• 제목/요약/키워드: training mode

검색결과 221건 처리시간 0.025초

문맥적응적 화면내 예측 모델 학습 및 부호화 성능분석 (Context-Adaptive Intra Prediction Model Training and Its Coding Performance Analysis)

  • 문기화;박도현;김재곤
    • 방송공학회논문지
    • /
    • 제27권3호
    • /
    • pp.332-340
    • /
    • 2022
  • 최근 딥러닝을 적용하는 비디오 압축에 대한 연구가 활발히 진행되고 있다. 특히, 화면내 예측 부호화의 성능 한계를 극복할 수 있는 방안으로 딥러닝 기반의 화면내 예측 부호화 기술이 연구되고 있다. 본 논문은 신경망 기반 문맥적응적 화면내 예측 모델의 학습기법과 그 부호화 성능분석을 제시한다. 즉, 본 논문에서는 주변 참조샘플의 문맥정보를 입력하여 현재블록을 예측하는 기존의 합성곱 신경망(CNN: Convolutional Neural network) 기반의 화면내 예측 모델을 학습한다. 학습된 화면내 예측 모델을 HEVC(High Efficiency Video Coding)의 참조 소프트웨어인 HM16.19에 추가적인 화면내 예측모드로 구현하고 그 부호화 성능을 분석하였다. 실험결과 학습한 예측 모델은 HEVC 대비 AI(All Intra) 모드에서 0.28% BD-rate 부호화 성능 향상을 보였다. 또한 비디오 부호화 블록분할 구조를 고려하여 학습한 경우의 성능도 확인하였다.

Effects of Electroencephalogram Biofeedback on Emotion Regulation and Brain Homeostasis of Late Adolescents in the COVID-19 Pandemic

  • Park, Wanju;Cho, Mina;Park, Shinjeong
    • 대한간호학회지
    • /
    • 제52권1호
    • /
    • pp.36-51
    • /
    • 2022
  • Purpose: The purpose of this study was to examine the effects of electroencephalogram (EEG) biofeedback training for emotion regulation and brain homeostasis on anxiety about COVID-19 infection, impulsivity, anger rumination, meta-mood, and self-regulation ability of late adolescents in the prolonged COVID-19 pandemic situation. Methods: A non-equivalent control group pretest-posttest design was used. The participants included 55 late adolescents in the experimental and control groups. The variables were evaluated using quantitative EEG at pre-post time points in the experimental group. The experimental groups received 10 sessions using the three-band protocol for five weeks. The collected data were analyzed using the Shapiro-Wilk test, Wilcoxon rank sum test, Wilcoxon signed-rank test, t-test and paired t-test using the SAS 9.3 program. The collected EEG data used a frequency series power spectrum analysis method through fast Fourier transform. Results: Significant differences in emotion regulation between the two groups were observed in the anxiety about COVID-19 infection (W = 585.50, p = .002), mood repair of meta-mood (W = 889.50, p = .024), self-regulation ability (t = - 5.02, p < .001), self-regulation mode (t = - 4.74, p < .001), and volitional inhibition mode (t = - 2.61, p = .012). Neurofeedback training for brain homeostasis was effected on enhanced sensory-motor rhythm (S = 177.00, p < .001) and inhibited theta (S = - 166.00, p < .001). Conclusion: The results demonstrate the potential of EEG biofeedback training as an independent nursing intervention that can markedly improve anxiety, mood-repair, and self-regulation ability for emotional distress during the COVID-19 pandemic.

CBT 환경을 기반으로 하는 쌍방향 자율모드 기반 RTE 시스템 개발 (Development of an Interactive self-control-mode based RTE System based on CBT)

