• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.735-747
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• 2023

In recent times, the sharp increase in extreme flood damages due to climate change has posed a challenge to effectively address flood-related issues solely relying on conventional flood management infrastructure. In response to this problem, this study aims to consider the effectiveness of nature-based flood management approaches, specifically levee retreat and relocation. To achieve this, we utilized a 1D numerical model, HEC-RAS, to analyze the flood reduction effects concerning floodwater levels, flow velocities, and time-dependent responses to a 100-year frequency flood event. The analysis results revealed that the effect of creating a flood buffer zone of the nature-based solution extends from upstream to downstream, reducing flood water levels by up to 30 cm. The selection of the flow roughness coefficient in consideration of the nature-based flood buffer space creation characteristics should be based on precise criteria and scientific evidence because it is sensitive to the flood control effect analysis results. Notably, floodwater levels increased in some expanded floodplain sections, and the reduction in flow velocities varied depending on the ratio of the expanded cross-sectional area. In conclusion, levee retreat and floodplain expansion are viable nature-based alternatives for effective flood management. However, a comprehensive design approach is essential considering flood control effects, flow velocity reduction, and the timing of peak water levels. This study offers insights into addressing the challenges of climate-induced extreme flooding and advancing flood management strategies.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.42-50
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• 2023

The development of technology has achieved digital innovation throughout the media industry, including production techniques and editing technologies, and has brought diversity in the form of consumer viewing through the OTT service and streaming era. The convergence of big data and deep learning networks automatically generated text in format such as news articles, novels, and scripts, but there were insufficient studies that reflected the author's intention and generated story with contextually smooth. In this paper, we describe the flow of pictures in the storyboard with image caption generation techniques, and the automatic generation of story-tailored scenarios through language models. Image caption using CNN and Attention Mechanism, we generate sentences describing pictures on the storyboard, and input the generated sentences into the artificial intelligence natural language processing model KoGPT-2 in order to automatically generate scenarios that meet the planning intention. Through this paper, the author's intention and story customized scenarios are created in large quantities to alleviate the pain of content creation, and artificial intelligence participates in the overall process of digital content production to activate media intelligence.

• Knowledge Management Research
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• v.25 no.1
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• pp.89-109
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• 2024

The development of digital technology has given rise to a new service model: self-service. This model introduces the concept of "digital shadow work", in which consumers conduct unpaid behind-the-scenes digital tasks instead of employees. While consumers are engaging in increasingly more digital shadow work in self-service environments, they are unaware of their unpaid labor. This raises concerns about consumer rights and businesses' long-term sustainability and health. This study aims to reveal the psychological awareness factors that influence consumers' decisions to engage in digital shadow work in self-service environments. This exploratory qualitative study utilizes a grounded theory approach and semi-structured interviews to reveal the psychological awareness factors that contribute to consumers' decision to engage in digital shadow work. By revealing the psychological awareness of decision-making factors, this study enhances consumer's understanding and awareness of digital shadow work, which helps increase their awareness of self-protection in the context of self-service technologies. Additionally, understanding consumers' decision-making psychology is crucial for non-face-to-face self-service technology companies and provides a theoretical basis for sustainable and healthy business development.

• Knowledge Management Research
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• v.25 no.1
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• pp.67-88
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• 2024

Smart tourism aims to maximize the utilization of local tourism resources, effectively manages cities and contributes to improving communication and quality of life between tourists and residents. Therefore, smart tourism emphasizes synergistic collaboration, considering both residents and tourists. This study explores smart tourism interaction and roles in enhancing regional competitiveness. By conducting thorough examination, focusing on integrating the four key elements of smart tourism city (smart experience, smart convenience, smart accessibility, and smart platform) with local residents, local businesses, regional resources, and ecosystem to foster positive synergies, Incheon smart tourism city project was employed as a single case study design. Research results indicate that the collaborative model of a smart tourism city positively impacts service satisfaction and strengthens regional tourism competitiveness. Building upon these results, this study aims to contribute to the development of smart tourism cities by proposing directions for future development and emphasizing the enhancement of regional competitiveness through the integration of smart technology and local tourism.

