• International Journal of Human Ecology
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• v.4 no.2
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• pp.77-87
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• 2003

This paper tried to construct statistical and econometric models on the basis of economic theory in order to discuss the issue of statistical efficiency and unbiasedness including the sample selection bias correcting problem. Comparative analytical tool were one stage Tobit of Maximum Likelihood estimation and Heckman's two-step Tobit of Ordinary Least Squares. The results showed that the adequacy of model for the analysis on demand and choice, we believe that there is no big difference in explanatory variables between the first selection model and the second linear probability model. Since the Lambda, the self- selectivity correction factor, in the Type II Tobit is not statistically significant, there is no self-selectivity in the Type II Tobit model, indicating that Type I Tobit model would give us better explanation in the demand for and choice which is less complicated statistical method rather than type II model.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.315-319
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• 2007

A technique to model plasma processes was presented. This was accomplished by combining the backpropagation neural network (BPNN) and genetic algorithm (GA). Particularly, the GA was used to optimize five training factor effects by balancing the training and test errors. The technique was evaluated with the plasma etch data, characterized by a face-centered Box Wilson experiment. The etch outputs modeled include Al etch rate, AI selectivity, DC bias, and silica profile angle. Scanning electron microscope was used to quantify the etch outputs. For comparison, the etch outputs were modeled in a conventional fashion. GABPNN models demonstrated a considerable improvement of more than 25% for all etch outputs only but he DC bias. About 40% improvements were even achieved for the profile angle and AI etch rate. The improvements demonstrate that the presented technique is effective to improving BPNN prediction performance.

• Journal of Labour Economics
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• v.26 no.1
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• pp.23-52
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• 2003

This study focus on unemployment and nonparticipation of youth. By dividing youth nonparticipants into 'house work and child care', 'studying and training', 'the others' categories, we estimate the potential wages with selectivity bias model and analyse the factors of choosing unemployment or nonparticipation with multinomial logit model. The differences between the potential market wage and the desired wage of the groups of 'studying and training', 'the others' in the nonparticipants are greater than those of the unemployment group. In the case of the man and lower age, and low schooling the differences of potential and desire wage are larger than woman and higher age, and high schooling. In the choice of unemployment and nonparticipation, man and higher age, and householder, and holder of qualification are not likely to opt nonparticipation. The experience of job lower the rate of probability to choose employment, but raise the rate of probability to choose unemployment and nonparticipation. These results mean that the quality of youth employment is very inferior.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1305-1308
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• 2003

A new technique is presented to construct a predictive model of plasma etch process. This was accomplished by combining a backpropagation neural network (BPNN) and a genetic algorithm (GA). The predictive model constructed in this way is referred to as a GA-BPNN. The GA played a role of controlling training factors simultaneously. The training factors to be optimized are the hidden neuron, training tolerance, initial weight magnitude, and two gradients of bipolar sigmoid and linear functions. Each etch response was optimized separately. The proposed scheme was evaluated with a set of experimental plasma etch data. The etch process was characterized by a $2^3$ full factorial experiment. The etch responses modeled are aluminum (A1) etch rate, silica profile angle, A1 selectivity, and dc bias. Additional test data were prepared to evaluate model appropriateness. The GA-BPNN was compared to a conventional BPNN. Compared to the BPNN, the GA-BPNN demonstrated an improvement of more than 20% for all etch responses. The improvement was significant in the case of A1 etch rate.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.599-602
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• 2003

A new technique is presented to construct predictive models of plasma etch processes. This was accomplished by combining a backpropagation neural network (BPNN) and a random generator (RC). The RG played a critical role to control neuron gradients in the hidden layer, The predictive model constructed in this way is referred to as a randomized BPNN (RG-BPNN). The proposed scheme was evaluated with a set of experimental plasma etch process data. The etch process was characterized by a 2$^3$ full factorial experiment. The etch responses modeled are 4, including aluminum (Al) etch rate, profile angle, Al selectivity, and do bias. Additional test data were prepared to evaluate model appropriateness. The performance of RC-BPNN was evaluated as a function of the number of hidden neurons and the range of gradient. for given range and hidden neurons, 100 sets of random neuron gradients were generated and among them one best set was selected for evaluation. Compared to the conventional BPNN, the proposed RC-BPNN demonstrated about 50％ improvements in all comparisons. This illustrates that the RG-BPNN of multi-valued gradients is an effective way to considerably improve the predictive ability of current BPNN of single-valued gradient.

• Journal of the Korean housing association
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• v.23 no.1
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• pp.55-66
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• 2012

Present study investigates on the impact of resources and characteristics of the tenure choice for divorced women in Korea. The authors utilize the micro data from the Korea Census (2% sample) provided by the National Statistical Office. The authors apply the bivariate probit model to eliminate selection bias that could incur due to sample selectivity, from a chain of marital disruption and tenure choices. This study starts with a descriptive explanation of homeownership after divorce from 1985 to 2005. It concluded that divorce results in a substantial attrition of homeownership. The authors found that out for many women, divorce initiates a process of downward mobility on the housing ladder. The probability to own housing is much lower for divorced women than for women who are not divorced. The present study concludes by suggesting some policy implications for divorced women who have limited access to housing stability. The authors also suggest some future studies that can compensate the empirical limitations of the present study.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.11a
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• pp.74-74
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• 2009

The ArF PR mask was. developed to overcome the limit. of sub 40nm patterning technology with KrF PR. But ArF PR difficult to meet the required PR selectivity by thin PR thickness. So need to the multi-stack mask such as amorphous carbon layer (ACL). Generally capacitively coupled plasma (CCP) etcher difficult to make the high density plasma and inductively coupled plasma (ICP) type etcher is more suitable for multi stack mask etching. Hybrid Coupled Plasma source (HCPs) etcher using the 13.56MHz RF power for ICP source and 2MHz and 27.12MHz for bias power was adopted to improve the process capability and controllability of ion density and energy independently. In the study, the oxide trench which has the multi stack layer process was investigated with the HCPs etcher (iGeminus-600 model DMS Corporation). The results were analyzed by scanning electron microscope (SEM) and it was found that etching characteristic of oxide trench profile depend on the multi-stack mask.

• Proceedings of the KOR-KST Conference
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• 1998.09a
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• pp.160-167
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• 1998

Recent world-wide interest in activity-based travel behavior modeling has generated an entirely new perspective on how the profession views the travel demand process. This paper seeks to further promote the case of activity-based travel behavior models by providing some empirical evidence of relationship between travel time and activity duration decision for worker with transportation panel data. The travel time from home to work and from work to home, without activity involvement, is estimated by the Ordinary Least Squares (OLS) method. And, the travel time to and from the selected activity and the activity duration are modeled simultaneously by the Three Stage Least Squares (3SLS) method due to the endogenous relationship between travel time and activity duration. Two kinds of models, OLS and 3SLS, include selectivity bias corrections in a discrete/continuous framework, because of the inter-relationship between the choice of activity type/travel mode (discrete) and the travel time/activity duration (continuous). Estimation is undertaken using a sample of over 1300 household two-day trip diaries collected from the same travelers in the Seattle area in 1989. The behavioral consequences of these models provide interesting and provocative findings that should be of value to transportation policy formulation and analysis.