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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： Zhong, Yidong Ao, Yang Li, Dingding Li, Jianguo Zeng, Deze South China Normal University School of Computer Science Guangdong Guangzhou510631 China China University of Geosciences School of Computer Science Hubei Wuhan430074 China

ISBN: (纸本)9798350329223

In traditional CPU scheduling systems, it is challenging to customize scheduling policies for datacenter workloads. Therefore, distributed cluster managers can only perform coarse-grained job scheduling rather than fine-grained CPU scheduling, although the latter is also critical to application performance in the clusters. Some scheduling systems have been proposed to facilitate the implementation and deployment of custom policies. However, these systems neither adequately capture the resource requirements of workloads nor cooperatively manage the CPU scheduling of distributed nodes, thus may failing to fully utilize CPU resources in the clusters. To tackle these issues, we present ADSwitch, a distributed CPU scheduling system that manages CPU resources adaptively in datacenter clusters. We train an efficient workload classification model based on a recurrent neural network (RNN), which enables ADSwitch to dynamically identify the demand of the running workload and switch to an appropriate scheduling policy through a reliable online update mechanism. We implement ADSwitch and evaluate it in a distributed cluster with synthetic workloads. The experimental results demonstrate that ADSwitch achieves up to 33% lower tail latency and 28% higher throughput than the existing scheduling systems without workload characteristic awareness. © 2023 IEEE.

关键词： Recurrent neural networks

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Telemetry Data Compression Algorithm Based on Balanced Cyclic neural network and Deep Learning

Telemetry Data Compression Algorithm Based on Balanced Cycli...

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2023 IEEE International Conference on Image processing and Computer Applications, ICIPCA 2023

作者： Huang, Min School of Computer Sichuan Technology and Business University Chengdu620000 China

ISBN: (纸本)9798350314670

With the rapid development of telemetry and telecontrol technology, the amount of information for acquiring telemetry data is increasing, which requires a large amount of storage space when storing, a large bandwidth when transmitting, and data processing as fast as possible. Therefore, data compression technology is needed to solve this problem. The redundancy of telemetry data is also very large. Statistics show that the redundancy of data transmitted in the standard telemetry system reaches more than 90%. In order to reduce the signal space and relieve the pressure of data processing, it is necessary and important to use data compression technology in telemetry system. Considering the space performance and time performance of the lossless compression algorithm, several commonly used lossless compression algorithms are compared for telemetry noise data. The compression space performance of each algorithm still needs to be improved. Considering that the measured engineering data is distributed in a typical balanced way, we studied the anomaly detection problem of time series data with inter class balance, and proposed an anomaly detection problem based on recurrent neural network (RNN). Finally, the overall test of the developed data acquisition and compression system is carried out. The test results show that the system has completed the real-time acquisition and compression of six channels of rapidly varying parameters, and the compression ratio is more than 1.8:1. All indicators meet the design requirements. © 2023 IEEE.

关键词： Redundancy

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Medical Remote Sensing and Internet of Things for Detection of Epileptic Seizure Using Deep neural networks 2

Medical Remote Sensing and Internet of Things for Detection ...

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2nd International Conference on distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Vinuja, G. Devi, N. Bharatha Saveetha School of Engineering Simats Department of Cse Thandalam India

ISBN: (纸本)9798350382952

Medical Remote sensing and the Internet of Things (IoT) have emerged as powerful tools in the field of disease detection and monitoring. Early detection of infectious diseases is crucial in order to prevent outbreaks and minimize their impact on public health. This paper aims to explore suitable methods and algorithms that can effectively utilize medical remote sensing data and IoT devices to detect and identify infectious diseases at an early stage. In this research, the novel real-time personal health monitoring system WISE (Wearable IoT-cloud-based health monitoring system) is introduced. In the present research, the body area network (BAN) is used to collect the patient's EEG signal. These signals are sent from the patient's location to the cloud via wifi. To turn the EEG input signal into an image, the discrete wavelet transform (DWT) is initially employed. This EEG input image first undergoes data de-noising pre-processing. Wavelet decomposition of EEG data accurately captures and pinpoints transient patterns in both temporal and frequency domains. Classifiers based on multilayer perceptron neural networks (MLPNN) were created and their classification accuracy for EEG signals was tested.. As compared to the existing algorithm the accuracy rate in predicting an abnormal EEG was 98.1 %. The classifier is employed to assess whether a region is typical or anomalous, and it also gauges its effectiveness. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Brain Tumour Segmentation and Volume Estimation using Efficient Convolution neural network for MRI Images 2

Brain Tumour Segmentation and Volume Estimation using Effici...

