In modern post-disaster rescue missions, the deployment of Multi-Robot Systems (MRSs) plays a key role in minimizing injuries and deaths among rescue personnel and civilians involved in the disaster. Achieving optimal...
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ISBN:
(纸本)9798350369458;9798350369441
In modern post-disaster rescue missions, the deployment of Multi-Robot Systems (MRSs) plays a key role in minimizing injuries and deaths among rescue personnel and civilians involved in the disaster. Achieving optimal MRS efficiency requires the implementation of a well-suited task allocation mechanism and a highly efficient pathfinding algorithm. However, due to inconsistent communication and low bandwidth, traditional frameworks in the mentioned domains may be impractical or may not work well. To address these problems, a novel Bi-Layer Joint Training Reinforcement (BJoT-RL) framework is proposed where, in the first layer, a Multi-Head Deep Q-Learning (MHDQN) is designed to perform task allocation;whereas, in the second layer, a Condition-Constrained Q-Learning (CCQ) is proposed to perform pathfinding. Noticeably, the output of each layer is used in the training of the other layer to realize a tight coupling, hence the innovative joint training. Thorough simulations show that the BJoT-RL framework performs better than state-of-the-art solutions in such applications.
Cloud have wide-ranging implications in several areas of computing, notably massive knowledge, cloud computing has simply up to the highest of any list of technology subjects. Furthermore, it is one of the most import...
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This paper investigates the symbiosis of Federated Learning (FL) and High-Performance computing (HPC) architectures, unraveling challenges introduced by the intricate interplay of heterogeneity and non-Independently a...
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ISBN:
(纸本)9798350363074;9798350363081
This paper investigates the symbiosis of Federated Learning (FL) and High-Performance computing (HPC) architectures, unraveling challenges introduced by the intricate interplay of heterogeneity and non-Independently and Identically distributed (non-IID) data. By leveraging the Flower framework, our research delves into the nuanced implications of FL in diverse HPC environments. We provide a comprehensive exploration of the heterogeneity within contemporary HPC architectures, spanning node organizations, memory hierarchies, and specialized accelerators, emphasizing adaptability to this complexity. Methodologically, we simulate a FL scenario within our research laboratory, leveraging Flower to orchestrate collaborative model training across heterogeneous nodes. The experiments involve variations in the Dirichlet beta parameter, offering insights into the effects of non-IID data. Results encompass communication efficiency, energy efficiency, and global model accuracy, providing a holistic understanding of the performances across diverse HPC infrastructures. This research contributes to the ongoing discourse on efficient and scalable algorithms, providing insights for collaborative learning in the era of diverse HPC architectures.
In order to cope with the intermittency and uncertainty of photovoltaic power generation and improve the accuracy of short-term load forecasting, a distributed photovoltaic grid connected short-term nonlinear load for...
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Exploration of a graph network means each node of the graph has to be visited by at least one robot. The problem of exploration has been studied in various networks like rings, trees, finite rectangular grids, etc. If...
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Multivariate time series (MTS) classification has been tackled using various methods, including Reservoir computing (RC), which generates efficient vectorized representations like reservoir state (RS). RS shines when ...
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ISBN:
(纸本)9798350383782;9798350383799
Multivariate time series (MTS) classification has been tackled using various methods, including Reservoir computing (RC), which generates efficient vectorized representations like reservoir state (RS). RS shines when handling extensive classes or training sets but demands longer processing and substantial memory. Addressing this, in this study we present the parallel Reservoir Echo State Network (PR-ESN), an optimized parallel training and evaluation algorithm rooted in the ESN principle. It leverages both CPU-shared memory and paralleldistributed memory architecture to efficiently capture reservoir state's optimal model space representation, addressing computational challenges in MTS analysis. Distinguishing itself from previous works, PR-ESN combines distributedparallel processing at the network level and shared memory multiprocessing at the node level. This results in reduced memory requirements and faster processing, making it a significant contribution to the field. Key features include PR-ESN's distributed training and evaluation, shared memory parallelization, and MSR concatenation for comprehensive analysis of distributed model space representations. Testing on real-world MTS and benchmark ECG data proves PR-ESN-based classifiers achieve superior accuracy and faster processing times with optimal memory usage. Testing on real-world MTS and benchmark ECG data proves PR-ESN-based classifiers achieve superior accuracy and faster processing times with optimal memory usage.
Brain tumours are among the most life-threatening diseases, and automatic segmentation of brain tumours from medical images is crucial for clinicians to identify and quantify tumour regions with high precision. While ...
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ISBN:
(纸本)9798350352900;9798350352894
Brain tumours are among the most life-threatening diseases, and automatic segmentation of brain tumours from medical images is crucial for clinicians to identify and quantify tumour regions with high precision. While traditional segmentation models have laid the groundwork, diffusion models have since been developed to better manage complex medical data. However, diffusion models often face challenges related to insufficient parallelcomputing power and inefficient GPU utilization. To address these issues, we propose the DF-SegDiff model, which includes diffusion segmentation, parallel data processing, a distributed training model, a dynamic balancing parameter and model fusion. This approach significantly reduces training time while achieving an average Dice score of 0.87, with several samples reaching Dice values close to 0.94. By combining BRATS2020 with the Medical Segmentation Decathlon dataset, we also integrated a comprehensive dataset containing 800 training samples and 53 test samples. Evaluation of the model using Dice, IoU, and other relevant metrics demonstrates that our method outperforms current state-of-the-art techniques.
An innovative approach to password cracking by leveraging a distributedcomputing model is developed. The sys- tem comprises a client-server architecture where clients receive segmented password ranges for parallelize...
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An innovative approach to password cracking by leveraging a distributedcomputing model is developed. The sys- tem comprises a client-server architecture where clients receive segmented password ranges for parallelized hashing attempts. The Java-based implementation employs MD5 hashing and divides the password into smaller units for distribution among multiple clients. Each client independently processes its assigned password range, attempting to match the hash against a pre- determined target. The server orchestrates the distribution of password segments and collects results from clients, facilitating the cracking process. Security measures include secure communication protocols, and ethical considerations center around legality, user consent, and emphasizing the educational value of responsible hacking practices. The work explores the tech- nical challenges of distributed password cracking, addressing efficiency, scalability, and security implications, while fostering a deeper understanding of cybersecurity and distributed systems.
distributed large memory offers the use of large virtual memory by using remote memory distributed over nodes in a cluster. The message passing interface (MPI) plays important role in DLM. MPI-based DLM manages the la...
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With the launch of the National Energy Administration's rooftop photovoltaic pilot construction in all counties and cities, power companies in various provinces and cities of the State grid are facing severe safet...
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ISBN:
(数字)9783031518263
ISBN:
(纸本)9783031518256;9783031518263
With the launch of the National Energy Administration's rooftop photovoltaic pilot construction in all counties and cities, power companies in various provinces and cities of the State grid are facing severe safety management challenges. For example, distributed power sources such as photovoltaic and wind power are affected by natural weather, and their output is intermittent and difficult to control. Large-scale integration into the power system will have an impact on the power system, making it unable to operate normally, and even leading to power system collapse and damage to power ***, it is necessary to form a low-cost solution that meets the basic requirements of stable and safe operation of the power grid. In this paper, we propose a regulation scheme of low-voltage distributed generation, which uses the edge computing capability deployed in the distribution transformer fusion terminal, and the communication capability of power line carrier and the intelligent circuit breaker integrated with function fusion to realize the observation and control of distributed energy and electrical equipment. The proposed scheme has been piloted and verified in two typical power distribution areas in Shenyang, and the pilot application has achieved the expected goals.
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