• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.629-635
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• 2021

This study seeks to examine and analyze the influence of institutional strengthening factors, and capacity building - communication, resources, and training - on the performance of defense policy implementation. This study conducted a quantitative analysis related to the implementation of the institutional strengthening policy. The data used are primary data with a research instrument in the form of a questionnaire. The population in this study were all people in the city of Bandung, Indonesia. The sample of this study consisted of 200 respondents consisting of civilians and soldiers who served in the city of Bandung. Data analysis uses the Structural Equation Model (SEM) measurement model. The results of this study reveals that institutional strengthening (X1) influences positively and significantly capacity building's communication (Y1), resources (Y2), and training (Y3). On the other hand, the performance of defense policy implementation (Y4) is positively and significantly affected by capacity building's communication (Y1), resources (Y2), and training (Y3). The interaction between institutions, consumption support, role of the healthcare sector, and effectiveness are the most important indicators reflecting capacity building (communication, resources, training) and the performance of defense policy implementation. Essentially, this study analyzes the performance of defense policy implementation based on capacity building.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.30 no.1
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• pp.44-49
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• 2022

This study performed precision instrument landing procedures for pilots with a commercial pilot license using VR HMD flight simulators, and assuming that the center of the pilot's gaze is in the front, 3-D.O.F. head tracking data and 2-D eye tracking of VR HMD worn by pilots gaze tracking was performed through. After that, AOI (Area of Interesting) was set for the instrument panel and external field of view of the cockpit to analyze how the pilot's gaze was distributed before and after the decision altitude. At the same time, the landing results were analyzed using the Localizer and G/S data as the pilot's precision instrument landing flight data. As a result, the pilot was quantitatively evaluated by reflecting the gaze tracking and the resulting landing result using a VR HMD simulator.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.149-158
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• 2024

Traditional manual identification of crop leaf diseases is challenging. Owing to the limitations in manpower and resources, it is challenging to explore crop diseases on a large scale. The emergence of artificial intelligence technologies, particularly the extensive application of deep learning technologies, is expected to overcome these challenges and greatly improve the accuracy and efficiency of crop disease identification. Crop leaf disease identification models have been designed and trained using large-scale training data, enabling them to predict different categories of diseases from unlabeled crop leaves. However, these models, which possess strong feature representation capabilities, require substantial training data, and there is often a shortage of such datasets in practical farming scenarios. To address this issue and improve the feature learning abilities of models, this study proposes a deep transfer learning adaptation strategy. The novel proposed method aims to transfer the weights and parameters from pre-trained models in similar large-scale training datasets, such as ImageNet. ImageNet pre-trained weights are adopted and fine-tuned with the features of crop leaf diseases to improve prediction ability. In this study, we collected 16,060 crop leaf disease images, spanning 12 categories, for training. The experimental results demonstrate that an impressive accuracy of 98% is achieved using the proposed method on the transferred ResNet-50 model, thereby confirming the effectiveness of our transfer learning approach.

• Korean Journal of Computational Design and Engineering
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• v.22 no.1
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• pp.80-88
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• 2017

Product data analytics (PDA) is a data-driven analysis method that uses product data management (PDM) databases as its operational data. It aims to understand and evaluate product development processes indirectly through the analysis of product data from the PDM databases. To educate and train PDA efficiently, this study proposed an approach that employs courses for both product development and PDA in a class. The participant group for product development provides a PDM database as a result of their product development activities, and the other group for PDA analyses the PDM database and provides analysis result to the product development group who can explain causes of the result. The collaboration between the two groups can enhance the efficiency of the education and training course on PDA. This study also includes an application example of the approach to a graduate class on PDA and discussion of its result.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.4
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• pp.145-152
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• 2020

