• Journal of the Semiconductor & Display Technology
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• v.11 no.2
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• pp.63-69
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• 2012

As decreasing size of chips, the need of wafer level packaging is increased in semi-conductor and display industries. Temperature uniformity is a crucial factor in vacuum soldering process to guarantee quality of bonding between chips and wafer. In this paper, a stepwise iterative algorithm has been suggested to obtain output profile of each heat source. Since this algorithm is based on open-loop stepwise iterative experimental technique, it is easier to implement and cost effective than real time feedback controls. Along with some experiments, it was shown that the suggested algorithm can remarkably improve temperature uniformity of wafer during whole heating process compared with the ordinary manual trial-and error method.

• Food Science of Animal Resources
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• v.42 no.3
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• pp.467-485
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• 2022

Supercooling storage refers to lowering the product temperature below its freezing point without phase transition and has the potential to extend shelf life. Nevertheless, supercooled objects are in a thermodynamically unstable state, and nucleation can occur spontaneously. To achieve supercooling storage, slow cooling and insulation are essential. Hence, a stepwise algorithm for the supercooling storage of pork loins was designed and validated in this study. Pork loins were stored at 3℃, -18℃, and -3℃ (freezing), and supercooled for 16 days. All samples remained in a supercooled state and were unfrozen at the end of storage. Supercooled pork loins were superior in terms of drip loss, cooking loss, and water-holding capacity compared to frozen samples. Additionally, supercooling treatment prevented discoloration, increase of volatile basic nitrogen, and microbial growth. Thus, supercooling of pork loin was achieved using a stepwise program and was effective to maintain meat quality.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.5
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• pp.314-326
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• 2016

The purpose of variable selection techniques is to select a subset of relevant variables for a particular learning algorithm in order to improve the accuracy of prediction model and improve the efficiency of the model. We conduct an empirical analysis to evaluate and compare seven well-known variable selection techniques for multiple linear regression model, which is one of the most commonly used regression model in practice. The variable selection techniques we apply are forward selection, backward elimination, stepwise selection, genetic algorithm (GA), ridge regression, lasso (Least Absolute Shrinkage and Selection Operator) and elastic net. Based on the experiment with 49 regression data sets, it is found that GA resulted in the lowest error rates while lasso most significantly reduces the number of variables. In terms of computational efficiency, forward/backward elimination and lasso requires less time than the other techniques.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.574-579
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• 2009

There have been much difficulties to construct an optimized neural network in complex nonlinear regression problems such as selecting the networks structure and avoiding overtraining problem generated by noise. In this paper, we propose a stepwise constructive method for neural networks using a flexible incremental algorithm. When the hidden nodes are added, the flexible incremental algorithm adaptively controls the number of hidden nodes by a validation dataset for minimizing the prediction residual error. Here, the ELM (Extreme Learning Machine) was used for fast training. The proposed neural network can be an universal approximator without user intervene in the training process, but also it has faster training and smaller number of hidden nodes. From the experimental results with various benchmark datasets, the proposed method shows better performance for real-world regression problems than previous methods.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.33-46
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• 2019

Purpose: This study proposed a DEA (Data Envelopment Analysis)-based stepwise benchmarking target selection for inefficient DMU (Decision Making Unit) to improve its efficiency gradually to reach most efficient frontier considering resource (DEA inputs and outputs) improvement preferences. Methods: The proposed method proceeded in two steps. First step evaluates efficiency of DMUs by using DEA, and an evaluated DMU selects benchmarking targets of HCU (Hypothesis Composit Unit) or RU (Real Unit) considering resource improvement preferences. Second step selects stepwise benchmarking targets of the inefficient DMU. To achieve this, this study developed a new DEA model, which can select a benchmarking target of an inefficient DMU in considering inputs or outputs improvement preference, and suggested an algorithm, which can select stepwise benchmarking targets of the inefficient DMU. Results: The proposed method was applied to 34 international ports for validation. In efficiency evaluation, five ports was evaluated as most efficient port, and the remaining 29 ports was evaluated as relative inefficient port. When port 34 was supposed as evaluated DMU, its can select its four stepwise benchmarking targets in assigning the preference weight to inputs (berth length, total area of pier, CFS, number of loading machine) as (0.82, 1.00, 0.41, 0.00). Conclusion: For the validation of the proposed method, it applied to the 34 major ports around the world and selected stepwise benchmarking targets for an inefficient port to improve its efficiency gradually. We can say that the proposed method enables for inefficient DMU to establish more effective and practical benchmarking strategy than the conventional DEA because it considers the resource (inputs or outputs) improvement preference in selecting benchmarking targets gradually.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.6
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• pp.361-368
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• 2002