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2nd IEEE International Conference on distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Jaichandran, R. Shobana, R. Senthilkumar, S. Srinivasan, A. Arora, Renuka Kumar, Ashok Department of Computer Science and Engineering Paiyanoor India Jagannath University Department of Computer Science and Engineering Haryana India Banasthali Vidyapith Department of Computer Science and Engineering Rajasthan India

ISBN: (纸本)9798350347456

A brain tumor is a type of blood clot in the form of cerebral disease. A brain tumor is a way to view and Magnetic Resonance Imaging (MRI) image detail is required. It is difficult to distinguish between normal tissue and tumor tissue from the brain. To should be an accurate analysis of brain tumours. Brain tumour segmentation is one of the most important and difficult tasks in the medical imaging field. Manual classification with human assistance can lead to incorrect predictions and diagnoses. Moreover, it is a difficult task when there is a large amount of data to help. Brain tumours are highly heterogeneous in appearance, and similarities between tumor and normal tissue make it difficult to extract tumour regions from images. In this paper, we proposed a method for extracting brain tumours from 2D magnetic resonance brain images (MRI) by a Fuzzy C-Means clustering algorithm with a traditional classifier and a convolutional neural network. Initially the pre-processing was carried out to apply the filter for image normalization with wiener filter and Robust active shape model was applied to predict the voluminous of data presence and segmentation was carried out through fuzzy c-means clustering and selected features was trained into optimize fuzzy c-means convolution neural network (FCM-CNN). This proposed system produces high detection accuracy in precision rate as well compared to other system. © 2023 IEEE.

关键词： Brain

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A Novel Maize Futures Price Prediction Model based on EEMD-CNN-IGRU 22

A Novel Maize Futures Price Prediction Model based on EEMD-C...

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22nd IEEE International Symposium on Parallel and distributed processing with Applications, ISPA 2024

作者： Wang, Jingyang Han, Jingyu Liu, Wei Li, Ying Sun, Qiuhong Zhou, Xiaokang Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China Kansai University Faculty of Business Data Science Osaka565-0823 Japan

ISBN: (纸本)9798331509712

Maize, a significant global food crop, is essential in agriculture and the economy. The price of maize futures is affected by many factors, and its data is a nonlinear, unstable, and long-term correlation, so it is difficult to predict it accurately. Accordingly, this paper presents a combined model based on Ensemble Empirical Mode Decomposition (EEMD), Convolutional neural network (CNN), and Improved Gated Recurrent Unit (IGRU). EEMD decomposes the maize price data to produce multiple Intrinsic Mode Function (IMF) components and residual sequences. Subsequently, the noisy IMF components are removed, and the remaining IMF components are reconstructed into low, medium, and high frequencies. The CNN is tasked with the extraction of eigenvalues for these components. The IGRU improves in two key respects on the original Gated Recurrent Unit (GRU) model. First, it enhances the update and reset gates. Second, it incorporates the Self-Attention (SA) module. Thereby, the model's predictive capabilities are improved. This study uses the primary maize futures trading data from the Dalian Commodity Exchange as the experimental data. A comparative analysis of the EEMD-CNN-IGRU model with seven baseline models shows that it outperforms other models in all evaluation indexes. © 2024 IEEE.

关键词： Convolutional neural networks

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An Efficient Sampling-Based SpMM Kernel for Balancing Accuracy and Speed in GNN Inference 22

An Efficient Sampling-Based SpMM Kernel for Balancing Accura...

