• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.587-592
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• 2019

The importance of software technologies is becoming more prominent because of the competition to secure a competitive edge in software, which has been intensified since the emergence of smartphones and IoT. Thus, to assure the initiative in the global software industry and to foster superior human resources, there is a growing need for outstanding software development professionals. This paper analyzes the factors that affect the basic perception of software, the need for software development, and the enhancement of software coding ability based on a compulsory software class, which aims to increase the workforce of the converged software industry. The analysis shows that among other technical practices to enhance coding ability, learner-centered technical contents showed the most positive effect regarding the recognition and motive of development and are an essential factor in improving coding skills. The findings indicate that the need for program development and active involvement in the development of the program are the most important factors in improving the practical ability. The analysis presents meaningful results by suggesting a methodology for improving software development capabilities.

• Agribusiness and Information Management
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• v.10 no.2
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• pp.1-7
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• 2018

In the present day context of changing information needs of the farmers and diversified production systems there is an urgent need to look for the effective extension support system for the small and marginal farmers in the developing countries like India. The rapid developments in the collection and analysis of field data by using the spatial technologies like GPS&GIS were made available for the extension functionaries and clientele for the diversified information needs. This article describes the GIS and GPS based decision support system in precision agriculture for the resource poor farmers. Precision farming techniques are employed to increase yield, reduce production costs, and minimize negative impacts to the environment. The parameters those can affect the crop yields, anomalous factors and variations in management practices can be evaluated through this GPS and GIS based applications. The spatial visualisation capabilities of GIS technology interfaced with a relational database provide an effective method for analysing and displaying the impacts of Extension education and outreach projects for small and marginal farmers in precision agriculture. This approach mainly benefits from the emergence and convergence of several technologies, including the Global Positioning System (GPS), geographic information system (GIS), miniaturised computer components, automatic control, in-field and remote sensing, mobile computing, advanced information processing, and telecommunications. The PPP convergence of person (farmer), project (the operational field) and pixel (the digital images related to the field and the crop grown in the field) will better be addressed by this decision support model. So the convergence and emergence of such information will further pave the way for categorisation and grouping of the production systems for the better extension delivery. In a big country like India where the farmers and holdings are many in number and diversified categorically such grouping is inevitable and also economical. With this premise an attempt has been made to develop a precision farming model suitable for the developing countries like India.

• Journal of Digital Convergence
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• v.17 no.6
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• pp.133-144
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• 2019

The purpose of this study is to empirically examine the ICT legal system and the ICT performance by new technology's investment for government organizational changes. I will show the impact of government ICT investment interest, competency, convergence and process change, and then present policy direction. A research method used the structural equations. As a result of analysis, ICT investment interest and operational competency showed the negative impact the ICT legal system and the role change of ICT process and convergence of new technologies showed the positive impact. The Framework Act on National Information showed the positive impact on organizational performance, but the E-Government Act showed the negative impact. The contribution in the study expanded organization research from MIS perspective, and each organization is required the conflict resolve by ICT investment. A future study will require longitudinal study of ICT capabilities from previous to present government.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.223-228
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• 2019

Recently, the United States has been trying to strengthen its cybersecurity by upgrading its position as an Unified Combatant Command that focuses on the Cyber Command in the United States, strengthening operations in cyberspace, and actively responding to cyber threats. Other major powers are also working to strengthen cyber capabilities, and they are working to strengthen their organization and power. The world demands economic power for its own interests rather than its own borders. But Cyber World is a world without borders and no defense. Therefore, a cyber weapon system is necessary for superiority in cyberspace (defense, attack) for national cybersecurity. In this paper, we analyze operational procedures for cyber weapons operation. And we design cyber weapons to analyze and develop the best cyber weapons to lead victory in cyberwarfare. It also conducts cyber weapons research to solve the confrontation between Cyber World.

• Journal of Korea Trade
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• v.25 no.1
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• pp.152-168
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• 2021

