• Neurospine
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• 제15권4호
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• pp.329-337
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• 2018

Objective: Machine learning algorithms excel at leveraging big data to identify complex patterns that can be used to aid in clinical decision-making. The objective of this study is to demonstrate the performance of machine learning models in predicting postoperative complications following anterior cervical discectomy and fusion (ACDF). Methods: Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), and random forest decision tree (RF) models were trained on a multicenter data set of patients undergoing ACDF to predict surgical complications based on readily available patient data. Following training, these models were compared to the predictive capability of American Society of Anesthesiologists (ASA) physical status classification. Results: A total of 20,879 patients were identified as having undergone ACDF. Following exclusion criteria, patients were divided into 14,615 patients for training and 6,264 for testing data sets. ANN and LR consistently outperformed ASA physical status classification in predicting every complication (p < 0.05). The ANN outperformed LR in predicting venous thromboembolism, wound complication, and mortality (p < 0.05). The SVM and RF models were no better than random chance at predicting any of the postoperative complications (p < 0.05). Conclusion: ANN and LR algorithms outperform ASA physical status classification for predicting individual postoperative complications. Additionally, neural networks have greater sensitivity than LR when predicting mortality and wound complications. With the growing size of medical data, the training of machine learning on these large datasets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.

• 대한지역사회영양학회지
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• 제17권4호
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• pp.376-389
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• 2012

The purpose of this study was to develop a fusion-food cooking program and apply that to sixth grade elementary school students, and to investigate its' effects on their knowledge, preferences, and perceptions of Korean traditional foods. The program focused on ten components. Students learned the general features of Korean traditional foods and Western foods in the 1st lesson. They learnt about kinds, nutrition value, and histories of kimchi, Tteok (rice cake), and Jeon (pan fried food), and cooked two fusion-foods of kimchi, Tteok, and Jeon each. As a result of learning about those food items, the students advanced their knowledge (p < 0.001) of kimchi, Tteok, and Jeon. Their preferences for Tteok (p < 0.05) and Jeon (p < 0.01) were increased, but those for kimchi remained unchanged. The interest (p < 0.05) and preferences (p < 0.01) for the general Korean traditional foods were increased. In conclusion, our results suggested that the fusion-food cooking program had the ability to improve elementary school students' perceptions of Korean traditional foods by increasing their knowledge, preferences, and interest in them. Furthermore it was considered that the program could help students understand the value of Korean traditional foods and in turn may encourage them to consume such food items more frequently. Since the fusion-food cooking activity program can be a good learning program as shown by the results of this study, more fusion-foods cooking activity programs, which are not discussed in this study, should be evaluated and developed in the future.

• 한국지능시스템학회논문지
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• 제18권1호
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• pp.20-26
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• 2008

지능형 로봇이나 컴퓨터가 일상생활 속에서 차지하는 비중이 점점 높아짐에 따라 인간과의 상호교류도 점점 중요시되고 있다. 이렇게 지능형 로봇(컴퓨터) - 인간의 상호 교류하는데 있어서 감정 인식 및 표현은 필수라 할 수 있겠다. 본 논문에서는 음성 신호와 얼굴 영상에서 감정적인 특징들을 추출한 후 이것을 Bayesian Learning과 Principal Component Analysis에 적용하여 5가지 감정(평활, 기쁨, 슬픔, 화남, 놀람)으로 패턴을 분류하였다. 그리고 각각 매개체의 단점을 보완하고 인식률을 높이기 위해서 결정 융합 방법과 특징 융합 방법을 적용하여 감정 인식 실험을 하였다. 결정 융합 방법은 각각 인식 시스템을 통해 얻어진 인식 결과 값을 퍼지 소속 함수에 적용하여 감정 인식 실험을 하였으며, 특징 융합 방법은 SFS(Sequential Forward Selection) 특징 선택 방법을 통해 우수한 특징들을 선택한 후 MLP(Multi Layer Perceptron) 기반 신경망(Neural Networks)에 적용하여 감정 인식 실험을 실행하였다. 그리고 인식된 결과 값을 2D 얼굴 형태에 적용하여 감정을 표현하였다.

• International journal of advanced smart convergence
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• 제4권2호
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• pp.20-28
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• 2015

In this paper, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame with posture variation and camera view point adaptation by employing the non-adaptive random projections that preserve the structure of the image feature space of objects. The existing online tracking algorithms update models with features from recent video frames and the numerous issues remain to be addressed despite on the improvement in tracking. The data-dependent adaptive appearance models often encounter the drift problems because the online algorithms does not get the required amount of data for online learning. So, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame.

• International journal of advanced smart convergence
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• 제8권1호
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• pp.166-175
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• 2019

Grit, a concept conceived in 2007 by Duckworth and others in the United States, is based on positive psychology that focuses on growth and development through individual strengths. Recently, "Grit", which means patience and enthusiasm for long-term goals, has emerged as a key factor of personality strength. In Korea, Joo-hwan Kim (2013) was the first to conceptualize and study the subject of Grit. However, there have been no overview studies that systematically summarize the overall trends and flow in the research of Grit so far. There have been 147 research papers on Grit published so far in Korea. The purpose of this study was to conduct trend analysis on the subject of Grit by analyzing forty-three (43) master's and doctoral dissertations, thus presenting the direction of future research on Grit through careful analysis. In the studies conducted, it was found that Grit is a very significant variable linked to self-efficacy. It is also a subjective belief that can help an individual achieve his/her educational goals, and go through failure resynchronization. In addition, Grit is very significant as a practical core indicator of how fusion talent is fostered for the fourth industrial revolution. Therefore, there is a need for more in-depth research from the viewpoints of workplace learning, experiential learning, or informal learning, as well as research into Grit characteristics.