  • 김성열;최보철;홍병두
    • 한국전자통신학회논문지
    • /
    • 제7권2호
    • /
    • pp.227-234
    • /
    • 2012
  • 컴퓨터와 인터넷의 발달은 사회 전 분야에 걸쳐 많은 변화를 가져왔다. 교육 시장도 이에 적극적으로 부응하여 원격교육, 사이버교육, 가상강의, e-Learning 등의 이름으로 제안되어지며 다양한 서비스를 제공하고 있다. 이를 위한 시스템들이 다양하게 제공되면서 컴퓨터를 이용하여 이루어지는 교육과 평가가 활발히 이루어지고 있다. 그러나 많은 시스템들이 제한적 기능을 제공하고 있는 것이 사실이다. 이에 본 논문에서는 학습자들의 교육 이해 수준 정도를 실시간으로 피드백(Feedback)함으로써 교수자가 맞춤식교육을 진행할 수 있는 CBT(Computer Based Training/Test)방식의 쌍방향 자율모드 기반 교육 환경을 제공하는 RTE(Real Training Environment) 시스템을 제안한다. 이 시스템을 활용하여 학습자의 능력에 맞는 맞춤식 교육 환경을 제공하고 학습자들의 학습동기를 극대화시킬 수 있을 것으로 생각된다.

Blue Print와 신뢰성 기법을 혼합한 고객만족도 향상에 관한 연구: 교육서비스 사례 (Customer Satisfaction Improvement by Combining the Blue Print and Reliability Technique: Education Service Case Study)

  • 백천주;구일섭;임익성;권홍규
    • 한국신뢰성학회지:신뢰성응용연구
    • /
    • 제12권1호
    • /
    • pp.13-24
    • /
    • 2012
  • This paper applied the Blue Print and FMEA (Failure Mode and Effect Analysis) to education service in order to raise the education service satisfaction. First, the Blue Print is deployed to come up with strategies to overcome the fail possibility point and waiting point. Next, in order to analyze the fail factors and alternative strategies, the Blue Print of education service is applied to FMEA. The results are as follows; first, the ommission from information document by web-mail or e-mail, Second, thing that selected in spite of company uneducated, thing that omitted despite the company is target, and the unsatisfaction of attendee about training contents. Third, the delay of counsel at the telephone reply, erroneous list of course name and attendee at HRD (Human Resource Development), omission of check whether attends or not. Except for unsatisfaction of attendee, all appears at the process that service delivered. And the unsatisfaction of attendee is about education contents. Both is the factor which have influence on the education service quality. The strategies to remove the failure mode are training and manual development on service and work, a thorough management and check of information system like as ERP (Enterprise Resoure Planning), HRD, education institution list DB (Data Base), on-line application system, a development of education program to offer best education that reflect the user needs and continuously changing environment.

CNN-based Fast Split Mode Decision Algorithm for Versatile Video Coding (VVC) Inter Prediction

  • Yeo, Woon-Ha;Kim, Byung-Gyu
    • Journal of Multimedia Information System
    • /
    • 제8권3호
    • /
    • pp.147-158
    • /
    • 2021
  • Versatile Video Coding (VVC) is the latest video coding standard developed by Joint Video Exploration Team (JVET). In VVC, the quadtree plus multi-type tree (QT+MTT) structure of coding unit (CU) partition is adopted, and its computational complexity is considerably high due to the brute-force search for recursive rate-distortion (RD) optimization. In this paper, we aim to reduce the time complexity of inter-picture prediction mode since the inter prediction accounts for a large portion of the total encoding time. The problem can be defined as classifying the split mode of each CU. To classify the split mode effectively, a novel convolutional neural network (CNN) called multi-level tree (MLT-CNN) architecture is introduced. For boosting classification performance, we utilize additional information including inter-picture information while training the CNN. The overall algorithm including the MLT-CNN inference process is implemented on VVC Test Model (VTM) 11.0. The CUs of size 128×128 can be the inputs of the CNN. The sequences are encoded at the random access (RA) configuration with five QP values {22, 27, 32, 37, 42}. The experimental results show that the proposed algorithm can reduce the computational complexity by 11.53% on average, and 26.14% for the maximum with an average 1.01% of the increase in Bjøntegaard delta bit rate (BDBR). Especially, the proposed method shows higher performance on the sequences of the A and B classes, reducing 9.81%~26.14% of encoding time with 0.95%~3.28% of the BDBR increase.

