The e-commerce fashion market has experienced a remarkable growth, leading to an overwhelming availability of shared information and numerous choices for users. In light of this, chatbots have emerged as a promising technological solution to enhance personalized services in this context. This study aimed to develop user-product attributes for a chatbot-based personalized fashion recommendation service using big data text mining techniques. To accomplish this, over one million consumer reviews from Coupang, an e-commerce platform, were collected and analyzed using frequency analyses to identify the upper-level attributes of users and products. Attribute terms were then assigned to each user-product attribute, including user body shape (body proportion, BMI), user needs (functional, expressive, aesthetic), user TPO (time, place, occasion), product design elements (fit, color, material, detail), product size (label, measurement), and product care (laundry, maintenance). The classification of user-product attributes was found to be applicable to the knowledge graph of the Conversational Path Reasoning model. A testing environment was established to evaluate the usefulness of attributes based on real e-commerce users and purchased product information. This study is significant in proposing a new research methodology in the field of Fashion Informatics for constructing the knowledge base of a chatbot based on text mining analysis. The proposed research methodology is expected to enhance fashion technology and improve personalized fashion recommendation service and user experience with a chatbot in the e-commerce market.
This paper addressed the analysis of the trend and direction of the US defense strategy based on their third offset strategy and presented the practical policy implication of ensuring the security of South Korea appropriately in the future national defense environment. The countermeasures for the development ability of advanced weapon systems and secure core technologies for Korea were presented in consideration of the US third offset strategy for the future national defense environment. First, to carry out the innovation of national defense in Korea based on artificial intelligence(AI), the long-term basis strategy for the operation of the unmanned robot and autonomous weapon system should be suggested. Second, the platform for AI has to be developed to obtain the development of algorithms and computing abilities for securing the collection/storage/management of national defense data. Lastly, advanced components and core technologies are identified, which the Korean government can join to develop with the US on a basis of the Korea-US alliance, and the technical cooperation with the US should be stronger.
Tan, Yi;Song, Yongze;Gan, Vincent J.L.;Mei, Zhongya;Wang, Xiangyu;Cheng, Jack C.P.
International conference on construction engineering and project management
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2017.10a
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pp.32-41
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2017
Accidents on offshore oil and gas platforms (OOGPs) usually cause serious fatalities and financial losses considering demanding environment platforms locate and complex topsides structure platforms own. Evacuation planning on platforms is usually challenging. The computational tool is a good choice to plan evacuation by emergency simulation. However, the complex structure of platforms and varied evacuation behaviors usually weaken the advantages of computational simulation. Therefore, this study developed a simulation model for OOGPs to evaluate different evacuation plans to improve evacuation performance by integrating building information modeling (BIM) and agent-based model (ABM). The developed model consists of four parts: evacuation model input, simulation environment modeling, agent definition, and simulation and comparison. Necessary platform information is extracted from BIM and then used to model simulation environment by integrating matrix model and network model. During agent definition, in addition to basic characteristics, environment sensing and dynamic escape path planning functions are also developed to improve simulation performance. An example OOGP BIM topsides with different emergent scenarios is used to illustrate the developed model. The results showed that the developed model can well simulate evacuation on OOGPs and improve evacuation performance. The developed model was also suggested to be applied to other industries such as the architecture, engineering, and construction industry.
This study explores how avatar customization influences the continuous intention to use the metaverse, mediated by self-expansion. The moderating effects of self-efficacy between avatar customization and self-expansion are also explored. Data were collected through an online survey using consumer panels. Participants were Zepeto users aged 18 or older who had used the platform within the previous six months. They were asked to recall a recent shopping experience of exploring the virtual fashion store via Zepeto. A total of 196 valid responses from participants were analyzed using SPSS 26.0 for descriptive statistics, reliability analysis, and PROCESS procedure, and AMOS 23.0 for confirmatory factor analysis. Results demonstrate that avatar customization increases continuous intention to use the metaverse; this effect is mediated by self-expansion. The moderated mediation effect of self-efficacy in the indirect path was significant and mediated by self-expansion. Specifically, the interplay effect of avatar customization and self-efficacy on self-expansion was statistically significant. For participants with high self-efficacy, avatar customization increases self-expansion, and it mediates the relationship between avatar customization and the continuous intention to use the metaverse. Findings contribute to expanding the literature on metaverse usage by testing the impact of avatar customization on self-expansion.
