• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.391-393
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• 2022

In recent years, technologies forecasting demand based on deep learning and big data have accelerated the smartification of the field of e-commerce, logistics and distribution areas. In particular, ports, which are the center of global transportation networks and modern intelligent logistics, are rapidly responding to changes in the global economy and port environment caused by the 4th industrial revolution. Port traffic forecasting will have an important impact in various fields such as new port construction, port expansion, and terminal operation. Therefore, the purpose of this study is to compare the time series analysis and deep learning analysis, which are often used for port traffic prediction, and to derive a prediction model suitable for the future container prediction of Busan Port. In addition, external variables related to trade volume changes were selected as correlations and applied to the multivariate deep learning prediction model. As a result, it was found that the LSTM error was low in the single-variable prediction model using only Busan Port container freight volume, and the LSTM error was also low in the multivariate prediction model using external variables.

• Asia Marketing Journal
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• v.11 no.1
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• pp.1-27
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• 2009

The research compared the motivational network of traits predictive of complaint attitudes across consumers in the U.S. and South Korean cultures. Overall, the results revealed a similar pattern of traits predictive of complaint attitudes in the two cultures. The traits of value consciousness, general self-efficacy, emotional instability, and the need for material resources were positively related to attitudes toward complaining. In contrast, conscientiousness was negatively related to complaint attitudes. The only trait predictor of complaining attitude that was significantly different between the Korean and U.S. samples was shopping enjoyment. It was negatively related to complaining attitude in the U.S. sample but unrelated to complaining attitude in the Korean sample. Understanding the personality traits predictive of complaint attitudes has the potential to help marketers develop messages that will encourage the low complaint prone to voice their dissatisfaction. This is important, because when a consumer complains about and unsatisfactory purchase, it gives the firm a chance to take actions to avoid losing a customer.

• Korean Journal of Culture and Social Issue
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• v.22 no.1
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• pp.87-118
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• 2016

The purpose of this present study was to investigate the influence of abusive supervision on strain-based work interference with family and interpersonal deviance. In addition, this study examined the mediating effect of subordinates' emotional labor toward supervisors and the moderating effect of hierarchical organizational climates on emotional labor, perceived organizational family support on strain-based work-family conflict, and social network on interpersonal deviance. The results are summarized as follows: (1) abusive supervision was positively related to subordinates' emotional labor toward supervisors. (2) Emotional labor was positively related to strain-based work-family conflict and interpersonal deviance. (3) Subordinates' emotional labor mediated the relationship between abusive supervision and the two outcome variables. (4) Hierarchical organizational climates moderated the relationship between abusive supervision and emotional labor. (5) Perceived organizational family did not have moderating effect between emotional labor and strain-based work-family conflict. (6) Social network had moderating effect but it did not influence interpersonal deviance as predicted by the hypothesis. Based on the results, implications of findings, limitations, and suggestions for future research were discussed.

• Asia Marketing Journal
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• v.9 no.2
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• pp.97-129
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• 2007

The critical link between consumer-based internet ordering and the delivery of the product to the consumer is a key success factor in online shopping. Market areas of online shopping company implies the range of space where products ordered from online shopping can be physically delivered to customers distributed over space with reasonable shipping cost and lead time through the physical distribution network. The average rate of growth in online shopping is 36% per year in Korea for the last 5 years. But there are no maps available that describe sales/delivery density of online shopping, few researches are focused on the short-term/long-term adaptation to demand increase by online shopping. In this paper (1) Maps of trade area are described indicating the sales/delivery density around the nation. (2) Empirical researches suggested that short-term adaptation to demand increase resulted in price reduction and service in enhancement of service quality in local transportation. But the long-term adaptation on the parts of parcel delivery industry are to be investigated in future researches.

• Knowledge Management Research
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• v.24 no.3
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• pp.53-71
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• 2023

Environmental, Social, and Governance (ESG) refers to factors that are important for assessing a firm's social and environmental effect, as well as its governance standards. This paper investigates the relationship between ESG-based corporate governance and SDGs strategy implementation by discussing about incorporating ESG issues into corporate operations. It digs into the advantages and disadvantages of aligning corporate governance with the SDGs, demonstrating the potential for delivering long-term value for both firms and society as a whole. In this paper, we investigate ESG-Based Knowledge Management (ESG-KM), a knowledge management system that incorporates sustainability principles. More specifically, the paper investigates how the synergy between ESG-KM and ESG-Based Corporate Governance (ESG-CG) might influence firms' long-term value creation, stakeholder involvement, and sustainable decision-making. Finally, this paper investigates how public organizations might use knowledge management to improve the implementation and effect of ESG-CG principles, resulting in better sustainable outcomes. Public enterprises may support responsible decision-making, increase stakeholder involvement, and achieve long-term performance by linking ESG principles with corporate governance standards. The paper then explores how ESG-KM might help public firms integrate these concepts into their governance structures. The scientific novelty of this paper resides in its thorough investigation, realistic implementation methodologies, and novel combination of ESG principles, corporate governance, and knowledge management. Furthermore, by providing actionable insights and emphasizing the application of these concepts in the context of public enterprises, the paper makes a valuable contribution to the field of management, propelling the discourse on responsible and sustainable business practices in both the private and public sectors.

