• 제목/요약/키워드: Training Samples

검색결과 570건 처리시간 0.022초

노인 불면에 대한 EFT 불면 치료 프로그램(EFT-I)의 효과 평가를 위한 예비적 연구 (A Preliminary study for the evaluation of the effects of EFT-I(EFT program for insomnia) for insomnia in the elderly)

  • 이정환;서현욱;정선용;김종우
    • 동의신경정신과학회지
    • /
    • 제22권4호
    • /
    • pp.101-109
    • /
    • 2011
  • Objectives : The aim of this study was to evaluate the effects of EFT-I(EFT program for insomnia) for insomnia in the elderly as a preliminary study. Methods : This study was a single group pre-post comparative study that involved 10 elderly women(mean age=$76.3{\pm}4.29$), who visited a senior welfare center, complained of insomnia symptoms. Subjects received 8 sessions(twice a week for 4 weeks and 1 hour for each session) of EFT-I group training. Insomnia severity, depression, state-anxiety, and life satisfaction of each subject were evaluated twice at pre and post of EFT-I. Korean Sleep Scale, Short form of Geriatric Depression Scale(SGDS), State-Trait Anxiety Inventory(STAI), and life satisfaction scale were used as evaluation tools. The data were analyzed using paired-samples T-test. Results : Insomnia severity, life satisfaction, depression, and state-anxiety were significantly improved by EFT-I. Conclusions : Result of this study showed that EFT-I can be a useful treatment program for elderly insomnia. Larger clinical trials are needed to verify effect of EFT-I as a community based insomnia management program for the elderly.

A new model approach to predict the unloading rock slope displacement behavior based on monitoring data

  • Jiang, Ting;Shen, Zhenzhong;Yang, Meng;Xu, Liqun;Gan, Lei;Cui, Xinbo
    • Structural Engineering and Mechanics
    • /
    • 제67권2호
    • /
    • pp.105-113
    • /
    • 2018
  • To improve the prediction accuracy of the strong-unloading rock slope performance and obtain the range of variation in the slope displacement, a new displacement time-series prediction model is proposed, called the fuzzy information granulation (FIG)-genetic algorithm (GA)-back propagation neural network (BPNN) model. Initially, a displacement time series is selected as the training samples of the prediction model on the basis of an analysis of the causes of the change in the slope behavior. Then, FIG is executed to partition the series and obtain the characteristic parameters of every partition. Furthermore, the later characteristic parameters are predicted by inputting the earlier characteristic parameters into the GA-BPNN model, where a GA is used to optimize the initial weights and thresholds of the BPNN; in the process, the numbers of input layer nodes, hidden layer nodes, and output layer nodes are determined by a trial method. Finally, the prediction model is evaluated by comparing the measured and predicted values. The model is applied to predict the displacement time series of a strong-unloading rock slope in a hydropower station. The engineering case shows that the FIG-GA-BPNN model can obtain more accurate predicted results and has high engineering application value.

인적자원 아웃소싱의 현황분석에 관한 연구 (The Study about analyzing the Present Situation of Human Resource Outsourcing)

  • 차성호;양동훈
    • 한국컴퓨터정보학회논문지
    • /
    • 제13권5호
    • /
    • pp.279-289
    • /
    • 2008
  • 본 연구는 우리나라 상장기업과 코스닥기업을 중심으로 인적자원 아웃소싱 정도의 실태조사를 통하여 그 현황을 파악하는데 1차적 목적을 두었으며, 상장기업과 코스닥기업의 인적자원 아웃소싱의 활성화에 유용한 기초 자료를 제공하기 위한 것이다. 필요한 자료 수집을 위해 2005년 8연 1일부터 2006년 3월 31일까지 조직단위로 상장기업 650개, 코스닥기업 850개 총 1,500개 기업체에 대해 인적자원 아웃소싱에 관한 설문조사를 행하였다. 현황분석에 관한 연구결과 의하면 첫째, 인적자원 아웃소싱이 도입된 분야는 교육훈련이 가장 높으며, 다음은 인사 정보시스템, 임직원 모집과 선발 순으로 나타났다. 둘째, 인적자원 아웃소싱으로 외부에 이전되는 업무는 복잡하고 특수한 업무인 기획 및 설계기능 보다는 일상적이고 정형적인 업무로써 위험부담이 없는 진행 및 운영기능이 먼저 외부화 되고 있음을 확인할 수 있었다. 본 연구의 결과는 다음과 같은 시사점을 제공한다. 첫째, 인적자원 아웃소싱 정도가 아직 다른 영역 보다 상대적으로 낮으며 활성화를 위한 전략적 운영이 필요하다. 둘째, 인적자원 아웃소싱의 활성화를 위하여 먼저 일상적이고 정형적인 단순업무를 위험부담 없이 운영해 본 후 발생된 문제점을 보완하여 복잡하고 비정형적인 핵심업무까지 확대시켜 나갈 수 있다.

