• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.3
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• pp.119-125
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• 2012

Caliper intergrated Electric Parking Brake (EPB) is an automatic parking brake system, attached to rear caliper. Because EPB uses luxury vehicles recently, the drivers of vehicles are sensitive to the EPB noise. EPB is operated by the motor and gear, so noise is generated by motor and gear. In order to reduce noise, One of EPB manufacturers uses helical gear and changes the shape of EPB housing. But these methods are not optimized for reduction of interior noise. There are many noise transfer paths into vehicle interior and it is difficult to identify the noise sources. Therefore, in this study, we performed contribution analysis and modal testing in the vehicle system. It is possible to distinguish between air-borne noise and structure-borne noise in the vehicle interior noise by comparing interior noise peak with resonance mode map.

• Journal of Plant Biotechnology
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• v.43 no.4
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• pp.457-465
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• 2016

Microsatellites are one of the most suitable markers for identification of variety, as they have the capability to discriminate between narrow genetic variations. The polymorphism level between 120 microsatellite primer pairs and 148 soybean varieties was investigated through the fluorescence based automatic detection system. A set of 16 primer pairs showed highly reproducible polymorphism in these varieties. A total of 204 alleles were detected using the 16 microsatellite markers. The number of alleles per locus ranged from 6 to 28, with an average of 12.75 alleles per locus. The average polymorphism information content (PIC) was 0.86, ranging from 0.75 to 0.95. The unweighted pair group method using the arithmetic averages (UPGMA) cluster analysis for 148 varieties were divided into five distinctive groups, reflecting the varietal types and pedigree information. All the varieties were perfectly discriminated by marker genotypes. These markers may be useful to complement a morphological assessment of candidate varieties in the DUS (distinctness, uniformity and stability) test, intervening of seed disputes relating to variety authentication, and testing of genetic purity in soybean varieties.

• The KIPS Transactions:PartA
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• v.11A no.7 s.91
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• pp.529-536
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• 2004

Smart homes integrated with sensors, actuators, wireless networks and context-aware middleware will soon become part of our daily life. This paper describes a context-aware middleware providing an automatic home service based on a user's preference. The context-aware middle-ware utilizes 6 basic data for learning and predicting the user's preference on the multimedia content : the pulse, the body temperature, the facial expression, the room temperature, the time, and the location. The six data sets construct the context model and are used by the context manager module. The log manager module maintains history information for multimedia content chosen by the user. The user-pattern learning and pre-dicting module based on a neural network predicts the proper home service for the user. The testing results show that the pattern of an in-dividual's preferences can be effectively evaluated and predicted by adopting the proposed context model.

• Atmosphere
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• v.17 no.4
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• pp.339-348
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• 2007

Global Environment Laboratory at Yonsei University in Seoul ($37.57^{\circ}N$, $126.95^{\circ}E$) has carried out the ozone layer monitoring program in the framework of the Global Ozone Observing System of the World Meteorlogical Organization (WMO/GAW/GO3OS Station No. 252) since May of 1984. The daily measurements of total ozone and the vertical distribution of ozone amount have been made with the Dobson Spectrophotometer (No.124) on the roof of the Science Building on Yonsei campus. From 2004 through 2006, major parts of the manual operations are automated in measuring total ozone amount and vertical ozone profile through Umkehr method, and calibrating instrument by standard lamp tests with new hardware and software including step motor, rotary encoder, controller, and visual display. This system takes full advantage of Windows interface and information technology to realize adaptability to the latest Windows PC and flexible data processing system. This automatic system also utilizes card slot of desktop personal computer to control various types of boards in the driving unit for operating Dobson spectrophotometer and testing devices. Thus, by automating most of the manual work both in instrument operation and in data processing, subjective human errors and individual differences are eliminated. It is therefore found that the ozone data quality has been distinctly upgraded after automation of the Dobson instrument.

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.1-16
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• 2000

This paper analyzes the approaches of Web-based expert systems by comparing their pros and cons. and proposes a methodology of implementing the Web-based backward inference engines with reduced burden to Web servers. There are several alternatives to implement expert systems under the WWW environment : CGI, Web servers embedding inference engines external viewers Java Applets and HTML. Each of the alternatives have advantages and disadvantages of each own in terms of development and deployment testing scalability portability maintenance and mass service. Especially inference engines implemented using HTML possess relatively large number of advantages compared with those implemented using other techniques. This paper explains the methodology to present rules and variables for backward inference by HTML and JavaScript and suggests a framework for design and development of HTML-based Expert System. A methodology to convert a traditional rule base to an Experts Diagram and then generate a new HTML-based Expert System from the Experts Diagram is also addressed.

