• Perspectives in Nursing Science
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• 제17권2호
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• pp.80-89
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• 2020

Purpose: The aim of this study is to review literature on anger management intervention programs for parents published over the last 10 years and to extract the key elements of the interventions through an integrative review. Methods: This research was carried out in stages following Whittemore and Knafl's integrative literature methodology. Key words in Korean and English were used to search the PubMed, MEDLINE, EMbase, CINAHL, RISS, KISS and National Assembly Library databases. Several intervention factors were extracted from the selected papers on the basis of the framework which was helpful to identify the intervention patterns and were classified into meaningful themes. Results: The extracted intervention factors from the final nine studies classified into four themes: 1) Modifying irrational beliefs through cognitive approaches, 2) Empowering parenting competencies through learning a parent's role, 3) Utilizing emotion management skills, and 4) Parent-child relationship improvement training based on self-reflection. Conclusion: Four main themes were drawn from the key components of the various interventions. These findings should be considered in practice, and further intervention development studies for parents using these findings should be conducted.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1670-1683
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• 2017

Mitotic event recognition is a crucial and challenging task in biomedical applications. In this paper, we introduce the slow feature analysis and propose a fully-automated mitotic event recognition method for cell populations imaged with time-lapse phase contrast microscopy. The method includes three steps. First, a candidate sequence extraction method is utilized to exclude most of the sequences not containing mitosis. Next, slow feature is learned from the candidate sequences using slow feature analysis. Finally, a hidden conditional random field (HCRF) model is applied for the classification of the sequences. We use a supervised SFA learning strategy to learn the slow feature function because the strategy brings image content and discriminative information together to get a better encoding. Besides, the HCRF model is more suitable to describe the temporal structure of image sequences than nonsequential SVM approaches. In our experiment, the proposed recognition method achieved 0.93 area under curve (AUC) and 91% accuracy on a very challenging phase contrast microscopy dataset named C2C12.

• 생명과학회지
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• 제17권5호
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• pp.714-718
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• 2007

유전자의 구조 예측 및 발현 기작에 대한 연구는 매우 중요한 사안으로 대두되고 있다. 특히 유전자 발현 제어에 중요한 역할을 하는 프로모터 영역을 예측하는 것은 전체 생명체 네트워크 규명을 위한 단초를 제공하기 때문에 많은 연구가 이루어지고 있다. 본 논문에서는 이러한 진핵생물의 유전자 프로모터 예측을 위한 Support Vector Machine(SVM) 활용방안에 대한 연구내용을 다루고 있다. 특성 벡터 값 생성을 위한 인코딩 방법 및 학습 데이터들의 구성에 대한 다양한 실험을 통해 SVM활용 방안에 대한 올바른 방향을 제시하고 있다.

• International Journal of Internet, Broadcasting and Communication
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• 제11권4호
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• pp.76-85
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• 2019

We introduce a vision-based object detection method for real-time video surveillance system in low-end edge computing environments. Recently, the accuracy of object detection has been improved due to the performance of approaches based on deep learning algorithm such as Region Convolutional Neural Network(R-CNN) which has two stage for inferencing. On the other hand, one stage detection algorithms such as single-shot detection (SSD) and you only look once (YOLO) have been developed at the expense of some accuracy and can be used for real-time systems. However, high-performance hardware such as General-Purpose computing on Graphics Processing Unit(GPGPU) is required to still achieve excellent object detection performance and speed. To address hardware requirement that is burdensome to low-end edge computing environments, We propose sub-frame analysis method for the object detection. In specific, We divide a whole image frame into smaller ones then inference them on Convolutional Neural Network (CNN) based image detection network, which is much faster than conventional network designed forfull frame image. We reduced its computationalrequirementsignificantly without losing throughput and object detection accuracy with the proposed method.

• The Journal of Asian Finance, Economics and Business
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• 제9권10호
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• pp.73-83
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• 2022

On the underpinning theory of the Resource Based Theory, this research examines the impact of corporate social responsibility and green marketing strategy on business performance with the moderating role balanced scorecard. Corporate social responsibility concentrates on related to government, the environment, customers, and employees. Green marketing strategy concentrates on approaches in terms of green products, green prices, green places, and green promotion. Business performance is measured by groups of indicators financial, and non-financial. The moderating role balanced the scorecard at the financial, customer, process, learning, and growth level. Research results with survey data from 419 managers at different food enterprises in Ho Chi Minh City processed through the structural analysis method, showed that corporate social responsibility has the strongest positive impact on business performance, followed by the green marketing strategy as the second strong positive impact on the business performance and results also showed that the balanced scorecard moderating role increases the level of the strong positive impact of the above relationship. Besides, it also showed the difference in the demographic control variables. Based on the findings, some implications are drawn to help the managers of enterprises improve the moderating role balanced scorecard when implementing corporate social responsibility and green marketing strategies thereby contributing to increasing business performance.

