• Journal of the Semiconductor & Display Technology
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• v.11 no.3
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• pp.51-56
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• 2012

In this paper, we propose a telemedicine system based ECG data using a bio-signal meter and a smart device for treating faraway patients. This system is composed of a patch-shaped portable bio-signal meter, patient's smart device application, and doctor's PC software. Using these components, doctors and patients can do telemedicine. First, a patient measures his own ECG signal with a bio-signal meter and send the data to a doctor using a smart device application. Then, the doctor checks the ECG data, and make and send a diagnosis chart to web server. Likewise, doctors and patients can be offered a medical environment without time and space restraints. Applying this system to real medical system can improve the problem of low accessibility and efficiency and also can reduce medical expenses.

• International journal of advanced smart convergence
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• v.2 no.2
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• pp.36-39
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• 2013

It is becoming more important as the growing of health information and increasing in cancer patients diagnose over the time gradually. Among the various types of cancer, we focuses on breast cancer diagnosis. The accuracy of breast cancer diagnosis is increasing when the diagnosis is based on evidence and statistics. To do this we use the weka data mining tools and analysis algorithms significantly associated with the decision tree uses rules. In addition, the data pre-processing and cross-validation are used to increase the reliability of the results. The number and cause of the disease becomes important to increase evidence-based medical doctors. As the evidence-based medical, the data obtained from patients in the past through the disease by calculating the probability for future patients to diagnose and predict disease and treatment plan. It can be found by improving the survival rate plays an important role.

• Smart Structures and Systems
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• v.12 no.3_4
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• pp.271-290
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• 2013

The acceleration information is significant for the structural health monitoring, which is the basic measurement to identify structural dynamic characteristics and structural vibration. The efficiency of the accelerometer is subsequently important for the structural health monitoring. In this paper, the distance measure matrix and the support level matrix are constructed firstly and the synthesized support level and the fusion method are given subsequently. Furthermore, the synthesized support level can be served as the determination for diagnosis on accelerometers, while the consensus data fusion method can be used to recover the acceleration information in frequency domain. The acceleration acquisition measurements from the accelerometers located on the real structure National Aquatics Center are used to be the basic simulation data here. By calculating two groups of accelerometers, the validation and stability of diagnosis and recovering on acceleration based on the data fusion are proofed in the paper.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1295-1302
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• 2016

In this paper, a black box, which is provided the reliability and user safety of home battery energy storage system connected with solar energy generation, is developed. In the developed scheme, a status and diagnosis data of battery management system, power conditioning system, solar energy generation and grid is measured. This status and diagnosis data is stored and displayed in the developed black box. In addition, this status and diagnosis data is stored and displayed in a monitoring system and a smart phone of user. A performance evaluation of the developed black box is carried out using emulator of home battery energy storage system connected with solar energy generation. Consequently, the developed black box is proved its superiority of the reliability and user safety.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.169-173
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• 2018

Iris diagnosis based on the color and texture information is one of a novel approach which can represent the current state of a certain organ inside body or the health condition of a person. In analysis of the iris images, there are critical image artifacts which can prevent of use interpretation of the iris textures on images. Here, we developed the iris diagnosis system based on a hand-held typed imaging probe which consists of a single camera sensor module with 8M pixels, two pairs of 400~700 nm LED, and a guide beam. Two original images with different light noise pattern were successively acquired in turns, and the light noise-free image was finally reconstructed and demonstrated by the proposed artifact removal approach.

• Land and Housing Review
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• v.9 no.2
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• pp.1-7
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• 2018

The purpose of this study is to suggest diagnosis indicators and checklist for urban regeneration projects by Korea Land & Housing Corporation(LH). There are already deprivation indices in the Urban Regeneration Act but not any additional guidelines in the practical aspect. In order to use the diagnosis indicators, the central government should supply more specific checklist to the actors in the regeneration field. The key actor of many stakeholders is LH as an operator and implementer in the regeneration projects. So far, LH has developed housing and cities and there haven't been any obvious changes to realize public benefit in the deteriorated area. From now on, It has to plan, implement, and manage a lot of regeneration projects entirely. Therefore, It is necessary to develope and apply the diagnosis indicators and checklist based on projects. This paper came up with the 6 factors related with LH business field : housing, urban infrastructure, public service, private service, environment, and smart city. For these, 32 diagnosis indicators and 72 checklists were selected that can include both physical and qualitative indicators. These can be used not only for the selection of regeneration projects but also for the process monitoring such as planning and implementation.

