• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.83-91
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• 2005

We analyze the performance of an adaptive hybrid search code acquisition algorithm for direct-sequence code division multiple access (DS-CDMA) systems under slowly-moving mobile environments. The code acquisition algorithm is designed to provide the desired feature of constant false alarm rate (CFAR) to cope with nonstationarity of the interference in CDMA forward links. An analytical expression for the mean acquisition time is first derived and the probabilities of detection, miss, and false alarm are then obtained for frequency-selective Rayleigh fading environments. The fading envelope of a received signal is assumed to be constant over the duration of post-detection integration (PDI), considering slow fading environments. Finally, the performance of the designed code acquisition algorithm shall be evaluated numerically to examine the effect of some design parameters such as the sub-window size, size of the PDI, decision threshold, and so on, considering cdma2000 environments.

• Proceedings of the KSPS conference
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• 1996.10a
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• pp.349-354
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• 1996

The phonetic mastery of English has been considered next to impossible to many non-native speakers of English, including even some teachers of English. This paper takes issue with this phonetic problem of second language acquisition and proposes that combination of cognitive and physical approaches can help master English faster and more easily.

• Journal of Information Processing Systems
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• v.7 no.3
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• pp.425-434
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• 2011

The success of iris recognition depends mainly on two factors: image acquisition and an iris recognition algorithm. In this study, we present a system that considers both factors and focuses on the latter. The proposed algorithm aims to find out the most efficient wavelet family and its coefficients for encoding the iris template of the experiment samples. The algorithm implemented in software performs segmentation, normalization, feature encoding, data storage, and matching. By using the Haar and Biorthogonal wavelet families at various levels feature encoding is performed by decomposing the normalized iris image. The vertical coefficient is encoded into the iris template and is stored in the database. The performance of the system is evaluated by using the number of degrees of freedom, False Reject Rate (FRR), False Accept Rate (FAR), and Equal Error Rate (EER) and the metrics show that the proposed algorithm can be employed for an iris recognition system.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.1-12
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• 2023

Ensuring the security of Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS) is paramount to safeguarding the reliability and safety of critical infrastructure. This paper addresses the significant threat posed by reconnaissance attacks on SCADA/ICS networks and presents an innovative methodology for enhancing their protection. The proposed approach strategically employs imbalance dataset handling techniques, ensemble methods, and feature engineering to enhance the resilience of SCADA/ICS systems. Experimentation and analysis demonstrate the compelling efficacy of our strategy, as evidenced by excellent model performance characterized by good precision, recall, and a commendably low false negative (FN). The practical utility of our approach is underscored through the evaluation of real-world SCADA/ICS datasets, showcasing superior performance compared to existing methods in a comparative analysis. Moreover, the integration of feature augmentation is revealed to significantly enhance detection capabilities. This research contributes to advancing the security posture of SCADA/ICS environments, addressing a critical imperative in the face of evolving cyber threats.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.12
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• pp.100-108
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• 2004

In reverse engineering, data acquisition methodology can generally be categorized into contacting and non-contacting types. Recently, researches on hybrid or sensor fusion of the two types have been increasing. In addition, efficient construction of a geometric model from the measurement data is required, where considerable amount of user interaction to classify and localize regions of interest is inevitable. Our research focuses on the classification of each bounded region into a pre-defined feature shape fer a hybrid measuring scheme, where the overall procedures are described as fellows. Firstly, the physical model is digitized by a non-contacting laser scanner which rapidly provides cloud-of-points data. Secondly, the overall digitized data are approximated to a z-map model. Each bounding curve of a region of interest (featured area) can be 1.aced out based on our previous research. Then each confined area is systematically classified into one of the pre-defined feature types such as floor, wall, strip or volume, followed by a more accurate measuring step via a contacting probe. Assigned to each feature is a specific digitizing path topology which may reflect its own geometric character. The research can play an important role in minimizing user interaction at the stage of digitizing path planning.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.159-167
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• 2006

Lipreading has been suggested as one of the methods to improve the performance of speech recognition in noisy environment. However, existing methods are developed and implemented only in software. This paper suggests a hardware design for real-time lipreading. For real-time processing and feasible implementation, we decompose the lipreading system into three parts; image acquisition module, feature vector extraction module, and recognition module. Image acquisition module capture input image by using CMOS image sensor. The feature vector extraction module extracts feature vector from the input image by using parallel block matching algorithm. The parallel block matching algorithm is coded and simulated for FPGA circuit. Recognition module uses HMM based recognition algorithm. The recognition algorithm is coded and simulated by using DSP chip. The simulation results show that a real-time lipreading system can be implemented in hardware.