Differential effects of type 1 diabetes mellitus and subsequent osteoblastic β-catenin activation on trabecular and cortical bone in a mouse mode

  • Chen, Sixu;Liu, Daocheng;He, Sihao;Yang, Lei;Bao, Quanwei;Qin, Hao;Liu, Huayu;Zhao, Yufeng;Zong, Zhaowen
    • Experimental and Molecular Medicine
    • /
    • 제50권12호
    • /
    • pp.3.1-3.14
    • /
    • 2018
  • Type 1 diabetes mellitus (T1DM) is a pathological condition associated with osteopenia. $WNT/{\beta}$-catenin signaling is implicated in this process. Trabecular and cortical bone respond differently to $WNT/{\beta}$-catenin signaling in healthy mice. We investigated whether this signaling has different effects on trabecular and cortical bone in T1DM. We first established a streptozotocin-induced T1DM mouse model and then constitutively activated ${\beta}$-catenin in osteoblasts in the setting of T1DM (T1-CA). The extent of bone loss was greater in trabecular bone than that in cortical bone in T1DM mice, and this difference was consistent with the reduction in the expression of ${\beta}$-catenin signaling in the two bone compartments. Further experiments demonstrated that in T1DM mice, trabecular bone showed lower levels of insulin-like growth factor-1 receptor (IGF-1R) than the levels in cortical bone, leading to lower $WNT/{\beta}$-catenin signaling activity through the inhibition of the IGF-1R/Akt/glycogen synthase kinase $3{\beta}$ ($GSK3{\beta}$) pathway. After ${\beta}$-catenin was activated in T1-CA mice, the bone mass and bone strength increased to substantially greater extents in trabecular bone than those in cortical bone. In addition, the cortical bone of the T1-CA mice displayed an unexpected increase in bone porosity, with increased bone resorption. The downregulated expression of WNT16 might be responsible for these cortical bone changes. In conclusion, we found that although the activation of $WNT/{\beta}$-catenin signaling increased the trabecular bone mass and bone strength in T1DM mice, it also increased the cortical bone porosity, impairing the bone strength. These findings should be considered in the future treatment of T1DM-related osteopenia.

시계열 선형 분석을 통한 뉴로피드백 훈련 전, 후의 주의력 결핍 성향과 정서적 성향에 미치는 영향에 관한 연구 (A Reserch on the Effect Neurofeedback Traing before & After About Emotional and Attention Deficit Characteristics by Timeseries Linear Analysis : for Primary Student)

  • 백기자;박병운;이선규
    • Journal of Information Technology Applications and Management
    • /
    • 제14권4호
    • /
    • pp.43-59
    • /
    • 2007
  • The purpose of the study was to examine the effectiveness of Neuro Feedback training by observing the pre and post brainwave measurement results of about 50 (experimental group 25. comparative group 25) subjects who have shown psychological difficulties in studying. attention deficit, and personalities. The study took place at Neuro Feedback training Center B. in between the months of July 2006 and May 2007. The methodology involved in the study included the Coloring Analysis Program of the Brain Quotient Test. As the brain waves are adjusted by timeseries linear analysis. the brain function quotients can reflect the functional states of the brain. Through the test, three parameters relaxation, attention and concentration-were initially measured for one minute each and the lowest parameter out of the three was selected as the training mode or improvement target. The training took place two or three times a week. for about 40 to 60 minutes per session. Because the clients have come to the training center at different times. the researcher sampled the results of only those who had attended more than 30 training sessions. The tool used to measure the psychological reaction was POMS (Profile of Mood State). while the tool used to measure the emotional and attention-deficit characteristics was the Amen Clinic ADD Type questionnaire. Hypothesis testing included t-test. The result of the study showed the Theta: SMR ratio of (left)p = .013. (right) p = .019. The result also confirmed the differences of both ATQ(left) p = .011. (right)p = .030 and SQ(left) p = .017. (right) p = .022. The result confirmed of emotional p = .000. attention-deficit characteristics p = .000. The result of the study suggest Neuro Feedback technique's possibility in positively affecting the subjects' mental state and attention-deficit characteristics.

