Progress of Researches on the Surface Topography Detection Techniques for Grinding Wheel

2013 ◽  
Vol 797 ◽  
pp. 505-510 ◽  
Author(s):  
Wei Liu ◽  
Zhao Hui Deng ◽  
Lin Lin Wan ◽  
Qiao Ping Wu ◽  
Hao Tang
Keyword(s):  
Surface Topography ◽  
Research Work ◽  
Research Direction ◽  
Detection Methods ◽  
Future Research ◽  
Grinding Wheel ◽  
Detection Techniques ◽  
Important Input ◽  
Work Done ◽  
Microscopic Surface

As one of the important input parameters in the grinding process, the surface topography characteristic of grinding wheel has a decisive impact on the grinding performance. To evaluate and analyze the microscopic surface topography, the premise must be able to detect the microscopic surface topography accurately. Based on the measurement of detection device and its spatial position, the surface topography detection methods have been classified, the principle has been specifically analyzed for some typical detection methods, and the research work done by domestic and foreign scholars in the area. Finally, the current problems and future research direction in the field have been analyzed.

scholarly journals Global Research Trends on the Use of Nanotechnology to Boost Meat Production: A Scientometric Analysis

2022 ◽  
Vol 6 ◽  
Author(s):  
Emrobowansan Monday Idamokoro ◽  
Yiseyon Sunday Hosu
Keyword(s):  
Research Work ◽  
Research Direction ◽  
Future Research ◽  
Meat Production ◽  
Scientometric Analysis ◽  
Annual Growth Rate ◽  
Total Citation ◽  
Production Research ◽  
Work Done ◽  
Scientometric Study

Meat production plays a vital socioeconomic role for sustainable development and for promoting food security in most countries. However, not much is known about research agendas done globally and the advancement of knowledge-generating networks in this area of study. The present study aims to reveal and analyze scientific research outputs on meat production linked with recent nanotechnology research work done till date. A compilation of research advancement and development within the sphere was realized through a scientometric study to comprehend the trend of research outputs, scientific impacts, authors' involvement, collaboration networks, and the advancement of knowledge gaps for future research endeavors on the current subject matter. Scholarly published articles were retrieved from the web of science (WOS) and Scopus databases from 1985 to 2020 and they were merged together using bibliometric package in R studio. All duplicated articles (438) from both data bases were excluded. A combination of terms (nano* AND (livestock* OR meat* OR beef* OR mutton* OR pork* OR chevon* OR chicken* OR turkey*)), and conversely analyzed for scientometric indices. A collection of 656 peer-reviewed, research articles were retrieved for the study period and authored by 2,133 researchers with a collaboration index of 3.31. The research outputs were highest in the year 2020 with total research outputs of 140 articles. The topmost three authors' keywords commonly used by authors were nanoparticles, meat, and chitosan with a respective frequency of 75, 62, and 57. China, Iran, and India ranked top in terms of meat production research outputs linked to nanotechnology and total citation with respective article productivity (total citations) of 160 (3,193), 111 (1,765), and 37 (552). Our findings revealed an increasing trend in research (with an annual growth rate of 25.18%) tending toward advancing meat production with the use of nanotechnology. Likewise, there is an increasing pointer to the fact that research work on nanotechnology and meat production has the prospect to influence positively, decision-making on research direction, and collaborations, hereby increasing the production of meat and its products in the future.

Deepfake videos: synthesis and detection techniques –a survey

2021 ◽  
pp. 1-21
Author(s):  
Shahela Saif ◽  
Samabia Tehseen
Keyword(s):  
Social Systems ◽  
Generative Models ◽  
Detection Methods ◽  
Future Research ◽  
Fake News ◽  
Research Directions ◽  
Detection Techniques ◽  
Future Research Directions ◽  
Video Creation ◽  
The One

Deep learning has been used in computer vision to accomplish many tasks that were previously considered too complex or resource-intensive to be feasible. One remarkable application is the creation of deepfakes. Deepfake images change or manipulate a person’s face to give a different expression or identity by using generative models. Deepfakes applied to videos can change the facial expressions in a manner to associate a different speech with a person than the one originally given. Deepfake videos pose a serious threat to legal, political, and social systems as they can destroy the integrity of a person. Research solutions are being designed for the detection of such deepfake content to preserve privacy and combat fake news. This study details the existing deepfake video creation techniques and provides an overview of the deepfake datasets that are publicly available. More importantly, we provide an overview of the deepfake detection methods, along with a discussion on the issues, challenges, and future research directions. The study aims to present an all-inclusive overview of deepfakes by providing insights into the deepfake creation techniques and the latest detection methods, facilitating the development of a robust and effective deepfake detection solution.

scholarly journals Salient Object Detection Techniques in Computer Vision—A Survey

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1174
Author(s):  
Ashish Kumar Gupta ◽  
Ayan Seal ◽  
Mukesh Prasad ◽  
Pritee Khanna
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Large Scale ◽  
Research Work ◽  
Salient Object Detection ◽  
Future Research ◽  
Automatic Identification ◽  
Salient Object ◽  
Detection Techniques

Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.

