• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.48 no.4
• /
• pp.116-128
• /
• 2011

In this paper, we propose the structure of a high-performance face-detection engine that responds well to facial rotating changes using rotation transformation which minimize the required memory usage compared to the previous face-detection engine. The validity of the proposed structure has been verified through the implementation of FPGA. For high performance face detection, the MCT (Modified Census Transform) method, which is robust against lighting change, was used. The Adaboost learning algorithm was used for creating optimized learning data. And the rotation transformation method was added to maintain effectiveness against face rotating changes. The proposed hardware structure was composed of Color Space Converter, Noise Filter, Memory Controller Interface, Image Rotator, Image Scaler, MCT(Modified Census Transform), Candidate Detector / Confidence Mapper, Position Resizer, Data Grouper, Overlay Processor / Color Overlay Processor. The face detection engine was tested using a Virtex5 LX330 FPGA board, a QVGA grade CMOS camera, and an LCD Display. It was verified that the engine demonstrated excellent performance in diverse real life environments and in a face detection standard database. As a result, a high performance real time face detection engine that can conduct real time processing at speeds of at least 60 frames per second, which is effective against lighting changes and face rotating changes and can detect 32 faces in diverse sizes simultaneously, was developed.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.9
• /
• pp.131-143
• /
• 2011

Previous alerting service based on LBS was caused severe overload problem of server by using the method to confirm the location of each moving object on server. In this paper, by loading an App on smart moving object, we proposed a novel algorithm named ADAS(Active Disaster Alert Service) for accessing to the server site with oneself location information as needed and implemented the disaster alerting service system with visualization for user. In the proposed method, running App access to the server periodically with the present location coordinate gained from GPS module or network module and the ID of moving object. Then, the server compare the present location coordinate of moving object and the coordinates of disasters registered in DIDB and transmit the n NDIs existed in near distance orderly from the coordinate of present moving object to the client. The App compares the coordinate of present location for moving object and the coordinates of NDI is transmitted from server by real time and executes the service with classifying levels of alert into three steps such as danger, carefulness and safety. And new NDIs are gained by accessing DIDB on Server periodically during running App. Therefore, this will be become a novel method for reducing fundamentally the server overload problem in comparison with previous alerting service that the career of moving object is managed on server.

• Journal of Internet Computing and Services
• /
• v.8 no.3
• /
• pp.19-32
• /
• 2007

This paper present a new face recognition method based on LVQ neural net to construct a real time face recognition system. The previous researches which used PCA, LDA combined neural net usually need much time in training neural net. The supervised LVQ neural net needs much less time in training and can maximize the separability between the classes. In this paper, the proposed method transforms the input face image by PCA and LDA sequentially into low-dimension feature vectors and recognizes the face through LVQ neural net. In order to make the system robust to external light variation, light compensation is performed on the detected face by max-min normalization method as preprocessing. PCA and LDA transformations are applied to the normalized face image to produce low-level feature vectors of the image. In order to determine the initial centers of LVQ and speed up the convergency of the LVQ neural net, the K-Means clustering algorithm is adopted. Subsequently, the class representative vectors can be produced by LVQ2 training using initial center vectors. The face recognition is achieved by using the euclidean distance measure between the center vector of classes and the feature vector of input image. From the experiments, we can prove that the proposed method is more effective in the recognition ratio for the cases of still images from ORL database and sequential images rather than using conventional PCA of a hybrid method with PCA and LDA.

• Journal of Korea Spatial Information System Society
• /
• v.8 no.2 s.17
• /
• pp.91-107
• /
• 2006

To handling the extreme situation that must manage positional information of a very large volume, at least millions of moving objects. A cluster-based sealable distributed computing system architecture, called the GALIS which consists of multiple data processors, each dedicated to keeping records relevant to a different geographical zone and a different time zone, was proposed. In this paper, we proposed a valid time management and time-zone shifting scheme, which are essential in realizing the long-term location data subsystem of GALIS, but missed in our previous prototype development. We explain how to manage valid time of moving objects to avoid ambiguity of location information. We also describe time-zone shifting algorithm with three variations, such as Real Time-Time Zone Shifting, Batch-Time Zone Shifting, Table Partitioned Batch-Time Zone Shifting, Through experiments related with query processing time and CPU utilization, we show the efficiency of the proposed time-zone shifting schemes.

• Science of Emotion and Sensibility
• /
• v.13 no.1
• /
• pp.197-206
• /
• 2010

For an emotion retrieval system implementation to support pedestrian navigation, coordinating the pedestrian emotion model with the system user's emotion is considered a key component. This study proposes a new method for capturing the user's model that corresponds to the pedestrian emotion model and examines the validity of the method. In the first phase, a database comprising a set of interior images that represent hypothetical destinations was developed. In the second phase, 10 subjects were recruited and asked to evaluate on navigation and satisfaction toward each interior image in five rounds of navigation experiments. In the last phase, the subjects' feedback data was used for of the pedestrian emotion model, which is called ‘learning' in this study. After evaluations by the subjects, the learning effect was analyzed by the following aspects: recall ratio, precision ratio, retrieval ranking, and satisfaction. Findings of the analysis verify that all four aspects significantly were improved after the learning. This study demonstrates the effectiveness of the learning algorithm for the proposed pedestrian emotion model. Furthermore, this study demonstrates the potential of such pedestrian emotion model to be well applicable in the development of various mobile contents service systems dealing with visual images such as commercial interiors in the future.