  • PDF

The development of training platform for CiADS using cave automatic virtual environment

  • Jin-Yang Li ;Jun-Liang Du ;Long Gu ;You-Peng Zhang;Xin Sheng ;Cong Lin ;Yongquan Wang
    • Nuclear Engineering and Technology
    • /
    • 제55권7호
    • /
    • pp.2656-2661
    • /
    • 2023
  • The project of China initiative Accelerator Driven Subcritical (CiADS) system has been started to construct in southeast China's Guangdong province since 2019, which is expected to be checked and accepted in the year 2025. In order to make the students in University of Chinese Academy of Sciences (UCAS) better understand the main characteristic and the operation condition in the subcritical nuclear facility, the training platform for CiADS has been developed based on the Cave Automatic Virtual Environment (CAVE) in the Institute of Modern Physics Chinese Academy of Sciences (IMPCAS). The CAVE platform is a kind of non-head mounted virtual reality display system, which can provide the immersive experience and the alternative training platform to substitute the dangerous operation experiments with strong radioactivity. In this paper, the CAVE platform for the training scenarios in CiADS system has been presented with real-time simulation feature, where the required devices to generate the auditory and visual senses with the interactive mode have been detailed. Moreover, the three dimensional modeling database has been created for the different operation conditions, which can bring more freedom for the teachers to generate the appropriate training courses for the students. All the user-friendly features will offer a deep realistic impression to the students for the purpose of getting the required knowledge and experience without the large costs in the traditional experimental nuclear reactor.

트레이닝 형태의 차이가 운동 특이성(exercise specificity)과 전사효과(transability)에 미치는 영향 (Effects of Different Exercise Training Mode on Exercise Specificity and Transability)

  • 김영일;곽이섭
    • 생명과학회지
    • /
    • 제19권7호
    • /
    • pp.968-975
    • /
    • 2009
  • 본 연구에서는 8주의 유산소 및 저항성 트레이닝 그룹으로 나누고 그에 따른 트레이닝이 서로 다른 테스트를 하였을 때, 운동 특이성 효과(specific effect)와 전사효과(transferabilty)에 영향을 미치는 지를 연구하였다. 결론적으로 8주간의 유산소 및 저항성 트레이닝은 동일한 테스트를 통하여 운동의 특이성은 나타냈으나 서로 다른 테스트를 해 봄으로써 전사효과의 향상은 나타내지 못했다. 전사효과를 나타내지 못한 가장 큰 이유는 8주라는 기간이 중추신경과 근육의 적응하기에는 다소 짧은 기간이라 사료되며 추후의 연구에서는 트레이닝 기간등을 고려한 좀 더 세분화된 연구가 필요하다고 생각된다.

리칭모드 제어기와 신경 회로망을 이용한 유도전동기의 위치제어 (The Position Control of Induction Motor using Reaching Mode Controller and Neural Networks)

  • 양오
    • 전자공학회논문지SC
    • /
    • 제37권3호
    • /
    • pp.72-83
    • /
    • 2000
  • 본 논문에서는 리칭모드 제어기와 신경 회로망을 이용하여 3상 유도전동기의 위치제어 시스템을 구현한다. 리칭모드 제어기는 위치오차와 속도오차의 궤적을 슬라이딩 평면으로 들어가도록 하고 신경회로망의 초기학습을 담당한다. 리칭모드 제어기의 구조는 슬라이딩 평면의 스위칭 함수로부터 간단히 구성하였다. 또한, 신경 회로망은 전향경로 신경망으로 구성되며 비선형 매핑능력과 탁월한 학습능력을 이용하여 유도전동기의 등가제어입력을 학습하도록 하였고 신경 회로망의 입력으로는 모터의 기준속도, 기준위치 및 엔코더를 이용하여 측정된 모터의 실제속도와 위치 등을 이용하였고 온라인 상태로 학습되도록 하였다. 이와 같이 복합적으로 구성된 제어기들을 유도전동기의 위치제어 시스템에 적용하였다. 본 논문에서 제안된 알고리즘의 타당성을 보이기 위해 기존의 PI 제어기와 비교평가를 하였으며 시뮬레이션과 실험결과로부터 초기운전 상태에서는 리칭모드 제어기가 주로 제어를 담당하지만 시간이 지남에 따라 신경회로망이 학습되어 신경 회로망이 주 제어기가 됨을 확인하였다. 아울러, 제안된 제어기가 PI 제어기보다 우수하고 특히 부하변동과 같은 외란에 강인함을 알 수 있었으며, 정상상태 오차가 현저히 감소하여 정밀한 제어가 가능함을 확인하였다.

  • PDF