• Proceedings of the Korea Concrete Institute Conference
• /
• 1999.10a
• /
• pp.767-770
• /
• 1999

The carbonation process is affected both by the concrete material properties such as W/C ratio, types of cement and aggregated, admixture characteristics and the environmental factors such as CO2 concentration, temperature, humidity. Based on results of preliminary research on carbonation, this study is to propose a carbonation prediction model by taking into account of prediction model by taking into account of CO2 concentration and W/C ratio among major factors affecting the carbonation process.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.04a
• /
• pp.1003-1008
• /
• 1997

In this paper, the prediction of cutting force and tool deflection in the ball-end milling process are studied. Identifying various cutting region using Z-map, cutting force in the ball-end milling process can be predicted. Cutting force deflects the tool and the tool deflection changes the cutting force. Tool deflection is included in the cutting force prediction. Tool deflecition also causes machining error of the machined surface. A series of experiments were performed to verify the simulated cutting force and machining error.

• Journal of Korean Society for Quality Management
• /
• v.26 no.4
• /
• pp.143-150
• /
• 1998

We propose an adaptive monitoring a, pp.oach for serially correlated data. This algorithm uses the adaptive linear prediction lattice filter (ALPLF) which makes it compute process parameters in real time and recursively update their estimates. It involves computation of the forward and backward prediction errors. CUSUM control charts are a, pp.ied to prediction errors simulaneously in both directions as an omnibus method for detecting changes in process parameters. Results of computer simulations demonstrate that the proposed adaptive monitoring a, pp.oach has great potentials for real-time industrial a, pp.ications, which vary frequently in their control environment.

• Journal of ILASS-Korea
• /
• v.10 no.2
• /
• pp.32-40
• /
• 2005

This paper presents the analytical approaches to predict cutting fluid aerosol formation characteristics in machining process. The prediction model which is based on the rotary atomization theory analyzes aerosol behaviors in terms of size and concentration. Experiments were tarried out to verify the aerosol formation prediction model under various operational conditions. The experimental results which are obtained by Dual-PDA measurement show resonable agreement with prediction results of aerosol concentration. This study can be provided as a basis to estimate and control the hazardous cutting fluid aerosol in machining process in view of environmental consciousness.

• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.1
• /
• pp.104-111
• /
• 2000

A fuzzy logic Run-by-Run(RbR) controller and an in -line wafer characteristics prediction scheme for the rapid thermal processing system have been developed for the study of process repeatability. The fuzzy logic RbR controller provides a framework for controlling a process which is subject to disturbances such as shifts and drifts as a normal part of its operation. The fuzzy logic RbR controller combines the advantages of both fuzzy logic and feedback control. It has two components : fuzzy logic diagnostic system and model modification system. At first, a neural network model is constructed with the I/O data collected during the designed experiments. The wafer state after each run is assessed by the fuzzy logic diagnostic system with featuring step. The model modification system updates the existing neural network process model in case of process shift or drift, and then select a new recipe based on the updated model using genetic algorithm. After this procedure, wafer characteristics are predicted from the in-line wafer characteristics prediction model with principal component analysis. The fuzzy logic RbR controller has been applied to the control of Titanium SALICIDE process. After completing all of the above, it follows that: 1) the fuzzy logic RbR controller can compensate the process draft, and 2) the in-line wafer characteristics prediction scheme can reduce the measurement cost and time.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.13 no.2
• /
• pp.81-88
• /
• 2014

Synthetic products such as casting tape and splints are rapidly replacing conventional plaster casts to treat orthopedic patients. Most synthetic products are produced through a polymerization process with related chemical agents. In this study, the effect of the process parameters on the residual NCO content within a prepolymer for casting tape and the hardening temperature for casting tape were experimentally evaluated. In order to verify the effects of the process parameters, an experimental method was adopted. From an S/N ratio analysis, optimal parameter combinations were determined to produce a pre-polymer with a suitable residual NCO content and alower hardening temperature. Prediction models for the NCO content and the hardening temperature were developed and confirmed.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1343-1346
• /
• 2005

In the cold rolling mill, coil breakage that generated in rolling process makes the various types of troubles such as the degradation of productivity and the damage of equipment. Recent researches were done by the mechanical analysis such as the analysis of roll chattering or strip inclining and the prevention of breakage that detects the crack of coil. But they could cover some kind of breakages. The prediction of Coil breakage was very complicated and occurred rarely. We propose to build effective prediction modes for coil breakage in rolling process, based on data mining model. We proposed three prediction models for coil breakage: (1) decision tree based model, (2) regression based model and (3) neural network based model. To reduce model parameters, we selected important variables related to the occurrence of coil breakage from the attributes of coil setup by using the methods such as decision tree, variable selection and the choice of domain experts. We developed these prediction models and chose the best model among them using SEMMA process that proposed in SAS E-miner environment. We estimated model accuracy by scoring the prediction model with the posterior probability. We also have developed a software tool to analyze the data and generate the proposed prediction models either automatically and in a user-driven manner. It also has an effective visualization feature that is based on PCA (Principle Component Analysis).

• Journal of applied mathematics & informatics
• /
• v.28 no.1_2
• /
• pp.439-450
• /
• 2010

In this paper, we propose a new idea to evaluate the prediction accuracy of user's preference generated by memory-based collaborative filtering algorithm before prediction process in the recommender system. Our analysis results show the possibility of a pre-evaluation before the prediction process of users' preference of item's transaction on the web. Classification functions proposed in this study generate a user's rating pattern under certain conditions. In this research, we test whether classification functions select users who have lower prediction or higher prediction performance under collaborative filtering recommendation approach. The statistical test results will be based on the differences of the prediction accuracy of each user group which are classified by classification functions using the generative probability of specific rating. The characteristics of rating patterns of classified users will also be presented.

• Journal of Korean Institute of Industrial Engineers
• /
• v.41 no.6
• /
• pp.572-578
• /
• 2015

Yield prediction is one of the most important issues in semiconductor manufacturing. Especially, for a fast-changing environment of the semiconductor industry, accurate and reliable prediction techniques are required. In this study, we propose a prediction model to predict wafer yield based on virtual metrology process parameters in semiconductor manufacturing. The proposed prediction model addresses imbalance problems frequently encountered in semiconductor processes so as to construct reliable prediction model. The effectiveness and applicability of the proposed procedure was demonstrated through a real data from a leading semiconductor industry in South Korea.

• Journal of the Korean Data and Information Science Society
• /
• v.20 no.3
• /
• pp.505-514
• /
• 2009

The researcher analyzes the relationship between the number of neighbors and the prediction accuracy in the preference prediction process using collaborative filtering system. The number of neighbors who are involved in the preference prediction process are divided into four groups. Each group shows a little difference in the preference prediction. By using prediction error averages in each group, linear functions are suggested. Through the result of this study, the accuracy of preference prediction can be raised when using linear functions by using the number of neighbors in the suggested system.