Purpose - This paper has an objective to suggest reasonable criteria in choosing Incoterms 2020 rules for efficient and effective logistics management in that the Incoterms rules affect not only the rights and obligations of the parties to the sales contract but also the control and management of logistics system and transaction costs in the transaction. Design/methodology - An analysis of the various factors is needed to assess the positive or negative impact on global value chain in choosing Incoterms rules from a total logistics view. This study analyzes the impact of which the content of individual incoterms rules can have on the operation of international logistics systems under the global value chain from a strategic perspective to suggest reasonable criteria for selection of Incoterms rules depending on the transaction situation. Findings - Results of this study shows that consideration of various aspects which includes the characteristics of the products, logistics capabilities, infrastructure, transaction volume, operational cost, customs regulations, tax and accounting should be reflected in choosing the appropriate Incoterms rules. Therefore, in order to minimize the total cost and improve logistics performance, it may be helpful to develop a decision support model which allows users to select appropriate Incoterms rules based on various influencing factors. Originality/value - This Study is different from previous research which has mainly focused on the rights and obligations of the parties to the transaction regarding the transfer of risks and costs under the Incoterms. In addition, this study has significance in that it provides implications for export and import companies that can be able to use Incoterms as a strategic tool to efficiently manage the global value chain and improve supply chain performance.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• Journal of The Geomorphological Association of Korea
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• v.28 no.1
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• pp.83-99
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• 2021

Burn severity analysis using satellite imagery has high capabilities for research and management in inaccessible areas. We extracted the forest fire area of the DMZ (Demilitarized Zone) in the western Imjin Estuary which is restricted to access due to the confrontation between South and North Korea. Then we analyzed the forest fire severity and recoverability using atmospheric corrected Surface Reflectance Level-2 data collected from Landsat-8 OLI (Operational Land Imagery) / TIRS (Thermal Infrared Sensor). Normalized Burn Ratio (NBR), differenced NBR (dNBR), and Relative dNBR (RdNBR) were analyzed based on changes in the spectral pattern of satellite images to estimate burn severity area and intensity. Also, we evaluated the recoverability after a forest fire using a land cover map which is constructed from the NBR, dNBR, and RdNBR analyzed results. The results of dNBR and RdNBR analysis for the six years (during May 30, 2014 - May 30, 2020) showed that the intensity of monthly burn severity was affected by seasonal changes after the outbreak and the intensity of annual burn severity gradually decreased after the fire events. The regrowth of vegetation was detected in most of the affected areas for three years (until May 2020) after the forest fire reoccurred in May 2017. The monthly recoverability (from April 2014 to December 2015) of forests and grass fields was increased and decreased per month depending on the vegetation growth rate of each season. In the case of annual recoverability, the growth of forest and grass field was reset caused by the recurrence of a forest fire in 2017, then gradually recovered with grass fields from 2017 to 2020. We confirmed that remote sensing was effectively applied to research of the burn severity and recoverability in the DMZ. This study would also provide implications for the management and construction statistics database of the forest fire in the DMZ.

• Asia Marketing Journal
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• v.14 no.4
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• pp.23-33
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• 2013

This case focuses on WE CAN Cookies, a social enterprise in South Korea that was founded in 2001 with the support of the Korean Roman Catholic Church. WE CAN Cookies specializes in the making of high quality organic cookies. As a nonprofit organization that uses a labor force of mostly mentally disabled workers, the company faces many challenges that normal companies do not experience. The company had to initially overcome the social prejudice that the handicapped cannot make good cookies. Despite the religious background and social agenda of the company, it started making inroads as a cookie-making business only after its managers, including the nuns who run it began adopting modern management philosophies and practices. The WE CAN Cookies case illustrates three main marketing-related concepts: One, WE CAN Cookies is a good example of how social enterprises face a broader spectrum of challenges when compared to conventional profit-seeking enterprises. Two, WE CAN Cookies demonstrates that social enterprises need flexibility in formulating their business strategies. Even though WE CAN Cookies is subject to many constraints, as a social enterprise it can also take advantage of new opportunities for obtaining support from the government and from the private sector. Three, WE CAN Cookies shows that these types of operations need to create greater balance in their social and business competencies to ensure the long term viability. Social enterprises are certified by governments with the stated goal of improving the lives and the wellbeing of special interest group. As important as achieving these objectives are, social enterprises also must additionally be able to build their operational capabilities not only in manufacturing but also in functions such as marketing.

• Journal of IKEEE
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• v.28 no.2
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• pp.234-238
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• 2024

This paper presents a study on the integration of 3D-stacked dual-tip RRAM with a CNN accelerator architecture, leveraging its low drive current characteristics and scalability in a 3D stacked configuration. The dual-tip structure is utilized in a parallel connection format in a synaptic array to implement multi-level capabilities. It is configured within a Network-on-chip style accelerator along with various hardware blocks such as DAC, ADC, buffers, registers, and shift & add circuits, and simulations were performed for the CNN accelerator. The quantization of synaptic weights and activation functions was assumed to be 16-bit. Simulation results of CNN operations through a parallel pipeline for this accelerator architecture achieved an operational efficiency of approximately 370 GOPs/W, with accuracy degradation due to quantization kept within 3%.