• Steel and Composite Structures
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• v.42 no.1
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• pp.123-137
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• 2022

The fundamental period (FP) is one of the most critical parameters for the seismic design of structures. In the reinforced concrete (RC) infilled frame, the infill walls significantly affect the FP because they change the stiffness and mass of the structure. Although several formulas have been proposed for estimating the FP of the RC infilled frame, they are often associated with high bias and variance. In this study, an efficient soft computing model, namely the group method of data handling (GMDH), is proposed to predict the FP of regular RC infilled frames. For this purpose, 4026 data sets are obtained from the open literature, and the quality of the database is examined and evaluated in detail. Based on the cleaning database, several GMDH models are constructed and the best prediction model, which considers the height of the building, the span length, the opening percentage, and the infill wall stiffness as the input variables for predicting the FP of regular RC infilled frames, is chosen. The performance of the proposed GMDH model is further underscored through comparison of its FP predictions with those of existing design codes and empirical models. The accuracy of the proposed GMDH model is proven to be superior to others. Finally, explicit formulas and a graphical user-friendly interface (GUI) tool are developed to apply the GMDH model for practical use. They can provide a rapid prediction and design for the FP of regular RC infilled frames.

• Journal of Distribution Science
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• v.20 no.4
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• pp.21-31
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• 2022

Purpose: The study focuses on the distribution of brand love on customers' behavioral intention at five-star hotels in Vietnam. Furthermore, the study also assesses the role of mediating variables and moderating variable involved in the research model. Research design, data and methodology This research surveys 458 customers using the services of five-star hotels in Vietnam through questionnaires on online platforms. Data cleaning and data analysis using SPSS 25.0 software combined with Smart-PLS 3.0 software were used in the research to evaluate the measurement model and structural model. Results: From the results of the structural model evaluation, it shows the positive distribution of brand love on customers' behavioral intentions towards five-star hotels in Vietnam. The mediating roles of brand engagement, brand equity, customer motivation, and the participation of the moderating variable, customer expectations, are determined by this research model. Conclusions: Based on the study results, the distribution of brand love positively affects customers' behavioral intention at five-star hotels in Vietnam, giving recommendations that have a positive impact on customer behavioral intention. In addition, the study shows the role of mediating variables as well as exploring the moderator's (customer expectations) in the distribution of relationships between customer motivation and customer behavioral intention.

• Journal of Distribution Science
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• v.22 no.5
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• pp.11-26
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• 2024

Purpose: The concept of News-finds-me Perception (NFMP) is gaining increasing scholarly interest due to its wide-ranging findings and implications in digital communications and marketing. From the retail marketing and communication approaches, social media is an effective tool to effectively communicate and persuade customers and stakeholders. Nevertheless, a scarcity of systematic review studies that systematically assemble prior research in the field is recognized. Consequently, this research investigated the Scopus database for articles pertaining to NFMP. Research design, data and methodology: The search was conducted on August 24, 2023, retrieving 46 documents. Following a data-cleaning process, 31 documents remained, providing evidence of the subject area's five-year development. The data was refined with OpenRefine and analyzed with VosViewer. Results: An overview of the subject's expansion is presented, which comprises the most cited documents, authors, organizations, journals, and countries. Furthermore, the investigation examines the influential studies that furnished scientists with essential knowledge and identify the current research trend of the research subject. Conclusions: Based on the results, the study proposes theoretical and practical implications, encouraging academics to further integrate the concept with various communication and marketing theories, as well as the retail marketing context, to gain a better understanding of its complex impacts.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.440-454
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• 2020

To prevent pollution from ships, the Energy Efficiency Design Index (EEDI) is a mandatory guideline for all new ships. The Ship Energy Efficiency Management Plan (SEEMP) has also been applied by MARPOL to all existing ships. SEEMP provides the Energy Efficiency Operational Indicator (EEOI) for monitoring the operational efficiency of a ship. By monitoring the EEOI, the shipowner or operator can establish strategic plans, such as routing, hull cleaning, decommissioning, new building, etc. The key parameter in calculating EEOI is Fuel Oil Consumption (FOC). It can be measured on board while a ship is operating. This means that only the shipowner or operator can calculate the EEOI of their own ships. If the EEOI can be calculated without the actual FOC, however, then the other stakeholders, such as the shipbuilding company and Class, or others who don't have the measured FOC, can check how efficiently their ships are operating compared to other ships. In this study, we propose a method to estimate the EEOI without requiring the actual FOC. The Automatic Identification System (AIS) data, ship static data, and environment data that can be publicly obtained are used to calculate the EEOI. Since the public data are of large capacity, big data technologies, specifically Hadoop and Spark, are used. We verify the proposed method using actual data, and the result shows that the proposed method can estimate EEOI from public data without actual FOC.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.36D no.10
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• pp.9-16
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• 1999

