• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4292-4307
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• 2021

For the research field of night infrared surveillance video, the target imaging in the video is easily affected by the light due to the characteristics of the active infrared camera and the classical ViBe algorithm has some problems for moving target extraction because of background misjudgment, noise interference, ghost shadow and so on. Therefore, an improved ViBe algorithm (I-ViBe) for moving target extraction in night infrared surveillance video is proposed in this paper. Firstly, the video frames are sampled and judged by the degree of light influence, and the video frame is divided into three situations: no light change, small light change, and severe light change. Secondly, the ViBe algorithm is extracted the moving target when there is no light change. The segmentation factor of the ViBe algorithm is adaptively changed to reduce the impact of the light on the ViBe algorithm when the light change is small. The moving target is extracted using the region growing algorithm improved by the image entropy in the differential image of the current frame and the background model when the illumination changes drastically. Based on the results of the simulation, the I-ViBe algorithm proposed has better robustness to the influence of illumination. When extracting moving targets at night the I-ViBe algorithm can make target extraction more accurate and provide more effective data for further night behavior recognition and target tracking.

• 한국전자거래학회지
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• 제27권1호
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• pp.1-13
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• 2022

인터넷, 모바일 등 네트워크 기술이 발전함에 따라 내외부 침입 및 위협으로부터 조직의 자원을 보호하기 위한 보안의 중요성이 커지고 있다. 따라서 최근에는 다양한 보안 로그 이벤트에 대하여 보안 위협 여부를 사전에 파악하고, 예방하는 이상징후 식별 알고리즘의 개발이 강조되고 있다. 과거 규칙 기반 또는 통계 학습에 기반하여 개발되어 온 보안 이상징후 식별 알고리즘은 점차 기계 학습과 딥러닝에 기반한 모델링으로 진화하고 있다. 본 연구에서는 다양한 기계 학습 분석 방법론을 활용하여 악의적 내부자 위협을 사전에 식별하는 최적 알고리즘으로 LSTM-autoencoder를 변형한 Deep-autoencoder 모형을 제안한다. 본 연구는 비지도 학습에 기반한 이상탐지 알고리즘 개발을 통해 적응형 보안의 가능성을 향상시키고, 지도 학습에 기반한 정탐 레이블링을 통해 기존 알고리즘 대비 오탐율을 감소시켰다는 점에서 학문적 의의를 갖는다.

• Computers and Concrete
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• 제29권3호
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• pp.187-199
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• 2022

The usability of waste materials as raw materials is necessary for sustainable production. This study investigates the effects of different powder materials used to replace cement (0%, 5% and 10%) and standard sand (0%, 20% and 30%) (basalt, limestone, and dolomite) on the compressive strength (f_c), flexural strength (f_r), and ultrasonic pulse velocity (UPV) of mortars exposed to freeze-thaw cycles (56, 86, 126, 186 and 226 cycles). Furthermore, the usability of artificial intelligence models is compared, and the prediction accuracy of the outputs is examined according to the inputs (powder type, replacement ratio, and the number of cycles). The results show that the variability of the outputs was significantly high under the freeze-thaw effect in mortars produced with waste powder instead of those produced with cement and with standard sand. The highest prediction accuracy for all outputs was obtained using the adaptive-network-based fuzzy inference system model. The significantly high prediction accuracy was obtained for the UPV, f_c, and f_r of mortars produced using waste powders instead of standard sand (R² of UPV, f_c and f_f is 0.931, 0.759 and 0.825 respectively), when under the freeze-thaw effect. However, for the mortars produced using waste powders instead of cement, the prediction accuracy of UPV was significantly high (R²=0.889) but the prediction accuracy of f_c and f_r was low (R²f_c=0.612 and R²f_f=0.334).