  • PDF

Development of an Optical Tissue Clearing Laser Probe System

  • Yeo, Changmin;Kang, Heesung;Bae, Yunjin;Park, Jihoon;Nelson, J. Stuart;Lee, Kyoung-Joung;Jung, Byungjo
    • Journal of the Optical Society of Korea
    • /
    • 제17권4호
    • /
    • pp.289-295
    • /
    • 2013
  • Although low-level laser therapy (LLLT) has been a valuable therapeutic technology in the clinic, its efficacy may be reduced in deep tissue layers due to strong light scattering which limits the photon density. In order to enhance the photon density in deep tissue layers, this study developed an optical tissue clearing (OTC) laser probe (OTCLP) system which can utilize four different OTC methods: 1) tissue temperature control from 40 to $10^{\circ}C$; 2) laser pulse frequency from 5 to 30 Hz; 3) glycerol injection at a local region; and 4) a combination of the aforementioned three methods. The efficacy of the OTC methods was evaluated and compared by investigating laser beam profiles in ex-vivo porcine skin samples. Results demonstrated that total (peak) intensity at full width at half maximum of laser beam profile when compared to control data was increased: 1) 1.21(1.39)-fold at $10^{\circ}C$; 2) 1.22 (1.49)-fold at a laser pulse frequency of 5 Hz; 3) 1.64 (2.41)-fold with 95% glycerol injection; 4) 1.86 (3.4)-fold with the combination method. In conclusion, the OTCLP system successfully improved the laser photon density in deep tissue layers and may be utilized as a useful tool in LLLT by increasing laser photon density.

딥러닝 사물 인식 알고리즘(YOLOv3)을 이용한 미세조류 인식 연구 (Microalgae Detection Using a Deep Learning Object Detection Algorithm, YOLOv3)

  • 박정수;백지원;유광태;남승원;김종락
    • 한국물환경학회지
    • /
    • 제37권4호
    • /
    • pp.275-285
    • /
    • 2021
  • Algal bloom is an important issue in maintaining the safety of the drinking water supply system. Fast detection and classification of algae images are essential for the management of algal blooms. Conventional visual identification using a microscope is a labor-intensive and time-consuming method that often requires several hours to several days in order to obtain analysis results from field water samples. In recent decades, various deep learning algorithms have been developed and widely used in object detection studies. YOLO is a state-of-the-art deep learning algorithm. In this study the third version of the YOLO algorithm, namely, YOLOv3, was used to develop an algae image detection model. YOLOv3 is one of the most representative one-stage object detection algorithms with faster inference time, which is an important benefit of YOLO. A total of 1,114 algae images for 30 genera collected by microscope were used to develop the YOLOv3 algae image detection model. The algae images were divided into four groups with five, 10, 20, and 30 genera for training and testing the model. The mean average precision (mAP) was 81, 70, 52, and 41 for data sets with five, 10, 20, and 30 genera, respectively. The precision was higher than 0.8 for all four image groups. These results show the practical applicability of the deep learning algorithm, YOLOv3, for algae image detection.

