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共 37018 条记录，以下是1571-1580 订阅

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Passivity-Based Fractional-Order Sliding Mode Control for Fractional-Order Hyperchaotic systems with Unknown Parameters

Passivity-Based Fractional-Order Sliding Mode Control for Fr...

引用

2023 ieee international conference on Memristive computing and applications, ICMCA 2023

作者： Guo, Junxuan Wu, Chaojun Fang, Liyi Yang, Ningning School of Electronics and Information Xi'An Polytechnic University Xi'an710048 China

ISBN: (纸本)9798350316230

In this paper we investigate the problem of passivity-based sliding mode control for fractional-order hyperchaotic systems with unknown parameters. Utilizing the fractional calculus, the fractional-order sliding surface is chosen. The state trajectories of the closed-loop system reach the fractional-order sliding surface in finite time under the control of the designed controller. The control law ensures the asymptotical stability on the sliding surface. A parameter adjustment scheme for fractional-order integral sliding surface is proposed by using the linear matrix inequality approach. Finally, the simulation results demonstrate the effectiveness of the proposed method for the synchronization control of fractional-order hyperchaotic systems with unknown parameters. © 2023 ieee.

关键词： Sliding mode control

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Advancement in Plant Pathology using Image Segmentation and Deep Learning

Advancement in Plant Pathology using Image Segmentation and ...

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2023 ieee international conference on Quantum Technologies, Communications, computing, Hardware and embedded systems Security, iQ-CCHESS 2023

作者： Thakkar, Sejal Patel, Chirag Suthar, Ved Parul University Cse Vadodara India Charusat University Changa India McMaster University & Indus University Systems and Technology Hamilton Canada

ISBN: (纸本)9798350314946

The study recommends utilizing image processing techniques and a dual-layer CNN for analyzing datasets related to root rot. The research emphases on detecting and diagnosing root rot over Machine Learning and image processing. In Past Authors had worked on one plant disease situations using openly available schema. However, this research incorporates actual-images taken from many farms in Village Rancharda in Ahmedabad District. The proposed model trains and tests on more than 50,000 images of 10 different plants. These images are classified into 36 definite classes, distinguishing healthy and diseased types based on disease labels. To refine the image data and enhance the diseased portion, image pre-processing techniques such as simple segmentation using HSV threshold, and segmentation using breadth first search is proposed deep learning model were compared. The resolution of images was adjusted to ensure consistency across the dataset. For image processing, a SWMAD technique was employed, creating 6channel images that were stacked onto the original image. The proposed model exhibited superior performance during validation, achieving an accuracy of up to 0.9791%. Moreover, the model was converted into a TFLite model, enabling its use in client applications without requiring a server. In this case, the model was implemented in an Android application. © 2023 ieee.

关键词： Diagnosis

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PowerSched - managing power consumption in overprovisioned systems

PowerSched - managing power consumption in overprovisioned s...

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2024 international conference on Cluster computing

作者： Simmendinger, Christian Marquardt, Marcel Maeder, Jan Schneider, Ralf Hewlett Packard Enterprise Ratingen Germany High Performance Comp Ctr Stuttgart Stuttgart Germany

ISBN: (纸本)9798350383461;9798350383454

Over the last decade both capital costs for the acquisition of modern HPC systems as well as costs to power and cool these systems have increased significantly. In addition to direct operational costs, the investment costs for the infrastructure required to provide the necessary energy and cooling capacity have also risen substantially. In order to shift the ratio between infrastructure costs and system costs in favor of the latter, the High Performance computing Center Stuttgart (HLRS) has decided to size the system Hawk based on energy requirements of typical user workloads. To not overload the data centre's infrastructure the power consumption of the Hawk system has to be constrained. The Hawk system hence is operated as an over-provisioned system where nameplate thermal design power exceeds provisioned power. In this work we explain how the dynamic power management solution called PowerSched which is implemented by Hewlett Packard Enterprise (HPE) at HLRS, is able to balance compute jobs with moderate demands for CPU-power against CPU-power hungry jobs and thus keeps the overall power consumption of the system and its components within the required limits. We elaborate how the solution minimizes power consumption while maximizing the overall performance of all applications executed in the system at any given time. We further demonstrate the effectiveness of the chosen approach and the effects of PowerSched on power consumption at system and component level. Finally, the performance impact at application level is compared against executions with static power capping per CPU and against executions with unconstrained power.

关键词： Power Management Overprovisioned systems Energy Efficiency Dynamic Golden Section Search

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Low-Power Approximate Multiplier Architecture for Error Resilient applications 11

Low-Power Approximate Multiplier Architecture for Error Resi...

