• Journal of Digital Convergence
• /
• v.11 no.12
• /
• pp.295-302
• /
• 2013

Many reported methods assume that the people in an image or an image sequence have been identified and localization. People detection is one of very important variable to affect for the system's performance as the basis technology about the detection of other objects and interacting with people and computers, motion recognition. In this paper, we present an efficient linear discriminant for multi-view people detection. Our approaches are based on linear discriminant. We define training data with fisher Linear discriminant to efficient learning method. People detection is considerably difficult because it will be influenced by poses of people and changes in illumination. This idea can solve the multi-view scale and people detection problem quickly and efficiently, which fits for detecting people automatically. In this paper, we extract people using fisher linear discriminant that is hierarchical models invariant pose and background. We estimation the pose in detected people. The purpose of this paper is to classify people and non-people using fisher linear discriminant.

• Journal of The Korean Association For Science Education
• /
• v.40 no.3
• /
• pp.321-336
• /
• 2020

We explore automated scoring models of scientific argumentation. We consider how a new analytical approach using a machine learning technique may enhance the understanding of spoken argumentation in the classroom. We sampled 2,605 utterances that occurred during a high school student's science class on molecular structure and classified the utterances into five argumentative elements. Next, we performed Text Preprocessing for the classified utterances. As machine learning techniques, we applied support vector machines, decision tree, random forest, and artificial neural network. For enhancing the identification of rebuttal elements, we used a heuristic feature-engineering method that applies experts' classification of morphemes of scientific argumentation.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.10
• /
• pp.125-133
• /
• 2014

This paper develops a bankruptcy prediction model on an Artificial Neural Network for hotel management. A bankruptcy prediction model has a specific feature to predict a bankruptcy of the whole hotel business after evaluate bankruptcy possibility on the basis of business performance data of each branch. here are many traditional statistical models for bankruptcy prediction such as Multivariate Discriminant Analysis or Logit Analysis. However, we chose Artificial Neural Network because the method has accuracy rates of prediction better than those of other methods. We first selected 100 good enterprises and 100 bankrupt enterprises as experimental data and set up a bankruptcy prediction model by use of a tool for Artificial Neural Network, NeuroShell. The model and its experiments, which demonstrated high efficiency, can certainly provide great help in decision making in the field of hotel management and in deciding on the bankruptcy or financial solidity of each branch of serviced residence hotel.

• Smart Structures and Systems
• /
• v.25 no.2
• /
• pp.155-167
• /
• 2020

This paper demonstrates a real-time hybrid substructuring (RTHS) shake table test to evaluate the seismic performance of a base isolated building. Since RTHS involves a feedback loop in the test implementation, the frequency dependent magnitude and inherent time delay of the actuator dynamics can introduce inaccuracy and instability. The paper presents a robust stability and performance analysis method for the RTHS test. The robust stability method involves casting the actuator dynamics as a multiplicative uncertainty and applying the small gain theorem to derive the sufficient conditions for robust stability and performance. The attractive feature of this robust stability and performance analysis method is that it accommodates linearized modeled or measured frequency response functions for both the physical substructure and actuator dynamics. Significant experimental research has been conducted on base isolators and dampers toward developing high fidelity numerical models. Shake table testing, where the building superstructure is tested while the isolation layer is numerically modeled, can allow for a range of isolation strategies to be examined for a single shake table experiment. Further, recent concerns in base isolation for long period, long duration earthquakes necessitate adding damping at the isolation layer, which can allow higher frequency energy to be transmitted into the superstructure and can result in damage to structural and nonstructural components that can be difficult to numerically model and accurately predict. As such, physical testing of the superstructure while numerically modeling the isolation layer may be desired. The RTHS approach has been previously proposed for base isolated buildings, however, to date it has not been conducted on a base isolated structure isolated at the ground level and where the isolation layer itself is numerically simulated. This configuration provides multiple challenges in the RTHS stability associated with higher physical substructure frequencies and a low numerical to physical mass ratio. This paper demonstrates a base isolated RTHS test and the robust stability and performance analysis necessary to ensure the stability and accuracy. The tests consist of a scaled idealized 4-story superstructure building model placed directly onto a shake table and the isolation layer simulated in MATLAB/Simulink using a dSpace real-time controller.

• Journal of Korea Multimedia Society
• /
• v.20 no.8
• /
• pp.1299-1311
• /
• 2017

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines using the sounds emitted by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We present here an automatic fault diagnosis system of hand drills using discrete wavelet transform (DWT) and pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The diagnosis system consists of three steps. Because of the presence of many noisy patterns in our signals, we first conduct a filtering analysis based on DWT. Second, the wavelet coefficients of the filtered signals are extracted as our features for the pattern recognition part. Third, PCA is performed over the wavelet coefficients in order to reduce the dimensionality of the feature vectors. Finally, the very first principal components are used as the inputs of an ANN based classifier to detect the wear on the drills. The results show that the proposed DWT-PCA-ANN method can be used for the sounds based automated diagnosis system.

• Journal of Korean Society of Transportation
• /
• v.33 no.3
• /
• pp.268-275
• /
• 2015

As Korea is predicted to be a super-aged society in the near future, transport policies that internalize the elderly have also drawn attentions. Even though some studies have examined the travel by the elderly with various motives, it is, however, difficult to find references that deal with the unique spatio-temporal characteristics of senior trips. For example, the models by time period have represented the temporal property while a set of independent variables associated with urban infrastructure have addressed the spatial feature. This study was conducted under a trip attraction model for transit. The result shows that transit facilities, commercial areas, and hospitals are the dominant factors to explain the travel by the elderly, particularly during 09:00-17:00.

