scholarly journals EAWNet: An Edge Attention-Wise Objector for Real-Time Visual Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zhichao Zhang ◽  
Hui Chen ◽  
Xiaoqing Yin ◽  
Jinsheng Deng
Keyword(s):  
Internet Of Things ◽  
Object Detection ◽  
Real Time ◽  
High Performance ◽  
Large Scale ◽  
Multiple Scales ◽  
Hot Spot ◽  
Training Data ◽  
Long Distance ◽  
Size Limitation

With the upgrading of the high-performance image processing platform and visual internet of things sensors, VIOT is widely used in intelligent transportation, autopilot, military reconnaissance, public safety, and other fields. However, the outdoor visual internet of things system is very sensitive to the weather and unbalanced scale of latent object. The performance of supervised learning is often limited by the disturbance of abnormal data. It is difficult to collect all classes from limited historical instances. Therefore, in terms of the anomaly detection images, fast and accurate artificial intelligence-based object detection technology has become a research hot spot in the field of intelligent vision internet of things. To this end, we propose an efficient and accurate deep learning framework for real-time and dense object detection in VIOT named the Edge Attention-wise Convolutional Neural Network (EAWNet) with three main features. First, it can identify remote aerial and daily scenery objects fast and accurately in terms of an unbalanced category. Second, edge prior and rotated anchor are adopted to enhance the efficiency of detection in edge computing internet. Third, our EAWNet network uses an edge sensing object structure, makes full use of an attention mechanism to dynamically screen different kinds of objects, and performs target recognition on multiple scales. The edge recovery effect and target detection performance for long-distance aerial objects were significantly improved. We explore the efficiency of various architectures and fine tune the training process using various backbone and data enhancement strategies to increase the variety of the training data and overcome the size limitation of input images. Extensive experiments and comprehensive evaluation on COCO and large-scale DOTA datasets proved the effectiveness of this framework that achieved the most advanced performance in real-time VIOT object detection.

scholarly journals HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time

2018 ◽  
Vol 7 (12) ◽  
pp. 467 ◽  
Author(s):  
Mengyu Ma ◽  
Ye Wu ◽  
Wenze Luo ◽  
Luo Chen ◽  
Jun Li ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
High Performance ◽  
Large Scale ◽  
Computation Time ◽  
Buffer Analysis ◽  
Data Volume ◽  
Time Buffer ◽  
Real World Datasets ◽  
Spatial Indexes

Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly with increasing data volume. In this paper, we introduce HiBuffer, a visualization-oriented model for real-time buffer analysis. An efficient buffer generation method is proposed which introduces spatial indexes and a corresponding query strategy. Buffer results are organized into a tile-pyramid structure to enable stepless zooming. Moreover, a fully optimized hybrid parallel processing architecture is proposed for the real-time buffer analysis of large-scale spatial data. Experiments using real-world datasets show that our approach can reduce computation time by up to several orders of magnitude while preserving superior visualization effects. Additional experiments were conducted to analyze the influence of spatial data density, buffer radius, and request rate on HiBuffer performance, and the results demonstrate the adaptability and stability of HiBuffer. The parallel scalability of HiBuffer was also tested, showing that HiBuffer achieves high performance of parallel acceleration. Experimental results verify that HiBuffer is capable of handling 10-million-scale data.

scholarly journals Attending to Entities for Better Text Understanding

2020 ◽  
Vol 34 (05) ◽  
pp. 7554-7561
Author(s):  
Pengxiang Cheng ◽  
Katrin Erk
Keyword(s):  
Large Scale ◽  
Human Performance ◽  
State Of The Art ◽  
Syntactic Structure ◽  
Semantic Knowledge ◽  
Training Data ◽  
Language Models ◽  
Long Distance ◽  
Future Directions ◽  
Text Understanding

Recent progress in NLP witnessed the development of large-scale pre-trained language models (GPT, BERT, XLNet, etc.) based on Transformer (Vaswani et al. 2017), and in a range of end tasks, such models have achieved state-of-the-art results, approaching human performance. This clearly demonstrates the power of the stacked self-attention architecture when paired with a sufficient number of layers and a large amount of pre-training data. However, on tasks that require complex and long-distance reasoning where surface-level cues are not enough, there is still a large gap between the pre-trained models and human performance. Strubell et al. (2018) recently showed that it is possible to inject knowledge of syntactic structure into a model through supervised self-attention. We conjecture that a similar injection of semantic knowledge, in particular, coreference information, into an existing model would improve performance on such complex problems. On the LAMBADA (Paperno et al. 2016) task, we show that a model trained from scratch with coreference as auxiliary supervision for self-attention outperforms the largest GPT-2 model, setting the new state-of-the-art, while only containing a tiny fraction of parameters compared to GPT-2. We also conduct a thorough analysis of different variants of model architectures and supervision configurations, suggesting future directions on applying similar techniques to other problems.