• Bulletin of the Society of Naval Architects of Korea
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• v.27 no.3
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• pp.131-136
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• 1990

여러해 전부터 선박의 생산실적이나 생산성 관련 자료를 기록하고 보완하는 작업을 꾸준히 개선토록 노력해온 결과중 중요한 것 하나는, 선박의 여러 가지 설계 검토과정에서 충분히 활용할 수 있는 함축성 있고 믿을만한 형태의 생산정보를 제공해줄 수 있게 되었다는 것이라고 말 할 수 있겠다. 이러한 자료들은 생산계획상 각 단계(stage)에서의 작업량, 예상재료비와 인건비의 산출등이 포함될 수 있으며, 선박이나 해상구조물의 전반적인 설계방법론(design methodology)을 개선코자 한다면 ''생산지향적 설계(Design for Production)''의 근간이 되는 선박건조전략(build strategy), 구매정책(purchasing policy)과 생산기술(production technology)에 대한 폭넓은 지식이 한데 어우러져야 한다. 최근에는 CIMS의 일부분에서 보는 바와 같은 경영관리, 설계 및 생산지원 시스템의 도입으로 이와 같은 설계 프로세스의 추진을 가능케하고 있다. 이와 병행하여 설계를 지원하기 위한 전산기술, 특히 대화형 화상처리기술(interactive graphics)의 발달은 설계자가 선박의 형상이나 구조 배치를 여러 가지로 변화시켜 가면서 눈으로 즉시 확인할 수 있도록 설계자의 능력을 배가시키는데 크게 기여하고 있다. 여러 가지의 설계안(alternative design arrangement)을 신속히 만들어내고 이를 즉시 검토 평가할 수 있는 능력을 초기설계 단계에서 가질 수 있다면 이는 분명히 큰 장점일 것이며, 더구나 설계초기 단계에 생산관련인자를 설계에서 고려할 수 있다면 이는 더욱 두드러진 발전일 것이다. 생산공법과 관련생산 비용을 정확히 반영한 각 가지의 설계안을 짧은 시간내에 검토하고 생산소요 비용을 산출하여 비교함으로써, 수주계약단계에서 실제적인 생산공법과 신뢰성있는 생산실적자료를 기준으로 하여 총 건조비(total production cost)를 최소로 하는 최적의 설계를 선택할 수 있도록 해 줄 것이다. 이제 이와 같은 새로운 설계도구(design tool)를 제공해 주므로써 초기설계에 각종 생산관련 정보나 지식 및 실적자료가 반영가능토록 발전되었다. 본 논문은 영국의 뉴카슬대학교(Univ. of Newcastle upon Type)에서 위에 언급한 특징들을 반영하여 새로운 선박구조 설계 방법을 개발한 연구결과를 보여주고 있다. 본 선계연구는 5단계로 구분되는데; (1) 컴퓨터 그라픽스를 이용하고 생산정보 데이타베이스와 연결시켜 구조형상(geometry)을 정의하고 구조부재 칫수(scantling) 계산/결정 (2) 블럭 분할(block division) 및 강재 배치(panel arrangement)의 확정을 위해 생산기술 및 건조방식에 대한 정보 제공 (3) 상기 (1) 및 (2)를 활용하여 아래 각 생산 단계에서의 생산작업 분석(work content assessment) a) 생산 준비 단계(Preparation) b) 가공 조립 단계(Fabrication/Assembly) c) 탑재 단계(Erection) (4) 각각의 설계(안)에 대하여 재료비(material cost), 인건비(labour cost) 및 오버헤드 비용(overhead cost)을 산출키 위한 조선소의 생산시설 및 각종 품셈 정보 (5) 총 건조 비용(total production cost)을 산출하여 각각의 설계안을 비교 검토. 본 설계 방식을 산적화물선(Bulk Carrier) 설계에 적용하여 구조배치(structural geometry), 표준화의 정도(levels of standardisation), 구조생산공법(structural topology) 등의 변화에 따른 설계 결과의 민감도를 분석(sensitivity studies)하였다. 전산장비는 설계자의 대화형 접근을 용이하도록 하기 위해 VAX의 화상 처리장치를 이용하여 각 설계안에 대한 구조형상과 작업분석, 건조비 현황 등을 제시할 수 있도록 하였다. 결론적으로 본 연구는 설계초기 단계에서 상세한 건조비 모델(detailed production cost model)을 대화형 화상 처리방법에 접합시켜 이를 이용하여 여러가지 설계안의 도출과 비교검토를 신속히 처리할 수 있도록 함은 물론, 각종 생산 실적정보를 초기설계에 반영하는 최초의 시도라고 믿으며, 생산지향적(Design for Production) 최적설계분야의 발전에 많은 도움이 되기를 기대해 마지 않는다. 참고로 본 시스템의 설계 적용결과를 부록에 요약 소개하며, 상세한 내용은 참고문헌 [4] 또는 [7]을 참조 요망한다.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.221-238
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• 2019

