• 대한음성학회지:말소리
• /
• 제65호
• /
• pp.51-65
• /
• 2008

The aim of this study is to compare ASD (Autism Spectrum Disorder) children with normal children in terms of the prosodic features. Materials are collected by the reading of Korean read sentences. They are composed of 10 declarative sentences, each of which was consisted of 5-6 words. Subjects are consisted of 10 ASD and 10 normal male children with a receptive vocabulary age of 5;0-6;5 years. We found out that both groups showed the differences not only in the tonal patterns at the end of the prosodic phrases, but also in both the degree of rising and falling slope related to pitch contour. While HL% and HLH% were highly emerged in sentence final position in normal group, HL% and HLH% were prominent in ASD group in the same position. LH% and LHL% IP types were observed only in ASD group in sentence medial position. The slope showing the variation in the fundamental frequency at the end of the prosodic phrase was twice as steep in the group of ASD children as in the group of normal children.

• 한국방사선학회논문지
• /
• 제4권3호
• /
• pp.5-11
• /
• 2010

본 논문에서는 이중 에너지 디지털 래디오그래피를 위한 이중 모드 검출기 개발을 제안한다. 이중 에너지 래디오그래피 모듈의 설계를 위하여 상용 BIS(Baggage Inspection System)에서 사용되고 있는 X-선 발생장치의 스펙트럼과 이중 모드 검출기에 대한 특징 및 방사선적 특성을 분석하였다. 제안하는 영상 검출기 모듈은 BIS에서 활용되고 있는 X-선관을 대상으로 X-선 스펙트럼을 모사하고, 모사한 스펙트럼을 통하여 새롭게 제안하는 검출기 모듈의 방사선적 특성을 고찰하였다. X-ray를 이용한 실험에서 구리 필터의 두께 증가에 따라 저에너지 검출기(LED)와 고에너지 검출기(HED)의 출력신호의 차이는 같이 증가하였다. 특히 HED에서의 출력신호의 크기는 구리 필터 두께가 증가 할수록 감소함을 알 수 있었다.

• Nuclear Engineering and Technology
• /
• 제44권7호
• /
• pp.781-790
• /
• 2012

The HyperGam program was developed for the analysis of complex HPGe ${\gamma}$-ray spectra. The previous version of HyperGam was mainly limited to the analysis of ${\gamma}$-ray peaks and the manual logging of the result. In this study, it is specifically developed into a tool for the isotopic analysis of spectra. The newly developed features include nuclide identification and activity determination. An algorithm for nuclide identification was developed to identify the peaks in the spectrum by considering the yield, efficiency, energy and peak area for the ${\gamma}$-ray lines emitted from the radionuclide. The detailed performance of nuclide identification and activity determination was accessed using the IAEA 2002 set of test spectra. By analyzing the test spectra, the numbers of radionuclides identified truly (true hit), falsely (false hit) or missed (misses) were counted and compared with the results from the IAEA 2002 tests. The determined activities of the radionuclides were also compared for four test spectra of several samples. The result of the performance test is promising in comparison with those of the well-known software packages for ${\gamma}$-ray spectrum analysis.

• 대한불안의학회지
• /
• 제6권2호
• /
• pp.93-101
• /
• 2010

Research on obsessive-compulsive disorder (OCD) has advanced substantially since the DSM-IV was published in 1994. It is time to reexamine the nosology of this disorder, reviewing conflicting views regarding the classification as well as subtypes and specifiers of OCD. Although there is ongoing debate, OCD experts have suggested that OCD be retained in the section related to anxiety disorders and also that along with OCD, this section include obsessive-compulsive spectrum disorders (OCSD), a group of disorders closely related to OCD. A combined 'anxiety and obsessive-compulsive spectrum chapter' has also been proposed to include OCSDs. A growing body of scientific data has provided empirical support for the inclusion of a 'tic-related' subtype of OCD in the DSM-V. However, it remains controversial as to whether to introduce OCD symptom dimensions as specifiers as well as items in the diagnostic criteria. With regard to compulsive hoarding, there has been sufficient evidence to recommend that it be classified in the DSM-V as a separate disorder. Much work remains in order to ensure that the DSM-V is as evidence based as possible. It is necessary to strive toward integrating the biological and psychological data related to OCD and OCSD based on their endophenotypic features.

• 한국전산구조공학회:학술대회논문집
• /
• 한국전산구조공학회 1998년도 가을 학술발표회 논문집
• /
• pp.439-446
• /
• 1998

One of the most important roles in the nonlinear dynamic structural analysis is to select a proper ground excitation, which dominates the response of a structure. Because of the lack of recorded accelerograms in Korea, a stochastic model of ground excitation with various dynamic properties rather than recorded accelerograms is necessarily required. If all information is not available at site, the information from other sites with similar features can be used by the procedure of seismic hazard analysis. Eliopoulos and Wen identified the parameters of the ground motion model by the empirical relations or expressions developed by Trifunac and Lee. Because the relations used in the parameter identification are largely empirical, it is required to apply the artificial neural networks instead of the empirical model. Additionally, neural networks have the advantage of the empirical model that it can continuously re-train the new recorded data, so that it can adapt to the change of the enormous data. Based on the redefined traditional processes, three neural-networks-based models (FAS_NN, PSD_NN and INT_NN) are proposed to individually substitute the Fourier amplitude spectrum, the parameter identification of power spectral density function and intensity function. The paper describes the first half of the research for the development of Neural-Networks-based model for the generation of an Artificial earthquake and a Response Spectrum(NNARS).