Detecting Pressure Anomalies While Drilling Using a Machine Learning Hybrid Approach

2021 ◽  
Author(s):  
Aurore Lafond ◽  
Maurice Ringer ◽  
Florian Le Blay ◽  
Jiaxu Liu ◽  
Ekaterina Millan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Large Scale ◽  
Hybrid Approach ◽  
Physical Models ◽  
Training Data ◽  
Digital Data ◽  
Machine Learning Techniques ◽  
New System

Abstract Abnormal surface pressure is typically the first indicator of a number of problematic events, including kicks, losses, washouts and stuck pipe. These events account for 60–70% of all drilling-related nonproductive time, so their early and accurate detection has the potential to save the industry billions of dollars. Detecting these events today requires an expert user watching multiple curves, which can be costly, and subject to human errors. The solution presented in this paper is aiming at augmenting traditional models with new machine learning techniques, which enable to detect these events automatically and help the monitoring of the drilling well. Today’s real-time monitoring systems employ complex physical models to estimate surface standpipe pressure while drilling. These require many inputs and are difficult to calibrate. Machine learning is an alternative method to predict pump pressure, but this alone needs significant labelled training data, which is often lacking in the drilling world. The new system combines these approaches: a machine learning framework is used to enable automated learning while the physical models work to compensate any gaps in the training data. The system uses only standard surface measurements, is fully automated, and is continuously retrained while drilling to ensure the most accurate pressure prediction. In addition, a stochastic (Bayesian) machine learning technique is used, which enables not only a prediction of the pressure, but also the uncertainty and confidence of this prediction. Last, the new system includes a data quality control workflow. It discards periods of low data quality for the pressure anomaly detection and enables to have a smarter real-time events analysis. The new system has been tested on historical wells using a new test and validation framework. The framework runs the system automatically on large volumes of both historical and simulated data, to enable cross-referencing the results with observations. In this paper, we show the results of the automated test framework as well as the capabilities of the new system in two specific case studies, one on land and another offshore. Moreover, large scale statistics enlighten the reliability and the efficiency of this new detection workflow. The new system builds on the trend in our industry to better capture and utilize digital data for optimizing drilling.

scholarly journals Cloud-Based Experimental Platform for the Space-Ground Integrated Network

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Xiaofeng Wang ◽  
Xinyu Chen ◽  
Haiyang Ye ◽  
Yuan Liu ◽  
Guizhu Zhang
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Random Number Generation ◽  
Long Distance ◽  
Integrated Network ◽  
Experimental Platform ◽  
Orbital Maneuvers ◽  
Ground Station ◽  
Bit Error Ratio ◽  
Time Varying Networks

The space-ground integrated network (SGIN) is an important direction of future network development and is expected to play an important role in edge computing for the Internet of Things (IoT). Through integration with an SGIN, IoT applications can provide services with long-distance and wide-coverage features. However, SGINs are typical large-scale and time-varying networks for which new network technologies, protocols, and applications must be rigorously evaluated and validated. Therefore, a reliable experimental platform is necessary for SGINs. This paper presents a cloud-based experimental platform for the SGIN context named SGIN-Stack. First, the architecture of SGIN-Stack, which combines the Systems Tool Kit (STK) and OpenStack, is described. Based on this architecture, a seamless linkage between OpenStack and STK is achieved to realize synchronous, dynamic, and real-time network emulation for an SGIN, and the dynamic differential compensation technology and a random number generation algorithm are applied to improve the emulation accuracy for satellite links. Finally, an emulation scenario is constructed that includes six space-based backbone nodes, sixty-six space-based access nodes, and a ground station. Based on this emulation scenario, experiments concerning the satellite link delays, bit error ratio (BER), and throughput are carried out to prove the high fidelity of our SGIN-Stack platform. Emulation experiments involving satellite orbital maneuvers and attitude adjustments show that SGIN-Stack can be used for dynamic and real-time SGIN emulation.

scholarly journals Attention-Aware Adversarial Network for Person Re-Identification

2019 ◽  
Vol 9 (8) ◽  
pp. 1550 ◽  
Author(s):  
Aihong Shen ◽  
Huasheng Wang ◽  
Junjie Wang ◽  
Hongchen Tan ◽  
Xiuping Liu ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Data Augmentation ◽  
Fundamental Problem ◽  
Training Data ◽  
Specific Data ◽  
Training Strategy ◽  
Adversarial Network ◽  
Benchmark Datasets ◽  
Adversarial Training