• Korean Journal of Clinical Laboratory Science
• /
• v.52 no.3
• /
• pp.229-236
• /
• 2020

Sixty-five molds isolated from clinical specimens were included in this study. All the isolates were molds that could be identified morphologically, strains that are difficult to identify because of morphological similarities, and strains that require species-level identification. PCR and direct sequencing were performed to target the internal transcribed spacer (ITS) region, the D1/D2 region, and the β-tubulin gene. Comparative sequence analysis using the GenBank database was performed using the basic local alignment search tool (BLAST) algorithm. The fungi identified morphologically to the genus level were 67%. Sequencing analysis was performed on 62 genera and species level of the 65 strains. Discrepancies were 14 (21.5%) of the 65 strains between the results of phenotypic and molecular identification. B. dermatitidis, T. marneffei, and G. argillacea were identified for the first time in Korea using the DNA sequencing method. Morphological identification is a very useful method in terms of the reporting time and costs in cases of frequently isolated and rapid growth, such as Aspergillus. When molecular methods are employed, the cost and clinical significance should be considered. On the other hand, the molecular identification of molds can provide fast and accurate results.

• Journal of the Institute of Electronics Engineers of Korea TE
• /
• v.42 no.1
• /
• pp.13-20
• /
• 2005

In this paper, I recognized Korean isolated words using CVEM which is suggested by M. Bilginer et al. CVEM is an algorithm which is easy to extract the common properties from training voice signals and also doesn't need complex calculation. In addition CVEM shows high accuracy in recognition results. But, CVEM has couple of problems which are impossible to use for many training voices and no discriminant information among extracted common vectors. To get the optimal common vectors from certain voice classes, various voices should be used for training. But CVEM is impossible to get continuous high accuracy in recognition because CVEM has a limitation to use many training voices and the absence of discriminant information among common vectors can be the source of critical errors. To solve above problems and improve recognition rate, k-clustering subspace method and DCVEM suggested. And did various experiments using voice signal database made by ETRI to prove the validity of suggested methods. The result of experiments shows improvements in performance. And with proposed methods, all the CVEM problems can be solved with out calculation problem.

• Journal of IKEEE
• /
• v.18 no.4
• /
• pp.471-477
• /
• 2014

In this paper, we propose a low-cost license plate recognition system based on smart cam system using Android. The proposed system consists of a portable device and server. Potable device Hardware consists of ARM Cortex-A9 (S5PV210) processor control unit, a power supply device, wired and wireless communication, input/output unit. We develope Linux kernel and dedicated device driver for WiFi module and camera. The license plate recognition algorithm is consisted of setting candidate plates areas with canny edge detector, extracting license plate number with Labeling, recognizing with template matching, etc. The number that is recognized by the device is transmitted to the remote server via the user mobile phone, and the server re-transfer the vehicle information in the database to the portable device. To verify the utility of the proposed system, user photographs the license plate of any vehicle in the natural environment. Confirming the recognition result, the recognition rate was 95%. The proposed system was suitable for low cost portable license plate recognition device, it enabled the stability of the system when used long time by using the Android operating system.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.4
• /
• pp.825-832
• /
• 2014

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. Even if some methods have the advantage in low complexity, but they generally suffer form low sensitivity. Also, it is difficult to detect PVC accurately because of the various QRS pattern by person's individual difference. Therefore it is necessary to design an efficient algorithm that classifies PVC based on QRS pattern in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose PVC classification based on QRS pattern using QS interval and R wave amplitude. For this purpose, we detected R wave, RR interval, QRS pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 PVC. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 93.72% in PVC classification.

• Journal of KIISE:Software and Applications
• /
• v.35 no.1
• /
• pp.22-32
• /
• 2008

To overcome the weakness of the image retrieval system using the existing Ontology and the distributed image based on the database having a simple structure, HERMES was suggested to ensure the self-control of various image suppliers and support the image retrieval based on semantic, the mentioned framework could not solve the problems which are not considered the deterioration in the capacity and scalability when many users connect to broker server simultaneously. In this paper the tables are written which in the case numerous users connect at the same time to the supply analogous level of services without the deterioration in the capacity installs Broker servers and then measures the performance time of each inner Broker Component through Monitoring System and saved and decides the ranking in saved data. As many Query performances are dispersed into several Servers User inputted from the users Interface with reference to Broker Ranking Table, Load Balancing system improving reliability in capacity is proposed. Through the experiment, the scheduling technique has proved that this schedule is faster than existing techniques.