• Journal of information and communication convergence engineering
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• v.13 no.3
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• pp.205-214
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• 2015

In this paper, we propose a new approach to learning a discriminative model for figure/ground segmentation by incorporating the bag-of-features and conditional random field (CRF) techniques. We advocate the use of image patches instead of superpixels as the basic processing unit. The latter has a homogeneous appearance and adheres to object boundaries, while an image patch often contains more discriminative information (e.g., local image structure) to distinguish its categories. We use pixel-level sparse coding to represent an image patch. With the proposed feature representation, the unary classifier achieves a considerable binary segmentation performance. Further, we integrate unary and pairwise potentials into the CRF model to refine the segmentation results. The pairwise potentials include color and texture potentials with neighborhood interactions, and an edge potential. High segmentation accuracy is demonstrated on three benchmark datasets: the Weizmann horse dataset, the VOC2006 cow dataset, and the MSRC multiclass dataset. Extensive experiments show that the proposed approach performs favorably against the state-of-the-art approaches.

• Journal of Communications and Networks
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• v.15 no.3
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• pp.276-282
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• 2013

Self-encoded spread spectrum (SESS) is a novel modulation technique that acquires its spreading code from a random information source, rather than using the traditional pseudo-random noise (PN) codes. In this paper, we present our study of the SESS system performance under pulsed-noise jamming and show that iterative detection can significantly improve the bit error rate (BER) performance. The jamming performance of the SESS with correlation detection is verified to be similar to that of the conventional direct sequence spread spectrum (DSSS) system. On the other hand, the time diversity detection of the SESS can completely mitigate the effect of jamming by exploiting the inherent temporal diversity of the SESS system. Furthermore, iterative detection with multiple iterations can not only eliminate the jamming completely but also achieve a gain of approximately 1 dB at $10^{-3}$ BER as compared with the binary phase shift keying (BPSK) system under additive white gaussian noise (AWGN) by effectively combining the correlation and time diversity detections.

• Speech Sciences
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• v.10 no.1
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• pp.95-105
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• 2003

This paper describes a new parameter for voice activity detection which serves as a front-end part for automatic speech recognition systems. The new parameter called run-ratio is derived from the runs test statistic which is used in the statistical test for randomness of a given sequence. The run-ratio parameter has the property that the values of the parameter for the random sequence are about 1. To apply the run-ratio parameter into the voice activity detection method, it is assumed that the samples of an inputted audio signal should be converted to binary sequences of positive and negative values. Then, the silence region in the audio signal can be regarded as random sequences so that their values of the run-ratio would be about 1. The run-ratio for the voiced region has far lower values than 1 and for fricative sounds higher values than 1. Therefore, the parameter can discriminate speech signals from the background sounds by using the newly derived run-ratio parameter. The proposed voice activity detector outperformed the conventional energy-based detector in the sense of error mean and variance, small deviation from true speech boundaries, and low chance of missing real utterances

• Journal of the Korean Physical Society
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• v.73 no.10
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• pp.1589-1595
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• 2018

Similarity index measures the topological proximity of node pairs in a complex network. Numerous similarity indices have been defined and investigated, but the dependency of structure on the performance of similarity indices has not been sufficiently investigated. In this study, we investigated the relationship between the performance of similarity indices and structural properties of a network by employing a two-state random network. A node in a two-state network has binary types that are initially given, and a connection probability is determined from the state of the node pair. The performances of similarity indices are affected by the number of links and the ratio of intra-connections to inter-connections. Similarity indices have different characteristics depending on their type. Local indices perform well in small-size networks and do not depend on whether the structure is intra-dominant or inter-dominant. In contrast, global indices perform better in large-size networks, and some such indices do not perform well in an inter-dominant structure. We also found that link prediction performance and the performance of similarity are correlated in both model networks and empirical networks. This relationship implies that link prediction performance can be used as an approximation for the performance of the similarity index when information about node type is unavailable. This relationship may help to find the appropriate index for given networks.

• Asia-Pacific Journal of Business
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• v.13 no.2
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• pp.107-122
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• 2022

Purpose - This study aims to develop machine learning models to predict administrative issue designation in KOSDAQ Market using financial data. Design/methodology/approach - Employing four classification techniques including logistic regression, support vector machine, random forest, and gradient boosting to a matched sample of five hundred and thirty-six firms over an eight-year period, the authors develop prediction models and explore the practicality of the models. Findings - The resulting four binary selection models reveal overall satisfactory classification performance in terms of various measures including AUC (area under the receiver operating characteristic curve), accuracy, F1-score, and top quartile lift, while the ensemble models (random forest and gradienct boosting) outperform the others in terms of most measures. Research implications or Originality - Although the assessment of administrative issue potential of firms is critical information to investors and financial institutions, detailed empirical investigation has lagged behind. The current research fills this gap in the literature by proposing parsimonious prediction models based on a few financial variables and validating the applicability of the models.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.39 no.3
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• pp.34-40
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• 2002

