• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5C
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• pp.514-522
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• 2003

Contrary to the previous methods, in this paper, we propose an enhanced shot change detection method using multiple features and binary decision tree. The previous methods usually used single feature and fixed threshold between consecutive frames. However, contents such as color, shape, background, and texture change simultaneously at shot change points in a video sequence. Therefore, in this paper, we detect the shot changes effectively using multiple features, which are supplementary each other, rather than using single feature. In order to classify the shot changes, we use binary classification tree. According to this classification result, we extract important features among the multiple features and obtain threshold value for each feature. We also perform the cross-validation and droop-case to verify the performance of our method. From an experimental result, it was revealed that the EI of our method performed average of 2% better than that of the conventional shot change detection methods.

• Ocean Systems Engineering
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• v.6 no.3
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• pp.217-231
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• 2016

This paper presents a new approach for underwater image analysis using the bi-dimensional empirical mode decomposition (BEMD) technique and the phase congruency information. The BEMD algorithm, fully unsupervised, it is mainly applied to texture extraction and image filtering, which are widely recognized as a difficult and challenging machine vision problem. The phase information is the very stability feature of image. Recent developments in analysis methods on the phase congruency information have received large attention by the image researchers. In this paper, the proposed method is called the EP model that inherits the advantages of the first two algorithms, so this model is suitable for processing underwater image. Moreover, the receiver operating characteristic (ROC) curve is presented in this paper to solve the problem that the threshold is greatly affected by personal experience when underwater image edge detection is performed using the EP model. The EP images are computed using combinations of the Canny detector parameters, and the binaryzation image results are generated accordingly. The ideal EP edge feature extractive maps are estimated using correspondence threshold which is optimized by ROC analysis. The experimental results show that the proposed algorithm is able to avoid the operation error caused by manual setting of the detection threshold, and to adaptively set the image feature detection threshold. The proposed method has been proved to be accuracy and effectiveness by the underwater image processing examples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.2289-2301
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• 2015

Mobile devices are becoming powerful enough to realize augmented reality (AR) application. This paper introduces two AR methods to estimate an environmental illumination distribution of a scene. In the first method, we extract the lighting direction and intensity from input images captured with a front-side camera of a mobile device, using its orientation sensor. The second method extracts shadow regions cast by three dimensional (3D) AR marker of known shape and size. Because previous methods examine per pixel shadow intensity, their performances are much affected by the number of sampling points, positions, and threshold values. By using a simple binary operation between the previously clustered shadow regions and the threshold real shadow regions, we can compute efficiently their relative area proportions according to threshold values. This area-based method can overcome point sampling problem and threshold value selection. Experiment results demonstrate that the proposed methods generate natural image with multiple smooth shadows in real-time.

• Journal of IKEEE
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• v.25 no.4
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• pp.584-590
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• 2021

This paper proposes a removal method of signal interference between ultrasound sensors where ghost signals due to interference are excluded and correct signal is recognized in distance mensurement using ultrasound sensors. The proposed method detects and excludes ghost signals when previous measured distance is compared to current measured distance and the distance difference exceeds a threshold. The threshold is fixed in conventional methods, so ghost signals cannot be correctly excluded when ultrasound sensor or target object move rapidly. On the contrary, to improve accuracy, the threshold is not fixed in the proposed method, and the threshold is adpatively determined based on the relative velocity when ultrasound sensor or target object move. Experiments of distance measurement with ultrasound signal interference are carried out where multiple ultrasound sensors of same type are exploited with maximum interference, and the results show that the proposed method efficiently exclude ghost signals.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.1
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• pp.141-149
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• 1999

In this paper, we consider multiple-class queueing systems in which the server starts a set-up as soon as the number of customers in the "start-up class" reaches threshold N. After the set-up the server starts his service. We obtain the Laplace-Stieltjes transform and the mean of the waiting times of each class of customers for FCFS and non-preemptive priority disciplines.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.244-251
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• 1997

In this paper, we propose a fault diagnosis algorithm to detect and isolate multiple faults in a system. The proposed fault diagnosis algorithm is based on a multiple fault classifier which consists of two ART2 NN(adaptive resonance theory2 neural network) modules and the algorithm is composed of three main parts - parameter estimation, fault detection and isolation. When a change in the system occurs, estimated parameters go through a transition zone in which residuals between the system output and the estimated output cross the threshold, and in this zone, estimated parameters are transferred to the multiple faults classifier for fault isolation. From the computer simulation results, it is verified that when the proposed diagnosis algorithm is performed successfully, it detects and isolates faults in the position control system of a DC motor.

