• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.183-189
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• 2023

A stroke is a medical disease where a blood vessel in the brain ruptures, causes damage to the brain. If the flow of blood and different nutrients to the brain is intermittent, symptoms may occur. Stroke is other reason for loss of life and widespread disorder. The prevalence of stroke is high in growing countries, with ischemic stroke being the high usual category. Many of the forewarning signs of stroke can be recognized the seriousness of a stroke can be reduced. Most of the earlier stroke detections and prediction models uses image examination tools like CT (Computed Tomography) scan or MRI (Magnetic Resonance Imaging) which are costly and difficult to use for actual-time recognition. Machine learning (ML) is a part of artificial intelligence (AI) that makes software applications to gain the exact accuracy to predict the end results not having to be directly involved to get the work done. In recent times ML algorithms have gained lot of attention due to their accurate results in medical fields. Hence in this work, Stroke disease identification system by using Machine Learning algorithm is presented. The ML algorithm used in this work is Artificial Neural Network (ANN). The result analysis of presented ML algorithm is compared with different ML algorithms. The performance of the presented approach is compared to find the better algorithm for stroke identification.

• 농업과학연구
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• 제47권4호
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• pp.1029-1037
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• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• 유통과학연구
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• 제16권9호
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• pp.65-77
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• 2018

Purpose - A trading area is very closely related to consumer life. A trading area is a cultural and social space that consumes culture and promotes human relationships as well as an economic space where consumers live their daily lives. In this context, a trading area research should be conducted objectively and empirically because it deals with the activities of consumer's life. The purpose of this study is to identify the intrinsic psychological motivation(push motivation) caused when consumers visit a trading area and to demonstrate how the push motivation for a trading area influence on consumer's flow, satisfaction, revisit intention. Research design, data, and methodology - In order to develop research hypotheses for this study, the development procedures for push motivation scale are as follows; (1) generating initial pool of items based on previous studies, (2) expert judgement to evaluate content and face validity, and (3) assessing convergent and discriminant validity using confirmatory factor analysis. In order to achieve these purposes, online surveys were conducted on frequent or familiar visitors to the trading areas around the Gangnam, Kunkuk University and Hongik University Station. Among the 1,343 questionnaires collected, 1,157 cases were analyzed by using SPSS 22.0 and SmartPLS 3.0 statistical package program, except for 186 responses in which responses were judged to be unfaithful. Results - The push motivation was classified into five sub-dimensions of excitement/stimulus, rest/relaxation, exit/refreshing, knowledge/learning and human relationship promotion as multidimensional and complex factors composed of individual and social-related dimensions. The excitement/stimulus and human relationship promotion of push motivation have positive effects on satisfaction. However, all dimensions of the push motivation have positive effects on flow. And flow has a positive effect on satisfaction and revisit intention. Meanwhile, the mediation test using boostrapping shows that flow plays a full mediating role in the relationship between rest/relaxation, exit/refreshing, knowledge/learning and satisfaction, but a partial mediating rol e between excitement/stimulus, human relationship promotion and satisfaction. Finally, satisfaction plays a partial mediating role between flow and revisit intention. Conclusions - This study shows that the push motivation is multidimensional and compositive depending on the situation of a consumer. In addition, it is found that the human relationship promotion(a social-related motivation) has a much more important effect on flow and satisfaction than other push motivations of individual dimensions. It also shows that satisfaction increases when consumers are being flowed at their visit and degree of revisit intention also grows as satisfaction increases. As implications of this study, a marketer should try to understand consumer's visit motivation at first and then develop factors that increase their flow, satisfaction, revisit intention. It also requires a marketer to approach subjects on a trading area more objectively and empirically based on the psychology and behavior of consumers, in order to establish a proper and efficient strategy on development of a trading area.

