• Safety and Health at Work
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• v.13 no.2
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• pp.240-247
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• 2022

Background: Although insomnia and constipation are highly prevalent worldwide, studies examining a possible association between them are lacking. We examined the relationship between insomnia and constipation in shift workers who have a high prevalence of insomnia and other diseases. Methods: This study had a multicenter cross-sectional design and conducted using health examination data including self-reported questionnaires. In total, 12,879 and 4,650 shift workers were enrolled in Severance Hospital and Wonju Severance Hospital, respectively, during 2015-2017. Multivariate logistic regression models and subgroup analysis were performed in each center with the same protocol, using a common data model. Results: The mean age of the total population was 44.35 (standard deviation = 8.75); the proportion of males was 56.9%. Female sex, being underweight and non-smoker were strongly associated with an increased risk of constipation symptom (p < 0.001). Pooled odds ratios (ORs) were calculated using ORs of both centers with weights; there was a significant dose-response relationship (sub-threshold 1.76 [95% confidence interval [CI] 1.62-1.91]; moderate 2.28 [95% CI 2.01-2.60]; severe 4.15 [95% CI 3.18-5.41] in the final model, p for trend < 0.001). Subgroup analysis performed by stratifying sex and pooled ORs showed a similar trend to that of the entire group. Conclusion: We observed a strong correlation between insomnia and constipation in this population. Our findings may help in formulating guidelines and policies to improve quality of life in shift workers through the management of sleep quality and proper bowel function. This study is the first to report this relationship among people working in shifts.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1853-1858
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• 2021

O-rings fill the gaps between mechanical parts. Until now, the sorting of defective products has been performed visually and manually, so classification errors often occur. Therefore, a camera-based defect classification system without human intervention is required. However, a binarization process is required to separate the required region from the background in the camera input image. In this paper, an adaptive binarization technique that considers the surrounding pixel values is applied to solve the problem that single-threshold binarization is difficult to apply due to factors such as changes in ambient lighting or reflections. In addition, the convex hull technique is also applied to compensate for the missing pixel part. And the learning model to be applied to the separated region applies the residual error-based deep learning neural network model, which is advantageous when the defective characteristic is non-linear. It is suggested that the proposed system through experiments can be applied to the automation of O-ring defect detection.

• Journal of Biomedical Engineering Research
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• v.42 no.5
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• pp.241-249
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• 2021

Recently, artificial intelligence for diagnosis system of obstetric diseases have been actively studied. Artificial intelligence diagnostic assist systems, which support medical diagnosis benefits of efficiency and accuracy, may experience problems of poor learning accuracy and reliability when inappropriate images are the model's input data. For this reason, before learning, We proposed an algorithm to exclude unread cervical imaging. 2,000 images of read cervical imaging and 257 images of unread cervical imaging were used for this study. Experiments were conducted based on the statistical method Radiomics to extract feature values of the entire images for classification of unread images from the entire images and to obtain a range of read threshold values. The degree to which brightness, blur, and cervical regions were photographed adequately in the image was determined as classification indicators. We compared the classification performance by learning read cervical imaging classified by the algorithm proposed in this paper and unread cervical imaging for deep learning classification model. We evaluate the classification accuracy for unread Cervical imaging of the algorithm by comparing the performance. Images for the algorithm showed higher accuracy of 91.6% on average. It is expected that the algorithm proposed in this paper will improve reliability by effectively excluding unread cervical imaging and ultimately reducing errors in artificial intelligence diagnosis.

• Journal of Ocean Engineering and Technology
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• v.36 no.5
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• pp.340-352
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• 2022

This article presents a modeling method for the uncorrelated measurement error of the ultra-short baseline (USBL) acoustic positioning system for aiding navigation of underwater vehicles. The Mahalanobis distance (MD) and principal component analysis are applied to decorrelate the errors of USBL measurements, which are correlated in the x- and y-directions and vary according to the relative direction and distance between a reference station and the underwater vehicles. The proposed method can decouple the radial-direction error and angular direction error from each USBL measurement, where the former and latter are independent and dependent, respectively, of the distance between the reference station and the vehicle. With the decorrelation of the USBL errors along the trajectory of the vehicles in every time step, the proposed method can reduce the threshold of the outlier decision level. To demonstrate the effectiveness of the proposed method, simulation studies were performed with motion data obtained from a field experiment involving an autonomous underwater vehicle and USBL signals generated numerically by matching the specifications of a specific USBL with the data of a global positioning system. The simulations indicated that the navigation system is more robust in rejecting outliers of the USBL measurements than conventional ones. In addition, it was shown that the erroneous estimation of the navigation system after a long USBL blackout can converge to the true states using the MD of the USBL measurements. The navigation systems using the uncorrelated error model of the USBL, therefore, can effectively eliminate USBL outliers without loss of uncontaminated signals.

