer Science University of Birmingham Birmingham United Kingdom University of Thessaly Dept. of Informatics and Telecommunications Lamia Greece Southern University of Science and Technology Dept. of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350337846

The trilemma trade-off problem between decentralisation, scalability, and security states that in blockchain systems the above properties are negatively correlated. Infrastructure, node configuration, choice of Consensus Protocol, and complexity of the underlying application are cited among the factors that affect the balance of the trade-off. Given that Blockchains are complex, dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This work proposes the use of Digital Twins as the means of optimising the trilemma trade-off of blockchain i.e., re-configuring system parameters such as to maximise scalability, decentralisation and security. Specifically, through a bi-directional feedback loop between the system and the digital twin, simulation, what-if analysis and machine learning techniques will be employed for the computation of an optimal configuration given the current system state. Furthermore, a dynamic update mechanism is proposed to allow for blockchain reconfiguration without violating the decentralisation of the system. © 2023 IEEE.

关键词： Blockchain

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Velocity-aided Channel Estimation for Spatially Selective mmWave Massive MIMO Communications 31

Velocity-aided Channel Estimation for Spatially Selective mm...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Vlachos, Evangelos Mavrokefalidis, Christos Berberidis, Kostas Industrial Systems Institute ATHENA Research Center Patras Greece Dept. of Computer Engineering and Informatics University of Patras Patras Greece

ISBN: (纸本)9789464593600

This paper focuses on addressing the challenge of estimating multiple-input multiple-output (MIMO) channels for wireless communication between a ground base-station and a moving vehicle. One recently recognised model for time-varying channels incorporates spatial selectivity, which is referred to as beam squint, and is particularly relevant in the millimeter-wave (mmWave) range. In such scenarios, it is essential to account for the beam squint when attempting to recover channel parameters using a training sequence. However, the use of a training sequence alone may be insufficient for this purpose. To overcome this issue, in this work, we propose a channel estimation approach that exploits information provided by the control module of the vehicle, namely its velocity. The estimation problem that is designed, regards the channel both in a parametric and a non-parametric form and the alternating direction method of multipliers is utilised to efficiently solve it. It is demonstrated via simulations that considerable gains can be achieved if information from the control unit of the vehicle can be appropriately introduced and exploited. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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An Empirical Study of On-Policy and Off-Policy Actor-Critic Algorithms in the Context of Exploration-Exploitation Dilemma 3

An Empirical Study of On-Policy and Off-Policy Actor-Critic ...

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3rd International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2023

作者： Seshagiri, Supriya Prema, K.V. Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Dept. of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350300604

The Exploration-Exploitation dilemma in Reinforcement Learning (RL) algorithms is about deciding whether to select a sub-optimal path to the outcome and acquire a more varied learning of the environment or to select the greedy path and seek to maximize rewards. It is a fundamental challenge in RL algorithms which influences their learning efficiency. The on-policy and off-policy design of RL algorithms has an influence on their ability to explore non-greedy actions thereby affecting their learning ability. The paper presents the results of experiments conducted to analyze the effect of on-policy and off-policy design and the use of entropy in exploration in actor-critic algorithms and investigate the root causes of effective learning in algorithms. An empirical comparison of Soft Actor Critic (SAC) which is off-policy and Proximal Policy Optimization (PPO), an on-policy algorithm, are performed through these experiments in several continuous OpenAI Gym environments and the effect of exploration strategies like entropy, off-policy target update, and Generalized Advantage Estimate (GAE) factor on the bias-variance balance of these algorithms are analyzed. © 2023 IEEE.

关键词： Reinforcement learning

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Human Gender Detection from Facial Image using Global and Local Feature

Human Gender Detection from Facial Image using Global and Lo...

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2022 International Conference on Electrical and Information Technology, IEIT 2022

作者： Ningrum, Lusiana Diyan Dewantara, Bima Sena Bayu Sari, Dewi Mutiara Politeknik Elektronika Negeri Surabaya Dept. of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665453035

ISBN: (纸本)9781665453035

Identifying gender is frequently difficult when physical characteristics show different facts, which can lead to public controversy. This paper describes a method for determining a person's gender solely based on physical characteristics, particularly facial shape and features. The method used combines global and local facial features. Face landmark detection is a global feature that displays a face's shape or contour in 2D. While the Histogram of Oriented Gradient (HOG) is a local feature that shows the detailed shape of facial components like eyebrows, eyes, nose, and mouth. The combined information from global and local features is fed into the Support Vector Machine (SVM) and classified as male or female. Based on the experimental results, our system was able to identify gender with an accuracy of 80% with an average F-measure is 0.8. © 2022 IEEE.

关键词： Support vector machines

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Action Localization and Recognition through Unsupervised I3D and TSN 3

Action Localization and Recognition through Unsupervised I3D...

