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Insurance Plan for Service Assurance in Cloud Computing Market with Incomplete Information

Journal of Communications and Information Networks 2022年第1期7卷 11-22页

作者： Yanru Zhang Chunxiao Jiang Nguyen H.Tran Shengrong Bu FeiRichard Yu Zhu Han College of Computer Science and Engineering the University of Electronic Science and Technology of ChinaChengdu 611731China Shenzhen Institute for Advanced Study UESTCLonghuaShenzhen 518110China Tsinghua Space Center Tsinghua UniversityBeijing 10084China School of Computer Science the University of SydneySydneyNew South WalesAustralia Department of Engineering Brock UniversityONCanada School of Information Technology Depart.of Systems and Computer EngineeringCarleton UniversityOttawa K1S 5B6ONCanada Department of Electrical and Computer Engineering the University of HoustonHouston 77004TXUSA

Nowadays,cloud computing has been identified as new opportunities for migrating to the expected agility,reuse,and adaptive capabilities that can support the ever changing IT trends and ***,the rapid evolution of those technologies also comes with open issues such as security,privacy,integrity,quality of services,and their possible detrimental *** this work,the concept of insurance is introduced to compensate the cloud computing customers when encountering those failures if service providers(SPs)have insurance ***,we consider the situation when the insurer is unable to see the system failure risk levels of the SPs,which is usually seen as an incomplete information market,in contrast with the optimal situation in a complete information ***,an insurance-based cloud computing architecture is proposed to build a monetary credit system in which the cloud computing SP pays a premium for a certain coverage to the ***,problem is formulated to solve the optimal insurance plan in complete and incomplete information markets,together with detail analysis of insurance policies in both cases are ***,simulation results show the properties of the two insurance plans and parameters that affect the design of the insurance plan.

关键词： service assurance cloud computing market incomplete information game theory insurance plan

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A New Approach for Long-Term Demand Forecasting

SSRN

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SSRN 2024年

作者： Paulos, José Pedro Azevedo, Filipe Fidalgo, José Nuno INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal The Department of Electrical Engineering ISEP IPP - School of Engineering Polytechnic Institute of Porto Portugal The Department of Electrical Engineering and Computers of the Faculty of Engineering of the University of Porto Portugal INESC TEC Porto Portugal

The methodology adopted in this article is based on the principle that load diagrams to be forecasted have two distinct components: shape and magnitude. The present work was focused on the shape component, which was essentially defined by historic data and calendar information. The implemented approach was applied to five European countries using four years for training and one year for testing. The goal was to forecast the hourly demand diagram for an entire future year. The forecasting models were based on three forecasting techniques: General Regression Neural Networks (GRNN), Multiple Linear Regression (MLR), and Artificial Neural Networks (ANN). The performance analysis showed a mean absolute percentage error roughly between 2% and 6%, depending on the country and year being forecasted. Investigating the effect of temperature on predictions was the aim of a second study, which allows us to define uncertainty margins around the forecasted demand curve. This outcome is an essential element for power system planning. © 2024, The Authors. All rights reserved.

关键词： Multiple linear regression

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Implementing SLAM for Robot Navigation with Lidar in ROS Platform

Implementing SLAM for Robot Navigation with Lidar in ROS Pla...

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Emerging Frontiers in electrical and Electronic Technologies (ICEFEET), International Conference on

作者： Gaurav Kumar Verma Surya Narayan Swar Ranjit Ray Gagan Deep Meena Dept. of Electrical Engineering National Institute of Technology Patna India Underwater Robotics & Autonomous Group CSIR-Central Mechanical Engineering Research Institute Durgapur India Department of Computer Science & Engineering Gandhi Institute of Engineering and Technology University Gunupur India

ISBN: (数字)9798331528614

ISBN: (纸本)9798331528621

This research covers the implementation of SLAM (Simultaneous Localization and Mapping) with Ouster OS0 LiDAR (Light Detection and Ranging) sensor in the Robot Operating System (ROS1) Noetic. Using the high-frequency capabilities of the ouster, this method works towards mapping and localization in structured and unstructured environments. The paper explains the Ouster sensor integration in the ROS1 Noetic environment, providing information on the sensor calibration, data acquisition, and pre-processing. The ROS ecosystem in Ubuntu 20.04 is set to process the data streams coming from the ouster LiDAR sensor which is further utilised to create map and localize the robot. The Ouster LiDAR sensor scans every object in its field of view (in 3D), the data is then used for the SLAM. SLAM is executed using the Direct Lidar Odometry (DLO) algorithm for mapping and simultaneously helping the robot with localization and enabling it to navigate through the environment smoothly and precisely.