• Smart Structures and Systems
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• v.12 no.3_4
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• pp.345-361
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• 2013

Locating and assessing the severity of damage in large or complex structures is one of the most challenging problems in the field of civil engineering. Considering that the wavelet packet transform (WPT) has the ability to clearly reflect the damage characteristics of structural response signals and the artificial neural network (ANN) is capable of learning in an unsupervised manner and of forming new classes when the structural exhibits change, this paper investigates a multi-stage structural damage diagnosis method by using the WPT and ANN based on "energy-damage" theory, in which, the wavelet packet component energies are first extracted to be damage sensitive feature and then adopted as input into an improved back propagation (BP) neural network model for damage diagnosis in a step by step mode. To validate the efficacy of the presented approach of the damage diagnosis, the benchmark structure of the American Society of Civil Engineers (ASCE) is employed in the case study. The results of damage diagnosis indicate that the method herein is computationally efficient and is able to detect the existence of different damage patterns in the simulated experiment where minor, moderate and severe damages corresponds to involving in the loss of stiffness on braces or the removal bracing in various combinations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.12
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• pp.1625-1630
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• 2018

In emergency situations, first aid workers are the main task of trauma evaluation and care. However, the scope of first-aid treatment is small and they are mainly carried out to the emergency room of the hospital. If a specialist who is in charge of an emergency situation is connected and emergency medical treatment through remote medical direction is performed, accurate diagnosis and appropriate care can be helped. This paper has developed an smart application(app) to support remote medical direction for emergency medical services. The developed smart app allows emergency rescuers to call a doctor at a remote location and transmit real-time status of emergency patients to vital sign and video. This will help to diagnose the patient's condition accurately. In addition, emergency care can be instructed and response in the emergency room can be made quickly.

• Journal of Korean Society for Quality Management
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• v.48 no.1
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• pp.87-105
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• 2020

Purpose: This paper aims to propose a practical strategy for smart factories and a step-by-step quality strategy according to the maturity of smart factory construction. Methods: The characteristics, compositional requirements, and diagnosis system are examined for smart factories through theoretical considerations. Several cases of implementing smart factory are studied considering the company maturity level from the aspect of the smartness concept. And specific quality techniques and innovation activities are carefully reviewed. Results: The maturity level of smart factory was classified into five phases: 1) ICT non-application, 2) basic, 3) intermediate 1, 4) intermediate 2, 5) advanced level. A five-step quality strategy was established on the basis of case studies; identify, measure, analyze, optimize, and customize. Some quality techniques are introduced for step-by-step implementation of quality strategies. Conclusion: To build a successful smart factory, it is necessary to establish a quality strategy that suits the culture and size of the company. The quality management strategy proposed in this paper is expected to contribute to the establishment of appropriate strategies for the size and purpose of the company.

• KNOM Review
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• v.24 no.1
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• pp.13-19
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• 2021

In recent, there is research to maximize production by preventing failures/accidents in advance through fault diagnosis/prediction and factory automation in the industrial field. Cloud technology for accumulating a large amount of data, big data technology for data processing, and Artificial Intelligence(AI) technology for easy data analysis are promising candidate technologies for accomplishing this. Also, recently, due to the development of fault diagnosis/prediction, the equipment maintenance method is also developing from Time Based Maintenance(TBM), being a method of regularly maintaining equipment, to the TBM of combining Condition Based Maintenance(CBM), being a method of maintenance according to the condition of the equipment. For CBM-based maintenance, it is necessary to define and analyze the condition of the facility. Therefore, we propose a machine learning-based system and data model for diagnosing the fault in this paper. And based on this, we will present a case of predicting the fault occurrence in advance.