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Multi-Objective Offloading Optimization in MEC and Vehicular-Fog Systems: A Distributed-TD3 Approach

IEEE Transactions on Intelligent Transportation Systems 2024年第11期25卷 16897-16909页

作者： Wakgra, Frezer Guteta Kar, Binayak Tadele, Seifu Birhanu Shen, Shan-Hsiang Khan, Asif Uddin The Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan The School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India

The emergence of 5G networks has enabled the deployment of a two-tier edge and vehicular-fog network. It comprises Multi-access Edge Computing (MEC) and Vehicular-Fogs (VFs), strategically positioned closer to Internet of Things (IoT) devices, reducing propagation latency compared to cloud-based solutions and ensuring satisfactory quality of service (QoS). However, during high-traffic events like concerts or athletic contests, MEC sites may face congestion and become overloaded. Utilizing offloading techniques, we can transfer computationally intensive tasks from resource-constrained devices to those with sufficient capacity, for accelerating tasks and extending device battery life. In this research, we consider offloading within a two-tier MEC and VF architecture, involving offloading from MEC to MEC and from MEC to VF. The primary objective is to minimize the average system cost, considering both latency and energy consumption. To achieve this goal, we formulate a multi-objective optimization problem aimed at minimizing latency and energy while considering given resource constraints. To facilitate decision-making for nearly optimal computational offloading, we design an equivalent reinforcement learning environment that accurately represents the network architecture and the formulated problem. To accomplish this, we propose a Distributed-TD3 (DTD3) approach, which builds on the TD3 algorithm. Extensive simulations, demonstrate that our strategy achieves faster convergence and higher efficiency compared to other benchmark solutions. © 2024 IEEE.

关键词： Quality of service

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Effect of Rigid Pitch Motion on Flexible Vibration Characteristics of a Wind Turbine Blade

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Energy engineering 2024年第10期121卷 2981-3000页

作者： Zhan Wang Liang Li Long Wang Weidong Zhu Yinghui Li Echuan Yang School of Mechanical Engineering Anhui University of Science and TechnologyHuainan232001China School of Mechanics and Optoelectronic Physics Anhui University of Science and TechnologyHuainan232001China Department of Mechanical Engineering University of MarylandBaltimoreMD 21250USA School of Mechanics and Aerospace Engineering Southwest Jiaotong UniversityChengdu610031China School of Mechanical Engineering Chongqing University of TechnologyChongqing400054China

Adynamic pitch strategy is usually adopted to improve the aerodynamic performance of the blade of awind *** dynamic pitch motion will affect the linear vibration characteristics of the ***,these influences have not been studied in previous *** this paper,the influences of the rigid pitch motion on the linear vibration characteristics of a wind turbine blade are *** blade is described as a rotating cantilever beam with an inherent coupled rigid-flexible vibration,where the rigid pitch motion introduces a parametrically excited vibration to the *** differential equations governing the nonlinear coupled pitch-bend vibration are proposed using the generalized Hamiltonian *** vibration characteristics of the inherent coupled rigid-flexible system are analyzed based on the combination of the assumed modes method and the multi-scales *** of static pitch angle,rotating speed,and characteristics of harmonic pitch motion on flexible natural frequencies andmode shapes are *** shows that the pitch amplitude has a dramatic influence on the natural frequencies of the blade,while the effects of pitch frequency and pith phase on natural frequencies are little.

关键词： Pitch motion wind turbine blade inherent rigid-flexible coupling vibration characteristics

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Improving Machine Translation Formality with Large Language Models

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computers, Materials & Continua 2025年第2期82卷 2061-2075页

作者： Murun Yang Fuxue Li School of Computer Science and Engineering Northeastern UniversityShenyang110819China College of Electrical Engineering Yingkou Institute of TechnologyYingkou115014China

Preserving formal style in neural machine translation (NMT) is essential, yet often overlooked as an optimization objective of the training processes. This oversight can lead to translations that, though accurate, lack formality. In this paper, we propose how to improve NMT formality with large language models (LLMs), which combines the style transfer and evaluation capabilities of an LLM and the high-quality translation generation ability of NMT models to improve NMT formality. The proposed method (namely INMTF) encompasses two approaches. The first involves a revision approach using an LLM to revise the NMT-generated translation, ensuring a formal translation style. The second approach employs an LLM as a reward model for scoring translation formality, and then uses reinforcement learning algorithms to fine-tune the NMT model to maximize the reward score, thereby enhancing the formality of the generated translations. Considering the substantial parameter size of LLMs, we also explore methods to reduce the computational cost of INMTF. Experimental results demonstrate that INMTF significantly outperforms baselines in terms of translation formality and translation quality, with an improvement of +9.19 style accuracy points in the German-to-English task and +2.16 COMET score in the Russian-to-English task. Furthermore, our work demonstrates the potential of integrating LLMs within NMT frameworks to bridge the gap between NMT outputs and the formality required in various real-world translation scenarios.

