• Journal of Korean Society for Quality Management
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• v.40 no.3
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• pp.327-336
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• 2012

Purpose: In this study, we research the hurdle rate method to suggest the robust control chart for a contaminated process less vulnerable to fault values than existing control charts. Methods: We produce the results of p, ARL values to compare the performance of two control charts, $\bar{x}-s$ that has been used typically and TM-TS that is suggested by this paper. We implement the simulation focusing on three cases, change of deviation, mean and both of them. Results: We draw a conclusion that the TM-TS control chart has better efficiency than $\bar{x}-s$ control chart over the three cases. Conclusion: We insist that applying TM-TS control chart for a polluted process is more effective than $\bar{x}-s$ control chart.

• Journal of the Semiconductor & Display Technology
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• v.10 no.1
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• pp.101-105
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• 2011

In case of high-tech process industries such as semiconductor and TFT-LCD manufactures, fault of a virtually finished product that is value-added one, since it has gone throughout the most of processes, may give rise to quality cost nearly amount to its selling price and can be a main cause that decreases the efficiency of manufacturing process. This paper proposes a real-time dispatching algorithm for semiconductor manufacturing process with rework. In order to evaluate the proposed algorithm, this paper examines the performance of the proposed method by comparing it with that of the existing dispatching algorithms, based on various experimental data.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.16-21
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• 2020

How accurately reproducible energy is delivered to the wafer in the process of making thin films using PE-CVD (Plasma enhanced chemical vapor deposition) during the semiconductor process. This is the most important technique, and most of the reaction on the wafer surface is made by thermal energy. In this study, we studied the method of monitoring the change of thermal energy transferred to the wafer surface by monitoring the temperature change according to the change of the thin film formed on the showerhead facing the wafer. Through this research, we could confirm the monitoring of wafer thin-film which is changed due to abnormal operation and accumulation of equipment, and we can expect improvement of semiconductor quality and yield through process reproducibility and equipment status by real-time monitoring of problem of deposition process equipment performance.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.131-135
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• 2023

Neural network-based time series models called time series neural networks (TSNNs) are trained by the error backpropagation algorithm and used to predict process shifts of parameters such as gas flow, RF power, and chamber pressure in reactive ion etching (RIE). The training data consists of process conditions, as well as principal components (PCs) of optical emission spectroscopy (OES) data collected in-situ. Data are generated during the etching of benzocyclobutene (BCB) in a SF₆/O₂ plasma. Combinations of baseline and faulty responses for each process parameter are simulated, and a moving average of TSNN predictions successfully identifies process shifts in the recipe parameters for various degrees of faults.

• Journal of the Korea Safety Management & Science
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• v.6 no.3
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• pp.237-247
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• 2004

The condition of the manufacturing process in a factory should be diagnosed and maintained efficiently because any unexpected disorder in the process will be reason to decrease the efficiency of the overall system. However, if an expert experienced in this system leaves, there will be a problem for the efficient process diagnosis and maintenance, because disorder diagnosis within the process is normally dependent on the expert's experience. This paper suggests a process diagnosis using data mining based on the collected data from the coil-spring manufacturing process. The rules are generated for the relations between the attributes of the process and the output class of the product using a decision tree after selecting the effective attributes. Using the generated rules from decision tree, the condition of the current process is diagnosed and the possible maintenance actions are identified to correct any abnormal condition. Then, the appropriate maintenance action is recommended using the decision network.

• Journal of the Korean Society of Safety
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• v.35 no.1
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• pp.107-115
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• 2020

This paper introduces the first vital area identification (VAI) process for the physical protection of nuclear power plants (NPPs) during low power and shutdown (LPSD) operation. This LPSD VAI is based on the 3rd generation VAI method which very efficiently utilizes probabilistic safety assessment (PSA) event trees (ETs). This LPSD VAI process was implemented to the virtual NPP during LPSD operation in this study. Korea Atomic Energy Research Institute (KAERI) had developed the 2nd generation full power VAI method that utilizes whole internal and external (fire and flooding) PSA results of NPPs during full power operation. In order to minimize the huge burden of the 2nd generation full power VAI method, the 3rd generation full power VAI method was developed, which utilizes ETs and minimal PSA fault trees instead of using the whole PSA fault tree. In the 3rd generation full power VAI method, (1) PSA ETs are analyzed, (2) minimal mitigation systems for avoiding core damage are selected from ETs by calculating system-level target sets and prevention sets, (3) relatively small sabotage fault tree that has the systems in the shortest system-level prevention set is composed, (4) room-level target sets and prevention sets are calculated from this small sabotage fault tree, and (5) the rooms in the shortest prevention set are defined as vital areas that should be protected. Currently, the 3rd generation full power VAI method is being employed for the VAI of Korean NPPs. This study is the first development and application of the 3rd generation VAI method to the LPSD VAI of NPP. For the LPSD VAI, (1) many LPSD ETs are classified into a few representative LPSD ETs based on the functional similarity of accident scenarios, (2) a few representative LPSD ETs are simplified with some VAI rules, and then (3) the 3rd generation VAI is performed as mentioned in the previous paragraph. It is well known that the shortest room-level prevention sets that are calculated by the 2nd and 3rd generation VAI methods are identical.

