• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.523-528
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• 2015

This paper proposes an algorithm for risk situation recognition using facial expression. The proposed method recognitions the surprise and fear expression among human's various emotional expression for recognizing risk situation. The proposed method firstly extracts the facial region from input, detects eye region and lip region from the extracted face. And then, the method applies Uniform LBP to each region, discriminates facial expression, and recognizes risk situation. The proposed method is evaluated for Cohn-Kanade database image to recognize facial expression. The DB has 6 kinds of facial expressions of human being that are basic facial expressions such as smile, sadness, surprise, anger, disgust, and fear expression. The proposed method produces good results of facial expression and discriminates risk situation well.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.331-334
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• 2002

It has been reported that maximum-likelihood position-estimation (MLPE) algorithms offer advantages of improved spatial resolution and linearity over conventional Anger algorithm in gamma cameras. The purpose of this study is to evaluate the performances of the noise models, Poisson and Gaussian, in MLPE for the localization of photons in a small gamma camera (SGC) using NaI(Tl) plate and PSPMT. The SGC consists of a single NaI(Tl) crystal, 10 cm diameter and 6 mm thick, optically coupled to a PSPMT (Hamamatsu R3292-07). The PSPMT was read out using a resistive charge divider, which multiplexes 28(X) by 28(Y) cross wire anodes into four channels. Poisson and Gaussian based MLPE methods have been implemented using experimentally measured light response functions. The system resolutions estimated by Poisson and Gaussian based MLPE were 4.3 mm and 4.0 mm, respectively. Integral uniformities were 29.7% and 30.6%, linearities were 1.5 mm and 1.0 mm and count rates were 1463 cps and 1388 cps in Poisson and Gaussian based MLPE, respectively. The results indicate that Gaussian based MLPE, which is convenient to implement, has better performances and is more robust to statistical noise than Poisson based MLPE.

• Journal of Advanced Navigation Technology
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• v.14 no.2
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• pp.191-197
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• 2010

Many does an application and application but the image analysis of face detection considerably is difficult. In order for with effect of the illumination which is irregular in the present paper America the illumination to range evenly in the face which is detected, detects a face territory, Complemented the result which detects only the front face of existing. With LAB color illumination revisions compared in Adaboost face detection of existing and 32% was visible the face detection result which improves. Bought two images which are input and executed Glassfire label rings. Compared Area critical price and became the area of above critical value and revised from RGB smooth anger and LAB images with LCFD system algorithm. The operational conversion image which is extracted like this executed a face territory detection in the object. In order to extract the feature which is necessary to a face detection used AdaBoost algorithms. The face territory remote login with the face territory which tilts in the present paper, until Multi-view face territory detections was possible. Also relationship without high detection rate seems in direction of illumination, With only the public PC application is possible was given proof user authentication field etc.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.773-777
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• 2009

In recent years, the field of synthesizing voice has been developed rapidly, and the technologies such as reading aloud an email or sound guidance of a car navigation system are used in various scenes of our life. The sound quality is monotonous like reading news. It is preferable for a text such as a novel to be read by the voice that expresses emotions wealthily. Therefore, we have been trying to develop a system reading aloud novels automatically that are expressed clear emotions comparatively such as juvenile literature. At first it is necessary to identify emotions expressed in a sentence in texts in order to make a computer read texts with an emotionally expressive voice. A method on the basis of the meaning interpretation that utilized artificial intelligence technology for a method to specify emotions of texts is thought, but it is very difficult with the current technology. Therefore, we propose a method to determine only emotion every sentence in a novel by a simpler way. This method determines the emotion of a sentence according to an emotion that words such as a verb in a Japanese verb sentence, and an adjective and an adverb in a adjective sentence, have. The emotional characteristics that these words have are prepared beforehand as a emotional words dictionary by us. The emotions used here are seven types: "joy," "sorrow," "anger," "surprise," "terror," "aversion" or "neutral."

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.20-26
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• 2008

As they have more and more intelligence robots or computers these days, so the interaction between intelligence robot(computer) - human is getting more and more important also the emotion recognition and expression are indispensable for interaction between intelligence robot(computer) - human. In this paper, firstly we extract emotional features at speech signal and facial image. Secondly we apply both BL(Bayesian Learning) and PCA(Principal Component Analysis), lastly we classify five emotions patterns(normal, happy, anger, surprise and sad) also, we experiment with decision fusion and feature fusion to enhance emotion recognition rate. The decision fusion method experiment on emotion recognition that result values of each recognition system apply Fuzzy membership function and the feature fusion method selects superior features through SFS(Sequential Forward Selection) method and superior features are applied to Neural Networks based on MLP(Multi Layer Perceptron) for classifying five emotions patterns. and recognized result apply to 2D facial shape for express emotion.

• The Journal of Korean Medicine
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• v.43 no.4
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• pp.102-130
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• 2022

Objectives: The persistence and unpredictability of coronavirus disease (COVID-19) and new measures to prevent direct medical intervention (e.g., social distancing and quarantine) have induced various psychological symptoms and disorders that require self-treatment approaches and integrative treatment interventions. To address these issues, the Korean Medicine Mental Health (KMMH) center developed a field manual by reviewing previous literature and preexisting manuals. Methods: The working group of the KMMH center conducted a keyword search in PubMed in June 2021 using "COVID-19" and "SARS-CoV-2". Review articles were examined using the following filters: "review," "systematic review," and "meta-analysis." We conducted a narrative review of the retrieved articles and extracted content relevant to previous manuals. We then created a treatment algorithm and recommendations by referring to the results of the review. Results: During the initial assessment, subjective symptom severity was measured using a numerical rating scale, and patients were classified as low- or moderate-high risk. Moderate-high-risk patients should be classified as having either a psychiatric emergency or significant psychiatric condition. The developed manual presents appropriate psychological support for each group based on the following dominant symptoms: tension, anxiety-dominant, anger-dominant, depression-dominant, and somatization. Conclusions: We identified the characteristics of mental health problems during the COVID-19 pandemic and developed a clinical mental health support manual in the field of Korean medicine. When symptoms meet the diagnostic criteria for a mental disorder, doctors of Korean medicine can treat the patients according to the manual for the corresponding disorder.

• Journal of the Korean Society of Radiology
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• v.13 no.3
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• pp.319-324
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• 2019

A depth-encoding detector module with silicon photomultipliers(SiPMs) using two layers of scintillation crystal array was designed, and the position measurement capability was verified using DETECT2000. The depth of interaction of the crystal pixels with the gamma rays was tracked through the image acquired with the combination of surface treatment of the crystal pixels and reflectors. The bottom layer was treated as a reflector except for the optically coupled surfaces, and the crystals of top layer were optically coupled each other except for the outer surfaces so that the light sharing was made easier than the bottom layer. Flood images were obtained through the combination of specular reflectors and random reflectors, grounded and polished surfaces of crystal pixels, and the positions at which layer images were generated were measured and analyzed. The images were reconstructed using the Anger algorithm, whose the SiPM signals were reduced as the 16-channels to 4-channels. In the combination of the grounded surface and all reflectors, the depth positions were discriminated into two layers, whereas it was impossible to separate the two layers in the all polished surface combinations. Therefore, using the combination of grounded surface crystal pixels and reflectors could improve the spatial resolution at the outside of the field of view by measuring the depth position in preclinical positron emission tomography.