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Underwater Waste Detection with YOLO: A High-Performance Computing Technique

Underwater Waste Detection with YOLO: A High-Performance Com...

引用

International conference on parallel distributed and Grid Computing (PDGC)

作者： Vivek Kumar Umangi Nigam Manan Goel Neeraj Rathore Ajitesh Kumar Mayank Srivastava School of Comp. Sc. Engg. & Tech. Bennett University Greater Noida India Operations & Technology Management UPES Dehradun India Computer Engineering and Application GLA University Mathura India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Waste accumulation in the oceans poses severe environmental challenges that require efficient mechanisms for detection and removal. The methods usually employ slow and resource-intensive techniques like sonar-based detection and manual inspection, hence the need for automated mechanisms of detection. Waste detection under water is a regular monitoring task. Thus, the work proposes rapid model training which allows real-time deployment and enhancement for prevention of degradation of water bodies for various type of wastes. The work uses high-performance computing techniques such as parallel processing, NVIDIA GPUs, CUDA, and YOLO. A deep learning model featuring Convolutional Neural Networks (CNN) is integrated with YOLO (You Only Look Once) to augment detection speed along with accuracy. The model training time is particularly important for real-time applications which allows rapid iteration for the researchers and practitioners. This work points to the fact that the GPU architecture has accelerated 41X in training deep models and improved over precision, and inference time. In all aspects the proposed model claims to perform real-time waste detection within permissible computational latency. This work is in demand for autonomous systems like underwater drones which demand the fast and timely decision-making capability to operate and maneuver properly in dynamic operational environments.

关键词： YOLO Training Deep learning Computational modeling High performance computing Graphics processing units Computer architecture parallel processing Real-time systems Convolutional neural networks

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Scaling Single-image Super-Resolution Training on Modern HPC Clusters: Early Experiences

Scaling Single-Image Super-Resolution Training on Modern HPC...

引用

35th IEEE International parallel and distributed processing Symposium (IPDPS)

作者： Anthony, Quentin Xu, Lang Subramoni, Hari Panda, Dhabaleswar K. Dk Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9781665435772

Deep Learning (DL) models for super-resolution (DLSR) are an emerging trend in response to the growth of ML/DL applications requiring high-resolution images. DLSR methods have also shown promise in domains such as medical imaging, surveillance, and microscopy. However, DLSR models are extremely computationally demanding, and require unreasonably long training times on modern Volta GPUs. In our experiments, we observed only 10.3 images/second on a single Volta GPU for training EDSR, a state-of-the-art DLSR model for single-image super-resolution. In comparison, a Volta GPU can process 360 images/second while training ResNet-50, a state-of-the-art model for image classification. Therefore, we believe supercomputers provide a good candidate to speed up DLSR model training. In this paper, we select EDSR as the representative DLSR PyTorch model. Further, we introduce Horovod-based distributed EDSR training. However, we observed poor default EDSR scaling performance on the Lassen HPC system at Lawrence Livermore National Laboratory. To investigate the performance degradations, we perform exhaustive communication profiling. These profiling insights are then used to optimize CUDA-Aware MPI for DLSR models by ensuring advanced MPI designs involving CUDA IPC and registration caching are properly applied by DL frameworks. We present a comprehensive scaling study of EDSR with MVAPICH2-GDR and NCCL up to 512 GPUs on Lassen. We demonstrate an improvement in scaling efficiency by 15.6% over default Horovod training, which translates to a 1.26x speedup in training performance.

关键词： DNN Training Performance Characterization MVAPICH2 MPI PyTorch Horovod Super Resolution

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Hazy Remote Sensing image Semantic Segmentation with Weak Annotations via Pre-training Optimization and Co-training

Hazy Remote Sensing Image Semantic Segmentation with Weak An...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Junda Xu Libao Zhang School of Artificial Intelligence Beijing Normal University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, weakly supervised semantic segmentation has emerged as a prominent research topic in the field of remote sensing image semantic segmentation due to its cost-effective labeling advantages. However, the presence of haze in remote sensing images poses significant challenges to accurate semantic segmentation. Despite the numerous haze removal methods developed for remote sensing images, their efficacy in the subsequent task of semantic segmentation remains inadequate. To address these issues, this paper aims to enhance the robustness of the segmentation network against haze interference by proposing a weakly supervised semantic segmentation framework based on pre-training optimization and dual-network co-training. The proposed approach employs a pseudo-label optimization network to effectively filter out noise interference and subsequently trains two parallel segmentation networks that mutually guide each other for enhanced robustness. Additionally, an edge optimization loss is introduced to improve prediction accuracy by incorporating both edge and texture information. Experimental comparisons with alternative methods across multiple datasets validate the superior performance of our proposed method.