引用

11th international conference on Intelligent systems and embedded Design, ISED 2023

作者： Kumari, Apurva Vandana, Ch. Rao, Chowta Mallikharjuna Kumar, Uppugunduru Anil Ahmed, Syed Ershad B v Raju Institute of Technology Dept. of Electronics and Communication Engineering Hyderabad India Lakireddy Bali Reddy College of Engineering Dept. of Electronics and Communication Engineering Andhra Pradesh India Department of Electronics and Communication Engineering Telangana Hyderabad501203 India Bits Pilani Department of Electrical and Electronics Engineering India

ISBN: (纸本)9798350318159

Approximate computing offers a solution since it is a potential technique to improve area, speed, and reduce power in cases when accurate calculation is not expected. In this work, an unsigned approximate multiplier architecture has been presented for error resilient applications. This architecture is divided into three segments: the least significant component remains constant and maintains the same accuracy, the middle portion, which is a partial product, is simplified using or gates, and the error is compensated utilizing an error correction module. According to results of 8-bit multiplier, 30.9% and 35.33% were reduced of power and power-delay products, respectively, compared state-of-the-art designs without sacrificing accuracy. Finally, proposed design is validated using error resilient applications. © 2023 ieee.

关键词： Error correction

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Machine Learning to Predict Risk Management applications Performance 5

Machine Learning to Predict Risk Management Applications Per...

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5th international conference on Autonomic computing and Self-Organizing systems

作者： De Giorgi, Laura Ardagna, Danilo Politecn Milan Dept Elect Informat & Bioengn Milan Italy Tinvent Srl Venice Italy

ISBN: (纸本)9798350389777;9798350389760

Machine Learning is increasingly crucial for predicting application performance, offering a black-box approach that does not require a deep understanding of the application internal workings. This method enables accurate predictions without delving into complex system models. Our study utilized ML to forecast the execution time of an industrial application dealing with risk measures as part of the Solvency II regulations for insurance companies. By conducting a comparative analysis of multiple models, XGBoost was identified as the most effective, achieving a Mean Absolute Percentage Error of 18%. The results demonstrated robust accuracy for intermediate durations, though limitations were observed for shorter and significantly longer times due to data scarcity. Overall, this study highlights the significant potential of ML in improving prediction accuracy for complex industrial applications, offering valuable insights for resource allocation and performance management.

关键词： Performance Machine Learning XGBoost

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Strong Co-Location Pattern Mining Incorporating Multi-Path and Distance Decay Effects 7

Strong Co-Location Pattern Mining Incorporating Multi-Path a...

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7th international conference on Algorithms, computing and Artificial Intelligence, ACAI 2024

作者： He, Yonggui Yang, Peizhong Wang, Lizhen Chen, Hongmei Yunnan University School of Information Science and Engineering Kunming China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing School of Information Science and Engineering Kunming China

ISBN: (纸本)9798331529314

Spatial co-location pattern mining (SCPM) is a sub-field of data mining, which aims to discover the subset of spatial features whose instances are frequently located in proximate areas. SCPM has broad prospects in many applications, such as ecology, public health, smart cities, etc. In recent years, improving and applying SCPM technology with the constraints of road networks has emerged as a prominent research focus. However, existing studies solely focus on the shortest path between instances when assessing the proximity relationships, neglecting other proximity paths, and thus may overlook co-location patterns with strong associations. To address this issue, this paper introduces a novel metric called Strong Proximity Score, which integrates both multi-path proximity and distance decay effects to measure the strength of proximity relationships. Additionally, the Minimum First Search (MFS) algorithm is presented, which utilizes the strategy of minimum instance pair search to accelerate the calculation of Strong Proximity Score. Extensive experiments conducted on real datasets of points of interest demonstrate the superiority of the method based on Strong Proximity Score over traditional SCPM methods and confirm the efficiency of MFS algorithm. © 2024 ieee.

关键词： Smart city

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real-time Calculation of Distributed Photovoltaic Safe Consumption Capacity in Low-Voltage Distribution Network 8

Real-Time Calculation of Distributed Photovoltaic Safe Consu...

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8th international conference on Power Energy systems and applications, ICoPESA 2024

作者： Yang, Lei Wang, Xianjun Xia, Lei Luo, Huiyong State Grid Henan Electric Power Company Marketing Service Center Zhengzhou China

ISBN: (纸本)9798350351668

With the increasing penetration of distributed photovoltaic (PV) in low-voltage distribution network (LVDN), the operational risks of LVDN are also increasing. To address the issue of insufficient security and stability in LVDN caused by the inability to fully consume PV output, a calculation method of distributed PV consumption capacity in LVDN is proposed. First, a linear model is constructed to calculate voltage in LVDN, and the model parameters are estimated based on historical operating data. Then, typical scenarios are obtained by clustering analysis. Finally, a stochastic programming model is developed to calculate the maximum PV consumption capacity while satisfying the voltage safety condition. Simulation results show that the proposed method is able to accurately calculate the safe dissipation capacity with less than 1.14% error even when the LVDN parameters are incomplete. © 2024 ieee.

关键词： DC distribution systems

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Polynesia: Enabling High-Performance and Energy-Efficient Hybrid Transactional/Analytical Databases with Hardware/Software Co-Design 38

Polynesia: Enabling High-Performance and Energy-Efficient Hy...