Jae Hyun Ahn;Hang Ju Lee;Se Yeon Lee;Ji Won Han;Won Joo Lee
Journal of the Korea Society of Computer and Information
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v.28
no.11
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pp.65-71
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2023
In this paper, we design and implement the Walking Life Festival application, which is based on the Android platform and provides information about domestic travel destinations and regional festivals in South Korea. This application utilizes various sensors found in smartphones, including the Step Counter sensor, Step Detector sensor, Acceleration sensor, and GPS sensor. Additionally, it makes use of Google Map API and Public Open API to offer information about domestic travel destinations and local festivals. The application also incorporates an automatic login feature using the Shared Preference API. When storing login information in the database, it encrypts the input plaintext data using a hash algorithm. For Google Maps integration, it creates objects using the Google.maps.LatLngBounds() method and extends the location information through the extends method. Furthermore, this application contributes to the activation of the domestic tourism industry by notifying users about the timing of local festivals related to domestic travel destinations, thus increasing their opportunities to participate in these festivals.
Project logistics is becoming increasingly important in overseas plant projects. Efficient logistics risk management is needed to reduce construction period and reduce costs. However, Korean construction firms bid unconditionally without sufficient experience and analysis on overseas plants contract, companies are gradually losing profitability on projects due to not considering profitability. Despite the significant effects on the profitability of Korean construction companies, and although these companies still continue to bid on overseas plant projects, policies to manage project logistics risks for safe transport and compliance with the contracted building schedule in the long term is still lacking. Hence, this study investigates the risk factors related to project logistics and to analyze the effect of project logistics risk on overseas plant business performance. We conducted a survey of project-related workers. The results of the analysis are as follows: First, among the logistics risk factors, overseas platform business people recognize operational risk and financial risk factors, which have a positive effect directly on overseas plant performance. Second, the ability to manage project logistics risks can have a significant impact on the success or failure of overseas plants. Finally, if logistics risk factors are managed on the basis of the research results confirmed through empirical analysis, it is possible to carry out more efficient and effective management of the project, which implies that this will have a positive effect on overseas plant business performance.
According to an analysis of 560 foreign-invested companies investing in South Korea's manufacturing industry, the following three facts were found. First, the proportion of sales by manufacturing foreign-invested companies is divided into 68.5 percent of domestic sales and 31.5 percent of exports. From 68.5 percent of domestic sales, sales to Korean companies are 60.5 percent, including 37.1 percent for large companies and 23.4 percent for small and medium-sized companies, while only 8.0 percent for domestic consumers. Second, the investment sectors of manufacturing foreign-invested enterprises are 'machine and equipment manufacturing', 'chemical and chemical-chemical material manufacturing-excluding pharmaceuticals', 'electronic components, computers, video, sound and communication equipment manufacturing' and 'vehicle and trailer manufacturing'. It overlaps with electric·electronics, petro-chemicals and automobiles, which are Korea's main industries and areas of Korean global companies. Third, 31.5 percent of the sales of foreign-invested companies in the manufacturing sector are exported. Foreign-invested companies export their products to use them for their parents or affiliates or to the third countries. The analysis shows that foreign-invested companies invested in Korea for B2B transactions with Korean companies. The implications are that Korea can attract foreign investments by utilizing Korean companies' demand for intermediate goods. Foreign-invested companies can invest in Korea in order to use Korea, which has signed free trade agreements with the US, the EU and ASEAN, as an export platform.
