• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.20-20
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• 2000

Back-propagation neural network (BPNN) is the most prevalently used paradigm in modeling semiconductor manufacturing processes, which as a neuron activation function typically employs a bipolar or unipolar sigmoid function in either hidden and output layers. In this study, applicability of another linear function as a neuron activation function is investigated. The linear function was operated in combination with other sigmoid functions. Comparison revealed that a particular combination, the bipolar sigmoid function in hidden layer and the linear function in output layer, is found to be the best combination that yields the highest prediction accuracy. For BPNN with this combination, predictive performance once again optimized by incrementally adjusting the gradients respective to each function. A total of 121 combinations of gradients were examined and out of them one optimal set was determined. Predictive performance of the corresponding model were compared to non-optimized, revealing that optimized models are more accurate over non-optimized counterparts by an improvement of more than 30%. This demonstrates that the proposed gradient-optimized teaming for BPNN with a linear function in output layer is an effective means to construct plasma models. The plasma modeled is a hemispherical inductively coupled plasma, which was characterized by a 24 full factorial design. To validate models, another eight experiments were conducted. process variables that were varied in the design include source polver, pressure, position of chuck holder and chroline flow rate. Plasma attributes measured using Langmuir probe are electron density, electron temperature, and plasma potential.

• Journal of the Korea Institute of Building Construction
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• v.7 no.3
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• pp.123-130
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• 2007

This study investigated the fundamental properties and shrinkage properties of high performance concrete with water/binder ratio of 0, 30 and with combination of expansive additive and shrinkage reducing agent. According to the results, the fluidity of high performance concrete showed lower the using method in combination with expansive additive and shrinkage reducing agent than the separately using method of that, so the amount of superplasticizer increased when the adding ratio of expansive additive and shrinkage reducing agent increased. However the air content of concrete increased when used in combination with expansive additive and shrinkage reducing agent, so the amount of AE agent decreased. The compressive strength showed the highest at 5% of expansive additive, and decreased with an increase of the amount of shrinkage reducing agent. Furthermore, in order to reduce the shrinkage of high performance concrete, it was found that the using method in combination with expansive additive and shrinkage reducing agent was more effective than separately using method of that. Autogenous shrinkage was predicted using JCI model. Because JCI model is unable to consider the effect of EA and SRA, correction factor should be added to enhance the accuracy.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.339-347
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• 2021

This study aims to forecast the exchange rate by a combination of different models as proposed by Poon and Granger (2003). For this purpose, we include three univariate time series models, i.e., ARIMA, Naïve, Exponential smoothing, and one multivariate model, i.e., NARDL. This is the first of its kind endeavor to combine univariate models along with NARDL to the best of our knowledge. Utilizing monthly data from January 2011 to December 2020, we predict the Pakistani Rupee against the US dollar by a combination of different forecasting techniques. The observations from M1 2020 to M12 2020 are held back for in-sample forecasting. The models are then assessed through equal weightage and var-cor methods. Our results suggest that NARDL outperforms all individual time series models in terms of forecasting the exchange rate. Similarly, the combination of NARDL and Naïve model again outperformed all of the individual as well as combined models with the lowest MAPE value of 0.612 suggesting that the Pakistani Rupee exchange rate against the US Dollar is dependent upon the macro-economic fundamentals and recent observations of the time series. Further evidence shows that the combination of models plays a vital role in forecasting, as stated by Poon and Granger (2003).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.9
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• pp.2415-2423
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• 2009

We consider organizational knowledge combination process. Previous literature investigated the issue from either managerial or social aspects, emphasizing on a part of the whole process and limiting to qualitative analyses. Here we propose an integrative and quantitative approach which considers knowledge combination process from engineering perspective. We employ a queueing network model and techniques to capture the process of interactions of entities in the knowledge combination environment. By doing so, we are able to understand the performance of the knowledge combination process. The performance measures derived can provide valuable implications for managerial decisions such as planning and controlling the knowledge combination process.

• The Korean Journal of Pain
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• v.25 no.1
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• pp.7-15
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• 2012

Background: Neuropathic pain is a chronic pain due to disorder in the peripheral or central nervous system with different pathophysiological mechanisms. Current treatments are not effective. Analgesic drugs combined can reduce pain intensity and side effects. Here, we studied the analgesic effect of nimesulide, nefopam, and morphine with different mechanisms of action alone and in combination with other drugs in chronic constriction injury (CCI) model of neuropathic pain. Methods: Male Wistar rats (n = 8) weighing 150-200 g were divided into 3 different groups: 1- Saline-treated CCI group, 2- Saline-treated sham group, and 3- Drug-treated CCI groups. Nimesulide (1.25, 2.5, and 5 mg/kg), nefopam (10, 20, and 30 mg/kg), and morphine (1, 3, and 5 mg/kg) were injected 30 minutes before surgery and continued daily to day 14 post-ligation. In the combination strategy, a nonanalgesic dose of drugs was used in combination such as nefopam + morphine, nefopam + nimesulide, and nimesulide + morphine. Von Frey filaments for mechanical allodynia and acetone test for cold allodynia were, respectively, used as pain behavioral tests. Experiments were performed on day 0 (before surgery) and days 1, 3, 5, 7,10, and 14 post injury. Results: Nefopam (30 mg/kg) and nimesulide (5 mg/kg) blocked mechanical and thermal allodynia; the analgesic effects of morphine (5 mg/kg) lasted for 7 days. Allodynia was completely inhibited in combination with nonanalgesic doses of nefopam (10 mg/kg), nimesulide (1.25 mg/kg), and morphine (3 mg/kg). Conclusions: It seems that analgesic drugs used in combination, could effectively reduce pain behavior with reduced adverse effects.

