• Journal of Information Processing Systems
• /
• v.18 no.6
• /
• pp.822-829
• /
• 2022

According to existing study into the remote fault diagnosis procedure, the current diagnostic approach has an imperfect decision model, which only supports communication in a close distance. An Internet of Things (IoT)-based remote fault diagnostic approach for wind power equipment is created to address this issue and expand the communication distance of fault diagnosis. Specifically, a decision model for active power coordination is built with the mechanical energy storage of power generation equipment with a remote diagnosis mode set by decision tree algorithms. These models help calculate the failure frequency of bearings in power generation equipment, summarize the characteristics of failure types and detect the operation status of wind power equipment through IoT. In addition, they can also generate the point inspection data and evaluate the equipment status. The findings demonstrate that the average communication distances of the designed remote diagnosis method and the other two remote diagnosis methods are 587.46 m, 435.61 m, and 454.32 m, respectively, indicating its application value.

• Biodesign
• /
• v.7 no.1
• /
• pp.24-27
• /
• 2019

Pnc1 converts nicotinamide to nicotinic acid to generate NAD⁺ through the Preiss-Handler pathway that is one of the NAD⁺-salvage pathway. By reducing levels of nicotinamide, an inhibitor of the NAD⁺-dependent histone deacetylase Sir2, yeast Pnc1 contributes gene silencing. In this study, to understand the structural features and molecular mechanism of nicotinamidase Pnc1, we overexpressed, purified, and crystallized the N-terminally His₆-tagged Pnc1 protein from Kluyveromyces lactis and obtained X-ray diffraction data at a resolution of 2.2 Å. The crystals of the K. lactis Pnc1 (KlPnc1) belonged to space group P2₁2₁2₁ with unit cell parameters a=38.5, b=77.3, c=83.3, and α=β=γ= 90°. There is one molecule in the asymmetric unit.

• Annual Conference of KIPS
• /
• 2022.11a
• /
• pp.40-42
• /
• 2022

People's focus steadily shifted toward fashion as a popular aesthetic expression as their quality of life improved. Humans are inevitably drawn to things that are more aesthetically appealing. This human proclivity has resulted in the evolution of the fashion industry over time. However, too many clothing alternatives on e-commerce platforms have created additional obstacles for clients in recognizing their suitable outfit. Thus, in this paper, we proposed a personalized Fashion Recommender system that generates recommendations for the user based on their previous purchases and history. Our model aims to generate recommendations using an image of a product given as input by the user because many times people find something that they are interested in and tend to look for products that are like that. In the system, we first reduce data dimensionality by component analysis to avoid the curse of dimensionality, and then the final suggestion is generated by neural network. To create the final suggestions, we have employed neural networks to evaluate photos from the H&M dataset and a nearest neighbor backed recommender.

• International conference on construction engineering and project management
• /
• 2013.01a
• /
• pp.188-195
• /
• 2013

Construction operations generate a significant amount of air pollutant emissions, including carbon emissions. The environmental performance of construction operations is closely relevant to the operational efficiency of each resource employed, which indicates how efficiently each resource (e.g., construction equipment) is utilized. In this context, monitoring the operational efficiency of construction equipment provides key information in managing and improving the environmental performance and productivity of construction operations. In this paper, we report our efforts to measure the operational efficiency of construction equipment, using low-cost accelerometers. An experimental study and real-world case studies are conducted to demonstrate the feasibility of the proposed approach. The results have shown the potential of this approach as an economically feasible means of monitoring the environmental performance of construction operations.

• ETRI Journal
• /
• v.45 no.4
• /
• pp.627-635
• /
• 2023

In vehicular edge computing (VEC) networks, the rapid expansion of intelligent transportation and the corresponding enormous numbers of tasks bring stringent requirements on timely task offloading. However, many tasks typically appear within a short period rather than arriving simultaneously, which makes it difficult to realize effective and efficient resource scheduling. In addition, some key information about tasks could be learned due to the regular data collection and uploading processes of sensors, which may contribute to developing effective offloading strategies. Thus, in this paper, we propose a model that considers the deterministic demand of multiple tasks. It is possible to generate effective resource reservations or early preparation decisions in offloading strategies if some feature information of the deterministic demand can be obtained in advance. We formulate our scenario as a 0-1 programming problem to minimize the average delay of tasks and transform it into a convex form. Finally, we proposed an efficient optimal offloading algorithm that uses the interior point method. Simulation results demonstrate that the proposed algorithm has great advantages in optimizing offloading utility.