다중 사용자 다중 안테나 네트워크를 위한 심화 학습기반 사용자 스케쥴링 (Deep Learning Based User Scheduling For Multi-User and Multi-Antenna Networks)

  • 반태원;이웅섭
    • 한국정보통신학회논문지
    • /
    • 제23권8호
    • /
    • pp.975-980
    • /
    • 2019
  • 본 논문에서는 차세대 이동통신 시스템의 핵심 요소 기술 중의 하나로 각광 받고 있는 다중 사용자 다중 안테나 네트워크에서 사용자 선택을 위한 심화 학습 기반 스케쥴링 기법을 제안한다. 제안된 신경망을 학습시키기 위하여 기존의 최적 방식을 통해서 90,000 데이터 샘플을 확보하였으며, 추가적인 10,000 데이터 샘플을 이용하여 최종 학습된 신경망의 과최적화 여부를 확인하였다. 제안된 신경망 기반의 스케쥴링 알고리즘은 초기 학습 시에는 상당한 복잡도와 학습 시간이 필요하지만, 일단 학습이 완료된 이후에는 추가적인 복잡도가 유발되지 않는 장점이 있다. 반면에, 기존의 최적 방식은 매 스케쥴링마다 동일한 복잡도의 계산이 지속적으로 요구된다. 다양한 컴퓨터 시뮬레이션 결과에 따르면, 제안된 심화 학습 기반의 스케쥴링 기법은 10dB 보다 낮은 SNR에서는 기존 최적 알고리즘의 약 88~96%에 이르는 평균 전송 속도의 합을 얻을 수 있으며, 10dB 이상의 SNR에서는 최적의 평균 전송 속도의 합을 얻을 수 있다.

Quality grading of Hanwoo (Korean native cattle breed) sub-images using convolutional neural network

  • Kwon, Kyung-Do;Lee, Ahyeong;Lim, Jongkuk;Cho, Soohyun;Lee, Wanghee;Cho, Byoung-Kwan;Seo, Youngwook
    • 농업과학연구
    • /
    • 제47권4호
    • /
    • pp.1109-1122
    • /
    • 2020
  • The aim of this study was to develop a marbling classification and prediction model using small parts of sirloin images based on a deep learning algorithm, namely, a convolutional neural network (CNN). Samples were purchased from a commercial slaughterhouse in Korea, images for each grade were acquired, and the total images (n = 500) were assigned according to their grade number: 1++, 1+, 1, and both 2 & 3. The image acquisition system consists of a DSLR camera with a polarization filter to remove diffusive reflectance and two light sources (55 W). To correct the distorted original images, a radial correction algorithm was implemented. Color images of sirloins of Hanwoo (mixed with feeder cattle, steer, and calf) were divided and sub-images with image sizes of 161 × 161 were made to train the marbling prediction model. In this study, the convolutional neural network (CNN) has four convolution layers and yields prediction results in accordance with marbling grades (1++, 1+, 1, and 2&3). Every single layer uses a rectified linear unit (ReLU) function as an activation function and max-pooling is used for extracting the edge between fat and muscle and reducing the variance of the data. Prediction accuracy was measured using an accuracy and kappa coefficient from a confusion matrix. We summed the prediction of sub-images and determined the total average prediction accuracy. Training accuracy was 100% and the test accuracy was 86%, indicating comparably good performance using the CNN. This study provides classification potential for predicting the marbling grade using color images and a convolutional neural network algorithm.

교사들이 인식하는 갈등 유형과 대응 방법 분석 (Analyses of Types of Conflict Perceived by Teachers and Coping Methods)

  • 김진철;윤소희
    • 산업융합연구
    • /
    • 제19권1호
    • /
    • pp.43-51
    • /
    • 2021
  • 본 연구는 교사들이 인식하는 갈등 유형과 대응 방법을 분석하기 위해 실시되었다. 연구를 위해 초·중등 교사 561명의 설문 결과를 독립표본 t-검증과 일원변량분석을 통해 분석하였다. 연구결과는 다음과 같다. 첫째, 동료교사, 학부모, 교육정책에 대한 갈등과 관련하여 담임 여부, 전입 여부, 근무 지역에 따라, 학생과의 갈등과 관련하여 담임 여부와 전입 여부에 따라, 관리자와의 갈등과 관련하여 근무 지역에 따라, 행정직원과의 갈등과 관련하여 담임 여부와 근무 지역에 따라 평균 차이가 있었으며, 이는 통계적으로 유의미한 수준이었다. 둘째, 학생 갈등 대응과 관련하여 성별과 전입 여부에 대한 응답의 차이가, 교육정책 갈등 대응과 관련하여 성별과 담임 여부에 대한 응답의 차이가 통계적으로 유의미한 것으로 나타났다. 연구 결과를 토대로 학교장의 조직 갈등 이해와 관리 역량 제고, 갈등 해소를 위한 교사들의 전략 학습이라는 시사점을 제시하였다.