MRC (Machine reading comprehension) is the AI NLP task that predict the answer for user's query by understanding of the relevant document and which can be used in automated consult services such as chatbots. Recently, the BERT (Pre-training of Deep Bidirectional Transformers for Language Understanding) model, which shows high performance in various fields of natural language processing, have two phases. First phase is Pre-training the big data of each domain. And second phase is fine-tuning the model for solving each NLP tasks as a prediction. In this paper, we have made the Patent MRC dataset and shown that how to build the patent consultation training data for MRC task. And we propose the method to improve the performance of the MRC task using the Pre-trained Patent-BERT model by the patent consultation corpus and the language processing algorithm suitable for the machine learning of the patent counseling data. As a result of experiment, we show that the performance of the method proposed in this paper is improved to answer the patent counseling query.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.949-958
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• 2004

race detection using a boosting based algorithm requires a very large size of face and nonface data. In addition, the fact that there always occurs a need for adding additional training data for better detection rates demands an efficient incremental teaming algorithm. In the design of incremental teaming based classifiers, the final classifier should represent the characteristics of the entire training dataset. Conventional methods have a critical problem in combining intermediate classifiers that weight updates depend solely on the performance of individual dataset. In this paper, for the purpose of application to face detection, we present a new method to combine an intermediate classifier with previously acquired ones in an optimal manner. Our algorithm creates a validation set by incrementally adding sampled instances from each dataset to represent the entire training data. The weight of each classifier is determined based on its performance on the validation set. This approach guarantees that the resulting final classifier is teamed by the entire training dataset. Experimental results show that the classifier trained by the proposed algorithm performs better than by AdaBoost which operates in batch mode, as well as by ${Learn}^{++}$.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.705-712
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• 2013

Active learning aims to improve the performance of a classification model by repeating the process to select the most helpful unlabeled data and include it to the training set through labelling by expert. In this paper, we propose a method for active learning based on hierarchical agglomerative clustering using Ward's linkage. The proposed method is able to construct a training set actively so as to include at least one sample from each cluster and also to reflect the total data distribution by expanding the existing training set. While most of existing active learning methods assume that an initial training set is given, the proposed method is applicable in both cases when an initial training data is given or not given. Experimental results show the superiority of the proposed method.

• Journal of Practical Engineering Education
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• v.13 no.2
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• pp.393-403
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• 2021

The purpose of this study is to propose a method of designing a systematic panel survey at the institutional level to lay the foundation for data-based decision-making using vocational training teachers and instructors as the population. In this study, the target population and sampling frame, which are the main elements necessary for planning a panel survey, are proposed. Also based on expert advice and empirical data analysis, the sampling unit and sampling method taking into account the outer and inner variables are presented, comprehensively considering the representativeness of data, the efficiency and sustainability of data collection. As a result of the study, with the unit of the panel as a vocational training institution, a two-stage stratified proportional sampling plan is proposed so that the institution selected as the panel and the vocational training teachers and instructors belonging to the institution can participate in the survey. Based on this, implications for the panel survey sample design are presented.

• Journal of Korea Entertainment Industry Association
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• v.13 no.1
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• pp.167-173
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• 2019

It is reported that the higher the satisfaction of students' clinical practice, the more the ability to perform their work. It is important to assess student satisfaction with clinical practice. This study is to investigate the satisfaction of occupational therapy students' clinical practice. The research data will be used as basic data for effective clinical practice. The subjects of this study were 319 occupational therapy students who completed clinical training. The data collection period was from August 1, 2017 to August 13, 2017. The questionnaire used in the previous literature was used to measure the satisfaction of clinical practice. The collected data were statistically processed using the SPSS 12.0 statistical program. The general characteristics of the study subjects were more female, university more. Satisfaction with major was the most satisfied and the training period was the most 24 weeks. Rehabilitation hospitals were the highest in the training institutions. The results showed that the satisfaction of clinical practice was higher in the order of clinical practice, satisfaction after clinical practice, clinical practice environment, teaching method of clinical practice, clinical practice time, evaluation of clinical practice, and internal conflicts in clinical practice. Clinical practice satisfaction according to general characteristics was significantly different according to the major satisfaction and the training period.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.136-144
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• 2022

Due to the lack of attendance management system, the training conducted by each existing institution was handwritten at the site and managed in writing about the attendance of participants. It not only takes a lot of manpower and time to store and search, but also has a lot of difficulty in storing. In this paper, we design and implement an integrated smart attendance management system using barcodes. Through this, the attendance system of training applicants is developed, security related to attendance is strengthened, and training attendance data is computerized and collected. In addition, standards for necessary data are selected so that each institution can efficiently manage and utilize training information data. The proposed system can add various institutions in a single construction, making it easy to expand institutional management and can expect additional cost reduction effects. In addition, it is expected that the quality of education will be improved by increasing the convenience of training managers who use the management system provided and controlling/managing attendance data of users.