• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.127-134
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• 2022

A modified folded monopole slot antenna for 3G WCDMA (1.91 ~ 2.17 GHz), 4G LTE (2.17 ~ 2.67 GHz), 3.5 GHz 5G (3.42 ~ 3.7 GHz) and Wi-Fi dual band (2.4 ~ 2.484 GHz / 5.15 ~ 5.825 GHz) was proposed for the first time. The proposed antenna is designed and fabricated on a FR-4 substrate with dielectric constant 4.3, thickness of 1.6 mm, and size of 35 × 60 mm². The measured impedance bandwidth of the proposed antenna is 2910 MHz(1.84 ~ 4.75 GHz) and 930 MHz(5.11 ~ 6.04 GHz), antenna gain in each frequency band is from 1.811 to 3.450 dBi. In particular, it was possible to obtain a commercially suitable omni-directional radiation pattern in all frequency bands of interest.

• Asia-Pacific Journal of Business
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• v.13 no.3
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• pp.293-309
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• 2022

Purpose - The purpose of this study is to explore the possibility of predicting the degree of smartphone overdependence based on mobile phone usage patterns. Design/methodology/approach - In this study, a survey conducted by Korea Internet and Security Agency(KISA) called "problematic smartphone use survey" was analyzed. The survey consists of 180 questions, and data were collected from 29,712 participants. Based on the data on the smartphone usage pattern obtained through the questionnaire, the smartphone addiction level was predicted using machine learning techniques. k-NN, gradient boosting, XGBoost, CatBoost, AdaBoost and random forest algorithms were employed. Findings - First, while various factors together influence the smartphone overdependence level, the results show that all machine learning techniques perform well to predict the smartphone overdependence level. Especially, we focus on the features which can be obtained from the smartphone log data (without psychological factors). It means that our results can be a basis for diagnostic programs to detect problematic smartphone use. Second, the results show that information on users' age, marriage and smartphone usage patterns can be used as predictors to determine whether users are addicted to smartphones. Other demographic characteristics such as sex or region did not appear to significantly affect smartphone overdependence levels. Research implications or Originality - While there are some studies that predict smartphone overdependence level using machine learning techniques, but the studies only present algorithm performance based on survey data. In this study, based on the information gain measure, questions that have more influence on the smartphone overdependence level are presented, and the performance of algorithms according to the questions is compared. Through the results of this study, it is shown that smartphone overdependence level can be predicted with less information if questions about smartphone use are given appropriately.

• Asia-Pacific Journal of Business
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• v.13 no.3
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• pp.311-328
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• 2022

Purpose - The purpose of this study was to examine the relationship between firm's non financial performance and its shareholder return by analyzing PCA while focusing on the classifying all the variables into three categories such as financial, characteristics, and non financial factors of the firms. Design/methodology/approach - By exploring the pattern of self tender from the 801 firms in KOSPI while focusing on the objective of stock disposal, this paper analyzes the change of shareholder return of the firm. Findings - First, the higher major ownership, the lower self tender gets, whereby the higher ownership by foreigners, the ratio of self tender is higher. Secondly, cash dividend has not significant impact on the disposal of self stock, and the high ratio of ownership by foreigners leads to the high probability of retirement rather than the general disposal. In contrast, the major ownership has a negative impact on the general disposal as well as retirement. Thirdly, the score of non financial factors such as Environment(E), Social responsibility(S), and Governance(G) shows the high value in case of the firms with self tender. More specifically, the firms with retirement has the highest value of ESG while it has the lowest value in case of the firms with general disposal. Research implications or Originality - The retirement which means the active shareholder return is strongly affected by the non financial factors. Specifically, the probability of retirement increases in case of the firms with retirement, and even such a tendency is found to the case of the firms with general disposal.