• 디지털융복합연구
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• 제20권5호
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• pp.39-46
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• 2022

교통사고와 사회·경제적 손실 간의 연계성이 확인됨에 따라 사고 데이터에 기반을 둔 안전 정책 마련 및 중상·사망 등 그 심각도가 높은 교통사고의 절감 방안의 필요성이 제기되고 있다. 본 연구에서는 인구 대비 교통사고 사망자 비율이 높은 대전시를 대상지역으로 설정하고 보행자 교통사고 데이터를 수집한 후, 기계학습을 통해 최적알고리즘과 심각도 분류의 주요 인자를 도출하였다. 연구의 결과에 따르면, 적용한 9개 알고리즘 중 앙상블 기반의 학습 기법인 AdaBoost (Adaptive Boosting)와 RF (Random Forest)가 최적의 성능을 보여주었다. 이를 기반으로 도출된 대전시 보행자 교통사고 심각도의 주요 인자는 보행자의 연령이 70대 및 20대이거나 사고유형이 횡단사고에 의한 경우로 나타남에 따라 대전시 보행자 사고 저감 대책을 위한 고려요인으로 제안하였다.

• 대한방사선기술학회지:방사선기술과학
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• 제46권4호
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• pp.277-285
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• 2023

In this study, pneumonia identification networks with the small number of layers were constructed by using chest X-ray images. The networks had similar trainable-parameters, and the performance of the trained models was quantitatively evaluated with the modification of the network architectures. A total of 6 networks were constructed: convolutional neural network (CNN), VGGNet, GoogleNet, residual network with identity blocks, ResNet with bottleneck blocks and ResNet with identity and bottleneck blocks. Trainable parameters for the 6 networks were set in a range of 273,921-294,817 by adjusting the output channels of convolution layers. The network training was implemented with binary cross entropy (BCE) loss function, sigmoid activation function, adaptive moment estimation (Adam) optimizer and 100 epochs. The performance of the trained models was evaluated in terms of training time, accuracy, precision, recall, specificity and F1-score. The results showed that the trained models with the small number of layers precisely detect pneumonia from chest X-ray images. In particular, the overall quantitative performance of the trained models based on the ResNets was above 0.9, and the performance levels were similar or superior to those based on the CNN, VGGNet and GoogleNet. Also, the residual blocks affected the performance of the trained models based on the ResNets. Therefore, in this study, we demonstrated that the object detection networks with the small number of layers are suitable for detecting pneumonia using chest X-ray images. And, the trained models based on the ResNets can be optimized by applying appropriate residual-blocks.

• 문화기술의 융합
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• 제8권3호
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• pp.203-212
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• 2022

본 연구는 정서중심심리코칭 경험의 본질이 무엇이며, 그 경험이 연구 참여자들에게 어떤 변화를 가져왔는지를 탐색하는 것을 목적으로 한다. 이를 위해 정서중심코칭을 5회 경험한 4인의 연구참여자를 대상으로 심층면접을 수행하였다. 면담자료는 Colaizzi의 현상학적 연구방법을 사용하여 분석하였다. 정서중심심리코칭은 Greenberg의 정서중심치료를 기반으로 ICF의 코칭 핵심역량과 함께 융합하여 정서를 중심으로 접근하는 새로운 코칭심리 모델이다. 분석결과, 정서중심코칭 경험 이전에는 자신의 정서를 회피하거나 억압한 면이 있었고, 이로 인해 행동에서도 효과적인 대인관계나 대안을 탐색하지 못하게 되는 결과를 보여주었다. 반면 경험 이후에는 정서에 대한 인식, 정서 조율, 정서 표현, 상대에 대한 정서 이해(공감) 등이 적응적인 형태로 발전하였으며, 효율적인 대안을 마련하게 된 것을 알 수 있었다.

• Geomechanics and Engineering
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• 제33권3호
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.

