检索结果-内蒙古大学图书馆

IET Blockchain 2022年第1期2卷 1-12页

作者： Iliakis, Konstantinos Koliogeorgi, Konstantina Litke, Antonios Varvarigou, Theodora Soudris, Dimitrios Microprocessors and Digital Systems Laboratory Department of Electrical and Computer Engineering National Technical University of Athens Zografou Campous 9 Iroon Polytechniou str Athens Greece Distributed Knowledge and Media Systems group Department of Electrical and Computer Engineering National Technical University of Athens Zografou Campous 9 Iroon Polytechniou str Athens Greece

Blockchain is a distributed ledger based on peer-to-peer networks, originally used for crypto-currency systems. Blockchains are being used as an enabling technology for decentralised applications in the areas of Internet-of-Things, finance, supply-chain and others. Consistency, data privacy, performance, and energy efficiency are of paramount importance in such applications. The full nodes of public, permissionless blockchains undertake the task of verifying the transactions generated by the network. Full nodes perform operations such as confirming balances, transactions, and history, i.e. mostly database search queries. Consequently, their throughput is crucial for the performance of blockchain systems. In this work, the benefits of accelerating the blockchain search and insert queries by leveraging GPU platforms are studied. An extensive comparison between the most dominant utilized database is provided, i.e. LevelDB, and MegaKV, a high-performance GPU-accelerated database. Realistic operations that take place in blockchain systems are emulated and evaluated over representative scenarios, showing three orders of magnitude gains in throughput and energy efficiency without compromising the security aspect. The extensive comparison between LevelDB and MegaKV indicates that GPU acceleration is an effective solution for runtime and energy efficiency enhancement of blockchain systems, and the integration of the two technologies is a promising field of research. © 2022 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Darly: Deep Reinforcement Learning for QoS-aware scheduling under resource heterogeneity Optimizing serverless video analytics

Darly: Deep Reinforcement Learning for QoS-aware scheduling ...

引用

IEEE International Conference on Cloud Computing, CLOUD

作者： Dimitrios Giagkos Achilleas Tzenetopoulos Dimosthenis Masouros Dimitrios Soudris Sotirios Xydis Microprocessors Laboratory and Digital Systems Lab (MicroLab) School of Electrical and Computer Engineering National Technical University of Athens

Today, video analytics are becoming extremely popular due to the increasing need for extracting valuable information from videos available in public sharing services through camera-driven streams. Typically, video analytics are organized as a set of separate tasks, each of which has different resource requirements (e.g., computational- vs. memory-intensive tasks). The serverless computing paradigm forms a very promising approach for mapping such types of applications, as it enables fine-grained deployment and management in a per-function manner. However, modern serverless frameworks suffer from performance variability issues, due to i) the interference introduced due to co-location of third-party workloads with the serverless funcations and ii) the increasing hardware heterogeneity introduced in public clouds. To this end, this work introduces Darly, a QoS- and heterogeneity-aware Deep Reinforcement Learning-based Scheduler for serverless video analytics deployments. The proposed framework incorporates a DRL agent which exploits low-level performance counters to identify the levels of interference and the degree of heterogeneity in the underlying infrastructure and combines this information along with user-defined QoS requirements to dynamically optimize resource allocations by deciding the placement, migration, or horizontal scaling of serverless functions. Promising results are produced withing our experiments, which are accompanied with the intent to further build upon this groundwork.

