• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.64-79
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• 2017

Identifying disease genes from human genome is a critical task in biomedical research. Important biological features to distinguish the disease genes from the non-disease genes have been mainly selected based on traditional feature selection approaches. However, the traditional feature selection approaches unnecessarily consider many unimportant biological features. As a result, although some of the existing classification techniques have been applied to disease gene identification, the prediction performance was not satisfactory. A small set of the most important biological features can enhance the accuracy of disease gene identification, as well as provide potentially useful knowledge for biologists or clinicians, who can further investigate the selected biological features as well as the potential disease genes. In this paper, we propose a new stepwise random forests (SRF) approach for biological feature selection and disease gene identification. The SRF approach consists of two stages. In the first stage, only important biological features are iteratively selected in a forward selection manner based on one-dimensional random forest regression, where the updated residual vector is considered as the current response vector. We can then determine a small set of important biological features. In the second stage, random forests classification with regard to the selected biological features is applied to identify disease genes. Our extensive experiments show that the proposed SRF approach outperforms the existing feature selection and classification techniques in terms of biological feature selection and disease gene identification.

• The Journal of Korean Society for School & Community Health Education
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• v.19 no.3
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• pp.79-93
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• 2018

Objectives: This study aimed to develop a smoking cessation education program and to provide basic data for evaluating program for university students majoring in health sciences. Methods: The education program was developed based on the instructional system design model(ADDIE), that was based on literature review, brainstorming, and interviews of related experts. Education program was implemented for 2 sessions of 3 hours to the 82 university students majoring in health science. Knowledge, competency, and self-efficacy for performance of smoking cessation were analyzed. One group pre- & post-test design was used for evaluation of this program. Results: In the analysis stage, a total of 5 duties, 16 tasks, and 55 task elements were derived. In the design and development stage, based on job analysis, education program contents were composed of understanding tobacco, planning and implementation of smoking cessation program, smoking cessation counseling and drug treatment. After this education program, students achieved remarkable improvement in increasing knowledge, competency, and self-efficacy for smoking cessation counselling and program. Conclusions: It is needed to be included the smoking cessation education in department related to health science regular course.

• Korean Journal of Agricultural Science
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• v.49 no.4
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• pp.785-793
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• 2022

In this study, tomato maturity was estimated based on deep learning for a harvesting robot. Tomato images were obtained using a RGB camera installed on a monitoring robot, which was developed previously, and the samples were cropped to 128 × 128 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the mean-variance loss was used to learn implicitly the distribution of the data features by class. In the test stage, the tomato maturity was estimated as a continuous index, which has a range of 0 to 1, by calculating the expected class value. The results show that the F1-score of the classification was approximately 0.94, and the performance was similar to that of a deep learning-based classification task in the agriculture field. In addition, it was possible to estimate the distribution in each maturity stage. From the results, it was found that our approach can not only classify the discrete maturation stages of the tomatoes but also can estimate the continuous maturity.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.223-224
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• 2023

Optimal fleet management in the planning stage is one of the most critical activities that guarantee successful construction projects. In South Korea, the construction standard production rate database (CSPRD) is normally employed. However, when it comes to a trade-off problem that involves decision-making on optimal sets of equipment to perform a certain task, the method will require the planners' in-depth knowledge and experience regarding the target process and a time consuming estimation of the performance of every possible scenario must be conducted for the deduction of the optimal fleet management. On this account, this research paper proposes a lightweight method of using mixed integer nonlinear programming (MINLP) in multi-objective problems based on CSPRD-based mathematical equations to assist planners in the preplanning stage of choosing the optimal sets of types and size machinery to efficiently arrange the construction scheduling and budgeting.

• Journal of Information Technology Application
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• v.2 no.1
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• pp.1-24
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• 2000

Project management in the Construction field inherently has more uncertainty and more risks relative to ones from other area. This is the very reason for why project management is recognized as the important task to construction companies. For getting better performance in the project management, we need a system that keeps the consistencies in a automatic or semi-automatic manner through the project management stages like as project definition stage, project planning stage, project design and implementation stage. But since the early stages such as definition and planning stages has many unstructured features and also are dependent to unique expertise or experience of a specific company, we have difficulty providing systematic support for the task of these stages. This kind of problem becomes harder to solve especially in the plant construction domain that is our target domain. Therefore, in this paper, we propose and also implement a systematic approach to resolve the problem mentioned for the early project management stages in the plant construction domain. The results of our approach can be used not only for the purpose of the early project management stages but also can be used automatically as an input to commercial project management tools for the middle project management stages. Because of doing in this way, the construction project can be consistently managed from the definition to implementation stage in a seamless manner. For achieving this purpose, we adopt knowledge based inference, CBR, and neural network as major methodologies and we also applied our approach to two real world cases, power plant and drainage treatment plant cases from a leading construction company in Korea. Since these two application cases showed us very successful results, we can say our approach was validated successfully to the plant construction area. Finally, we believe our approach will contribute to many project management problems from more broader construction area.