关键词： Accuracy Semantic segmentation image edge detection Noise Interference Robustness Sensors Speech processing Optimization Remote sensing

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parallel Data Augmentation for Text-based Person Re-identification

Parallel Data Augmentation for Text-based Person Re-identifi...

引用

IEEE International conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Cai, Han-Qing Li, Xin Ji, Yi Li, Ying Liu, Chun-Ping Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Given textual descriptions, text-based person re-identification aims at retrieving the matched target person in a large-scale image pool. In contrast to the traditional person re-identification (Re-ID) task, text-based person Re-ID requires extra extracted discriminative textual representations and then aligns two modal features to narrow down the semantic gap between linguistic domain and visual domain. A majority of previous works design complex network structures and concatenate multi-branch features while failing to pay much attention to problems with the dataset, which requires more parameters learning and might lead to over-fitting. Hence, in this paper, we propose a parallel Data Augmentation method (PDA) to reduce over-fitting and make the model occlusion resistant without increasing the number of training parameters. Specifically, prior to the training, for an image, we randomly choose a rectangular region of variable size and erase the region with a constant value. Similar to image processing, we randomly add a mask of random length words to a sentence, then the processed data is sent to the TIPCB framework for training. Extensive experimentations on the large-scale CUHK-PEDES dataset show the effectiveness of our method and verify that our method exceeds the state-of-the-art methods.

关键词： Person re-identification cross-modality data augmentation

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Multi Node parallel Feature Extraction and 3D Scene Construction for Wide Detection images

Multi Node Parallel Feature Extraction and 3D Scene Construc...

引用

Energy and Electrical Engineering (EEE), International conference on

作者： Ning Kang Ziliang Shangguan Yuecheng Liu Fangfang Cao Yunlong Pan System Engineering Research Office Beijing Aerospace Automatic Control Research Institute Beijing China System Scheme Design Office Beijing Aerospace Automatic Control Research Institute Beijing China

ISBN: (数字)9798350341768

ISBN: (纸本)9798350341775

Remote sensing images are the true representation of various natural and human elements in the surface scene. Due to their good adaptability and wide detection range, they are widely used in the military field, and play an important role in fire reconnaissance, intrusion warning, flight guidance, landform mapping, etc. important role. For a remote sensing image, how to quickly and efficiently complete its feature evaluation has gradually become a hot research issue in recent years. This paper builds a set of visual parallel processing remote sensing image evaluation system. The system can support three analysis modes: pure terrain, pure scene, and scene plus terrain. The grid window is the basic unit, and multiple CPUs are used to perform parallel feature extraction and analysis on remote sensing images, and mirror and display the evaluated results in the $3\mathrm{D}$ scene. Remote sensing image data. Compared to traditional methods, software significantly improves computational efficiency and result availability.

关键词： Support vector machines Three-dimensional displays Machine learning algorithms Software algorithms parallel processing Feature extraction Software Windows Computational efficiency Remote sensing

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Deep learning methods for malicious URL detection using embedding techniques as Logistic Regression with Lasso penalty and Random Forest 7

Deep learning methods for malicious URL detection using embe...

引用

7th International conference on parallel, distributed and Grid Computing, PDGC 2022

作者： Thakur, Isha Panda, Kajal Kumar, Sanjeev Centre for Development of Advanced Computing Cyber Security Technology Division Mohali India

ISBN: (纸本)9781665454018

In the Internet ecosystem, URLs (Uniform Resource Locators) are widely used to propagate malicious infections through spam, spear-phishing, drive-by-download exploitation, malware embedding, etc. The blacklisting, pattern analysis, and signature-matching approaches are widely used to detect such threats. However, these techniques are very effective in detecting known types of malicious URLs but unable to detect new types of attacks launched by the malicious URLs. Moreover, traditional machine learning-based malicious URL detection is highly dependent upon manual feature engineering, which is a costly approach. , This research study proposes deep learning-based malicious URL detection with less dependent on manual feature engineering to overcome the above-mentioned challenges. The Natural Language processing (NLP) technique, i.e., Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer, along with the N-gram parameter, is used for extracting the features. Afterward, two feature selection techniques as Embedded methods are used - Logistic Regression with L1 (Lasso) penalty and Random Forest Selection. Neural network models such as Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Convolution Neural Networks (CNN) are constructed to detect malicious URLs. The experiment results demonstrate that the DNN model gives the best performance results as 96.95% accuracy, 99% precision, 100% recall, and 99% F1-score with logistic regression along with the L1 (Lasso) penalty as a feature selection method. Finally, this study compares state-of-art implemented deep learning methods for malicious URL detection. © 2022 IEEE.

