• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.148-157
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• 1996

Availability of input trajectories corresponding to desired output trajectories is often important in designing control systems for batch and other transient processes. In this paper, we propose a predictive control-type model-based iterative learning algorithm which is applicable to finding the nominal input trajectories of a linear time-invariant batch process. Unlike the other existing learning control algorithms, the proposed algorithm can be applied to nonsquare systems and has an ability to adjust noise sensitivity as well as convergence rate. A simple model identification technique with which performance of the proposed learning algorithm can be significantly enhanced is also proposed. Performance of the proposed learning algorithm is demonstrated through numerical simulations.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.615-619
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• 2000

This paper presents an implementation of digital motion control system of Surface Permanent-Magnet Synchronous Motor(SPMSM) vector drives with a direct torque control(DTC) using the 16bit DSP TMS320F240 The DSP controller enable enhanced real time algorithm and cost-effective design of intelligent control for motors which can be yield enhanced operation fewer system components lower system cost increased efficiency and high performance The system presented are stator flux and torque observer of stator flux feedback model that inputs are current and voltage sensing of motor terminal and angle for a low speed operating area two hysteresis band controllers an optimal switching look-up table and IGBT voltage source inverter by using fully integrated control software. The developed control system are shown a good motion control response characteristic results and high performance features using 1.0Kw purposed servo drive SPMSM.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.1036-1038
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• 2000

This paper presents an implementation of digital motion control system of induction motor vector drives with a direct torque control(DTC) using the 16bit DSP TMS 320F240. The DSP controller enable enhanced real time algorithm and cost-effective design of intelligent controllers for induction motors which can be yield enhanced operation, fewer system components, lower system cost, increased efficiency and high performance. The system presented are stator flux observer of current model that inputs are current sensing of motor terminal and rotor angle, and optimal switching look-up table by using fully integrated control software. The developed system are shown a good motion control response characteristic results and high performance features using 2.2Kw general purposed induction motor.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3538-3556
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• 2014

This paper proposes a novel video stitching method that improves real-time performance and visual quality of a multi-camera video surveillance system. A two-stage seam searching algorithm based on enhanced dynamic programming is proposed. It can obtain satisfactory result and achieve better real-time performance than traditional seam-searching methods. The experiments show that the computing time is reduced by 66.4% using the proposed algorithm compared with enhanced dynamic programming, while the seam-searching accuracy is maintained. A real-time local update scheme reduces the deformation effect caused by moving objects passing through the seam, and a seam-based local color transfer model is constructed and applied to achieve smooth transition in the overlapped area, and overcome the traditional pixel blending methods. The effectiveness of the proposed method is proved in the experiements.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1246-1262
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• 2021

Recent studies have demonstrated the strong ability of deep convolutional neural networks (CNNs) to significantly boost the performance in single image super-resolution (SISR). The key concern is how to efficiently recover and utilize diverse information frequencies across multiple network layers, which is crucial to satisfying super-resolution image reconstructions. Hence, previous work made great efforts to potently incorporate hierarchical frequencies through various sophisticated architectures. Nevertheless, economical SISR also requires a capable structure design to balance between restoration accuracy and computational complexity, which is still a challenge for existing techniques. In this paper, we tackle this problem by proposing a competent architecture called Enhanced U-Net Network (EUN), which can yield ready-to-use features in miscellaneous frequencies and combine them comprehensively. In particular, the proposed building block for EUN is enhanced from U-Net, which can extract abundant information via multiple skip concatenations. The network configuration allows the pipeline to propagate information from lower layers to higher ones. Meanwhile, the block itself is committed to growing quite deep in layers, which empowers different types of information to spring from a single block. Furthermore, due to its strong advantage in distilling effective information, promising results are guaranteed with comparatively fewer filters. Comprehensive experiments manifest our model can achieve favorable performance over that of state-of-the-art methods, especially in terms of computational efficiency.

• Wind and Structures
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• v.19 no.3
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• pp.321-337
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• 2014

This study has developed an Enhanced Remote-Sensing (ERS) scale to improve the accuracy and efficiency of using remote-sensing images of residential building to predict their damage conditions. The new scale, by incorporating multiple damage states observable on remote-sensing imagery, substantially reduces measurement errors and increases the amount of information retained. A ground damage survey was conducted six days after the Joplin EF 5 tornado in 2011. A total of 1,400 one- and two-family residences (FR12) were selected and their damage states were evaluated based on Degree of Damage (DOD) in the Enhanced Fujita (EF) scale. A subsequent remote-sensing survey was performed to rate damages with the ERS scale using high-resolution aerial imagery. Results from Ordinary Least Square regression indicate that ERS-derived damage states could reliably predict the ground level damage with 94% of variance in DOD explained by ERS. The superior performance is mainly because ERS extracts more information. The regression model developed can be used for future rapid assessment of tornado damages. In addition, this study provides strong empirical evidence for the effectiveness of the ERS scale and remote-sensing technology for assessment of damages from tornadoes and other wind events.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3266-3285
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• 2023

With the popularity of online learning, intelligent tutoring systems are starting to become mainstream for assisting online question practice. Surrounded by abundant learning resources, some students struggle to select the proper questions. Personalized question recommendation is crucial for supporting students in choosing the proper questions to improve their learning performance. However, traditional question recommendation methods (i.e., collaborative filtering (CF) and cognitive diagnosis model (CDM)) cannot meet students' needs well. The CDM-based question recommendation ignores students' requirements and similarities, resulting in inaccuracies in the recommendation. Even CF examines student similarities, it disregards their knowledge proficiency and struggles when generating questions of appropriate difficulty. To solve these issues, we first design an enhanced cognitive diagnosis process that integrates students' affection into traditional CDM by employing the non-compensatory bidimensional item response model (NCB-IRM) to enhance the representation of individual personality. Subsequently, we propose an affection-enhanced personalized question recommendation (AE-PQR) method for online learning. It introduces NCB-IRM to CF, considering both individual and common characteristics of students' responses to maintain rationality and accuracy for personalized question recommendation. Experimental results show that our proposed method improves the accuracy of diagnosed student cognition and the appropriateness of recommended questions.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.157.4-157
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• 2001

An effective approach to detect and diagnose multiple failures in a dynamic system is proposed for the case where the measurement noise is correlated sequentially in time. It is based on the modified interacting multiple-model (MIMM) estimation algorithm in which a generalized decorrelation process is developed by employing the autoregressive (AR) model for the correlated measurement noise. Numerical example for the nuclear steam generator is provided to illustrate the enhanced performance of the proposed algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.30
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• pp.185-192
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• 1994

The complexity and consistency are important factors that affect human information processing in use of product. In this study, complexity and consistency of product(remote controller) are measured by Task Action Grammar(TAG) model. Also, new design alternative of the user interface is presented and evaluated. The results show that the consistent system and the good correspondence between hierarchical structure of system and user's mental model lead to the reduction of errors and enhanced user's performance.