• 자동차안전학회지
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• 제13권4호
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• pp.33-38
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• 2021

This paper presents a comparative study between the parameter-optimized Pacejka model and artificial neural network model for the tire force estimation. The two different approaches are investigated and compared in this study. First, offline optimization is conducted based on Pacejka Magic Formula model to determine the proper parameter set for the minimization of tire force error between the model and test data set. Second, deep neural network model is used to fit the model to the tire test data set. The actual tire forces are measured using MTS Flat-Track test platform and the measurements are used as the reference tire data set. The focus of this study is on the applicability of machine learning technique to tire force estimation. It is shown via the regression results that the deep neural network model is more effective in describing the tire force than the parameter-optimized Pacejka model.

• 기독교교육논총
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• 제65권
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• pp.9-27
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• 2021

종교적 언어의 학습은 아동의 영적 성숙을 위해서 중요하다. 아동이 거룩함을 말하는 것이 격려되는 상황 속에서는 아동의 동료 관계, 하나님과 자연의 건강한 관계를 형성하는 역량이 강화된다. 종교교육가의 윤리적 당위성은 종교를 가르치며 삶의 경험을 긍정적으로 갱신하는 과정 속에서 확보된다. 특히, 종교교사는 언어적, 인지적, 그리고 영적 발달을 저해하는 교리적 훈육에 저항해야 하는 책무에 능동적으로 반응할 필요가 있다. 문화적 영향은 아동의 신비와 탐구, 자아발견의 계발에 긍정적인 역할을 한다. 문화적 접근을 통해서 종교교사는 아동에게 종교적 언어를 가르치고 의미-형성과 표현을 위한 도구로 활용이 가능하다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2020년도 추계학술대회
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• pp.252-255
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• 2020

Unsupervised deep learning methods have shown impressive results for the challenging monocular depth estimation task, a field of study that has gained attention in recent years. A common approach for this task is to train a deep convolutional neural network (DCNN) via an image synthesis sub-task, where additional views are utilized during training to minimize a photometric reconstruction error. Previous unsupervised depth estimation networks are trained within a fixed depth estimation range, irrespective of its possible range for a given image, leading to suboptimal estimates. To overcome this suboptimal limitation, we first propose an unsupervised adaptive depth estimation method guided by minimum and maximum (min-max) depth priors for a given input image. The incorporation of min-max depth priors can drastically reduce the depth estimation complexity and produce depth estimates with higher accuracy. Moreover, we propose a novel network architecture for adaptive depth estimation, called the AdaMM-DepthNet, which adopts the min-max depth estimation in its front side. Intensive experimental results demonstrate that the adaptive depth estimation can significantly boost up the accuracy with a fewer number of parameters over the conventional approaches with a fixed minimum and maximum depth range.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.686-693
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• 2022

Construction is a collaborative endeavor. The complexity in delivering construction projects successfully is impacted by the effective collaboration needs of a multitude of stakeholders throughout the project life-cycle. Technologies such as Building Information Modelling and relational project delivery approaches such as Alliancing and Integrated Project Delivery have developed to address this conundrum. However, with the onset of the pandemic, the digital economy has surged world-wide and advances in technology such as in the areas of machine learning (ML) and Artificial Intelligence (AI) have grown deep roots across specializations and domains to the point of matching its capabilities to the human mind. Several recent studies have both explored the role of AI in the construction process and highlighted its benefits. In contrast, literature in the organization studies field has highlighted the fear that tasks currently done by humans will be done by AI in future. Motivated by these insights and with the understanding that construction is a labour intensive sector where knowledge is both fragmented and predominantly tacit in nature, this paper explores the integration of AI in construction processes across project phases from planning, scheduling, execution and maintenance operations using literary evidence and experiential insights. The findings show that AI can complement human skills rather than provide a substitute for them. This preliminary study is expected to be a stepping stone for further research and implementation in practice.

• 스마트미디어저널
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• 제13권3호
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• pp.53-61
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• 2024

본 논문은 기존 방법보다 우수한 성능을 달성하는 피부 병변 분할을 위한 새로운 M자 모양 인코더-디코더 아키텍처를 제안한다. 제안된 아키텍처는 왼쪽과 오른쪽 다리를 활용하여 다중 스케일 특징 추출을 가능하게 하고, 스킵 연결 내에서 어텐션 메커니즘을 통합하여 피부 병변 분할 성능을 더욱 향상시킨다. 입력 영상은 네 가지 다른 패치로 분할되어 입력되며 인코더-디코더 프레임워크 내에서 피부 병변 분할 성능의 향상된 처리를 가능하게 한다. 제안하는 방법에서 어텐션 메커니즘을 통해 입력 영상의 특징에 더 많은 초점을 맞추어 더욱 정교한 영상 분할 결과를 도출하는 것이다. 실험 결과는 제안된 방법의 효과를 강조하며, 기존 방법과 비교하여 우수한 정확도, 정밀도 및 Jaccard 지수를 보여준다.