• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.4
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• pp.399-405
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• 2010

In this paper, the movement faces the problem of the difficult points upon the gaze of the user that corrective action is needed to solve the identification system offers a new perspective. Using the Kalman filter using the position information of the current head position estimated the future. In order to determine the authenticity of the face features of the face structural element information and the processing time is relatively fast horizontal and vertical histogram analysis method to detect the elements of the face. and people grow and infrared bright pupil effect obtained by constructing a real-time pupil detection, tracking and pupil - geulrinteu vectors are extracted.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.6
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• pp.973-980
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• 2011

In this paper, we gaze upon the movement faces the problem points are difficult to identify a user based on points and that corrective action is needed to solve the identification system is proposed a new eye. Kalman filter, the current head of the location information was used to estimate the future position in order to determine the authenticity of the face facial features and structural elements, the information and the processing time is relatively fast horizontal and vertical elements of the face using the histogram analysis to detect. And an infrared illuminator obtained by constructing a bright pupil effect in real-time detection of the pupil, the pupil was tracked - geulrinteu vectors are extracted.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.3
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• pp.331-338
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• 2016

In this paper, we propose a GRNN(: Generalized Regression Neural Network) algorithms for new eyes and face recognition identification system to solve the points that need corrective action in accordance with the existing problems of facial movements gaze upon it difficult to identify the user and. Using a Kalman filter structural information elements of a face feature to determine the authenticity of the face was estimated future location using the location information of the current head and the treatment time is relatively fast horizontal and vertical elements of the face using a histogram analysis the detected. And the light obtained by configuring the infrared illuminator pupil effects in real-time detection of the pupil, the pupil tracking was - to extract the text print vector.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.684-690
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• 2007

We describe our method to predict the direction of conditional probabilistic dependencies between clusters of random variables. Selected variables called 'gateway variables' are used to predict the conditional probabilistic dependency relations between clusters. The direction of conditional probabilistic dependencies between clusters are predicted by finding directed acyclic graph (DAG)-shaped dependency structure between the gateway variables. We show that our method shows meaningful prediction results in determining directions of conditional probabilistic dependencies between clusters.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.329-344
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• 2023

In this study, we comprehensively analyze the generalization performance of various deep learning-based active sonar target classifiers when applied to small and imbalanced active sonar datasets. To generate the active sonar datasets, we use data from two different oceanic experiments conducted at different times and ocean. Each sample in the active sonar datasets is a time-frequency domain image, which is extracted from audio signal of contact after the detection process. For the comprehensive analysis, we utilize 22 Convolutional Neural Networks (CNN) models. Two datasets are used as train/validation datasets and test datasets, alternatively. To calculate the variance in the output of the target classifiers, the train/validation/test datasets are repeated 10 times. Hyperparameters for training are optimized using Bayesian optimization. The results demonstrate that shallow CNN models show superior robustness and generalization performance compared to most of deep CNN models. The results from this paper can serve as a valuable reference for future research directions in deep learning-based active sonar target classification.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1295-1304
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• 2004

In this paper, the Bayesian recurrent neural network is proposed to predict time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one needs to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, the weights vector is set as a state vector of state space method, and its probability distributions are estimated in accordance with the particle filtering process. This approach makes it possible to obtain more exact estimation of the weights. In the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent neural network with Bayesian inference, what we call Bayesian recurrent neural network (BRNN), is expected to show higher performance than the normal neural network. To verify the proposed method, the time series data are numerically generated and various kinds of neural network predictor are applied on it in order to be compared. As a result, feedback structure and Bayesian learning are better than feedforward structure and backpropagation learning, respectively. Consequently, it is verified that the Bayesian reccurent neural network shows better a prediction result than the common Bayesian neural network.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.471-476
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• 2004

Recently, provides information which is most necessary to the user the research against the web information recommendation system for the Internet shopping mall is actively being advanced. the back which it will drive in the object. In that Dynamic Web Recommendation Method Using SOM (Self-Organizing Feature Maps) has the advantages of speedy execution and simplicity but has the weak points such as the lack of explanation on models and fired weight values for each node of the output layer on the established model. The method proposed in this study solves the lack of explanation using the Bayesian reasoning method. It does not give fixed weight values for each node of the output layer. Instead, the distribution includes weight using Hybrid SOM. This study designs and implements Dynamic Web Recommendation Method Using Hybrid SOM. The result of the existing Web Information recommendation methods has proved that this study's method is an excellent solution.

• Journal of KIISE:Software and Applications
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• v.35 no.10
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• pp.589-599
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• 2008

Accurate software effort estimation has always been a challenge for the software industrial and academic software engineering communities. Many studies have focused on effort estimation methods to improve the estimation accuracy of software effort. Although data quality is one of important factors for accurate effort estimation, most of the work has not considered it. In this paper, we investigate the influence of outlier elimination on the accuracy of software effort estimation through empirical studies applying two outlier elimination methods(Least trimmed square regression and K-means clustering) and three effort estimation methods(Least squares regression, Neural network and Bayesian network) associatively. The empirical studies are performed using two industry data sets(the ISBSG Release 9 and the Bank data set which consists of the project data collected from a bank in Korea) with or without outlier elimination.

• The KIPS Transactions:PartD
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• v.13D no.1 s.104
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• pp.133-138
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• 2006

Electric commerce of surpassing the traditional one appeared before the public and has currently led the change in the management of enterprises. To establish and maintain good relations with customers, electric commerce has various channels for customers that understand what they want to and suggest it to them. The bulletin board and e-mail among em are inbound information that enterprises can directly listen to customers' opinions and are different from other channels in characters. Enterprises can effectively manage the bulletin board and e-mail by understanding customers' ideas as many as possible and provide them with optimum answers. It is one of the important factors to improve the reliability of the notice board and e-mail as well as the whole electric commerce. Therefore this thesis researches into methods to classify various kinds of documents automatically in electric commerce; they are possible to solve existing problems of the bulletin board and e-mail, to operate effectively and to manage systematically. Moreover, it researches what the most suitable algorithm is in the automatic classification of Q&A documents by experiment the classifying performance of Naive Bayesian, TFIDF, Neural Network, k-NN