scholarly journals A Survey on Text-based Modeling in Model Evolution and Management

2019 ◽  
Vol 63 (1) ◽  
pp. 51-65
Author(s):  
Ferenc A. Somogyi ◽  
Mark Asztalos
Keyword(s):  
Source Code ◽  
Research Area ◽  
Research Field ◽  
Future Research ◽  
Model Evolution ◽  
Model Driven ◽  
Advantages And Disadvantages ◽  
Model Differencing ◽  
Graphical Notations ◽  
Active Research

Model-driven software engineering methodologies like model-driven engineering aim to improve the productivity of software development by using graph-based models as the main artifacts during development, and generating the source code from these models. The models are usually displayed and edited using a graphical notation. However, they can also be described using a textual notation. This has some advantages and disadvantages compared to the graphical approach. For example, while editing the model, we can better focus on the details instead of a broad overview. Similarly to source code, models evolve rapidly during development. Handling and managing the evolution of models is an important task in model-driven methodologies and is an active research area today. However, there exist few research on text-based modeling approaches, compared to graph-based ones. This paper introduces the text-based modeling research field based on existing literature, and presents the state-of-the-art of the field related to model evolution and management. Our goal is to identify challenges and directions for future research in this field. The main topics covered are model differencing and merging, and the synchronization of the textual and graphical notations.

scholarly journals A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

2021 ◽  
Author(s):  
Peng Liu
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Remote Sensing Data ◽  
Generative Adversarial Networks ◽  
Future Research ◽  
Observation Data ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Adversarial Networks ◽  
Active Research

In the past decades, remote sensing (RS) data fusion has always been an active research community. A large number of algorithms and models have been developed. Generative Adversarial Networks (GAN), as an important branch of deep learning, show promising performances in variety of RS image fusions. This review provides an introduction to GAN for remote sensing data fusion. We briefly review the frequently-used architecture and characteristics of GAN in data fusion and comprehensively discuss how to use GAN to realize fusion for homogeneous RS data, heterogeneous RS data, and RS and ground observation data. We also analyzed some typical applications with GAN-based RS image fusion. This review takes insight into how to make GAN adapt to different types of fusion tasks and summarizes the advantages and disadvantages of GAN-based RS data fusion. Finally, we discuss the promising future research directions and make a prediction on its trends.

scholarly journals Intelligent Injection Molding on Sensing, Optimization, and Control

2020 ◽  
Vol 2020 ◽  
pp. 1-22 ◽  
Author(s):  
Peng Zhao ◽  
Jianfeng Zhang ◽  
Zhengyang Dong ◽  
Junye Huang ◽  
Hongwei Zhou ◽  
...  
Keyword(s):  
Injection Molding ◽  
Control Strategies ◽  
Research Field ◽  
Future Research ◽  
Injection Molding Process ◽  
Molding Process ◽  
Geometric Precision ◽  
Optimization And Control ◽  
Active Research ◽  
And Control

Injection molding is one of the most significant material processing methods for mass production of plastic products. It is widely used in various industry sectors, and its products are ubiquitous in our daily life. The settings and optimization of the injection molding process dictate the geometric precision and mechanical properties of the final products. Therefore, sensing, optimization, and control of the injection molding process have a crucial influence on product quality and have become an active research field with abundant literature. This paper defines the concept of intelligent injection molding as the integral application of these three procedures—sensing, optimization, and control. This paper reviews recent studies on methods for the detection of relevant physical variables, optimization of process parameters, and control strategies of machine variables in the molding process. Finally, conclusions are drawn to discuss future research directions and technologies, as well as algorithms worthy of being explored and developed.

scholarly journals Deep Learning for Anomaly Detection

2021 ◽  
Vol 54 (2) ◽  
pp. 1-38
Author(s):  
Guansong Pang ◽  
Chunhua Shen ◽  
Longbing Cao ◽  
Anton Van Den Hengel
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Novelty Detection ◽  
Research Area ◽  
Unique Problem ◽  
Fine Grained ◽  
Advantages And Disadvantages ◽  
Active Research ◽  
High Level ◽  
Critical Direction

Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection , has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.

scholarly journals A Bibliometric Analysis of Exosomes in Cardiovascular Diseases From 2001 to 2021

2021 ◽  
Vol 8 ◽  
Author(s):  
Dan Ma ◽  
Baoyi Guan ◽  
Luxia Song ◽  
Qiyu Liu ◽  
Yixuan Fan ◽  
...  
Keyword(s):  
Stem Cells ◽  
Cardiovascular Diseases ◽  
Bibliometric Analysis ◽  
Research Field ◽  
Future Research ◽  
Visual Methods ◽  
Knowledge Maps ◽  
Active Research ◽  
Medical University ◽  
Research Hotspots

