scholarly journals Temporal and non-temporal contextual saliency analysis for generalized wide-area search within unmanned aerial vehicle (UAV) video

2021 ◽  
Author(s):  
Simon G. E. Gökstorp ◽  
Toby P. Breckon
Keyword(s):  
Short Term Memory ◽  
State Of The Art ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Temporal Reasoning ◽  
The State ◽  
Search And Rescue ◽  
Wide Area ◽  
Deep Model ◽  
The Impact

AbstractUnmanned aerial vehicles (UAV) can be used to great effect for wide-area searches such as search and rescue operations. UAV enable search and rescue teams to cover large areas more efficiently and in less time. However, using UAV for this purpose involves the creation of large amounts of data, typically in video format, which must be analysed before any potential findings can be uncovered and actions taken. This is a slow and expensive process which can result in significant delays to the response time after a target is seen by the UAV. To solve this problem we propose a deep model architecture using a visual saliency approach to automatically analyse and detect anomalies in UAV video. Our Temporal Contextual Saliency (TeCS) approach is based on the state-of-the-art in visual saliency detection using deep Convolutional Neural Networks (CNN) and considers local and scene context, with novel additions in utilizing temporal information through a convolutional Long Short-Term Memory (LSTM) layer and modifications to the base model architecture. We additionally evaluate the impact of temporal vs non-temporal reasoning for this task. Our model achieves improved results on a benchmark dataset with the addition of temporal reasoning showing significantly improved results compared to the state-of-the-art in saliency detection.

Visual Saliency and Perceptual Quality Assessment of 3D Meshes

2018 ◽  
pp. 38-115
Author(s):  
Anass Nouri ◽  
Christophe Charrier ◽  
Olivier Lezoray
Keyword(s):  
Quality Assessment ◽  
State Of The Art ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Ground Truth ◽  
The State ◽  
Perceptual Quality ◽  
Adaptive Smoothing ◽  
Definition Of ◽  
Mesh Database

This chapter concerns the visual saliency and the perceptual quality assessment of 3D meshes. Firstly, the chapter proposes a definition of visual saliency and describes the state-of-the-art methods for its detection on 3D mesh surfaces. A focus is made on a recent model of visual saliency detection for 3D colored and non-colored meshes whose results are compared with a ground-truth saliency as well as with the literature's methods. Since this model is able to estimate the visual saliency on 3D colored meshes, named colorimetric saliency, a description of the construction of a 3D colored mesh database that was used to assess its relevance is presented. The authors also describe three applications of the detailed model that respond to the problems of viewpoint selection, adaptive simplification and adaptive smoothing. Secondly, two perceptual quality assessment metrics for 3D non-colored meshes are described, analyzed, and compared with the state-of-the-art approaches.

scholarly journals A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach

2021 ◽  
Vol 11 (17) ◽  
pp. 8074
Author(s):  
Tierui Zou ◽  
Nader Aljohani ◽  
Keerthiraj Nagaraj ◽  
Sheng Zou ◽  
Cody Ruben ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
State Of The Art ◽  
Real Life ◽  
The State ◽  
Wide Area ◽  
Network Parameter ◽  
False Data Injection ◽  
Network Parameters ◽  
Injection Attacks

Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in power systems heavily depend on the state estimator outputs. While much effort has been given to measurements of false data injection attacks, seldom reported work is found on the broad theme of false data injection on the database of network parameters. State-of-the-art physics-based model solutions correct false data injection on network parameter database considering only available wide-area measurements. In addition, deterministic models are used for correction. In this paper, an overdetermined physics-based parameter false data injection correction model is presented. The overdetermined model uses a parameter database correction Jacobian matrix and a Taylor series expansion approximation. The method further applies the concept of synthetic measurements, which refers to measurements that do not exist in the real-life system. A machine learning linear regression-based model for measurement prediction is integrated in the framework through deriving weights for synthetic measurements creation. Validation of the presented model is performed on the IEEE 118-bus system. Numerical results show that the approximation error is lower than the state-of-the-art, while providing robustness to the correction process. Easy-to-implement model on the classical weighted-least-squares solution, highlights real-life implementation potential aspects.

Researching Telework

2000 ◽  
pp. 255-276 ◽  
Author(s):  
Nicole B. Ellison
Keyword(s):  
Organizational Culture ◽  
State Of The Art ◽  
The State ◽  
Future Research ◽  
Scholarly Literature ◽  
Home Based ◽  
The Family ◽  
The Individual ◽  
The Impact

This chapter examines the state of the art in telework research. The author reviews the most central scholarly literature examining the phenomenon of telework (also called home-based work or telecommuting) and develops a framework for organizing this body of work. She organizes previous research on telework into six major thematic concerns relating to the definition, measurement, and scope of telework; management of teleworkers; travel-related impacts of telework; organizational culture and employee isolation; boundaries between “home” and “work” and the impact of telework on the individual and the family. Areas for future research are suggested.

scholarly journals (Sub)titles in cinematic virtual reality: a descriptive study

2021 ◽  
Author(s):  
Belén Agulló ◽  
◽  
Anna Matamala ◽  
Keyword(s):  
New York ◽  
Virtual Reality ◽  
State Of The Art ◽  
New York Times ◽  
Descriptive Study ◽  
The State ◽  
Corpus Analysis ◽  
Content Creation ◽  
Standard Practices ◽  
The Impact

