• ETRI Journal
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• v.46 no.1
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• pp.22-34
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• 2024

Exposure to varied noisy environments impairs the recognition performance of artificial intelligence-based speech recognition technologies. Degraded-performance services can be utilized as limited systems that assure good performance in certain environments, but impair the general quality of speech recognition services. This study introduces an audiovisual speech recognition (AVSR) model robust to various noise settings, mimicking human dialogue recognition elements. The model converts word embeddings and log-Mel spectrograms into feature vectors for audio recognition. A dense spatial-temporal convolutional neural network model extracts features from log-Mel spectrograms, transformed for visual-based recognition. This approach exhibits improved aural and visual recognition capabilities. We assess the signal-to-noise ratio in nine synthesized noise environments, with the proposed model exhibiting lower average error rates. The error rate for the AVSR model using a three-feature multi-fusion method is 1.711%, compared to the general 3.939% rate. This model is applicable in noise-affected environments owing to its enhanced stability and recognition rate.

• Journal of Korea Water Resources Association
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• v.45 no.8
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• pp.839-851
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• 2012

When we evaluate the water supply capacity of a river basin, it is a common practice to gradually increase the water demand and check if the water demands are met. This practice is not only used in the simulation approach, but also in the optimization approach. However, this trial and error approach is a tedious task. Hence, we propose a two-phase method. In the first phase, by assuming that the decision maker has complete information on inflow data, we use a goal programming model that can generate the maximum water supply capacity at one time. In the second phase, we simulate the real-time operation for the critical period by utilizing the water supply capacity given by the goal programming model under the condition that there is no foresight of inflow. We applied the two-phase method to the Geum-River basin, where multi-purpose weirs were newly constructed. By comparing the results of the goal programming model with those of the real-time simulation model we could comprehend and estimate the effect of perfect inflow data on the water supply capacity.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.385-396
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• 2005

In this paper, a new structural approach to on-line signature verification is presented. A primitive pattern is defined as a part segmented by a local minimal position of speed. And a structural description of signature is composed of subpatterns which are defined as such forms as rotation shape, cusp shape and bell shape, acquired by composition of the primitives regarding the directional changes. As the matching method to find identical parts between two signatures, a modified DP(dynamic programming) matching algorithm is presented. And also, variation and complexity of local parts are computed from the training samples, and reference model and decision boundary are derived from these. Error rate, execution time and memory usage are compared among the functional approach, the parametric approach and the proposed structural approach. It is found that the average error rate can be reduced from 14.2% to 4.05% when the local parts of a signature are weighted and the complexity is used as a factor of decision threshold. Though the error rate is similar to that of functional approaches. time consumption and memory usage of the proposed structural approach are shown to be very effective.

• Structural Engineering and Mechanics
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• v.85 no.4
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• pp.469-484
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• 2023

A deep recursive bidirectional Cuda Deep Neural Network Long Short Term Memory (Bi-CuDNNLSTM) layer is recruited in this paper to predict the entire force time histories, and the corresponding hysteresis and backbone curves of reinforced concrete (RC) bridge piers using experimental fast and slow cyclic tests. The proposed stacked Bi-CuDNNLSTM layers involve multiple uncertain input variables, including horizontal actuator displacements, vertical actuators axial loads, the effective height of the bridge pier, the moment of inertia, and mass. The functional application programming interface in the Keras Python library is utilized to develop a deep learning model considering all the above various input attributes. To have a robust and reliable prediction, the dataset for both the fast and slow cyclic tests is split into three mutually exclusive subsets of training, validation, and testing (unseen). The whole datasets include 17 RC bridge piers tested experimentally ten for fast and seven for slow cyclic tests. The results bring to light that the mean absolute error, as a loss function, is monotonically decreased to zero for both the training and validation datasets after 5000 epochs, and a high level of correlation is observed between the predicted and the experimentally measured values of the force time histories for all the datasets, more than 90%. It can be concluded that the maximum mean of the normalized error, obtained through Box-Whisker plot and Gaussian distribution of normalized error, associated with unseen data is about 10% and 3% for the fast and slow cyclic tests, respectively. In recapitulation, it brings to an end that the stacked Bi-CuDNNLSTM layer implemented in this study has a myriad of benefits in reducing the time and experimental costs for conducting new fast and slow cyclic tests in the future and results in a fast and accurate insight into hysteretic behavior of bridge piers.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.1
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• pp.115-121
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• 1999