• Journal of Korean Society on Water Environment
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• v.33 no.3
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• pp.348-358
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• 2017

The process of evaluating and selecting the best available techniques presents various characteristics for each country. In the case of EU, BAT is selected through TWG meeting after first screening, mass and energy balance, impact assessment and decision support process. Korea has proposed four principles to select BAT that can be carbon neutral for each environmental infrastructure in order to reduce greenhouse gas emissions. In order to evaluate and select the best available technique, it is necessary to differentiate the method according to whether it is a technique generally applied at the current workplace, whether it is a single technique or a combination technique, and whether it is a technology technique or management technique. In the case of a single technique, it should be evaluated whether it is a technique applied in the workplace, excessive cost, superior environmental technique over BAT, and secondary environmental pollution. In the case of multiple techniques, it is necessary to examine whether the emission standards are met and whether the pollutants can be treated at the same level as BAT. In the case of BAT candidates for management techniques, whether or not they contribute directly or indirectly to lowering the emission level of pollutants can be an important evaluation item. In the case of environmental techniques that are not generally applied in the workplace, it is recommended that the following 8 steps be carried out, including those prescribed by law. In the first stage, the list of performance evaluation factors is listed. In the second stage, the level of disposal of pollutants and the level of satisfaction with standards are listed. In the third stage, the environmental evaluation elements are listed. In the fourth stage, Is to list the economic evaluation elements, step 6 is to list the pollution and accident prevention evaluation factors, step 7 is the quantitative evaluation of the technical working group, and step 8 is BAT confirmation through deliberation of the central environmental policy committee.

• Journal of the Korean Society for Railway
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• v.5 no.4
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• pp.231-236
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• 2002

Todays, crash safety requirements of the railway vehicle structures become important design criterion according to the increased driving speed and the lightweight construction. Although the crash analysis using computer simulation can be effectively applied to predict the crash performance of the railway vehicles in the early design stage, the optimized design w.r.t the crash safety could be realized by the crash tests with actual prototype vehicles. However, it is very expensive and time-consuming task to perform the crash test of the railway vehicles. As a measure to cope with the problem, in this paper, the scale modeling technique is suggested and experimentally verified to predict the impact energy absorption characteristics of full scale model of aluminum extrusions sub-structures and the high-speed railway vehicle structure.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.8
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• pp.834-841
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• 2006

Recently, many enterprises are installing AMSs(Automated Manufacturing Systems) for their competitive advantages. As the level of automation increases, proper design and validation of control logic is a imperative task for the successful operation of AMSs. However, current discrete event simulation methods mainly focus on the performance evaluation. As a result, they lack the modeling capabilities for the detail logic of automated manufacturing system controller. Proposed in this paper is a method of validation of the controller logic for automated material handling system using an object-oriented design and simulation. Using this method, FA engineers can validate the controller logic easily in earlier stage of system design, so they can reduce the time for correcting the logic errors and enhance the productivity of control program development Generated simulation model can also be used as a communication tool among FA engineers who have different experiences and disciplines.

• International Journal of Contents
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• v.9 no.2
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• pp.8-13
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• 2013

Locating text data in images automatically has been a challenging task. In this approach, we build a three stage system for text detection purpose. This system utilizes tensor voting and Completed Local Binary Pattern (CLBP) to classify text and non-text regions. While tensor voting generates the text line information, which is very useful for localizing candidate text regions, the Nearest Neighbor classifier trained on discriminative features obtained by the CLBP-based operator is used to refine the results. The whole algorithm is implemented in MATLAB and applied to all images of ICDAR 2011 Robust Reading Competition data set. Experiments show the promising performance of this method.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.285-294
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• 2023

Autonomous radiation source detection has long been studied for radiation emergencies. Compared to conventional data-driven or path planning methods, deep reinforcement learning shows a strong capacity in source detection while still lacking the generalized ability to the geometry in unknown environments. In this work, the detection task is decomposed into two subtasks: exploration and localization. A hierarchical control policy (HC) is proposed to perform the subtasks at different stages. The low-level controller learns how to execute the individual subtasks by deep reinforcement learning, and the high-level controller determines which subtasks should be executed at the current stage. In experimental tests under different geometrical conditions, HC achieves the best performance among the autonomous decision policies. The robustness and generalized ability of the hierarchy have been demonstrated.