International journal of advanced smart convergence
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v.12
no.4
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pp.208-216
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2023
The purpose of our study is to design datasets for Artificial Intelligence learning for cold sea fish farming. Salmon is considered one of the most popular fish species among men and women of all ages, but most supplies depend on imports. Recently, salmon farming, which is rapidly emerging as a specialized industry in Gangwon-do, has attracted attention. Therefore, in order to successfully develop salmon farming, the need to systematically build data related to salmon and salmon farming and use it to develop aquaculture techniques is raised. Meanwhile, the catch of pollack continues to decrease. Efforts should be made to improve the major factors affecting pollack survival based on data, as well as increasing the discharge volume for resource recovery. To this end, it is necessary to systematically collect and analyze data related to pollack catch and ecology to prepare a sustainable resource management strategy. Image data was obtained using CCTV and underwater cameras to establish an intelligent aquaculture strategy for salmon and pollock, which are considered representative fish species in Gangwon-do. Using these data, we built learning data suitable for AI analysis and prediction. Such data construction can be used to develop models for predicting the growth of salmon and pollack, and to develop algorithms for AI services that can predict water temperature, one of the key variables that determine the survival rate of pollack. This in turn will enable intelligent aquaculture and resource management taking into account the ecological characteristics of fish species. These studies look forward to achievements on an important level for sustainable fisheries and fisheries resource management.
Drone-mounted hyperspectral sensors (DHSs) have revolutionized remote sensing in agriculture by offering a cost-effective and flexible platform for high-resolution spectral data acquisition. Their ability to capture data at low altitudes minimizes atmospheric interference, enhancing their utility in agricultural monitoring and management. This study focused on addressing the challenges of radiometric and geometric distortions in preprocessing drone-acquired hyperspectral data. Radiometric correction, using the empirical line method (ELM) and spectral reference panels, effectively removed sensor noise and variations in solar irradiance, resulting in accurate surface reflectance values. Notably, the ELM correction improved reflectance for measured reference panels by 5-55%, resulting in a more uniform spectral profile across wavelengths, further validated by high correlations (0.97-0.99), despite minor deviations observed at specific wavelengths for some reflectors. Geometric correction, utilizing a rubber sheet transformation with ground control points, successfully rectified distortions caused by sensor orientation and flight path variations, ensuring accurate spatial representation within the image. The effectiveness of geometric correction was assessed using root mean square error(RMSE) analysis, revealing minimal errors in both east-west(0.00 to 0.081 m) and north-south directions(0.00 to 0.076 m).The overall position RMSE of 0.031 meters across 100 points demonstrates high geometric accuracy, exceeding industry standards. Additionally, image mosaicking was performed to create a comprehensive representation of the study area. These results demonstrate the effectiveness of the applied preprocessing techniques and highlight the potential of DHSs for precise crop health monitoring and management in smart agriculture. However, further research is needed to address challenges related to data dimensionality, sensor calibration, and reference data availability, as well as exploring alternative correction methods and evaluating their performance in diverse environmental conditions to enhance the robustness and applicability of hyperspectral data processing in agriculture.
The concept of sharing economy has received rich attention in recent years. As a typical type of business model in the sharing economy, online short rent has been paid attention by both industry and academia. In this study, we find trust to be a critical determinant to the success of online short rent platforms. Based on three dimensions of trust theory, i.e., ability, benevolence and integrity, we investigate the factors influencing tenant' willingness to participate in online short rent. We further examine the extent to which trust can influence the number of sales and comments of rooms listed at online short-term rent platforms, which can represent tenant' willingness to participate in the sharing economy. The results show that the trust dimensions represented by a landlord's personal characteristics have significant positive correlations with the number of sales and comments. For example, the real name authentication and the sesame score can represent the trust integrity; online replay ratio and the average confirmation time representing the trust sincerity, and the order acceptance ratio representing the trust ability. On this basis, we proposed some recommendations for both platforms and landlords. For example, the landlords can improve the tenants' trust by authenticating his/her real name, replying actively and timely. For platforms, when they make housing list ranking rules, they can take the landlord's personal attributes that may affect trust into consideration. Moreover, platforms can also allow landlords to supply value-added services to improve service quality and ultimately promote the virtuous circle of the platform ecosphere. Through conducting the empirical research on a particular application of the sharing economy, we aim to fill the research gap of this field in China and provide theoretical and practical contributions to the future development of online short rent.
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