• 대한치매학회지
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• 제23권1호
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• pp.1-10
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• 2024

Background and Purpose: Voice, reflecting cerebral functions, holds potential for analyzing and understanding brain function, especially in the context of cognitive impairment (CI) and Alzheimer's disease (AD). This study used voice data to distinguish between normal cognition and CI or Alzheimer's disease dementia (ADD). Methods: This study enrolled 3 groups of subjects: 1) 52 subjects with subjective cognitive decline; 2) 110 subjects with mild CI; and 3) 59 subjects with ADD. Voice features were extracted using Mel-frequency cepstral coefficients and Chroma. Results: A deep neural network (DNN) model showed promising performance, with an accuracy of roughly 81% in 10 trials in predicting ADD, which increased to an average value of about 82.0%±1.6% when evaluated against unseen test dataset. Conclusions: Although results did not demonstrate the level of accuracy necessary for a definitive clinical tool, they provided a compelling proof-of-concept for the potential use of voice data in cognitive status assessment. DNN algorithms using voice offer a promising approach to early detection of AD. They could improve the accuracy and accessibility of diagnosis, ultimately leading to better outcomes for patients.

• 대한수학교육학회지:학교수학
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• 제18권4호
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• pp.749-771
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• 2016

이 논문에서는 우리나라와 미국 수학 교과서에서 다루고 있는 평행사변형이 되기 위한 조건 관련 과제를 과제의 구조, 증명과 추론 유형, 그리고 인지적 노력 수준에 따라 비교 분석하였다. 이를 통해 두 나라 교과서 과제의 공통점과 차이점을 분석하였다. 그 결과는 다음과 같다. 첫째, 과제 구조와 관련하여, 우리나라 교과서에 비해 미국 교과서에 제시된 과제의 구조가 더 다양하다. 둘째, 증명과 추론 유형과 관련하여, 우리나라와 미국 교과서 모두 IC 과제와 DA 과제의 구성 비율이 높으며, 우리나라 교과서에 비해 미국 교과서에 제시된 과제의 유형이 더 다양하다. 셋째, 과제의 인지적 노력 수준과 관련하여, 우리나라와 미국 교과서 모두 PNC 과제와 PWC 과제가 대부분을 차지하며, 우리나라의 경우 미국에 비해 구체적인 알고리즘적 절차를 이용하는 수학 과제를 제시하는 비율이 높다. 차이점을 토대로 우리나라 교과서 재구성에 필요한 다음과 같은 시사점을 얻을 수 있었다. 첫째, 과제의 구조 및 증명과 추론 유형과 관련하여, 구성의 다양성을 높여야 한다. 둘째, 과제의 인지적 노력 수준과 관련하여, PNC 과제에 대한 편중현상을 완화해야 하며, 과제 유형별 인지적 노력 수준에 대한 재고가 필요하다. 셋째, 과제의 주제 또는 소재와 관련하여, 수학 내적, 외적인 상황과의 연결성이 강화된 과제를 도입할 수 있는 방안의 재고가 필요하다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.5723-5743
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• 2018

The evolving internet-based services demand high-speed data transmission in conjunction with scalability. The next generation optical network has to exploit artificial intelligence and cognitive techniques to cope with the emerging requirements. This work proposes a novel way to solve the dynamic provisioning problem in optical network. The provisioning in optical network involves the computation of routes and the reservation of wavelenghs (Routing and Wavelength assignment-RWA). This is an extensively studied multi-objective optimization problem and its complexity is known to be NP-Complete. As the exact algorithms incurs more running time, the heuristic based approaches have been widely preferred to solve this problem. Recently the software-defined networking has impacted the way the optical pipes are configured and monitored. This work proposes the dynamic selection of path computation algorithms in response to the changing service requirements and network scenarios. A software-defined controller mechanism with a novel packet matching feature was proposed to dynamically match the traffic demands with the appropriate algorithm. A software-defined controller with Path Computation Element-PCE was created in the ONOS tool. A simulation study was performed with the case study of dynamic path establishment in ONOS-Open Network Operating System based software defined controller environment. A java based NOX controller was configured with a parent path computation element. The child path computation elements were configured with different path computation algorithms under the control of the parent path computation element. The use case of dynamic bulk path creation was considered. The algorithm selection method is compared with the existing single algorithm based method and the results are analyzed.

• 대한치매학회지
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• 제23권3호
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• pp.127-135
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• 2024

Background and Purpose: To ensure data privacy, the development of defacing processes, which anonymize brain images by obscuring facial features, is crucial. However, the impact of these defacing methods on brain imaging analysis poses significant concern. This study aimed to evaluate the reliability of three different defacing methods in automated brain volumetry. Methods: Magnetic resonance imaging with three-dimensional T1 sequences was performed on ten patients diagnosed with subjective cognitive decline. Defacing was executed using mri_deface, BioImage Suite Web-based defacing, and Defacer. Brain volumes were measured employing the QBraVo program and FreeSurfer, assessing intraclass correlation coefficient (ICC) and the mean differences in brain volume measurements between the original and defaced images. Results: The mean age of the patients was 71.10±6.17 years, with 4 (40.0%) being male. The total intracranial volume, total brain volume, and ventricle volume exhibited high ICCs across the three defacing methods and 2 volumetry analyses. All regional brain volumes showed high ICCs with all three defacing methods. Despite variations among some brain regions, no significant mean differences in regional brain volume were observed between the original and defaced images across all regions. Conclusions: The three defacing algorithms evaluated did not significantly affect the results of image analysis for the entire brain or specific cerebral regions. These findings suggest that these algorithms can serve as robust methods for defacing in neuroimaging analysis, thereby supporting data anonymization without compromising the integrity of brain volume measurements.