• Journal of Korea Trade
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• v.24 no.6
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• pp.37-60
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• 2020

Purpose - The purpose of this study is to investigate the direct and indirect impact of innovation space factors on the growth of semiconductor enterprises. Design/methodology - This empirical study uses the financial statements of 83 semiconductor listed companies in 23 provinces from 2004 to 2019 approved by CSRC (2019). A stepwise regression and backward regression are employed in order to examine the role of innovation space to expand technology investment in promoting business growth and uses South Korean Samsung's investment in China as a test case. Findings - Results indicate that innovation space, technology input, geographical area, owner's background, operating years and financing liabilities all contribute to a boost in business growth. Factors such as carbon emission, financial liberalization, government efficiency, technology input, and financing liabilities further influence management growth. Innovation space follows a nonlinear pattern, and this plays a positive role in magnifying the influence of technology on management growth. Additionally, operations of the state-owned companies and expansionary financing enterprises are influenced by the external economy. Regarding the spatial distribution, the Samsung investment in 24 companies in China shows that Samsung focuses on the acquisition of scarce resources for semiconductor production as a component of its investment and innovation strategy. Originality/value - Even though prior research has considered the concepts studied here, this study contributes to empirically evaluate the direct impact of innovation space on business growth, and the indirect impact of innovation space on business growth through technology investment. This study includes an in-depth discussion of the practical effects that innovation space has on China's economy, using a case of South Korean Samsung's investment in China as a test the empirical findings.

• Advances in Computational Design
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• v.7 no.2
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• pp.113-128
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• 2022

Water consumption is strongly affected by numerous factors, such as population, climatic, geographic, and socio-economic factors. Therefore, the implementation of a reliable predictive model of water consumption pattern is challenging task. This study investigates the performance of predictive models based on multi-layer perceptron (MLP), multiple linear regression (MLR), and support vector regression (SVR). To understand the significant factors affecting water consumption, the stepwise regression (SW) procedure is used in MLR to obtain suitable variables. Then, this study also implements three predictive models based on these significant variables (e.g., SWMLR, SWMLP, and SWSVR). Annual data of water consumption in Thailand during 2006 - 2015 were compiled and categorized by provinces and distributors. By comparing the predictive performance of models with all variables, the results demonstrate that the MLP models outperformed the MLR and SVR models. As compared to the models with selected variables, the predictive capability of SWMLP was superior to SWMLR and SWSVR. Therefore, the SWMLP still provided satisfactory results with the minimum number of explanatory variables which in turn reduced the computation time and other resources required while performing the predictive task. It can be concluded that the MLP exhibited the best result and can be utilized as a reliable water demand predictive model for both of all variables and selected variables cases. These findings support important implications and serve as a feasible water consumption predictive model and can be used for water resources management to produce sufficient tap water to meet the demand in each province of Thailand.

• The Journal of Information Systems
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• v.31 no.4
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• pp.145-160
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• 2022

Purpose The purpose of this study is to develop a virtual try-on deep learning model that can efficiently learn front and back clothes images. It is expected that the application of virtual try-on clothing service in the fashion and textile industry field will be vitalization. Design/methodology/approach The data used in this study used 232,355 clothes and product images. The image data input to the model is divided into 5 categories: original clothing image and wearer image, clothing segmentation, wearer's body Densepose heatmap, wearer's clothing-agnosting. We advanced the HR-VITON model in the way of Mixed-Precison, Gradient Accumulation, and sharing model weights. Findings As a result of this study, we demonstrated that the weight-shared MP-GA HR-VITON model can efficiently learn front and back fashion images. As a result, this proposed model quantitatively improves the quality of the generated image compared to the existing technique, and natural fitting is possible in both front and back images. SSIM was 0.8385 and 0.9204 in CP-VTON and the proposed model, LPIPS 0.2133 and 0.0642, FID 74.5421 and 11.8463, and KID 0.064 and 0.006. Using the deep learning model of this study, it is possible to naturally fit one color clothes, but when there are complex pictures and logos as shown in <Figure 6>, an unnatural pattern occurred in the generated image. If it is advanced based on the transformer, this problem may also be improved.

• Steel and Composite Structures
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• v.52 no.4
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• pp.391-403
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• 2024

The flexural behavior of composite sandwich wall panels with different thicknesses, numbers of holes, and hole forms, and arrangement form of longitudinal steel bar (uniform type and concealed-beam type) are investigated. A total of twelve composite sandwich wall panels are prepared, utilizing modified polystyrene particles mixed with foam concrete for the flexural performance test. The failure pattern of the composite sandwich wall panels is influenced by the extruded polystyrene panel (XPS) panel thickness and the reinforcement ratio in combination, resulting in both flexural and shear failure modes. Increasing the XPS panel thickness causes the specimens to transition from flexural failure to shear failure. An increase in the reinforcement ratio leads to the transition from flexural failure to shear failure. The hole form on the XPS panel and the steel bar arrangement form affect the loading behavior of the specimens. Plum-arrangement hole form specimens exhibit lower steel bar strain and deflection compared to linear-arrangement hole form specimens. Additionally, specimens with concealed beam-type steel bar display lower steel bar strain and deflection than uniform-type steel bar specimens. However, the hole form and steel bar arrangement form have a limited impact on the ultimate load. Theoretical formulas for cracking load are provided for both fully composite and non-composite states. When compared to the experimental values, it is observed that the cracking load of the specimens with XPS panels closely matches the calculations for the non-composite state. An accurate prediction model for the ultimate load of fully composite wall panels is developed. These findings offer valuable insights into the behavior of composite sandwich wall panels and provide a basis for predicting their performance under various design factors and conditions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.1-9
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• 2024

