• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1540-1561
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• 2024

This research proposes a novel approach for Tamil Handwritten Character Recognition (THCR) that combines feature selection and ensemble learning techniques. The Tamil script is complex and highly variable, requiring a robust and accurate recognition system. Feature selection is used to reduce dimensionality while preserving discriminative features, improving classification performance and reducing computational complexity. Several feature selection methods are compared, and individual classifiers (support vector machines, neural networks, and decision trees) are evaluated through extensive experiments. Ensemble learning techniques such as bagging, and boosting are employed to leverage the strengths of multiple classifiers and enhance recognition accuracy. The proposed approach is evaluated on the HP Labs Dataset, achieving an impressive 95.56% accuracy using an ensemble learning framework based on support vector machines. The dataset consists of 82,928 samples with 247 distinct classes, contributed by 500 participants from Tamil Nadu. It includes 40,000 characters with 500 user variations. The results surpass or rival existing methods, demonstrating the effectiveness of the approach. The research also offers insights for developing advanced recognition systems for other complex scripts. Future investigations could explore the integration of deep learning techniques and the extension of the proposed approach to other Indic scripts and languages, advancing the field of handwritten character recognition.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.688-696
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• 2017

This paper presents an integrated roll-pitch-yaw autopilot using an equivalent based sliding mode control for skid-to-turn nonlinear time-varying missile system with lumped disturbances in its six-equations of motion. The considered missile model are developed to integrate the model uncertainties, external disturbances, and parameters perturbation as lumped disturbances. Moreover, it considers the coupling effect between channels, the variation of missile velocity and parameters, and the aerodynamics nonlinearity. The presented approach is employed to achieve a good tracking performance with robustness in all missile channels simultaneously during the entire flight envelope without demand of accurate modeling or output derivative to avoid the noise existence in the real missile system. The proposed autopilot consisting of a two-loop structure, controls pitch and yaw accelerations, and stabilizes the roll angle simultaneously. The Closed loop stability is studied. Numerical simulation is provided to evaluate performance of the suggested autopilot and to compare it with an existing autopilot in the literature concerning the robustness against the lumped disturbances, and the aforesaid considerations. Finally, the proposed autopilot is integrated in a six degree of freedom flight simulation model to evaluate it with several target scenarios, and the results are shown.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1220-1230
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• 2006

In this paper, we propose a flew technique to the design and real-time control of an adaptive controller for robotic manipulator based on digital signal processors. The Texas Instruments DSPs(TMS320C80) chips are used in implementing real-time adaptive control algorithms to provide enhanced motion control performance for dual-arm robotic manipulators. In the proposed scheme, adaptation laws are derived from model reference adaptive control principle based on the improved Lyapunov second method. The proposed adaptive controller consists of an adaptive feed-forward and feedback controller and time-varying auxiliary controller elements. The proposed control scheme is simple in structure, fast in computation, and suitable for real-time control. Moreover, this scheme does not require any accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the proposed adaptive controller is illustrated by simulation and experimental results for a dual arm robot manipulator with eight joints. joint space and cartesian space.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.78-78
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• 2006

Gene expression analysis can be performed by one-color (intensity-based) or two-color (ratio-based) microarray platforms depending on the specific applications and needs of the researcher. The traditional two-color approach is well founded from a historical and scientific standpoint, and the one-color approach, when paired with high quality microarrays and a robust workflow, offers additional flexibility in experimental design. Two of the major requirements of any microarray platform are system reproducibility, which provides the means for high confidence experiments and accurate comparison across multiple samples; and high sensitivity, for the detection of significant gene expression changes, including small fold changes across multiple gene sets. Each of these requirements is fulfilled by the Agilent One-color Gene Expression Platform as illustrated by the data included in this study. As a result, researchers have the ability to take advantage of the enhanced performance and sensitivity of Agilent's 60-mer oligonucleotide microarrays, and experience the first commercial microarray platform compatible with both one- and two-color detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4300-4314
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• 2019

With the development of video-related applications, media content has increased dramatically through applications. There is a substantial amount of near-duplicate videos (NDVs) among Internet videos, thus NDVR is important for eliminating near-duplicates from web video searches. This paper proposes a novel NDVR system that supports large-scale retrieval and contributes to the efficient and accurate retrieval performance. For this, we extracted keyframes from each video at regular intervals and then extracted both commonly used features (LBP and HSV) and new image features from each keyframe. A recent study introduced a new image feature that can provide more robust information than existing features even if there are geometric changes to and complex editing of images. We convert a vector set that consists of the extracted features to binary code through a set of hash functions so that the similarity comparison can be more efficient as similar videos are more likely to map into the same buckets. Lastly, we calculate similarity to search for NDVs; we examine the effectiveness of the NDVR system and compare this against previous NDVR systems using the public video collections CC_WEB_VIDEO. The proposed NDVR system's performance is very promising compared to previous NDVR systems.

