• KIPS Transactions on Computer and Communication Systems
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• v.9 no.1
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• pp.9-16
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• 2020

The k-anonymity scheme has been widely used to protect private information when Big Data are distributed to a third party for research purposes. When the scheme is applied, an optimal k value determination is one of difficult problems to be resolved because many factors should be considered. Currently, the determination has been done almost manually by human experts with their intuition. This leads to degrade performance of the anonymization, and it takes much time and cost for them to do a task. To overcome this problem, a simple idea has been proposed that is based on machine learning. This paper describes implementations and experiments to realize the proposed idea. In thi work, a deep neural network (DNN) is implemented using tensorflow libraries, and it is trained and tested using input dataset. The experiment results show that a trend of training errors follows a typical pattern in DNN, but for validation errors, our model represents a different pattern from one shown in typical training process. The advantage of the proposed approach is that it can reduce time and cost for experts to determine k value because it can be done semi-automatically.

• Smart Structures and Systems
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• v.29 no.1
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• pp.129-140
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• 2022

The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.

• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.480-494
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• 2018

The purpose of this study is to develop a nursing activity cost calculation program based on Lee's doctoral dissertation using TD-ABC. The developed program has been supplemented with data storage, print out, and graph conversion functions to expand the application possibility. The development of the program consisted of three steps: program requirements analysis, program design and development, and program validation. This program was designed not only to do the cost calculation, but also to compare the cost-effectiveness and cost consumption trends. Consequently, this program is meaningful in that the nursing manager can obtain the cost information necessary for nursing unit management and extend the utilization so that the cost management strategy can be established based on the cost information. Therefore, we propose that the cost-management capacity of clinical nurses should be strengthened and the nursing performance measurement research should be expanded by applying it to various actual clinical nursing management settings. It is suggested that this program should be used as a training medium to strengthen nurse cost management capacity by combining nursing management curriculum at undergraduate level.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.139-148
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• 2020

Due to change in circumstances in the 2000s such as severe birthrate decline and shortened military service period, the armed forces of the Republic of Korea is currently turning to technologies and equipments from manpower, developing it to become high-tech, high-speed, and complex, resulting in an environment in which a single mistake could cause a mass mortality crisis.It is also evident that, considering aspects such as safety training curriculums and achievements of advanced countries and private education, hands-on training is a must in preventing suicides and accidents in the military, and establishing safety training centers is crucial for systematic and effective hands-on training.Soldiers who are joining the army as of now have experienced the Internet ever since they were born and easily use both virtual and augmented reality, and the current level of science and technology has developed to the point where most of the public safety experience centers are able to be replaced by virtual and augmented reality (VR/AR). Therefore, considering the aspects such as installation space, construction costs, maintenance costs, user characteristics, and education effects, other than for those trainings where real models and objects are more effective such as first aid training, it is with a strong recommendation that establishing military safety training facilities with VR/AR (Virtual and augmented reality) is a must in the coming future. We have derived the need for hands-on training by considering the development of virtual and augmented reality (VR/AR), analysis of operation status of the public safety experience centers, characteristics of military units, installation and maintenance costs, and proposed plan to establish safety training centers where effective training performance can be achieved at a lower cost than the public safety experience center. In addition, we suggested the scale of the required safety training center and the composition of the experience rooms considering the number of trainees and the environment of each military units. Given this analysis it will contribute to the prevention of military safety and suicide by building a safety training center in the future.

• Korean Journal of Health Education and Promotion
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• v.31 no.5
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• pp.81-95
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• 2014

Objectives: The purpose of this study was to apply participatory action-oriented training (PAOT) principle for the management of metabolic syndrome and to assess its effectiveness in the workplace. Methods: The metabolic syndrome management program applying participatory action-oriented training (PAOT) principle is constructed action checklist, Group discussion, self-creation of an action plan, after 3 months, 6 months, 1 year, the practice carried out check up and encourage practice activity. Results: The seventy-seven workers who participated in the metabolic syndrome management program made a total of 246 action plans, 3.8 action plans per person and 234 action plans were achieved after one year, so that 81.0% of action plans have been achieved. After 1 year, in the intervention group, systolic blood pressure (SBP) and diastolic blood pressure (DBP), body mass index (BMI), the risk factors score decreased statistically significant, and metabolic syndrome prevalence decreased 54.8%. whereas the control group also showed a significant decreased in the mean systolic and diastolic blood pressure, the risk factors score. but, low-density lipoprotein (LDL) was significantly increased. Conclusions: To improve the effectiveness of metabolic syndrome management program applying participatory action-oriented training (PAOT) principle, the need to continues implementing programs and analysis of the long-term effects are required. Participatory action-oriented training (PAOT) principle can applying without cost and time, the human burden for the prevention and management of chronic diseases such as metabolic syndrome.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.665-667
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• 2014