关键词： Neural machine translation formality large language model text style transfer style evaluation reinforcement learning

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Robust interval stability/stabilization and H∞ feedback control for uncertain stochastic Markovian jump systems based on the linear operator

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science China(Information sciences) 2022年第4期65卷 169-181页

作者： Huasheng ZHANG Jianwei XIA Guangming ZHUANG Hao SHEN School of Mathematics Science Liaocheng University School of Electrical and Information Engineering Anhui University of Technology

This paper focuses on the problem of robust H∞state feedback control with interval pole constraints for uncertain stochastic Markovian jump systems（MJSs）. First, we present the sufficient conditions of robust interval stability using a linear operator and its spectrum. A robust interval stabilization controller is designed for MJSs; it ensures the stability of MJSs and adjusts the rate of convergence. In addition, the robust H∞controller with interval pole constraints is designed with the admissible parametric uncertainties and a prescribed H∞disturbance attenuation level; this guarantees that the closed-loop system is robust and asymptotically stable with an ideal rate of convergence. A numerical example is provided to demonstrate the effectiveness of the proposed method.

关键词： Markovian jump systems linear operator interval stability interval stabilization robust $H_{\infty}$ control

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A Server Placement Algorithm for Reducing Risk and Improving Power Utilization in Data Centers

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Tsinghua science and technology 2024年第1期29卷 158-173页

作者： Rui Chen Huikang Huang Xiaoxuan Luo Weiwei Lin School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China Peng Cheng Laboratory Shenzhen 518066China

As the power demand in data centers is increasing,the power capacity of the power supply system has become an essential resource to be *** many data centers use power oversubscription to make full use of the power capacity,there are unavoidable power supply risks associated with ***,how to improve the data center power capacity utilization while ensuring power supply security has become an important *** solve this problem,we first define it and propose a risk evaluation metric called Weighted Power Supply Risk(WPSRisk).Then,a method,named Hybrid Genetic Algorithm with Ant Colony System(HGAACS),is proposed to improve power capacity utilization and reduce power supply risks by optimizing the server placement in the power supply *** uses historical power data of each server to find a better placement solution by population *** possesses not only the remarkable local search ability of Ant Colony System(ACS),but also enhances the global search capability by incorporating genetic operators from Genetic Algorithm(GA).To verify the performance of HGAACS,we experimentally compare it with five other placement *** experimental results show that HGAACS can perform better than other algorithms in both improving power utilization and reducing the riskof powersupply system.

关键词： server placement power utilization power supply risk swarm intelligence algorithm

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Psychology-informed Rumor and Stance Detection 5

Psychology-informed Rumor and Stance Detection

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5th International Conference on Big Data and Artificial Intelligence and Software engineering, ICBASE 2024

作者： Wei, Jingru Cai, Yanping Yan, Dengcheng Wang, Huabin Zhong, Hong Wang, Qingren Anhui University School of Computer Science and Technology Hefei230601 China

ISBN: (纸本)9798331506612

In recent years, online social networks have developed rapidly, the spread of rumors becomes convenient and rapid, so it is necessary to study rumor detection and stance classification. In social networks, users with different personality behaves differently during the spread of rumors and their stance is usually affected by their personality. Therefore, it is particularly important to analyze the personality of social network users. In this paper, we add the Big Five personality analysis to the model and design a multi-task learning model that combines rumor detection, stance classification, and personality recognition tasks. Firstly, daily tweets of users were crawled on the official Twitter website, and the Linguistic Inquiry and Word Count (LIWC) was used to obtain the true value of each user's Big Five personality. Secondly, the shared Long Short-Term Memory (LSTM) layer is used to train the input embedding of rumor detection and stance classification tasks, and the Multilayer Perceptron (MLP) model is used to obtain the Big Five personality prediction value of each user. Finally, tweet embedding is combined with user personality embedding to jointly train the rumor detection and stance classification tasks. Compared with baseline rumor detection and stance classification models on the RumourEval and PHEME datasets, our model has the best performance on each task. © 2024 IEEE.

关键词： Tweets

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BEV-Locator:an end-to-end visual semantic localization network using multi-view images

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science China(Information sciences) 2025年第2期68卷 134-150页

作者： Zhihuang ZHANG Meng XU Wenqiang ZHOU Tao PENG Liang LI Stefan POSLAD School of Vehicle and Mobility Tsinghua University School of Information Technology & Management University of International Business and Economics Qcraft Inc. School of Electronic Engineering and Computer Science Queen Mary University of London

Accurate localization ability is fundamental in autonomous driving. Traditional visual localization frameworks approach the semantic map-matching problem with geometric models, which rely on complex parameter tuning and thus hinder large-scale deployment. In this paper, we propose BEV-Locator: an end-to-end visual semantic localization neural network using multi-view camera images. Specifically, a visual BEV(bird-eye-view) encoder extracts and flattens the multi-view images into BEV space. While the semantic map features are structurally embedded as map query sequences. Then a cross-model transformer associates the BEV features and semantic map queries. The localization information of ego-car is recursively queried out by cross-attention modules. Finally, the ego pose can be inferred by decoding the transformer outputs. This end-to-end model speaks to its broad applicability across different driving environments, including high-speed scenarios. We evaluate the proposed method in large-scale nuScenes and Qcraft datasets. The experimental results show that the BEV-Locator is capable of estimating the vehicle poses under versatile scenarios, which effectively associates the cross-model information from multi-view images and global semantic maps. The experiments report satisfactory accuracy with mean absolute errors of 0.052 m, 0.135 m and 0.251° in lateral, longitudinal translation and heading angle degree.

