• Fashion & Textile Research Journal
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• v.13 no.3
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• pp.398-408
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• 2011

This study shows that the self-satisfaction individually is rising and social life is attracted effective and successful in image making field by structuring the facial image improvement program through experimental study in order to improve unbalance of women's face. Experiment is conducted by electing 3 samples for 12 weeks and analyzing the measurement and visual analysis, infrared thermography, and evaluation of experts in order to check the facial unbalance. Subject 1 had the effect at approximately in 4 weeks with the severely distorted chin line and mouth appendage. The facial outline became softer to turn entire image to be softer and more feminine. Subject 2 had the severe distortion of location and size of eyes and nose. But the skin was getting better at first, followed by eyes getting clearer with the location changed in left and right. Subject 3 had the twisted nose and lower chin, but after two weeks, the eye area and skin were better and the width of left and right chin was similarly changed. On the basis of the above research result, the program to effectively improve the image was structured and presented with the resolution of facial unbalance. Program is consist of the training of breathing method, face washing method, facial muscle exercise.

• Advances in concrete construction
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• v.3 no.2
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• pp.91-102
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• 2015

In this paper, the load-deflection analysis of the Carbon Fiber Reinforced Polymer (CFRP) strengthened Reinforced Concrete (RC) slab using Recurrent Neural Network (RNN) is investigated. Six reinforced concrete slabs having dimension $1800{\times}400{\times}120mm$ with similar steel bar of 2T10 and strengthened using different length and width of CFRP were tested and compared with similar samples without CFRP. The experimental load-deflection results were normalized and then uploaded in MATLAB software. Loading, CFRP length and width were as neurons in input layer and mid-span deflection was as neuron in output layer. The network was generated using feed-forward network and a internal nonlinear condition space model to memorize the input data while training process. From 122 load-deflection data, 111 data utilized for network generation and 11 data for the network testing. The results of model on the testing stage showed that the generated RNN predicted the load-deflection analysis of the slabs in acceptable technique with a correlation of determination of 0.99. The ratio between predicted deflection by RNN and experimental output was in the range of 0.99 to 1.11.

• Journal of Navigation and Port Research
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• v.37 no.4
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• pp.359-366
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• 2013

The objective of the research was to study the determinants affecting the decision to study nautical science program within a Thai maritime institute in preparation for working post-graduation as a ship officer on a merchant ship. The samples are classified by institute, academic year, cumulative score level, domicile, and parent's monthly income. The total sample of study was 386 Thai merchant marine students. The data collection method was a one to five rating scale questionnaire. The statistical methods applied in analyzing the data were percentage, mean, standard deviation, t-test, one way analysis of variance and a Sheffe's test. The study shows seven factors that influenced the decision in descending order; expectations, tuition and scholarships, selection system, quality of the institute, background and private capability, generality of the institute and external influences on the decision. The decision to select an institute was classified by institute and revealed that different institutes had distinct determinants that led to the decision. The students were from The Merchant Marine Training Center and from The International Maritime College, Kasetsart University were differences. There were no dissimilarity between academic year, cumulative score level, domicile, and parent's monthly income.

• Journal of Fashion Business
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• v.23 no.5
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• pp.111-123
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• 2019

The objective of this study is to provide basic marketing data that is useful for domestic cosmetics companies by investigating purchase decision factors in cosmetics shopping. To this end, a mobile survey was conducted with a total of 300 men and women, aged 20-30, residing in Seoul and the surrounding Gyeonggi province. The collected data was analyzed by using SPSSWIN 21.0. Next, frequency analysis, factor analysis, reliability analysis, descriptive statistical analysis, correlation analysis and multiple regression analysis were performed. The results outlined consumer's purchase decision factors and suggest retailers should focus on services, such as additional events and samples, convenience of the shop itself, training salespeople to be kind and informative, promotion and services by carefully choosing models and celebrities to advertise and encourage impulse purchases, increasing product quality, improving their reputation in SNS, improve the perceived reliability of the shop by stocking famous brands prominently, advertisement, promotion and being reliably trendy and being reliably trendy. However, product loyalty was not an important factor consumers aged 20-30, these people are using SNS a lot so buy a wide variety of products. In this way, the cosmetics companies should establish marketing strategies in line with consumer habits.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.421-441
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• 2021

Fruit detection in orchards is one of the most crucial tasks for designing the visual system of an automated harvesting robot. It is the first and foremost tool employed for tasks such as sorting, grading, harvesting, disease control, and yield estimation, etc. Efficient visual systems are crucial for designing an automated robot. However, conventional fruit detection methods always a trade-off with accuracy, real-time response, and extensibility. Therefore, an improved method is proposed based on coarse-to-fine multitask cascaded convolutional networks (MTCNN) with three aspects to enable the practical application. First, the architecture of Fruit-MTCNN was improved to increase its power to discriminate between objects and their backgrounds. Then, with a few manual labels and operations, synthetic images and labels were generated to increase the diversity and the number of image samples. Further, through the online hard example mining (OHEM) strategy during training, the detector retrained hard examples. Finally, the improved detector was tested for its performance that proved superior in predicted accuracy and retaining good performances on portability with the low time cost. Based on performance, it was concluded that the detector could be applied practically in the actual orchard environment.

