• Journal of Korean Medical classics
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• v.34 no.1
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• pp.47-65
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• 2021

Objectives : An accurate judgment of prognosis when treating diseases is crucial. While the 『Shanghanlun(傷寒論)』 deals with the prognosis of the Three Yin Diseases with great importance, full-scale studies have been lacking. This paper aims to study the Three Yin Diseases with a focus on prognosis analysis. Methods : Among the Three Yin Disease verses, those that could provide clues to prognosis were selected and analysed. Conclusions & Results : When Yang pulse patterns such as long(長脈)·floating(浮脈)·rapid(數脈) pulses and Yang symptoms such as fever, vexing heat, mild perspiration, thirst, warmth in hands and feet are present in Yin disease, it could be taken as signs of Yang Qi restoration. In these situations, Yin Cold pattern such as diarrhea and reversal cold disappear and the prognosis is positive. However, despite Yang pulse patterns and symptoms, there are cases where diarrhea happens as a result of cold dampness being eliminated due to Yang Qi restoration. Also, when Yang Qi starts communicating smoothly after its restoration in the Three Yin Diseases, perspiration can happen. When diarrhea and reversal cold, which are patterns of Yin Cold get worse, with pulse patterns such as unfelt(脈不至)·replete(實脈)·fulminating(脈暴出) pulses, false heat symptoms such as fever and hot flashes happen, accompanied with Yang Qi depleted symptoms such as inability to lie down due to agitation, continuous perspiration, sore throat, dyspnea, and exaggerated breathing happen. When fast pulse, fever, and perspiration are present due to depression and stagnation of ministerial fire, symptoms such as bloody stool with pus, purulent abscess, sore throat, and inability to lie down due to agitation show, which signal negative prognosis. In bad cases of Reverting Yin Disease, there is continuous diarrhea and bloody stool with pus, which can be due to either Kidney Yang deficiency or depression and stagnation of ministerial fire. It could also be caused by excessive heat.

• Imaging Science in Dentistry
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• v.51 no.2
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• pp.149-154
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• 2021

Purpose: This study aimed to investigate the computed tomography and magnetic resonance imaging features of giant cell tumors in the temporomandibular joint region to facilitate accurate diagnoses. Materials and Methods: From October 2007 to June 2020, 6 patients (2 men and 4 women) at Yonsei University Dental Hospital had histopathologically proven giant cell tumors in the temporomandibular joint. Their computed tomography and magnetic resonance imaging findings were reviewed retrospectively, and the cases were classified into 3 types based on the tumor center and growth pattern observed on the radiologic findings. Results: The age of the 6 patients ranged from 25 to 53 years. Trismus was found in 5 of the 6 cases. One case recurred. The mean size of the tumors, defined based on their greatest diameter, was 32 mm (range, 15-41 mm). The characteristic features of all cases were a heterogeneously-enhancing tumorous mass with a lobulated margin on computed tomographic images and internal multiplicity of signal intensity on T2-weighted magnetic resonance images. According to the site of origin, 3 tumors were bone-centered, 2 were soft tissue-centered, and 1 was peri-articular. Conclusion: Computed tomography and magnetic resonance imaging yielded a tripartite classification of giant cell tumors of the temporomandibular joint according to their location on imaging. This study could help clinicians in the differential diagnosis of giant cell tumors and assist in proper treatment planning for tumorous diseases of the temporomandibular joint.

• Investigative Magnetic Resonance Imaging
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• v.25 no.1
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• pp.23-34
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• 2021

Purpose: Differentiating between glioblastoma and solitary metastasis is very important for the planning of further workup and treatment. We assessed the ability of various morphological parameters using conventional MRI and diffusion-based techniques to distinguish between glioblastomas and solitary metastases in tumoral and peritumoral regions. Materials and Methods: We included 38 patients with solitary brain tumors (21 glioblastomas, 17 solitary metastases). To find out if there were differences in the morphologic parameters of enhancing tumors, we analyzed their shape, margins, and enhancement patterns on postcontrast T1-weighted images. During analyses of peritumoral regions, we assessed the extent of peritumoral non-enhancing lesion on T2- and postcontrast T1-weighted images. We also aimed to detect peritumoral neoplastic cell infiltration by visual assessment of T2-weighted and diffusion-based images, including DWI, ADC maps, and exponential DWI, and evaluated which sequence depicted peritumoral neoplastic cell infiltration most clearly. Results: The shapes, margins, and enhancement patterns of tumors all significantly differentiated glioblastomas from metastases. Glioblastomas had an irregular shape, ill-defined margins, and a heterogeneous enhancement pattern; on the other hand, metastases had an ovoid or round shape, well-defined margins, and homogeneous enhancement. Metastases had significantly more extensive peritumoral T2 high signal intensity than glioblastomas had. In visual assessment of peritumoral neoplastic cell infiltration using T2-weighted and diffusion-based images, all sequences differed significantly between the two groups. Exponential DWI had the highest sensitivity for the diagnosis of both glioblastoma (100%) and metastasis (70.6%). A combination of exponential DWI and ADC maps was optimal for the depiction of peritumoral neoplastic cell infiltration in glioblastoma. Conclusion: In the differentiation of glioblastoma from solitary metastatic lesions, visual morphologic assessment of tumoral and peritumoral regions using conventional MRI and diffusion-based techniques can also offer diagnostic information.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3614-3619
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• 2022

