• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.202-205
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• 2008

This paper presents an optimization of permanent magnet (PM) in a brushless dc (BLDC) motor using the response surface methodology (RSM). Size and angle of the PM are optimized to minimize the cogging torque, while reducing the magnitude of harmonic at a dominant frequency and maintaining the operating torque. A fitted RS model is constructed by verifying the high reliability of the total variation and the variation of estimated error. The optimized design is validated by carrying out the reanalysis and comparing to the initial model using the nonlinear transient finite element analysis.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.537-538
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• 2008

A step-down DC-DC converter with On-chip Compensation for battery-operated portable electronic devices which are designed in 0.18um CMOS standard process. In an effort to improve low load efficiency, this paper proposes the PFM (Pulse Frequency modulation) voltage mode 1MHz switching frequency step-down DC-DC converter with on-chip compensation. Capacitor multiplier method can minimize error amplifier compensation block size by 20%. It allows the compensation block of DC-DC converter be easily integrated on a chip and occupy less layout area. But capacitor multiplier operation reduces DC-DC converter efficiency. As a result, this converter shows maximum efficiency over 87% for the output voltage of 1.8V (input voltage : 3.3V), maximum load current 500mA, and 0.14% output ripple voltage. The total core chip area is $mm^2$.

• Management Science and Financial Engineering
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• v.15 no.2
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• pp.69-100
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• 2009

In this paper, we consider a relatively new two-machine flow shop scheduling problem where the unit time WIP cost increases as a job passes through various stages in the production process, and the objective is to minimize the total WIP (work-in-process) cost. Specifically, we study three special cases of the problem. First, we consider the problem where processing times on machine 1 are identical. Second, the problem with identical processing times on machine 2 is examined. The recognition version of the both problems is unary NP-complete (or NP-complete in strong sense). For each problem, we suggest two simple and intuitive heuristics and find the worst case bound on relative error. Third, we consider the problem where the processing time of a job on each machine is proportional to a base processing time. For this problem, we show that a known heuristic finds an optimal schedule.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.209-215
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• 2014

Sequence tagging is the task of predicting frame-wise labels for a given input sequence and has important applications to diverse domains. Conventional methods such as maximum likelihood (ML) learning matches global features in empirical and model distributions, rather than local features, which directly translates into frame-wise prediction errors. Recent probabilistic sequence models such as conditional random fields (CRFs) have achieved great success in a variety of situations. In this paper, we introduce a novel discriminative CRF learning algorithm to minimize local feature mismatches. Unlike overall data fitting originating from global feature matching in ML learning, our approach reduces the total error over all frames in a sequence. We also provide an efficient gradient-based learning method via gradient forward-backward recursion, which requires the same computational complexity as ML learning. For several real-world sequence tagging problems, we empirically demonstrate that the proposed learning algorithm achieves significantly more accurate prediction performance than standard estimators.

• Design & Manufacturing
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• v.15 no.1
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• pp.21-25
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• 2021

When the product is removed from the mold after molding during the sheet metal molding process, elastic recovery causes a springback phenomenon. Much research has been done to minimize this phenomenon. In this study, V-bending experiments were conducted using galvanized steel sheets, stainless steel, and aluminum sheet materials, using a total of nine types of thin sheet materials of 1.0t, 1.5t, and 2.0t, respectively. Molding analysis and experimental data were compared and analyzed. In the case of galvanized steel sheets, it was considered that the springback phenomenon occurs more frequently in molding analysis than in experiments. It was considered that the springback phenomenon occurs greatly in the experiment, not the interpretation of the molding of the stainless steel plate and the aluminum plate. It was considered that the springback occurrence tendency of the molding analysis and the experiment was the same, and the springback occurrence error rate of the molding analysis and the experimental result was about 4.0%.

• Journal of Korean Society on Water Environment
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• v.30 no.6
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• pp.585-595
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• 2014

The temporal trends of the prioritized toxic water pollutants generated and discharged from the industrial facilities in the Geum-River basin, Korea were analyzed with the results of the nationwide Water Pollution Source Survey conducted in 2001 - 2012. The statistical results indicated rapid increase in the volume of raw toxic wastewaters whereas the amount of each toxic pollutant kept fluctuating for 12 years. Serious discrepancies in the survey data of the same type of industries demonstrated a low reliability of the survey result, which stemmed from several error factors. A unit-load for each type of industrial facility was devised to estimate the amount of prioritized toxic water pollutant based on the total volume of industrial wastewater generated from the same type of industrial facilities. The supplementary measures with an effective permit issuance policy and adding survey parameters of terminal wastewater treatment plants to use them as references to the Water Pollution Source Survey were suggested as means to minimize the errors associated with the false reports from the industries.

