• Journal of Technology Innovation
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• v.29 no.4
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• pp.59-93
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• 2021

This article discusses the innovation activities of firms that aim for system transformation from the perspective of 'Transformative Innovation Policies'. Here, for the sustainable transformation of our society, a firm that finds the 'purpose' of business activities in solving social problems and implements a new business model is defined as a 'transition-oriented firm'. The main characteristics of a transition-oriented firm are examined in terms of 1) transition vision and mission setting, 2) business model innovation for transition, 3) network formation for system transition, and 4) securing legitimacy of transition. And through case studies, the approach, significance, and limitations of the transition-oriented corporate innovation theory are discussed. The case study is from a Eisai Korea, which aims for an integrated prevention, treatment, and care system centered on residents and citizens.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1239-1249
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• 2018

Optical remote sensing sensors provide visually more familiar images than radar images. However, it is difficult to discriminate sea ice types in optical images using spectral information based machine learning algorithms. This study addresses two topics. First, we propose a semantic segmentation which is a part of the state-of-the-art deep learning algorithms to identify ice types by learning hierarchical and spatial features of sea ice. Second, we propose a new approach by combining of semi-supervised and active learning to obtain accurate and meaningful labels from unlabeled or unseen images to improve the performance of supervised classification for multiple images. Therefore, we successfully added new labels from unlabeled data to automatically update the semantic segmentation model. This should be noted that an operational system to generate ice type products from optical remote sensing data may be possible in the near future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.7-14
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• 2021

The emergence of the automated smart grid has become an essential device for responding to these problems and is bringing progress toward a smart grid-based society. Smart grid is a new paradigm that enables two-way communication between electricity suppliers and consumers. Smart grids have emerged due to engineers' initiatives to make the power grid more stable, reliable, efficient and safe. Smart grids create opportunities for electricity consumers to play a greater role in electricity use and motivate them to use electricity wisely and efficiently. Therefore, this study focuses on power demand management through machine learning. In relation to demand forecasting using machine learning, various machine learning models are currently introduced and applied, and a systematic approach is required. In particular, the GP learning model has advantages over other learning models in terms of general consumption prediction and data visualization, but is strongly influenced by data independence when it comes to prediction of smart meter data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.1
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• pp.81-91
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• 2012

Presently in 2011, there are countless attacking tools oriented to invading security on the internet. And most of the tools are possible to conduct the actual invasion. Also, as the program sources attacking the weaknesses of PS3 were released in 2010 and also various sources for attacking agents and attacking tools such as Stuxnet Source Code were released in 2011, the part for defense has the greatest burden; however, it can be also a chance for the defensive part to suggest and develop methods to defense identical or similar patterned attacking by analyzing attacking sources. As a way to cope with such attacking, this study divides the network areas targeted for attack based on load balancing by the approach gateways and communication lines according to the defensive policies by attacking types and also suggests methods to multiply communication lines. The result of this paper will be provided as practical data to realize defensive policies based on high hardware performances through enhancing the price competitiveness of hardware infrastructure with 2010 as a start.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.115-123
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• 2021

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.

• Genomics & Informatics
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• v.21 no.1
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• pp.8.1-8.19
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• 2023

Cancer of the stomach is the second most frequent cancer-related death worldwide. The survival rate of patients with gastric cancer (GC) remains fragile. There is a requirement to discover biomarkers for prognosis approaches. Helicobacter pylori in the stomach is closely associated with the progression of GC. We identified the genes associated with poor/favorable prognosis in H. pylori-induced GC. Multivariate statistical analysis was applied on the Gene Expression Omnibus (GEO) dataset GSE54397 to identify differentially expressed miRNAs (DEMs) in gastric tissues with H. pylori-induced cancer compared with the H. pylori-positive with non-cancerous tissue. A protein interaction map (PIM) was built and subjected to DEMs targets. The enriched pathways and biological processes within the PIM were identified based on substantial clusters. Thereafter, the most critical genes in the PIM were illustrated, and their prognostic impact in GC was investigated. Considering p-value less than 0.01 and |Log2 fold change| as >1, five microRNAs demonstrated significant changes among the two groups. Gene functional analysis revealed that the ubiquitination system, neddylation pathway, and ciliary process are primarily involved in H. pylori-induced GC. Survival analysis illustrated that the overexpression of DOCK4, GNAS, CTGF, TGF-b1, ESR1, SELE, TIMP3, SMARCE1, and TXNIP was associated with poor prognosis, while increased MRPS5 expression was related to a favorable prognosis in GC patients. DOCK4, GNAS, CTGF, TGF-b1, ESR1, SELE, TIMP3, SMARCE1, TXNIP, and MRPS5 may be considered prognostic biomarkers for H. pylori-induced GC. However, experimental validation is necessary in the future.