关键词：

来源：评论

学校读者我要写书评

暂无评论

From Circuits to SoC Processors: Arithmetic Approximation Techniques & Embedded Computing Methodologies for DSP Acceleration

arXiv

引用

arXiv 2023年

作者： Leon, Vasileios K. National Technical University of Athens School of Electrical and Computer Engineering Division of Computer Science Microprocessors and Digital Systems Laboratory 9 Heroon Polytechniou Zografou Campus Athens15780 Greece

The recent end of Dennard’s Scaling and the declining Moore’s Law have signified a new era for computing systems. Power efficiency has now become a critical factor for both cloud and edge computing. Concurrently, the rapid growth of compute-intensive applications from the digital Signal Processing (DSP) and Artificial Intelligence (AI) domains challenges the resources of computing systems. As a result, the computing industry is forced to find alternative design approaches and computing platforms to sustain increased power efficiency, while providing sufficient performance. Among the examined solutions, Approximate Computing, Hardware Acceleration, and Heterogeneous Computing have gained great momentum. Approximate Computing is a novel design paradigm that exploits the inherent error resilience of DSP/AI applications to deliver gains in power, area, and/or performance by reducing the quality of the results. Hardware Acceleration refers to the execution of demanding computational tasks on specialized hardware, such as the Application-Specific Integrated Circuits (ASICs) and the Field-Programmable Gate Arrays (FPGAs), rather than general-purpose processors. Finally, Heterogeneous Computing refers to versatile processing architectures, such as the Vision Processing Units (VPUs), which integrate more than one type of processor and various memory technologies. In this Dissertation, we introduce design solutions and methodologies, built on top of the preceding computing paradigms, for the development of energy-efficient DSP and AI accelerators. In particular, we adopt the promising paradigm of Approximate Computing and apply new approximation techniques in the design of arithmetic circuits. Based on our methodology, these arithmetic approximation techniques are then combined with hardware design techniques to implement approximate ASIC- and FPGA-based DSP and AI accelerators. Moreover, we propose methodologies for the efficient mapping of DSP/AI kernels on distinctive embe

关键词： digital signal processing

来源：评论

学校读者我要写书评

暂无评论

A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

引用

IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

来源：评论

学校读者我要写书评

暂无评论

Implementing digital Twins for Enhanced Energy Management in Three Case Studies

Implementing Digital Twins for Enhanced Energy Management in...

引用

2024 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2024

作者： Testasecca, Tancredi Stamatopoulos, Stathis Natalini, Andrea Lazzaro, Marilena Capizzi, Chiara Maria Sarmas, Elissaios Arnone, Diego Università degli Studi di Palermo Dipartimento di Ingegneria Palermo Italy School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece Engineering Ingegneria Informatica S.p.A. Digital Twin and IOT Solutions Department Rome Italy Digital Hubs Engineering Ingegneria Informatica S.p.A. Palermo Italy

ISBN: (纸本)9798350385014

digitalization is recognized as a powerful enabler for decarbonizing the building sector. The International Energy Agency has highlighted its potential to improve safety, productivity, accessibility, and sustainability of energy systems. This work aims to present innovative applications of digital twins within the European Project DigiBUILD, focusing on optimizing energy systems for enhanced efficiency and reduced carbon footprint. The architecture of the developed digital twins w detailed, focusing on data collection, services description, and their implementation. Through three detailed case studies, the paper explores real applications of digital twins: the Heron pilot, whose aim is to manage a cluster of buildings and optimally charges electric vehicles to reduce carbon emissions;Veolia, where a district heating networks to optimally manage heat sources in the production plant to reduce gas consumption and emissions;and Emotion, focusing on a building equipped with photovoltaic panels and electric vehicles and implementing services for optimal vehicle charging to maximize self-consumption, in day-ahead scheduling but also in real time. A discussion finally highlights the challenges and limitations encountered in the deployment of digital twins, emphasizing the importance of user interaction in facilitating efficient energy management. This research contributes in underlining the significant potential of digital twins in advancing the digitalization of the energy sector, contributing to the European Union's goals for sustainability and reduced environmental impact. © 2024 IEEE.

关键词： Carbon footprint

来源：评论

学校读者我要写书评

暂无评论

Adrias: Interference-Aware Memory Orchestration for Disaggregated Cloud Infrastructures

Adrias: Interference-Aware Memory Orchestration for Disaggre...