Recently, companies and public institutions have been actively introducing chatbot services in the field of customer counseling and response. The introduction of the chatbot service not only brings labor cost savings to companies and organizations, but also enables rapid communication with customers. Advances in data analytics and artificial intelligence are driving the growth of these chatbot services. The current chatbot can understand users' questions and offer the most appropriate answers to questions through machine learning and deep learning. The advancement of chatbot core technologies such as NLP, NLU, and NLG has made it possible to understand words, understand paragraphs, understand meanings, and understand emotions. For this reason, the value of chatbots continues to rise. However, technology-oriented chatbots can be inconsistent with what users want inherently, so chatbots need to be addressed in the area of the user experience, not just in the area of technology. The Fourth Industrial Revolution represents the importance of the User Experience as well as the advancement of artificial intelligence, big data, cloud, and IoT technologies. The development of IT technology and the importance of user experience have provided people with a variety of environments and changed lifestyles. This means that experiences in interactions with people, services(products) and the environment become very important. Therefore, it is time to develop a user needs-based services(products) that can provide new experiences and values to people. This study proposes a chatbot development process based on user needs by applying the design thinking approach, a representative methodology in the field of user experience, to chatbot development. The process proposed in this study consists of four steps. The first step is 'setting up knowledge domain' to set up the chatbot's expertise. Accumulating the information corresponding to the configured domain and deriving the insight is the second step, 'Knowledge accumulation and Insight identification'. The third step is 'Opportunity Development and Prototyping'. It is going to start full-scale development at this stage. Finally, the 'User Feedback' step is to receive feedback from users on the developed prototype. This creates a "user needs-based service (product)" that meets the process's objectives. Beginning with the fact gathering through user observation, Perform the process of abstraction to derive insights and explore opportunities. Next, it is expected to develop a chatbot that meets the user's needs through the process of materializing to structure the desired information and providing the function that fits the user's mental model. In this study, we present the actual construction examples for the domestic cosmetics market to confirm the effectiveness of the proposed process. The reason why it chose the domestic cosmetics market as its case is because it shows strong characteristics of users' experiences, so it can quickly understand responses from users. This study has a theoretical implication in that it proposed a new chatbot development process by incorporating the design thinking methodology into the chatbot development process. This research is different from the existing chatbot development research in that it focuses on user experience, not technology. It also has practical implications in that companies or institutions propose realistic methods that can be applied immediately. In particular, the process proposed in this study can be accessed and utilized by anyone, since 'user needs-based chatbots' can be developed even if they are not experts. This study suggests that further studies are needed because only one field of study was conducted. In addition to the cosmetics market, additional research should be conducted in various fields in which the user experience appears, such as the smart phone and the automotive market. Through this, it will be able to be reborn as a general process necessary for 'development of chatbots centered on user experience, not technology centered'.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Spatial Information Research
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• v.11 no.3
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• pp.261-274
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• 2003