Background: Exosomes in cardiovascular diseases (CVDs) have become an active research field with substantial value and potential. Nevertheless, there are few bibliometric studies in this field. We aimed to visualize the research hotspots and trends of exosomes in CVDs using a bibliometric analysis to help understand the future development of basic and clinical research.Methods: The articles and reviews regarding exosomes in the CVDs were culled from the Web of Science Core Collection, and knowledge maps were generated using CiteSpace and VOSviewer software.Results: A total of 1,039 articles were included. The number of exosome articles in the CVDs increased yearly. These publications came from 60 countries/regions, led by the US and China. The primary research institutions were Shanghai Jiao Tong University and Nanjing Medical University. Circulation Research was the journal and co-cited journal with the most studies. We identified 473 authors among which Lucio Barile had the most significant number of articles and Thery C was co-cited most often. After analysis, the most common keywords are myocardium infarction, microRNA and mesenchymal stem cells. Ischemic heart disease, pathogenesis, regeneration, stem cells, targeted therapy, biomarkers, cardiac protection, and others are current and developing areas of study.Conclusion: We identified the research hotspots and trends of exosomes in CVDs using bibliometric and visual methods. Research on exosomes is flourishing in the cardiovascular medicine. Regenerative medicine, exosome engineering, delivery vehicles, and biomarkers will likely become the focus of future research.

scholarly journals 3D Gestural Interaction: The State of the Field

2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Joseph J. LaViola
Keyword(s):  
Recognition Performance ◽  
Research Area ◽  
The State ◽  
Future Research ◽  
Set Size ◽  
Gestural Interaction ◽  
Active Research ◽  
Active Research Area ◽  
Natural Way ◽  
State Of The Field

3D gestural interaction provides a powerful and natural way to interact with computers using the hands and body for a variety of different applications including video games, training and simulation, and medicine. However, accurately recognizing 3D gestures so that they can be reliably used in these applications poses many different research challenges. In this paper, we examine the state of the field of 3D gestural interfaces by presenting the latest strategies on how to collect the raw 3D gesture data from the user and how to accurately analyze this raw data to correctly recognize 3D gestures users perform. In addition, we examine the latest in 3D gesture recognition performance in terms of accuracy and gesture set size and discuss how different applications are making use of 3D gestural interaction. Finally, we present ideas for future research in this thriving and active research area.

scholarly journals A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

2021 ◽  
Author(s):  
Peng Liu
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Remote Sensing Data ◽  
Generative Adversarial Networks ◽  
Future Research ◽  
Observation Data ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Adversarial Networks ◽  
Active Research

In the past decades, remote sensing (RS) data fusion has always been an active research community. A large number of algorithms and models have been developed. Generative Adversarial Networks (GAN), as an important branch of deep learning, show promising performances in variety of RS image fusions. This review provides an introduction to GAN for remote sensing data fusion. We briefly review the frequently-used architecture and characteristics of GAN in data fusion and comprehensively discuss how to use GAN to realize fusion for homogeneous RS data, heterogeneous RS data, and RS and ground observation data. We also analyzed some typical applications with GAN-based RS image fusion. This review takes insight into how to make GAN adapt to different types of fusion tasks and summarizes the advantages and disadvantages of GAN-based RS data fusion. Finally, we discuss the promising future research directions and make a prediction on its trends.

Challenges of implementing bridge model updating in industry practice

2021 ◽  
Author(s):  
Cong Ye ◽  
Campbell Middleton ◽  
Sin-Chi Kuok ◽  
Liam Butler
Keyword(s):  
Model Updating ◽  
Element Model ◽  
Physical Structure ◽  
Research Field ◽  
Future Research ◽  
Analysis Model ◽  
Structured Interviews ◽  
Bridge Model ◽  
Industry Practice ◽  
Active Research

<p>Model updating aims to update an analysis model (e.g. a finite element model) of an engineering structure in order to closely represent the true condition and performance of the physical structure. Model updating of bridges has been an active research field for more than two decades, yet the confidence and practical usefulness of bridge model updating results may be subject to questioning. While model updating may have worked well for many other engineering applications, it has found to be challenging and problematic to implement such practice on bridge structures. More recently, there has been a vision of developing bridge digital twins which can automatically update the model in near real time as new monitoring data become available. This paper aims to elaborate on the critical issues that have not been addressed properly to enable real-world implementation of bridge model updating.</p><p>A series of industry facing semi-structured interviews have been conducted with 19 bridge professionals (owners, operators and consultants) to aid in investigating the technical and practical challenges of implementing bridge model updating in practice. It is envisioned that the outcomes of this paper will inform future research regarding model updating and digital twin development for bridge applications.</p>

scholarly journals Omni-channel retailing research – state of the art and intellectual foundation

2018 ◽  
Vol 48 (4) ◽  
pp. 365-390 ◽  
Author(s):  
Erdem Galipoglu ◽  
Herbert Kotzab ◽  
Christoph Teller ◽  
Isik Özge Yumurtaci Hüseyinoglu ◽  
Jens Pöppelbuß
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
State Of The Art ◽  
Research Area ◽  
Research Field ◽  
Future Research ◽  
Chain Management ◽  
Content Type ◽  
Analytical Research ◽  
Multi Method Approach

