• 대한의용생체공학회:의공학회지
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• 제44권4호
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• pp.255-263
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• 2023

Clinical ultrasound (US) is a widely used imaging modality with various clinical applications. However, capturing a large field of view often requires specialized transducers which have limitations for specific clinical scenarios. Panoramic imaging offers an alternative approach by sequentially aligning image sections acquired from freehand sweeps using a standard transducer. To reconstruct a 3D volume from these 2D sections, an external device can be employed to track the transducer's motion accurately. However, the presence of optical or electrical interferences in a clinical setting often leads to incorrect measurements from such sensors. In this paper, we propose a deep learning (DL) framework that enables the prediction of scan trajectories using only US data, eliminating the need for an external tracking device. Our approach incorporates diverse data types, including correlation volume, optical flow, B-mode images, and rawer data (IQ data). We develop a DL network capable of effectively handling these data types and introduce an attention technique to emphasize crucial local areas for precise trajectory prediction. Through extensive experimentation, we demonstrate the superiority of our proposed method over other DL-based approaches in terms of long trajectory prediction performance. Our findings highlight the potential of employing DL techniques for trajectory estimation in clinical ultrasound, offering a promising alternative for panoramic imaging.

• 한국컴퓨터정보학회논문지
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• 제28권8호
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• pp.87-93
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• 2023

본 논문에서는 사용자의 골격 자세를 분석하여 네트워크 학습 기반으로 거북목 자세를 정확하고 효율적으로 판별하는 시스템을 제안한다. 거북목 증후군이란 목이 구부정하게 앞으로 나오는 자세를 오래 유지함으로써 목의 자세가 바뀌고 뒷목, 어깨, 허리 등에 통증이 생기는 증상을 말하며, 수술이나 약물치료보다 평소의 자세 습관이 효과적이라고 알려져 있다. 기존의 방법들은 웹캠을 이용한 합성곱 신경망을 이용하였고, 이러한 접근법은 영상의 명도와 조명, 피부 색 등에 영향을 받기 때문에 특정 인물에 대해서만 수행되는 문제가 있다. 본 논문에서는 이 문제를 완화하고 자 영상으로부터 골격을 추출하고, 정면보다는 측면에 해당하는 데이터를 학습하여 이전 기법보다 효율적이고 정확하게 거북목 자세를 찾아낸다. 결과적으로 이전 기법에 비해 다양한 실험 장면에서 정확도가 되었음을 보여준다.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2021년도 제64차 하계학술대회논문집 29권2호
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• pp.305-307
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• 2021

Alzheimer's Disease (AD) is a cognitive disorder characterized by memory impairment that can be assessed at early stages based on administering clinical tests. However, the AD pathophysiological mechanism is still poorly understood due to the difficulty of distinguishing different levels of AD severity, even using a variety of brain modalities. Therefore, in this study, we present a hybrid EEG-fNIRS modalities to compensate for each other's weaknesses with the help of Machine Learning (ML) techniques for classifying four subject groups, including healthy controls (HC) and three distinguishable groups of AD levels. A concurrent EEF-fNIRS setup was used to record the data from 41 subjects during Oddball and 1-back tasks. We employed both a traditional neural network (NN) and a CNN-LSTM hybrid model for fNIRS and EEG, respectively. The final prediction was then obtained by using majority voting of those models. Classification results indicated that the hybrid EEG-fNIRS feature set achieved a higher accuracy (71.4%) by combining their complementary properties, compared to using EEG (67.9%) or fNIRS alone (68.9%). These findings demonstrate the potential of an EEG-fNIRS hybridization technique coupled with ML-based approaches for further AD studies.

• Korean Chemical Engineering Research
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• 제61권4호
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• pp.487-495
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• 2023

암모니아는 인류의 식량문제를 해결할 수 있는 비료 생산의 주요 원료임과 동시에 무탄소 연료이면서 친환경적인 수소 운반자로서 중요한 에너지원으로 알려져 있다. 그래서 지금까지도 암모니아를 합성하거나 분해하는 기술들이 각광을 받고 있다. 암모니아 합성 및 분해 반응을 촉진시키기 위해서는 반드시 촉매 재료가 필요하다. 고성능 및 값싼 암모니아 합성 및 분해용 신촉매를 설계하기 위해서는 무수히 많은 합성 가능한 촉매 후보군들을 다루어야만 하는데 전통적인 접근법만으로 탐색 및 분석을 하기엔 시간적, 경제적인 비용이 많이 들 수밖에 없다. 최근에 4차 산업혁명의 핵심기술에 속하는 머신러닝을 이용하여 이용하여 고성능 촉매를 빠르고 정확하게 찾을 수 있는 탐색 모델이 개발되어 왔다. 본 연구에서는 암모니아 합성 및 분해용 반응 메커니즘에 대해서 알아보고, 고성능 및 경제적인 암모니아 합성 및 분해 촉매를 효율적으로 탐색할 수 있는 머신러닝 기반 방법에 대한 최신 연구 및 전망을 기술하였다.

