Although there have been significant processor technology enhancements in terms of speed, data compression algorithms still do not accomplish the required task in a convenient time for voluminous data. the parallelism...
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080 M40 Medium carbon steel has potential applications in axles, shafts, bolts, studs, spindles, automotive components and many more. In high friction applications these steels possess better wear resistance, and afte...
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080 M40 Medium carbon steel has potential applications in axles, shafts, bolts, studs, spindles, automotive components and many more. In high friction applications these steels possess better wear resistance, and after weld overlay, they can exhibit superior properties. Additive manufacturing (AM) is an advanced method in which material in the form of powder or wire is used to form a desired shape. Laser Metal Deposition (LMD) is an AM method which is used in industrial application to recreate, repair and produce corrosion resistance weld overlay on a part. In comparison with conventional overlay techniques such as Plasma Transfer Arc (PTA), Metal Inert Gas (MIG) & Tungsten Inert Gas (TIG);Laser weld overlays are faster techniques which can make thin sections with good metallurgical bond and with high productivity and flexibility. Apart from that it has also advantage of low heat affected zone, minimum metal dilution, and can produce thin layers with good aesthetics. In this paper, 080 M40 steel samples were used as substrate and WC- NiCr powder was weld overlayed using LMD method. Such kind of weld overlays will be had potential application in the field of mining. the weld overlay was deposited in parallel layered fashion and effect of set of parameters like feed rate, dilution, size of the bead and processing speed were used to produce weld overlays of 1 mm thickness. metallurgical, micro structural and mechanical properties of these weld overlay materials have been investigated. these properties are very important in the processing of mining tools as well as other parts produced using the LMD process and also for other future applications. Further effect of laser processing parameters on the resulting properties of these steels weld overlays are discussed. (c) 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 5thinternationalconference on Advances in Steel, Power and Construction Technology
the throughput and energy efficiency of compute-centric architectures for memory intensive Deep Neural Networks (DNN) applications are limited by memory bound issues like high data-access energy, long latencies, and l...
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ISBN:
(数字)9781665490054
ISBN:
(纸本)9781665490054
the throughput and energy efficiency of compute-centric architectures for memory intensive Deep Neural Networks (DNN) applications are limited by memory bound issues like high data-access energy, long latencies, and limited bandwidth. processing-in-Memory (PIM) is a very promising approach to address these challenges and bridge the memory-computation gap. PIM places computational logic inside the memory to exploit minimum data movement and massive internal data parallelism. there are currently two PIM trends: 1) Use of emerging non-volatile memories to perform highly parallel analog computation of MAC operations and implicit storage of weights within the memory arrays, and 2) exploiting mature memory technologies that are enhanced by additional logic to enable efficient computation of MAC operations near the memory arrays. In this paper, we will compare both trends from an architectural perspective. Our study mainly emphasizes on FeFET memories (an emerging memory candidate) and DRAM memories (a mature memory candidate). We will highlight the major architectural constraints of these memory candidates that impact the PIM designs and their overall performance. Finally, we will assess feasible choice of candidate for different computations or DNN task types.
To address the mismatching issues in photovoltaic (PV) module and sub-module level, distributed power processing (DPP) displays advantages in power-improvement and efficiency. there are two main methods utilized in DP...
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the graph coloring problem, a fundamental NP-hard challenge, has numerous applications in scheduling, register allocation, and network optimization. Traditional sequential algorithms for graph coloring are computation...
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ISBN:
(数字)9798350365887
ISBN:
(纸本)9798350365894
the graph coloring problem, a fundamental NP-hard challenge, has numerous applications in scheduling, register allocation, and network optimization. Traditional sequential algorithms for graph coloring are computationally expensive, particularly for large-scale graphs. In this paper, we propose the parallel BitColor Algorithm (PBitCo), an extension of the BitColor framework, designed to exploit the parallelprocessing capabilities of modern CPU and GPU architectures. the PBitCo algorithm utilizes bitwise operations to reduce computation time and employs parallel execution on widely accessible platforms to further enhance performance. We implemented and tested the algorithm on various graph instances, comparing its performance against conventional graph coloring methods. Our results demonstrate that PBitCo achieves significant speedups, withthe GPU implementation delivering up to a 10x improvement over baseline methods.
Histogram equalization is a method of contrast adjustment in image processing using the image’s histogram. However, as modern imaging systems become more complex, these traditional algorithms for histogram equalizati...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Histogram equalization is a method of contrast adjustment in image processing using the image’s histogram. However, as modern imaging systems become more complex, these traditional algorithms for histogram equalization are no longer efficient. In response to this problem, researchers have studied several strategies for improving the performance of histogram equalization in digital images. An option is to use parallelprocessing and multi-threading approaches to distribute the computational burden, thereby speeding up the execution of histogram equalization. Another methodology includes using machine learning algorithms to adapt histogram equalization parameters according to the input image. Furthermore, using advanced hardware architectures like Field Programmable Gate Arrays (FPGA), Graphic processing Units (GPU), or Application Specific Integrated Circuits can significantly enhance the speed and efficiency of a Histogram Equalization. the performance optimization techniques have provided encouraging results, which significantly refine image processing time and visual perception. Modern imaging systems may benefit tremendously from their use in the new age.
Machine Learning (ML) rises as a highly useful tool to analyze the vast amount of data generated in every field of science nowadays. Simultaneously, data movement inside computer systems gains more focus due to its hi...
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ISBN:
(纸本)9781665414555
Machine Learning (ML) rises as a highly useful tool to analyze the vast amount of data generated in every field of science nowadays. Simultaneously, data movement inside computer systems gains more focus due to its high impact on time and energy consumption. In this contest. the Near-Data processing (NDP) architectures emerged as a prominent solution to increasing data by drastically reducing the required amount of data movement. For NDP, we see three main approaches, Application-Specific Integrated Circuits (ASICs), full Central processing units (CPUs) and Graphics processing Units (GPUs), or vector units integration. However, previous work considered only ASICs, CPUs and GPUs when executing ML algorithms inside the memory. In this paper, we present an approach to execute ML algorithms near-data, using a general-purpose vector architecture and applying near-data parallelism to kernels from KNN, MLP, and CNN algorithms. To facilitate this process, we also present an NDP intrinsics library to ease the evaluation and debugging tasks. Our results show speedups up to 10x for KNN, 11x for MLP, and 3x for convolution when processing near-data compared to a high-performance x86 baseline.
A widely used computationally intensive scientific kernel, the matrix multiplication algorithm is at the heart of many scientific routines. Resurging fields, such as artificial intelligence (AI), strongly benefit from...
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the current redundant sequence deduplication algorithms cannot remove structural repetitive DNA short reads such as mirror, reverse, paired, and complementary palindromes in high-throughput genomics sequencing data. M...
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At present, the application demand of building information model in railway engineering project is more and more urgent, and the BIM model with jumbled data seriously restricts the use of BIM technology in practical *...
At present, the application demand of building information model in railway engineering project is more and more urgent, and the BIM model with jumbled data seriously restricts the use of BIM technology in practical *** the processing and application of this big data model, this paper uses a parallel model processing algorithm based on *** the format conversion of the model and the design of GPU parallel algorithm, the method realizes the rapid and lightweight analysis and display of the large data model in the railway equipment room. the results show that this method can significantly reduce the volume of the large data model under the condition of realizing the universal format *** withthe mainstream surface reduction algorithm QEM, the GPU parallel algorithm has a multi-fold increase in the speed of analyzing 3D *** is conducive to the development of the field of big data processing and promotes the practical application of BIM technology in the railway industry.
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