• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.4
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• pp.327-336
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• 2021

In this paper, we will introduce the image processing methods for the remote pupillary light reflex measurement using the video taken by a general smartphone camera without a special device such as an infrared camera. We propose an algorithm for estimate the size of the pupil that changes with light using image data analysis without a learning process. In addition, we will introduce the results of visualizing the change in the pupil size by removing noise from the recorded data of the pupil size measured for each frame of the video. We expect that this study will contribute to the construction of an objective indicator for remote pupillary light reflex measurement in the situation where non-face-to-face communication has become common due to COVID-19 and the demand for remote diagnosis is increasing.

• Industrial Engineering and Management Systems
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• v.14 no.4
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• pp.392-400
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• 2015

Monitoring of plasma etch processes for fault detection is one of the hallmark procedures in semiconductor manufacturing. Optical emission spectroscopy (OES) has been considered as a gold standard for modeling plasma etching processes for on-line diagnosis and monitoring. However, statistical quantitative methods for processing the OES data are still lacking. There is an urgent need for a statistical quantitative method to deal with high-dimensional OES data for improving the quality of etched wafers. Therefore, we propose a robust relevance vector machine (RRVM) for regression with statistical quantitative features for modeling etch rate and uniformity in plasma etch processes by using OES data. For effectively dealing with the OES data complexity, we identify seven statistical features for extraction from raw OES data by reducing the data dimensionality. The experimental results demonstrate that the proposed approach is more suitable for high-accuracy monitoring of plasma etch responses obtained from OES.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.3302-3309
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• 2011

This study showed the reduced greenhouse gas(GHG) emission through GHG inventory establishment and process diagnosis for a response to climatic change. Also, it presented a direction for company's response to climatic change. Ulsan from its industrial complex has many energy-intensive companies such as petrochemistry, automobile and shipbuilding, and as we judged that the systematic reduction of GHG emission would make a considerable reduction of GHG emission in national dimension we executed this study from 10 companies. It showed the high rate of direction GHG emissions by its process that 5 of 10 companies calculated GHG emission and built its inventory. Also, in order to reduce energy and GHG, it produced about 227,554 million won of its economic effect and 50,740 ton/yr of its sparing effect.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.6
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• pp.381-389
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• 2021

Most diagnosis approaches rely on historical failure data that might not be feasible in real operating conditions because the battery voltage and internal parameters are nonlinear according to various operating conditions, such as cell-to-cell configuration and initial condition. To overcome this issue, the estimator and the predictor require integrated approaches that consider comprehensive data, with the degradation process and measured data taken into account. In this paper, vector autoregressive models (VAR) with various parameters that affect overdischarge to the cell in the battery pack were constructed, and the cell-to-cell parameters were identified using an adaptive model to analyze the influence of failure prognosis. The theoretical analysis is validated using experimental results in terms of the feasibility and advantages of fault prognosis.

• Journal of the Korean Society of Safety
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• v.15 no.2
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• pp.36-45
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• 2000

Automated production system is composed of many complicated techniques and it become a very difficult task to control, monitor and diagnose this compound system. Moreover, it is required to develop an effective diagnosing technique and reduce the diagnosing time while operating the system in parallel under many faults occurring concurrently. This study develops a Modular Artificial Neural Network(MANN) which can perform a diagnosing function of multiple faults with the following steps: 1) Modularizing a complicated system into subsystems. 2) Formulating a hierarchical structure by dividing the subsystem into many detailed elements. 3) Planting an artificial neural network into hierarchical module. The system developed is implemented on workstation platform with $X-Windows^{(r)}$ which provides multi-process, multi-tasking and IPC facilities for visualization of transaction, by applying the software written in $ANSI-C^{(r)}$ together with $MOTIF^{(r)}$ on the fault diagnosis of PI feedback controller reactor. It can be used as a simple stepping stone towards a perfect multiple diagnosing system covering with various industrial applications, and further provides an economical approach to prevent a disastrous failure of huge complicated systems.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2687-2698
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• 2020

Most of the machine learning-based intrusion detection tools developed for Industrial Control Systems (ICS) are trained on network packet captures, and they rely on monitoring network layer traffic alone for intrusion detection. This approach produces weak intrusion detection systems, as ICS cyber-attacks have a real and significant impact on the process variables. A limited number of researchers consider integrating process measurements. However, in complex systems, process variable changes could result from different combinations of abnormal occurrences. This paper examines recent advances in intrusion detection algorithms, their limitations, challenges and the status of their application in critical infrastructures. We also introduce the discussion on the similarities and conflicts observed in the development of machine learning tools and techniques for fault diagnosis and cybersecurity in the protection of complex systems and the need to establish a clear difference between them. As a case study, we discuss special characteristics in nuclear power control systems and the factors that constraint the direct integration of security algorithms. Moreover, we discuss data reliability issues and present references and direct URL to recent open-source data repositories to aid researchers in developing data-driven ICS intrusion detection systems.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.28-34
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• 2021