• Advances in nano research
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• v.15 no.6
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• pp.521-531
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• 2023

Quantum-dot cellular automata (QCA) has shown great potential in the nanoscale regime as a replacement for CMOS technology. This work presents a specific approach to static random-access memory (SRAM) cell based on 2:1 multiplexer, 4-bit SRAM array, and 32-bit SRAM array in QCA. By utilizing the proposed SRAM array, a single-layer 16×32-bit SRAM with the read/write capability is presented using an optimized signal distribution network (SDN) crossover technique. In the present study, an extremely-optimized 2:1 multiplexer is proposed, which is used to implement an extremely-optimized SRAM cell. The results of simulation show the superiority of the proposed 2:1 multiplexer and SRAM cell. This study also provides a more efficient and accurate method for calculating QCA costs. The proposed extremely-optimized SRAM cell and SRAM arrays are advantageous in terms of complexity, delay, area, and QCA cost parameters in comparison with previous designs in QCA, CMOS, and FinFET technologies. Moreover, compared to previous designs in QCA and FinFET technologies, the proposed structure saves total energy consisting of overall energy consumption, switching energy dissipation, and leakage energy dissipation. The energy and structural analyses of the proposed scheme are performed in QCAPro and QCADesigner 2.0.3 tools. According to the simulation results and comparison with previous high-quality studies based on QCA and FinFET design approaches, the proposed SRAM reduces the overall energy consumption by 25%, occupies 33% smaller area, and requires 15% fewer cells. Moreover, the QCA cost is reduced by 35% compared to outstanding designs in the literature.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.121-130
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• 2024

In this paper, we propose an installation approach for Naval Combat Management System(CMS) software that identifies potential data anomalies during installation. With the popularization of wireless communication methods, such as Low Earth Orbit(LEO) satellite communications, various utilization methods using wireless networks are being discussed in CMS. One of these methods includes the use of wireless network communications for installation, which is expected to enhance the real-time performance of the CMS. However, wireless networks are relatively more vulnerable to security threats compared to wired networks, necessitating additional security measures. This paper presents a method where files are transmitted to multiple nodes using encryption, and after the installation of the files, a validity check is performed to determine if there has been any tampering or alteration during transmission, ensuring proper installation. The feasibility of applying the proposed method to Naval Combat Systems is demonstrated by evaluating transmission performance, security, and stability, and based on these evaluations, results sufficient for application to CMS have been derived.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.471-476
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• 2024

With the spread of various smart devices and computing devices, big data generation is occurring widely. Machine learning is an algorithm that performs reasoning by learning data patterns. Among the various machine learning algorithms, the algorithm that attracts attention is deep learning based on neural networks. Deep learning is achieving rapid performance improvement with the release of various applications. Recently, among deep learning algorithms, attempts to analyze data using graph structures are increasing. In this study, we present a graph generation method for transferring to a deep learning network. This paper proposes a method of generalizing node properties and edge weights in the graph generation process and converting them into a structure for deep learning input by presenting a matricization We present a method of applying a linear transformation matrix that can preserve attribute and weight information in the graph generation process. Finally, we present a deep learning input structure of a general graph and present an approach for performance analysis.

• Information Systems Review
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• v.12 no.1
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• pp.1-22
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• 2010

Demands onenterprise applications are changing drastically in terms of service and value. Currently enterprises have started to view these applications as service systems, as they combine technology with organizational networks designed to deliver services that satisfy the needs of customers and marketing operations. Social networking is playing a crucial role in this direction and provides organizations with the critical data that enable to build strong relationships with their customers and partners. Enterprises have started using this concept, by integrating social networking services with their enterprise applications such as CRM. In this paper, we combine an open source social networking engine with a CRM (Customer Relationship Management) application to constitute a social CRM system. This can bring the customers closer to the enterprise and facilitate better communication with them. Social Networking Analysis constructs were used to analyze the effectiveness of service system. In the current competitive and economically challenging conditions, salespeople needs to quickly and effectively establish meaningful communication with customers. Our approach can address this issue, by handling the changing customer demands in minimal time, and increases service quality and business value.