Rational Research of Deep Concave Open-Pit Slope Angle of Lower Part

2014 ◽  
Vol 716-717 ◽  
pp. 248-251
Author(s):  
Xiao Long Ma ◽  
Guang Zhang ◽  
Qing Guo Ren ◽  
Xiu Ling Jiang
Keyword(s):  
Slope Stability ◽  
Research Work ◽  
Slope Angle ◽  
Mineral Resources ◽  
Research Direction ◽  
Economic Benefits ◽  
Open Pit Mine ◽  
Open Pit ◽  
Future Research ◽  
Factors Affecting

With the rapid advance of industrialization,the consumption of mineral resources is increasing.For open pit mine,many of them went into deep mining.The current,widespread adoption of open pit slope angle makes deep concave mining has a huge stripping ratio.So many mines in trouble,on the premise of guarantee the deep sunken open pit slope stability increase slope toe can bring huge economic benefits,and a lot of research work has been done.This paper describes the factors affecting the stability of open-pit mine slope,the slope rock mass stress analysis,to calculate the lower steep slope economic benefits,puts forward reasonable suggestions the future research direction of the slope stability of open-pit mine.

scholarly journals Islanding Fault Detection in Microgrids—A Survey

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3479 ◽  
Author(s):  
Mehdi Hosseinzadeh ◽  
Farzad Rajaei Salmasi
Keyword(s):  
Fault Detection ◽  
Detection Methods ◽  
Future Research ◽  
Extensive Literature ◽  
Detection Techniques ◽  
Comprehensive List ◽  
Advantages And Disadvantages ◽  
Utility Grid

This paper provides an overview of islanding fault detection in microgrids. Islanding fault is a condition in which the microgrid gets disconnected from the microgrid unintentionally due to any fault in the utility grid. This paper surveys the extensive literature concerning the development of islanding fault detection techniques which can be classified into remote and local techniques, where the local techniques can be further classified as passive, active, and hybrid. Various detection methods in each class are studied, and advantages and disadvantages of each method are discussed. A comprehensive list of references is used to conduct this survey, and opportunities and directions for future research are highlighted.

Green supply chain management

2014 ◽  
Vol 11 (1) ◽  
pp. 20-46 ◽  
Author(s):  
Sunil Luthra ◽  
Dixit Garg ◽  
Abid Haleem
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Research Work ◽  
Research Direction ◽  
Research Field ◽  
Green Supply Chain Management ◽  
Green Supply Chain ◽  
Future Research ◽  
Chain Management ◽  
Content Type

Purpose – The purpose of this paper is to introduce and provide an overview of the various issues related to Green Supply Chain Management (GSCM) and suggest further scope and direction of research in this emerging field. Design/methodology/approach – The work relies on experiences, case studies and other literature related to GSCM. Literature has been segregated to understand various GSCM issues. A detailed review is used to sort out the literature and develop the research direction of the study. The review is focussed on development of GSCM including all those researchers which is relevant to environmental and social sustainability toward operation management and the supply chain. A literature review seems to be a valid approach, as a necessary step in structuring a research field. Findings – The objectives of this paper are to identify major research work conducted on GSCM and to classify them to identify gaps in literature and opportunities for future research. The paper has provided an integrative framework for study, design, implementation and GSCM performance. The findings also identify a number of issues that need to be addressed. Research limitations/implications – Implication of the work is that the knowledge of the research gap can be used to focus efforts on key areas so as to ensure speedy and comprehensive implementation of GSCM practices. Practical implications – The paper may prove to be a very useful source of information to practitioners and regulators in their green practices implementation programs. Originality/value – This paper provides some of the very first insights into development of GSCM theories. The methodological review will provide better understanding of the current state of research in the discipline.

scholarly journals A Novel Hierarchical Model in Ensemble Environment for Road Detection Application

2021 ◽  
Vol 13 (6) ◽  
pp. 1213
Author(s):  
Yang Gu ◽  
Bingfeng Si ◽  
Bushi Liu
Keyword(s):  
Image Segmentation ◽  
Research Work ◽  
Intelligent Transportation ◽  
Research Direction ◽  
Optimal Choice ◽  
Detection Methods ◽  
Natural Environments ◽  
Road Detection ◽  
Noise Data ◽  
Key Issues