When the conventional IPD (inter-poly-dielctrics) layer with ONO(oxide-nitride-oxide) structure was used in the Flash EEPROM cell, its data retention characteristics were significanfly degraded because the top oxide of the ONO layer was etched off due to the cleaning process used in the gate oxidation process for the peripheral MOSFETs. When the IPD layer with the ONON(oxide-nitride-oxide-nitride) was used there, however, its data retention characteristics were much improved because the top nitride of the ONON layer protected the top oxide from being etched in the cleaning process. For the modelling of the data retention characteristics of the Flash EEPROM cell with the ONON IPD layer, the decrease of the threshold voltage cue to the charge loss during the bake was here given by the empirical relation ${\Delta}V_t\; = \;{\beta}t^me^{-ea/kT}$ and the values of the ${\beta}$=184.7, m=0.224, Ea=0.31 eV were obtained with the experimental measurements. The activation energy of 0.31eV implies that the decrease of the threshold voltage by the back was dur to the movement of the trapped electrons inside the inter-oxide nitride layer. On the other hand, the results of the computer simulation using the model were found to be well consistent with the results of the electrical measurements when the thermal budget of the bake was not high. However, the latter was larger then the former in the case of the high thermal budger, This seems to be due to the leakage current generated by the extraction of the electrons with the bake which were injected into the inter-oxide niride later and were trapped there during the programming, and played the role to prevent the leakage current. To prevent the generation of the leakage current, it is required that the inter-oxide nitride layer and the top oxide layer be made as thin and as thick as possible, respectively.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.36D no.10
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• pp.17-22
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• 1999

In this paper, the results of the studies about a new proposal where the ONON(oxide-nitride-oxide-nitride) layer instead of the conventional ONO(oxide-nitride-oxide) layer is used as the IPD(inter-poly-dielectrics) layer to improve the data retention problem in the Flash EEPROM cell, have been discussed. For these studies, the stacked-gate Flash EEPROM cell with an about 10nm thick gate oxide and on ONO or ONON IPD layer have been fabricated. The measurement results have shown that the data retention characteristics of the devices with the ONO IPD layer are significantly degraded with an activation energy of 0.78 eV. which is much lower than the minimum value (1.0 eV) required for the Flash EEPROM cell. This is believed to be due to the partial or whole etching of the top oxide of the IPD layer during the cleaning process performed just prior to the dry oxidation process to grow the gate oxide of the peripheral MOSFET devices. Whereas the data retention characteristics of the devices with the ONON IPD layer have been found to be much (more than 50%) improved with an activation energy of 1.10 eV. This must be because the thin nitride layer on the top oxide layer in the ONON IPD layer protected the top oxide layer from being etched during the cleaning process.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.2
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• pp.215-229
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• 2023

Objectives: To validate the effectiveness of a real-time chemical exposure monitoring system developed by KOSHA (Korea Occupational Safety and Health Agency), we applied the system to a workplace in the electronics industry for 153 days. Methods: The monitoring system consisted of a PID chemical sensor, a LTE communication equipment, and a web-based platform. To monitor chemical exposure, four sets of sensors were placed in two manufacturing tasks - inspection and jig cleaning - which used TCE as a degreasing agent. We reviewed previous reports of work environment measurements and conducted a new work environment measurement on one day during the period. The PID sensor systems detected the chemical exposure levels in the workplace every second and transmitted it to the platform. Daily average and maximum chemical exposure levels were also recorded. Results: We compared the results from the real-time monitoring system and the work environment measurement by traditional methods. Generally, the data from the real-time monitoring system showed a higher level because the sensors were closer to the chemical source. We found that 28% of jig cleaning task data exceeded the STEL. Peak exposure levels of sensor data were useful for understanding the characteristics of the task's chemical use. Limitations and implications were reviewed for the adoption of the system for preventing poisoning caused by chemical substances. Conclusions: We found that the real-time chemical exposure monitoring system was an efficient tool for preventing occupational diseases caused by chemical exposure, such as acute poisoning. Further research is needed to improve the reliability and applicability of the system. We also believe that forming a social consensus around the system is essential.

• Smart Structures and Systems
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• v.29 no.1
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• Water Engineering Research
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• v.1 no.1
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• pp.39-48
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• 2000

This work was completed in partial fulfillment of an on-going research ot descover the effective management of urban nonpoint sources. The current data was obtained from the area of Shingal, Kyunni-do. The investigation was are predominant soures of storm-runoff load and drainage. As a result of the investigation, the road was found to be most seriously contaminated and a significant potential source deteriorating the quality of streams and lakes in the vicinity of the town. Thus, in could be concluded that an effective and systematic cleaning technique must be developed as soon as possible and be frequently applied to the road.

• Journal of the Korean Society of Clothing and Textiles
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• v.24 no.5
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• pp.772-783
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• 2000

The purpose of this study was to investigate product evaluation on consumer's buying behavior of domestic & imported golf wear brands. The questionnaires were sent to 200 consumers who play golf. The 119 data were analyzed by mean, t-test, ANOVA and chi-square. The results of the study were as follows: There were significant differences on consumers' evaluation of apparel quality on fabric and style between groups. Consumers evaluated that the imported golf wears made of more soft, light and unique fabric than domestic, and had a unique and characteristic style. The evaluation of apparel quality according to demographic information has significant difference. Consumers(46-55 ages, business managers and professional) evaluated imported brands were made of soft and light fabric. Consumers(business managers) buying imported brand evaluated dry-cleaning was inconvenient. Consumers who engaged in service industry evaluated domestic brands were easy to coordinate with other items.