Person re-identification (re-ID) is a fundamental problem in the field of computer vision. The performance of deep learning-based person re-ID models suffers from a lack of training data. In this work, we introduce a novel image-specific data augmentation method on the feature map level to enforce feature diversity in the network. Furthermore, an attention assignment mechanism is proposed to enforce that the person re-ID classifier focuses on nearly all important regions of the input person image. To achieve this, a three-stage framework is proposed. First, a baseline classification network is trained for person re-ID. Second, an attention assignment network is proposed based on the baseline network, in which the attention module learns to suppress the response of the current detected regions and re-assign attentions to other important locations. By this means, multiple important regions for classification are highlighted by the attention map. Finally, the attention map is integrated in the attention-aware adversarial network (AAA-Net), which generates high-performance classification results with an adversarial training strategy. We evaluate the proposed method on two large-scale benchmark datasets, including Market1501 and DukeMTMC-reID. Experimental results show that our algorithm performs favorably against the state-of-the-art methods.

Visualising large-scale geodynamic simulations: How to Dive into Earth's Mantle with Virtual Reality

2020 ◽  
Author(s):  
Markus Wiedemann ◽  
Bernhard S.A. Schuberth ◽  
Lorenzo Colli ◽  
Hans-Peter Bunge ◽  
Dieter Kranzlmüller
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
High Performance ◽  
Large Scale ◽  
Large Data ◽  
Optimization Techniques ◽  
Physical Parameters ◽  
Mantle Flow ◽  
Data Set ◽  
Precise Knowledge

<p>Precise knowledge of the forces acting at the base of tectonic plates is of fundamental importance, but models of mantle dynamics are still often qualitative in nature to date. One particular problem is that we cannot access the deep interior of our planet and can therefore not make direct in situ measurements of the relevant physical parameters. Fortunately, modern software and powerful high-performance computing infrastructures allow us to generate complex three-dimensional models of the time evolution of mantle flow through large-scale numerical simulations.</p><p>In this project, we aim at visualizing the resulting convective patterns that occur thousands of kilometres below our feet and to make them "accessible" using high-end virtual reality techniques.</p><p>Models with several hundred million grid cells are nowadays possible using the modern supercomputing facilities, such as those available at the Leibniz Supercomputing Centre. These models provide quantitative estimates on the inaccessible parameters, such as buoyancy and temperature, as well as predictions of the associated gravity field and seismic wavefield that can be tested against Earth observations.</p><p>3-D visualizations of the computed physical parameters allow us to inspect the models such as if one were actually travelling down into the Earth. This way, convective processes that occur thousands of kilometres below our feet are virtually accessible by combining the simulations with high-end VR techniques.</p><p>The large data set used here poses severe challenges for real time visualization, because it cannot fit into graphics memory, while requiring rendering with strict deadlines. This raises the necessity to balance the amount of displayed data versus the time needed for rendering it.</p><p>As a solution, we introduce a rendering framework and describe our workflow that allows us to visualize this geoscientific dataset. Our example exceeds 16 TByte in size, which is beyond the capabilities of most visualization tools. To display this dataset in real-time, we reduce and declutter the dataset through isosurfacing and mesh optimization techniques.</p><p>Our rendering framework relies on multithreading and data decoupling mechanisms that allow to upload data to graphics memory while maintaining high frame rates. The final visualization application can be executed in a CAVE installation as well as on head mounted displays such as the HTC Vive or Oculus Rift. The latter devices will allow for viewing our example on-site at the EGU conference.</p>

scholarly journals An Efficient Data Stream Analytics Model for Real Time Internet of Things (Iot) Applications

2020 ◽  
Vol 8 (6) ◽  
pp. 495-500
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Private Information ◽  
Large Scale ◽  
Layer System ◽  
Collection Data ◽  
Efficient Data ◽  
Multiple Environments ◽  
Iot Devices

Internet of Things (IoT), data analytics is supporting multiple applications. These numerous applications try to gather data from different environments, here the gathered data may be homogeneous or heterogeneous, but most of the data collected from multiple environments were heterogeneous, the task of gathering, processing, storing and the analysis that is being performed on data are still challenging. Providing security to all these things is also a challenging task due to untrusted networks and big data. Big data management in the ever-expanding network may rise several non-trivial concerns on data collection, data-efficient processing, analytics, and security. However, the above said scenarios depends on large scale sensor deployed. Sensors continuously transmit data to clouds for real time use, which can raise the issue of privacy disclosure because IoT devices may gather data including a kind of sensitive private information. In this context, we propose a two-layer system or model for analyzing IoT data, collected from multiple applications. The first layer is mainly used for gathering data from multiple environments and acts as a service-oriented interface to ingest data. The second layer is responsible for storing and analyses data securely. The Proposed solutions are implemented by the use of open source components.

