scholarly journals Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review

2021 ◽  
Vol 11 (2) ◽  
pp. 821
Author(s):  
Lei Hou ◽  
Haosen Chen ◽  
Guomin (Kevin) Zhang ◽  
Xiangyu Wang
Keyword(s):  
Deep Learning ◽  
Safety Management ◽  
Research Progress ◽  
Structural Defects ◽  
Future Research ◽  
Wide Range ◽  
Future Research Directions ◽  
Methodological Problems ◽  
Future Work ◽  
Aec Industry

Safety is an essential topic to the architecture, engineering and construction (AEC) industry. However, traditional methods for structural health monitoring (SHM) and jobsite safety management (JSM) are not only inefficient, but also costly. In the past decade, scholars have developed a wide range of deep learning (DL) applications to address automated structure inspection and on-site safety monitoring, such as the identification of structural defects, deterioration patterns, unsafe workforce behaviors and latent risk factors. Although numerous studies have examined the effectiveness of the DL methodology, there has not been one comprehensive, systematic, evidence-based review of all individual articles that investigate the effectiveness of using DL in the SHM and JSM industry to date, nor has there been an examination of this body of evidence in regard to these methodological problems. Therefore, the objective of this paper is to disclose the state of the art of current research progress and determine the relevant gaps, challenges and future work. Methodically, CiteSpace was employed to summarize the research trends, advancements and frontiers of DL applications from 2010 to 2020. Next, an application-focused literature review was conducted, which led to a summary of research gaps, recommendations and future research directions. Overall, this review gains insight into SHM and JSM and aims to help researchers formulate more types of effective DL applications which have not been addressed sufficiently for the time being.

scholarly journals Plasmonic optical tweezers based on nanostructures: fundamentals, advances and prospects

Nanophotonics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 1227-1245 ◽  
Author(s):  
Domna G. Kotsifaki ◽  
Síle Nic Chormaic
Keyword(s):  
Optical Tweezers ◽  
Near Field ◽  
Metallic Nanostructures ◽  
Research Progress ◽  
Future Research ◽  
Research Directions ◽  
Precise Control ◽  
Wide Range ◽  
Wavelength Scale ◽  
Future Research Directions

AbstractThe ability of metallic nanostructures to confine light at the sub-wavelength scale enables new perspectives and opportunities in the field of nanotechnology. Making use of this unique advantage, nano-optical trapping techniques have been developed to tackle new challenges in a wide range of areas from biology to quantum optics. In this work, starting from basic theories, we present a review of research progress in near-field optical manipulation techniques based on metallic nanostructures, with an emphasis on some of the most promising advances in molecular technology, such as the precise control of single biomolecules. We also provide an overview of possible future research directions of nanomanipulation techniques.

Android Malware Detection Techniques: A Literature Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Meghna Dhalaria ◽  
Ekta Gandotra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Future Research Directions ◽  
To Come ◽  
Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

scholarly journals A matrix-less method to approximate the spectrum and the spectral function of Toeplitz matrices with real eigenvalues

2021 ◽  
Author(s):  
Sven-Erik Ekström ◽  
Paris Vassalos
Keyword(s):  
Distribution Function ◽  
Asymptotic Distribution ◽  
Numerical Experiments ◽  
Toeplitz Matrices ◽  
Future Research ◽  
Research Directions ◽  
Numerical Examples ◽  
Working Hypothesis ◽  
Wide Range ◽  
Future Research Directions

AbstractIt is known that the generating function f of a sequence of Toeplitz matrices {Tn(f)}n may not describe the asymptotic distribution of the eigenvalues of Tn(f) if f is not real. In this paper, we assume as a working hypothesis that, if the eigenvalues of Tn(f) are real for all n, then they admit an asymptotic expansion of the same type as considered in previous works, where the first function, called the eigenvalue symbol $\mathfrak {f}$ f , appearing in this expansion is real and describes the asymptotic distribution of the eigenvalues of Tn(f). This eigenvalue symbol $\mathfrak {f}$ f is in general not known in closed form. After validating this working hypothesis through a number of numerical experiments, we propose a matrix-less algorithm in order to approximate the eigenvalue distribution function $\mathfrak {f}$ f . The proposed algorithm, which opposed to previous versions, does not need any information about neither f nor $\mathfrak {f}$ f is tested on a wide range of numerical examples; in some cases, we are even able to find the analytical expression of $\mathfrak {f}$ f . Future research directions are outlined at the end of the paper.