关键词： Inverse problems

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Fault Diagnosis Based on Digital Twin Modeling of Bearings

Fault Diagnosis Based on Digital Twin Modeling of Bearings

引用

Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Zhenxin Liang Ziwen Wu School of Mechanical Engineering Xinjiang University Urumqi China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

As critical rotating machinery components, the monitoring of the health status and fault diagnosis of bearings become particularly important. In this paper, a bearing digital twin model and fault diagnosis system based on ANSYS and Matlab is proposed. First, a high-precision digital twin model of the bearing was constructed using ANSYS software, which can simulate the physical behavior of the bearing under different working conditions. Subsequently, a Matlab-based fault diagnosis system was developed, which is able to read vibration data automatically and extract fault features through an improved spectral cliff algorithm and deconvolution algorithm. The system is designed with a modular architecture, including key aspects such as data acquisition, signal preprocessing, feature extraction and fault diagnosis. The experimental results show that the proposed system outperforms the traditional methods in terms of the accuracy of fault detection and the efficiency of diagnosis.

关键词： Fault diagnosis Deconvolution Software algorithms Signal processing algorithms Feature extraction Prediction algorithms Mathematical models

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Edge-enhanced GAN with Vanishing Points for image Inpainting

Edge-enhanced GAN with Vanishing Points for Image Inpainting

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Summer Virtual conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD-Summer), ACIS International

作者： Kei Masaoka Irawati Nurmala Sari Weiwei Du Department of Information Science Kyoto Institute of Technology Kyoto Japan Information and Human Science Kyoto Institute of Technology Kyoto Japan

ISBN: (纸本)9798350396386

Reconstructing the damaged images with perspective views has an extensive range in the field of image inpainting. However, most existing methods generated inadequately realistic restored images. Accomplishing this problem, we propose an edge-enhanced image generation model considering viewpoints. Our method applies edge map information to guide image generation based on the perspective views of an image using vanishing points detection. Texture synthesis will be presented as post-processing to complete the remaining missing regions. Experiment shows that our approach can generate perspective images with convincing details, such as indoor and outdoor facades.

关键词： image synthesis image edge detection Generative adversarial networks image restoration Artificial intelligence image reconstruction Software engineering

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Pseudo Multi-Source Domain Extension and Selective Pseudo-Labeling for Unsupervised Domain Adaptive Medical image Segmentation

Pseudo Multi-Source Domain Extension and Selective Pseudo-La...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xiaokang Liu Zhiqiang Wang Kai Hu Xieping Gao Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China College of Medical Imaging Laboratory and Rehabilitation Xiangnan University Chenzhou China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China

Unsupervised domain adaptation (UDA) attracts extra attention in medical image processing because no additional labels are required when adapting to different distributions. In this work, we propose a novel unsupervised domain adaptation framework named as Domain Expansion and PseudoLabeling (DEPL). We extend the domain of a labeled source domain data to four different distributed domains and use adversarial learning to align the image appearance level and feature level from the four different domains to the unlabeled target domain. In addition, we propose a selective pseudolabeling mechanism, namely using strong confidence pseudolabeling to boost model performance. We evaluate our model for the MR to CT adaptation segmentation task on the public dataset MMWHS. Compared to seven other state-of-the-art segmentation methods, our DEPL achieves the best Dice similarity coefficient by 82.4%, which is at least 3.9% higher than the other UDA segmentation methods.

关键词： image segmentation Adaptation models Computed tomography distributed databases Signal processing Adversarial machine learning Biomedical image processing

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Research on AGV Cluster Scheduling Method Based on parallel Search Algorithm 22

Research on AGV Cluster Scheduling Method Based on Parallel ...

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3rd Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2022

作者： Zhao, Yuejun Jiangsu Automation Research Institute Jiangsu Lianyungang222061 China

ISBN: (纸本)9781450395786

With gradually popularity of the application of AGVs in automated factories, the rational and efficient path planning and scheduling of AGVs can improve the operational efficiency of factories. The search efficiency of the existing A∗algorithm is not high and the adaptation of the genetic algorithm remains to be improved. Considering the above problems, this paper proposes a self-adaptive cluster scheduling strategy, adopts the parallel computing to improve the search efficiency of the A∗algorithm, and puts forward an improved genetic algorithm based on the dynamic fitness function which does not need to modify the structure of the genetic algorithm according to the changes of the environment so that the adaptability of the algorithm can be improved. Simulation experiments of multi-AGV scheduling are also conducted. The results show that the method adopted in this study has obvious advantages in path solving speed and can adapt to different numbers of obstacles, which provides a reference for the development and application of AGV cluster scheduling methods in real operation scenarios. © 2022 ACM.

关键词： Genetic algorithms

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