• Journal of Hydrogen and New Energy
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• v.28 no.3
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• pp.287-294
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• 2017

EVs manufacturers typically target a range of 300 km on a fully charged battery. Many studies have been conducted to improve these disadvantages. As a results, the mileage of EVs is expected to increase significantly. However, as the distance traveled by EVs increases, current test method (SCT) have many difficulties. The biggest problem is that it takes a lot of time to test an EVs and greatly increases the error rate during the test period. In order to solve these problems, this paper discusses the fuel economy test method of EVs for energy efficiency and mileage. The comparison of test methods was achieved by chassis dynamometer test about EVs. These review of test methods are intended to both improve testing efficiency and provide a practical testing methodology that can be easily adapted to accommodate future testing enhancements. In conclusion, the results of MCT mode and SCT mode comparison show similar results within 3 %, confirming that the test method is appropriate. Also, as the CSCM distance becomes shorter in the MCT mode, the mileage becomes longer and the fuel economy becomes lower. As a result, the error from the SCT test results is expected to increase. In order to minimize the error of SCT measurement fuel economy, it is recommended to maximize the CSCM driving distance. However, since the timing of the EOT is not clearly known, it is reasonable to define the allowable range of the CSCE to be within 20 % of the MCT total mileage.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.4
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• pp.38-47
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• 2020

A parametric study for analytical correction using measurement results was performed to minimize errors in the predictions of column shortening in RC tall building. The parameters of the column shortening analytical correction are the execution standard of analytical correction, the value of the analytical correction, and the measurement location, and the analytical correction models with the parameters were applied to the construction sequence analysis of a 41-story RC building to compare and analyze the correction effect according to the parameter. The reduction ratio of the error value for each floor was compared with the number of corrections and the total corrected value, and it was confirmed that the error tended to be minimized when the execution standard of analytical correction was performed based on a regular interval, when the analysis correction value was corrected by the error value, and when the measurement position was measured every floor. From this, it was confirmed that the most appropriate analytical correction model can be derived by applying multiple analytical correction models to the actual analysis model.

• Journal of the Korean Operations Research and Management Science Society
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• v.1 no.1
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• pp.151-170
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• 1976

There are many cases of production processes which intermittently produce several different kinds of products for stock through one set of physical facility. In this case, an important question is what size of production run should be prduced once we do set-up for a product in order to minimize the total cost, that is, the sum of the set-up, carrying, and stock-out costs. This problem is used to be called scheduling of multiple products through a single facility in the production management field. Despite the very common occurrence of this type of production process, no one has yet devised a method for determining the optimal production schedule. The purpose of this study is to develop quantitative analytical models which can be used practically and give us rational production schedules. The study is to show improved models with application to a can-manufacturing plant. In this thesis the economic production quantity (EPQ) model was used as a basic model to develop quantitative analytical models for this scheduling problem and two cases, one with stock-out cost, the other without stock-out cost, were taken into consideration. The first analytical model was developed for the scheduling of products through a single facility. In this model we calculate No, the optimal number of production runs per year, minimizing the total annual cost above all. Next we calculate No$_{i}$ is significantly different from No, some manipulation of the schedule can be made by trial and error in order to try to fit the product into the basic (No schedule either more or less frequently as dictated by) No$_{i}$, But this trial and error schedule is thought of inefficient. The second analytical model was developed by reinterpretation by reinterpretation of the calculating process of the economic production quantity model. In this model we obtained two relationships, one of which is the relationship between optimal number of set-ups for the ith item and optimal total number of set-ups, the other is the relationship between optimal average inventory investment for the ith item and optimal total average inventory investment. From these relationships we can determine how much average inventory investment per year would be required if a rational policy based on m No set-ups per year for m products were followed and, alternatively, how many set-ups per year would be required if a rational policy were followed which required an established total average inventory inventory investment. We also learned the relationship between the number of set-ups and the average inventory investment takes the form of a hyperbola. But, there is no reason to say that the first analytical model is superior to the second analytical model. It can be said that the first model is useful for a basic production schedule. On the other hand, the second model is efficient to get an improved production schedule, in a sense of reducing the total cost. Another merit of the second model is that, unlike the first model where we have to know all the inventory costs for each product, we can obtain an improved production schedule with unknown inventory costs. The application of these quantitative analytical models to PoHang can-manufacturing plants shows this point.int.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.