关键词： visual localization semantic map bird-eye-view transformer pose estimation

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Efficient and Scalable: Physical Layer Design to Taming Interference in Large-Scale Concurrent Backscatter Communications 12

Efficient and Scalable: Physical Layer Design to Taming Inte...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： He, Xin Zhang, Aohua Jiang, Weiwei Yang, Panlong School of Computer and Information Anhui Normal University Anhui241002 China School of Computer Science Nanjing University of Information Science and Technology Jiangsu210044 China

ISBN: (纸本)9798331511074

The ever-increasing number of IoT devices can communicate with ultra-low power consumption through backscattering technology, which opens up more possibilities for ubiquitous IoT communication. However, large-scale tag communication brings certain challenges. In this study, we propose a scheme that uses the successive interference cancellation (SIC) algorithm based on concatenated convolutional coding and iterative decoding design, to address interference issues that may arise from multiple tag communication. The channel is modeled as MISO in the Rayleigh fading environment. Our system employs Walsh codes for spreading to achieve direct serial code division multiple access (DS-CDMA) and combines iterative decoding to enhance anti-interference capability. The SIC algorithm is applied to detect and decode tags separately while eliminating interference from other tags to improve decoding accuracy. Our proposed scheme successfully achieves concurrent communication of 90+ tags with notable BER results. Moreover, when nodes transmit data at 400 kbps, the throughput reaches 33 Mbps, which is 1.5 times higher than the throughput of the scheme without the SIC algorithm. © 2024 IEEE.

关键词： Iterative decoding

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Remote Interference Source Localization: A Multi-UAV-Based Cooperative Framework

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Chinese Journal of Electronics 2022年第3期31卷 442-455页

作者： WU Guangyu GU Jiangchun Department of Computer Science and Technology University of Science and Technology of China School of Communication Engineering Army Engineering University of PLA

Interference source localization with high accuracy and time efficiency is of crucial importance for protecting spectrum resources. Due to the flexibility of unmanned aerial vehicles(UAVs), exploiting UAVs to locate the interference source has attracted intensive research interests. The off-the-shelf UAV-based interference source localization schemes locate the interference sources by employing the UAV to keep searching until it arrives at the target. This obviously degrades time efficiency of localization. To balance the accuracy and the efficiency of searching and localization, this paper proposes a multi-UAV-based cooperative framework alone with its detailed scheme, where search and remote localization are iteratively performed with a swarm of UAVs. For searching, a low-complexity Q-learning algorithm is proposed to decide the direction of flight in every time interval for each UAV. In the following remote localization phase, a fast Fourier transformation based location prediction algorithm is proposed to estimate the location of the interference source by fusing the searching result of different UAVs in different time intervals. Numerical results reveal that in the proposed scheme outperforms the stateof-the-art schemes, in terms of the accuracy, the robustness and time efficiency of localization.

关键词： unmanned aerial vehicles low-complexity Q-learning algorithm path planning fast Fourier transforms remote interference source localization multi-robot systems UAV swarm mobile robots remote localization location prediction algorithm aerospace robotics autonomous aerial vehicles learning (artificial intelligence) fast Fourier transformation cooperative communication spectrum resources radiofrequency interference mobile radio flight direction multiUAV-based cooperative framework

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Analytical review and study on various course recommendation systems

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International Journal of Wireless and Mobile Computing 2023年第2期25卷 160-170页

作者： Anupama, V. Elayidom, M. Sudheep Division of Computer Science School of Engineering Cochin University of Science and Technology Kerala Kochi India

In the educational system, online courses are significant in developing the knowledge of users. The selection of courses is important for college students because of large unknown optional courses. The course recommendation systems are provided with suggestions and improve course selection during the pre-registration stage. This survey presents the analysis of 50 research papers for course recommendation. The course recommendation systems are grouped under three categories, namely machine learning-based techniques, collaborative-based and data mining-based techniques. Besides, the classification of techniques, utilised tools, implemented software tools and performance metrics are considered for analysis. Moreover, the research gaps identified in the existing course recommendation system are discussed. The machine learningbased approach is mostly used for course recommendation among several approaches. Most existing course recommendation techniques use Java as the implementation tool and the Moodle log database. Also, F-measure, MAE, accuracy and RMS have been commonly used as performance metrics. © 2023 Inderscience Enterprises Ltd.

关键词： Collaborative filtering

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