• Journal of KIISE
• /
• v.44 no.3
• /
• pp.306-313
• /
• 2017

Named entity recognition (NER) seeks to locate and classify named entities in text into pre-defined categories such as names of persons, organizations, locations, expressions of times, etc. Recently, many state-of-the-art NER systems have been implemented with bidirectional LSTM CRFs. Deep learning models based on long short-term memory (LSTM) generally depend on word representations as input. In this paper, we propose an approach to expand word representation by using pre-trained word embedding, part of speech (POS) tag embedding, syllable embedding and named entity dictionary feature vectors. Our experiments show that the proposed approach creates useful word representations as an input of bidirectional LSTM CRFs. Our final presentation shows its efficacy to be 8.05%p higher than baseline NERs with only the pre-trained word embedding vector.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2017.04a
• /
• pp.44-44
• /
• 2017

The objective of this study was to evaluate firmness, and sweetness index (SI) of tomatoes (Lycopersicum esculentum) by using hyperspectral imaging (HSI) in the range of 1000-1400 nm. The mean spectra of the 95 matured tomato samples were extracted from the hyperspectral images, and the reference firmness and sweetness index of the same sample were measured and calibrated with their corresponding spectral data by partial least squares (PLS) regression with different preprocessing method. The results showed that the regression model developed by PLS regression based on Savitzky-Golay (S-G) second-derivative preprocessed spectra resulted in better performance for firmness, and SI of tomatoes compared to models developed by other preprocessing methods, with correlation coefficients (rpred) of 0.82, and 0.74 with standard error of prediction (SEP) of 0.86 N, and 0.63 respectively. Then, the feature wavelengths were identified using model-based variable selection method, i.e., variable important in projection (VIP), resulting from the PLS regression analyses and finally chemical images were derived by applying the respective regression coefficient on the spectral image in a pixel-wise manner. The resulting chemical images provided detailed information on firmness, and sweetness index (SI) of tomatoes. Therefore, these research demonstrated that HIS technique has a potential for rapid and non-destructive evaluation of the firmness and sweetness index of tomatoes.

• The Journal of Information Systems
• /
• v.17 no.2
• /
• pp.49-72
• /
• 2008

Today, the World Wide Web (WWW) impacts many facets of our lives, including communication, entertainment, social activities, shopping, etc. The web portal is the most accessed type of site and is advertising-supported the more users who visit the site, the more income it generates. User perception to a web site is very important much research has focused on the internet users' behavior. Some well-known theories, such as the technology acceptance model have been used to examine variables that motivate individuals to accept and use an IS. But Understanding continued use is the goal of this study. We focus on user beliefs (specifically, perceived usefulness) and attitude because pier studies of IT usage, predominantly based on the technology acceptance model (TAM) and similar models, have established these perceptions as the dey determinants of both initial IT usage (acceptance) and long-term usage (continuance) intention and behavior (Bhattacherjee 2001; Davis et al. 1989). Any change in beliefs or attitudes will likely have a corresponding impact on, and may even revers, users' continuance intention and behavior. Also, continuance use have some features which are prior use, habit, feature-centric view of technology. So this research reflected continuance use features. Examination of the paths in the model revealed several interesting results. First, Perceived usefulness was a stronger predictor of acceptance intention in TAM than attitude, But attitude was a stronger predictor of continuance intention in this study than perceive usefulness. Second, confirmation was not affect directly to attitude. Last, Habit was strongest predictor of continuance intention in this study.

• Annals of Clinical Neurophysiology
• /
• v.2 no.2
• /
• pp.119-124
• /
• 2000

Background : We studied EEG changes during pilocarpine-induced status epilepticus(SE), a widely used model whose EEG characteristics have not been fully described previously. Methods : Male Sprague-Dawley rats weighing 250-350 grams were used as subjects. SE was induced 5-7 days after placement of chronic epidural electrodes, using 360-380 mg/Kg pilocarpine IP. Rats were observed with continuous EEG recording following pilocarpine injection until end of the SE episode. Results : SE occurred in 10/12 rats studied. SE began with a series of discrete seizures $11.1{\pm}3.93$ minutes after pilocarpine injection. $5.2{\pm}2.71$ seizures occurred over $10.9{\pm}4.62$ minutes, until the EEG converted to a waxing and waning pattern, during which the amplitude and frequency of epileptiform activity increased. After $1.4{\pm}1.82$ minutes, a pattern of continuous high amplitude rapid spiking was established. Continuous spiking continued for $3.4{\pm}0.48$ hours with a very gradual decline in amplitude and frequency, until periodic epileptiform discharges(PEDs) began to occur. The EEG consisted primarily of PEDs for another $7.4{\pm}3.09$ hours, until electrographic generalized seizures began to occur. These continued for $5.8{\pm}4.82$ hours until death. Duration of SE was $17.0{\pm}5.88$ hours. Flat periods were a prominent feature during all EEG patterns in this model. Conclusion : EEG features distinctive in pilocarpine SE(but not unique to it) include flat periods during all patterns and resumption of continuous spiking episodes after the onset of PEDs. The sequence of discrete seizures to waxing and waning to continuous spiking to PEDs was identical to that which has been described in humans and other animal models.