scholarly journals Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1932
Author(s):  
Malik Haris ◽  
Adam Glowacz
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Large Scale ◽  
Single Shot ◽  
Automated Driving ◽  
Convolutional Network ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Automated driving and vehicle safety systems need object detection. It is important that object detection be accurate overall and robust to weather and environmental conditions and run in real-time. As a consequence of this approach, they require image processing algorithms to inspect the contents of images. This article compares the accuracy of five major image processing algorithms: Region-based Fully Convolutional Network (R-FCN), Mask Region-based Convolutional Neural Networks (Mask R-CNN), Single Shot Multi-Box Detector (SSD), RetinaNet, and You Only Look Once v4 (YOLOv4). In this comparative analysis, we used a large-scale Berkeley Deep Drive (BDD100K) dataset. Their strengths and limitations are analyzed based on parameters such as accuracy (with/without occlusion and truncation), computation time, precision-recall curve. The comparison is given in this article helpful in understanding the pros and cons of standard deep learning-based algorithms while operating under real-time deployment restrictions. We conclude that the YOLOv4 outperforms accurately in detecting difficult road target objects under complex road scenarios and weather conditions in an identical testing environment.

scholarly journals Learning from the 2018 Western Japan Heavy Rains to Detect Floods during the 2019 Hagibis Typhoon

2020 ◽  
Vol 12 (14) ◽  
pp. 2244
Author(s):  
Luis Moya ◽  
Erick Mas ◽  
Shunichi Koshimura
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Local Governments ◽  
Large Scale ◽  
Damage Identification ◽  
Remote Sensing Data ◽  
Early Response ◽  
Training Data ◽  
Supervised Machine Learning ◽  
A Current

Applications of machine learning on remote sensing data appear to be endless. Its use in damage identification for early response in the aftermath of a large-scale disaster has a specific issue. The collection of training data right after a disaster is costly, time-consuming, and many times impossible. This study analyzes a possible solution to the referred issue: the collection of training data from past disaster events to calibrate a discriminant function. Then the identification of affected areas in a current disaster can be performed in near real-time. The performance of a supervised machine learning classifier to learn from training data collected from the 2018 heavy rainfall at Okayama Prefecture, Japan, and to identify floods due to the typhoon Hagibis on 12 October 2019 at eastern Japan is reported in this paper. The results show a moderate agreement with flood maps provided by local governments and public institutions, and support the assumption that previous disaster information can be used to identify a current disaster in near-real time.

scholarly journals Geospatial Object Detection on High Resolution Remote Sensing Imagery Based on Double Multi-Scale Feature Pyramid Network

2019 ◽  
Vol 11 (7) ◽  
pp. 755 ◽  
Author(s):  
Xiaodong Zhang ◽  
Kun Zhu ◽  
Guanzhou Chen ◽  
Xiaoliang Tan ◽  
Lifei Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Object Detection ◽  
Large Scale ◽  
Training Data ◽  
Validation Dataset ◽  
Remote Sensing Imagery ◽  
Scale Feature ◽  
Multi Scale ◽  
Feature Pyramid

Object detection on very-high-resolution (VHR) remote sensing imagery has attracted a lot of attention in the field of image automatic interpretation. Region-based convolutional neural networks (CNNs) have been vastly promoted in this domain, which first generate candidate regions and then accurately classify and locate the objects existing in these regions. However, the overlarge images, the complex image backgrounds and the uneven size and quantity distribution of training samples make the detection tasks more challenging, especially for small and dense objects. To solve these problems, an effective region-based VHR remote sensing imagery object detection framework named Double Multi-scale Feature Pyramid Network (DM-FPN) was proposed in this paper, which utilizes inherent multi-scale pyramidal features and combines the strong-semantic, low-resolution features and the weak-semantic, high-resolution features simultaneously. DM-FPN consists of a multi-scale region proposal network and a multi-scale object detection network, these two modules share convolutional layers and can be trained end-to-end. We proposed several multi-scale training strategies to increase the diversity of training data and overcome the size restrictions of the input images. We also proposed multi-scale inference and adaptive categorical non-maximum suppression (ACNMS) strategies to promote detection performance, especially for small and dense objects. Extensive experiments and comprehensive evaluations on large-scale DOTA dataset demonstrate the effectiveness of the proposed framework, which achieves mean average precision (mAP) value of 0.7927 on validation dataset and the best mAP value of 0.793 on testing dataset.

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