• 한국전자통신학회논문지
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• 제15권6호
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• pp.1099-1104
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• 2020

인터넷을 기반으로 다양한 종류의 응용 서비스들을 제공하기 위해서 각 흐름 별로 서비스 품질을 보장하는 것은 이상적이지만, 이를 실현하는 것은 매우 어려운 일이다. 서비스 품질 요구조건이 같거나 비슷한 여러 흐름들을 동일한 그룹으로 지정하고, 그룹별로 서비스 품질을 제공하는 방안이 효율적이다. 라우터에서 적용되는 큐 관리 메커니즘은 데이터의 효율적으로 전송하고, 서비스 별로 차별화된 서비스 품질을 지원하기 위하여 매우 중요한 역할을 수행한다. 다양한 멀티미디어 서비스를 효율적으로 지원하기 위해서 지능적이고 적응적인 큐 관리 메커니즘 기능이 필요하다. 본 논문은 일정 기간 유입되는 각 흐름 그룹의 트래픽 정보와 현재의 네트워크 상태 정보를 기반으로 그룹별 패킷의 전달 여부를 결정하는 심층강화학습 기반의 지능형 큐관리 메커니즘을 제안한다.

• The Journal of Korean Physical Therapy
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• 제15권2호
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• pp.75-84
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• 2003

The purpose of this study was to review of mechanism and application methodology about mental practice. The mental practice is symbolic rehearsal of physical activity in the absence of any gross muscular movements. Human have the ability to generate mental correlates of perceptual and motor events without any triggering external stimulus, a function known as imagery, Practice produces both internal and external sensory consequences which are thought to be essential for learning to occur, It is for this reason that mental practice, rehearsal of skill in imagination rather than by overt physical activity, has intrigued theorists, especially those interested in cognitive process. Several studies in sport psychology have shown that mental practice can be effective in optimizing the execution of movements in athletes and help novice learner in the incremental acquisition of new skilled behaviors. There are many theories of mental practice for explaining the positive effect In skill learning and performance. Most tenable theories are symbolic learning theory, psyconeuromuscular theory, Paivio's theory, regional cerebral blood flow theory, motivation theory, modeling theory, mental and muscle movement nodes theory, insight theory, selective attention theory, and attention-arousal set theory etc.. The factors for influencing to effects of mental practice are application form, application period, time for length of the mental practice, number of repetition, existence of physical practice.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.458-461
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• 2021

유동인구 예측은 상권의 특성에 따른 점포의 입지 선정 및 고객 맞춤형 마케팅 등 민간 분야에서부터 교통망 등 사회 간접 자본 설계를 위한 공공 분야에 이르기까지 다양한 목적으로 연구되어 왔으며, 최근에는 Covid-19 의 확산에 따라 그 중요도가 더욱 높아지고 있다. 보다 정교한 예측을 위해서는 전체적인 유동 인구 뿐만 아니라 특성 별로 세분화된 하위 그룹에 대해서도 정확한 예측이 요구되나, 기존의 예측 모델들은 이러한 데이터의 계층 구조를 고려하지 않았다. 본 연구에서는 세분화된 하위 그룹 별 유동인구의 예측 정확도를 높이기 위해 전체 유동인구의 패턴을 동시에 활용하는 Global-Local 구조 기반의 Deep Learning 유동인구 분석 모델을 제안한다. 실험 결과 단일 시계열 데이터만을 사용하는 경우 대비 5.4%~52.6%의 예측 오류 감소 효과가 있음을 확인하였다.

• ETRI Journal
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• 제46권3호
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• pp.513-525
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• 2024

Deep neural networks trained on labeled medical data face major challenges owing to the economic costs of data acquisition through expensive medical imaging devices, expert labor for data annotation, and large datasets to achieve optimal model performance. The heterogeneity of diseases, such as Alzheimer's disease, further complicates deep learning because the test cases may substantially differ from the training data, possibly increasing the rate of false positives. We propose a reconstruction-based self-supervised anomaly detection model to overcome these challenges. It has a dual-subnetwork encoder that enhances feature encoding augmented by skip connections to the decoder for improving the gradient flow. The novel encoder captures local and global features to improve image reconstruction. In addition, we introduce an entropy-based image conversion method. Extensive evaluations show that the proposed model outperforms benchmark models in anomaly detection and classification using an encoder. The supervised and unsupervised models show improved performances when trained with data preprocessed using the proposed image conversion method.

