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Sustainable Growth in Engineering and Technology Management
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Sustainable Growth in Engineering and Technology Management

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 19177

Special Issue Editors

Prof. Dr. Tae-Eung Sung

E-Mail Website
Guest Editor
Department of Computer and Telecommunications Engineering, College of Science and Technology, Yonsei University, Wonju 26493, Republic of Korea
Interests: artificial intelligence and big data analyses in open innovation; new generation communication and networking systems
Prof. Dr. Ki-Il Kim

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
Interests: wireless sensor networks; wireless body area networks; real-time communications; protocol engineering; QoS
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Eungdo Kim

E-Mail Website
Guest Editor
Graduate School of Biomedical Convergence, College of Medicine, Chungbuk National University, Cheongju 28644, Korea
Interests: technology management; technology innovation; technology valuation; technology commercialization

Special Issue Information

Dear Colleagues,

The recent COVID-19 epidemic and economic recession have been redefining our daily life and work culture. We are hopefully anticipating the possibility to overcome these unfavorable situations such as the COVID-19 pandemic, global economic crisis, global warming, etc. by contributing to innovative technologies and their commercialization related to sustainable growth in engineering and science.

One area that has been gaining significant attention is sustainable platforms of mobile networks for realizing smart factories, smart IoT, green communications, etc. Thanks to technological advances in the wireless and mobile networks environment, cutting-edge technologies in protocols and applications require reliability, robustness, and timeliness in the information exchange between devices.

Another area that seeks visualized outcomes for innovative technologies in engineering and science is sustainable growth in technology commercialization. Most technology-based enterprises are not always commercially successful in unexpected economic situations, but they need to be equipped with knowledge, skills and competences in aspects of technology innovation and management.

Against all adversity, we will have to move forward to enable sustainable growth in engineering and technology management. Proactive effort to change unwanted crisis to sustainable opportunity will make the world advance with state-of-the-art technologies of collaboration between digital resources and human intelligence.

In this Special Issue, we aim to explore the latest research, application, and adoption of reliable mobile networks in industrial fields and technology management in technology commercialization, with a focus on sustainability. We willingly welcome all researchers to consider technological advances in mobile networks and the integration of interdisciplinary engineering/science technologies, and further, to contribute to sustainable growth.

Topics include, but are not limited to:

  • Systems and applications for industrial wireless and mobile networks;
  • Sustainable growth platforms, such as smart factories, smart IoT, green communications (5G and beyond);
  • Machine and deep learning algorithms in sustainable development;
  • Data analytics and data-driven applications for sustainability;
  • Challenges of innovation and technology management for sustainability;
  • Sustainable methodologies, models and information systems for technology commercialization;
  • Any subjects relevant to sustainability in mobile networks and technology innovation management.

Looking forward to your contributions.

Prof. Dr. Tae-Eung Sung
Prof. Dr. Ki-Il Kim
Prof. Dr. Eungdo Kim
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. Sustainability 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 2400 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.

Keywords

  • sustainable growth
  • mobile networks
  • internet of things
  • open innovation
  • technology management
  • technology commercialization

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Published Papers (5 papers)