• Annual Conference of KIPS
• /
• 2023.05a
• /
• pp.585-587
• /
• 2023

Long-tailed datasets have an imbalanced distribution because they consist of a different number of data samples for each class. However, there are problems of the performance degradation in tail-classes and class-accuracy imbalance for all classes. To address these problems, this paper suggests a learning method for training of long-tailed dataset. The proposed method uses and combines two methods; one is a resampling method to generate a uniform mini-batch to prevent the performance degradation in tail-classes, and the other is a reweighting method to address the accuracy imbalance problem. The purpose of our proposed method is to train the learning models to have uniform accuracy for each class in a long-tailed dataset.

• ETRI Journal
• /
• v.46 no.3
• /
• pp.473-484
• /
• 2024

We introduce a machine learning-based web application to help travel agents plan a package tour schedule. K-nearest neighbor (KNN) classification predicts the optimal tourists' dwelling time based on a variety of information to automatically generate a convenient tour schedule. A database collected in collaboration with an established travel agency is fed into the KNN algorithm implemented in the Python language, and the predicted dwelling times are sent to the web application via a RESTful application programming interface provided by the Flask framework. The web application displays a page in which the agents can configure the initial data and predict the optimal dwelling time and automatically update the tour schedule. After conducting a performance evaluation by simulating a scenario on a computer running the Windows operating system, the average response time was 1.762 s, and the prediction consistency was 100% over 100 iterations.

• Journal of Information Technology Applications and Management
• /
• v.30 no.4
• /
• pp.11-28
• /
• 2023

This paper proposes a method of integrating ChatGPT with traditional chatbot systems to enhance conversational artificial intelligence(AI) and create more efficient conversational systems. Traditional chatbot systems are primarily based on classification models and are limited to intent classification and simple response generation. In contrast, ChatGPT is a state-of-the-art AI technology for natural language generation, which can generate more natural and fluent conversations. In this paper, we analyze the business service areas that can be integrated with ChatGPT and traditional chatbots, and present methods for conducting conversational scenarios through case studies of service types. Additionally, we suggest ways to integrate ChatGPT with traditional chatbot systems for intent recognition, conversation flow control, and response generation. We provide a practical implementation example of how to integrate ChatGPT with traditional chatbots, making it easier to understand and build integration methods and actively utilize ChatGPT with existing chatbots.

• Journal of Information Technology Applications and Management
• /
• v.30 no.4
• /
• pp.65-76
• /
• 2023

On social media, companies create brand experiences while customers actively seek, consume, and generate brand-related content. Customer engagement plays a vital role in the marketing performance of social media-driven brands. This study explores the positive relationship between brand authenticity, aligning brand identity with image, and self-brand connection, aligning brand identity with consumers' self-concepts, on customer engagement and its subsequent impact on brand loyalty. The study surveyed 243 consumers engaged with brand-related social media content, validating hypotheses using structural equation modeling. Results confirmed that brand authenticity and self-brand connection positively affect customer engagement, which, in turn, boosts brand loyalty. These findings highlight the importance of companies enhancing brand authenticity and self-brand connection to drive customer engagement, with theoretical and practical implications provided.

• East Asian Economic Review
• /
• v.28 no.2
• /
• pp.143-174
• /
• 2024

This paper empirically examines the relationship between outward foreign direct investment (OFDI) of Korean manufacturing firms and the servicification of domestic employment using a firm-level panel data. In this study, considering the issue of low productivity in the Korean service sector, we categorize service employment into core and non-core services and investigate their relationship with OFDI using the firm-fixed effects model. The empirical results show that the share of core service employment exhibits a positive correlation with the extensive OFDI. On the other hand, the share of non-core service employment, which is expected to generate relatively low value-added, does not show a significant relationship with the extensive OFDI. When we divide the samples based on host countries and the type of subsidiaries, the impact on servicification varies depending on the technological capabilities of host countries and their participation in global value chains. Our study suggests that Korean manufacturing firm's internationalization strategies may facilitate a transition from labor-intensive employment, like the cases in advanced countries, to technology-intensive employment through OFDI and other means.