• Journal of Korea Trade
• /
• v.23 no.2
• /
• pp.14-33
• /
• 2019

Purpose - This paper aims to articulate determinants of inter-organizational cooperation based on to the extent to which inter-organizational tasks are related to product development and production processes. Design/Methodology - This research conducted OLS regression analysis based on the data acquired from questionnaire survey in Korean auto industry. Findings - Our analysis has verified that complementary and compatible resources, as well as physical and human asset specificities, positively affect inter-organizational product development cooperation. Conversely, in the production process, only complementary resources positively affect inter-organizational cooperation, whereas compatible resources and physical asset specificity have a negative influence. The changing characteristics of compatible resources (with IT innovations and AI), and physical asset specificity (influenced by a rising need to reduce production costs), cause inter-organizational cooperation in production to decrease. Originality/value - This research attempts to expound upon these determining factors of inter-organizational cooperation by considering both complementary-compatible resources and asset specificity in product development and production simultaneously. The reason why the impact of complementary-compatible resources and asset specificity on inter-organizational cooperation is critical in understanding the determinants of inter-organizational cooperation is that the attributes of complementary-compatible resources and asset specificity in production have changed drastically due to the continuing diffusion of IT innovations and AI (Artificial Intelligence).

• The Research Journal of the Costume Culture
• /
• v.25 no.5
• /
• pp.708-717
• /
• 2017

Industrial innovation in Britain, during the eighteenth and nineteenth centuries, stimulated the introduction of the factory system and the migration of people from rural agricultural communities to urban industrial societies. The factory system brought elevated levels of economic growth to the purveyors of capitalism, but forced people to migrate into cities where working conditions in factories were, in general, harsh and brutal, and living conditions were cramped, overcrowded and unsanitary. Industrial developments, known collectively as the 'Industrial Revolution', were driven initially by the harnessing of water and steam power, and the widespread construction of rail, shipping and road networks. Parallel with these changes, came the development of purchasing 'middle class', consumers. Various technological ripples (or waves of innovative activity) continued (worldwide) up to the early-twenty-first century. Of recent note are innovations in digital technology, with associated developments, for example, in artificial intelligence, robotics, 3-D printing, materials technology, computing, energy storage, nano-technology, data storage, biotechnology, 'smart textiles' and the introduction of what has become known as 'e-commerce'. This paper identifies the more important early technological innovations, their influence on textile manufacture, distribution and consumption, and the changed role of the designer and craftsperson over the course of these technological ripples. The implications of non-ethical production, globalisation and so-called 'fast fashion' and non-sustainability of manufacture are examined, and the potential benefits and opportunities offered by new and developing forms of social media are considered. The message is that hand-crafted products are ethical, sustainable and durable.

• The Research Journal of the Costume Culture
• /
• v.2 no.1
• /
• pp.93-104
• /
• 1994

Nowadays computer technology is being applied in various areas of apparel design. In particular, since the task of pattern making is to be performed by a set of predefined drawing rules, the effect of computer application in pattern making will be significant, There have been a large number of studies on pattern making program. For instance, the previous studies have developed computer programs for pattern making of women's wear, men's wear, children's wear, Han-Bok, etc. Most of them have focused on the development of computer program for a particular kind of apparel only and, however, have disregarded the feasibility of developing a multi-purposed computer program so that is just can be modified to adopt for various styles. For example, by widening the hem-wide of the basic H-Line skirt and then connecting its waist line and widened hem-wide, we can draw the A-Line skirt. Therefore, we have developed a program which can make a pattern for the basic skirt and can mae, with a slight change of he program, other patterns for various style as well. The objective of this paper is to identify and organize modules which will be used for developing a general pattern making computer system. This general pattern making system is a computer program by which we can draw a variety of apparel styles. This system is restricted to skirt pattern making only. there presentation scheme used in organizing these modules is an AND-OR tree, the one being often used in representing a complex problem in artificial intelligence domain.

