Parallel Process. Lett.最新文献_第2页

FPGA-based Learning Acceleration for LSTM Neural Network 基于 FPGA 的 LSTM 神经网络学习加速

Parallel Process. Lett. Pub Date : 2022-10-27 DOI: 10.1142/s0129626423500019

G. Dec

引用次数: 1

Fractional Competitive Fruitfly Optimized Secure Routing Protocol under Mobile Sink Based WSN with Deep QNN Based Prediction 基于深度QNN预测的基于移动汇聚WSN的分数竞争果蝇优化安全路由协议

Parallel Process. Lett. Pub Date : 2022-09-13 DOI: 10.1142/s0129626422500049

A. Saoji, Srinivasa Rao Giduturi

{"title":"Fractional Competitive Fruitfly Optimized Secure Routing Protocol under Mobile Sink Based WSN with Deep QNN Based Prediction","authors":"A. Saoji, Srinivasa Rao Giduturi","doi":"10.1142/s0129626422500049","DOIUrl":"https://doi.org/10.1142/s0129626422500049","url":null,"abstract":"Wireless Sensor Networks (WSN) consists of numerous of low cost and less-energy sensor nodes that are responsible to gather and transmit the data packets from one node to destination point. WSN has a wide range of applications over agriculture, military, traffic monitoring, instrument surveillance, and security monitoring. In WSN, the nodes are located in a specific region to create a wireless network. The effective data communication among sensors is a challenging task because of different complex parameters. Typically, clustering is a well-preferred methodology to provide the effective communication by partitioning the nodes into different clusters. Every cluster possesses individual cluster head that transmits the data to other sensor nodes. Therefore, it is substantial to choose optimal cluster head and optimal route for effective transmission with less energy consumption and less delay. To increase the network efficiency and sink utilization, an energy aware routing algorithm called Fractional Competitive Fruit Fly Optimizer (FrCFFO) is designed, which is an integration of Fractional concept into the Competitive Fruit Fly Optimizer (CFFO). Here, the energy prediction is performed using Deep Quantum Neural Network (QNN). Effective CH selection and routing is done using the proposed FrCFFO and the fitness parameter is considered depending upon the factors like energy, distance, link lifetime, trust, and delay. Moreover, the developed FrCFFO has achieved effective performance with minimum delay of 0.098sec, maximum energy of 0.233J, and maximum PDR of 90.81%.","PeriodicalId":422436,"journal":{"name":"Parallel Process. Lett.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128378524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved Parallel Prefix Sums Algorithm 一种改进的并行前缀和算法

Parallel Process. Lett. Pub Date : 2022-09-13 DOI: 10.1142/s0129626422500086

H. M. Bahig, Khaled A. Fathy

引用次数: 0

Cyclic Vertex (Edge) Connectivity of Burnt Pancake Graphs 焦化煎饼图的循环顶点(边)连通性

Parallel Process. Lett. Pub Date : 2022-09-06 DOI: 10.1142/s0129626422500062

Xiaoqing Liu, Shuming Zhou, Hong Zhang

引用次数: 0

Fitness Sorted Red Deer-Cat Swarm Optimization-based Autonomous QoS-aware Multicast Communication System in MANET 基于适应度排序红鹿猫群优化的MANET自治qos感知多播通信系统

Parallel Process. Lett. Pub Date : 2022-09-01 DOI: 10.1142/s0129626422500074

Sanjaya Kumar Sarangi, M. Panda, P. K. Behera

{"title":"Fitness Sorted Red Deer-Cat Swarm Optimization-based Autonomous QoS-aware Multicast Communication System in MANET","authors":"Sanjaya Kumar Sarangi, M. Panda, P. K. Behera","doi":"10.1142/s0129626422500074","DOIUrl":"https://doi.org/10.1142/s0129626422500074","url":null,"abstract":"A mobile ad hoc network (MANET) is a set of autonomous mobile devices connected by wireless links in a distributed manner and without a fixed infrastructure. Mobile Ad-Hoc Networks (MANETs) is considered as an infrastructure-less and constant self-configured network of wireless devices. Here, multicast has emerged as an efficient tool for communication. Recently, distinct research works have been studied in wireless MANETs for designing multicast routing protocols. Multicasting is a procedure used for allowing the users to send the same messages simultaneously to a set of users. Though, it suffers from different problems regarding implementation in ad-hoc networks owing to its shorter battery lifetime, and lack of bandwidth dynamic nature of the mobile devices. Hence, the main aim of this paper is to propose a new QoS-aware multi-cast routing protocol in MANET using the hybrid meta-heuristic algorithm. The proposed routing protocol will focus on the development of a hybrid meta-heuristic-based “reliable neighbor nodes selection” scheme over the Multicast Ad-Hoc on Demand Distance Vector routing protocol (MAODV) protocol. The “reliable neighbor nodes selection” is accomplished by Fitness sorted Red Deer-Cat Swarm Optimization (FRD-CSO) algorithm focusing on the constraints like energy model, mobility model, and reliability pair factor. Once the optimal node selection is performed, the proposed QoS-aware protocol is validated by the route discovery mechanism and route reply process. The simulation has been performed to show the efficiency of the designed multicast routing over other multicast routing protocols that demonstrates the noteworthy enhancement in terms of diverse performance measures.","PeriodicalId":422436,"journal":{"name":"Parallel Process. Lett.","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132177995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Supply Chain Management of Marginal Cost-Intensive Green Products under Random Demand Model 随机需求模型下边际成本密集型绿色产品的供应链管理