引用

IEEE Symposium on High-Performance computer Architecture

作者： Dimosthenis Masouros Christian Pinto Michele Gazzetti Sotirios Xydis Dimitrios Soudris Microprocessors and Digital Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece IBM Research Europe Dublin Ireland Department of Informatics and Telematics Harokopio University of Athens Greece

Workload co-location has become the de-facto approach for hosting applications in Cloud environments, leading, however, to interference and fragmentation in shared resources of the system. To this end, hardware disaggregation is introduced as a novel paradigm, that allows fine-grained tailoring of cloud resources to the characteristics of the deployed applications. Towards the realization of hardware disaggregated clouds, novel orchestration frameworks must provide additional knobs to manage the increased scheduling *** present Adrias, a memory orchestration framework for disaggregated cloud systems. Adrias exploits information from low-level performance events and applies deep learning techniques to effectively predict the system state and performance of arriving workloads on memory disaggregated systems, thus, driving cognitive scheduling between local and remote memory allocation modes. We evaluate Adrias on a state-of-art disaggregated testbed and show that it achieves 0.99 and 0.942 R 2 score for system state and application’s performance prediction on average respectively. Moreover, Adrias manages to effectively utilize disaggregated memory, by offloading almost 1/3 of deployed applications with less than 15% performance overhead compared to a conventional local memory scheduling, while clearly outperforms naive scheduling approaches (random and round-robin), by providing up to ×2 better performance.

关键词： Deep learning Memory management Interference Hardware Resource management

来源：评论

学校读者我要写书评

暂无评论

The applied principles of EEG analysis methods in neuroscience and clinical neurology

引用

Military Medical Research 2024年第6期11卷 907-941页

作者： Hao Zhang Qing-Qi Zhou He Chen Xiao-Qing Hu Wei-Guang Li Yang Bai Jun-Xia Han Yao Wang Zhen-Hu Liang Dan Chen Feng-Yu Cong Jia-Qing Yan Xiao-Li Li School of Systems Science Beijing Normal UniversityBeijing 100875China College of Electrical and Control Engineering North China University of TechnologyBeijing 100041China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Department of Psychology the State Key Laboratory of Brain and Cognitive Sciencesthe University of Hong KongHong Kong SAR 999077China HKU-Shenzhen Institute of Research and Innovation Shenzhen 518057GuangdongChina Department of Health Technology and Informatics the Hong Kong Polytechnic UniversityHong Kong SAR 999077China Department of Rehabilitation Medicine the First Afliated Hospital of Nanchang UniversityNanchang 330006China Rehabilitation Medicine Clinical Research Center of Jiangxi Province Nanchang 330006China Beijing Key Laboratory of Learning and Cognition School of PsychologyCapital Normal UniversityBeijing 100048China School of Communication Science Beijing Language and Culture UniversityBeijing 100083China Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004HebeiChina School of Computer Science Wuhan UniversityWuhan 430072China School of Biomedical Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalian 116081LiaoningChina Guangdong Artifcial Intelligence and Digital Economy Laboratory(Guangzhou) Guangzhou 510335China.

Electroencephalography(EEG)is a non-invasive measurement method for brain *** to its safety,high resolution,and hypersensitivity to dynamic changes in brain neural signals,EEG has aroused much interest in scientific research and medical *** article reviews the types of EEG signals,multiple EEG signal analysis methods,and the application of relevant methods in the neuroscience feld and for diagnosing neurological ***,3 types of EEG signals,including time-invariant EEG,accurate event-related EEG,and random event-related EEG,are ***,5 main directions for the methods of EEG analysis,including power spectrum analysis,time-frequency analysis,connectivity analysis,source localization methods,and machine learning methods,are described in the main section,along with diferent sub-methods and effect evaluations for solving the same ***,the application scenarios of different EEG analysis methods are emphasized,and the advantages and disadvantages of similar methods are *** article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives,provide references for subsequent research,and summarize current issues and prospects for the future.

