• The Journal of the Korea Contents Association
• /
• v.20 no.12
• /
• pp.49-57
• /
• 2020

This study predicts the ratio of added value, which represents the competitiveness of export industries in South Korea, using various machine learning techniques. To enhance the accuracy and stability of prediction, forecast combination technique was applied to predicted values of machine learning techniques. In particular, this study improved the efficiency of the prediction process by selecting key variables out of many variables using recursive feature elimination method and applying them to machine learning techniques. As a result, it was found that the predicted value by the forecast combination method was closer to the actual value than the predicted values of the machine learning techniques. In addition, the forecast combination method showed stable prediction results unlike volatile predicted values by machine learning techniques.

• Nuclear Engineering and Technology
• /
• v.56 no.6
• /
• pp.2266-2273
• /
• 2024

A machine learning platform is proposed for the diagnosis of a severe accident progression in a nuclear power plant. To predict the key parameters for accident management including lost signals, a long short term memory (LSTM) network is proposed, where multiple accident scenarios are used for training. Training and test data were produced by MELCOR simulation of the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident at unit 3. Feature variables were selected among plant parameters, where the importance ranking was determined by a recursive feature elimination technique using RandomForestRegressor. To answer the question of whether a reduced order ML model could predict the complex transient response, we performed a systematic sensitivity study for the choices of target variables, the combination of training and test data, the number of feature variables, and the number of neurons to evaluate the performance of the proposed ML platform. The number of sensitivity cases was chosen to guarantee a 95 % tolerance limit with a 95 % confidence level based on Wilks' formula to quantify the uncertainty of predictions. The results of investigations indicate that the proposed ML platform consistently predicts the target variable. The median and mean predictions were close to the true value.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.22 no.8
• /
• pp.1152-1163
• /
• 1998

In the present work, the image processing technique for measurement of drop sizes has been improved. Firstly, the local processing concept was adopted in addition to the global processing technique to take account of non-uniformity of the illumination intensity ; thereby, basically, the measurement error can be reduced. Also, the unfocussed image of drops can be eliminated more precisely since the elimination process is based on the local normalized contrast. Secondly the algorithms to process the partially detected or overlapped drop images and the non-spherical drop images were developed. Finally, the improved algorithm was tested by using an artificially prepared image-frame, where the partial or overlapped particles and the non-spherical particles are mixed with the normal spherical ones (with their true size-distributions known a priori). The results showed that both the recognition rate of the number of particles and the measurement accuracy were improved prominently.

• Archives of Plastic Surgery
• /
• v.43 no.1
• /
• pp.88-92
• /
• 2016

There are two general categories of lateral osteotomy techniques-the external perforating method and the internal continuous method. Regardless of which technique is used, procedural effectiveness is hampered by limited visualization in the surgical field. Considering this point, we devised a new technique that involves using a wide subperiosteal dissection and internal perforation under direct visualization. Using an intranasal approach, whereby the visibility of the intended fracture line was maintained, enabled a greater degree of control, and in turn, results that were more precise, and thus predictable and reproducible. Traditionally, it has been taken as dogma that the periosteum must be preserved, considering the potential for dead space and bony instability; however, under sufficient visualization of the surgical field with an internal perforating method, complete osteotomy with fully preserved intranasal mucosa could be conducted exactly as intended. This intact mucosal lining compensates for the elevated periosteum. Compressive dressing and drainage through a Silastic angio-needle catheter enabled the elimination of dead space. Therefore, precise, reproducible, and predictable osteotomy minimizing the potential for associated complications such as ecchymosis, that is, bruising owing to hemorrhage, could be performed. In this article, we introduce a novel technique for lateral osteotomy with improved visualization.

• Radiation Oncology Journal
• /
• v.1 no.1
• /
• pp.25-28
• /
• 1983

Anterior and posterior parallel opposed field technique covering entire neck is desirable for elimination of junctional problems in treating upper neck with bilateral parallel opposed fields and lower neck with anterior one field. For good dose distribution in neck, dose in build-up region should be high for anterior field and should be low for posterior field. And so, with 10 MV X-ray, lead absorber was used for anterior field only. The adequate thickness of lead absorber, absorber-skin separation, width of central cord block for posterior field and anterior & posterior field weight were studied using film dosimery. The results are as follows. 1. As the thickness of the lead absorber increased the dose in build-up region increased. 2. As the absorber-skin separation decreased the dose in build-up region increased. 3. The adequate thickness of lead absorber was around 5.6mm. 4. The adequate absorber-skin separation was around 5cm. 5. The adequate posterior cord block width was 3cm. 6. 4:1 weighting for anterior and posterior field was adequate. And so with this technique, adequate dose distribution could be made as well as elimination junction problems.