引用

3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Umran, Munzir Muchtar, Kahlil Abidin, Taufik Fuadi Arnia, Fitri Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Universitas Syiah Kuala Faculty of Mathematics and Natural Sciences Department of Informatics Banda Aceh Indonesia

ISBN: (纸本)9798350321487

Since CCTV technology was widely introduced as a video surveillance system, it has become very popular due to increased security concerns. However, detecting suspicious behavior is still done manually by humans, which is inefficient in terms of accuracy and manpower (prone to distraction and fatigue). Many studies use the entire frame of a video to recognize human action, whereas in this study first localize the area of object movement using the unsupervised inflated 3D (I3D) network which produces several frames that have been determined as the region of interest (ROI) from the total frames in the video. Then the collection of ROI frames is generated into a video which will then be classified to recognize human action through the Temporal Segment Networks (TSN). For efficiency purposes, the proposed pipeline can be run separately in the edge (for localization), and cloud environment (for recognition), respectively. The results show that there are 54 classes of ROI video data sets that have an average human action recognition prediction similarity rate above 84.6% which is the average of the prediction similarity rate of the entire ROI video data. In terms of prediction accuracy, 37 classes of data (36.63%) are better than the original class, including 25 classes of data (24.75%) above the threshold, and the processing speed of classification with ROI video data is on average 5 times faster than full-frame video data. © 2023 IEEE.

关键词： Forecasting

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The Integration of 6G and Blockchain into an Efficient AIoT-Based Smart education Model 6

The Integration of 6G and Blockchain into an Efficient AIoT-...

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6th World Symposium on Communication engineering, WSCE 2023

作者： Lioupa, Anna Memos, Vasileios A. Stergiou, Christos L. Ishibashi, Yutaka Psannis, Konstantinos E. School of Information Sciences University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

ISBN: (纸本)9798350339505

The technological advances impact the future classrooms and the whole education system. The evolution of 6G promises faster speeds based on terahertz, wireless communication, the leverage of artificial intelligence (AL), virtually reality (VR) and generally a fully connected world. The use of applications of IoT and AIoT improves the education environment, but the intelligent network services undoubtedly gather, store and analyze big data which are vulnerable to attackers. As a solution we may consider the technology of blockchain. The trust and security of next generation intelligent networks are essential part of their design. In this article we suggest a data security system based on blockchain for AL applications in 6G networks. First, we present two Al-enabled applications, indoor positioning and mobile payment within the framework of 6G in the university area. We present the efficiency of blockchain in data security for Al-enabled systems using two case studies of indoor positioning system and mobile payment. The 6G system which is fully supported by Al and the integration of this in technology of blockchain, is ensured the security in future wireless communication. © 2023 IEEE.

关键词： Blockchain

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Gender Detection Using Short Speech Utterances: A Deep Learning Approach with Wavelet Cepstral Coefficients

Gender Detection Using Short Speech Utterances: A Deep Learn...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Syahroni Hidayat Budi Sunarko Uswatun Hasanah Faila Nadhifatul Aryza Dept. of Electrical Engineering Universitas Negeri Semarang Semarang Indonesia Dept. of Informatics and Computer Engineering Education Universitas Negeri Semarang Semarang Indonesia Dept. of Computer Engineering Universitas Negeri Semarang Semarang Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The human voice is critical in recognizing the speaker’s identity and gender. This study aims to develop an effective gender detection system using short utterances of less than one second. Wavelet Cepstral Coefficient (WCC) features were used for feature extraction, and a Long Short-Term Memory (LSTM) model was applied for classification. Primary and secondary short voice datasets, including syllables and the Audio MNIST dataset, underwent acoustic pretreatment and were trained using LSTM architectures with 32, 64, and 128 units. Results indicated that the LSTM model achieved high accuracy, with the syllable dataset reaching 0.99 accuracy and the Audio MNIST dataset showing perfect accuracy (1.00) using a 32-unit configuration. The findings demonstrate that combining WCC features and LSTM models can efficiently handle short-duration speech for accurate gender detection.

关键词： Seminars Accuracy Cepstral analysis Memory architecture Speech recognition Feature extraction Speaker recognition Intelligent systems Information technology Long short term memory

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Human Pose Estimation for Fitness Exercise Movement Correction 24

Human Pose Estimation for Fitness Exercise Movement Correcti...

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24th International Electronics Symposium, IES 2022

作者： Rahmadani, Atharian Dewantara, Bima Sena Bayu Sari, Dewi Mutiara Politeknik Elektronika Negeri Surabaya Dept. of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

Based on computer vision technology, this research suggests an application to identify and assess a fitness practitioner's movements. Several fitness movements such as lifting weights, squat jumps, and pull-ups that are very beneficial for health and body fitness become the main movement for body building. However, those kinds of activities may be very dangerous if done incorrectly. Based on the problem, we developed an application based on computer vision to recognize and correct the pose accuracy of fitness practitioners by using input in the form of videos that record the movements of fitness practitioners continuously. To categorize the many forms of fitness sport movements, this system uses the support vector machine (SVM) method. On the monitor screen, the classification results will be visible. The result shows that the accuracy of the system is 96.87% by using SVM with the Radial Basis Function (RBF) kernel type and can make corrections to four types of fitness movements with a testing accuracy of 90.62%. © 2022 IEEE.

关键词： Sports

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