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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

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Evaluating Classifier performance in Mapping Paddy Rice Fields Using Multi-Temporal SAR Data on Google Earth Engine

Evaluating Classifier Performance in Mapping Paddy Rice Fiel...

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2024 IEEE Region 10 conference, TENCON 2024

作者： Santiago, Robert Martin Quilang, Eduardo Jimmy Sybingco, Edwin De La Salle University Dept. of Electronics & Computer Eng'g Manila Philippines Philippine Rice Information System Philippine Rice Research Institute Science City of Muñoz Philippines

ISBN: (纸本)9798350350821

Mapping paddy rice fields is crucial for monitoring rice production, which is vital for food security and economic stability. Prior studies have utilized remote sensing data along with various machine learning algorithms to accomplish this task. However, the performance of these classifiers under different conditions remains an area worth investigating. In this study, rice-growing areas are delineated from other crops and land cover types using multi-temporal synthetic aperture radar (SAR) imageries recorded by Sentinel-1 and classification algorithms - Naïve Bayes, CART, and Random Forest - on the Google Earth Engine (GEE) platform. Results show that the random forest classifier outperforms the others with an overall accuracy of 85.90% and a kappa coefficient of 0.7180. This superior performance can be attributed to its capability to handle complex, high-dimensional data, which aligns well with the properties of the multi-temporal SAR data used to observe the growth cycle of paddy rice. The differing performances of the three classifiers underscored the importance of selecting an appropriate algorithm, especially when leveraging the distinctive radar signatures of paddy rice over time, to enhance the accuracy of mapping paddy rice fields. © 2024 IEEE.

关键词： Deforestation

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PCB Defect Detection Method Based on G-YOLOv8

PCB Defect Detection Method Based on G-YOLOv8

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2024 International conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Gao, Chao Bai, Mengting Lian, Xiaoqin Hu, Xiangyu Han, Shuangshuang Chen, Shichao Beijing Technology and Business University School of Computer and Artificial Intelligence Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Chinese Academy of Sciences Beijing Huairou Academy of Parallel Sensing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China

ISBN: (纸本)9798350376739

In the electronics industry, PCB is crucial for ensuring the product quality. However, these defects of PCB generally have the characteristics of complex backgrounds, small sizes and similar features, resulting in the issues of high computational complexity or low detection precision of PCB defect detection algorithms based on deep learning. To resolve the aforementioned concerns, this paper proposed a lightweight PCB defect detection model named G-YOLOv8 which was based on the YOLOv8. The G-YOLOv8 can effectively reduce the computational complexity by integrating the Ghost Conv into the Backbone and Neck networks. Furthermore, the G-C2f module was introduced into Neck network to strengthen the capability of G-YOLOv8 to perceive the feature information at different scales and semantic levels, thus improving the detection performance of the model for the PCB defect. Meanwhile, the performance of G-YOLOv8 model was evaluated on the public data set. The experimental results show that the parameter number of G-YOLOv8 is 2.6 million, which is decreased by 89.7% and 13.3% compared with SSD and YOLOv8, respectively. The computational load of G-YOLOv8 is 7.5 GFLOPs, which is decreased by 93.5% and 7.4% compared with SSD and YOLOv8, respectively. The mAP@0.5 of G-YOLOv8 is 94.4%, which is increased by 4.8% and 1.3% compared with SSD and YOLOv8, respectively. Hence, in the task of PCB defect detection, the G-YOLOv8 model can reduce the computational complexity while preserving a high detection precision, which can meet the requirements of PCB defect detection in industrial scenarios. © 2024 IEEE.

关键词： Electronics industry

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An Energy-Efficient 3D Point Cloud Neural Network Accelerator With Efficient Filter Pruning, MLP Fusion, and Dual-Stream Sampling 42

An Energy-Efficient 3D Point Cloud Neural Network Accelerato...