• Journal of Aerospace System Engineering
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• v.17 no.4
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• pp.18-27
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• 2023

In this paper, the structural-thermal-optical performance analysis of the mirror was performed by setting the laser heat source as the boundary condition of the thermal analysis. For the laser heat source model, the Beer-Lambert model considering semi-transparent optical material based on Gaussian beam was selected as the boundary condition, and the mechanical part was not considered, to analyze the performance of only the mirror. As a result of the thermal analysis, thermal stress and thermal deformation data due to temperature change on the surface of the mirror were obtained. The displacement data of the surface due to thermal deformation was fitted to a Zernike polynomial to calculate the optical performance, through which the performance of the mirror when a high-energy laser was incident on the mirror could be predicted.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.51-60
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• 2024

Aiming at the problem of low contrast and difficult to recognize video images in low-light environment, we propose an adaptive contrast compensation enhancement algorithm based on human visual perception. First of all, the video image characteristic factors in low-light environment are extracted: AL (average luminance), ABWF (average bandwidth factor), and the mathematical model of human visual CRC(contrast resolution compensation) is established according to the difference of the original image's grayscale/chromaticity level, and the proportion of the three primary colors of the true color is compensated by the integral, respectively. Then, when the degree of compensation is lower than the bright vision precisely distinguishable difference, the compensation threshold is set to linearly compensate the bright vision to the full bandwidth. Finally, the automatic optimization model of the compensation ratio coefficient is established by combining the subjective image quality evaluation and the image characteristic factor. The experimental test results show that the video image adaptive enhancement algorithm has good enhancement effect, good real-time performance, can effectively mine the dark vision information, and can be widely used in different scenes.

• The Journal of the Acoustical Society of Korea
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• v.26 no.8
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• pp.397-402
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• 2007

In this paper, we apply a support vector machine(SVM) that incorporates an optimized nonlinear decision rule over different sets of feature vectors to improve the performance of statistical model-based voice activity detection(VAD). Conventional method performs VAD through setting up statistical models for each case of speech absence and presence assumption and comparing the geometric mean of the likelihood ratio (LR) for the individual frequency band extracted from input signal with the given threshold. We propose a novel VAD technique based on SVM by treating the LRs computed in each frequency bin as the elements of feature vector to minimize classification error probability instead of the conventional decision rule using geometric mean. As a result of experiments, the performance of SVM-based VAD using the proposed feature has shown better results compared with those of reported VADs in various noise environments.

• Journal of Life Science
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• v.20 no.4
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• pp.496-502
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• 2010

The acidic saline animal model of pain has been suggested to mimic fibromyalgia (FM). Oligomeric proanthocyanidin complexes (OPC) from grape seeds are known to act as an antioxidant. We studied the effects of OPC on the pain threshold in the acidic saline animal model of pain. The left gastrocnemius muscle was injected with $100\;{\mu}l$ of saline at pH 4.0 under brief isoflurane anesthesia on days 0 and 5. Control rats (n=5) received identical injections of physiological saline (pH 7.2) on the same schedule. Rats (n=10) with acidic saline injection were separated into two study subgroups. After measurement of pre-drug pain thresholds, rats were injected intraperitoneally with either saline or OPC 300 mg/kg. Paw withdrawal thresholds to pressure were again measured 60 min after intraperitoneal injection. Nociceptive thresholds were measured with a Dynamic Plantar Aesthesiometer by applying an increasing pressure to right or left hind paw until the rat withdrew the paw. Compared to baseline (day 0), acid injections produced mechanical hyper-responsiveness on day 7 (pre-drug) in these rats [p<0.05]. A potent antihyperalgesic effect was observed when rats were injected intraperitoneally with OPC 300 mg/kg [injected paw, p=0.001; contralateral paw, p=0.002]. OPC treatment decreased the expression of acid sensing ion channel 3 in the brain motor cortex area on immunohistochemical staining when OPC 300 mg/kg was administered intraperitoneally in the animal model of FM pain [p<0.05]. Further research is required to determine the efficacy of OPC treatments in FM pain in humans.

• Journal of the Korea Concrete Institute
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• v.15 no.1
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• pp.134-142
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• 2003

Autogenous shrinkage, a significant contributor of early-age cracking of high strength concrete (HSC), must be avoided or minimized from an engineering point of view. Therefore, it is necessary to study how to reduce and to predict autogenous shrinkage with respect to tile control of early-age cracking. In this study, autogenous shrinkage of HSC with various water-binder ratio (W/B) ranging from 0.50 to 0.27 and fly ash content of 0, 10, 20, and 30% were investigated. Based on the test results, thereafter, a prediction model for autogenous shrinkage was proposed. Test results show that autogenous shrinkage increased and more rapidly developed with decreasing the W/B. Also, the higher fly ash contents, the smaller autogenous shrinkage. In particular, even if much autogenous shrinkage occurs at very early-ages, stress may not be developed while the stiffness of concrete is low. In order to consider the change of concrete stiffness, the transition time referred as stiffening threshold, was obtained by monitoring of ultrasonic pulse velocity evolution and considered in the autogenous shrinkage model. From a practical point of view, the proposed model can be effectively used to predict autogenous shrinkage and to estimate stress induced by autogenous shrinkage.

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.379-386
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• 2004

Video segmentation is a fundamental task in video indexing and it includes two kinds of shot change detections such as the abrupt transition and the gradual transition. The abrupt shot boundaries are detected by computing the image-based distance between adjacent frames and comparing this distance with a pre-determined threshold value. However, the gradual shot boundaries are difficult to detect with this approach. To overcome this difficulty, we propose the method that detects gradual transition in the MPEG compressed video using the HMM (Hidden Markov Model). We take two different HMMs such as a discrete HMM and a continuous HMM with a Gaussian mixture model. As image features for HMM's observations, we use two distinct features such as the difference of histogram of DC images between two adjacent frames and the difference of each individual macroblock's deviations at the corresponding macroblock's between two adjacent frames, where deviation means an arithmetic difference of each macroblock's DC value from the mean of DC values in the given frame. Furthermore, we obtain the DC sequences of P and B frame by the first order approximation for a fast and effective computation. Experiment results show that we obtain the best detection and classification performance of gradual transitions when a continuous HMM with one Gaussian model is taken and two image features are used together.