Dimensionality Reduction With Multi-Fold Deep Denoising Autoencoder

2020 ◽  
pp. 154-165
Author(s):  
Pattabiraman V. ◽  
Parvathi R.
Keyword(s):  
Large Scale ◽  
Curse Of Dimensionality ◽  
Data Sources ◽  
Sensor Data ◽  
Future Research ◽  
Computational Power ◽  
Nonlinear Methods ◽  
Research Areas ◽  
Learning Techniques ◽  
Natural Data

Natural data erupting directly out of various data sources, such as text, image, video, audio, and sensor data, comes with an inherent property of having very large dimensions or features of the data. While these features add richness and perspectives to the data, due to sparsity associated with them, it adds to the computational complexity while learning, unable to visualize and interpret them, thus requiring large scale computational power to make insights out of it. This is famously called “curse of dimensionality.” This chapter discusses the methods by which curse of dimensionality is cured using conventional methods and analyzes its performance for given complex datasets. It also discusses the advantages of nonlinear methods over linear methods and neural networks, which could be a better approach when compared to other nonlinear methods. It also discusses future research areas such as application of deep learning techniques, which can be applied as a cure for this curse.

scholarly journals Evapotranspiration Estimation with Small UAVs in Precision Agriculture

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6427
Author(s):  
Haoyu Niu ◽  
Derek Hollenbeck ◽  
Tiebiao Zhao ◽  
Dong Wang ◽  
YangQuan Chen
Keyword(s):  
Energy Balance ◽  
High Resolution ◽  
Spatial Resolution ◽  
Precision Agriculture ◽  
Large Scale ◽  
Bowen Ratio ◽  
Future Research ◽  
Accurate Estimation ◽  
Research Areas ◽  
Energy Balance Approach

Estimating evapotranspiration (ET) has been one of the most critical research areas in agriculture because of water scarcity, the growing population, and climate change. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, researchers use water balance, soil moisture, weighing lysimeters, or an energy balance approach, such as Bowen ratio or eddy covariance towers to estimate ET. However, these ET methods are point-specific or area-weighted measurements and cannot be extended to a large scale. With the advent of satellite technology, remote sensing images became able to provide spatially distributed measurements. However, the spatial resolution of multispectral satellite images is in the range of meters, tens of meters, or hundreds of meters, which is often not enough for crops with clumped canopy structures, such as trees and vines. Unmanned aerial vehicles (UAVs) can mitigate these spatial and temporal limitations. Lightweight cameras and sensors can be mounted on the UAVs and take high-resolution images. Unlike satellite imagery, the spatial resolution of the UAV images can be at the centimeter-level. UAVs can also fly on-demand, which provides high temporal imagery. In this study, the authors examined different UAV-based approaches of ET estimation at first. Models and algorithms, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC), the two-source energy balance (TSEB) model, and machine learning (ML) are analyzed and discussed herein. Second, challenges and opportunities for UAVs in ET estimation are also discussed, such as uncooled thermal camera calibration, UAV image collection, and image processing. Then, the authors share views on ET estimation with UAVs for future research and draw conclusive remarks.

Affective Images of Tourism Destinations

1997 ◽  
Vol 35 (4) ◽  
pp. 11-15 ◽  
Author(s):  
Seyhmus Baloglu ◽  
David Brinberg
Keyword(s):  
Large Scale ◽  
Space Structure ◽  
Destination Image ◽  
Future Research ◽  
Scaling Analysis ◽  
Tourism Destination ◽  
Cognitive Component ◽  
Research Areas ◽  
Mediterranean Countries ◽  
Tourism Destinations

The destination image and positioning studies in tourism have been limited to those dealing with the image's perceptual or cognitive component. This study examined the applicability of Russel and his colleagues' proposed affective space structure to large-scale environments (i.e., tourism destination countries) as well as its potential as a positioning structure to study affective images of tourism destinations. The multidimensional scaling analysis of 11 Mediterranean countries along with proposed affective space structure indicated that Russel and his colleagues' proposed affective space can also be applied to places that are not perceived directly. It also showed potential for studying the affective image positioning of tourism destinations. The article concludes with some theoretical and practical implications and future research areas regarding tourism destination images.

scholarly journals Linking Instruction and Student Achievement. A research design for a new generation of classroom studies

