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Parameters of several families of binary duadic codes and their related codes

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arXiv 2023年

作者： Liu, Hai Li, Chengju Qian, Haifeng MoE Engineering Research Center of Software/Hardware Co-design Technology and Application East China Normal University Shanghai200062 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Software Engineering East China Normal University Shanghai200062 China

Binary duadic codes are an interesting subclass of cyclic codes since they have large dimensions and their minimum distances may have a square-root bound. In this paper, we present several families of binary duadic codes of length 2m−1 and develop some lower bounds on their minimum distances by using the BCH bound on cyclic codes, which partially solves one case of the open problem proposed in [10]. It is shown that the lower bounds on their minimum distances are close to the square root bound. Moreover, the parameters of the dual and extended codes of these binary duadic codes are investigated. copyright © 2023, The Authors. All rights reserved.

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Dynamically detecting invariants for automatic testing PLC programs 33

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Dynamically detecting invariants for automatic testing PLC p...

33rd International conference on software engineering and Knowledge engineering, SEKE 2021

作者： Lu, Zeyu Mao, Xia Huang, Yanhong Shi, Jianqi Yang, Yang National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai China Hardware/software Co-Design Technology and Application Engineering Research Center Shanghai China Shanghai Key Laboratory of Trustworthy Computing Shanghai China

ISBN: (纸本)1891706527

Since programmable logic controllers (PLCs) control safety-critical infrastructures, examining the PLC software satisfies the high-reliability specifications necessary to ensure the safeness of PLCs. However, prior works have limitations in finding defects in the PLC source code. Static verification techniques suffer from notable false positives without capturing runtime behavior. The symbolic execution and conformance testing technique captures the relations of inputs and outputs. It is not sufficient to consider only the data constraints as the PLC operates in real-time. In this paper, we propose a novel approach in the detection of the runtime behavior of PLC programs with incorporated time constraints. This testing approach automatically finds implementation errors in PLC programs by mining invariants from runtime traces. As the existing tools mine only data or time invariants which are inadequate to test PLC programs, our approach focuses on the interplay of data and time invariants. Dynamically detected data-time invariants are then checked with the safety specifications. We evaluate the usefulness of our approach in a real-life case. The experimental results show that the proposed approach can find errors in PLC programs effectively. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Real time systems

FcoSMask: Fully convolutional One-Stage Face MaskWearing Detection Based on MobileNetV3 21

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FCOSMask: Fully Convolutional One-Stage Face MaskWearing Det...

5th International conference on computer Science and application engineering, CSAE 2021

作者： Yu, Yang Lu, Jie Huang, Chao Xiao, Bo MoE Engineering Research Center for Software/Hardware Co Design Technology East China Normal University Shanghai200062 China Software Engineering Institute East China Normal University Shanghai200062 China

ISBN: (纸本)9781450389853

method against the worldwide coronavirus disease 2019 (coVID- 19). This paper proposes FcoSMask, a fully convolutional one-stage face mask wearing detector based on the lightweight network, for emergency epidemic control and long-term epidemic prevention work. MobileNetV3 is applied as the backbone network to reduce computational overhead. Thus, complex calculation related to anchor boxes is avoided in the anchor-free method, and complete Intersection over Union (CIoU) loss is selected as the bounding box regression loss function to speed up model convergence. Experiments show that compared to other anchor-based methods, detection speed of FcoSMask is improved around 3 to 4 times on self-established datasets and mean average precision (mAP) achieves 92.4%, which meets the accuracy and real-time requirements of the face mask wearing detection task in most public areas. Finally, a Web-based face mask wearing system is developed that can support public epidemic prevention and control management.. © 2021 Association for computing Machinery. All rights reserved.

关键词： convolution

research and design of Sensor Data Management System Based on Distributed Storage

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Research and Design of Sensor Data Management System Based o...

2020 International conference on computer Science and Management technology, ICCSMT 2020

作者： Yang, Jing Chi, Xiaotong Zhu, Minghua Wang, Wenjie East China Normal University MoE Engineering Research Center for Software/Hardware Co-design Technology and Application Shanghai China

ISBN: (纸本)9781728186689

Sensor data is one of the basic source of big data in the Internet of Things(IoT). It is the data that is sensed, measured, and transmitted by sensors with real-time and dynamic characteristics. With the development of the IoT and big data technologies, new industries such as smart transportation, smart cities, and smart agriculture have gradually formed, resulting in an increasing demand for large-scale data analysis and calculations. However, the storage architecture based on relational databases has been unable to meet the current needs in terms of data storage and data read and write speed. Therefore, this paper mainly studies the sensor data management system based on distributed storage, builds a Hadoop cluster, proposes a storage structure that combines the relational database MySQL with the distributed database HBase, designs a data migration module to satisfy real-time sensor data monitoring and massive data storage requirements. In order to meet the needs of enterprises in actual production, SSM framework technology is used to build a sensor data management platform, covering the functions of system management, sensor data management, data exception management, sensor data real-time monitoring, sensor node status monitoring, data statistical analysis, etc. The platform has good security and reliability, and can effectively improve the management efficiency of the enterprise. Finally, the experiment provides that the system has obvious advantages in data upload and query ability compared with the traditional storage architecture. It provides a good data environment for data analysis and data mining in the future. © 2020 IEEE.

关键词： Sensor nodes

OpenNet: Incremental Learning for Autonomous Driving Object Detection with Balanced Loss

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OpenNet: Incremental Learning for Autonomous Driving Object ...

