• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.442-448
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• 2023

Artificial Intelligence (AI) technology has recently grown explosively and is being used in a variety of application fields. Accordingly, the number of AI models is rapidly increasing. AI models are adapted and developed to fit a variety of data types, tasks, and environments, and the variety and volume of models continues to grow. The need to share models and collaborate within the AI community is becoming increasingly important. Collaboration is essential for AI models to be shared and improved publicly and used in a variety of applications. Therefore, with the advancement of AI, the introduction of Model Hub has become more important, improving the sharing, reuse, and collaboration of AI models and increasing the utilization of AI technology. In this paper, we collect data on the model hub and analyze the characteristics of the model hub and the AI models provided. The results of this research can be of great help in developing various multimodal AI models in the future, utilizing AI models in various fields, and building services by fusing various AI models.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.97-110
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• 2017

This study investigates the efficiencies of machine learning models, including artificial neural network (ANN), generalized regression neural network (GRNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF), for reservoir water level forecasting in the Chungju Dam, South Korea. The models' efficiencies are assessed based on model efficiency indices and graphical comparison. The forecasting results of the models are dependent on lead times and the combination of input variables. For lead time t = 1 day, ANFIS1 and ANN6 models yield superior forecasting results to RF6 and GRNN6 models. For lead time t = 5 days, ANN1 and RF6 models produce better forecasting results than ANFIS1 and GRNN3 models. For lead time t = 10 days, ANN3 and RF1 models perform better than ANFIS3 and GRNN3 models. It is found that ANN model yields the best performance for all lead times, in terms of model efficiency and graphical comparison. These results indicate that the optimal combination of input variables and forecasting models depending on lead times should be applied in reservoir water level forecasting, instead of the single combination of input variables and forecasting models for all lead times.

• Asian Journal for Public Opinion Research
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• v.9 no.4
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• pp.352-367
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• 2021

Purpose: This research aims to examine gender preferences for men and women advertising models in Saudi advertisements. Saudi Arabia is known as one of the most gender-segregated society in the world, and it has gender-specific roles, characteristics, and behaviors that are undesirable for the other gender. Methodology: The questionnaire was developed with the help of earlier studies on perceptions towards advertising models and validated by a jury of experts and focus groups. The gender preferences for ten product categories (including automobiles, baby care products, cigarettes, cosmetics for women, fashion, food & beverages, motorcycles, personal care for men, personal care for women, sporting goods) were examined for men and women models. Similarly, three personal preferences characteristics for both genders (face beauty, voice quality, and Islamic dress), two characteristics for women models (body shape, femininity), and two characteristics for men models (height-weight balance, masculinity) were examined for men and women models separately. Finally, a survey was conducted to solicit responses from respondents (N=412). Findings: Results indicated significant gender preferences for gender-specific product categories and typical gender stereotypes in advertising models. Men models were preferred in men-specific products, and women models were required in women-specific products. Some product categories (including personal care for men and sporting goods) were ranked higher for men advertising models, while for women advertising models, other product categories (including personal care for women and cosmetics for women) were ranked higher. Masculinity was ranked highest as the preferred personal characteristic for men advertising models, while voice quality was highest for women advertising models. Finally, there is a significant difference between the preferred personal characteristic for men and women advertising models for three characteristics, including face beauty, Islamic dress, and masculinity and femininity. Implications: Saudi Arabia is a unique society with predominantly unique cultural dominance. Consequently, local culture greatly influences advertisements. It has stereotyped gender roles even in advertisements. This study will establish a baseline for further research on the subject area.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.55-67
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• 2023

Lightweight face recognition models, as one of the most popular and long-standing topics in the field of computer vision, has achieved vigorous development and has been widely used in many real-world applications due to fewer number of parameters, lower floating-point operations, and smaller model size. However, few surveys reviewed lightweight models and reimplemented these lightweight models by using the same calculating resource and training dataset. In this survey article, we present a comprehensive review about the recent research advances on the end-to-end efficient lightweight face recognition models and reimplement several of the most popular models. To start with, we introduce the overview of face recognition with lightweight models. Then, based on the construction of models, we categorize the lightweight models into: (1) artificially designing lightweight FR models, (2) pruned models to face recognition, (3) efficient automatic neural network architecture design based on neural architecture searching, (4) Knowledge distillation and (5) low-rank decomposition. As an example, we also introduce the SqueezeFaceNet and EfficientFaceNet by pruning SqueezeNet and EfficientNet. Additionally, we reimplement and present a detailed performance comparison of different lightweight models on the nine different test benchmarks. At last, the challenges and future works are provided. There are three main contributions in our survey: firstly, the categorized lightweight models can be conveniently identified so that we can explore new lightweight models for face recognition; secondly, the comprehensive performance comparisons are carried out so that ones can choose models when a state-of-the-art end-to-end face recognition system is deployed on mobile devices; thirdly, the challenges and future trends are stated to inspire our future works.

• Physical Therapy Korea
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• v.8 no.1
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• pp.97-106
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• 2001

Physical therapists should under stand motor control models and apply various models to evaluation and treatment of neurologically impaired patients. Thus, this paper reviews motor control models and applications in clinical settings. Assumptions and limitations of reflex models, hierarchical models, and systems models are presented. This paper also delineates goals and dissatisfaction of neurologic rehabilitation approaches for neurologically impaired patients. Muscle reeducation approach, neurotherapeutic facilitation approach, and contemporary task-oriented approach are explained.

• Korean Management Science Review
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• v.11 no.2
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• pp.99-131
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• 1994

This paper shows the direction in which logistics modellers should make their effort by examining the gap between desirable characteristics which logistics decision models should possess and deficiencies from which existing models suffer. For this purpose, we(1) categorized logistics models into facility planning, inventory management and transportation/delivery planning models, (2) carried out a wide survey of theoretical and industry models within each category and (3) assessed recent development of integrated logistics models.

• Journal of the Korean Operations Research and Management Science Society
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• v.5 no.1
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• pp.31-38
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• 1980

The purpose of this paper is to exhibit existing replacement models and to develop new replacement models for managing equipments in large organizations, private or public. Some of the models in this paper are well known and in use, and some are not. All these models are classified, depending on main factors which play key roles on the models. One group is the models in which the economic factor plays a key role, and the other is those in which both the economic factor and the effectiveness factor play key roles.

• Journal of the Korea Society for Simulation
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• v.1 no.1
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• pp.77-86
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• 1992

In this paper, we study validation test methods of DEVSA(Descrete Event system Specification) models using SPN(Stochastic Petri Net) models. We discuss conventional validation test methods, by which DEVS models can be transformed to SPN models, by reviewing the features of DEVS model. Based on the model transformation method, we define a new homogeneous function for validation test and suggest a new validation test method of DEVS models using the property of SPN models and the new homogeneous function.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.167-177
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• 2010

Recent competitive and technological changes during the past decade have accelerated the need for better capital recovery methods. Competition and technology have together shortened the expected lives of property which could not have been forecasted several years ago. Since the usage of technological growth models has been prevalent in various technological forecasting environments, the various forms of growth models have become numerous. Of six such models studied, some models do significantly better than others, especially at low penetration levels in predicting future levels of growth. A set of criteria for choosing an appropriate model for technological growth models was developed. Two major characteristics of an S-shaped curve were elected which differentiate the various models; they are the skewness of the curve and underlying assumptions regarding the variance of error structure of the model.