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Research

25 pages, 21499 KiB  
Article
A Study on Intelligent Technology Valuation System: Introduction of KIBO Patent Appraisal System II
by Min-Seung Kim, Chan-Ho Lee, Ji-Hye Choi, Yong-Ju Jang, Jeong-Hee Lee, Jaesik Lee and Tae-Eung Sung
Sustainability 2021, 13(22), 12666; https://doi.org/10.3390/su132212666 - 16 Nov 2021
Cited by 10 | Viewed by 3450
Abstract
Technology finance, which has attracted worldwide attention for the successful business development of small-and-medium enterprises (SMEs) or start-ups, has advanced an innovation or stagnation way-out resolution strategy for companies in line with the low-growth economic trends. Although the development of new technologies and [...] Read more.
Technology finance, which has attracted worldwide attention for the successful business development of small-and-medium enterprises (SMEs) or start-ups, has advanced an innovation or stagnation way-out resolution strategy for companies in line with the low-growth economic trends. Although the development of new technologies and the establishment of active R&D and commercialization strategies are essential factors in a company’s management sustainability, the activation of the technology market in practice is still in progress for its golden age. In this study, to promote a technology transfer-based company’s growth and to run technology-based various financial support activities, we develop and propose a new intelligent, deep learning-based technology valuation system that enables technology holders to estimate the economic values of their innovative technologies and further to establish a firm’s commercialization strategy. For the last years, the KIBO Patent Appraisal System (KPAS-II) herein proposed has been advanced by KIBO as a web-based, artificial intelligence (AI) and evaluation data applications valuation system that automatically calculates and estimates a technology’s feasible economic value by utilizing both the intrinsic and extrinsic index information of a patent and the commercialization entity’s business capabilities, and by applying to the discounted cash flow (DCF) method in valuation theory, and finally integrating with deep learning results based on the in-advance previously established patent DB and the financial DB. The KPAS-II proposed in this study can be said to have dramatically overcome the long-term preparation period and high levels of R&D and commercialization costs in terms of the limitations that the existing technology valuation method possesses by enhancing the reliability of approximate economic values from the deep learning results based on financial data and completed valuation data. In addition, it is expected that technology marketing coordinators, researchers, and non-specialty business agents, not limited to valuation experts, can easily estimate the economic values of their patents or technologies, and they can be actively utilized in a technology-based company’s decision-making and technologically dependent financial activities. Full article
(This article belongs to the Special Issue Sustainable Growth in Engineering and Technology Management)
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25 pages, 560 KiB  
Article
Evaluating and Enhancing the Robustness of Sustainable Neural Relationship Classifiers Using Query-Efficient Black-Box Adversarial Attacks
by Ijaz Ul Haq, Zahid Younas Khan, Arshad Ahmad, Bashir Hayat, Asif Khan, Ye-Eun Lee and Ki-Il Kim
Sustainability 2021, 13(11), 5892; https://doi.org/10.3390/su13115892 - 24 May 2021
Cited by 2 | Viewed by 3091
Abstract
Neural relation extraction (NRE) models are the backbone of various machine learning tasks, including knowledge base enrichment, information extraction, and document summarization. Despite the vast popularity of these models, their vulnerabilities remain unknown; this is of high concern given their growing use in [...] Read more.
Neural relation extraction (NRE) models are the backbone of various machine learning tasks, including knowledge base enrichment, information extraction, and document summarization. Despite the vast popularity of these models, their vulnerabilities remain unknown; this is of high concern given their growing use in security-sensitive applications such as question answering and machine translation in the aspects of sustainability. In this study, we demonstrate that NRE models are inherently vulnerable to adversarially crafted text that contains imperceptible modifications of the original but can mislead the target NRE model. Specifically, we propose a novel sustainable term frequency-inverse document frequency (TFIDF) based black-box adversarial attack to evaluate the robustness of state-of-the-art CNN, CGN, LSTM, and BERT-based models on two benchmark RE datasets. Compared with white-box adversarial attacks, black-box attacks impose further constraints on the query budget; thus, efficient black-box attacks remain an open problem. By applying TFIDF to the correctly classified sentences of each class label in the test set, the proposed query-efficient method achieves a reduction of up to 70% in the number of queries to the target model for identifying important text items. Based on these items, we design both character- and word-level perturbations to generate adversarial examples. The proposed attack successfully reduces the accuracy of six representative models from an average F1 score of 80% to below 20%. The generated adversarial examples were evaluated by humans and are considered semantically similar. Moreover, we discuss defense strategies that mitigate such attacks, and the potential countermeasures that could be deployed in order to improve sustainability of the proposed scheme. Full article
(This article belongs to the Special Issue Sustainable Growth in Engineering and Technology Management)
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20 pages, 548 KiB  
Article
Partial Discharge Online Detection for Long-Term Operational Sustainability of On-Site Low Voltage Distribution Network Using CNN Transfer Learning
