• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.355-366
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• 2022

This paper proposes a microcode-based neural network accelerator controller for artificial intelligence accelerators that can be reconstructed using a programmable architecture and provide the advantages of low-power and ultra-small chip size. In order for the target accelerator to support various neural network models, the neural network model can be converted into microcode through microcode compiler and mounted on accelerator to control the operators of the accelerator such as datapath and memory access. While the proposed controller and accelerator can run various CNN models, in this paper, we tested them using the YOLOv2-Tiny CNN model. Using a system clock of 200 MHz, the Controller and accelerator achieved an inference time of 137.9 ms/image for VOC 2012 dataset to detect object, 99.5ms/image for mask detection dataset to detect wearing mask. When implementing an accelerator equipped with the proposed controller as a silicon chip, the gate count is 618,388, which corresponds to 65.5% reduction in chip area compared with an accelerator employing a CPU-based controller (RISC-V).

• Research in Plant Disease
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• v.28 no.4
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• pp.209-220
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• 2022

Charcoal canker of oak, which has recently increased in southern Iran, could pose a serious threat to the entire forest ecosystem in the near future. In addition, it seems that climate change and its consequences, such as drought in the southern regions of Iran, have exacerbated this phenomenon. Consequently, the objective of this study was to identify the fungal pathogens that could cause charcoal canker disease in the oak forests of South Zagros. It was also sought to find associations between changes in the occurrence/exacerbation of charcoal canker disease under non and intense drought stress in non-inoculated or inoculated Quercus brantii seedlings. In total, 120 isolates were obtained from eight oak forests located in the Zagros Mountains of Southern Iran, Kohgiluyeh & Boyer-Ahmad and Fars provinces, which were classified as Biscogniauxia mediterranea based on morphological assessment. Subsequently, molecular assay confirmed the result by phylogenetic inference of internal transcribed spacer-rDNA regions, α-actin, and β-tubulin genes. The results of the pathogenicity test showed that the response of isolates of B. mediterranea (Iran-G1 and Iran-M70) was varied in different environments for the measured necrotic lesion length. In comparison with the control moisture treatments (non-stress), the necrotic lesion length in inoculated treatments increased under intense drought stress. In general, inoculated oak seedlings' exposure to water-deficient stress by the pathogen of B. mediterranea could affect the spread/severity of the charcoal canker disease.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.2
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• pp.218-222
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• 2014

In this paper, we introduce an autonomous calibration method for a 2D laser displacement sensor (e.g. laser vision sensor and laser range finder) by matching a single point on a flat structure. Many arc welding robots install a 2D laser displacement sensor to expand their application by recognizing their environment (e.g. base metal and seam). In such systems, sensing data should be transformed to the robot's coordinates, and the geometric relation (i.e. rotation and translation) between the robot's coordinates and sensor coordinates should be known for the transformation. Calibration means the inference process of geometric relation between the sensor and robot. Generally, the matching of more than 3 points is required to infer the geometric relation. However, we introduce a novel method to calibrate using only 1 point matching and use a specific flat structure (i.e. circular hole) which enables us to find the geometric relation with a single point matching. We make the rotation component of the calibration results as a constant to use only a single point by moving a robot to a specific pose. The flat structure can be installed easily in a manufacturing site, because the structure does not have a volume (i.e. almost 2D structure). The calibration process is fully autonomous and does not need any manual operation. A robot which installed the sensor moves to the specific pose by sensing features of the circular hole such as length of chord and center position of the chord. We show the precision of the proposed method by performing repetitive experiments in various situations. Furthermore, we applied the result of the proposed method to sensor based seam tracking with a robot, and report the difference of the robot's TCP (Tool Center Point) trajectory. This experiment shows that the proposed method ensures precision.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.191-198
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• 2020

In this paper we propose a for high school students who are attending a nationwide city with experience in Internet pornography, we would like to find out the impact of Internet pornography experience and self-regulation on sex crime harmful behavior. For this study, an Internet panel survey was conducted using a purposeful method of significant allocation inference. During the period, 246 copies of the questionnaire were distributed for about a month from May to June 2018 and 210 parts were analyzed except for 36 parts with no experience of pornographic material, and further analysis was conducted on 85 respondents with experience in harmful behavior of sexual violence. To this end, analysis tools used the SPS WIN 20.0 program version. The research results are as follows. First, we could find that Internet pornography has a negative effect on teenagers. This shows the probability of developing sexual violence into behavior as people can experience pornographic material regardless of their will due to the high Internet access. Second, the self-regulation of sexual violence behavior is found to have no direct impact. This is not just the adolescent's will to do so, but it is affected by the external environment. Third, self-regulation has proven its role as a modulator to mitigate negative perceptions of Internet pornography. Based on this, the proposal for limiting current prices was discussed.

• Journal of the Korean Society for Railway
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• v.20 no.4
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• pp.491-499
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• 2017

Railway turnout systems are one of the most important systems in a railway and abnormal turnout systems can cause serious accidents. To detect an abnormal state of a turnout, turnout clearance detectors are widely used. These devices consider a failure of a turnout clearance detectors to be a failure of the turnout system, that could hinder train operations. Analysis of turnout clearance detector failures is very important to ensure normal train operation. We categorized failures of detectors into four groups to identify failure characteristics of the 140 detectors, which are composed of main line detectors (A), side tracks (B), detectors that are in operation more than 80 times a day (C) and detectors that are in operation fewer than 10 times per day. Failures of detectors have mainly been caused in the control part, in the cables and sensors; failures are classified into four groups (A, B, C and D). We have tried to find failure density distributions for each type of failures, inferring the parameter distributions a priori. Finally, using the Bayesian inference we proposed a maintenance time for control parts through the mean time of the detector, life and the life expectancy.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.921-932
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• 2015