• Journal of the Korean Society of Radiology
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• v.6 no.5
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• pp.409-414
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• 2012

This study acquired molecular lmaging using nano-contrast agents, and the general condition of the same image acquisition to analyze the difference between molecular imaging and general imaging, two images are converted into DWT (Discrete Wavelet Transform). Nano-contrast agent imaging using MRI and molecular imaging using PET study of molecular imaging technology mainstream. DWT analysis of the same lesions using MRI imaging and molecular imaging block lesions are present in the lesions, illustrating the value of a high-frequency feature both highly general imaging and molecular imaging could know that. The high frequency region of the feature extraction values appear higher molecular imaging.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.674-679
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• 2006

In this paper a gesture recognition system using 3D feature data is described. The system relies on a novel 3D sensor that generates a dense range mage of the scene. The main novelty of the proposed system, with respect to other 3D gesture recognition techniques, is the capability for robust recognition of complex hand postures such as those encountered in sign language alphabets. This is achieved by explicitly employing 3D hand features. Moreover, the proposed approach does not rely on colour information, and guarantees robust segmentation of the hand under various illumination conditions, and content of the scene. Several novel 3D image analysis algorithms are presented covering the complete processing chain: 3D image acquisition, arm segmentation, hand -forearm segmentation, hand pose estimation, 3D feature extraction, and gesture classification. The proposed system is tested in an application scenario involving the recognition of sign-language postures.

• Smart Structures and Systems
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• v.1 no.2
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• pp.141-158
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• 2005

A new design of distributed crack sensors based on the topological change of transmission line cables is presented for the condition assessment of reinforced concrete (RC) structures during and immediately after an earthquake event. This study is primarily focused on the performance of cable sensors under dynamic loading, particularly a feature that allows for some "memory" of the crack history of an RC member. This feature enables the post-earthquake condition assessment of structural members such as RC columns, in which the earthquake-induced cracks are closed immediately after an earthquake event due to gravity loads, and are visually undetectable. Factors affecting the onset of the feature were investigated experimentally with small-scale RC beams under cyclic loading. Test results indicated that both crack width and the number of loading cycles were instrumental in the onset of the memory feature of cable sensors. Practical issues related to dynamic acquisition with the sensors are discussed. The sensors were proven to be fatigue resistant from shake table tests of RC columns. The sensors continued to show useful performance after the columns can no longer support additional loads.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.673-687
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• 2023

Convolutional neural network-based deep learning technology is the most commonly used in image identification, but it requires large-scale data for training. Therefore, application in specific fields in which data acquisition is limited, such as in the military, may be challenging. In particular, the identification of ground weapon systems is a very important mission, and high identification accuracy is required. Accordingly, various studies have been conducted to achieve high performance using small-scale data. Among them, the ensemble method, which achieves excellent performance through the prediction average of the pre-trained models, is the most representative method; however, it requires considerable time and effort to find the optimal combination of ensemble models. In addition, there is a performance limitation in the prediction results obtained by using an ensemble method. Furthermore, it is difficult to obtain the ensemble effect using models with imbalanced classification accuracies. In this paper, we propose a transfer learning-based feature fusion technique for heterogeneous models that extracts and fuses features of pre-trained heterogeneous models and finally, fine-tunes hyperparameters of the fully connected layer to improve the classification accuracy. The experimental results of this study indicate that it is possible to overcome the limitations of the existing ensemble methods by improving the classification accuracy through feature fusion between heterogeneous models based on transfer learning.