• 한국분말재료학회지
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• 제30권3호
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• pp.210-216
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• 2023

In this study, machine learning models are proposed to predict the Vickers hardness of AlSi10Mg alloys fabricated by laser powder bed fusion (LPBF). A total of 113 utilizable datasets were collected from the literature. The hyperparameters of the machine-learning models were adjusted to select an accurate predictive model. The random forest regression (RFR) model showed the best performance compared to support vector regression, artificial neural networks, and k-nearest neighbors. The variable importance and prediction mechanisms of the RFR were discussed by Shapley additive explanation (SHAP). Aging time had the greatest influence on the Vickers hardness, followed by solution time, solution temperature, layer thickness, scan speed, power, aging temperature, average particle size, and hatching distance. Detailed prediction mechanisms for RFR are analyzed using SHAP dependence plots.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권7호
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• pp.1907-1928
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• 2024

In recent years, multi-focus image fusion has emerged as a prominent area of research, with transformers gaining recognition in the field of image processing. Current approaches encounter challenges such as boundary artifacts, loss of detailed information, and inaccurate localization of focused regions, leading to suboptimal fusion outcomes necessitating subsequent post-processing interventions. To address these issues, this paper introduces a novel multi-focus image fusion technique leveraging the Swin Transformer architecture. This method integrates a frequency layer utilizing Wavelet Transform, enhancing performance in comparison to conventional Swin Transformer configurations. Additionally, to mitigate the deficiency of local detail information within the attention mechanism, Convolutional Neural Networks (CNN) are incorporated to enhance region recognition accuracy. Comparative evaluations of various fusion methods across three datasets were conducted in the paper. The experimental findings demonstrate that the proposed model outperformed existing techniques, yielding superior quality in the resultant fused images.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.107-109
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• 2004

In this paper, authors propose the sensor fusion system that can recognize multiple 3D objects from 2D projection images and tactile information. The proposed system focuses on improving recognition performance of 3D object. Unlike the conventional object recognition system that uses image sensor alone, the proposed method uses tactual sensors in addition to visual sensor. Neural network is used to fuse these informations. Tactual signals are obtained from the reaction force by the pressure sensors at the fingertips when unknown objects are grasped by four-fingered robot hand. The experiment evaluates the recognition rate and the number of teaming iterations of various objects. The merits of the proposed systems are not only the high performance of the learning ability but also the reliability of the system with tactual information for recognizing various objects even though visual information has a defect. The experimental results show that the proposed system can improve recognition rate and reduce learning time. These results verify the effectiveness of the proposed sensor fusion system as recognition scheme of 3D object.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권3호
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• pp.156-165
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• 2005

Human being recognizes the physical world by integrating a great variety of sensory inputs, the information acquired by their own action, and their knowledge of the world using hierarchically parallel-distributed mechanism. In this paper, authors propose the sensor fusion system that can recognize multiple 3D objects from 2D projection images and tactile informations. The proposed system focuses on improving recognition performance of 3D objects. Unlike the conventional object recognition system that uses image sensor alone, the proposed method uses tactual sensors in addition to visual sensor. Neural network is used to fuse the two sensory signals. Tactual signals are obtained from the reaction force of the pressure sensors at the fingertips when unknown objects are grasped by four-fingered robot hand. The experiment evaluates the recognition rate and the number of learning iterations of various objects. The merits of the proposed systems are not only the high performance of the learning ability but also the reliability of the system with tactual information for recognizing various objects even though the visual sensory signals get defects. The experimental results show that the proposed system can improve recognition rate and reduce teeming time. These results verify the effectiveness of the proposed sensor fusion system as recognition scheme for 3D objects.

• Nuclear Engineering and Technology
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• 제55권1호
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• pp.100-108
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• 2023

In this study, a neural network model inspired by a one-dimensional convolution U-net is developed to automatically accelerate edge localized mode (ELM) detection from big diagnostic data of fusion devices and increase the detection accuracy regardless of the hyperparameter setting. This model recognizes the input signal patterns and overcomes the problems of existing detection algorithms, such as the prominence algorithm and those of differential methods with high sensitivity for the threshold and signal intensity. To train the model, 10 sets of discharge radiation data from the KSTAR are used and sliced into 11091 inputs of length 12 ms, of which 20% are used for validation. According to the receiver operating characteristic curves, our model shows a positive prediction rate and a true prediction rate of approximately 90% each, which is comparable to the best detection performance afforded by other algorithms using their optimized hyperparameters. The accurate and automatic ELM-burst detection methodology used in our model can be beneficial for determining plasma properties, such as the ELM frequency from big data measured in multiple experiments using machines from the KSTAR device and ITER. Additionally, it is applicable to feature detection in the time-series data of other engineering fields.