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Data-driven Multi-stage Vehicle Trajectory Prediction for Complex Scenarios

Data-driven Multi-stage Vehicle Trajectory Prediction for Co...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Jian Zhang Mengyuan Wang Zikun Feng Shunli Zhang School of Software Engineering Beijing Jiaotong Univerisity Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Owing to the advancements in deep learning methods, predicting vehicle trajectories based on vast amounts of traffic trajectory data is no longer an elusive objective. However, traditional time-series prediction methods struggle to effectively address issues such as V-V interactions, road guidance, and the inherent uncertainty in vehicle trajectories within road traffic. To tackle these issues, this paper proposes a data-driven multistage prediction method. Firstly, in terms of map representation, a slide-window-driven lane node aggregation is employed to compensate for the absence of scale information in lane nodes. Secondly, distance-wise attributes for complex areas are propagated and encoded to obtain enhanced features. Thirdly, by defining the hist features, the perception range of vehicle interactions is expanded both temporally and spatially. Finally, a multi-stage scheme is utilized to achieve the final aggregation of trajectory features, and a feature decoder integrated with a focal loss is employed to obtain multi-modal prediction results. Both qualitative and quantitative analyses of the experimental results demonstrate the effectiveness of our proposed method.

关键词： Measurement Deep learning distributed processing Uncertainty Statistical analysis Roads Prediction methods Road traffic Encoding Trajectory

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Systematic Literature Review of VANET Simulators: Comparative Analysis, Technological Advancements, and Research Challenges

Systematic Literature Review of VANET Simulators: Comparativ...

引用

parallel Computing and distributed systems (PCDS), international symposium on

作者： Reham Almutairi Giacomo Bergami Graham Morgan School of Computing Newcastle University UK College of Computer Science and Engineering University of Hafr Albatin Saudi Arabia School of Computing Newcastle University Newcastle Upon Tyne UK

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Vehicular Ad-hoc Networks (VANETs) are integral to the future of smart vehicles and transportation, facilitating real-time information sharing and communication among vehicles to enhance safety, improve traffic flow, and reduce accidents. With the rise of self-driving vehicles and emerging technologies such as 5G and edge computing, new challenges arise for VANET simulators. These simulators must now accommodate dynamic environments, necessitating features such as high scalability, real-time large-scale experiment support, and node mobility. Furthermore, they should support battery management, recharging during movement, and integrate renewable energy sources. This survey evaluates current VANET simulators against these criteria, comparing their features, discussing advancements in VANET technology, and identifying ongoing research challenges. Our findings reveal that no existing VANET simulator fully supports the integration of renewable energy sources or the real-time recharging of vehicle batteries. Additionally, there is a significant gap in simulating the effects of encryption without implementing specific encryption algorithms.

关键词： Renewable energy sources 5G mobile communication Vehicular ad hoc networks Transportation Battery management systems Real-time systems Encryption Batteries Testing Edge computing

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Coverage Holes Recovery in Hybrid Sensor Networks Based on Key Perceptual Intersection

Coverage Holes Recovery in Hybrid Sensor Networks Based on K...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： He Li Shixian Sun Chuang Dong Zhufeng Fu Cong Zhao Qinglei Qi School of Artificial Intelligence and Software Engineering University of Nanyang Normal Nanyang China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Wireless sensor networks are widely used in various applications such as military surveillance, wildlife monitoring, industrial process monitoring and so on. Detecting and recovering from coverage holes is a major issue in wireless sensor networks. The gradual energy depletion of sensor nodes with limited battery energy leads to dysfunction of some of the nodes, thus creating holes in the monitored area. In this paper, propose a method to detect and recover coverage holes in wireless sensor networks. This method first divides the network into equally sized units, and then selects a representative node for each unit, called an agent. Then, the percentage of each cell covered by nodes can be accurately calculated and holes can be detected. Finally, the holes are recovered using the average of the key perceptual intersections as the initial value of the global optimal point of the particle swarm optimization algorithm. Simulation indicates show that the algorithm reduces the network energy consumption, reduces the distance travelled by the mobile nodes and increases the percentage of network holes recovery as compared to other algorithms.