• International Journal of Contents
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• 제12권3호
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• pp.22-28
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• 2016

Tensor with missing or incomplete values is a ubiquitous problem in various fields such as biomedical signal processing, image processing, and social network analysis. In this paper, we considered how to reconstruct a dataset with missing values by using tensor form which is called tensor completion process. We applied Tucker factorization to solve tensor completion which was built base on optimization problem. We formulated the optimization objective function using components of Tucker model after decomposing. The weighted least square matric contained only known values of the tensor with low rank in its modes. A first order optimization method, namely Nonlinear Conjugated Gradient, was applied to solve the optimization problem. We demonstrated the effectiveness of the proposed method in EEG signals with about 70% missing entries compared to other algorithms. The relative error was proposed to compare the difference between original tensor and the process output.

• 한국컴퓨터정보학회논문지
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• 제21권1호
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• pp.181-189
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• 2016

This study examined three elementary school computing curriculum - the CSTA K-12 computer science standards, the computing programme of the national curriculum in England, and the 2015 national curriculum in Korea - focusing on the educational objectives with the perspective of the revision of Bloom's Taxonomy of Educational Objectives. The CSTA K-12 computer science standards mainly addressed applying procedural knowledge and using digital technology is the main theme. The computing programme in England concentrated on understanding factual and conceptual knowledge of computer science, such as algorithms. The 2015 national curriculum also addressed applying procedural knowledge, but the main focus is making softwares and robots. The findings of this comparative analysis suggest that it is needed to set up concrete educational objectives for lower grade and make them related to the secondary education to make more coherent elementary-level learning objectives. And elementary-level computing learning objectives are needed to be organized with the perspective of knowledge and cognitive process level.

• Journal of Information Processing Systems
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• 제15권4호
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• pp.717-723
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• 2019

Smart systems and services aim to facilitate growing urban populations and their prospects of virtual-real social behaviors, gig economies, factory automation, knowledge-based workforce, integrated societies, modern living, among many more. To satisfy these objectives, smart systems and services must comprises of a complex set of features such as security, ease of use and user friendliness, manageability, scalability, adaptivity, intelligent behavior, and personalization. Recently, artificial intelligence (AI) is realized as a data-driven technology to provide an efficient knowledge representation, semantic modeling, and can support a cognitive behavior aspect of the system. In this paper, an integration of AI with the smart systems and services is presented to mitigate the existing challenges. Several novel researches work in terms of frameworks, architectures, paradigms, and algorithms are discussed to provide possible solutions against the existing challenges in the AI-based smart systems and services. Such novel research works involve efficient shape image retrieval, speech signal processing, dynamic thermal rating, advanced persistent threat tactics, user authentication, and so on.

• 한국수학교육학회지시리즈A:수학교육
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• 제58권1호
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• pp.139-160
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• 2019

This study attempts to examine statistical tasks in the middle-school mathematics textbooks of Korea and Connected Mathematics 3 [CMP] of the US in terms of an opportunity-to-learn for statistical reasoning. We utilized an analytical framework consisting of types of context, statistical reasoning level, cognitive demand of the tasks, and types of student response. The findings from the task analysis suggested that Korean textbooks focused on finding answers by applying previously learned algorithms or formulas and thus provided students with very limited opportunities to experience statistical reasoning. Also, the results proposed that the mathematical tasks in statistics unit of CMP3 offer more essential and complex tasks that promote students' conceptual understanding of various statistical ideas and statistical reasoning in a meaningful way.

• Journal of Oral Medicine and Pain
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• 제47권2호
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• pp.107-108
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• 2022

Artificial intelligence (AI) refers to the use of machines to mimic intelligent human behavior. It involves interactions with humans in clinical settings, and augmented intelligence is considered as a cognitive extension of AI. The importance of AI in healthcare and medicine has been emphasized in recent studies. Machine learning models, such as genetic algorithms, artificial neural networks (ANNs), and fuzzy logic, can learn and examine data to execute various functions. Among them, ANN is the most popular model for diagnosis based on image data. AI is rapidly becoming an adjunct to healthcare professionals and is expected to be human-independent in the near future. The introduction of AI to the diagnosis and treatment of oral diseases worldwide remains in the preliminary stage. AI-based or assisted diagnosis and decision-making will increase the accuracy of the diagnosis and render treatment more precise and personalized. Therefore, dental professionals must actively initiate and lead the development of AI, even if they are unfamiliar with it.

• 대한불안의학회지
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• 제15권1호
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• pp.13-19
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• 2019

Objective : The purpose of this study was to investigate consensus relative to treatment strategies for psychosocial treatment in panic disorder, that represents one subject addressed by the Korean guidelines for treatment of panic disorder 2018. Methods : The executive committee developed questionnaires relative to treatment strategies for patients with panic disorder based on guidelines, algorithms, and clinical trials previously published in foreign countries and Korea. Seventy-two (61.0%) of 112 experts on a committee reviewing panic disorder responded to the questionnaires. We classified the consensus of expert opinions into three categories (first-line, second-line, and third-line treatment strategies), and identified treatment of choice using the Chi-square test and 95% confidence intervals. Results : For psychosocial treatment of panic disorder, individual and group cognitive behavior therapy (CBT) were recommended treatments of choice, and mindfulness based cognitive therapy (MBCT) was recommended as first line strategy. There was statistically significant consensus among experts regarding usefulness of each component of CBT and MBCT, for treatment of patients with panic disorder. Conclusion : Results, that reflect recent studies and clinical experiences, may provide the guideline for psychosocial treatment strategies for panic disorder.