2017 ◽  
Vol 11 (3) ◽  
pp. 10 ◽  
Author(s):  
Kirsti Klette ◽  
Marte Blikstad-Balas ◽  
Astrid Roe
Keyword(s):  
Student Achievement ◽  
Large Scale ◽  
Classroom Practices ◽  
Instructional Quality ◽  
Theoretical Background ◽  
Video Data ◽  
Data Sources ◽  
Future Research ◽  
Ongoing Research ◽  
Achievement Data

AbstractEducational research into instructional quality would benefit from macro- and meso-level instructional data – such as achievement data or large-scale student surveys – in relation to data from the micro level – such as detailed analyses of classroom practices. Several scholars have specifically asked for studies that correlate achievement data with records of learning processes and teaching strategies, and ongoing projects attempting to do so have shown promising results. Linking different data sources on instructional quality is quite demanding because it requires a concerted effort by researchers from different fields of expertise and different traditions. A main ambition of our ongoing research project is precisely to advance such integration. As the title of the project reveals, we are dedicated to Linking Instruction and Student Achievement (LISA). In this article, we start by providing a theoretical background and status of knowledge related to instructional quality. We go on to argue that video data has shown particular promise in studies aiming to obtain systematic data from a range of classrooms in order to compare classroom practices. We then present the three components of the LISA project’s design – student perception surveys, systematic classroom observation, and achievement gains in national tests – and the value of combining these three data sources. Finally, we will outline some of our findings thus far and point to future research possibilities.Key words: instructional quality; classroom practices; video studies; mathematics; language arts Å koble undervisning med elevprestasjoner - Forskningsdesign for en ny generasjon klasseromsstudierSammendragFor å studere undervisningskvalitet vil det være en fordel å kombinere data fra et makro og meso- nivå  med detaljerte studier av hva som skjer i klasserommet. Flere har etterlyst studier som ser på sammenhenger mellom målbar faglig fremgang og lærerens undervisning. Å få til slike studier er krevende, da det forutsetter et tett samarbeid mellom forskere fra ulike felt med ulik ekspertise innenfor nokså ulike forskningstradisjoner. En hovedambisjon i vårt pågående forskningsprosjekt er nettopp å få til en slik integrasjon. Som tittelen avslører, er vi dedikert til «Linking Instruction and Student Achievement (LISA)». I denne artikkelen presenterer vi det teoretiske og empiriske grunnlaget knyttet til undervisningskvalitet. Videre argumenterer vi for verdien av videodata i studier som sammenligner undervisningspraksiser fra ulike klasserom på en systematisk måte. Deretter presenterer vi de tre datakildene i LISA-prosjektets forskningsdesign – spørreskjemaer til elever om deres oppfatninger om lærerens undervisning, systematiske klasseromsobservasjoner, og målt fremgang på nasjonale prøver i lesing og regning. Verdien av å kombinere nettopp disse tre datakildene vil også bli diskutert. Avslutningsvis deler vi noen av våre tidlige forskningsfunn.Nøkkelord: undervisningskvalitet; klasseromspraksis; video studier; matematikk; norskfaget

scholarly journals GrandBase: generating actionable knowledge from Big Data

2017 ◽  
Vol 1 (2) ◽  
pp. 105-126 ◽  
Author(s):  
Xiu Susie Fang ◽  
Quan Z. Sheng ◽  
Xianzhi Wang ◽  
Anne H.H. Ngu ◽  
Yihong Zhang
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Empirical Studies ◽  
Knowledge Extraction ◽  
Data Sources ◽  
Future Research ◽  
Content Type ◽  
Actionable Knowledge ◽  
True Value ◽  
Truth Discovery

Purpose This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase. Design/methodology/approach In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed. Findings Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed. Originality/value To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

scholarly journals Finnish Maritime Archaeology through its Publications

2021 ◽  
Author(s):  
Kristin Ilves ◽  
◽  
Marko Marila ◽  
Keyword(s):  
Knowledge Production ◽  
Large Scale ◽  
Maritime Archaeology ◽  
Research Community ◽  
Future Research ◽  
Historical Period ◽  
Research Areas ◽  
International Visibility ◽  
History Of ◽  
Heritage Discourse

This article analyses Finnish maritime archaeology through a compiled bibliography of 621 scientific and popular works published between 1942–2020. General trends and turning points in the history of the discipline are identified and discussed vis-a-vis temporal and topical foci discerned in the publications. Special attention is drawn to the concentration in Finnish research on shipwrecks from the historical period, and the low international visibility of scientific production is problematised. While large-scale projects have been carried out in Finnish maritime archaeology, knowledge production within the authorised heritage discourse in particular has aimed to fulfil the needs of local and national rather than international audiences. Our compiled bibliography, which is hereby made available to the wider research community, has potential to become a valuable tool for identifying and developing future research areas.