• Journal of the Korean earth science society
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• v.44 no.3
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• pp.210-221
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• 2023

The purpose of this study is to evaluate the content knowledge and problem solving process used by pre-service earth science teachers while discriminating faults on geological maps. For this, we collected and evaluated data on fixation duration and gaze plot, while pre-service earth science teachers (N=12) solved the problem on faults interpretation using an eye tracker (Tobii Pro Glass 2 model). The results were as follows. First, most of the pre-service earth science teachers know the concepts of the normal and reverse fault but they do not know the procedural knowledge essential for fault interpretation on geological maps. Second, the pre-service earth science teachers did not draw a geological cross-sectional map to interpret the fault on the geological map and interpreted the fault based on two-dimensional information collected from the geological map rather than three-dimensional information. Therefore, it is essential to improve the teaching and learning environment so that pre-service earth science teachers who will become earth science teachers in the future can learn procedural knowledge essential to comprehend natural phenomena including understanding natural phenomena. The results of this study can substantially help organize a new earth science curriculum or develop materials on teachers' education in the future.

• Journal of Korean Society on Water Environment
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• v.24 no.1
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• pp.91-98
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• 2008

This work was performed to develop a model possible to predict the influent flow and influent components, which are one of main disturbances causing process problems at the operation of municipal wastewater treatment plant. In this study, artificial neural network (ANN) was used in order to develop a model that was able to predict the influent flow, $COD_{Mn}$, SS, TN 1 day-ahead, 2day-ahead and 3 day ahead. Multi-layer feed-forward back-propagation network was chosen as neural network type, and tanh-sigmoid function was used as activation function to transport signal at the neural network. And Levenberg-Marquart (LM) algorithm was used as learning algorithm to train neural network. Among 420 data sets except missing data, which were collected between 2005 and 2006 at field plant, 210 data sets were used for training, and other 210 data sets were used for validation. As result of it, ANN model for predicting the influent flow and components 1-3day ahead could be developed successfully. It is expected that this developed model can be practically used as follows: Detecting the fault related to effluent concentration that can be happened in the future by combining with other models to predict process performance in advance, and minimization of the process fault through the establishment of various control strategies based on the detection result.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.467-474
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• 1999

In this paper, Fault tolerant digital governor, using duplex I/O module and triplex CPU module and also 2 out of 3 voting algorithm and adding self diagnostic ability, is designed to realize ceaseless controlling and to improve the reliability of control system. The processor module of the system(SIDG-3000) is developed based on MC68EC040 32 Bit of Motorola, which guaranteed high quality of the module ,and SRAM for data also SRAM for command are separated. The process module also includes inter process communication function and power back up function (SRAM for back-up). System reliability is estimated by using the model of Markov process. The reliability of triplex system in mission time can be improved about 1.8 times in reliability 86%. 2.8 times in 95 %, 6 times in 99 % compared with a single control system. Designed digital governor system is applied after modelling of the steam turbine generator system of Buk-Cheju Thermal Power Plant. Simulation is carried out to prove the effectiveness of the designed digital governor system

• Journal of the Korean Institute of Gas
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• v.23 no.4
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• pp.19-27
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• 2019

The consumption of liquefied natural gas (LNG) has increased annually due to the strengthening of international environmental regulations. In order to produce stable and efficient LNG, it is essential to divide the global (overall) operating condition and construct a quick and accurate monitoring system for each operation condition. In this study, multi-mode monitoring system is proposed to the LNG plant fractionation process. First, global normal operation data is divided to local (subdivide) normal operation data using global principal component analysis (PCA) and k-means clustering method. And then, the data to be analyzed were matched with the local normal mode. Finally, it is determined the state of process abnormality through the local PCA. The proposed method is applied to 45 fault case and it proved to be more than 5~10% efficient compared to the global PCA and univariate monitoring.