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38th ieee international conference on Data Engineering (ICDE)

作者： Boroumand, Amirali Ghose, Saugata Oliveira, Geraldo F. Mutlu, Onur Google Mountain View CA 94043 USA Univ Illinois Urbana IL USA Swiss Fed Inst Technol Zurich Switzerland

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

A growth in data volume, combined with increasing demand for real-time analysis (using the most recent data), has resulted in the emergence of database systems that concurrently support transactions and data analytics. These hybrid transactional and analytical processing (HTAP) database systems can support real-time data analysis without the high costs of synchronizing across separate single-purpose databases. Unfortunately, for many applications that perform a high rate of data updates, state-of-the-art HTAP systems incur significant losses in transactional (up to 74.6%) and/or analytical (up to 49.8%) throughput compared to performing only transactional or only analytical queries in isolation, due to (1) data movement between the CPU and memory, (2) data update propagation from transactional to analytical workloads, and (3) the cost to maintain a consistent view of data across the system. We propose Polynesia, a hardware-software co-designed system for in-memory HTAP databases that avoids the large throughput losses of traditional HTAP systems. Polynesia (1) divides the HTAP system into transactional and analytical processing islands, (2) implements new custom hardware that unlocks software optimizations to reduce the costs of update propagation and consistency, and (3) exploits processing-in-memory for the analytical islands to alleviate data movement overheads. Our evaluation shows that Polynesia outperforms three state-of-the-art HTAP systems, with average transactional/analytical throughput improvements of 1.7x/3.7x, and reduces energy consumption by 48% over the prior lowest-energy HTAP system.

关键词： Costs Data analysis conferences Throughput Propagation losses real-time systems Database systems

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Constructing Future Vehicle Digital Twins with Insights from Cloud-Edge Collaboration 21

Constructing Future Vehicle Digital Twins with Insights from...

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21st ieee international Symposium on Parallel and Distributed Processing with applications, 13th ieee international conference on Big Data and Cloud computing, 16th ieee international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Shi, Xin Zhao, Liang Lin, Na Xu, Lexi Hawbani, Ammar Bi, Yuanguo Shenyang Aerospace University Shenyang China China Unicom Beijing China Northeastern University Shenyang China

ISBN: (纸本)9798350329223

Intelligent Transportation System (ITS) is embarking on a new development road, in which Digital Twins (DT) provides crucial support for improving the smartness standard of vehicles and realizing a high-level of traffic management in this process. However, the existing solutions face challenges such as limited computing resources, data scarcity and data distribution, which hinder the application prospects of DT for ITS. First, we propose the concept of future-oriented DT aim to address these issues. Specifically, lightweight local DTs are deployed at the different edge servers to provide services for vehicles, and a strong performance global DT is deployed in the cloud server to manage and optimize the local DTs. Then, we introduce a collaborative learning method between two models to construct the DT network architecture and realize the vision of the future-oriented. This method makes use of rich global knowledge and local data, so that the model can dexterously integrate personalization and generalization capabilities. Finally, We use an Incremental Learning algorithm to improve the ability of the model that adapt to new scenarios and changing input data. All experiments are based on real traffic data, and trajectory prediction task as application carrier. Experimental results demonstrate the effectiveness of the proposed method, and the stability and performance of DTs have achieved excellent performance in the construction process. © 2023 ieee.

关键词： Learning systems

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Clustering-based Cooperative Computation Offloading Game for dependent tasks in Industrial Internet of Things systems

Clustering-based Cooperative Computation Offloading Game for...

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ieee international conference on Communications (ieee ICC)

作者： Chouikhi, Samira Esseghir, Moez Merghem-Boulahia, Leila Univ Technol Troyes Comp Sci & Digital Soc LIST3N Lab Troyes France

ISBN: (纸本)9781538674628

With the expansion of connected devices used for industrial purposes, the Industrial Internet of Things (IIoT) has emerged as a specific branch of the Internet of Things (IoT) for Industry 4.0. Its applications in industrial domains include monitoring, smart manufacturing, and virtual and augmented reality. However, the huge amount of data generated by IIoT devices with limited computing resources makes it challenging to guarantee the different required quality of service (QoS) of the applications while minimizing the computation cost in terms of energy consumption. especially in terms of latency. In this paper, we opt for the offloading of dependent computation-intensive tasks to edge and cloud servers with more powerful computation capacities. Our proposed model aims to minimize the energy consumption of each IIoT device while respecting the maximal tolerant deadline of task completion. We propose to cluster the IIoT devices that have dependent tasks together to better handle this dependency. Moreover, we propose a distributed cooperative game that allows each device to decide whether it is beneficial for it and for its cluster, in terms of task completion and energy consumption, to offload its task or execute it locally. We prove that the Nash Equilibrium exists by proving that our game is a weighted potential game. Finally, we propose a practical distributed offloading algorithm to implement the cooperative game. The performance evaluation results show that the proposal optimizes energy consumption whilst increasing the number of tasks completed on time.

关键词： Computation offloading edge computing industrial Internet of Things game theory dependent computation tasks cooperative offloading game

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