• Journal of the Korean Society for information Management
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• v.19 no.4
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• pp.35-51
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• 2002

Inductive learning and classification techniques have been employed in various research and applications that organize textual data to solve the problem of information access. In this study, we develop hybrid model combination methods which incorporate the concepts and techniques for multiple modeling algorithms to improve the accuracy of text classification, and conduct experiments to evaluate the performances of proposed schemes. Boosted stacking, one of the extended stacking schemes proposed in this study yields higher accuracy relative to the conventional model combination methods and single classifiers.

• Journal of information and communication convergence engineering
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• v.17 no.4
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• pp.261-266
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• 2019

Differential evolution (DE) is an uncomplicated and serviceable developmental algorithm. Nevertheless, its execution depends on strategies and regulating structures. The combination of several strategies between subpopulations helps to stabilize the probing on DE. In this paper, we propose a unique k-mean soft island model DE(KSDE) algorithm which maintains population diversity through soft island model (SIM). A combination of various approaches, called KSDE, intended for migrating the subpopulation information through SIM is developed in this study. First, the population is divided into k subpopulations using the k-means clustering algorithm. Second, the mutation pattern is singled randomly from a strategy pool. Third, the subpopulation information is migrated using SIM. The performance of KSDE was analyzed using 13 benchmark indices and compared with those of high-technology DE variants. The results demonstrate the efficiency and suitability of the KSDE system, and confirm that KSDE is a cost-effective algorithm compared with four other DE algorithms.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.268-273
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• 2021

In this paper, we propose a sentiment analysis model that improves performance on small-scale data. A sentiment analysis model for small-scale data is proposed and verified through experiments. To this end, we propose Bagging-Bi-GRU, which combines Bi-GRU, which learns GRU, which is a variant of LSTM (Long Short-Term Memory) with excellent performance on sequential data, in both directions and the bagging technique, which is one of the ensembles learning methods. In order to verify the performance of the proposed model, it is applied to small-scale data and large-scale data. And by comparing and analyzing it with the existing machine learning algorithm, Bi-GRU, it shows that the performance of the proposed model is improved not only for small data but also for large data.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.131-157
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• 2018

We propose a robust event date model to estimate the date of a target event by a combination of individual dates obtained from archaeological artifacts assumed to be contemporaneous. These dates are affected by errors of different types: laboratory and calibration curve errors, irreducible errors related to contaminations, and taphonomic disturbances, hence the possible presence of outliers. Modeling based on a hierarchical Bayesian statistical approach provides a simple way to automatically penalize outlying data without having to remove them from the dataset. Prior information on individual irreducible errors is introduced using a uniform shrinkage density with minimal assumptions about Bayesian parameters. We show that the event date model is more robust than models implemented in BCal or OxCal, although it generally yields less precise credibility intervals. The model is extended in the case of stratigraphic sequences that involve several events with temporal order constraints (relative dating), or with duration, hiatus constraints. Calculations are based on Markov chain Monte Carlo (MCMC) numerical techniques and can be performed using ChronoModel software which is freeware, open source and cross-platform. Features of the software are presented in Vibet et al. (ChronoModel v1.5 user's manual, 2016). We finally compare our prior on event dates implemented in the ChronoModel with the prior in BCal and OxCal which involves supplementary parameters defined as boundaries to phases or sequences.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.97-110
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• 2017

This study investigates the efficiencies of machine learning models, including artificial neural network (ANN), generalized regression neural network (GRNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF), for reservoir water level forecasting in the Chungju Dam, South Korea. The models' efficiencies are assessed based on model efficiency indices and graphical comparison. The forecasting results of the models are dependent on lead times and the combination of input variables. For lead time t = 1 day, ANFIS1 and ANN6 models yield superior forecasting results to RF6 and GRNN6 models. For lead time t = 5 days, ANN1 and RF6 models produce better forecasting results than ANFIS1 and GRNN3 models. For lead time t = 10 days, ANN3 and RF1 models perform better than ANFIS3 and GRNN3 models. It is found that ANN model yields the best performance for all lead times, in terms of model efficiency and graphical comparison. These results indicate that the optimal combination of input variables and forecasting models depending on lead times should be applied in reservoir water level forecasting, instead of the single combination of input variables and forecasting models for all lead times.