Special Issue "Ambidextrous Open Innovation of Electronics"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 2600

Special Issue Editors

Prof. Dr. Avimanyu Sahoo
E-Mail Website
Guest Editor
Division of Engineering and Technology, Oklahoma State university, 570 Engineering North, Stillwater, OK 74075, USA
Interests: event-triggered control; neural network control; optimal control and adaptive dynamic programming; nonlinear adaptive control; diagnostic and prognostics
Prof. Dr. Min-Ren Yan
E-Mail Website
Guest Editor
Department of International Business Administration, Chinese Culture University, Taipei, Taiwan
Interests: system dynamics of open innovation
Prof. Dr. JinHyo Joseph Yun
E-Mail Website
Chief Guest Editor
1. DGIST (Daegu Gyeongbuk Institute of Science and Technology), 333, Techno jungang-daero, Hyeonpung-eup, Dalseong-gun, Daegu, Korea
2. Graduate School of Public Administration, Seoul National University, 1 Gwanak-ro, Gwank-gu, Seoul 08826, Korea
Interests: open innovation; business model; open innovation economy; social open innovation; Schumpeterian dynamics; complexity; game theory; political economics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, nearly all industries, including the autonomous car industry, intelligent robots, e-commerce, smart city, and smart construction, are being fused or converged with the electronic industry. In addition, dynamic new combinations between technology and market are exposed with the appearance of the 4th industrial revolution. It is for this reason that we have organized this Special Issue on “Ambidextrous Open Innovation of Electronics”, which will cover very diverse ambidextrous open innovations, such as between exploration and exploitation, between technology and market, between inbound and outbound, etc.

The ambidextrous open innovation of electronics is appearing as a kind of dominant phenomenon for firms of all types, such as start-ups, SMEs, or MNCs, in order to succeed in the fourth industrial revolution.

In order to encourage scientists and researchers to present their progress in the above fields, we would like to set up this Special Issue to publish the latest research work on, but not limited to, the following topics:

  • Ambidextrous open innovation in the second IT revolution;
  • Ambidextrous dynamic open innovation;
  • Ambidextrous open innovation of smart cities;
  • Ambidextrous open innovation of autonomous cars;
  • Ambidextrous open innovation of intelligent robots;
  • Ambidextrous open innovation of block chain to the sharing economy;
  • Smart wearable technology;
  • Machine intelligence in mechatronics and robotics;
  • The impact of fire protection and safety technologies;
  • Construction education;
  • Innovation diversity for emerging economies;
  • Digital innovation and governance policy;
  • Open Innovation; The role of Japan
  • Quantum management, oriental wisdom, and management innovation;
  • The 4th industrial revolution: A sustainable innovation;
  • Dynamics of open innovation in the biomedical industry;
  • Open innovation and technology commercialization;
  • Technology, innovation, and collaborative governance;
  • Innovation performance;
  • Knowledge transfer and convergence identification with intellectual property strategies;
  • The Roles of HR, organizational capabilities, and open innovation in the 4th industrial revolution;
  • Innovation, entrepreneurship, and sustainability;
  • Digital transformation and innovation in the public sector;
  • Green governance;
  • Digital innovation and entrepreneurship;
  • Industry innovation ecosystem design and strategic development;
  • Open innovation and strategic competitiveness;
  • Measuring business excellence and case studies for sustainability;
  • Patent analysis and open innovation;
  • Innovation ecosystem for sustainable development in China;
  • Beyond innovation, beyond smart cities;
  • Entrepreneurship and technology-based firms;
  • Climate change and enhancement of public awareness toward industry;
  • Stories about the Korean innovation companies from start-ups and ventures to medium-large enterprises;
  • Transdisciplinary research and education in the deep-net age;
  • Open innovation in the tourism sector;
  • Efficiency issues in R&D and supply chain;
  • Open Innovation Study of University of South Wales, Business School;
  • Open innovation with inter-rationality;
  • Design, community innovation, creativity and culture;
  • Innovation and knowledge creation.

Time schedule of this special issue:

  • Special issue Open: 10th June 2020

Any SOItmC 2020 authors in addition to the planned papers can submit to this special issue after full paper submission to SOItmC 2020 platform and paying the registration fee until 10 June 2020 from 10th June 2020

  • Close: 31 December 2020

All papers should be submitted to this special issue until 31 December 2020.

Prof. Dr. JinHyo Joseph Yun
Prof. Dr. Avimanyu Sahoo
Prof. Dr. Min-Ren Yan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

Article
Developing an Open-Source Lightweight Game Engine with DNN Support
Electronics 2020, 9(9), 1421; https://doi.org/10.3390/electronics9091421 - 01 Sep 2020
Cited by 2 | Viewed by 934
Abstract
With the growth of artificial intelligence and deep learning technology, we have many active research works to apply the related techniques in various fields. To test and apply the latest machine learning techniques in gaming, it will be very useful to have a [...] Read more.
With the growth of artificial intelligence and deep learning technology, we have many active research works to apply the related techniques in various fields. To test and apply the latest machine learning techniques in gaming, it will be very useful to have a light-weight game engine for quick prototyping. Our game engine is implemented in a cost-effective way, in comparison to well-known commercial proprietary game engines, by utilizing open source products. Due to its simple internal architecture, our game engine is especially beneficial for modifying and reviewing the new functions through quick and repetitive tests. In addition, the game engine has a DNN (deep neural network) module, with which the proposed game engine can apply deep learning techniques to the game features, through applying deep learning algorithms in real-time. Our DNN module uses a simple C++ function interface, rather than additional programming languages and/or scripts. This simplicity enables us to apply machine learning techniques more efficiently and casually to the game applications. We also found some technical issues during our development with open sources. These issues mostly occurred while integrating various open source products into a single game engine. We present details of these technical issues and our solutions. Full article
(This article belongs to the Special Issue Ambidextrous Open Innovation of Electronics)
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Article
Pattern Detection Model Using a Deep Learning Algorithm for Power Data Analysis in Abnormal Conditions
Electronics 2020, 9(7), 1140; https://doi.org/10.3390/electronics9071140 - 14 Jul 2020
Cited by 3 | Viewed by 1427
Abstract
Building a pattern detection model using a deep learning algorithm for data collected from manufacturing sites is an effective way for to perform decision-making and assess business feasibility for enterprises, by providing the results and implications of the patterns analysis of big data [...] Read more.
Building a pattern detection model using a deep learning algorithm for data collected from manufacturing sites is an effective way for to perform decision-making and assess business feasibility for enterprises, by providing the results and implications of the patterns analysis of big data occurring at manufacturing sites. To identify the threshold of the abnormal pattern requires collaboration between data analysts and manufacturing process experts, but it is practically difficult and time-consuming. This paper suggests how to derive the threshold setting of the abnormal pattern without manual labelling by process experts, and offers a prediction algorithm to predict the potentials of future failures in advance by using the hybrid Convolutional Neural Networks (CNN)–Long Short-Term Memory (LSTM) algorithm, and the Fast Fourier Transform (FFT) technique. We found that it is easier to detect abnormal patterns that cannot be found in the existing time domain after preprocessing the data set through FFT. Our study shows that both train loss and test loss were well developed, with near zero convergence with the lowest loss rate compared to existing models such as LSTM. Our proposition for the model and our method of preprocessing the data greatly helps in understanding the abnormal pattern of unlabeled big data produced at the manufacturing site, and can be a strong foundation for detecting the threshold of the abnormal pattern of big data occurring at manufacturing sites. Full article
(This article belongs to the Special Issue Ambidextrous Open Innovation of Electronics)
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