Parallel Process. Lett. Pub Date : 2022-08-26 DOI: 10.1142/s0129626422500037

Yi Wang, Mengxue Du

引用次数: 0

Self-Adaptive Optimization Assisted Deep Learning Model for Partial Discharge Recognition 局部放电识别的自适应优化辅助深度学习模型

Parallel Process. Lett. Pub Date : 2022-03-01 DOI: 10.1142/s0129626421500249

Rajat Srivastava, V. Avasthi, R. K. Priya

{"title":"Self-Adaptive Optimization Assisted Deep Learning Model for Partial Discharge Recognition","authors":"Rajat Srivastava, V. Avasthi, R. K. Priya","doi":"10.1142/s0129626421500249","DOIUrl":"https://doi.org/10.1142/s0129626421500249","url":null,"abstract":"In the power system, research is being conducted in diagnosing and monitoring the condition of power equipment in a precise way. The Partial Discharges (PD) estimations under high voltage is recognized to be the most renowned and useful approach for accessing the electrical behaviour of the insulation material. The PD is good at localizing the dielectric failures even in the smaller regions before the occurrence of the dielectric breakdown. Therefore, the PD condition monitoring with accurate feature specification will be the appropriate model for enhancing the life span of the electrical apparatus. In this research work, a novel data-driven approach is introduced to detect the PD pulses in power cables using optimization based machine learning models. The proposed model will encompass two major phases: feature extraction and recognition. The first phase of the proposed method concentrates on extracting the wavelet scattering transform-based features. In the second phase, these features are fed as the input to optimized Deep Belief Network (DBN), whose count of the hidden neuron is optimized via a Self Adaptive Border Collie Optimization algorithm (SA-BCO). Finally, the performance evaluation is done in terms of diverse performance measures.","PeriodicalId":422436,"journal":{"name":"Parallel Process. Lett.","volume":"57 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126142157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accounting Start-up Time of Parallel Processes in Amdahl's Law 阿姆达尔定律中并行过程启动时间的计算

Parallel Process. Lett. Pub Date : 2021-12-13 DOI: 10.1142/s0129626421500262

E. Eremin

引用次数: 0

Effects of Continuous vs Discrete Frequency Scaling and Core Allocation on Energy Efficiency of Static Schedules for Moldable Tasks 连续与离散频率比例和核心分配对可塑任务静态调度能量效率的影响

Parallel Process. Lett. Pub Date : 2021-12-10 DOI: 10.1142/s0129626421500250

Sebastian Litzinger, J. Keller

{"title":"Effects of Continuous vs Discrete Frequency Scaling and Core Allocation on Energy Efficiency of Static Schedules for Moldable Tasks","authors":"Sebastian Litzinger, J. Keller","doi":"10.1142/s0129626421500250","DOIUrl":"https://doi.org/10.1142/s0129626421500250","url":null,"abstract":"Models for energy-efficient static scheduling of parallelizable tasks with deadlines on frequency-scalable parallel machines comprise moldable vs. malleable tasks and continuous vs. discrete frequency levels, plus preemptive vs. non-preemptive task execution with or without task migration. We investigate the tradeoff between scheduling time and energy efficiency when going from continuous to discrete core allocation and frequency levels on a multicore processor, and from preemptive to non-preemptive task execution. To this end, we present a tool to convert a schedule computed for malleable tasks on machines with continuous frequency scaling [Sanders and Speck, Euro-Par (2012)] into one for moldable tasks on a machine with discrete frequency levels. We compare the energy efficiency of the converted schedule to the energy consumed by a schedule produced by the integrated crown scheduler [Melot et al., ACM TACO (2015)] for moldable tasks and a machine with discrete frequency levels. Our experiments with synthetic and application-based task sets indicate that the converted Sanders Speck schedules, while computed faster, consume more energy on average than crown schedules. Surprisingly, it is not the step from malleable to moldable tasks that is responsible but the step from continuous to discrete frequency levels. One-time frequency scaling during a task’s execution can compensate for most of the energy overhead caused by frequency discretization.","PeriodicalId":422436,"journal":{"name":"Parallel Process. Lett.","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124360331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FPGA-based Neural Net for Failures Prediction in the Cold Forging Process 基于fpga的冷锻失效预测神经网络

Parallel Process. Lett. Pub Date : 2021-11-18 DOI: 10.1142/s0129626421500237

G. Dec

引用次数: 1