Present Directions and Future Research in Vadose Zone Hydrology

1999 ◽  
Author(s):  
William A. Jury
Keyword(s):  
Vadose Zone ◽  
Large Scale ◽  
Future Research ◽  
Soil Science Society ◽  
Measurement Monitoring ◽  
Research Areas ◽  
Future Research Directions ◽  
New Generation ◽  
Vadose Zone Hydrology ◽  
Monitoring Devices

The last decade has been an active one for research in vadose zone hydrology (VZH). There are a host of new experimental devices, lots of new theories, and a bright new generation of scientists eager to unlock the mysteries of the discipline. It would be tempting to say that we are well on our way to conquering the most difficult problems that face experimentalists and modelers in the VZH field. However, as is so often the case in science, new problems are discovered in the process of solving other ones. I have been given the task of providing an overview of the current directions in our field, and of pointing out unsolved problems and future research directions for the discipline. Execution of such a task is well beyond my abilities or vision, so what you will get is a compendium of my personal preferences and bias. I chose several methods carry out my charge. First, I examined the poster abstracts to get an idea of the content and breadth of the offerings for the symposium “Vadose Zone Hydrology—Cutting Across Disciplines.” Next, I examined 1 year’s worth of articles in the S-l section of the Soil Science Society of America Journal and in Water Resources Research at a 10-year interval to get an idea of the changes in people’s interests in research over that time span. Finally, I polled my own research group and asked some colleagues what the really tough problems were in the discipline of VZH. One way to find out what is going on in the world of VZH is to examine the poster abstracts from the above-named conference. Table 17.1 presents an organizational summary of the 78 posters by subject matter. It is clear that the most active areas are property measurement, monitoring, and characterizing large-scale systems, which reflects both the influx of new monitoring devices and also increased attention paid to details of scale-dependence, interpolation, disturbance during monitoring, and other issues that have become research areas in their own right.

scholarly journals Semantic Networks for Engineering Design: State of the Art and Future Directions

2021 ◽  
pp. 1-45
Author(s):  
Ji Han ◽  
Serhad Sarica ◽  
Feng Shi ◽  
Jianxi Luo
Keyword(s):  
Engineering Design ◽  
Large Scale ◽  
Design Research ◽  
State Of The Art ◽  
Semantic Network ◽  
Semantic Networks ◽  
Knowledge Bases ◽  
Data Sources ◽  
Future Research ◽  
Support Engineering

Abstract In the past two decades, there has been increasing use of semantic networks in engineering design for supporting various activities, such as knowledge extraction, prior art search, idea generation and evaluation. Leveraging large-scale pre-trained graph knowledge databases to support engineering design-related natural language processing (NLP) tasks has attracted a growing interest in the engineering design research community. Therefore, this paper aims to provide a survey of the state-of-the-art semantic networks for engineering design and propositions of future research to build and utilize large-scale semantic networks as knowledge bases to support engineering design research and practice. The survey shows that WordNet, ConceptNet and other semantic networks, which contain common-sense knowledge or are trained on non-engineering data sources, are primarily used by engineering design researchers to develop methods and tools. Meanwhile, there are emerging efforts in constructing engineering and technical-contextualized semantic network databases, such as B-Link and TechNet, through retrieving data from technical data sources and employing unsupervised machine learning approaches. On this basis, we recommend six strategic future research directions to advance the development and uses of large-scale semantic networks for artificial intelligence applications in engineering design.

Change of Attitude, Technology and Practice: Identifying the Change for Increased Value Creation with Customer Co-creation

2017 ◽  
Vol 5 (1) ◽  
pp. 70-82
Author(s):  
Soumi Paul ◽  
Paola Peretti ◽  
Saroj Kumar Datta
Keyword(s):  
Social Media ◽  
New Product Development ◽  
Value Creation ◽  
Large Scale ◽  
Customer Orientation ◽  
Customer Relationships ◽  
Future Research ◽  
Management Competencies ◽  
Business Decisions ◽  
Prime Concern