• MALSORI
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• no.59
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• pp.27-51
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• 2006

For speech recognition systems, rejection function as well as decoding function is necessary to improve the reliability. There have been many research efforts on out-of-vocabulary word rejection, however, little attention has been paid on non-target sentence rejection. Recently pronunciation approaches using speech recognition increase the need for non-target sentence rejection to provide more accurate and robust results. In this paper, we proposed filler model method and word/phoneme detection ratio method to implement non-target sentence rejection system. We made performance evaluation of filler model along to word-level, phoneme-level, and sentence-level filler models respectively. We also perform the similar experiment using word-level and phoneme-level word/phoneme detection ratio method. For the performance evaluation, the minimized average of FAR and FRR is used for comparing the effectiveness of each method along with the number of words of given sentences. From the experimental results, we got to know that word-level method outperforms the other methods, and word-level filler mode shows slightly better results than that of word detection ratio method.

• Wind and Structures
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• v.36 no.6
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• pp.367-377
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• 2023

This study proposes a few-shot learning model for extrapolating the wind pressure of scaled experiments to full-scale measurements. The proposed ML model can use scaled experimental data and a few full-scale tests to accurately predict the remaining full-scale data points (for new specimens). This model focuses on extrapolating the prediction to different scales while existing approaches are not capable of accurately extrapolating from scaled data to full-scale data in the wind engineering domain. Also, the scaling issue observed in wind tunnel tests can be partially resolved via the proposed approach. The proposed model obtained a low mean-squared error and a high coefficient of determination for the mean and standard deviation wind pressure coefficients of the full-scale dataset. A parametric study is carried out to investigate the influence of the number of selected shots. This technique is the first of its kind as it is the first time an ML model has been used in the wind engineering field to deal with extrapolation in wind performance prediction. With the advantages of the few-shot learning model, physical wind tunnel experiments can be reduced to a great extent. The few-shot learning model yields a robust, efficient, and accurate alternative to extrapolating the prediction performance of structures from various model scales to full-scale.

• Transactions of the Society of Information Storage Systems
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• v.3 no.1
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• pp.5-12
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• 2007

This paper investigates the design of disturbance observer for rotational vibration suppression in disk drive systems. The design aims to provide an optimal controller which satisfies both vibration performance and robust stability. It consists of an inversion method, a special filter, and optimization scheme. Firstly a new inversion method is introduced, which provides more accurate inversion compared to conventional zero phase error method. The inversion is to deal with unstable zeros in the plant model. Secondly a special filter for disturbance selection is given, which features adjustable gain and band pass characteristics so that it enables flexible shaping of the loop considering the trade-off between performance and stability margins. And finally the parameters of disturbance observer are optimized in conjunction with external disturbance model. Simulation and experiment on commercial hard disk drives confirm that the design is very effective to such disturbance which is hard to be handled by conventional approach.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.10
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• pp.2554-2566
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• 1994

Robot position/force control has been a difficult task owing to the interaction between a robot and an environment with a rather high stiffness. In addition to the dynamic instability, the interaction causes the following problem : 1) chattering at steady-state, 2) dynamic coupling effect of robot, and 3) performance degradation due to a titled environment. To solve the problem, the Time Delay Control(TDC), which has been known to be quiet robust to plant uncertainties and disturbances, has been applied. In conjunction to TDC, the following three ideas were also used : 1) To reduce the amplitude of the chattering at the steady state, a novel scheme was adopted to enhance the resolution type solution of A/D conversion for the force sensor. 2) To reduce the dynamic coupling, a trajectory type position command was tried on a comparative basis to the step command, as well as a more accurate mass matrix was used instead of the constant mass matrix. 3) And finally to improve the performance in the tilted environment, force derivatives instead of position derivatives were used in the TDC law. Computer simulations and experiments resulted in obvious improvements on the quality of the hybrid control, thereby clearly demonstrating the effectiveness of TDC with the proposed ideas.

• Journal of the Korea Computer Industry Society
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• v.2 no.8
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• pp.1117-1122
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• 2001

Recently, issues on the internet searching of image information through various multimedia databases have drawn an tremendous attention and several researches on image information retrieval methods are on progress. By incorporating wavelet transform and correlation matrixes, we propose a novel and highly efficient feature vector extraction algorithm that has an capability of a robust similarity matching. The simulation results have yielded a faster and highly accurate candidate image retrieval performance in comparison to those of the conventional algorithms. Such an improved performance can be obtained because the used feature vectors were compressed to 256：1 while the correlation matrixes are incorporated to provide a fuel information for the better matching.