• Toxicological Research
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• v.34 no.4
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• pp.281-290
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• 2018

Exposure to chemical agents is an inevitable consequence of modern society; some of these agents are hazardous to human health. The effects of chemical carcinogens are of great concern in many countries, and international organizations, such as the World Health Organization, have established guidelines for the regulation of these chemicals. Carcinogens are currently categorized into two classes, genotoxic and non-genotoxic carcinogens, which are subject to different regulatory policies. Genotoxic carcinogens are chemicals that exert carcinogenicity via the induction of mutations. Owing to their DNA interaction properties, there is thought to be no safe exposure threshold or dose. Genotoxic carcinogens are regulated under the assumption that they pose a cancer risk for humans, even at very low doses. In contrast, non-genotoxic carcinogens, which induce cancer through mechanisms other than mutations, such as hormonal effects, cytotoxicity, cell proliferation, or epigenetic changes, are thought to have a safe exposure threshold or dose; thus, their use in society is permitted unless the exposure or intake level would exceed the threshold. Genotoxicity assays are an important method to distinguish the two classes of carcinogens. However, some carcinogens have negative results in in vitro bacterial mutation assays, but yield positive results in the in vivo transgenic rodent gene mutation assay. Non-DNA damage, such as spindle poison or topoisomerase inhibition, often leads to positive results in cytogenetic genotoxicity assays such as the chromosome aberration assay or the micronucleus assay. Therefore, mechanistic considerations of tumor induction, based on the results of the genotoxicity assays, are necessary to distinguish genotoxic and non-genotoxic carcinogens. In this review, the concept of threshold of toxicological concern is introduced and the potential risk from multiple exposures to low doses of genotoxic carcinogens is also discussed.

• The Journal of Information Systems
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• v.26 no.3
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• pp.63-90
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• 2017

Purpose This study proposes a novel system trading model using case-based reasoning (CBR) based on absolute similarity threshold. The proposed model is designed to optimize the absolute similarity threshold, feature selection, and instance selection of CBR by using genetic algorithm (GA). With these mechanisms, it enables us to yield higher returns from stock market trading. Design/Methodology/Approach The proposed CBR model uses the absolute similarity threshold varying from 0 to 1, which serves as a criterion for selecting appropriate neighbors in the nearest neighbor (NN) algorithm. Since it determines the nearest neighbors on an absolute basis, it fails to select the appropriate neighbors from time to time. In system trading, it is interpreted as the signal of 'hold'. That is, the system trading model proposed in this study makes trading decisions such as 'buy' or 'sell' only if the model produces a clear signal for stock market prediction. Also, in order to improve the prediction accuracy and the rate of return, the proposed model adopts optimal feature selection and instance selection, which are known to be very effective in enhancing the performance of CBR. To validate the usefulness of the proposed model, we applied it to the index trading of KOSPI200 from 2009 to 2016. Findings Experimental results showed that the proposed model with optimal feature or instance selection could yield higher returns compared to the benchmark as well as the various comparison models (including logistic regression, multiple discriminant analysis, artificial neural network, support vector machine, and traditional CBR). In particular, the proposed model with optimal instance selection showed the best rate of return among all the models. This implies that the application of CBR with the absolute similarity threshold as well as the optimal instance selection may be effective in system trading from the perspective of returns.

• Annals of Clinical Neurophysiology
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• v.19 no.1
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• pp.34-39
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• 2017

Background: Automated nerve excitability testing is used to assess various peripheral neuropathies and motor neuron diseases. Comparing these excitability parameters with normal data provides information regarding the axonal excitability properties and ion biophysics in diseased axons. This study measured and compared normal values of axonal excitability parameters in both the distal motor and sensory axons of normal Koreans. Methods: The axonal excitability properties of 50 distal median motor axons and 30 distal median sensory axons were measured. An automated nerve excitability test was performed using the QTRACW threshold-tracking software (Institute of Neurology, University College London, London, UK) with the TRONDF multiple excitability recording protocol. Each parameter of stimulus-response curves, threshold electrotonus, current-voltage relationship, and recovery cycle was measured and calculated. Results: Our Korean normal data on axonal excitability showed ranges of values and characteristics similar to previous reports from other countries. We also reaffirmed that there exist characteristic differences in excitability properties between motor and sensory axons: compared to motor axons, sensory axons showed an increased strength-duration time constant, more prominent changes in threshold to hyperpolarizing threshold electrotonus (TE) and less prominent changes in threshold to depolarizing TE, and more prominent refractoriness and less prominent subexcitability and superexcitability. Conclusions: We report normal data on axonal excitability in Koreans. These data can be used to compare various pathological conditions in peripheral nerve axons such as peripheral neuropathies and motor neuron disease.

• Annals of Clinical Neurophysiology
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• v.14 no.2
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• pp.64-71
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• 2012

Background: It is generally accepted that upper motor neuron (UMN) lesion can alter lower motor neuron (LMN) function by the plasticity of neural circuit. However there have been only few researches regarding the axonal excitability of LMN after UMN injury especially during the acute stage. The aim of this study was to investigate the nerve excitability properties of the LMNs following an acute to subacute supratentorial corticospinal tract lesion. Methods: An automated nerve excitability test (NET) using the threshold tracking technique was utilized to measure multiple excitability indices in median motor axons of 15 stroke patients and 20 controls. Testing of both paretic and non-paretic side was repeated twice, during the acute stage and subacute stage. The protocols calculated the strength-duration time constant from the duration-charge curve, parameters of threshold electrotonus (TE), the current-threshold relationship from sequential sub-threshold current, and the recovery cycle from sequential supra-threshold stimulation. Results: On the paretic side, compared with the control group, significant decline of superexcitablity and increase in the relative refractory period were observed during the subacute stage of stroke. Additionally, despite the absence of statistical significance, a mildly collapsing in ('fanning in') of the TE was found. Conclusions: Our results suggest that supratentorial brain lesions can affect peripheral axonal excitability even during the early stage. The NET pattern probably suggests background membrane depolarization of LMNs. These features could be associated with trans-synaptic regulation of UMNs to LMNs as one of the "neural plasticity" mechanisms in acute brain injury.