This study analyzed military personnel survivability in regards to offensive operations according to the scientific military training data of a reinforced infantry battalion. Scientific battle training was conducted at the Korea Combat Training Center (KCTC) training facility and utilized scientific military training equipment that included MILES and the main exercise control system. The training audience freely engaged an OPFOR who is an expert at tactics and weapon systems. It provides a statistical analysis of data in regards to state-of-the-art military training because the scientific battle training system saves and utilizes all training zone data for analysis and after action review as well as offers training control during the training period. The methodologies used the Cox PH modeling (which does not require parametric distribution assumptions) and decision tree modeling for survival data such as CART, GUIDE, and CTREE for richer and easier interpretation. The variables that violate the PH assumption were stratified and analyzed. Since the Cox PH model result was not easy to interpret the period of service, additional interpretation was attempted through univariate local regression. CART, GUIDE, and CTREE formed different tree models which allow for various interpretations.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.255-260
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• 2022

This study aimed to develop a service model that uses a deep learning algorithm for detecting diseases and pests in strawberries through image data. In addition, the pest detection performance of deep learning models was further improved by proposing segmented image data sets specialized in disease and pest symptoms. The CNN-based YOLO deep learning model was selected to enhance the existing R-CNN-based model's slow learning speed and inference speed. A general image data set and a proposed segmented image dataset was prepared to train the pest and disease detection model. When the deep learning model was trained with the general training data set, the pest detection rate was 81.35%, and the pest detection reliability was 73.35%. On the other hand, when the deep learning model was trained with the segmented image dataset, the pest detection rate increased to 91.93%, and detection reliability was increased to 83.41%. This study concludes with the possibility of improving the performance of the deep learning model by using a segmented image dataset instead of a general image dataset.

• Smart Structures and Systems
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• v.20 no.1
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• pp.61-74
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• 2017

One of the most reliable and simplest tools for structural vibration control in civil engineering is Tuned Mass Damper, TMD. Provided that the frequency and damping parameters of these dampers are tuned appropriately, they can reduce the vibrations of the structure through their generated inertia forces, as they vibrate continuously. To achieve the optimal parameters of TMD, many different methods have been provided so far. In old approaches, some formulas have been offered based on simplifying models and their applied loadings while novel procedures need to model structures completely in order to obtain TMD parameters. In this paper, with regard to the nonlinear decision-making of fuzzy systems and their enough ability to cope with different unreliability, a method is proposed. Furthermore, by taking advantage of both old and new methods a fuzzy system is designed to be operational and reduce uncertainties related to models and applied loads. To design fuzzy system, it is required to gain data on structures and optimum parameters of TMDs corresponding to these structures. This information is obtained through modeling MDOF systems with various numbers of stories subjected to far and near field earthquakes. The design of the fuzzy systems is performed by three methods: look-up table, the data space grid-partitioning, and clustering. After that, rule weights of Mamdani fuzzy system using the look-up table are optimized through genetic algorithm and rule weights of Sugeno fuzzy system designed based on grid-partitioning methods and clustering data are optimized through ANFIS (Adaptive Neuro-Fuzzy Inference System). By comparing these methods, it is observed that the fuzzy system technique based on data clustering has an efficient function to predict the optimal parameters of TMDs. In this method, average of errors in estimating frequency and damping ratio is close to zero. Also, standard deviation of frequency errors and damping ratio errors decrease by 78% and 4.1% respectively in comparison with the look-up table method. While, this reductions compared to the grid partitioning method are 2.2% and 1.8% respectively. In this research, TMD parameters are estimated for a 15-degree of freedom structure based on designed fuzzy system and are compared to parameters obtained from the genetic algorithm and empirical relations. The progress up to 1.9% and 2% under far-field earthquakes and 0.4% and 2.2% under near-field earthquakes is obtained in decreasing respectively roof maximum displacement and its RMS ratio through fuzzy system method compared to those obtained by empirical relations.

• Horticultural Science & Technology
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• v.29 no.4
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• pp.374-381
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• 2011

Stilbenes are polyphenolic natural products, which have antioxidative and antifungal activities. In some plants, including grapevine, the stilbene compounds, as resveratrol derivatives, exist in very diverse forms. Experiments to identify the individual stilbene compounds were carried out first to quantify them in UV-irradiated grapevine leaves. For this, stilbene glycosides were extracted from grapevine leaves which irradiated intensively with UV light. The glycoside samples were hydrolyzed by ${\beta}$-glucosidase, before analyzed by HPLC-mass spectrometer at each m/z corresponding to the mass of specific stilbenes. As results, in chromatograms, the enzymatic hydrolysis resulted in decrease and increase of the peaks expected for glycosides and aglycones, respectively. The samples were also exposed to sunlight in order to photo-isomerize the stilbene compounds. The light exposure resulted in disappearance and appearance of peaks expected for trans- and cis-isomers of stilbenes, respectively. Such a change of the peaks in chromatograms provided information needed for the inference to peak components. In this way, it was possible to identify 16 kinds of stilbene compounds from grapevine leaves. The identified stilbenes were quantified from grapevine leaves irradiated mildly by UV light. The UV-irradiation increased markedly in the content of stilbene compounds, especially trans-resveratrol by several hundredfold. In addition, piceatannol, which is a mere minor component of stilbenes in control leaves and a more active radical scavenger than resveratrol, was also increased by several tenfold by the treatment. The increase in stilbene contents as influenced by UV irradiation seems to be one of the stress coping responses of grapevine as a hormesis phenomenon.