• 문화기술의 융합
• /
• 제10권4호
• /
• pp.607-612
• /
• 2024

생성형 AI(Generative Artificial Intelligence)는 다양한 형태의 데이터를 생성하는 인공지능(Artificial Intelligence, AI) 기술이다. ChatGPT의 성공 이후, 생성형 AI 시장은 빠르게 성장하고 있다. 생성형 AI 기술 및 시장의 성장에 따라, 다양한 산업 분야에서는 이를 적극적으로 활용하고 있다. 본 논문에서는 생성형 AI의 현황과 활용사례에 대해 살펴보고, 생성형 AI의 전반적인 발전 방향에 대해 논의한다. 현재의 생성형 AI는 도메인 지식(Domain Knowledge)과 데이터를 기반으로 학습되어 특정 산업 분야에 특화된 수직적 AI(Vertical AI)의 형태로 발전되고 있다. 머지않은 미래에 생성형 AI는 학습되지 않은 사항도 사람처럼 스스로 판단하여 처리하는 일반 인공지능(범용 인공지능, Artificial General Intelligence, AGI)로 확장되어 다양한 환경에 더욱 유연하게 활용할 수 있을 것으로 기대한다.

• 인터넷정보학회논문지
• /
• 제13권4호
• /
• pp.73-82
• /
• 2012

전세계적 인지 잉여의 급격한 증가와 이를 이용한 소셜 저작의 성공적인 사례가 리눅스 프로젝트 및 위키피디아 등에서 나타나고 있다. 본 연구에서는 리눅스, 위키피디아 등의 소셜 저작 시스템을 분석하였으며 이를 기반으로 소셜 저작의 주요 성공 요소를 추출하였다. 더불어 페이스북 등의 소셜 미디어에서 적용되는 도구로서, 기존 리눅스와 위키피디아에서는 보이지 않았던 새로운 성공 요소가 존재하는 지 확인하였으며, 이를 기반으로 소셜 저작 시스템에 대한 개선 사항을 제시하였다. 소셜 저작 시스템에 요구되는 주요소들을 구체적으로 제시하여 향후 성공적 소셜 시스템 설계요인을 제시하였다.

• Journal of Information Technology Applications and Management
• /
• 제30권6호
• /
• pp.1-15
• /
• 2023

With the advent of the Fourth Industrial Revolution, propelled by digital technology, we are transitioning into an era of hyperconnectivity, where everything and objects are becoming interconnected. A smart supply chain refers to a supply chain system where various sensors and RFID tags are attached to objects such as machinery and products used in the manufacturing and transportation of goods. These sensors and tags collect and analyze process data related to the products, providing meaningful information for operational use and decision-making in the supply chain. Before the spread of COVID-19, the fundamental principles of supply chain management were centered around 'cost minimization' and 'high efficiency.' A smart supply chain overcomes the linear delayed action-reaction processes of traditional supply chains by adopting real-time data for better decision-making based on information, providing greater transparency, and enabling enhanced collaboration across the entire supply chain. Therefore, in this study, a hierarchical model for building a smart supply chain was constructed to systematically derive the importance of key factors that should be strategically considered in the construction of a smart supply chain, based on the major factors identified in previous research. We applied AHP (Analytical Hierarchy Process) techniques to identify urgent improvement areas in smart SCM initiatives. The analysis results showed that the external supply chain integration is the most urgent area to be improved in smart SCM initiatives.

• 아동학회지
• /
• 제16권1호
• /
• pp.133-146
• /
• 1995

The purpose of this study was to compare the metacognitive abilities of low and middle-achievers in elementary school. Forty-nine low-and fifty middle-achieving 6th graders were selected from two elementary schools in Seoul. The tower of Hanoi with three discs was used to explore the children's abilities. The subjects were asked to move the three discs on a post to another post five times. All children's performances on the Hanoi tower were video taped. KEDI-WISC, an intelligence test was also used to see whether the children's intelligence scores affect their performances. The results showed that: (1) there was no significant difference between the two groups in the rate of success for the tasks; (2) low-achievers took more time to finish the tasks than middle-achievers, but the time difference decreased dramatically after the first trial; (3) no significant differences was found in self monitoring abilities, though the low-achievers needed more time to start monitoring themselves; (4) low-achievers had much more difficulty in representing the tasks; (5) the IQ scores of the middle-achievers were significantly higher than the low-achievers, but the IQ scores of low achievers were more scattered than those of middle-achievers; that is, IQ scores significantly affected the performance of the children.