• Journal of Educational Research in Mathematics
• /
• v.17 no.2
• /
• pp.91-109
• /
• 2007

This study deals with the problem of transition from arithmetic to algebra and the relationship between elementary and secondary school mathematics for systems of linear equations. In elementary school, activity for solving word problems related to systems of linear equations in two variables falls broadly into using two strategies: Guess and check and making a table. In secondary school, those problems are solved algebraically, for example, by solving systems of equations using the technique of elimination. The analysis of mathematics textbooks shows that there is no link between strategies of elementary school mathematics and secondary school mathematics. We devised an alternative way to reinforce link between elementary and secondary school mathematics for systems of linear equations. Drawing a diagram can be introduced as a strategy solving word problems related to systems of linear equations in two variables in elementary school. Moreover it is closely related to the idea of the technique of elimination of secondary school mathematics. It may be a critical juncture of elementary-secondary school mathematics in the case of systems of linear equations in two variables.

• International Journal of Computer Science & Network Security
• /
• v.24 no.2
• /
• pp.31-42
• /
• 2024

Different industries mostly rely on quality certification for promoting their products or brands. Although getting quality certification, specifically by human experts is a tough job to do. But the field of machine learning play a vital role in every aspect of life, if we talk about quality certification, machine learning is having a lot of applications concerning, assigning and assessing quality certifications to different products on a macro level. Like other brands, wine is also having different brands. In order to ensure the quality of wine, machine learning plays an important role. In this research, we use two datasets that are publicly available on the "UC Irvine machine learning repository", for predicting the wine quality. Datasets that we have opted for our experimental research study were comprised of white wine and red wine datasets, there are 1599 records for red wine and 4898 records for white wine datasets. The research study was twofold. First, we have used a technique called backward elimination in order to find out the dependency of the dependent variable on the independent variable and predict the dependent variable, the technique is useful for predicting which independent variable has maximum probability for improving the wine quality. Second, we used a robust machine learning algorithm known as "XGBoost" for efficient prediction of wine quality. We evaluate our model on the basis of error measures, root mean square error, mean absolute error, R2 error and mean square error. We have compared the results generated by "XGBoost" with the other state-of-the-art machine learning techniques, experimental results have showed, "XGBoost" outperform as compared to other state of the art machine learning techniques.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.6
• /
• pp.2310-2318
• /
• 2018

Critical conduction mode (CRM) operation is more efficient than continuous conduction mode (CCM) operation at low power levels because of the valley switching of switches and elimination of the reverse recovery losses of boost diodes. When using a sensorless digital control method, an error occurs between the actual and the estimated current. Because of the error, it operates as CCM or discontinuous conduction mode (DCM) during CRM operation and also has an adverse effect on THD of input current. In this paper, a current sensorless technique is presented in an inverter system using a bridgeless boosted power factor correction converter, and a compensation method is proposed to reduce CRM calculation error. The validity of the proposed method is verified by simulation and experiment.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.39 no.1
• /
• pp.13-27
• /
• 2014

In this paper, we deal with a Steiner Ring Star (SRS) problem arising from the design of survivable telecommunication networks. We develop two mixed integer programming formulations for the SRS problem by implementing Miller-Tucker-Zemlin (MTZ) and Sarin-Sherali-Bhootra (SSB) subtour elimination constraints, and then apply the reformulation-linearization technique (RLT) to enhance the lower bound obtained by the LP relaxation. By exploiting the ring-star structure of underlying network, we devise some valid inequalities that tighten the LP relaxation. Computational results demonstrate the effectiveness of the proposed solution procedure.

• Proceedings of the KIPE Conference
• /
• 2004.07b
• /
• pp.587-591
• /
• 2004

This paper proposes a novel current compensation technique that can eliminate the harmonic currents included in line currents without computation of harmonic current components. A current controller with fast dynamics for an active filter is described. Harmonic currents are directly controlled without the need for sensing and computing the harmonic current of the load current, thus simplifying the control system. Current compensation is done in the time domain, allowing a fast time response. The DC voltage control loop keeps the voltage across the DC capacitor constant. High power factor control by an active filter is described. All control functions are implemented in software using a single-chip microcontroller, thus simplifying the control circuit. Any current-controlled synchronous rectifier can be used as a shunt active filter through only the simple modification of the software and the addition of current sensors. It is shown through experimental results that the proposed controller gives good performance for the shunt active filter.