关键词： Location awareness Point cloud compression Laser radar Simultaneous localization and mapping Three-dimensional displays Navigation Throughput Robot localization Real-time systems Streams

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Variational Mode Decomposition as Trusted Data Augmentation in ML-based Power System Stability Assessment ⁎

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IFAC-PapersOnLine 2024年第15期58卷 520-525页

作者： Tetiana Bogodorova Denis Osipov Luigi Vanfretti Electrical Computer and Systems Engineering Department Rensselaer Polytechnic Institute Troy NY 12180 USA System Planning & Analysis New York Power Authority Albany NY 12207 USA

Balanced data is required for deep neural networks (DNNs) when learning to perform power system stability assessment. However, power system measurement data contains relatively few events from where power system dynamics can be learnt. To mitigate this imbalance, we propose a novel data augmentation strategy preserving the dynamic characteristics to be learnt. The augmentation is performed using Variational Mode Decomposition. The detrended and the augmented data are tested for distributions similarity using Kernel Maximum Mean Discrepancy test. In addition, the effectiveness of the augmentation methodology is validated via training an Encoder DNN utilizing original data, testing using the augmented data, and evaluating the Encoder’s performance employing several metrics.

关键词： Convolutional neural networks data augmentation deep learning power system stability assessment variational mode decomposition

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Transferable Learning of GCN Sampling Graph Data Clusters from Different Power systems

Transferable Learning of GCN Sampling Graph Data Clusters fr...

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Annual Allerton Conference on Communication, Control, and Computing

作者： Tong Wu Anna Scaglione Daniel Arnold Tianyi Chen The Department of Electrical and Computer Engi-neering University of Central Florida Orlando FL USA The Department of Electrical and Computer Engineering Cornell Tech Cornell University New York City NY USA Lawrence Berkeley National Laboratory The Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798331541033

ISBN: (纸本)9798331541040

Contemporary neural network (NN) detectors for power systems face two primary challenges. First, each power system requires individual training of NN detectors to accommodate its unique configuration and base demands. Second, significant changes within the power system, such as the introduction of new substations or new generators, necessitate retraining. To overcome these issues, we introduce a novel architecture, the Nodal Graph Convolutional Neural Network (NGCN), which utilizes graph convolutions at each bus and its neighborhoods. This approach allows the training process to encompass multiple power systems and include all buses, thereby enhancing the transferability of the method across different power systems. The NGCN is particularly effective for detection tasks, such as cyber-attacks on smart inverters and false data injection attacks. Our tests demonstrate that the NGCN significantly improves performance over traditional NNs, boosting detection accuracy from approximately 85% to around 97% for the aforementioned task. Furthermore, the transferable NGCN, which is trained by samples from multiple power systems, performs considerably better in evaluations than the NGCN trained on a single power system.

关键词： Training Accuracy Substations Detectors computer architecture Inverters Power systems Cyberattack Faces Monitoring

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Temporally Robust Multi-Agent STL Motion Planning in Continuous Time

Temporally Robust Multi-Agent STL Motion Planning in Continu...

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American Control Conference (ACC)

作者： Joris Verhagen Lars Lindemann Jana Tumova Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Computer Science University of Southern California Los Angeles CA USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Signal Temporal Logic (STL) is a formal language over continuous-time signals (such as trajectories of a multiagent system) that allows for the specification of complex spatial and temporal system requirements (such as staying sufficiently close to each other within certain time intervals). To promote robustness in multi-agent motion planning with such complex requirements, we consider motion planning with the goal of maximizing the temporal robustness of their joint STL specification, i.e. maximizing the permissible time shifts of each agent's trajectory while still satisfying the STL specification. Previous methods presented temporally robust motion planning and control in a discrete-time optimization scheme. In contrast, we parameterize the trajectory by continuous Bézier curves, where the curvature and the time-traversal of the trajectory are parameterized individually. We show an algorithm generating continuous-time temporally robust trajectories and prove soundness of our approach. Moreover, we empirically show that our parametrization realizes this with a considerable speed-up compared to state-of-the-art methods based on constant interval time discretization.

关键词： Redundancy Formal languages Robustness Encoding Trajectory Planning Logic

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SRG Analysis of Lur’e systems and the Generalized Circle Criterion

arXiv

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arXiv 2024年

作者： Krebbekx, Julius P.J. Tóth, Roland Das, Amritam Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Lab HUN-REN Institute for Computer Science and Control Budapest Hungary

Scaled Relative Graphs (SRGs) provide a novel graphical frequency-domain method for the analysis of nonlinear systems. However, we show that the current SRG analysis suffers from some pitfalls that limit its applicability in analysing practical nonlinear systems. We overcome these pitfalls by modifying the SRG of a linear time invariant operator, combining the SRG with the Nyquist criterion, and apply our result to Lur’e systems. We thereby obtain a generalization of the celebrated circle criterion, which deals with broader class of nonlinearities, and provides (incremental) L2-gain performance bounds. We illustrate the power of the new approach on the analysis of the controlled Duffing oscillator. © 2024, CC BY.