引用

42nd IEEE/ACM International conference on computer-Aided Design (ICCAD)

作者： Zhou, Changchun Fu, Yuzhe Liu, Min Qiu, Siyuan Li, Ge He, Yifan Jiao, Hailong Peking Univ Shenzhen Grad Sch Sch Elect & Comp Engn Shenzhen Peoples R China Reconova Technol Co Ltd Xiamen Peoples R China

ISBN: (纸本)9798350322255

Three-dimensional (3D) point cloud has been employed in a wide range of applications recently. As a powerful weapon for point cloud analysis, point-based point cloud neural networks (PNNs) have demonstrated superior performance with less computation complexity and parameters, compared to sparse 3D convolution-based networks and graph-based convolutional neural networks. However, point-based PNNs still suffer from high computational redundancy, large off-chip memory access, and low parallelism in hardware implementation, thereby hindering the applications on edge devices. In this paper, to address these challenges, an energy-efficient 3D point cloud neural network accelerator is proposed for on-chip edge computing. An efficient filter pruning scheme is used to skip the redundant convolution of pruned filters and zero-value feature channels. A block-wise multi-layer perceptron (MLP) fusion method is proposed to increase the on-chip reuse of features, thereby reducing off-chip memory access. A dual-stream blocking technique is proposed for higher parallelism while maintaining inference accuracy. Implemented in an industrial 28-nm CMOS technology, the proposed accelerator achieves an effective energy efficiency of 12.65 TOPS/W and 0.13 mJ/frame energy consumption for PointNeXt-S at 100 MHz, 0.9 V supply voltage, and 8-bit data width. Compared to the state-of-the-art point cloud neural network accelerators, the proposed accelerator enhances the energy efficiency by up to 66.6x and reduces the energy consumption per frame by up to 70.2x.

关键词： Three-dimensional point cloud off-chip memory access data reuse high parallelism speedup energy efficiency

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FROZENHOT Cache: Rethinking Cache Management for Modern Hardware 23

FROZENHOT Cache: Rethinking Cache Management for Modern Hard...

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18th European conference on computer Systems (EuroSys)

作者： Qiu, Ziyue Yang, Juncheng Zhang, Juncheng Li, Cheng Ma, Xiaosong Chen, Qi Yang, Mao Xu, Yinlong Univ Sci & Technol China Hefei Peoples R China Microsoft Res Hefei Peoples R China Anhui Prov Key Lab High Performance Comp Hefei Peoples R China Carnegie Mellon Univ Pittsburgh PA 15213 USA HBKU Qatar Comp Res Inst Doha Qatar

ISBN: (纸本)9781450394871

Caching is crucial for accelerating data access, employed as a ubiquitous design in modern systems at many parts of computer systems. With increasing core count, and shrinking latency gap between cache and modern storage devices, hit-path scalability becomes increasingly critical. However, existing production in-memory caches often use list-based management with promotion on each cache hit, which requires extensive locking and poses a significant overhead for scaling beyond a fewcores. Moreover, existing techniques for improving scalability either (1) only focus on the indexing structure and do not improve cache management scalability, or (2) sacrifice efficiency or miss-path scalability. Inspired by highly skewed data popularity and short-term hotspot stability in cache workloads, we propose Frozen-Hot, a generic approach to improve the scalability of list-based caches. FROZENHOT partitions the cache space into two parts: a frozen cache and a dynamic cache. The frozen cache serves requests for hot objects with minimal latency by eliminating promotion and locking, while the latter leverages the existing cache design to achieve workload adaptivity. We built FROZENHOT as a library that can be easily integrated into existing systems. We demonstrate its performance by enabling FROZENHOT in two production systems: HHVM and RocksDB using under 100 lines of code. Evaluated using production traces from MSR and Twitter, FROZENHOT improves the throughput of three baseline cache algorithms by up to 551%. Compared to stock RocksDB, FROZENHOT-enhanced RocksDB shows a higher throughput on all YCSB workloads with up to 90% increase, as well as reduced tail latency.

关键词： Caching Storage Concurrency Algorithm performance Scalability

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Foes or Friends: Embracing Ground Effect for Edge Detection on Lightweight Drones 24

Foes or Friends: Embracing Ground Effect for Edge Detection ...