As a popular research direction in the field of intelligent transportation, road detection has been extensively concerned by many researchers. However, there are still some key issues in specific applications that need to be further improved, such as the feature processing of road images, the optimal choice of information extraction and detection methods, and the inevitable limitations of detection schemes. In the existing research work, most of the image segmentation algorithms applied to road detection are sensitive to noise data and are prone to generate redundant information or over-segmentation, which makes the computation of segmentation process more complicated. In addition, the algorithm needs to overcome objective factors such as different road conditions and natural environments to ensure certain execution efficiency and segmentation accuracy. In order to improve these issues, we integrate the idea of shallow machine-learning model that clusters first and then classifies in this paper, and a hierarchical multifeature road image segmentation integration framework is proposed. The proposed model has been tested and evaluated on two sets of road datasets based on real scenes and compared with common detection methods, and its effectiveness and accuracy have been verified. Moreover, it demonstrates that the method opens up a new way to enhance the learning and detection capabilities of the model. Most importantly, it has certain potential for application in various practical fields such as intelligent transportation or assisted driving.

scholarly journals Recent Advancement in Anomaly Detection in Surveillance Videos

2019 ◽  
Vol 8 (2) ◽  
pp. 964-970
Keyword(s):  
Computer Vision ◽  
Anomaly Detection ◽  
Video Data ◽  
Detection Methods ◽  
Future Research ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Recent Computer ◽  
Banking Education ◽  
To Come

Detection of Anomaly is of a notable and emergent problem into many diverse fields like information theory, deep learning, computer vision, machine learning, and statistics that have been researched within the various application from diverse domains including agriculture, health care, banking, education, and transport anomaly detection. Newly, numbers of important anomaly detection techniques along with diverseness of sort have been watched. The main aim of this paper to come up with a broad summary of the present development on detection of an anomaly, exclusively for video data with mixed types and high dimensionalities, where identifying the anomalous behaviors and event or anomalous patterns is a significant task. The paper expresses the advantages and disadvantages of the detection methods the experiments tried on the publically available benchmark dataset to assess numerous popular and classical methods and models. The objective of this analysis is to furnish an understanding of recent computer vision and machine algorithms methods and also state-of-the-art deep learnings techniques to detect anomalies for researchers. At last, the paper delivered roughly directions for future research on an anomalies detection.

A SCIENTIFIC REVIEW ON PHYTO-PHARMACOLOGICAL PROPERTIES OF TRIBULUS TERRESTRIS LINN.

INDIAN DRUGS ◽  
2018 ◽  
Vol 55 (10) ◽  
pp. 7-15
Author(s):  
M Waseem ◽  
◽  
A. Rauf ◽  
S. Rehman
Keyword(s):  
Research Work ◽  
Future Research ◽  
Tribulus Terrestris ◽  
Pharmacological Properties ◽  
Classical Literature ◽  
Electronic Database ◽  
Scientific Review ◽  
Ancient Times ◽  
Work Done ◽  
Different Parts

Tribulus terrestris Linn. (Zygophyllaceae) has been used since ancient times to treat various health ailments. Different parts of the plant have been used by traditional physicians, however the dried entire fruit is more commonly used as ‘Gokshura’ and ‘Ikshugandha’ in Ayurveda and as ‘Khar-e-Khasak Khurd’ in Unani System of Medicine for its diuretic, aphrodisiac, emmenagogue, laxative, lactagogue, lithotriptic, demulcent, stomachic and astringent properties. It is therapeutically employed for its efficacy in vesicular calculi, urinary discharge, strangury, sexual debility, dysuria, burning micturation, ammenorrhoea, cough and asthma. Fruits contain traces of alkaloids, flavonoids, fixed oil, small quantity of essential oil, resins and nitrates. This review aims to provide an electronic database regarding phytopharmacological properties of T. terrestris, in particular with its description in Unani classical literature along with the recent research work done by many authors; so that future research work can be made at ease and it will help in revalidating scientifically the claimed activities of the drug mentioned in classical literatures and further exploration of any new therapeutic activity based on phyto-chemistry.

scholarly journals A review of applications of visual inspection technology based on image processing in the railway industry

2019 ◽  
Vol 1 (3) ◽  
pp. 185-204 ◽  
Author(s):  
Scarlett Liu ◽  
Quandong Wang ◽  
Yiping Luo
Keyword(s):  
Image Processing ◽  
Visual Inspection ◽  
Research Direction ◽  
Detection Methods ◽  
Future Research ◽  
Future Research Direction ◽  
Railway Transportation ◽  
Railway Industry ◽  
Railway System ◽  
Low Efficiency

Abstract In order to ensure the safety of railway transportation, it is necessary to regularly check for faults and defects in the railway system. Visual inspection technology is conducive to improving the low efficiency, poor economy and inaccurate detection results of traditional detection methods. This paper introduces the research and contribution of various scholars in the field of visual inspection, summarizes the application and development of visual inspection technology in the railway industry, and finally forecasts the future research direction of visual inspection technology.

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