To operate the ion cyclotron resonance heating (ICRH) antennas in a better heating state and produce relatively low impurities, it is necessary to control the antenna spectrum by changing the antenna phasing. As the electrical length of the antenna feeding transmission lines is changing as a matter of the standing wave pattern at the ceramic supports, 90° elbows, T-connectors and antenna loops, we chose to measure the current at the grounding points of the antenna loops by antenna strap probe. The voltage drops along a small, several millimeter-long paths at the end of the antenna loops give a signal that is proportional to the current in the antenna loop. Through the simulation of the antenna strap probe and the actual measurement of the antenna phasing under vacuum conditions, the reliability of the antenna strap probe based diagnostic system have been successfully proved. Moreover, this system was successfully applied to the ICRH daily experiments in the spring of 2021. In the near future, the active real-time feedback control of the antenna phasing system will be developed based on this diagnostic system in the EAST tokamak.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.55-67
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• 2022

Sensor data can provide fault diagnosis for equipment. However, the cause analysis for fault results of equipment is not often provided. In this study, we propose an explainable convolutional neural network framework for the sensor-based time series classification model. We used sensor-based time series dataset, acquired from vehicles equipped with sensors, and the Wafer dataset, acquired from manufacturing process. Moreover, we used Cycle Signal dataset, acquired from real world mechanical equipment, and for Data augmentation methods, scaling and jittering were used to train our deep learning models. In addition, our proposed classification models are convolutional neural network based models, FCN, 1D-CNN, and ResNet, to compare evaluations for each model. Our experimental results show that the ResNet provides promising results in the context of time series classification with accuracy and F1 Score reaching 95%, improved by 3% compared to the previous study. Furthermore, we propose XAI methods, Class Activation Map and Layer Visualization, to interpret the experiment result. XAI methods can visualize the time series interval that shows important factors for sensor data classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.656-661
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• 2022

Password with a combination of 4-digit numbers has been widely adopted for various authentication systems (such as credit card authentication, digital door lock systems and so on). However, this system could not be safe because the 4-digit password can easily be stolen by predicting it from the fingermarks on the keypad or display screen. Furthermore, due to the prolonged COVID-19 pandemic, contactless method has been preferred over contact method in authentication. This paper suggests a new password input method based on eyeblink pattern analysis in video sequence. In the proposed method, when someone stands in front of a camera, the sequence of eyeblink motions is captured (according to counting signal from 0 to 9 or 9 to 0), analyzed and encoded, producing the desired 4-digit decimal numbers. One does not need to touch something like keypad or perform an exaggerated action, which can become a very important clue for intruders to predict the password.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.365-378
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• 2022

Complete blood cell count(CBC) is a technique that counts leukocytes for each type of blood cell being analyzed. The principle is that laser is incident to ex vivo flowing leukocytes in a microcapillary tube and scattered light occurs by laser and leukocytes. By collecting the scattered light, we can count the types of cells because different cells generate different light-scattering patterns. However, the technique has an intrinsic limitation, scattering pattern is shown in a wide range region in the resulting, which makes it difficult to accurate analyze and use fluorescent dyes. To overcome this limitation, a new design of CBC with a dual laser, which irradiates with orthogonal angles for collecting quad-scattering information was proposed. Before development, the scattering difference depending on wavelength must be investigated to only catch up to the scattered signal by angles. Some studies, which focused on simple particles, have been conducted to theoretically and experimentally investigate different scatterings by wavelength. In this study, we propose an optical system for measuring scattered light and investigate a complex particle. As a result, the green laser made strong scattering signals in both the forward and side direction: 10% and 30%, respectively.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.749-754
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• 2022

High adhesion and water resistance of the skin surface are required for wearable and skin-attachable electronic patches in various medical applications. In this study, we report a stretchable electronic patch that mimics the drainable structure pattern of the hexagonal channels of frog's pads and the sucker of an octopus based on carbon-based conductive polymer composite materials. The hexagonal channel structure that mimics the pads of frogs drains water and improves adhesion through crack arresting effect, and the suction structure that mimics an octopus sucker shows high adhesion on wet surfaces. In addition, the high-adhesive electronic patch has excellent adhesion to various surfaces such as silicone wafer (max. 4.06 N/cm²) and skin replica surface (max. 1.84 N/cm²) in dry and wet conditions. The high skin-adhesive electronic patch made of a polymer composite material based on a polymer matrix and carbon particles can reliably detect electrocardiogram (ECG) in dry and humid environments. The proposed electronic patch presents potential applications for wearable and skin-attachable electronic devices for detecting various biosignals.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.457-462
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• 2023

We proposed a flexible link configuration in a system combining mid-span spectral inversion (MSSI) and dispersion management used for long-distance transmission of high-capacity optical signals such as wavelength division multiplexing signals, and examined specific methods to increase chromatic dispersion and nonlinear distortion compensation effects. The dispersion map proposed to increase the flexibility of dispersion-managed link configuration has a 'random-inverse' structure. That is, in the proposed dispersion map, the residual dispersion per span (RDPS) of each fiber span in the first half section up to the optical phase conjugator is randomly distributed, and the RDPS distribution in the second half section reverses the distribution pattern of the first section. Although the proposed dispersion map has a random distribution of RDPS, it was confirmed that the distortion compensation effect is improved due to the fact that the dispersion profile is symmetrical with respect to the optical phase conjugator. In the dispersion map of the 'random-inverse' configuration, it was also confirmed that the compensation effect of the distorted wavelength division multiplexing signal becomes improved when the magnitude of the RDPS allocated to each fiber span is large.