• Advances in Computational Design
• /
• 제5권4호
• /
• pp.445-465
• /
• 2020

This paper presents a study conducted with the aim of developing a model of tendering based on a technique of artificial intelligence by managing and controlling the factors of success or failure of construction projects through the evaluation of the process of invitation to tender. Aiming to solve this problem, analysis of the current environment based on SWOT (Strengths, Weaknesses, Opportunities, and Threats) is first carried out. Analysis was evaluated through a case study of the construction projects in Algeria, to bring about the internal and external factors which affect the process of invitation to tender related to the construction projects. This paper aims to develop a mean to identify threats-opportunities and strength-weaknesses related to the environment of various national construction projects, leading to the decision on whether to continue the project or not. Following a SWOT analysis, novel artificial intelligence models in forecasting the project status are proposed. The basic principal consists in interconnecting the different factors to model this phenomenon. An artificial neural network model is first proposed, followed by a model based on fuzzy logic. A third model resulting from the combination of the two previous ones is developed as a hybrid model. A simulation study is carried out to assess performance of the three models showing that the hybrid model is better suited in forecasting the construction project status than RNN (recurrent neural network) and FL (fuzzy logic) models.

• 지식경영연구
• /
• 제20권3호
• /
• pp.17-38
• /
• 2019

Rrecently, companies have made great efforts to satisfy various needs and heightened expectations of customers, and the importance of customer center as customer contact department for customer relationship management is increasing. In the knowledge ecosystem, corporate customer centers are emerging as a new alternative to acquiring corporate competitiveness by increasing sales and increasing market share by improving marketing support activities and customer relationship management at customer contact points. As a result, the interest in the customer center has increased rapidly because it provides the opportunity to contact with the customer. In addition, in the era of the fourth industrial revolution, the customer center, which is a collection of information and communication technologies, has a big databased voice recognition technology to elaborate customer service, thereby enhancing customer satisfaction and contributing to marketing through continuous interaction with customers. Of course, we have the opportunity to transform into the frontline business intelligence front for customer knowledge. This study is a comparative case study on how the customer center of K Life Insurance that takes the lead in the customer center industry has successfully renewed and established their key information systems to improve customer services and reinforce marketing support competencies. Based on the above, this study will present factors affecting successful implementation and settlement of the customer service information systems of customer centers by independently analyzing two individual cases.

• 한국멀티미디어학회논문지
• /
• 제24권8호
• /
• pp.1000-1011
• /
• 2021

In recent years, using Deep Learning methods to apply for medical and biomedical image analysis has seen many advancements. In clinical, using Deep Learning-based approaches for cancer image analysis is one of the key applications for cancer detection and treatment. However, the scarcity and shortage of labeling images make the task of cancer detection and analysis difficult to reach high accuracy. In 2015, the Unet model was introduced and gained much attention from researchers in the field. The success of Unet model is the ability to produce high accuracy with very few input images. Since the development of Unet, there are many variants and modifications of Unet related architecture. This paper proposes a new approach of using Unet++ with pretrained EfficientNet as backbone architecture for breast tumor cell nuclei segmentation and uses the multi-organ transfer learning approach to segment nuclei of breast tumor cells. We attempt to experiment and evaluate the performance of the network on the MonuSeg training dataset and Triple Negative Breast Cancer (TNBC) testing dataset, both are Hematoxylin and Eosin (H & E)-stained images. The results have shown that EfficientUnet++ architecture and the multi-organ transfer learning approach had outperformed other techniques and produced notable accuracy for breast tumor cell nuclei segmentation.