Research Progress of Effect of Heat Treatment on Microstructure, Phase Transformation Behaviors and Memory Properties in Ti-Ni Based Shape Memory Alloys

2021 ◽  
Vol 1036 ◽  
pp. 20-31
Author(s):  
Jun Jie Ye ◽  
Zhi Rong He ◽  
Kun Gang Zhang ◽  
Yu Qing Du
Keyword(s):  
Heat Treatment ◽  
Phase Transformation ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Research Progress ◽  
Future Research ◽  
Research Directions ◽  
Influence Mechanism ◽  
Treatment Technologies ◽  
Future Research Directions

Ti-Ni based shape memory alloys (SMAs) are of excellent shape memory effect, superelasticity and damping property. These properties of the alloys can be fully displayed only after proper heat treatment. In this paper, the research progresses of the effect of the heat treatment on the microstructure, phase composition, phase transformation behaviors and shape memory properties in Ti-Ni based SMAs are reviewed, the correlation influence mechanism is summarized, and the future research directions in this field are pointed out. It is expected to provide reference for the development of Ti-Ni based SMAs and their heat treatment technologies.

Electrochemical polymerization for two-dimensional conjugated polymers

2018 ◽  
Vol 6 (40) ◽  
pp. 10672-10686 ◽  
Author(s):  
Qing Zhang ◽  
Huanli Dong ◽  
Wenping Hu
Keyword(s):  
Conjugated Polymers ◽  
Electrochemical Polymerization ◽  
Research Progress ◽  
Future Research ◽  
Special Focus ◽  
Two Dimensional ◽  
Research Directions ◽  
Potential Applications ◽  
Future Research Directions ◽  
Recent Research Progress

This article places special focus on the recent research progress of the EP method in synthesizing CPs. In particular, their potential applications as 2D CPs are summarized, with a basic introduction of the EP method, its use in synthesizing CPs as well as the promising applications of the obtained CPs in different fields. Discussions of current challenges in this field and future research directions are also given.

scholarly journals CubeSat Communications: Recent Advances and Future Challenges

2020 ◽  
Author(s):  
Nasir Saeed ◽  
Ahmed Elzanaty ◽  
Heba Almorad ◽  
Hayssam Dahrouj ◽  
Tareq Y. Al-Naffouri ◽  
...  
Keyword(s):  
Channel Modeling ◽  
Future Research ◽  
New Paradigm ◽  
Earth Remote Sensing ◽  
Research Directions ◽  
Space Industry ◽  
Recent Advances ◽  
Wide Range ◽  
Future Challenges ◽  
Future Research Directions

<pre><pre>Given the increasing number of space-related applications, research in the emerging space industry is becoming more and more attractive. One compelling area of current space research is the design of miniaturized satellites, known as CubeSats, which are enticing because of their numerous applications and low design-and-deployment cost. </pre><pre>The new paradigm of connected space through CubeSats makes possible a wide range of applications, such as Earth remote sensing, space exploration, and rural connectivity.</pre><pre>CubeSats further provide a complementary connectivity solution to the pervasive Internet of Things (IoT) networks, leading to a globally connected cyber-physical system.</pre><pre>This paper presents a holistic overview of various aspects of CubeSat missions and provides a thorough review of the topic from both academic and industrial perspectives.</pre><pre>We further present recent advances in the area of CubeSat communications, with an emphasis on constellation-and-coverage issues, channel modeling, modulation and coding, and networking.</pre><pre>Finally, we identify several future research directions for CubeSat communications, including Internet of space things, low-power long-range networks, and machine learning for CubeSat resource allocation.</pre></pre>

scholarly journals Research Trends of Human–Computer Interaction Studies in Construction Hazard Recognition: A Bibliometric Review

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6172
Author(s):  
Jiaming Wang ◽  
Rui Cheng ◽  
Mei Liu ◽  
Pin-Chao Liao
Keyword(s):  
Human Computer Interaction ◽  
Industrial Revolution ◽  
Safety Management ◽  
Research Progress ◽  
Physiological Data ◽  
Future Research ◽  
Research Subjects ◽  
Management Platform ◽  
Computer Interaction ◽  
Hazard Recognition

