• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6063-6079
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• 2019

When the length of sampling data sequence is too large, the method of gesture recognition based on traditional Dynamic Time Warping (DTW) algorithm will lead to too long calculation time, and the accuracy of recognition result is not high.Support vector machine (SVM) has some shortcomings in precision, Edit Distance on Real Sequences(EDR) algorithm does not guarantee that noise suppression will not suppress effective data.A new method based on Improved Interpolation Dynamic Time Warping (IIDTW)algorithm is proposed to improve the efficiency of gesture recognition and the accuracy of gesture recognition. The results show that the computational efficiency of IIDTW algorithm is more than twice that of SVM-DTW algorithm, the error acceptance rate is FAR reduced by 0.01%, and the error rejection rate FRR is reduced by 0.5%.Gesture recognition based on IIDTW algorithm can achieve better recognition status. If it is applied to unlock mobile phone, it is expected to become a new generation of unlock mode.

• Speech Sciences
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• v.12 no.4
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• pp.237-245
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• 2005

The purpose of this study was to investigate the characteristics for cohesion in the narratives of fluent aphasics. Ten fluent aphasics and ten normal adults matched chronological-age and education level with aphasics participated in this study. Story retelling task was given to the participants individually. And all narratives they produced were recorded and transcribed for analysis. The frequency of cohesive markers and the rate of accuracy were analyzed. The result were as follows; (1) The fluent aphasics used cohesive devices significantly less than the normal adult group, and the rate of accuracy of cohesive devices used was lower than the normal adults. (2) Both groups used lexical cohesion more than pro-forms and ellipsis, and the difference of two groups was larger in lexical cohesion than pro-forms and ellipsis. (3) The fluent aphasics used lexical cohesion more accurately than pro-forms and ellipsis but the normal adults used all three cohesive markers accurately. The difference of two groups was large in pro-forms and ellipsis.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.283-288
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• 2019

Neural networks have been reborn as a Deep Learning thanks to big data, improved processor, and some modification of training methods. Neural networks used to initialize weights in a stupid way, and to choose wrong type activation functions of non-linearity. Weight initialization contributes as a significant factor on the final quality of a network as well as its convergence rate. This paper discusses different approaches to weight initialization. MNIST dataset is used for experiments for comparing their results to find out the best technique that can be employed to achieve higher accuracy in relatively lower duration.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.445-458
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• 2012

Image feature extraction is one of the basic works for biometric analysis. This paper presents the novel concept of application of ridgelets for iris recognition systems. Ridgelet transforms are the combination of Radon transforms and Wavelet transforms. They are suitable for extracting the abundantly present textural data that is in an iris. The technique proposed here uses the ridgelets to form an iris signature and to represent the iris. This paper contributes towards creating an improved iris recognition system. There is a reduction in the feature vector size, which is 1X4 in size. The False Acceptance Rate (FAR) and False Rejection Rate (FRR) were also reduced and the accuracy increased. The proposed method also avoids the iris normalization process that is traditionally used in iris recognition systems. Experimental results indicate that the proposed method achieves an accuracy of 99.82%, 0.1309% FAR, and 0.0434% FRR.

• Journal of the Korea Society of Computer and Information
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• v.24 no.6
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• pp.67-72
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• 2019

This paper proposes a computer vision-based banknote recognition system intended to assist the blind. This system is robust and fast in recognizing banknotes on videos recorded with a smartphone on real-life scenarios. To reduce the computation time and enable a robust recognition in cluttered environments, this study segments the banknote candidate area from the background utilizing a technique called Pixel-Based Adaptive Segmenter (PBAS). The Speeded-Up Robust Features (SURF) interest point detector is used, and SURF feature vectors are computed only when sufficient interest points are found. The proposed algorithm achieves a recognition accuracy of 98%, a 100% true recognition rate and a 0% false recognition rate. Although Korean banknotes are used as a working example, the proposed system can be applied to recognize other countries' banknotes.