IEEE International conference on Systems, Man and Cybernetics

作者： Zezhou Wang Guitao Cao Xidong Xi Jiangtao Wang MoE Engineering Research Center of SW/HW Co-design Technology and Application East China Normal University Shanghai China Software Engineering Institute East China Normal University Shanghai China

Automated driving object detection has always been a challenging task in computer vision due to environmental uncertainties. These uncertainties include significant differences in object sizes and encountering the class unseen. It may result in poor performance when traditional object detection models are directly applied to automated driving detection. Because they usually presume fixed categories of common traffic participants, such as pedestrians and cars. Worsely, the huge class imbalance between common and novel classes further exacerbates performance degradation. To address the issues stated, we propose OpenNet to moderate the class imbalance with the Balanced Loss, which is based on Cross Entropy Loss. Besides, we adopt an inductive layer based on gradient reshaping to fast learn new classes with limited samples during incremental learning. To against catastrophic forgetting, we employ normalized feature distillation. By the way, we improve multi-scale detection robustness and unknown class recognition through FPN and energy-based detection, respectively. The Experimental results upon the coDA dataset show that the proposed method can obtain better performance than that of the existing methods.

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Satellite Federated Fine-Tuning for Foundation Models in Space computing Power Networks

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arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collaborative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

OpenNet: Incremental Learning for Autonomous Driving Object Detection with Balanced Loss

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arXiv 2023年

作者： Wang, Zezhou Cao, Guitao Xi, Xidong Wang, Jiangtao MoE Engineering Research Center of SW HW Co-design Technology Application East China Normal University Shanghai China Software Engineering Institute East China Normal University Shanghai China

关键词： Object detection

Energy-Efficient Shop Scheduling Using Space-cooperation Multi-Objective Optimization

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IEEE Transactions on Sustainable Computing

IEEE Transactions on Sustainable computing 2024年第3期10卷 601-615页

作者： Ding, Jiepin Xia, Jun Yang, Yaning Zhou, Junlong Chen, Mingsong Li, Keqin East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai200062 China Ningxia Normal University School of Physics and Electronic Information Engineering Guyuan756500 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China State University of New York Department of Computer Science New PaltzNY12561 United States

Since Industry 5.0 emphasizes that manufacturing enterprises should raise awareness of social contribution to achieve sustainable development, more and more meta-heuristic algorithms are investigated to save energy in manufacturing systems. Although non-dominated sorting-based meta-heuristics have been recognized as promising multi-objective optimization methods for solving the energy-efficient flexible job shop scheduling problem (EFJSP), it is hard to guarantee the quality of the Pareto front (e.g., total energy consumption, makespan) due to the lack of population diversity. This is mainly because an improper individual comparison inevitably reduces population diversity, thus limiting exploration and exploitation abilities during population updates. To achieve efficient population evolution, this paper introduces a novel space-cooperation multi-objective optimization (SCMO) method that can effectively solve EFJSP to obtain scheduling schemes with better trade-offs. By cooperatively evaluating the similarity among individuals in both the decision space and objective space, we propose a space-cooperation population update method based on a three-vector representation that can accurately eliminate repetitive individuals to derive higher-quality Pareto solutions. To further improve search efficiency, we propose a difference-driven local search, which selectively changes the positions of operations with higher differences to search for neighbors effectively. Based on the Taguchi method, we conduct experiments to obtain a suitable parameter combination of SCMO. comprehensive experimental results show that, compared to state-of-the-art methods, our SCMO method achieves the highest HV and NR and the lowest IGD, with an average of 0.990, 0.952, and 0.001, respectively. Meanwhile, compared to traditional local search approaches, our difference-driven local search obtains twice the HV on instance Mk12 and reduces the solving time from 1521s to 475s. © 2016 IEEE.

关键词： Job shop scheduling

PET: Multi-agent Independent PPO-based Automatic ECN Tuning for High-Speed Data center Networks

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arXiv 2024年

作者： Cheng, Kai Wang, Ting Du, Xiao Du, Shuyi Cai, Haibin The Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education The Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai200050 China The Department of Houston International Institute Dalian Maritime University Liaoning Dalian116026 China

Explicit congestion Notification (ECN)-based congestion control schemes have been widely adopted in high-speed data center networks (DCNs), where the ECN marking threshold plays a determinant role in guaranteeing a packet lossless DCN. However, existing approaches either employ static settings with immutable thresholds that cannot be dynamically self-adjusted to adapt to network dynamics, or fail to take into account many-to-one traffic patterns and different requirements of different types of traffic, resulting in relatively poor performance. To address these problems, this paper proposes a novel learning-based automatic ECN tuning scheme, named PET, based on the multi-agent Independent Proximal Policy Optimization (IPPO) algorithm. PET dynamically adjusts ECN thresholds by fully considering pivotal congestion-contributing factors, including queue length, output data rate, output rate of ECN-marked packets, current ECN threshold, the extent of incast, and the ratio of mice and elephant flows. PET adopts the Decentralized Training and Decentralized Execution (DTDE) paradigm and combines offline and online training to accommodate network dynamics. PET is also fair and readily deployable with commodity hardware. comprehensive experimental results demonstrate that, compared with state-of-the-art static schemes and the learning-based automatic scheme, our PET achieves better performance in terms of flow completion time, convergence rate, queue length variance, and system robustness. © 2024, CC BY.

关键词： Mammals