Characteristics of Aerobic Exercise as Determinants of Blood Pressure Control in Hypertensive Patients: A Systematic Review and Meta-Analysis

  • Lee, Sun Hee;Chae, Young Ran
    • 대한간호학회지
    • /
    • 제50권6호
    • /
    • pp.740-756
    • /
    • 2020
  • Purpose: The purpose of this study was to evaluate the effect on blood pressure (BP) and heart rate (HR) according to aerobic exercise characteristics in adults with hypertension using a systematic review and meta-analysis. Methods: The related researches were selected from PubMed, EMBASE, Cochrane library, CINAHL, PsycINFO, SPORTDiscus and 5 domestic databases up to September 4, 2019. To estimate the effect size, random effect models were used to derive weighted mean differences (WMD) and their 95% confidence intervals (CI) of aerobic exercise on BP and HR. Results: A total of 37 RCTs with 1,813 samples were included. Aerobic exercise was found to significantly reduce systolic BP (WMD, - 8.29 mmHg; 95% CI, - 10.12 to - 6.46), diastolic BP (WMD, - 5.19 mmHg; 95% CI, - 6.24 to - 4.14) and HR (WMD, - 4.22 beats/min; 95% CI, - 5.36 to -3.09). In detail, systolic BP and diastolic BP were significantly decreased in all groups of exercise types, frequency and duration. Systolic BP and diastolic BP were significantly decreased in the moderate and vigorous-intensity group. Exercise characteristics with the most dramatical change in systolic BP were water-based training, moderate-intensity, 3 times a week and 8 to 11 weeks of duration. In diastolic BP, the greatest effect size was over 24 weeks of exercise. Conclusion: Moderate aerobic exercise, especially water-based exercise can be an important part of lifestyle modification for hypertensive patients. Also, it can be recommended in a variety of clinical settings for lowering BP and HR. However, there is insufficient evidence that low-intensity exercise is effective in lowering BP.

Evaluation of a multi-stage convolutional neural network-based fully automated landmark identification system using cone-beam computed tomography-synthesized posteroanterior cephalometric images

  • Kim, Min-Jung;Liu, Yi;Oh, Song Hee;Ahn, Hyo-Won;Kim, Seong-Hun;Nelson, Gerald
    • 대한치과교정학회지
    • /
    • 제51권2호
    • /
    • pp.77-85
    • /
    • 2021
  • Objective: To evaluate the accuracy of a multi-stage convolutional neural network (CNN) model-based automated identification system for posteroanterior (PA) cephalometric landmarks. Methods: The multi-stage CNN model was implemented with a personal computer. A total of 430 PA-cephalograms synthesized from cone-beam computed tomography scans (CBCT-PA) were selected as samples. Twenty-three landmarks used for Tweemac analysis were manually identified on all CBCT-PA images by a single examiner. Intra-examiner reproducibility was confirmed by repeating the identification on 85 randomly selected images, which were subsequently set as test data, with a two-week interval before training. For initial learning stage of the multi-stage CNN model, the data from 345 of 430 CBCT-PA images were used, after which the multi-stage CNN model was tested with previous 85 images. The first manual identification on these 85 images was set as a truth ground. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the errors in manual identification and artificial intelligence (AI) prediction. Results: The AI showed an average MRE of 2.23 ± 2.02 mm with an SDR of 60.88% for errors of 2 mm or lower. However, in a comparison of the repetitive task, the AI predicted landmarks at the same position, while the MRE for the repeated manual identification was 1.31 ± 0.94 mm. Conclusions: Automated identification for CBCT-synthesized PA cephalometric landmarks did not sufficiently achieve the clinically favorable error range of less than 2 mm. However, AI landmark identification on PA cephalograms showed better consistency than manual identification.