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22nd IEEE International Symposium on Parallel and distributed processing with Applications, ISPA 2024

作者： Song, Yingchen Wang, Yaobin Xiong, Chaoyu Wang, Tianhai Tang, Pingping Southwest University of Science and Technology School of Computer Science and Technology Mianyang621010 China

ISBN: (纸本)9798331509712

How to coordinate the design of sampling and Sparse-dense Matrix Multiplication (SpMM) is important in Graph neural network (GNN) acceleration. However, existing methods have an imbalance between accuracy and speed in performing GNN inference tasks due to irrational sampling strategies. To solve this problem, we propose an adaptive edge sampling strategy SpMM kernel. It considers the relationship between the number of non-zero elements in each matrix row and the shared memory width. The edge sampling scheme is adaptively selected according to the different situations of each row. Our method reduces the graph size by adaptive edge sampling to fit into the GPU's shared memory, which decreases the computational cost and increases the data locality ultimately achieving a balance between accuracy and speed in GNN inference. We conducted experiments on NVIDIA RTX 4060 Ti GPU using representative GNN model and datasets. Experimental results show that our designed kernel outperforms the cuSPARSE SpMM kernel and GE-SpMM by up to 20.2× and 17.3× respectively, with less than 1% accuracy loss. Compared to ES-SpMM, it reduces the accuracy loss by 4.7% on average and achieves an average 1.26× speedup. © 2024 IEEE.

关键词： Graph neural networks

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Deep-learning-based automatic evaluation of rice seed germination rate

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JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023年第4期103卷 1912-1924页

作者： Zhao, Jinfeng Ma, Yan Yong, Kaicheng Zhu, Min Wang, Yueqi Luo, Zhaowei Wei, Xin Huang, Xuehui Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 200234 Peoples R China Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai Peoples R China Shanghai Normal Univ Coll Life Sci Shanghai Key Lab Plant Mol Sci Shanghai Peoples R China

BACKGROUNDRice is an important food crop plant in the world and is also a model plant for genetics and breeding research. The germination rate is an important indicator that measures the performance of rice seeds. Currently, solutions involving image processing techniques have substantial challenges in the identification of seed germination. The detection of rice seed germination without human intervention involves challenges because the rice seeds are small and densely distributed. RESULTSIn this article, we develop a convolutional neural network (YOLO-r) that can detect the germination status of rice seeds and automatically evaluate the total number of germinations. Image partition, the Transformer encoder, a small target detection layer, and CDIoU loss are exploited in YOLO-r to improve the detection accuracy. A total of 21 429 seeds were collected, which have different phenotypic characteristics in length, shape, and color. The results show that the mean average precision of YOLO-r is 0.9539, which is higher than the compared models. Moreover, the average detection time per image of YOLO-r was 0.011 s, which meets the real-time requirements. The experimental results demonstrate that YOLO-r is robust to complex situations such as water stains, impurities, awns, adhesion, and so on. The results also show that the mean absolute error of the predicted germination rate mainly exists within 0.1. CONCLUSIONSNumerous experimental studies have demonstrated that YOLO-r can predict rice germination rate in a fast, easy, and accurate manner. (c) 2022 Society of Chemical Industry.

关键词： germination rate YOLO image partitioning neural network

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An image classification approach for hole detection in wireless sensor networks

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JOURNAL OF SUPERCOMPUTING 2022年第6期78卷 8774-8809页

作者： Cheong, Se-Hang Ng, Kim-Hou Si, Yain-Whar Univ Macau Fac Sci & Technol Macau Peoples R China

Hole detection is a crucial task for monitoring the status of wireless sensor networks (WSN) which often consist of low-capability sensors. Holes can form in WSNs due to the problems during placement of the sensors or power/hardware failure. In these situations, sensing or transmitting data could be affected and can interrupt the normal operation of the WSNs. It may also decrease the lifetime of the network and sensing coverage of the sensors. The problem of hole detection is especially challenging in WSNs since the exact location of the sensors is often unknown. In this paper, we propose a novel hole detection approach called FD-CNN which is based on Force-directed (FD) Algorithm and Convolutional neural network (CNN). In contrast to existing approaches, FD-CNN is a centralized approach and is able to detect holes from WSNs without relying on the information related to the location of the sensors. The proposed approach also alleviates the problem of high computational complexity in distributed approaches. The proposed approach accepts the network topology of a WSN as an input and generates the identity of the nodes surrounding each detected hole in the network as the final output. In the proposed approach, an FD algorithm is used to generate the layout of the wireless sensor networks followed by the identification of the holes in the layouts using a trained CNN model. In order to prepare labeled datasets for training the CNN model, an unsupervised pre-processing method is also proposed in this paper. After the holes are detected by the CNN model, two algorithms are proposed to identify the regions of the holes and corresponding nodes surrounding the regions. Extensive experiments are conducted to evaluate the proposed approach based on different datasets. Experimental results show that FD-CNN can achieve 80% sensitivity and 93% specificity in less than 2 minutes.