关键词： Electroencephalogram analysis methods Applied principles Neuroscience Diagnosis Neurological diseases

来源：评论

学校读者我要写书评

暂无评论

Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

引用

IEEE Transactions on Automatic Control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

Implementing digital Twins for Enhanced Energy Management in Three Case Studies

Implementing Digital Twins for Enhanced Energy Management in...

引用

Metrology for Living Environment (MetroLivEn), IEEE International Workshop on

作者： Tancredi Testasecca Stathis Stamatopoulos Andrea Natalini Marilena Lazzaro Chiara Maria Capizzi Elissaios Sarmas Diego Arnone Dipartimento di Ingegneria Università degli Studi di Palermo Palermo Italy Decision Support Systems Laboratory School of Electrical & Computer Engineering National Technical University of Athens Athens Greece Digital Twin and IOT Solutions Department Engineering Ingegneria Informatica S.p.A. Rome Italy Digital Hubs Engineering Ingegneria Informatica S.p.A. Palermo Italy

ISBN: (数字)9798350385014

ISBN: (纸本)9798350385021

digitalization is recognized as a powerful enabler for decarbonizing the building sector. The International Energy Agency has highlighted its potential to improve safety, productivity, accessibility, and sustainability of energy systems. This work aims to present innovative applications of digital twins within the European Project DigiBUILD, focusing on optimizing energy systems for enhanced efficiency and reduced carbon footprint. The architecture of the developed digital twins w detailed, focusing on data collection, services description, and their implementation. Through three detailed case studies, the paper explores real applications of digital twins: the Heron pilot, whose aim is to manage a cluster of buildings and optimally charges electric vehicles to reduce carbon emissions; Veolia, where a district heating networks to optimally manage heat sources in the production plant to reduce gas consumption and emissions; and Emotion, focusing on a building equipped with photovoltaic panels and electric vehicles and implementing services for optimal vehicle charging to maximize self-consumption, in day-ahead scheduling but also in real time. A discussion finally highlights the challenges and limitations encountered in the deployment of digital twins, emphasizing the importance of user interaction in facilitating efficient energy management. This research contributes in underlining the significant potential of digital twins in advancing the digitalization of the energy sector, contributing to the European Union's goals for sustainability and reduced environmental impact.

关键词： Productivity Buildings Focusing Europe Electric vehicles digital twins Smart grids

来源：评论

学校读者我要写书评

暂无评论

Secure State Estimation against Sparse Attacks on a Time-varying Set of Sensors

引用

IFAC-PapersOnLine 2023年第2期56卷 270-275页

作者： Zishuo Li Muhammad Umar B. Niazi Changxin Liu Yilin Mo Karl H. Johansson Department of Automation Tsinghua University Beijing 100084 China Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm SE-100 44 Sweden Laboratory for Information and Decision Systems Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA

This paper studies the problem of secure state estimation of a linear time-invariant (LTI) system with bounded noise in the presence of sparse attacks on an unknown, time-varying set of sensors. At each time, the attacker has the freedom to choose an arbitrary set of no more than p sensors and manipulate their measurements without restraint. To this end, we propose a secure state estimation scheme and guarantee a bounded estimation error irrespective of the attack signals subject to 2p-sparse observability and a mild, technical assumption that the system matrix has no degenerate eigenvalues. The proposed scheme comprises a design of decentralized observers for each sensor based on the local observable subspace decomposition. At each time step, the local estimates of sensors are fused by a median operator to obtain a secure estimation, which is then followed by a local detection-and-resetting process of the decentralized observers. The estimation error is shown to be upper-bounded by a constant which is determined only by the system parameters and noise magnitudes. Moreover, we design the detector threshold to ensure that the benign sensors never trigger the detector. The efficacy of the proposed algorithm is demonstrated by its application on a benchmark example of IEEE 14-bus system. We show that our proposed scheme can effectively tolerate sparse attacks on an unknown set of sensors, ensuring a bounded estimation error and effectively detecting and resetting the attacked sensors.

关键词： Estimation and filtering Design of fault tolerant/reliable systems Estimation and fault detection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：