• Journal of Internet Computing and Services
• /
• v.24 no.4
• /
• pp.37-45
• /
• 2023

Adaptive has gained significant attention in Education Technology (EdTech), with personalized learning experiences becoming increasingly important. Next-generation chatbots, including models like ChatGPT, are emerging in the field of education. These advanced tools show great potential for delivering personalized and adaptive learning experiences. This paper reviews previous research on adaptive learning and the role of chatbots in education. Based on this, the paper explores current and future chatbot technologies to propose a framework for using ChatGPT or similar chatbots in adaptive learning. The framework includes personalized design, targeted resources and feedback, multi-turn dialogue models, reinforcement learning, and fine-tuning. The proposed framework also considers learning attributes such as age, gender, cognitive ability, prior knowledge, pacing, level of questions, interaction strategies, and learner control. However, the proposed framework has yet to be evaluated for its usability or effectiveness in practice, and the applicability of the framework may vary depending on the specific field of study. Through proposing this framework, we hope to encourage learners to more actively leverage current technologies, and likewise, inspire educators to integrate these technologies more proactively into their curricula. Future research should evaluate the proposed framework through actual implementation and explore how it can be adapted to different domains of study to provide a more comprehensive understanding of its potential applications in adaptive learning.

• Journal of Distribution Science
• /
• v.22 no.2
• /
• pp.103-113
• /
• 2024

Purpose: With the popularity of artificial intelligence (AI) in the service industry and occurrence ofservice failures in AI-based services, understanding human-robot interaction issues in service failure situations is especially important. Some issues which deserve further empirical investigation are whether consumers can develop the same tolerance for chatbots after service failure as they have for human agents, and the relationship between agent type and tolerance is mediated by the mechanisms of perceived warmth and perceived competence. Research Design, Data, and Methodology: This research experimentally collected and analyzed data from 119 university students who had experienced chatbots service failures. Differences in tolerance towards human agents and chatbots after experiencing service failures were explored, with a further examination of the mediating pathways between this relationship via perceived warmth and perceived competence. Results: Consumers are more tolerant ofservice failure with chatbots compared to service failure with human agents. Significant mediation of the relationship between service agent and service failure tolerance by perceived competence, while perceived warmth has no significant mediating effect. Conclusions: This research enhances our understanding of AI-assisted services, human-computer interaction, improves the service functionality of existing smart devices, and deepens the understanding of the relationship between consumer responses and behaviors.

• Journal of IKEEE
• /
• v.22 no.4
• /
• pp.1079-1087
• /
• 2018

Recently, computer vision field based deep learning artificial intelligence has become a hot topic among various image analysis boundaries. In this study, flames are detected in fire images using the Faster R-CNN algorithm, which is used to detect objects within the image, among various image recognition algorithms based on deep learning. In order to improve fire detection accuracy through a small amount of data sets in the learning process, we use image augmentation techniques, and learn image augmentation by dividing into 6 types and compare accuracy, precision and detection rate. As a result, the detection rate increases as the type of image augmentation increases. However, as with the general accuracy and detection rate of other object detection models, the false detection rate is also increased from 10% to 30%.

• Journal of Advanced Navigation Technology
• /
• v.25 no.6
• /
• pp.576-581
• /
• 2021

Compared to a low concentration, a high concentration clearly entails limitations in terms of predictive performance owing to differences in its frequency and environment of occurrence. To resolve this problem, in this study, an artificial intelligence neural network algorithm was used to classify low and high concentrations; furthermore, two prediction models trained using the characteristics of the classified concentration types were used for prediction. To this end, we constructed training datasets using weather and air pollutant data collected over a decade in the Cheonan region. We designed a DNN-based classification model to classify low and high concentrations; further, we designed low- and high-concentration prediction models to reflect characteristics by concentration type based on the low and high concentrations classified through the classification model. According to the results of the performance assessment of the prediction model by concentration type, the low- and high-concentration prediction accuracies were 90.38% and 96.37%, respectively.