• Animal Bioscience
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• 제37권4호
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• pp.622-630
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• 2024

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

• 한국과학교육학회지
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• 제26권1호
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• pp.16-24
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• 2006

본 연구에서는 실생활 소재를 사용한 문제해결형과 과제해결형 탐구 활동을 적용했을 때, 학생 간 상호작용의 양상을 조사하였다. 연구를 위해, 충북 청원군 소재 중학교 3학년 학생 5명의 실험 수업을 관찰하고, 녹음 및 녹화 후 기록 원고를 작성 분석하였다. 학생들의 대화를 질문, 설명, 사고, 메타인지의 4가지 상호작용 유형으로 분류하고, 각 유형별 학습 접근 수준은 심층적-피상적 접근으로 분류하였다. 실험 형태별 언어적 상호작용의 수와 학습 접근 수준 비교 결과, 문제해결형 탐구실험은 문제점 발견에서 해결까지 상호작용의 수가 문제 발생 이전에 비해 2배 가량 증가하는 것을 볼 수 있었으며, 심층적 접근 수준의 상호작용의 수도 4배 정도 증가하는 것으로 나타났다. 한편, 과제해결형 탐구실험에서는 실험과정 중 상호작용의 수가 고루 분포하였다. 또한 학생들은 문제해결형 탐구실험에서 보다 많은 심층적 접근의 상호작용을 보이는 것으로 나타났다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.105-125
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• 2024

Gliomas are the most common malignant brain tumor and cause the most deaths. Manual brain tumor segmentation is expensive, time-consuming, error-prone, and dependent on the radiologist's expertise and experience. Manual brain tumor segmentation outcomes by different radiologists for the same patient may differ. Thus, more robust, and dependable methods are needed. Medical imaging researchers produced numerous semi-automatic and fully automatic brain tumor segmentation algorithms using ML pipelines and accurate (handcrafted feature-based, etc.) or data-driven strategies. Current methods use CNN or handmade features such symmetry analysis, alignment-based features analysis, or textural qualities. CNN approaches provide unsupervised features, while manual features model domain knowledge. Cascaded algorithms may outperform feature-based or data-driven like CNN methods. A revolutionary cascaded strategy is presented that intelligently supplies CNN with past information from handmade feature-based ML algorithms. Each patient receives manual ground truth and four MRI modalities (T1, T1c, T2, and FLAIR). Handcrafted characteristics and deep learning are used to segment brain tumors in a Global Convolutional Neural Network (GCNN). The proposed GCNN architecture with two parallel CNNs, CSPathways CNN (CSPCNN) and MRI Pathways CNN (MRIPCNN), segmented BraTS brain tumors with high accuracy. The proposed model achieved a Dice score of 87% higher than the state of the art. This research could improve brain tumor segmentation, helping clinicians diagnose and treat patients.

• 한국멀티미디어학회논문지
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• 제21권5호
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• pp.617-625
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• 셀메드
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• 제5권3호
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• pp.20.1-20.4
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• 2015

Ayurveda is often criticized for having empirical and non-evidence based approach to treat the patients. At the same time, modern medicine is also being criticized for having a non-holistic, reductionist and mechanistic approach of treating the patients which do not help in many real clinical situations. An open minded deduction of treatment approaches in both of these systems for a common patient however makes us to rethink that ideally both systems are similar with a common objective of offering a cure although in a manner which is better understood through their own methods of learning. The differences therefore, are more superficial rather than being deeply rooted in the understanding. A more tolerant viewpoint towards the competitive medical systems may therefore be a better approach to offer optimal health care to our people through a genuine amalgamation of these two health care sciences through an integrated approach. Once this tolerance is developed, it will give us an opportunity to think for a focused selection of type of health care depending upon the type of the disease and strength of the particular system in that area.

• 재무관리논총
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• 제3권2호
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• pp.267-279
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• 1996

The prediction of future demand is a vital task in managing business operations. To this end, traditional approaches often focused on statistical techniques such as exponential smoothing and moving average. The need for better accuracy has led to nonlinear techniques such as neural networks and case based reasoning. In addition, experimental design techniques such as orthogonal arrays may be used to assist in the formulation of an effective methodology. This paper investigates a multistrategy approach involving neural nets, case based reasoning, and orthogonal arrays. Neural nets and case based reasoning are employed both separately and in combination, while orthoarrays are used to determine the best architecture for each approach. The comparative evaluation is performed in the context of an application relating to the prediction of Treasury notes.