• 한국의학물리학회지:의학물리
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• 제33권4호
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• pp.142-149
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• 2022

Purpose: This study seeks to compare the dosimetric parameters of the bulk electron density (ED) approach and synthetic computed tomography (CT) image in terms of position variation of the air cavity in magnetic resonance-guided radiotherapy (MRgRT) for patients with pancreatic cancer. Methods: This study included nine patients that previously received MRgRT and their simulation CT and magnetic resonance (MR) images were collected. Air cavities were manually delineated on simulation CT and MR images in the treatment planning system for each patient. The synthetic CT images were generated using the deep learning model trained in a prior study. Two more plans with identical beam parameters were recalculated with ED maps that were either manually overridden by the cavities or derived from the synthetic CT. Dose calculation accuracy was explored in terms of dose-volume histogram parameters and gamma analysis. Results: The D_95% averages were 48.80 Gy, 48.50 Gy, and 48.23 Gy for the original, manually assigned, and synthetic CT-based dose distributions, respectively. The greatest deviation was observed for one patient, whose D_95% to synthetic CT was 1.84 Gy higher than the original plan. Conclusions: The variation of the air cavity position in the gastrointestinal area affects the treatment dose calculation. Synthetic CT-based ED modification would be a significant option for shortening the time-consuming process and improving MRgRT treatment accuracy.

• 우울조울병
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• 제9권2호
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• pp.78-86
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• 2011

Objectives : The aim of this study was to investigate relationship between perceived stress level, cognitive emotion regulation (CER) strategy and hope in group with high depressive symptom and higher-level perceived stress (H-H) and group with high depressive symptom and lower-level perceived stress (H-L) in patients with depression. Method : A total of 85 patients (over score of 16 by Beck depression Inventory; BDI) were surveyed with Cognitive emotion regulation questionnaire (CERQ), Perceived stress scale (PSS), and The state hope scale (SHS). Mean scores of CERQ and SHS were compared between relatively higher perceived stress and lower perceived stress groups. Correlation analysis and multiple linear regression analyses were performed to identify the effect of BDI, PSS and CER strategy on SHS in two groups. Results : In ANOVA, the level of hope and maladaptive CERQ score proved to be significantly lower among the H-H group than among the H-L group, while adaptive CERQ scores were not. In Regression analysis, the effective CER strategy in SHS were 'Refocus on planning' in H-H group, while it was 'Acceptance' CER strategy in H-L group. The final regression model explained 36% of the variance of SHS in H-H group and explained 21% of SHS in H-L group. Conclusion : These findings suggest that 'Refocus on planning' and 'Acceptance' cognitive emotion strategy are helpful in promotion of state hope on depression. Especially, 'Refocus on planning' strategy is more effective in high depressive symptom and high-perceived stress level, while 'Acceptance' strategy help to promote hope in high depressive symptom and low-perceived stress level in patients with depression.

• IMMUNE NETWORK
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• 제21권6호
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• pp.42.1-42.20
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• 2021

Eosinophils play critical roles in the maintenance of homeostasis in innate and adaptive immunity. Although primarily known for their roles in parasitic infections and the development of Th2 cell responses, eosinophils also play complex roles in other immune responses ranging from anti-inflammation to defense against viral and bacterial infections. However, the contributions of pattern recognition receptors in general, and NOD-like receptors (NLRs) in particular, to eosinophil involvement in these immune responses remain relatively underappreciated. Our in vivo studies demonstrated that NLRC4 deficient mice had a decreased number of eosinophils and impaired Th2 responses after induction of an allergic airway disease model. Our in vitro data, utilizing human eosinophilic EoL-1 cells, suggested that TLR2 induction markedly induced pro-inflammatory responses and inflammasome forming NLRC4 and NLRP3. Moreover, activation by their specific ligands resulted in caspase-1 cleavage and mature IL-1β secretion. Interestingly, Th2 responses such as secretion of IL-5 and IL-13 decreased after transfection of EoL-1 cells with short interfering RNAs targeting human NLRC4. Specific induction of NLRC4 with PAM3CSK4 and flagellin upregulated the expression of IL-5 receptor and expression of Fc epsilon receptors (FcεR1α, FcεR2). Strikingly, activation of the NLRC4 inflammasome also promoted expression of the costimulatory receptor CD80 as well as expression of immunoregulatory receptors PD-L1 and Siglec-8. Concomitant with NLRC4 upregulation, we found an increase in expression and activation of matrix metalloproteinase (MMP)-9, but not MMP-2. Collectively, our results present new potential roles of NLRC4 in mediating a variety of eosinopilic functions.