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Multi-agent deep reinforcement learning for traffic signal control with Nash Equilibrium 23

Multi-agent deep reinforcement learning for traffic signal c...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wei, Wei Wu, Qiang Wu, Jianqing Du, Bo Shen, Jun Li, Tinghong School of Computer Science and Engineering Xi'an University of Technology Xi'an China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China University of Wollongong Smart Infrastructure Facility Wollongong Australia China Mobile Communications Corporation Lanzhou China

ISBN: (纸本)9781665494571

Traffic signal control is an essential and chal-lenging real-world problem, which aims to alleviate traffic congestion by coordinating vehicles' movements at road in-tersections. Deep reinforcement learning (DRL) combines deep neural networks (DNNs) with a framework of reinforcement learning, which is a promising method for adaptive traffic signal control in complex urban traffic networks. Now, multi-agent deep reinforcement learning (MARL) has the potential to deal with traffic signal control at a large scale. However, current traffic signal control systems still rely heavily on simplified rule- based methods in practice. In this paper, we propose: (1) a MARL algorithm based on Nash Equilibrium and DRL, namely Nash Asynchronous Advantage Actor-Critic (Nash-A3C);(2) an urban simulation environment (SENV) to be essentially close to the real-world scenarios. We apply our method in SENV, obtaining better performance than benchmark traffic signal control methods by 22.1%, which proves that Nash-A3C to be more suitable for large industrial level deployment. © 2021 IEEE.

关键词： Performance evaluation Deep learning Computational modeling Roads Neural networks Reinforcement learning Nash equilibrium

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Forwarding Policy Verification of Hybrid Multi-domain Software-Defined Network 23

Forwarding Policy Verification of Hybrid Multi-domain Softwa...

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作者： Yao, Jiangyuan Jiang, Zheng Yang, Weiping Wang, Minrui Li, Deshun School of Computer Science and Technology Hainan University Haikou China School of Cyberspace Security Hainan University Haikou China

ISBN: (纸本)9781665494571

Software-Defined Network (SDN) is now widely used in the industry and the exploration of multi-domain SDN is currently underway. Because of the different manager, the forwarding policy conflict occurs frequently in multi-domain SDN. However, the previous verification of network policy conflicts is only limited to traditional networks or single domain SDN. Therefore, we carry out our work on hybrid multi-domain SDN which is a type of multi-domain SDN. We merge the SDN flow table entries with border router configuration files to construct a formal model HM-SDN of hybrid multi-domain SDN, and then generate a graph structure SDNIG according to HM-SDN which can depict the real network environment, finally present a verifier VeriGraph based on graph algorithms. After evaluation, we can construct our graph structure no more than 0.13s and can verify common attributes less than 3s in the small and medium networks. © 2021 IEEE.

关键词： Industries Smart cities Hybrid power systems Servers

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An Empirical Study of Online Tools Use in Online international Student Design Workshop 9

An Empirical Study of Online Tools Use in Online Internation...

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9th IEEE/ACIS international conference on Big data, Cloud computing, and data science, BCD 2024

作者： Inasaka, Akiyoshi Chiba Institute of Technology Faculty of Creative Engineering Department of Design Narashino Chiba Japan

ISBN: (纸本)9798350394191

In contemporary design education, the influx of diverse design accomplishments and methodologies, especially those involving interdisciplinary and cross-regional collaboration, poses a significant challenge. Understanding and expressing user and environmental needs within technology, aesthetics, and culture is central to addressing these issues. Since 2016, we have addressed this challenge through a joint design workshop course for 3rd and 4th-year students, aiming to foster cross-cultural understanding and communication skills, thus developing international expertise in design. This course employs a project-based learning (PBL) approach and is a staple in our department's curriculum. It involves mixed groups from our university and international partners working on specific design problems and presenting their solutions. The course, conducted primarily in English, emphasizes non-native linguistic and non-verbal communication through visual expressions like sketches and drawings. One objective is to provide students with the skills necessary for effective cross-cultural communication. Typically, this intensive summer course alternates annually between Japan and partner countries. However, the COVID-19 pandemic 2020 necessitated a shift to an online format to maintain the course's objectives despite travel restrictions. This paper highlights the adaptations made from FY2020 to FY2021 to continue the program online. Our international collaborations led to developing a PBL-based active learning method, crucial for enhancing cross-cultural understanding and communication in design education. Utilizing the technology and methods refined during the pandemic, we successfully conducted a hybrid online/on-site workshop connecting Japan and Taiwan. Online tools facilitated remote group work, discussions, and feedback, accommodating a diverse, multicultural cohort through synchronous and asynchronous communication. © 2024 IEEE.