• Structural Monitoring and Maintenance
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• v.6 no.1
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• pp.47-66
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• 2019

One method of retrofitting concrete structures is to use fiber reinforced polymers (FRP). In this research, the shear, torsional and flexural strengthening of self-compacting reinforced concrete (RC) girders are fulfilled with glass fiber reinforced polymer (GFRP) and carbon fiber reinforced polymer (CFRP) materials. At first, for verification, the experimental results were compared with numerical modeling results obtained from ABAQUS software version 6.10. Then the reinforcing sheets were attached to concrete girders in one and two layers. Studying numerical results obtained from ABAQUS software showed that the girders stiffness decreased with the propagations of cracks in them, and then the extra stresses were tolerated by adhesive layers and GFRP and CFRP sheets, which resulted in increasing the bearing capacity of the studied girders. In fact, shear, torsion and bending strengths of the girders increased by reinforcing girders with adding GFRP and CFRP sheets. The samples including two layers of CFRP had the maximum efficiencies that were 90, 76 and 60 percent of improvement in shear, torsion and bending strengths, respectively. It is worth noting that the bearing capacity of concrete girders with adding one layer of CFRP was slightly higher than the ones having two layers of GFRP in all circumstances; therefore, despite the lower initial cost of GFRP, using CFRP can be more economical in some conditions.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1385-1397
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• 2018

There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to low prediction accuracy for wind speed. According to this situation, this paper constructs the short-time forecasting model based on the least squares support vector machines (LSSVM) to forecast the wind speed. The basis of the model used in this paper is support vector regression (SVR), which is used to calculate the regression relationships between the historical data and forecasting data of wind speed. In order to improve the forecast precision, historical data is clustered by cluster analysis so that the historical data whose changing trend is similar with the forecasting data can be filtered out. The filtered historical data is used as the training samples for SVR and the parameters would be optimized by particle swarm optimization (PSO). The forecasting model is tested by actual data and the forecast precision is more accurate than the industry standards. The results prove the feasibility and reliability of the model.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.643-655
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• 2021

This study investigated the effect of the Amanah Ikhtiar Malaysia (AIM) microfinancing on the economic, social, and household empowerment of women borrowers in Malaysia. The study used a quantitative approach based on primary data. For this study, the participants comprised 384 AIM borrowers from Terengganu, Kelantan, and Pahang in the east coast region of Malaysia. Purposive stratified random sampling was used as well as the Krejcie and Morgan method to count the number of samples. Descriptive statistics and the Women Empowerment Index (WEI) were used in the analysis. The study findings reveal that AIM microfinancing affects the economic, social, and household empowerment of women borrowers in Malaysia. However, in comparing the three categories, women enjoyed more freedom in social and household decision-making than in economic decision-making. The present study recommends policies for the successful and effective operation of microfinance programs by providing the necessary guidelines for the control of AIM loan for women borrowers; increasing income-generating activities, sufficient access of credit, and proper education for the borrowers; and giving economic freedom of choice with necessary skill training policymaking options for the government and NGOs with the aim to improve the total household income and empowerment of the microcredit borrowers in Malaysia.

• Journal of Distribution Science
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• v.19 no.2
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• pp.5-14
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• 2021

Purpose: We examined informational justice, cognitive trust, and satisfaction in healthcare distribution market and their associations within the physician-patient (provider-purchaser) relationship. Methodology: 253 valid survey samples collected from patients and used structural equation modelling for analysis. Findings: We postulated that (1) physicians' informational justice has a positive impact on patients' cognitive trust, (2) patients' cognitive trust has a positive impact on satisfaction, and (3) patients' perceived informational justice has a positive impact on satisfaction. Participants were 253 people who had visited a hospital in South Korea in the past year. Results confirmed that the presence of informational justice has a positive impact on patients' cognitive trust and satisfaction in the physician-patient relationship. Additionally, once cognitive trust was built, it positively influenced patients' satisfaction. We discussed the concept and the impacts of informational justice in light of our analyses regarding patients' perceived cognitive trust and their satisfaction in the physician-patient relationship. Implications: These results emphasize the importance of ethics in healthcare, particularly physicians' frankness and honesty when providing information to patients. Further, these findings present implications for physician education, as part of their training must involve building their patients' cognitive trust as a prerequisite for developing patient satisfaction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2098-2114
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• 2021

Light intensity variation is one of the key factors which affect the accuracy of vehicle face re-identification, so in order to improve the robustness of vehicle face features to light intensity variation, a Nonnegative Matrix Factorization model with the constraint of image acquisition time difference is proposed. First, the original features vectors of all pairs of positive samples which are used for training are placed in two original feature matrices respectively, where the same columns of the two matrices represent the same vehicle; Then, the new features obtained after decomposition are divided into stable and variable features proportionally, where the constraints of intra-class similarity and inter-class difference are imposed on the stable feature, and the constraint of image acquisition time difference is imposed on the variable feature; At last, vehicle face matching is achieved through calculating the cosine distance of stable features. Experimental results show that the average False Reject Rate and the average False Accept Rate of the proposed algorithm can be reduced to 0.14 and 0.11 respectively on five different datasets, and even sometimes under the large difference of light intensities, the vehicle face image can be still recognized accurately, which verifies that the extracted features have good robustness to light variation.