• International Journal of Aeronautical and Space Sciences
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• 제14권4호
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• pp.297-309
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• 2013

Design optimization is a mathematical process to find an optimal solution through the use of formal optimization algorithms. Design plays a vital role in the engineering field; therefore, using design tools in education and research is becoming more and more important. Recently, numerical design optimization in fluid mechanics, which uses computational fluid dynamics (CFD), has numerous applications in the engineering field, because of the rapid development of high-performance computing resources. However, it is difficult to find design optimization software and contents for educational purposes in aerospace engineering. In the present study, we have developed an aerodynamic design framework specifically for an airfoil, based on the EDucation-research Integration through Simulation On the Net (EDISON) portal. The airfoil design framework is composed of three subparts: a geometry kernel, CFD flow analysis, and an optimization algorithm. Through a seamless interface among the subparts, an iterative design process is conducted. In addition, the CFD flow analysis and the design framework are provided through a web-based portal system, while the computation is taken care of by a supercomputing facility. In addition to the software development, educational contents are developed for lectures associated with design optimization in aerospace and mechanical engineering education programs. The software and content developed in this study is expected to be used as a tool for e-learning material, for education and research in universities.

• 컴퓨터교육학회논문지
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• 제15권5호
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• pp.11-22
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• 2012

본 연구는 로봇을 활용한 STEAM 수업에서 '도전과 기술의 조화'와 같은 플로우 요인이 학습자의 학습지속 태도에 영향을 주는 과정을 TAM 모형을 적용해서 분석하고자 했다. 연구는 초등학교 6학년 2학기 과학교과의 '에너지와 도구' 단원을 재구성했으며 모두 189명에게 적용하여 유효한 174명에 대한 설문만을 분석에 활용했다. 분석결과 학생들의 학습 몰입요인(도전과 기술의 조화 요인)은 학습의 유용성 보다는 학습의 용이성 요인에 영향을 더 주었고 이는 다시 학습의 가치성 요인을 통해서 최종적으로 학습의 지속의 도에 영향을 준 것으로 파악되었다. 연구의 결과 파악된 시사점으로는 로봇을 STEAM 수업에서의 학습지속의도는 학습자의 학습에 대한 적절한 적극적인 태도와 로봇에 대한 기본적인 소양이 기본적으로 필요하며, 이를 바탕으로 STEAM 수업에 로봇이 도움을 주며 학습의 결과에 영향을 줄 수 있다는 가치적 측면이 고려되어야 함을 알 수 있었다. 반면, 학습의 용이성 및 도전과 기술의 조화 요인은 각각 학습지속의도 및 학습의 가치성 요인에 직접적인 (+)의 영향을 주지 못했다. 다만 두 요인은 유효한 범위 안에서 각각의 종속변수에게 간접적인 영향을 주고 있는 것으로 나타나 이것에 대한 분석결과도 포함시켰다.

• 한국과학교육학회지
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• 제44권2호
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• pp.219-229
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• 2024

본 연구의 목적은 2022 개정 과학과 교육과정에 의한 통합과학 교과서를 개발하는 저자들이 교육과정을 이해하고 적용하는 과정에서 느낀 고민과 어려움을 분석하는 것이다. 이를 위하여 교육과정에 제시된 용어 및 서술어의 이해, 학습 내용의 구성, 탐구 및 활동, 학습내용의 수준과 범위 등으로 범주로 교과서 저자들의 의견 89개를 분석하였다. 분석 결과 교과서 저자들은 학습 내용의 수준과 범위를 정하는데 가장 많은 어려움을 나타내었다. 그리고 용어와 서술어의 중의적 표현과 모호함에 대해 많은 고민과 어려움을 나타내었다. 학습 내용의 구성 측면에서는 성취기준의 반복적 기술, 성취기준의 배열 순서가 학습의 흐름과 일치하지 않은 점과 관련한 어려움이 제시되었으며, 탐구 및 활동과 관련해서는 체험하거나 실제 구현하기 어려운 탐구 활동 제시, 학습량 적정화에 따른 활동 구성의 제약 등과 관련하여 교과서 집필의 어려움이 제시되었다. 국가적으로 양질의 교과서 개발은 양질의 과학 교육을 위해 필요하기 때문에, 교육과정 개발 주체와 교과서 저자와의 교육과정 이해를 위한 소통이 필요하며, 교과서 개발을 위한 지원 체제가 요구된다