This paper presents a new data path synthesis algorithm which takes into account simultaneously three important design criteria: testability, design area, and total execution time. We define a goodness measure on the testability of a circuit based on three rules of thumb introduced in prior work on synthesis for testability. We then develop a stepwise refinement synthesis algorithm which carries out the scheduling and allocation tacks in an integrated fashion. Experimental results for benchmark and other circuit examples show that we are able to enhance the testability of circuits with very little overheads on design area and execution time.

• Journal of Biomedical Engineering Research
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• v.5 no.2
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• pp.173-182
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• 1984

This paper represents a algorithm which improves the some drawbacks in the past methods for detecting QRS Complex waves. This proposed algorithm is very useful to detect correctly QRS Complex not only in a normal ECG, but in the abnormal ECG such as contaminating the noise with high amplitude, the existence of sharp T wave, and abrupt stepwise fluctuation of the base line.

• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.425-435
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• 2016

In this paper, we propose a classification model by analyzing raw material images recorded using a color CCD camera to automatically classify good and defective agricultural products such as rice, coffee, and green tea, and raw materials. The current classifying agricultural products mainly depends on visual selection by skilled laborers. However, classification ability may drop owing to repeated labor for a long period of time. To resolve the problems of existing human dependant commercial products, we propose a vision based automatic raw material classification combining mean shift clustering and stepwise region merging algorithm. In this paper, the image is divided into N cluster regions by applying the mean-shift clustering algorithm to the foreground map image. Second, the representative regions among the N cluster regions are selected and stepwise region-merging method is applied to integrate similar cluster regions by comparing both color and positional proximity to neighboring regions. The merged raw material objects thereby are expressed in a 2D color distribution of RG, GB, and BR. Third, a threshold is used to detect good and defective products based on color distribution ellipse for merged material objects. From the results of carrying out an experiment with diverse raw material images using the proposed method, less artificial manipulation by the user is required compared to existing clustering and commercial methods, and classification accuracy on raw materials is improved.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.291-304
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• 2002

This article is concerned with an effective algorithm for the identification of multiple outliers in linear regression. It proposes a hybrid algorithm which employs the least median of squares estimator, instead of the least squares estimator, to construct an Initial clean subset in the stepwise forward search scheme. The performance of the proposed algorithm is evaluated and compared with the existing competitor via an extensive Monte Carlo simulation. The algorithm appears to be superior to the competitor for the most of scenarios explored in the simulation study. Particularly it copes with the masking problem quite well. In addition, the orthogonal decomposition and Its updating techniques are considered to improve the computational efficiency and numerical stability of the algorithm.

• Korean Journal of Adult Nursing
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• v.22 no.4
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• pp.353-364
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• 2010

Purpose: The purpose of this study was to develop an algorithm for preventing and managing of pressure ulcer and to verify the its appropriateness. Methods: The first step was development of a pre-algorithm through a literature review and expert opinion. The second step was to establish content validity by submitting the algorithm questionnaires about the content to 12 experts. The third step was the revision of the algorithm. The fourth and last step was to establish the clinical validity of the algorithm with 25 experienced nurses. Results: For the ease of the practitioner the algorithm for prevention and the management of pressure ulcers was confined to one page depicting the main algorithm pathway and seven stepwise guidelines. The guidelines included skin care of pressure ulcer prevention, mechanical loading care, support surface care, reposition care of pressure ulcer, and Stages II, III and IV explanations along with debridement/wound irrigation and infection control. Most of all algorithm courses chosen more than 80% of agreement by expert index of content validity. The usefulness, appropriateness, and convenience of the algorithm were demonstrated through clinical validity with intensive care unit and ward nurses. Conclusion: The algorithm will improve the quality of pressure ulcer nursing care as it provides a model for decision making for clinical nurses as well as providing consistent and integrated nursing care for patients with pressure ulcer throughout an institution.