Purpose The purpose of this paper is twofold: to identify, evaluate and structure the research that focusses on omni-channel retailing from the perspective of logistics and supply chain management; and to reveal the intellectual foundation of omni-channel retailing research. Design/methodology/approach The paper applies a multi-method approach by conducting a content-analysis-based literature review of 70 academic papers. Based on the reference lists of these papers, the authors performed a citation and co-citation analysis based on the 34 most frequently cited papers. This analysis included multidimensional scaling, a cluster analysis and factor analysis. Findings The study reveals the limited consideration of logistics and supply chain management literature in the foundation of the omni-channel retailing research. Further, the authors see a dominance of empirical research as compared to conceptual and analytical research. Overall, there is a focus on the Western retail context in this research field. The intellectual foundation is embedded in the marketing discipline and can be characterised as lacking a robust theoretical foundation. Originality/value The contribution of this research is identifying, evaluating and structuring the literature of omni-channel research and providing an overview of the state of the art of this research area considering its interdisciplinary nature. This paper thus supports researchers looking to holistically comprehend, prioritise and use the underpinning literature central to the phenomena of omni-channel retailing. For practitioners and academics alike, the findings can trigger and support future research and an evolving understanding of omni-channel retailing.

scholarly journals Study of Content Based Image Retrieval Using Data Mining Techniques

2020 ◽  
pp. 397-403
Author(s):  
S. Dhinakaran
Keyword(s):  
Image Retrieval ◽  
Research Area ◽  
Research Field ◽  
Content Based Image Retrieval ◽  
High Dimensional ◽  
Query Image ◽  
Clustering Techniques ◽  
Clustering Technique ◽  
Active Research ◽  
And Performance

<p>The field of image retrieval has been an active research area for several decades and has been paid more and more attention in recent years as a result of the dramatic and fast increase in the volume of digital images. Content-based image retrieval (CBIR) is a new but widely adopted method for finding images from vast and un annotated image databases. In recent years, a variety of techniques have been developed to improve the performance of CBIR. In reaction to the needs of users, who feel problems connected with traditional methods of image searching and indexing, researchers focus their interest on techniques for retrieving images on the basis of automatically-derived features, often denoted as Content-Based Image Retrieval (CBIR). CBIR systems index the media documents using salient features extracted from the actual media rather than by textual annotations. Query by content is nowadays a very active research field, with many systems being developed by industrial and academic teams. Results performed by these teams are really promising. The situation gets diametrically different when we move our attention from the usual CBIR task, i.e. the retrieval of images which are similar (as a whole) to the query image, to the task “find all images that contain the query image”. The proposed CBIR technique uses more than one clustering techniques to improve the performance of CBIR. This optimized method makes use of K-means and Hierarchical clustering technique to improve the execution time and performance of image retrieval systems in high dimensional sets. In this similarity measure is totally based on colors. In this paper more focus area is the way of combination of clustering technique in order to get faster output of images. In this paper the clustering techniques are discussed and analyzed. Also, we propose a method HDK that uses more than one clustering technique to improve the performance of CBIR. This method makes use of hierarchical and divides and conquers K-means clustering technique with equivalency and compatible relation concepts to improve the performance of the K-Means for using in high dimensional datasets. It also introduced the feature like color, texture and shape for accurate and effective retrieval system.</p>

Commissioning of a multi-beamline femtoslicing facility at SOLEIL

2018 ◽  
Vol 25 (2) ◽  
pp. 385-398 ◽  
Author(s):  
Marie Labat ◽  
Jean-Blaise Brubach ◽  
Alessandra Ciavardini ◽  
Marie-Emmanuelle Couprie ◽  
Erik Elkaim ◽  
...  
Keyword(s):  
Energy Range ◽  
Photon Energy ◽  
Research Area ◽  
Research Field ◽  
Photon Energy Range ◽  
Dispersion Function ◽  
Ultrafast Dynamics ◽  
Picosecond Pulses ◽  
X Ray ◽  
Active Research

The investigation of ultrafast dynamics, taking place on the few to sub-picosecond time scale, is today a very active research area pursued in a variety of scientific domains. With the recent advent of X-ray free-electron lasers (XFELs), providing very intense X-ray pulses of duration as short as a few femtoseconds, this research field has gained further momentum. As a consequence, the demand for access strongly exceeds the capacity of the very few XFEL facilities existing worldwide. This situation motivates the development of alternative sub-picosecond pulsed X-ray sources among which femtoslicing facilities at synchrotron radiation storage rings are standing out due to their tunability over an extended photon energy range and their high stability. Following the success of the femtoslicing installations at ALS, BESSY-II, SLS and UVSOR, SOLEIL decided to implement a femtoslicing facility. Several challenges were faced, including operation at the highest electron beam energy ever, and achievement of slice separation exclusively with the natural dispersion function of the storage ring. SOLEIL's setup also enables, for the first time, delivering sub-picosecond pulses simultaneously to several beamlines. This last feature enlarges the experimental capabilities of the facility, which covers the soft and hard X-ray photon energy range. In this paper, the commissioning of this original femtoslicing facility is reported. Furthermore, it is shown that the slicing-induced THz signal can be used to derive a quantitative estimate for the degree of energy exchange between the femtosecond infrared laser pulse and the circulating electron bunch.

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