The effective probability-based tile pre-fetching algorithm and the collaborative cache replacement algorithm are able to reduce the response time for user's requests by transferring tiles which will be used in advance and determining tiles which should be removed from the restrictive cache space of a client based on the future access probabilities in Web GISs(Geographical Information Systems). The Web GISs have multi-level abstracted data for the quick response time when zoom-in and zoom-out queries are requested. But, the previous pre-fetching algorithm is applied on only two-dimensional pre-fetching space, and doesn't consider expanded pre-fetching space for multi-level abstracted data in Web GISs. In this thesis, a probability-based pre-fetching algorithm for multi-level abstracted in Web GISs was proposed. This algorithm expanded the previous two-dimensional pre-fetching space into three-dimensional one for pre-fetching tiles of the upper levels or lower levels. Moreover, we evaluated the effect of the proposed pre-fetching algorithm by using a simulation method. Through the experimental results, the response time for user requests was improved 1.8%∼21.6% on the average. Consequently, in Web GISs with multi-level abstracted data, the proposed pre-fetching algorithm and the collaborative cache replacement algorithm can reduce the response time for user requests substantially.

• Management & Information Systems Review
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• v.36 no.4
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• pp.1-19
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• 2017

These days, the concept of creating shared value is drawn keen attentions to. This interest comes out of the expectation that Creating Shared Value(CSV) can offer an answer to some social issues by creating societal and economic values on the top of the achievements that existing Corporate Social Responsibility(CSR) has made. However, it is difficult to make a clear distinction between the achievements that the activities of CSR and CSV have made. In this regard, developing a methodology to make an actual proof analysis on the accomplishments of CSV and to verify customer's awareness of and attitude towards the CSV is necessarily required. A company needs to gain a competitive advantage in the marketplace as well as resolve a social issue by innovating value chain. The research has verified the cause and effect relationship between the CSV from the point of view of value chain and the purchase intention aroused by its economic, societal and cultural values through the company image and credibility with actual proof analysis and come up with following results. First, a societal and cultural value resulted in giving positive impact on a company's image, which implies that CSV activities can be the thin end of the wedge through which customers have a good image of the company involved in CSV. Second, a societal value makes a positive influence on the credibility of a company. In this regard, CSV should be recognized not just as a thing that generates a cost, but a way to win-win as well as future development. Third and last, the research results show that both company image and credibility influence on purchase intention. Considering that CSV generates a positive evaluation on a company that will ultimately cause continuous profit-making, the company's ultimate goal of activities, it should be approached from the perspective of making a mid-and-long term strategy.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.155-159
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• 2004

Recently, there are many trials about Artificial neural networks : ANNs structure and studying method of researches for forecasting traffic volume. ANNs have a powerful capabilities of recognizing pattern with a flexible non-linear model. However, ANNs have some overfitting problems in dealing with a lot of parameters because of its non-linear problems. This research deals with the application of a variety of model selection criterion for cancellation of the overfitting problems. Especially, this aims at analyzing which the selecting model cancels the overfitting problems and guarantees the transferability from time measure. Results in this study are as follow. First, the model which is selecting in sample does not guarantees the best capabilities of out-of-sample. So to speak, the best model in sample is no relationship with the capabilities of out-of-sample like many existing researches. Second, in stability of model selecting criterion, AIC3, AICC, BIC are available but AIC4 has a large variation comparing with the best model. In time-series analysis and forecasting, we need more quantitable data analysis and another time-series analysis because uncertainty of a model can have an effect on correlation between in-sample and out-of-sample.