The Fourth Industrial Revolution has led to the development of drones for commercial and private applications. Therefore, the malfunction of drones has become a prominent problem. Failure mode and effect analysis was used in this study to analyze the primary cause of drone failure, and blade breakage was observed to have the highest frequency of failure. This was tested using a vibration sensor placed on drones along the breakage length of the blades. The data exhibited a significant increase in vibration within the drone body for blade fracture length. Principal component analysis was used to reduce the data dimension and classify the state with machine learning algorithms such as support vector machine, k-nearest neighbor, Gaussian naive Bayes, and random forest. The performance of machine learning was higher than 0.95 for the four algorithms in terms of accuracy, precision, recall, and f1-score. A follow-up study on failure prediction will be conducted based on the results of fault diagnosis.

• Journal of Society of Preventive Korean Medicine
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• v.4 no.1
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• pp.32-50
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• 2000

On the process of research in the plan on oriental medical participation in the industrial health sponsored by BK21 project, we carried out the oriental medical health examination program for workers during former half-year We reached the conclusion as follows, 1. The oriental medical health examination program is contents and formalities that should be determined by present industrial health system, based on the oriental medical system and scholastic character, and included probability of the western and oriental medical cooperation. 2. The oriental medical health examination program can promote capability of individual health management and productive power of workers, and it is capable to manage on the self-conscious symptoms and macroschophically approach to their environment 3. The oriental medical health examination program that we have developed, is flow as questionare, understanding of working environment, information of result and later management. It is composed of three fields as follow , first, use of pulse diagnostic apparatus, understanding of the health promotion life style, and diagnosis of the oriental medical doctor, second, analysis of constitution, third, photographing for understanding of the musculoskeletal disorders, questionare for musculoskeletal self-conscious symptoms, and diagnosis of oriental medical doctor. 4. The oriental medical health examination program that we have developed, progressive from the view point of health, makes the oriental medical doctor's roll more important. It is the first trial at the western and oriental medical cooperation and characterized by excellence about musouloskeletal disorders. But it need to be improved in aspects of time and specialist on the health examination, diagnostic apparatus, control of examinant and later management. So we think that it needs research on the employment of health examination specialist, establishment of later management system, development of significantly diagnosable standard and assessable form on the health examination, and contents of health examination on the western and oriental medical cooperation.

• Nuclear Engineering and Technology
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• v.38 no.4
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• pp.383-390
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• 2006

Having its roots in medical applications, industrial gamma ray CT has opened up new roads far investigating and modeling industrial processes. Using a line of research related to industrial gamma ray CT, the authors set up a system of single source and detector gamma transmission tomography for wood timber and a packed bed phantom. The hardware of the CT system consists of two servo motors, a data logger, a computer, a radiation source and a radiation detector. One motor simultaneously moves the source and the detector for a parallel beam scanning, whereas the other motor rotates the scan table at a preset projection angle. The image is reconstructed from the measured projections by the filtered back projection method. The phantom was designed to simulate a cross section of a packed bed with a void. The radiation source was 20mCi of Cs-137 and the detector was a 1 inch $\times$ 1 inch NaI (TI) scintillator shielded by a lead collimator. The experimental gamma ray CT image has sufficient resolution to reveal air holes and the density distribution inside the phantom. The system could possibly be applied to a packed bed column or a pipe flow in a petrochemical plant.

• Industrial Engineering and Management Systems
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• v.10 no.4
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• pp.272-278
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• 2011

Decision tree as a classification tool is being used successfully in many areas such as medical diagnosis, customer churn prediction, signal detection and so on. The main advantage of decision tree classifiers is their capability to break down a complex structure into a collection of simpler structures, thus providing a solution that is easy to interpret. Since decision tree is a top-down algorithm using a divide and conquer induction process, there is a risk of reaching a local optimal solution. This paper proposes a procedure of optimally determining thresholds of the chosen variables for a decision tree using an adaptive particle swarm optimization (APSO). The proposed algorithm consists of two phases. First, we construct a decision tree and choose the relevant variables. Second, we find the optimum thresholds simultaneously using an APSO for those selected variables. To validate the proposed algorithm, several artificial and real datasets are used. We compare our results with the original CART results and show that the proposed algorithm is promising for improving prediction accuracy.