• Knowledge Management Research
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• v.24 no.3
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• pp.73-94
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• 2023

With the virtual asset market's rapid growth, government regulations on listing and trading procedures are expected. However, specific measures are currently lacking. To ensure stable inclusion in the institutional framework, precise regulations are needed for market development and investor protection. This study compares self-regulatory guidelines of the top domestic virtual asset exchanges with Korea Exchange's Preliminary Listing Examination Standards (2022) to enhance timeliness and relevance. It defines IEO, IPO, and ICO concepts and addresses conflicts of interest in IEO. Analyzing delisted virtual assets, it categorizes issues and classifies listing examination guidelines into formal and qualitative requirements. The study examines self-regulatory guidelines based on continuity, transparency, stability, corporate characteristics, and investor protection criteria, along with five special requirements for virtual assets. Improvement measures include regular disclosures of governance structure, circulation volume, and the establishment of independent audit institutions. This research further analyzes delisting cases, classifies issues, and proposes solutions. Considering stock market similarities, it offers measures based on the institutional framework.

• Therapeutic Science for Rehabilitation
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• v.12 no.4
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• pp.23-37
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• 2023

Objective : This study systematically reviewed the collaborative team interventions of the Individualized Education Plan (IEP) using the International Classification of Functioning, Disability, and Health-Children and Youth (ICF-CY) framework to establish the professional domain of occupational therapists in Korea and their role as experts in IEP cooperative team interventions in special education. Methods : Articles were collected from the EBSCOhost, ProQuest, and PubMed databases. International search terms included "Special education," "Individualized education plan (IEP)," "IEP process," "IEP implementation," and "Occupational therapy." The study period was limited from January 2013 to February 2023, and the final 10 studies were analyzed using secondary classification. Results : Most studies were randomized experiments targeting individuals with autism, and often employed environmental improvements. The IEP collaborative team interventions using the ICF-CY framework emphasized goals related to activity (five studies), participation (four studies), and body structure/function (one study). Conclusion : Occupational therapists play a crucial role in collaborative IEP team interventions. This study established expertise in the context of special education in South Korea.

• Journal of the Korea Computer Graphics Society
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• v.29 no.5
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• pp.21-30
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• 2023

Accurate segmentation of the kidney tumor is necessary to identify shape, location and safety margin of tumor in abdominal CT images for surgical planning before renal partial nephrectomy. However, kidney tumor segmentation is challenging task due to the various sizes and locations of the tumor for each patient and signal intensity similarity to surrounding organs such as intestine and spleen. In this paper, we propose a semi-supervised learning-based mean teacher network that utilizes both labeled and unlabeled data using a kidney local guided map including kidney local information to segment small-sized kidney tumors occurring at various locations in the kidney, and analyze the performance according to the kidney tumor size. As a result of the study, the proposed method showed an F1-score of 75.24% by considering local information of the kidney using a kidney local guide map to locate the tumor existing around the kidney. In particular, under-segmentation of small-sized tumors which are difficult to segment was improved, and showed a 13.9%p higher F1-score even though it used a smaller amount of labeled data than nnU-Net.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.408-416
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• 2023

The IUCN Red List of Ecosystems serves as a global standard for assessing and identifying ecosystems at high risk of biodiversity loss, providing scientific evidence necessary for effective ecosystem management and conservation policy formulation. The IUCN Red List of Ecosystems has been designated as a key indicator (A.1) for Goal A of the Kunming-Montreal Global Biodiversity Framework. The assessment of the Red List of Ecosystems discerns signs of ecosystem collapse through specific criteria: reduction in distribution (Criterion A), restricted distribution (Criterion B), environmental degradation (Criterion C), changes in biological interaction (Criterion D), and quantitative estimation of the risk of ecosystem collapse (Criterion E). Since 2014, the IUCN Red List of Ecosystems has been evaluated in over 110 countries, with more than 80% of the assessments conducted in terrestrial and inland water ecosystems, among which tropical and subtropical forests are distributed ecosystems under threat. The assessment criteria are concentrated on spatial signs (Criteria A and B), accounting for 68.8%. There are three main considerations for applying the Red List of Ecosystems assessment domestically: First, it is necessary to compile applicable terrestrial ecosystem types within the country. Second, it must be determined whether the spatial sign assessment among the Red List of Ecosystems categories can be applied to the various small-scale ecosystems found domestically. Lastly, the collection of usable time series data (50 years) for assessment must be considered. Based on these considerations, applying the IUCN Red List of Ecosystems assessment domestically would enable an accurate understanding of the current state of the country's unique ecosystem types, contributing to global efforts in ecosystem conservation and restoration.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.69-75
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• 2024

This paper proposes an innovative cognitive IoT framework specifically designed for fog computing (FC) in the context of industrial Internet of Things (IIoT). The discourse in this paper is centered on the intricate design and functional architecture of the Cognitive IoT platform. A crucial feature of this platform is the integration of machine learning (ML) and artificial intelligence (AI), which enhances its operational flexibility and compatibility with a wide range of industrial applications. An exemplary application of this platform is highlighted through the Predictive Maintenance-as-a-Service (PdM-as-a-Service) model, which focuses on real-time monitoring of machine conditions. This model transcends traditional maintenance approaches by leveraging real-time data analytics for maintenance and management operations. Empirical results substantiate the platform's effectiveness within a fog computing milieu, thereby illustrating its transformative potential in the domain of industrial IoT applications. Furthermore, the paper delineates the inherent challenges and prospective research trajectories in the spheres of Cognitive IoT and Fog Computing within the ambit of Industrial Internet of Things (IIoT).