• Journal of the Korea Safety Management & Science
• /
• v.9 no.6
• /
• pp.113-122
• /
• 2007

Quality function deployment(QFD) is becoming a widely used customer oriented approach. The aim of this paper is to present an analytic method of quality function deployment that is to maximize customer satisfaction, using a customer satisfaction survey conducted in the college in Korea. Combining weights and satisfaction indices, "performance/important" diagrams are to develop and this grid can be used in order to identify priorities for decision making. Also, this paper shows a 0-1 integer programming model for maximizing customer satisfaction subject to a budget constraint in QFD planning process with case study.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.12
• /
• pp.116-123
• /
• 1994

A modified Hybrid learning rule(MHLR) is proposed, which is derived from combining the Back Propagation algorithm that is known as an excellent classifier with modified Hebbian by changing the orginal Hebbian which is a good feature extractor. The network architecture of MHLR is multi-layered neural network. The weights of MHLR are calculated from sum of the weight of BP and the weight of modified Hebbian between input layer and higgen layer and from the weight of BP between gidden layer and output layer. To evaluate the performance, BP, MHLR and the proposed Hybrid learning rule (HLR) are simulated by Monte Carlo method. As the result, MHLR is the best in recognition rate and HLR is the second. In learning speed, HLR and MHLR are much the same, while BP is relatively slow.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.16 no.6
• /
• pp.125-132
• /
• 2017

Generally, in machining operations, the required machining performance can be obtained by properly combining several machining parameters properly. In this research, we construct a simulation model, which that predicts the relationship between the input variables and output variables in the turning operation. Input variables necessary for the turning operation include cutting speed, feed, and depth of cut. Surface roughness and electrical current consumption are used as the output variables. To construct the simulation model, an Artificial Neural Network (ANN) is employed. With theIn ANN, training is necessary to find appropriate weights, and the Ant Colony Optimization (ACO) technique is used as a training tool. EspeciallyIn particular, for the continuous domain, ACOR is adopted and athe related algorithm is developed. Finally, the effects of the algorithm on the results are identified and analyzsed.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.11
• /
• pp.49-57
• /
• 2016

In this paper, we propose the novel tracking method which uses the weighted discriminative correlation filter (DCF). We also propose the PSPR instead of conventional PSR as tracker performance evaluation method. The proposed tracking method uses multiple DCF to estimates the target position. In addition, our proposed method reflects more weights on the correlation response of the tracker which is expected to have more performance using PSPR. While existing multi-DCF-based tracker calculates the final correlation response by directly summing correlation responses from each tracker, the proposed method acquires the final correlation response by weighted combining of correlation responses from the selected trackers robust to given environment. Accordingly, the proposed method can provide high performance tracking in various and complex background compared to multi-DCF based tracker. Through a series of tracking experiments for various video data, the presented method showed better performance than a single feature-based tracker and also than a multi-DCF based tracker.

• ETRI Journal
• /
• v.33 no.2
• /
• pp.295-298
• /
• 2011

We propose a motion-compensated frame interpolation method in which an accurate backward/forward motion vector pair (MVP) is estimated based on a parabolic motion model. A reliability measure for an MVP is also proposed to select the most reliable MVP for each interpolated block. The possibility of deformation of bidirectional corresponding blocks is estimated from the selected MVP. Then, each interpolated block is produced by combining corresponding blocks with the weights based on the possibility of deformation. Experimental results show that the proposed method improves PSNR performance by up to 2.8 dB as compared to conventional methods and achieves higher visual quality without annoying blockiness artifacts.

• Journal of health informatics and statistics
• /
• v.43 no.4
• /
• pp.300-306
• /
• 2018

Objectives: The purpose of this study is to demonstrate empirically through a specialist AHP analysis what factors should be more important in the development of the public health care system. In addition, we will use Analytic Hierarchy Process (AHP) method for experts to achieve research purpose. Methods: The data analysis method of this study is as follows. First, we set up three metrics in order to measure the relative importance between the factors to be improved for the development of the public health care system and each of the sub-factors. A total of nine measurements (items) were set by combining the three measurement criteria for each measurement index. Second, the relative importance and priority analysis use the AHP analysis. Third, the subjects of this study were 15 experts in the field of public health care. The statistical processing was performed using the Expert Choice 2000 statistical program. Results: In order to development of the public health care system, experts ranked the most important as improvement in the systematic aspect of public health care (56%) as the first priority. Next, the relative importance analysis of the measurement items considering the multiple-weights of the sub-factors is as follows. The strengthen institutional improvement (revitalization of secondary public function hospital) was the number one, strengthen cooperation between agencies was the second, and Re-establishing the role of local public health care system was the third place. Conclusions: Considering the relative importance, factors that are considered to be important in the first place may not be improved as the best policy alternative due to limitations in spatial, temporal, financial, and institutional aspects. In this case, we suggest that we should choose the best policy alternative by using prioritization considering relative weights.