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30th International conference on Mobile Computing and Networking, ACM MobiCom 2024

作者： Zhao, Chenyu Ruan, Ciyu Xu, Jingao Wang, Haoyang Wang, Shengbo Li, Jiaqi Zha, Jirong Yang, Zheng Liu, Yunhao Zhang, Xiao-Ping Chen, Xinlei Shenzhen International Graduate School Tsinghua University China Computer Science Department Carnegie Mellon University United States Pengcheng Laboratory China RISC-V International Open Source Laboratory China School of Software Tsinghua University China

ISBN: (纸本)9798400704895

Drone-based rapid and accurate environmental edge detection is highly advantageous for tasks such as disaster relief and autonomous navigation. Current methods, using radar or cameras, raise deployment costs and burden lightweight drones with high computational demands. In this paper, we propose AirTouch, a system that transforms the ground effect from a stability "foe"in traditional flight control views, into a "friend"for accurate and efficient edge detection. Our key insight is that analyzing drone sensor readings and flight commands allows us to detect ground effect changes. Such changes typically indicate the drone flying over an edge, making this information valuable for edge detection. We approach this insight through theoretical analysis, algorithm design, and implementation, fully leveraging the ground effect as a new sensing modality without compromising drone flight stability, thereby achieving accurate and efficient scene edge detection. Extensive evaluations demonstrate that our system achieves a high detection accuracy with mean detection distance errors of 0.051m, outperforming the baseline performance by 86%. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Drones

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program Simulation of Virtual Instrument Integrated Measurement and Control System Based on Machine Learning Algorithm

Program Simulation of Virtual Instrument Integrated Measurem...

引用

2023 International conference on Networking, Informatics and Computing, ICNETIC 2023

作者： Shi, Lixia Changchun University of Science and Technology Jilin Changchun130022 China

ISBN: (纸本)9798350313314

program simulation of virtual instrument integrated measurement and control system based on machine learning algorithm with China's entry into WTO, China's economy will enter a new period of development. The rapid economic development is bound to accelerate the pace of technological transformation of enterprises, and the demand for advanced instruments and equipment is bound to be stronger. Virtual instrument is a new measurement and control instrument technology with the development of computer software technology. It overcomes the disadvantages of traditional instruments, and uses the powerful computing power of computer and flexible software technology to realize completely different instrument systems that meet the needs on the basis of limited hardware. Compared with traditional instruments, virtual instruments used in this paper can be found to have good operability, powerful functions, openness, high cost performance, ease of use, flexibility and efficiency. The advantages embodied in virtual instruments can replace traditional measurement and control instruments to a certain extent. The results show that the training samples are divided by multiple random down sampling, and the balanced subset is mapped to the test set for defect prediction to obtain the prediction subset. The final prediction model is constructed by integrating the prediction subsets, and the virtual instrument software defect prediction model based on machine learning is completed. The design of software defect prediction model has significantly improved the defect prediction performance compared with the traditional defect prediction model on different unbalanced data sets with different marker ratios, and the average data set is generally improved by 30.28%. The established defect prediction model can focus more on learning defect categories, and can detect the defects in the data set. In the class overlapping and unbalanced data set, it can effectively improve the ability of defect pred

关键词： Forecasting

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Feature Engineering for Injection Attack Detection: An Exploration from SQLI to XSS 7

Feature Engineering for Injection Attack Detection: An Explo...

引用

7th Cyber Security in Networking conference, CSNet 2023

作者： Loginov, Alexander Zincir-Heywood, Nur Heywood, Malcolm Dalhousie University Computer Science Halifax Canada

ISBN: (纸本)9798350342871

This paper explores the effects of data preprocessing and feature engineering on injection attacks using a classification and anomaly detection approach. To this end, the proposed data preprocessing and feature engineering method is compared against state-of-the-art SQL injection detection on two different datasets using KNN, DT and SVM as classifiers, whereas OCSVM as an anomaly detector. Evaluation results of the classifiers and the anomaly detector not only show the effectiveness and high performance of the proposed data preprocessing and feature engineering method on SQL injection attacks but also demonstrate the generalizability on other injection and non-injection attacks. © 2023 IEEE.

关键词： Classification (of information)

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SCORE REGULARIZED POLICY OPTIMIZATION THROUGH DIFFUSION BEHAVIOR 12

SCORE REGULARIZED POLICY OPTIMIZATION THROUGH DIFFUSION BEHA...