Advanced digital computer technology enables the computer-based controllers to replace the traditional analog controllers used in factory automations. This replacement, however, brings up the side effects caused by the quantization error and non-real-time execution of control software. This paper describes the structure of real-time simulator and controller that can be used for design and verification of real-time digital controllers. The virtual machine concept adopted by the proposed real-time simulator makes the proposed simulator be independent from the specific hardware platforms. The proposed system can also be used in the loosely coupled distributed environments connected through local area network using real-time message passing algorithm and virtual data table based on the shared memory mechanism.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.1
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• pp.56-61
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• 2000

Majority of industrial robots are controlled by a simple independent joint control of joint actuators rather than complex controllers based on the nonlinear dynamic model of the robot manipulator. In this independent joint control scheme, the performance of actuator control is influenced significantly by the joint disturbance torques including gravity, Coriolis and centrifugal torques, which result in the trajectory tracking error in the joint control system. The control performance of a redundant manipulator under independent joint control can be improved by minimizing this joint disturbance torque in resolving the kinematic redundancy. A 3 DOF planar robot is studied as an example, and the dynamic programming method is used to find the globally optimal joint trajectory that minimize the joint disturbance torque over the entire motion. The resulting solution is compared with the solution obtained by the conventional joint torque minimization, and it is shown that joint disturbance can be reduced using the kinematic redundancy.

• Journal of Advanced Marine Engineering and Technology
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• v.25 no.2
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• pp.383-394
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• 2001

The stabilization control of inverted pendulum (IP) system is difficult because of its nonlinearity and structural unstability. In this paper, an Evolving Neural Network Controller (ENNC) without Error Back Propagation (EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC)are compared with the one of conventional optimal controller and the conventional evolving neural network controller (CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.29-31
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• 2013

본 논문에서는 기존라디오(primary) 네트워크와 스펙트럼을 공유하는(spectrum sharing) 인지라디오(cognitive radio) 네트워크에서 비재생적(non-regenerative) 다중안테나 중계 (relay) 시스템을 위한 소스(source) 및 중계기 프리코딩(precoding) 기법을 제안한다. 제안된 기법은 소스와 중계기 프리코딩 행렬의 최적해를 구하기 위해 QCQP(Quadratically Constrained Quadratic Programming) 문제를 통해 구한다. 제안된 기법은 기존라디오 수신기에서의 간섭세기 제한을 만족하면서 낮은 MSE(Mean squared error)와 높은 MI(Mutual Information)를 달성함을 모의실험결과를 통해 보인다. 또한 아주 빠른 속도로 최적해로 수렴함을 보이고 있다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.2
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• pp.23-28
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• 2003

This research explains visualization of diagnostic system of 3D CAD data, IGES (Initial Graphics Exchange Specification), by using JNI(Java Native Interface) to connect between C++ and Java programming. The diagnostic system is to analyze IGES clearly by identifying errors and anomalies with respect to the diagnosis of geometry and topology of entities. The output of the system is IGES file including . error information which can be visualized with different colors by several commercial visualization systems. The paper focuses on the visualization of the result IGES which can be extended to web based application over internet.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.984-987
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• 1996

We consider a feedback control system including interval plant where uncertain parameters expressed in the hyperrectangular box X. Here we define the maximum value of the integral of the error(ISE) as the worst performance index(WPI) due to the plant parameter uncertainty. Suppose that the closed loop system retains robust stability and it belongs to type I. Then we show that the WPI occurs only on the exposed edges of Q. In particular, it is also shown that if ISE is a convex function relative to X, the WPI is attained at one of vertices of X. Some examples are given.