• Journal of Environmental Health Sciences
• /
• v.50 no.2
• /
• pp.125-137
• /
• 2024

Background: The Korea Risk Information Surveillance System (K-RISS) was developed to enable the early detection of food and drug safety-related issues. Its goal is to deliver real-time risk indicators generated from ongoing food and drug risk monitoring. However, the existing K-RISS system suffers under several limitations. Objectives: This study aims to augment K-RISS with more detailed indicators and establish a severity standard that takes into account structural changes in the daily time series of K-RISS values. Methods: First, a Delphi survey was conducted to derive the required weights. Second, a control chart, commonly used in statistical process controls, was utilized to detect outliers and establish caution, attention, and serious levels for K-RISS values. Furthermore, Bai and Perron's method was employed to determine structural changes in K-RISS time series. Results: The study incorporated 'closeness to life' and 'sustainability' indicators into K-RISS. It obtained the necessary weights through a survey of experts for integrating variables, combining indicators by data source, and aggregating sub K-RISS values. We defined caution, attention, and serious levels for both average and maximum values of daily K-RISS. Furthermore, when structural changes were detected, leading to significant variations in daily K-RISS values according to different periods, the study systematically verified these changes and derived respective severity levels for each period. Conclusions: This study enhances the existing K-RISS system and introduces more advanced indicators. K-RISS is now more comprehensively equipped to serve as a risk warning index. The study has paved the way for an objective determination of whether the food safety risk index surpasses predefined thresholds through the application of severity levels.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.42 no.4 s.304
• /
• pp.43-50
• /
• 2005

This paper proposes a new pattern recognition system combining the new adaptive feature weighting based on the genetic algorithm and the modified KNN(K Nearest-Neighbor) rules. The new feature weighting proposed herein avoids the overfitting and finds the Proper feature weighting value by determining the middle value of weights using GA. New GA operators are introduced to obtain the high performance of the system. Moreover, a class dependent feature weighting strategy is employed. Whilst the classical methods use the same feature space for all classes, the Proposed method uses a different feature space for each class. The KNN rule is modified to estimate the class of test pattern using adaptive feature space. Experiments were performed with the unconstrained handwritten numeral database of Concordia University in Canada to show the performance of the proposed method.

• Journal of Korea Multimedia Society
• /
• v.23 no.8
• /
• pp.891-902
• /
• 2020

The Re-Identification(Re-ID) is one of the most popular researches in the field of computer vision due to a variety of applications. To achieve a high-level re-identification performance, recently other methods have developed the deep learning based networks that are specialized for only person or vehicle. However, most of the current methods are difficult to be used in real-world applications that require re-identification of both person and vehicle at the same time. To overcome this limitation, this paper proposes a deep neural network learning method that combines triplet and softmax loss to improve performance and re-identify people and vehicles simultaneously. It's possible to learn the detailed difference between the identities(IDs) by combining the softmax loss with the triplet loss. In addition, weights are devised to avoid bias in one-side loss when combining. We used Market-1501 and DukeMTMC-reID datasets, which are frequently used to evaluate person re-identification experiments. Moreover, the vehicle re-identification experiment was evaluated by using VeRi-776 and VehicleID datasets. Since the proposed method does not designed for a neural network specialized for a specific object, it can re-identify simultaneously both person and vehicle. To demonstrate this, an experiment was performed by using a person and vehicle re-identification dataset together.

• The KIPS Transactions:PartB
• /
• v.13B no.4 s.107
• /
• pp.489-498
• /
• 2006

Clustering results differ according to the datasets and the performance worsens even while using web documents which are manually processed by an indexer, because although representative clusters for a feature can be obtained by statistical feature selection methods, irrelevant features(i.e., non-obvious features and those appearing in general documents) are not eliminated. Those irrelevant features should be eliminated for improving clustering performance. Therefore, this paper proposes three feature-filtering algorithms which consider feature values per document set, together with distribution, frequency, and weights of features per document set: (l) features filtering algorithm in a document (FFID), (2) features filtering algorithm in a document matrix (FFIM), and (3) a hybrid method combining both FFID and FFIM (HFF). We have tested the clustering performance by feature selection using term frequency and expand co link information, and by feature filtering using the above methods FFID, FFIM, HFF methods. According to the results of our experiments, HFF had the best performance, whereas FFIM performed better than FFID.