In this paper, a new encryption method for a binary image using Phase modulation and Fourier transform is proposed. For decryption we use the characteristics of square law detector. In encryption process, a key image is obtained by phase modulation of 256 level random pattern and its Fourier transformation, and input image is encrypted by Fourier transforming the multiplication of the phase modulated random pattern and phase modulated input image. The encrypted image and key image have only phase information, so they can not be copied or counterfeited and the original image can not be decrypted without the key image. To reconstruct the original image, each phase mask of the key image and the encrypted image must be placed on each path of the Mach-Zehnder interferometry with Fourier transform lens and the output image is obtained in the form of intensity in the CCD(Charge Coupled Device) camera. The real-time decryption is possible in the proposed system by use of a LCD as a phase modulator and a CCD camera as an intensity detector. The proposed method shows a good performance in the computer simulation and optical experiment as an encryption scheme.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.268-275
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• 2020

A lightweight hardware design of self-timed ring based true random number generator (TRNG) suitable for information security applications is described. To reduce hardware complexity of TRNG, an entropy extractor with feedback structure was proposed, which minimizes the number of ring stages. The number of ring stages of the FSTR-TRNG was determined to be a multiple of eleven, taking into account operating clock frequency and entropy extraction circuit, and the ratio of tokens to bubbles was determined to operate in evenly-spaced mode. The hardware operation of FSTR-TRNG was verified by FPGA implementation. A set of statistical randomness tests defined by NIST 800-22 were performed by extracting 20 million bits of binary sequences generated by FSTR-TRNG, and all of the fifteen test items were found to meet the criteria. The FSTR-TRNG occupied 46 slices of Spartan-6 FPGA device, and it was implemented with about 2,500 gate equivalents (GEs) when synthesized in 180 nm CMOS standard cell library.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.2
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• pp.155-166
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• 2024

Purpose : This study aims to identify key factors for predicting dropout risk at the university level and to provide a foundation for policy development aimed at dropout prevention. This study explores the optimal machine learning algorithm by comparing the performance of various algorithms using data on college students' dropout risks. Methods : We collected data on factors influencing dropout risk and propensity were collected from N University. The collected data were applied to several machine learning algorithms, including random forest, decision tree, artificial neural network, logistic regression, support vector machine (SVM), k-nearest neighbor (k-NN) classification, and Naive Bayes. The performance of these models was compared and evaluated, with a focus on predictive validity and the identification of significant dropout factors through the information gain index of machine learning. Results : The binary logistic regression analysis showed that the year of the program, department, grades, and year of entry had a statistically significant effect on the dropout risk. The performance of each machine learning algorithm showed that random forest performed the best. The results showed that the relative importance of the predictor variables was highest for department, age, grade, and residence, in the order of whether or not they matched the school location. Conclusion : Machine learning-based prediction of dropout risk focuses on the early identification of students at risk. The types and causes of dropout crises vary significantly among students. It is important to identify the types and causes of dropout crises so that appropriate actions and support can be taken to remove risk factors and increase protective factors. The relative importance of the factors affecting dropout risk found in this study will help guide educational prescriptions for preventing college student dropout.

• Clean Technology
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• v.26 no.3
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• pp.161-167
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• 2020

For the design of the prevention and mitigation measures in process industries involving flammable substances, reliable safety data are required. An important property used to estimate the risk of fire and explosion for a flammable liquid is the flash point. Flammability is an important factor to consider when developing safe methods for storing and handling solids and liquids. In this study, the flash point data were measured for the binary systems {2-butanol + 2,2,4-trimethylpentane}, {2-butanol + methylcyclohexane} and {2-butanol + toluene} at 101.3 kPa. Experiments were performed according to the standard test method (ASTM D 3278) using a Stanhope-Seta closed cup flash point tester. A minimum flash point behavior was observed in the binary systems as in the many cases for the hydrocarbon and alcohol mixture that were observed. The measured flash points were compared with the predicted values calculated via the following activity coefficient (GE) models: Wilson, Non-Random Two-Liquid (NRTL), and UNIversal QUAsiChemical (UNIQUAC) models. The predicted data were only adequate for the data determined by the closed-cup test method and may not be appropriate for the data obtained from the open-cup test method because of its deviation from the vapor liquid equilibrium. The predicted results of this work can be used to design safe petrochemical processes, such as the identification of safe storage conditions for non-ideal solutions containing flammable components.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.3A
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• pp.172-178
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• 2012

This paper studies the application of a fuzzy-ARTMAP (FAM) neural network to multi-user detector (MUD) for direct sequence (DS)-code division multiple access (CDMA) system. This method shows new solution for solving the problems, such as complexity and long training, which is found when implementing the previously developed neural-basis MUDs. The proposed FAM based MUD is fast and easy to train and includes capabilities not found in other neural network approaches; a small number of parameters, no requirements for the choice of initial weights, automatic increase of hidden units, no risk of getting trapped in local minima, and the capabilities of adding new data without retraining previously trained data. In simulation studies, binary signals were generated at random in a linear channel with Gaussian noise. The performance of FAM based MUD is compared with other neural net based MUDs in terms of the bit error rate.