Wellness is IT fused with the user manage and maintain the health of a service can help you. If you are using an existing Fitness Center to yourself by choosing appliances that fit with the risk of injury in order to learn how the efficient movement had existed for a long time was needed. To resolve, use the personal training but more expensive cost of people's problems, and shown again in the habit of exercising alone will have difficulty. This paper provides a variety of smart phones based on a hybrid app with compatibility with the platform and personalized training market system. Users of the Fitness Center is built into smart phones in the history of their movement sensors or transmits to the Web by typing directly. This is based on exercise programs tailored to users via the training market. Personal training marketplace has a variety of users, check the history of this movement he can recommend an exercise program for themselves can be applied by selecting the. This provides users with the right exercise program can do long-term exercise habits can be proactive and goal setting.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.419-432
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• 2019

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.569-575
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• 2022

It is most desirable to build a crane training program in the same evvironment as the actual port, but it has problem such as time constraint and cost. To overcome these limitations, next-generation training programs based on AR/VR are receiving a lot of attention. In this paper, a prototype of a harbor crane training system based on virtual reality was designed and implemented. The system implemented in this paper consists of two elements: an Arduino-based IoT terminal and an HMD equipped with a Unity application program. The IoT terminal consists of 2 controllers, 2 toggle switches, and 8 button switches to process data generated according to the user's operation. The HMD uses Oculus Quest2 and is connected to the IoT terminal through wireless communication to provide user convenience. The training system implemented in this paper is expected to provide trainees with a training environment independent of time and place through virtual reality and to save time and money.

• International Journal of Quality Innovation
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• v.8 no.3
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• pp.81-112
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• 2007

In the competitive global market, firms have to keep profit from innovation activities. A firm makes profits by offering products or services at a lower cost than its competitors or by offering differentiated products at premium prices that more than compensate for the extra cost of differentiation. The IC Package and Testing technology industries were the first high technological industry to build in Taiwan. The Package and Testing industries in Taiwan adopted competitive innovation activities to become stronger. In our study, we want to know how innovation activities influence a firm operating in the IC Package and Testing industries. Our study used a questionnaire and Likert five-point scale to survey the innovation activities, customer and feedback in innovation performance in the IC Package and Testing industry. The wafer level chip size packing technology in our study indicates the innovation activities. Because we need to compare the difference between the wafer level chip size packing technology and wire bonding technology to recognize innovation and how the innovator and customer were influenced. Our conclusions are described below: (1) When the innovator adopts innovation activities that can be maintained using experiments and knowledge, using machine and decision variables more quickly will produce success; (2) Innovators should adopt innovation activities that focus on customers that use knowledge and experimentation, training time and cost. If an innovation forces customers to spend much time and cost to learn new technology or applications, the innovation will not be adopted; (3) Innovators that create innovation performance higher than his customers must also consider the impact upon their customers. We have to remind innovator to focus on why their customers have a different level of evolution in the same innovation activities.

• Journal of the military operations research society of Korea
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• v.36 no.3
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• pp.69-81
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• 2010

Since the mid-nineties, Republic of Korean Army has officially adapted information technology. Starting with the Chang-Jo 21('99) training M&S Model, around seventy types of the M&S system software, including Ground Operations & Resources Requirement Analysis Model ('10), developed by Republic of Korea Army or foreign counterparts have been utilized. However, as the existing cost estimations of the Defense M&S system software fail to reflect the acquisition and development environment, the estimated costs have been frequently overestimated or underestimated. This paper provides a suitable cost estimation model for the Defense M&S system software by presenting the software cost drivers which reflect the unique software development environment of the Defense.