关键词： Wireless communication Process monitoring Lead acid batteries Wireless sensor networks Energy consumption distributed processing Surveillance Wildlife Mobile nodes Particle swarm optimization

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A Performance Prediction Model for Structured Grid Based Applications in HPC Environments

A Performance Prediction Model for Structured Grid Based App...

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international symposium on parallel and distributed Computing

作者： Md Bulbul Sharif Thomas Hines Sheikh Ghafoor Mario Morales-Hernández Tigstu Dullo Alfred Kalyanapu Computer Science Tennessee Technological University Cookeville TN USA Computational Science and Engineering Oak Ridge National Laboratory Oak Ridge TN USA Civil and Environmental Engineering Tennessee Technological University Cookeville TN USA

Predicting the performance of parallel applications at scale is a challenging problem. We have developed a performance prediction model for structured grid-based scientific applications for High Performance Computing systems. Our model can capture the system complexity and consider computation and communication attributes of application performance on HPC architectures. We have also proposed a methodology for obtaining the realistic value of the model parameters by small-scale sample runs of an application on the target system. We have used our model to predict the performance of an actual application (2D Flood Simulation) and a synthetic application (Game of Life) on the Summit supercomputer at Oak Ridge National Laboratory and Stampede2 at Texas Advanced Computing Center. The experimental results indicate that our model’s predictive performance is acceptable, and our model was able to predict the performance of these two applications on Summit and Stampede2 with more than 90% accuracy.

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A Crowdsensing Service Pricing Method in Vehicular Edge Computing

A Crowdsensing Service Pricing Method in Vehicular Edge Comp...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Zheng Li Sizhe Tang Hao Tian Haolong Xiang Xiaolong Xu Wanchun Dou State Key Laboratory of Novel Software Technology Nanjing University Nanjing China School of Software Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The rapid advancements in vehicular networking technology have enabled onboard users to access a variety of emerging services like precise navigation and real-time hazard avoidance. However, as the diversity and volume of required data expand and demands intensify, most of vehicular services are unable to satisfy user expectations. Although crowdsensing architectures based on edge computing have been proposed in vehicular networks, how to optimally allocate the sensing service time of roadside units and set appropriate prices for services to maximize user benefits still remain significant challenges. To solve the above issue, in this paper, a crowdsensing service pricing method is proposed in vehicular edge computing. Specifically, a vehicular networking crowdsensing framework based on the edge computing is designed. Then, the optimization problem of perception time pricing during the crowdsensing process is modeled into a Stackelberg game and further the multi-agent deep deterministic policy gradient-based algorithm is employed to obtain the optimal pricing strategy with user profits maximization. Finally, simulation results demonstrate that the proposed method significantly increases the payoff for all participants during the crowdsensing process.

关键词： Crowdsensing Simulation Pricing Games Reinforcement learning Robustness Real-time systems Sensors Vehicle dynamics Edge computing

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loT-enabled Rapid Global Maximum Power Point Tracking for Multiple Photovoltaic Inverters

loT-enabled Rapid Global Maximum Power Point Tracking for Mu...

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IEEE international symposium on Power Electronics for distributed Generation systems (PEDG)

作者： Kangjia Zhou Feng Gao Caiyun Qin Xiangjian Meng School of Electrical Engineering Shandong University Jinan China School of Control Science and Engineering Shandong University Jinan China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350361001

ISBN: (纸本)9798350361018

Maximum power point tracking (MPPT) is a crucial technology for enhancing photovoltaic (PV) array power generation efficiency. Under scenarios with partial shading, the output power characteristic curve of a PV array often displays multiple peaks, necessitating the use of global peak-seeking techniques. Particle Swarm Optimization (PSO) and its advanced variants have been widely adopted to address global optimization challenges. However, the current landscape primarily includes PSO based MPPT methods designed for single inverters, which tend to exhibit slow optimization speeds. This paper presents an Internet of Things (IoT)-based multi- inverters approach for global MPPT. In this approach, all inverters within the network are treated as a single particle. This transformation shifts the conventional serial PSO operation into a parallel one, enabling data exchange among inverters to jointly track the maximum power point. Simulation results demonstrate that the proposed methodology significantly accelerates the optimization process, effectively mitigating the limitations of traditional approaches.