关键词： Students

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AP3: Adaptive Power Prediction Framework based on Spatial Partition Multi-Phase Model 23

AP3: Adaptive Power Prediction Framework based on Spatial Pa...

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作者： Chen, Juan Ou, Zhixin Guo, Yifei Qi, Xinxin Sun, Yuyang Deng, Lin Chen, Hongyu Lin, Zihan College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665494571

The accuracy of processor power modeling is an important foundation for power management and optimization on parallel computing system. It is difficult to build a high-accuracy instantaneous CPU/DRAM power prediction model. One of the main reasons for the low accuracy is that the processor architecture sometimes influence greatly on the accuracy of power prediction. For example, the accuracy of processor power model is affected by inaccurate modeling of the uncore part of processor. Another reason comes from the limitation of static models for instantaneous power prediction. Despite the existence of various optional linear/nonlinear models, the fixed training set and model coefficients are insufficient to make high-accuracy instantaneous predictions for various target programs. Aiming at the above two issues, this paper proposes an Adaptive Power Prediction framework based on spatial Partition multi-phase model (AP^{3}). Spatial partition mainly solves the impact of uncore power on the prediction accuracy, and adaptability solves the limitation of the static model on the power prediction accuracy. According to the experimental results on both ARM-based and x86-based processor platforms, AP^{3} greatly increases the CPU and DRAM power instantaneous prediction accuracy. Spatial partition reduce the prediction error (MRE) by 0.3%-8.2% compared to previous single model, while adaptive update further reduces the error (MRE) by 1.7%-7.1% compared to previous static model. © 2021 IEEE.

关键词： Training Adaptation models Smart cities Multicore processing Computational modeling Power system management Random access memory

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data Clustering Approach on the Basis of data Sensitivity for Implementation of Secure Cloud computing Environment 3rd

Data Clustering Approach on the Basis of Data Sensitivity fo...

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3rd international conference on Machine Learning, Image Processing, Network Security and data sciences, MIND 2021

作者： Singh, Rajdeep Pateriya, R.K. Department of Computer Science & Engineering Mewar University Rajasthan Chittorgarh India

ISBN: (纸本)9789811958670

In the work illustrated, an effective approach has been proposed for handling data for the cloud computing environment. Here, the sensitivity of data is derived from data weights that in turn based on the size of the file and attribute scale for the preprocessing. The categorization of the sensitivity is based on the 10 attributes, and it has been selected on the basis of the size and information of data. Afterward, for clustering of similar elements, K-means clustering algorithm is applied which clusters the data in maximum of 5 clusters. It is been used to perform data sensitivity analysis and encryption. In terms of data categorization and processing in a cloud computing environment, the approach demonstrates its efficacy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud security

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The Parallelized Cuckoo Filter for Cold data Representation 23

The Parallelized Cuckoo Filter for Cold Data Representation

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作者： Sun, Bowen Luo, Lailong Li, Shangsen Chen, Yingwen Guo, Deke College of Computer National University of Defense Technology China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China College of Computer National University of Defense Technology National Lab for Parallel and Distributed Processing China

ISBN: (纸本)9781665494571

Cold data contributes a large portion of the big data today and is usually stored in secondary storage. Various sketch data structures are implemented to represent the stored elements and provide constant-time membership queries. Considering the large volume and infrequent access of cold data, we envision three rationales of cold data representation: 1) parallelized insertion, 2) accurate representation, and 3) constant-time query/deletion. The existing sketches, however, fail to achieve these rationales simultaneously. To this end, we propose a novel sketch named Parallelized cuckoo Filter (PCF) in this paper. PCF divides the standard cuckoo filter into multiple homogenous segments. An additional hash function is introduced to assign an element to a segment. The element will be inserted into the selected segment. With such a design, element insertions in PCF can be naturally parallelized. Besides, as a variant of the cuckoo filter, PCF naturally realizes the accurate representation and constant-time query/deletion requirements. Comprehensive experiments further indicate that PCF achieves the three rationales simultaneously. © 2021 IEEE.

关键词： Hash functions Smart cities Big data data structures Standards

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Edge Intelligence-Based UAV Human Target Recognition with Improved YOLOV5 Algorithm 23

Edge Intelligence-Based UAV Human Target Recognition with Im...