• 산업경영시스템학회지
• /
• 제37권1호
• /
• pp.1-8
• /
• 2014

This paper proposes an emotion classifier from EEG signals based on Bayes' theorem and a machine learning using a perceptron convergence algorithm. The emotions are represented on the valence and arousal dimensions. The fast Fourier transform spectrum analysis is used to extract features from the EEG signals. To verify the proposed method, we use an open database for emotion analysis using physiological signal (DEAP) and compare it with C-SVC which is one of the support vector machines. An emotion is defined as two-level class and three-level class in both valence and arousal dimensions. For the two-level class case, the accuracy of the valence and arousal estimation is 67% and 66%, respectively. For the three-level class case, the accuracy is 53% and 51%, respectively. Compared with the best case of the C-SVC, the proposed classifier gave 4% and 8% more accurate estimations of valence and arousal for the two-level class. In estimation of three-level class, the proposed method showed a similar performance to the best case of the C-SVC.

• 한국군사과학기술학회지
• /
• 제8권3호
• /
• pp.73-82
• /
• 2005

This paper describes shock waveform synthesis methods for a shock response spectnlm over a short time interval with which intereference between parts within a test item is increased to perform a sufficient shock test for damage or malfunction which may be caused by the interference between parts, and a digital filter for obtaining a shock response history required for the shock waveform synthesis and a digital inverse filter for restoration by inversely using the digital filter. The time at which the maximax value occurs in the response history is detected in order to establish a delay time which is one of the parameters in the wavelet, on the condition that the natural frequency of SDOF system with a Q (quality factor) of 10 equals to the wavelet frequency of the zero delay wavelet input. A shock response spectrum over a short time interval and an abrupt change in the acceleration for an instant are illustrated as features of the synthesized waveform.

• Journal of Power Electronics
• /
• 제10권4호
• /
• pp.342-350
• /
• 2010

Randomizing the switching frequency (RSF) to reduce the electromagnetic interference (EMI) of switching power converters is a well-known technique that has been previously discussed. The randomized pulse position (RPP) technique, in which the switching frequency is kept fixed while the pulse position (the delay from the starting of the switching cycle to the turn-on instant within the cycle) is randomized, has been previously addressed in the literature for the same purpose. This paper presents a double-hybrid technique (DHB) for EMI reduction in dc-dc switching regulators. The proposed technique employed both the RSF and the RPP techniques. To effectively spread the conducted-noise frequency spectrum and at the same time attain a satisfactory output voltage quality, two parameters (switching frequency and pulse position) were randomized, and a third parameter (the duty ratio) was controlled by a digital compensator. Implementation was achieved using field programmable gate array (FPGA) technology, which is increasingly being adopted in industrial electronic applications. To evaluate the contribution of the proposed DHB technique, investigations were carried out for each basic PWM, RPP, RSF, and DHB technique. Then a comparison was made of the performances achieved. The experimentally investigated features include the effect of each technique on the common-mode, differential-mode, and total conducted-noise characteristics, and their influence on the converter’s output ripple voltage.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
• /
• 제30권4호
• /
• pp.145-152
• /
• 2019

Objectives: The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective datadriven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics. Methods: Based on our search and exclusion criteria, we reviewed 13 studies. Results: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data. Conclusion: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.

• Korean Journal of Radiology
• /
• 제24권12호
• /
• pp.1260-1283
• /
• 2023

Magnetic resonance imaging (MRI) has been extensively applied in the study of multiple sclerosis (MS), substantially contributing to diagnosis, differential diagnosis, and disease monitoring. MRI studies have significantly contributed to the understanding of MS through the characterization of typical radiological features and their clinical or prognostic implications using conventional MRI pulse sequences and further with the application of advanced imaging techniques sensitive to microstructural damage. Interpretation of results has often been validated by MRI-pathology studies. However, the application of MRI techniques in the study of neuromyelitis optica spectrum disorders (NMOSD) remains an emerging field, and MRI studies have focused on radiological correlates of NMOSD and its pathophysiology to aid in diagnosis, improve monitoring, and identify relevant prognostic factors. In this review, we discuss the main contributions of MRI to the understanding of MS and NMOSD, focusing on the most novel discoveries to clarify differences in the pathophysiology of focal inflammation initiation and perpetuation, involvement of normal-appearing tissue, potential entry routes of pathogenic elements into the CNS, and existence of primary or secondary mechanisms of neurodegeneration.