关键词： Maximum power point trackers Photovoltaic systems Accuracy Simulation Inverters distributed power generation Internet of Things

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Mining Relational Similarity in Social Networks for Enhanced Recommendations

Mining Relational Similarity in Social Networks for Enhanced...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Pengcheng Guo Qingyang Wang Jielin Jiang Siyu Wu Haolong Xiang Xinyue Ji Shengjun Xue Nanjing University of Information Science and Technology Nanjing China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing China Silicon Lake College Suzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Social perception recommendation systems can effectively alleviate the user cold start problem by leveraging the side information of social networks. However, most social perception recommendation systems treat user relations as independently existing entities for learning, thereby overlooking potential connections between relations. Additionally, as the number of relations increases, it inevitably imposes a significant computational burden on the servers. To address these issues, we propose the Social perception recommendation based on relational clustering(SPRRC). SPRRC conducts relational clustering of social networks and projects knowledge graphs, effectively mining information about the similarity between relations. First, we cluster relationships in item knowledge graphs and social networks through unsupervised learning. After that, we use local weighted smoothing to aggregate the map information of the clustered items and social network virtual subgraphs respectively, and use the attention networks to learn the representation of users and items in the interactive bipartite graph. Finally, a large number of experiments have verified the high accuracy of our method, compared with the latest methods.

关键词： distributed processing Smoothing methods Social networking (online) Aggregates Knowledge graphs Bipartite graph Servers Artificial intelligence Recommender systems Unsupervised learning

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1st Workshop on Composable systems (COMPSYS 2022)

1st Workshop on Composable Systems (COMPSYS 2022)

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

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HPPython: Extending Python with HPspmd for Data parallel Programming 23

HPPython: Extending Python with HPspmd for Data Parallel Pro...

引用

Proceedings of the 2023 2nd international symposium on Computing and Artificial Intelligence

作者： Judah Josiah Nava Jaden Jinu Lee Hanku Lee Computer Science & Information Systems Department Minnesota State University Moorhead USA

ISBN: (纸本)9798400708954

In light of previous endeavors and trends in the realm of parallel programming, HPPython emerges as an essential superset that enhances the accessibility of parallel programming for developers, facilitating scalability across multiple nodes. Despite Python's popularity as a programming language in scientific and engineering applications and its native support for executing various processes, HPPython brings substantial simplification to the development of parallel programs and empowers program distribution across heterogeneous clusters consisting of multiple physical computers. HPPython leverages the MPI standard for its underlying communication, thereby harnessing the benefits of the SPMD model. Additionally, HPPython introduces novel syntax and constructs, such as parallel loops and distributed lists, while endeavoring to retain the natural essence of the original language. This paper delves into the distinct components of HPPython and elucidates their integration, establishing HPPython as a viable solution for parallel programming in today's data-driven world.

关键词： Data parallel Language distributed Computing High-Performance Computing parallel Computing

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Knowledge Tracing Based on Semantic Enhancement of Exercise Relevance

Knowledge Tracing Based on Semantic Enhancement of Exercise ...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Yueyang Huang Kun Liang Huitao Zhang Xilong Chang Chao Wang Xiankun Zhang College of Artificial Intelligence Tianjin University of Science and Technology Tianjin China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Knowledge tracing is a key technology in online education platforms such as Intelligent Tutoring systems (ITSs) and Massive Open Online Courses (MOOCs), which model the state of an individual’s knowledge based on the learner’s historical sequence of interactions to predict future performance. However, existing knowledge-tracing approaches lack attention to high- and low-order features and exercise relevance, while few models consider the real-world situation of online tutoring systems where learners can only interact with a limited number of exercises and data is often sparse. Therefore, this paper proposes a knowledge tracing method based on the fusion of exercise relevance and hybrid attention network. Firstly, the parallel GRU captures the high and low order features of learners, which are processed by the extraction network to fit the information representation of learner-interacted exercises. Second, the correlation-enhancing features of target exercises and historical interaction exercises are captured. Finally, a prediction network is used to fuse multiple features in order to effectively capture learners’ knowledge states and improve prediction accuracy in the face of sparse data. Extensive experiments on real online education datasets have shown that ERKT achieves better prediction results compared to existing mainstream methods.

关键词： Knowledge engineering Fuses Education Transfer learning Semantics Knowledge based systems Information representation Predictive models Feature extraction Faces

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