Human–computer interaction, an interdisciplinary discipline, has become a frontier research topic in recent years. In the fourth industrial revolution, human–computer interaction has been increasingly applied to construction safety management, which has significantly promoted the progress of hazard recognition in the construction industry. However, limited scholars have yet systematically reviewed the development of human–computer interaction in construction hazard recognition. In this study, we analyzed 274 related papers published in ACM Digital Library, Web of Science, Google Scholar, and Scopus between 2000 and 2021 using bibliometric methods, systematically identified the research progress, key topics, and future research directions in this field, and proposed a research framework for human–computer interaction in construction hazard recognition (CHR-HCI). The results showed that, in the past 20 years, the application of human–computer interaction not only made significant contributions to the development of hazard recognition, but also generated a series of new research subjects, such as multimodal physiological data analysis in hazard recognition experiments, development of intuitive devices and sensors, and the human–computer interaction safety management platform based on big data. Future research modules include computer vision, computer simulation, virtual reality, and ergonomics. In this study, we drew a theoretical map reflecting the existing research results and the relationship between them, and provided suggestions for the future development of human–computer interaction in the field of hazard recognition from a practical perspective.

scholarly journals A Systematic Survey on Deep Generative Models for Graph Generation

2020 ◽  
Author(s):  
Xiaojie Guo ◽  
Liang Zhao
Keyword(s):  
Generative Models ◽  
Future Research ◽  
Specific Domain ◽  
Research Directions ◽  
Wide Range ◽  
Data Representations ◽  
Future Research Directions ◽  
Definition Of ◽  
Rich History ◽  
Graph Generation

Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in this area, graph generation considers learning the distributions of given graphs and generating more novel graphs. Owing to its wide range of applications, generative models for graphs have a rich history, which, however, are traditionally hand-crafted and only capable of modeling a few statistical properties of graphs. Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. This article provides an extensive overview of the literature in the field of deep generative models for graph generation. Firstly, the formal definition of deep generative models for the graph generation as well as preliminary knowledge is provided. Secondly, two taxonomies of deep generative models for unconditional, and conditional graph generation respectively are proposed; the existing works of each are compared and analyzed. After that, an overview of the evaluation metrics in this specific domain is provided. Finally, the applications that deep graph generation enables are summarized and five promising future research directions are highlighted.

Deep Learning and Medical Imaging

2019 ◽  
pp. 101-147
Author(s):  
Nourhan Mohamed Zayed ◽  
Heba A. Elnemr
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image Analysis ◽  
Future Research ◽  
Research Directions ◽  
Conventional Machine ◽  
Future Research Directions ◽  
Nested Hierarchy ◽  
Great Potency

Deep learning (DL) is a special type of machine learning that attains great potency and flexibility by learning to represent input raw data as a nested hierarchy of essences and representations. DL consists of more layers than conventional machine learning that permit higher levels of abstractions and improved prediction from data. More abstract representations computed in terms of less abstract ones. The goal of this chapter is to present an intensive survey of existing literature on DL techniques over the last years especially in the medical imaging analysis field. All these techniques and algorithms have their points of interest and constraints. Thus, analysis of various techniques and transformations, submitted prior in writing, for plan and utilization of DL methods from medical image analysis prospective will be discussed. The authors provide future research directions in DL area and set trends and identify challenges in the medical imaging field. Furthermore, as quantity of medicinal application demands increase, an extended study and investigation in DL area becomes very significant.

Music Onset Detection

2010 ◽  
pp. 297-316
Author(s):  
Ruohua Zhou ◽  
Josh D Reiss
Keyword(s):  
Performance Measures ◽  
Essential Role ◽  
General Scheme ◽  
Future Research ◽  
Onset Detection ◽  
Time Frequency ◽  
Detection Algorithms ◽  
Music Signal ◽  
Wide Range ◽  
Future Research Directions

Music onset detection plays an essential role in music signal processing and has a wide range of applications. This chapter provides a step by step introduction to the design of music onset detection algorithms. The general scheme and commonly-used time-frequency analysis for onset detection are introduced. Many methods are reviewed, and some typical energy-based, phase-based, pitch-based and supervised learning methods are described in detail. The commonly used performance measures, onset annotation software, public database and evaluation methods are introduced. The performance difference between energy-based and pitch-based method is discussed. The future research directions for music onset detection are also described.

Sign in / Sign up

Export Citation Format

Share Document