Building customer relationships and customer equity is the prime concern in today’s business decisions. The emergence of internet, especially social media like Facebook and Twitter, changed traditional marketing thought to a great extent. The importance of customer orientation is reflected in the axiom, “The customer is the king”. A good number of organizations are engaging customers in their new product development activities via social media platforms. Co-creation, a new perspective in which customers are active co-creators of the products they buy and use, is currently challenging the traditional paradigm. The concept of co-creation involving the customer’s knowledge, creativity and judgment to generate value is considered not only an upcoming trend that introduces new products or services but also fitting their need and increasing value for money. Knowledge and innovation are inseparable. Knowledge management competencies and capacities are essential to any organization that aspires to be distinguished and innovative. The present work is an attempt to identify the change in value creation procedure along with one area of business, where co-creation can return significant dividends. It is on extending the brand or brand category through brand extension or line extension. This article, through an in depth literature review analysis, identifies the changes in every perspective of this paradigm shift and it presents a conceptual model of company-customer-brand-based co-creation activity via social media. The main objective is offering an agenda for future research of this emerging trend and ensuring the way to move from theory to practice. The paper acts as a proposal; it allows the organization to go for this change in a large scale and obtain early feedback on the idea presented. 

Overview of Subsynchronous Oscillation in Grid-connected Wind Farm

2020 ◽  
Vol 13 (7) ◽  
pp. 969-979
Author(s):  
Xu Pei-Zhen ◽  
Lu Yong-Geng ◽  
Cao Xi-Min
Keyword(s):  
Power Systems ◽  
Wind Turbine ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Induction Generator ◽  
Future Research ◽  
Stable Operation ◽  
Subsynchronous Oscillation ◽  
Different Types

Background: Over the past few years, the subsynchronous oscillation (SSO) caused by the grid-connected wind farm had a bad influence on the stable operation of the system and has now become a bottleneck factor restricting the efficient utilization of wind power. How to mitigate and suppress the phenomenon of SSO of wind farms has become the focus of power system research. Methods: This paper first analyzes the SSO of different types of wind turbines, including squirrelcage induction generator based wind turbine (SCIG-WT), permanent magnet synchronous generator- based wind turbine (PMSG-WT), and doubly-fed induction generator based wind turbine (DFIG-WT). Then, the mechanisms of different types of SSO are proposed with the aim to better understand SSO in large-scale wind integrated power systems, and the main analytical methods suitable for studying the SSO of wind farms are summarized. Results: On the basis of results, using additional damping control suppression methods to solve SSO caused by the flexible power transmission devices and the wind turbine converter is recommended. Conclusion: The current development direction of the SSO of large-scale wind farm grid-connected systems is summarized and the current challenges and recommendations for future research and development are discussed.

A Review of Minimum Quantity Lubrication (MQL) based on Bibliometry

2020 ◽  
Vol 13 ◽  
Author(s):  
Gaurav Gaurav ◽  
Abhay Sharma ◽  
G S Dangayach ◽  
M L Meena
Keyword(s):  
Descriptive Analysis ◽  
Minimum Quantity Lubrication ◽  
Citation Network ◽  
Machining Process ◽  
Cutting Fluid ◽  
Future Research ◽  
Research Directions ◽  
Minimum Quantity ◽  
Research Areas ◽  
Future Research Directions

Background: Minimum quantity lubrication (MQL) is one of the most promising machining techniques that can yield a reduction in consumption of cutting fluid more than 90 % while ensuring the surface quality and tool life. The significance of the MQL in machining makes it imperative to consolidate and analyse the current direction and status of research in MQL. Objective: This study aims to assess global research publication trends and hot topics in the field of MQL among machining process. The bibliometric and descriptive analysis are the tools that the investigation aims to use for the data analysis of related literature collected from Scopus databases. Methods: Various performance parameters are extracted, such as document types and languages of publication, annual scientific production, total documents, total citations, and citations per article. The top 20 of the most relevant and productive sources, authors, affiliations, countries, word cloud, and word dynamics are assessed. The graphical visualisation of the bibliometric data is presented in terms of bibliographic coupling, citation, and co-citation network. Results: The investigation reveals that the International Journal of Machine Tools and Manufacture (2611 citations, 31 hindex) is the most productive journal that publishes on MQL. The most productive institution is the University of Michigan (32 publications), the most cited country is Germany (1879 citations), and the most productive country in MQL is China (124 publications). The study shows that ‘Cryogenic Machining’, ‘Sustainable Machining’, ‘Sustainability’, ‘Nanofluid’ and ‘Titanium alloy’ are the most recent keywords and indications of the hot topics and future research directions in the MQL field. Conclusion: The analysis finds that MQL is progressing in publications and the emerging with issues that are strongly associated with the research. This study is expected to help the researchers to find the most current research areas through the author’s keywords and future research directions in MQL and thereby expand their research interests.

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