关键词： Frequency domain analysis

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Automated Disassembly Sequence Prediction for Industry 4.0 Using Enhanced Genetic Algorithm

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computers, Materials & Continua 2021年第11期69卷 2531-2548页

作者： Anil Kumar Gulivindala M.V.A.Raju Bahubalendruni R.Chandrasekar Ejaz Ahmed Mustufa Haider Abidi Abdulrahman Al-Ahmari Department of Mechanical Engineering Industrial Robotics and Manufacturing Automation LaboratoryNational Institute of TechnologyKariakal609609India Department of Computer Science Engineering National Institute of TechnologyKariakal609609India Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadh11421Saudi Arabia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The evolution of Industry 4.0 made it essential to adopt the Internet of Things(IoT)and Cloud Computing(CC)technologies to perform activities in the new age of *** technologies enable collecting,storing,and retrieving essential information from the manufacturing *** collected at sites are shared with others where execution automatedly *** obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing ***,information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major *** current research validates the information optimally to offer a minimum set of activities to complete the disassembly *** optimal disassembly sequence plan(DSP)can possess valid information to organize the necessary actions in ***,finding an optimal DSP is complex because of its combinatorial *** genetic algorithm(GA)is a widely preferred artificial intelligence(AI)algorithm to obtain a near-optimal solution for the DSP *** converging nature at local optima is a limitation in the traditional *** study improvised the GA workability by integrating with the proposed priori crossover *** optimality function is defined to reduce disassembly effort by considering directional changes as *** enhanced GA method is tested on a real-time product to evaluate the *** obtained results reveal that diversity control depends on the operators employed in the disassembly *** proposed method’s solution can be stored in the cloud and shared through IoT devices for effective resource allocation and disassembly for maximum recovery of the *** effectiveness of the proposed enhanced GA method is determined by making a comparative assessment with traditional GA and other AI methods at different population sizes.

关键词： Automation internet of things disassembly sequence planning priori cross over operator enhanced GA disassembly predicates

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Constant-time Motion Planning with Anytime Refinement for Manipulation

arXiv

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arXiv 2023年

作者： Mishani, Itamar Feddock, Hayden Likhachev, Maxim The Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States Electrical and Computer Engineering Department Pittsburgh University United States

Robotic manipulators are essential for future autonomous systems, yet limited trust in their autonomy has confined them to rigid, task-specific systems. The intricate configuration space of manipulators, coupled with the challenges of obstacle avoidance and constraint satisfaction, often makes motion planning the bottleneck for achieving reliable and adaptable autonomy. Recently, a class of constant-time motion planners (CTMP) was introduced. These planners employ a preprocessing phase to compute data structures that enable online planning provably guarantee the ability to generate motion plans, potentially sub-optimal, within a user defined time bound. This framework has been demonstrated to be effective in a number of time-critical tasks. However, robotic systems often have more time allotted for planning than the online portion of CTMP requires, time that can be used to improve the solution. To this end, we propose an anytime refinement approach that works in combination with CTMP algorithms. Our proposed framework, as it operates as a constant time algorithm, rapidly generates an initial solution within a user-defined time threshold. Furthermore, functioning as an anytime algorithm, it iteratively refines the solution's quality within the allocated time budget. This enables our approach to strike a balance between guaranteed fast plan generation and the pursuit of optimization over time. We support our approach by elucidating its analytical properties, showing the convergence of the anytime component towards optimal solutions. Additionally, we provide empirical validation through simulation and real-world demonstrations on a 6 degree-of-freedom robot manipulator, applied to an assembly domain. Copyright © 2023, The Authors. All rights reserved.

关键词： Budget control

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StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks

StereoVoxelNet: Real-Time Obstacle Detection Based on Occupa...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Hongyu Li Zhengang Li Neşet Ünver Akmandor Huaizu Jiang Yanzhi Wang Taşkın Padır Institute for Experiential Robotics Northeastern University Khoury College of Computer Sciences Northeastern University Department of Electrical and Computer Engineering Northeastern University Boston MA USA

Obstacle detection is a safety-critical problem in robot navigation, where stereo matching is a popular vision-based approach. While deep neural networks have shown impressive results in computer vision, most of the previous obstacle detection works only leverage traditional stereo matching techniques to meet the computational constraints for real-time feedback. This paper proposes a computationally efficient method that employs a deep neural network to detect occupancy from stereo images directly. Instead of learning the point cloud correspondence from the stereo data, our approach extracts the compact obstacle distribution based on volumetric representations. In addition, we prune the computation of safety irrelevant spaces in a coarse-to-fine manner based on octrees generated by the decoder. As a result, we achieve real-time performance on the onboard computer (NVIDIA Jetson TX2). Our approach detects obstacles accurately in the range of 32 meters and achieves better IoU (Intersection over Union) and CD (Chamfer Distance) scores with only 2% of the computation cost of the state-of-the-art stereo model. Furthermore, we validate our method's robustness and real-world feasibility through autonomous navigation experiments with a real robot. Hence, our work contributes toward closing the gap between the stereo-based system in robot perception and state-of-the-art stereo models in computer vision. To counter the scarcity of high-quality real-world indoor stereo datasets, we collect a 1.36 hours stereo dataset with a mobile robot which is used to fine-tune our model. The dataset, the code, and further details including additional visualizations are available at https://***/stereovoxelnet/.

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