• Asia pacific journal of information systems
• /
• 제29권4호
• /
• pp.752-770
• /
• 2019

The purpose of this study is to incorporate telemarketing processes to improve telemarketing performance. For this application, we have attempted to mix the model of machine learning to extract potential customers with personalisation techniques to derive recommended products from actual contact. Most of traditional recommendation systems were mainly in ways such as collaborative filtering, which predicts items with a high likelihood of future purchase, based on existing purchase transactions or preferences for products. But, under these systems, new users or items added to the system do not have sufficient information, and generally cause problems such as a cold start that can not obtain satisfactory recommendation items. Also, indiscriminate telemarketing attempts can backfire as they increase the dissatisfaction and fatigue of customers who do not want to be contacted. To this purpose, this study presented a multi-purpose hybrid recommendation algorithm to achieve two goals: to select customers with high possibility of contact, and to recommend products to selected customers. In addition, we used subscription data from telemarketing agency that handles insurance products to derive realistic applicability of the proposed recommendation system. Our proposed recommendation system would certainly solve the cold start and scarcity problem of existing recommendation algorithm by using contents information such as customer master information and telemarketing history. Also. the model could show excellent performance not only in terms of overall performance but also in terms of the recommendation success rate of the unpopular product.

• 디지털융복합연구
• /
• 제16권9호
• /
• pp.405-412
• /
• 2018

영화 흥행의 예측이 필요한 시점은 영화 제작 전에 시나리오에 대한 투자를 결정하는 시점이다. 이런 요구에 따라 최근 인공지능 기반 시나리오 분석 서비스가 출시되었으나, 아직 그 알고리즘이 완벽하지는 않다. 본 연구의 목적은 인간의 뇌 작동 기작에 기반 하여, 영화 시나리오 흥행 예측 모형을 제시하는 것이다. 이를 위해 베버의 자극 반응 법칙과 뇌의 자극 기작 이론 등을 적용하여, 디즈니 애니메이션 흥행작의 시각, 청각, 인지적 자극의 타임 스펙트럼 패턴 도출을 시도한 결과는 다음과 같다. 첫째, 흥행작에서 나타난 뇌 자극의 빈도가 비 흥행작보다 약 1.79배가 많았다. 둘째로, 흥행작에서는 지각 자극 코드들이 타임 스펙트럼 상에 고른 분포를 보인 반면에 비흥행작에서는 집중 분포를 보였다. 셋째로, 흥행작에서는 인지적 부담이 큰 인지적 자극은 주로 단독적으로 등장한 반면에, 인지적 부담이 적은 시각적, 청각적 자극은 두 가지가 동시에 등장하였다.

• 유통과학연구
• /
• 제15권4호
• /
• pp.69-78
• /
• 2017

Purpose - The study was AI as exploratory study on artificial intelligence (AI) shopping information services, to explore the possibility of a new business of the distribution industry. For research, we compare to IBM of consumer awareness surveys an AI shopping information service for retailing channel and target goods group. Finally, we present to service scenario for distribution service using AI. Research design, data, and methodology - First, to identify possible the success of the information service shopping using AI, AI technology for the consumer is very important for the acceptance of judgement. Therefore, we explored the possibility of AI information service for business as a shopping. The experimental data were used to interpret the meaning of the relevant literature and the IBM Institute of Business Value (IBV) Report 2015. This research is based on the use of a technical acceptance model (TAM) to determine whether the consumer would adopt the 'AI shopping information service' technology. Step 1 of the process assumes that the consumer adopts AI technology. In step 2, consumers find their preference channels and goods targeted at them as per their preferences. Finally Step 3, we present scenario for 'AI shopping information service' based on the results of Step 1 and 2. Results - Consumers have expressed their high interests in the new shopping information services, especially the on/off line distribution channels can use shopping information to increase the efficiency in provision of goods. Digital channel (such as SNS, online shopping etc.) is especially high value goods such as cars, furniture, and home appliances by displaying it to an appropriate product group. Conclusions - The study reveals the potential for the use of new business models such as 'AI shopping information service' by the distribution industry. We present seven scenario related AI application refer from IBM suggestion, and the findings would enable the distribution industry to approach target consumers with their products, especially high value goods. 'Shopping advisor' is considered to the most effective. In order to apply to the other field of the distribution industry business, which utilizes AI technology, it should be accompanied by additional empirical data analysis should be undertaken.