• ETRI Journal
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• v.41 no.4
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• pp.437-451
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• 2019

Outlier detection techniques play an important role in enhancing the reliability of data communication in wireless sensor networks (WSNs). Considering the importance of outlier detection in WSNs, many outlier detection techniques have been proposed. Unfortunately, most of these techniques still have some potential limitations, that is, (a) high rate of false positives, (b) high time complexity, and (c) failure to detect outliers online. Moreover, these approaches mainly focus on either temporal outliers or spatial outliers. Therefore, this paper aims to introduce novel algorithms that successfully detect both temporal outliers and spatial outliers. Our contributions are twofold: (i) modifying the Hampel Identifier (HI) algorithm to achieve high accuracy identification rate in temporal outlier detection, (ii) combining the Gaussian process (GP) model and graph-based outlier detection technique to improve the performance of the algorithm in spatial outlier detection. The results demonstrate that our techniques outperform the state-of-the-art methods in terms of accuracy and work well with various data types.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1734-1737
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• 2019

This paper presents a prediction structure with a Hybrid Dynamic Branch Prediction (HDBP) scheme which decreases the number of stalls. In the application, a branch history register is dynamically adjusted to produce more unique index values of pattern history table (PHT). The number of stalls is also reduced by using the modified gshare predictor with a long history register folding scheme. The aliasing rate decreased to 44.1% and the miss prediction rate decreased to 19.06% on average compared with the gshare branch predictor, one of the most popular two-level branch predictors. Moreover, Compared with the gshare, an average improvement of 1.28% instructions per cycle (IPC) was achieved. Thus, with regard to the accuracy of branch prediction, the HDBP is remarkably useful in boosting the overall performance of the superscalar processor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.1174-1192
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• 2021

With the assistance of non-orthogonal multiple access (NOMA), the spectrum efficiency and the number of users in cloud radio access network (CRAN) can be greatly improved. In this paper, the system performance of NOMA-assisted CRAN is investigated. Specially, the outage probability (OP) and ergodic sum rate (ESR), are derived for performance evaluation of the system, respectively. Based on this, by minimizing the OP of the system, a suboptimal power allocation (PA) scheme with closed-form PA coefficients is proposed. Numerical simulations validate the accuracy of the theoretical results, where the derived OP has more accuracy than the existing one. Moreover, the developed PA scheme has superior performance over the conventional fixed PA scheme but has smaller performance loss than the optimal PA scheme using the exhaustive search method.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.179-192
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• 2023

The widespread use of Cloud Computing, Internet of Things (IoT), and social media in the Information Communication Technology (ICT) field has resulted in continuous and unavoidable cyber-attacks on users and critical infrastructures worldwide. Traditional security measures such as firewalls and encryption systems are not effective in countering these sophisticated cyber-attacks. Therefore, Intrusion Detection and Prevention Systems (IDPS) are necessary to reduce the risk to an absolute minimum. Although IDPSs can detect various types of cyber-attacks with high accuracy, their performance is limited by a high false alarm rate. This study proposes a new technique called Fuzzy Logic - Objective Risk Analysis (FLORA) that can significantly reduce false positive alarm rates and maintain a high level of security against serious cyber-attacks. The FLORA model has a high fuzzy accuracy rate of 90.11% and can predict vulnerabilities with a high level of certainty. It also has a mechanism for monitoring and recording digital forensic evidence which can be used in legal prosecution proceedings in different jurisdictions.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.427-427
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• 2021

During the rainy season, heavy downpours are always a source of concern for the world. Flooding and heavy rains can devastate communities, disrupt agriculture, and contribute to traffic accidents.. Weir and flow hall effect sensors are the conventional analytical methods for measuring flow rate; in this paper, we analyzed manhole flowrate statistics. The measurement of the flow rate of a notch/weir is a time-consuming task that necessitates continuous mathematical analysis. . We created three types of IoT sensors in this study: (HC-SR04 ultrasonic, YF-S201 magnetic, and HB100 radar), which take the sensor's real-time input signal and estimate the flow using a notch equation and a previously calibrated optimized coefficient of discharge. The proposed systems are cost-effective, but in terms of accuracy, we found that the HC-SR04 ultrasonic sensor is the best of the three systems