Virtual reality has attracted the attention of industry and researchers. Its applications for entertainment and audiovisual content creation are endless. Filmmakers are experimenting with different techniques to create immersive stories. Also, subtitle creators and researchers are finding new ways to implement (sub)titles in this new medium. In this article, the state-of-the-art of cinematic virtual reality content is presented and the current challenges faced by filmmakers when dealing with this medium and the impact of immersive content on subtitling practices are discussed. Moreover, the different studies on subtitles in 360º videos carried out so far and the obtained results are reviewed. Finally, the results of a corpus analysis are presented in order to illustrate the current subtitle practices by The New York Times and the BBC. The results have shed some light on issues such as position, innovative graphic strategies or the different functions, challenging current subtitling standard practices in 2D content.

scholarly journals Impact of Cryogenics on Cavitation through an Orifice: A Review

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8319
Author(s):  
Claudia Esposito ◽  
Johan Steelant ◽  
Maria Rosaria Vetrano
Keyword(s):  
State Of The Art ◽  
The State ◽  
Hydrodynamic Cavitation ◽  
Knowledge Gaps ◽  
Propulsion Systems ◽  
Clear Vision ◽  
Basic Concepts ◽  
The Impact

Cryogenic cavitation affects the operation of liquid propulsion systems during the first phase of a launch. Its effects within orifices or turbopumps can range from mild instabilities to catastrophic damages to the structures, jeopardizing the launch itself. Therefore, to ensure the proper designing of propulsion systems, cavitation phenomena cannot be neglected. Although hydrodynamic cavitation has been studied for decades, the impact of the nature of the fluid has been sparsely investigated. Therefore, this review, beginning from the basic concepts of cavitation, analyzes the literature dedicated to hydrodynamic cryogenic cavitation through an orifice. Our review provides a clear vision of the state-of-the-art from experimental and modeling viewpoints, identifies the knowledge gaps in the literature, and proposes a way to further investigate cryogenic cavitation in aerospace science.

scholarly journals Digital Archiving for Archaeology: the state of the art in Argentina

2021 ◽  
Author(s):  
Andrés D. Izeta ◽  
Roxana Cattáneo
Keyword(s):  
State Of The Art ◽  
National Level ◽  
Legal Framework ◽  
The State ◽  
Digital Data ◽  
Archaeological Research ◽  
General Evaluation ◽  
Research Infrastructures ◽  
The Status ◽  
The Impact

This article discusses the state-of-the art of digital archives for archaeological research in Argentina. It also presents and characterises the national and international legal framework and the role played by funding agencies and professional bodies in archaeological practice. In addition, it reports how legal corpora regulate the impact on the management of archaeological digital data. Research infrastructures available at the national level are described, such as the Suquía, an institutional digital archive devoted to archaeology since 2016. Finally, we make a general evaluation of the status quo of research infrastructures mostly concerned with preserving and disseminating data from archaeological research at the national level.

scholarly journals Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

2019 ◽  
Vol 9 (20) ◽  
pp. 4237 ◽  
Author(s):  
Tuong Le ◽  
Minh Thanh Vo ◽  
Bay Vo ◽  
Eenjun Hwang ◽  
Seungmin Rho ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Performance Metrics ◽  
Short Term Memory ◽  
State Of The Art ◽  
Electric Energy ◽  
The State ◽  
Short Term ◽  
Electric Energy Consumption ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

The electric energy consumption prediction (EECP) is an essential and complex task in intelligent power management system. EECP plays a significant role in drawing up a national energy development policy. Therefore, this study proposes an Electric Energy Consumption Prediction model utilizing the combination of Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) that is named EECP-CBL model to predict electric energy consumption. In this framework, two CNNs in the first module extract the important information from several variables in the individual household electric power consumption (IHEPC) dataset. Then, Bi-LSTM module with two Bi-LSTM layers uses the above information as well as the trends of time series in two directions including the forward and backward states to make predictions. The obtained values in the Bi-LSTM module will be passed to the last module that consists of two fully connected layers for finally predicting the electric energy consumption in the future. The experiments were conducted to compare the prediction performances of the proposed model and the state-of-the-art models for the IHEPC dataset with several variants. The experimental results indicate that EECP-CBL framework outperforms the state-of-the-art approaches in terms of several performance metrics for electric energy consumption prediction on several variations of IHEPC dataset in real-time, short-term, medium-term and long-term timespans.

scholarly journals Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification

2019 ◽  
Vol 11 (14) ◽  
pp. 1665 ◽  
Author(s):  
Tianle He ◽  
Chuanjie Xie ◽  
Qingsheng Liu ◽  
Shiying Guan ◽  
Gaohuan Liu
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Winter Wheat ◽  
Large Scale ◽  
Short Term Memory ◽  
State Of The Art ◽  
Training Sample ◽  
Central China ◽  
Surface Reflectance ◽  
The Impact

Machine learning comprises a group of powerful state-of-the-art techniques for land cover classification and cropland identification. In this paper, we proposed and evaluated two models based on random forest (RF) and attention-based long short-term memory (A-LSTM) networks that can learn directly from the raw surface reflectance of remote sensing (RS) images for large-scale winter wheat identification in Huanghuaihai Region (North-Central China). We used a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images over one growing season and the corresponding winter wheat distribution map for the experiments. Each training sample was derived from the raw surface reflectance of MODIS time-series images. Both models achieved state-of-the-art performance in identifying winter wheat, and the F1 scores of RF and A-LSTM were 0.72 and 0.71, respectively. We also analyzed the impact of the pixel-mixing effect. Training with pure-mixed-pixel samples (the training set consists of pure and mixed cells and thus retains the original distribution of data) was more precise than training with only pure-pixel samples (the entire pixel area belongs to one class). We also analyzed the variable importance along the temporal series, and the data acquired in March or April contributed more than the data acquired at other times. Both models could predict winter wheat coverage in past years or in other regions with similar winter wheat growing seasons. The experiments in this paper showed the effectiveness and significance of our methods.

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