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作者： Cai, Zeyu Qu, Yuben Dong, Chao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (纸本)9781665494571

Edge intelligent computing is often combined with the tasks of Unmanned Aerial Vehicle (UAV). Most applications of UAV require the location and identity information of human targets. Traditional human target detection and face recognition algorithms require a lot of time in edge intelligent computing. In addition, the final recognition result is not accurate enough. To this end, this paper proposes an improved YOLOV5-Cozy target detection algorithm to detect human targets and faces in a fast and accurate way. The algorithm uses MobileNetV3, depthwise separable convolution and shuffleconv to improve the original YOLOV5 (You Only Look Once version 5) algorithm. Compared with the original algorithm, the improved algorithm has an average detection accuracy drop of less than 5%, and its calculation time has been reduced by 20%. In terms of face recognition, we use the FaceNet algorithm with the addition of a face augmentation module. The module enhances the face recognition ability of the FaceNet algorithm in different environments, and the accuracy of face recognition has increased by 20%. We implement the proposed algorithm on the embedded computer Jetson Agx Xavier for UAV edge intelligent computing, and the average FPS achieves at about 23.6. © 2021 IEEE.

关键词： Target recognition Smart cities Convolution Face recognition Image edge detection Computational modeling Object detection

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Organs-At-Risk Segmentation in Medical Imaging Based on the U-Net with Residual and Attention Mechanisms 23

Organs-At-Risk Segmentation in Medical Imaging Based on the ...

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作者： Lin, Xiao-Wei Li, Ni Qi, Qi School of Computer Science and Technology Hainan University Haikou China School of Mathematics and Statistics Hainan Normal University Hainan Normal University Key Laboratory of Data Science and Intelligence Education of Ministry of Education Haikou China

ISBN: (纸本)9781665494571

In this paper, we proposed a new organs-at-risk segmentation method for Medical images of heads and necks based on the U-Net with residuals and attention mechanism. A Project & Excite (PE) block combined with a residual method is proposed as Residual-PE Block in the U-Net encoding stage in order to enhance the feature expression ability. Attention Gates (AGs) are introduced in the decoding stage to increase the relevance of feature weighting in accord with segmentation tasks. The proposed method has been evaluated through a set of real world medical images including 22 organs at risk in the head and neck. Experimental results showed that our proposed method improved the average segmentation accuracy by 3-4% compared with current existing methods. The average Dice Similarity Coefficient (DSC) score on the segmentation of the 22 types of head-and-neck related organs at risk is 79.5% and the average 95% Hausdorff Distance (HD) is 6.99mm. It demonstrates that this approach is promising for clinical doctors to improve their accuracy and efficiency in radiotherapy. © 2021 IEEE.

关键词： Image segmentation Head Three-dimensional displays Smart cities Medical services Logic gates Encoding

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SFPGRN: Spectral detection method of plant diseases based on deep learning 3

SFPGRN: Spectral detection method of plant diseases based on...

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3rd international conference on Artificial Intelligence, Automation, and High-Performance computing, AIAHPC 2023

作者： Zhou, Yi Pan, Zeng Liu, Zuhao He, Tingwei Yan, Jingwen Department of Information Science and Technology Southwest Jiaotong University China Department of Biomedical Engineering and Instrument Science Zhejiang University China Department of Electronic Information Engineering Shantou University China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

At present, most of the spectral detection methods with plant diseases are based on machine learning, we present a novel approach to spectral analysis using a spectral feature pseudo-graph-based residual network, referred to as SFPGRN, for data in the near-infrared band. The proposed method involves constructing a residual network model using a characteristic surface obtained via natural neighborhood interpolation based on preprocessed near-infrared spectral reflection signal and first-order differential spectral index. The input layer is a pseudo spectral map that is generated through surface projection of the characteristic surface. The generated spectral pseudo image contains a diverse set of spectral features that augment the representation capability of the pseudo-image while retaining the spectral band reflectance information. We evaluated the proposed method on the 2015 spectral dataset of apple leaf diseases and insect pests in China and found that it outperforms traditional machine learning algorithms, achieving a classification accuracy of 93.21%. © 2023 SPIE.

关键词： Image recognition

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Three quantum neural network architectures for multi-class image classification 3

Three quantum neural network architectures for multi-class i...

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3rd international conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Qu, Guangkai Yu, Xianglong Wang, Zhimin Gu, Yongjian Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

Quantum neural networks (QNNs) integrate the advantages of quantum information with the formidable capabilities of classical neural networks, exhibiting notable potential in image classification tasks. However, the performance of current QNNs in processing multi-class image classification tasks still leaves room for improvement. In this paper, we propose three QNN architectures to enhance the accuracy of multi-class image classification by utilizing techniques of overparameterization, dense pooling, and local feature extraction. These networks are capable of mapping conventional data to the amplitudes of quantum states via amplitude encoding, eschewing the need for traditional dimensionality reduction techniques. Following processing with parameterized quantum circuits (PQCs), we conduct quantum measurements to extract feature information. We validate the proposed architectures using the MNIST dataset for eight-class image classification problems. The experimental results unequivocally demonstrate that the three techniques we employed significantly enhance the accuracy of QNNs in multi-class image classification tasks. © 2025 SPIE.

关键词： Quantum electronics

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