• Journal of Biomedical Engineering Research
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• v.38 no.1
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• pp.25-31
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• 2017

Testing TB in chest X-ray images is a typical method to diagnose presence and magnitude of PTB lesion. However, the method has limitation due to inter-reader variability. Therefore, it is essential to overcome this drawback with automatic interpretation. In this study, we propose a novel method for detection of PTB using SegNet, which is a deep learning architecture for semantic pixel wise image labelling. SegNet is composed of a stack of encoders followed by a corresponding decoder stack which feeds into a soft-max classification layer. We modified parameters of SegNet to change the number of classes from 12 to 2 (TB or none-TB) and applied the architecture to automatically interpret chest radiographs. 552 chest X-ray images, provided by The Korean Institute of Tuberculosis, used for training and test and we constructed a receiver operating characteristic (ROC) curve. As a consequence, the area under the curve (AUC) was 90.4% (95% CI:[85.1, 95.7]) with a classification accuracy of 84.3%. A sensitivity was 85.7% and specificity was 82.8% on 431 training images (TB 172, none-TB 259) and 121 test images (TB 63, none-TB 58). This results show that detecting PTB using SegNet is comparable to other PTB detection methods.

• Journal of Korea Multimedia Society
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• v.7 no.3
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• pp.308-318
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• 2004

A subject to recognize the damaged facial image is becoming an important issue in commercialization of automatic face recognition. The method to recognize a face on a damaged image is divided into two types. The one is to recognize remainders after removing the damaged information and the other is to recognize a total face after recovering the damaged information. On this paper, we present the reconstruction method by analyzing the main materials after extracting the damaged region through Kohonen network. The suggested algorithm in this paper estimates feature vectors of the damaged region using eigen-faces in PCA and then reconstructs the damaged image. This allows also the reconstruction under the untrained images. Through testing the artificial images where the eye and the mouth which have many effects to face recognition are damaged, the recognition rate of the proposed results showed similar results with the method which used Kohonen network, and improved about 11.8% more than symmetrical property method. Also, in case of the untrained image, our results improved about 14% more than that of the Kohonen method and about 7% more than that of the symmetrical property method.

• Journal of Technologic Dentistry
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• v.30 no.1
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• pp.57-63
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• 2008

The purpose of this study was to ascertain the effect of different abutment height and different taper of abutment on retention force of cemented implant-supported prostheses. Test specimens consisted of different abutment height group(3mm, 4mm, 5mm, 6mm, 7mm) and different taper(degrees) abutment group($4^{\circ},\;5^{\circ},\;6^{\circ},\;7^{\circ},\;8^{\circ}$). The surfaces of abutments and crowns were manufactured and finished by automatic lathe(CNC). Luting cement(Tokuso Ionomer) was prepared according to the manufacturer's instruction. And the cylinders were sealed onto the abutments and loaded in compression at 5kg for 10minutes. Excess cement was removed from the abutment-cylinder junction and the specimens were stored at room temparature for 24 hours. Specimens were tested in tension using a universal testing machine. Within the limits of this study, the following conclusions were drawn: 1. The increase in abutment height result in improvement in retention strength(P<0.05). 2. The increase in taper of abutment result in decrease in retention strength(P<0.05). 3. The decrease in abutment height result in decrease in retention strength, besides has a significantly lower retention strength at 3mm abutment height. 4. The increase in taper of abutment result in decrease in retention strength, besides has a significantly lower retention strength at $7^{\circ}$ abutment.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.61.1-61.1
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• 2017

As the application of deep-learning methods has been succeeded in various fields, they have a high potential to be applied to space weather forecasting. Convolutional neural network, one of deep learning methods, is specialized in image recognition. In this study, we apply the AlexNet architecture, which is a winner of Imagenet Large Scale Virtual Recognition Challenge (ILSVRC) 2012, to the forecast of daily solar flare occurrence using the MatConvNet software of MATLAB. Our input images are SOHO/MDI, EIT $195{\AA}$, and $304{\AA}$ from January 1996 to December 2010, and output ones are yes or no of flare occurrence. We consider other input images which consist of last two images and their difference image. We select training dataset from Jan 1996 to Dec 2000 and from Jan 2003 to Dec 2008. Testing dataset is chosen from Jan 2001 to Dec 2002 and from Jan 2009 to Dec 2010 in order to consider the solar cycle effect. In training dataset, we randomly select one fifth of training data for validation dataset to avoid the over-fitting problem. Our model successfully forecasts the flare occurrence with about 0.90 probability of detection (POD) for common flares (C-, M-, and X-class). While POD of major flares (M- and X-class) forecasting is 0.96, false alarm rate (FAR) also scores relatively high(0.60). We also present several statistical parameters such as critical success index (CSI) and true skill statistics (TSS). All statistical parameters do not strongly depend on the number of input data sets. Our model can immediately be applied to automatic forecasting service when image data are available.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.74-78
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• 1992

This paper describes an intelligent system to automatic evaluation of eddy current(EC) signal for Inspection of steam generator(SG) tubes in nuclear power plant. Some features of the intelligent system design in the proposed system are : (1) separation of representation scheme ,or event capturing knowledge in EC signal and for structural inspection knowledge in SG tubes inspection; (2) each representation scheme is implemented in different methods, one is syntactic pattern grammar and the other is rule based production. This intelligent system also includes an data base system and an user interface system to support integration of the hybrid knowledge processing methods. The intelligent system based on the proposed concept is useful in simplifying the knowledge elicitation process of the rule based production system, and in increasing the performance in real time signal inspection application.