New motor development requires high-speed load testing using dynamo equipment to calculate the efficiency of the motor. Abnormal noise and vibration may occur in the test equipment rotating at high speed due to misalignment of the connecting shaft or looseness of the fixation, which may lead to safety accidents. In this study, three single-axis vibration sensors for X, Y, and Z axes were attached on the surface of the test motor to measure the vibration value of vibration. Analog data collected from these sensors was used in classification models for anomaly detection. Since the classification accuracy was around only 93%, commonly used hyperparameter optimization techniques such as Grid search, Random search, and Bayesian Optimization were applied to increase accuracy. In addition, Response Surface Method based on Design of Experiment was also used for hyperparameter optimization. However, it was found that there were limits to improving accuracy with these methods. The reason is that the sampling data from an analog signal does not reflect the patterns hidden in the signal. Therefore, in order to find pattern information of the sampling data, we obtained descriptive statistics such as mean, variance, skewness, kurtosis, and percentiles of the analog data, and applied them to the classification models. Classification models using descriptive statistics showed excellent performance improvement. The developed model can be used as a monitoring system that detects abnormal conditions of the motor test.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.5
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• pp.845-852
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• 2024

In this paper, we propose mult-band antenna included Sub-6 GHz band for WLAN system. The proposed antenna has the fourth strip line and slot in the partial ground plane to obtain impedance matching. The total substrate size is 48.0 mm (W)×50.0 mm (L), thickness (h) 1.0 mm, and the dielectric constant is 4.4, which is made of 26.0 mm (W2)×42.0 mm (L1+L2+4.0(L1+L2+4.0 mm+L8+L9) antenna size on the FR-4 substrate. From the fabrication and measurement results, bandwidths of 115 MHz (0.825 to 0.940 GHz) for 900 MHz band, 210.0 MHz (2.29 to 2.50 GHz) for 2.4 GHz band, 270.0 MHz (3.45 to 3.72 GHz) for 3.5 GHz band, and 930.0 MHz (4.95 to 5.88 GHz) for 5.0 GHz band were obtained on the basis of -10 dB. Also, gain and radiation pattern characteristics are measured and shown in the frequency triple band as required.

• Korea Journal of Hospital Management
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• v.29 no.3
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• pp.54-69
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• 2024

Purposes: This study aims to analyze the location patterns, concentration, and accessibility of clinics in Seoul with the grids of a geographic information system(GIS) and provide useful indicators for research on hospital and clinic management and policies based on the results. The study especially sets out to identify areas falling behind in the service of the essential medical care departments based on an analysis with 250m×250m grids and contribute to efficient medical business management and public healthcare policies based on the results. Methodology: The study analyzed clinics in Seoul in terms of concentration, distribution and clustering patterns, accessibility, and hot spots by dividing the entire city into 500m×500m cells and generating grids. The accessibility analysis for the essential medical care departments was especially in detail based on grids in a 250m×250m cell size. Findings: The Herfindahl results show that plastic surgery and rehabilitation medicine recorded the highest concentration level. General medicine and dermatology recorded the highest Moran's I value, and internal medicine was the highest in hot spots. Obstetrics and gynecology capable of child delivery showed considerably lower accessibility in the grids corresponding to Gangseo-gu, Yangcheon-gu, and Gwanak-gu. Surgery had low accessibility in the grids corresponding to the northern parts of Gangseo-gu for the total population. Practical Implication: Unlike previous studies whose analyses covered wide areas, the present study examined minute spatial patterns, concentration, and accessibility based on an analysis with 500m×500m or 250m×250m grids. Based on its findings, the study expects a more minute analysis for future research on medical business management and policies.