• Journal of Ocean Engineering and Technology
• /
• v.33 no.6
• /
• pp.590-596
• /
• 2019

Numerous deaths and substantial property damage have occurred recently due to frequent disasters of the highest intensity according to the abnormal climate, which is caused by various problems, such as global warming, all over the world. Such large-scale disasters have become an international issue and have made people aware of the disasters so they can implement disaster-prevention measures. Extensive information on disaster prevention actively has been announced publicly to support the natural disaster reduction measures throughout the world. In Japan, diverse developmental studies on disaster prevention systems, which support hazard map development and flood control activity, have been conducted vigorously to estimate external forces according to design frequencies as well as expected maximum frequencies from a variety of areas, such as rivers, coasts, and ports based on broad disaster prevention data obtained from several huge disasters. However, the current reduction measures alone are not sufficiently effective due to the change of the paradigms of the current disasters. Therefore, in order to obtain the synergy effect of reduction measures, a study of the establishment of an integrated system is required to improve the various disaster prevention technologies and the current disaster prevention system. In order to develop a similar typhoon search system and establish a disaster prevention infrastructure, in this study, techniques will be developed that can be used to forecast typhoons before they strike by using artificial intelligence (AI) technology and offer primary disaster prevention information according to the direction of the typhoon. The main function of this model is to predict the most similar typhoon among the existing typhoons by utilizing the major typhoon information, such as course, central pressure, and speed, before the typhoon directly impacts South Korea. This model is equipped with a combination of AI and DNN forecasts of typhoons that change from moment to moment in order to efficiently forecast a current typhoon based on similar typhoons in the past. Thus, the result of a similar typhoon search showed that the quality of prediction was higher with the grid size of one degree rather than two degrees in latitude and longitude.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.10
• /
• pp.4214-4230
• /
• 2020

This paper introduces a pilot allocation scheme for massive MIMO systems based on deep convolutional neural network (CNN) learning. This work is an extension of a prior work on the basic deep learning framework of the pilot assignment problem, the application of which to a high-user density nature is difficult owing to the factorial increase in both input features and output layers. To solve this problem, by adopting the advantages of CNN in learning image data, we design input features that represent users' locations in all the cells as image data with a two-dimensional fixed-size matrix. Furthermore, using a sorting mechanism for applying proper rule, we construct output layers with a linear space complexity according to the number of users. We also develop a theoretical framework for the network capacity model of the massive MIMO systems and apply it to the training process. Finally, we implement the proposed deep CNN-based pilot assignment scheme using a commercial vanilla CNN, which takes into account shift invariant characteristics. Through extensive simulation, we demonstrate that the proposed work realizes about a 98% theoretical upper-bound performance and an elapsed time of 0.842 ms with low complexity in the case of a high-user-density condition.

• Smart Structures and Systems
• /
• v.15 no.5
• /
• pp.1363-1372
• /
• 2015

Smart structures and intelligent systems play pivotal roles in numerous areas of applied sciences ranging from civil engineering to computer and communications systems engineering. Although such structures and systems have been intensively deployed in these areas, they have been, interestingly, very rarely deployed in the field of cultural heritage preservation.This paper presents one of thefirst such attempts. A new methodology is describedthat deploys smart structures andlinks them with artificial intelligence methods.These solutions are referred toas advanced hybrid engineering artefacts. By their use,important environmental factors can be monitoredin hard to access, remote or unsafe locationsby minimizing the need for human involvement. In addition toproviding safety the methodologyalso reduces costs and, most importantly,providesa new way to modelany particular micro-environment in a much more efficient way than this is possible with traditional ways. Last but not least, although themethodology has been developed for cultural heritage preservation, its application areas are much broader and it is expected that it will find its applicationin other domains like civil engineering and ecology.