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12th International conference on Learning Representations, ICLR 2024

作者： Chen, Huayu Lu, Cheng Wang, Zhengyi Su, Hang Zhu, Jun Department of Computer Science & Technology Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University China Guangdong Guangzhou China

Recent developments in offline reinforcement learning have uncovered the immense potential of diffusion modeling, which excels at representing heterogeneous behavior policies. However, sampling from diffusion policies is considerably slow because it necessitates tens to hundreds of iterative inference steps for one action. To address this issue, we propose to extract an efficient deterministic inference policy from critic models and pretrained diffusion behavior models, leveraging the latter to directly regularize the policy gradient with the behavior distribution's score function during optimization. Our method enjoys powerful generative capabilities of diffusion modeling while completely circumventing the computationally intensive and time-consuming diffusion sampling scheme, both during training and evaluation. Extensive results on D4RL tasks show that our method boosts action sampling speed by more than 25 times compared with various leading diffusion-based methods in locomotion tasks, while still maintaining state-of-the-art performance. Code: https://***/thu-ml/SRPO. © 2024 12th International conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Diffusion

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SpecGaussian with Latent Features: A high-quality Modeling of the View-dependent Appearance for 3D Gaussian Splatting 24

SpecGaussian with Latent Features: A High-quality Modeling o...

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32nd ACM International conference on Multimedia, MM 2024

作者： Wang, Zhiru Xie, Shiyun Pan, Chengwei Wang, Guoping Sino-French Engineer School Beihang University Beijing China School of Artificial Intellegence Beihang University Beijing China School of Artificial Intellegence Beihang Uinveristy & Zhongguancun Laboratory Beijing China School of Computer Science Peking University & ShuangHu Laboratory Beijing China

ISBN: (纸本)9798400706868

Recently, the 3D Gaussian Splatting (3D-GS) method has achieved great success in novel view synthesis, providing real-time rendering while ensuring high-quality rendering results. However, this method faces challenges in modeling specular reflections and handling anisotropic appearance components, especially in dealing with view-dependent color under complex lighting conditions. Additionally, 3D-GS uses spherical harmonic to learn the color representation, which has limited ability to represent complex scenes. To overcome these challenges, we introduce Lantent-SpecGS, an approach that utilizes a universal latent neural descriptor within each 3D Gaussian. This enables a more effective representation of 3D feature fields, including appearance and geometry. Moreover, two parallel CNNs are designed to decoder the splatting feature maps into diffuse color and specular color separately. A mask that depends on the viewpoint is learned to merge these two colors, resulting in the final rendered image. Experimental results demonstrate that our method obtains competitive performance in novel view synthesis and extends the ability of 3D-GS to handle intricate scenarios with specular reflections. © 2024 ACM.

关键词： Rendering (computer graphics)

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Generating Work Efficient Scan Implementations for GPUs the Functional Way 28th

Generating Work Efficient Scan Implementations for GPUs the ...

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28th International conference on Parallel and Distributed Computing (Euro-Par)

作者： Pizzuti, Federico Steuwer, Michel Dubach, Christophe Univ Edinburgh Edinburgh Midlothian Scotland McGill Univ Montreal PQ Canada

ISBN: (纸本)9783031125973;9783031125966

Scan is a core parallel primitive. high-performance work-efficient implementations are usually hard-coded, leading to performance portability issues. performance portability is usually achieved using a generative approach, which decomposes the primitive in simpler, composable parts, expressing the implementation space. Data parallel functional languages excel at expressing programs as composition of simple patterns. Lift, Furthark, Accelerate have successfully applied this technique to patterns such as parallel reduction and tiling. However, work-efficient parallel scan is still provided as a hard-coded builtin. This paper shows how to decompose a classical GPU work-efficient parallel scan in terms of other data-parallel functional primitives. This enables the automatic exploration of the implementation design space, using a set of simple rewrite rules. As the evaluation shows, this technique outperforms hand-written baselines and Furthark, a state of the art high performance code generator. In particular, this composable approach achieves a speedup of up to 1.5x over hard-coded implementations on two different Nvidia GPUs.

关键词： program processors

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