by Jinseok Kim and Ki-Il Kim
Sustainability 2021, 13(9), 4692; https://doi.org/10.3390/su13094692 - 22 Apr 2021
Cited by 14 | Viewed by 2664
Abstract
Partial discharge (PD) detection studies aiming at the fault diagnosis for facilities and power cables in transmission networks have been conducted over the years. Recently, the deep learning models for PD detection have been used to diagnose the PD fault of facilities and [...] Read more.
Partial discharge (PD) detection studies aiming at the fault diagnosis for facilities and power cables in transmission networks have been conducted over the years. Recently, the deep learning models for PD detection have been used to diagnose the PD fault of facilities and cables. Most PD studies have been conducted in the field, such as gas-insulated switchgear (GIS) and power cables for high voltage transmission networks. There are few studies of PD fault detection for on-site low-voltage distribution networks. Additionally, there are few studies of PD detection algorithms for improving the accuracy of the deep learning models using small real PD data only. In this study, a PD online detection system and a model for long-term operational sustainability of on-site low voltage distribution networks are proposed using convolutional neural network (CNN) transfer-learning. The proposed PD online system makes it possible to acquire as many real PD data as possible through continuous monitoring of PD occurrence. The PD detection accuracy results showed that the proposed CNN transfer-learning models are more effective models for obtaining improved accuracy (97.4%) than benchmark models, such as CNN and support vector machine (SVM) using only small real PD data acquired from PD online detection system. Full article
(This article belongs to the Special Issue Sustainable Growth in Engineering and Technology Management)
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17 pages, 354 KiB  
Article
Factors Affecting Outbound Open Innovation Performance in Bio-Pharmaceutical Industry-Focus on Out-Licensing Deals
by Eungdo Kim, InGyu Lee, Hongbum Kim and Kwangsoo Shin
Sustainability 2021, 13(8), 4122; https://doi.org/10.3390/su13084122 - 7 Apr 2021
Cited by 14 | Viewed by 4559
Abstract
Due to the high risk in development process, the bio-pharmaceutical industry has transformed itself into an open innovation framework in order to overcome economic risk. This study examines the relationship between outbound open innovation and financial performance in bio-pharmaceutical industry. Specifically, this study [...] Read more.
Due to the high risk in development process, the bio-pharmaceutical industry has transformed itself into an open innovation framework in order to overcome economic risk. This study examines the relationship between outbound open innovation and financial performance in bio-pharmaceutical industry. Specifically, this study extends knowledge-based view to link the open innovation performance and licensor’s sustainability. In order to provide empirical evidence, this study uses econometric methodology with several databases including bio-pharmaceutical firms. The analysis shows firm’s desorptive capabilities have a significant effect on financial performance, confirming the application of knowledge capacity framework. The result of the study can suggest the way how the licensors can maintain the sustainability of competitiveness in bio-pharmaceutical industry. Full article
(This article belongs to the Special Issue Sustainable Growth in Engineering and Technology Management)
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27 pages, 1240 KiB  
Article
Entropy Based Features Distribution for Anti-DDoS Model in SDN
by Raja Majid Ali Ujjan, Zeeshan Pervez, Keshav Dahal, Wajahat Ali Khan, Asad Masood Khattak and Bashir Hayat
Sustainability 2021, 13(3), 1522; https://doi.org/10.3390/su13031522 - 1 Feb 2021
Cited by 35 | Viewed by 3994
Abstract
In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as severe network security threats. For conventional network security tools it is extremely difficult to distinguish between the higher traffic volume of a DDoS attack and large number of legitimate users [...] Read more.
In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as severe network security threats. For conventional network security tools it is extremely difficult to distinguish between the higher traffic volume of a DDoS attack and large number of legitimate users accessing a targeted network service or a resource. Although these attacks have been widely studied, there are few works which collect and analyse truly representative characteristics of DDoS traffic. The current research mostly focuses on DDoS detection and mitigation with predefined DDoS data-sets which are often hard to generalise for various network services and legitimate users’ traffic patterns. In order to deal with considerably large DDoS traffic flow in a Software Defined Networking (SDN), in this work we proposed a fast and an effective entropy-based DDoS detection. We deployed generalised entropy calculation by combining Shannon and Renyi entropy to identify distributed features of DDoS traffic—it also helped SDN controller to effectively deal with heavy malicious traffic. To lower down the network traffic overhead, we collected data-plane traffic with signature-based Snort detection. We then analysed the collected traffic for entropy-based features to improve the detection accuracy of deep learning models: Stacked Auto Encoder (SAE) and Convolutional Neural Network (CNN). This work also investigated the trade-off between SAE and CNN classifiers by using accuracy and false-positive results. Quantitative results demonstrated SAE achieved relatively higher detection accuracy of 94% with only 6% of false-positive alerts, whereas the CNN classifier achieved an average accuracy of 93%. Full article
(This article belongs to the Special Issue Sustainable Growth in Engineering and Technology Management)
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