关键词： Wireless sensor networks Hole detection Force-directed algorithm Convolutional neural network

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Assessment of the status and trends of photovoltaic distributed generation in Brazil: An in-depth approach based on big data processing

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SOLAR ENERGY 2023年 249卷 694-711页

作者： Costa, Vinicius B. F. Scianni, Lucas Miranda, Rafael C. Bonatto, Benedito Univ Fed Itajuba Inst Elect Syst & Energy BR-37500903 Itajuba MG Brazil Univ Fed Itajuba Inst Prod Engn & Management BR-37500903 Itajuba MG Brazil

The deployment of photovoltaic (PV) distributed generation (DG) has been increasing substantially in Brazil. In this context, the present paper exploits a robust dataset provided by the Brazilian Electricity Regulatory Agency (ANEEL) to evaluate the status and trends concerning PV DG. As of Nov/2022, such a dataset consists of more than 1,4 million lines (one for each system) and thirty columns (various information concerning the systems). For an in-depth assessment, three items are addressed: (i) the application of machine learning algorithms to estimate the installed power of individual systems based on other information available in the dataset, (ii) the application of forecasting models to predict the installed power of PV DG over time in Brazil and its regions, and (iii) the application of the data envelopment analysis (DEA) method to rank Brazilian states in terms of efficiency in PV DG deployment. While items (i)-(iii) present distinct specific goals, they use the same dataset and provide essential insights concerning PV DG in Brazil. In item (i), elastic net (EN), decision tree (DT), random forest (RF), extra tree (ET), AdaBoost (AB), and gradient boosting (GB) are applied to select the most accurate algorithm. In item (ii), autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), Holt-Winters exponential smoothing (HWES), Bass diffusion model (BDM), and multilayer perceptron artificial neural network (MLP-ANN) are applied. In item (iii), output-oriented DEA CCR and BCC are applied. Concerning item (i), results demonstrate that estimating the installed power of individual systems is not very simple. Nonetheless, the machine learning algorithms imply a significant accuracy increase compared to taking the dataset's average as the estimation, especially the DT and RF algorithms with a coefficient of determination of 29%. Regarding item (ii), results demonstrate that the installed capacity in Brazil and its re

关键词： Artificial intelligence Big data Data envelopment analysis Forecasting Machine learning Photovoltaic distributed generation

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distributed LINK SPARSIFICATION FOR SCALABLE SCHEDULING USING GRAPH neural networkS 47

DISTRIBUTED LINK SPARSIFICATION FOR SCALABLE SCHEDULING USIN...

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47th IEEE International Conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Zhao, Zhongyuan Swami, Ananthram Segarra, Santiago Rice Univ Houston TX 77251 USA US Armys DEVCOM Army Res Lab Adelphi MD USA

ISBN: (纸本)9781665405409

distributed scheduling algorithms for throughput or utility maximization in dense wireless multi-hop networks can have overwhelmingly high overhead, causing increased congestion, energy consumption, radio footprint, and security vulnerability. For wireless networks with dense connectivity, we propose a distributed scheme for link sparsification with graph convolutional networks (GCNs), which can reduce the scheduling overhead while keeping most of the network capacity. In a nutshell, a trainable GCN module generates node embeddings as topology-aware and reusable parameters for a local decision mechanism, based on which a link can withdraw itself from the scheduling contention if it is not likely to win. In mediumsized wireless networks, our proposed sparse scheduler beats classical threshold-based sparsification policies by retaining almost 70% of the total capacity achieved by a distributed greedy max-weight scheduler with 0.4% of the point-to-point message complexity and 2.6% of the average number of interfering neighbors per link.

关键词： Independent set graph neural networks scheduling overhead massive access distributed scheduling

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