• 농업과학연구
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• 제46권1호
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• pp.67-78
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• 2019

The purpose of this study was to predict the water quality using the RNN (recurrent neutral network) and LSTM (long short-term memory). These are advanced forms of machine learning algorithms that are better suited for time series learning compared to artificial neural networks; however, they have not been investigated before for water quality prediction. Three water quality indexes, the BOD (biochemical oxygen demand), COD (chemical oxygen demand), and SS (suspended solids) are predicted by the RNN and LSTM. TensorFlow, an open source library developed by Google, was used to implement the machine learning algorithm. The Okcheon observation point in the Geum River basin in the Republic of Korea was selected as the target point for the prediction of the water quality. Ten years of daily observed meteorological (daily temperature and daily wind speed) and hydrological (water level and flow discharge) data were used as the inputs, and irregularly observed water quality (BOD, COD, and SS) data were used as the learning materials. The irregularly observed water quality data were converted into daily data with the linear interpolation method. The water quality after one day was predicted by the machine learning algorithm, and it was found that a water quality prediction is possible with high accuracy compared to existing physical modeling results in the prediction of the BOD, COD, and SS, which are very non-linear. The sequence length and iteration were changed to compare the performances of the algorithms.

• Nuclear Engineering and Technology
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• 제54권4호
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• pp.1167-1174
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• 2022

Cast austenitic stainless steels (CASSs) are widely used as structural materials in the nuclear industry. The main drawback of CASSs is the reduction in fracture toughness due to long-term exposure to operating environment. Even though ultrasonic non-destructive testing has been conducted in major nuclear components and pipes, the detection of cracks is difficult due to the scattering and attenuation of ultrasonic waves by the coarse grains and the inhomogeneity of CASS materials. In this study, the ultrasonic signals measured in thermally aged CASS were discriminated for the first time with the simple ultrasonic technique (UT) and machine learning (ML) models. Several different ML models, specifically the K-nearest neighbors (KNN), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) models, were used to classify the ultrasonic signals as thermal aging condition of CASS specimens. We identified that the ML models can predict the category of ultrasonic signals effectively according to the aging condition.

• 컴퓨터교육학회논문지
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• 제6권4호
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• pp.181-193
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• 2003

본 연구의 목적은 하이퍼텍스트 유형과 자기주도성이 학업성취에 미치는 효과를 규명하려는 것이다. 이를 위해 경기 H시 소재 H고등학교 1학년 2학급 69명을 대상으로 자기주도성 검사를 실시하여 평균점수 이상을 얻은 학생을 자기주도성이 높은 집단으로, 그 미만을 얻은 학생을 자기주도성이 낮은 집단으로 분류하여 실험처치를 하였다. 연구 결과, 하이퍼텍스트 유형은 학업성취에 영향을 미치지 않았으나 학습자의 자기주도성은 학업성취도에 영향을 미치는 것으로 나타났다. 또한 하이퍼텍스트 유형과 학습자의 자기주도성이 학업성취도에 미치는 상호작용 효과가 있는 것으로 밝혀졌다. 본 연구는 비교적 짧은 기간에 이루어져 지속적인 학습효과를 검증하는데 한계가 있기 때문에 장기간에 걸친 연구가 필요하다. 또한 자기주도성 이외의 학습자 특성을 고려한 연구와 컴퓨터일반 교과 외 다양한 학습내용으로 하는 후속 연구가 필요하다.

• 산업경영시스템학회지
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• 제42권4호
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• pp.135-144
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• 2019

In this study, BSC model of center for teaching and learning was developed using balanced scorecard suitable for non-profit organization. Firstly, relevant literature surveys and evaluation indicators of various CTL and institution with similar characteristics were examined. Next, a draft BSC model was designed through interviews of specialists. Lastly, the BSC model was proposed by verifying the content validity of the evaluation model by conducting two Delphi surveys. The BSC model of CTL has 4 perspectives: resource, customer, internal process, learning and growth, 9 critical success factors: 2 factors in resource, customer and learning and growth perspectives, 3 factors in internal process perspective, and 23 key performance Indicators: 4 indicators in resource and learning and growth, 7 indicators in customer perspective, 8 indicators in internal process perspective. The implications of this study through the results were as follows: firstly, the proposed BSC model showed an evaluation model suitable for a non-profit organization. Second, the BSC model was linked to the organization's mission and vision. Third, it could contribute to the long-term development of CTL. Lastly, if it could be applied to management, and evaluated, it is expected to play a role of providing basic data for the budget support and spread of the university.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• 실천공학교육논문지
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• 제14권1호
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• pp.137-147
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• 2022

대학은 학생들이 전공 관련 직무역량을 갖추고 졸업할 수 있도록 교과 및 비교과에서의 경험 학습을 제공해야 하며, 경험 학습에는 교내에서 수행하는 캡스톤디자인과 산업체에서 수행하는 현장실습 등이 있다. 특히 2021년 하반기부터 도입된 표준현장실습제도의 시행으로 산업체의 단기(4주) 및 중기현장실습(8주) 참여 감소현상은 학생들이 실무경험을 쌓을 기회를 더욱 더 어렵게 만들고 있다. 이를 해결하기 위해 본 논문에서는 기업의 애로기술을 해결하는 캡스톤디자인과 현장실습을 결합한 산업체 참여형 캡스톤디자인과 현장실습 연계 모형을 제안한다. 참여 학생들은 캡스톤디자인 과정에서 기업애로기술의 해결책을 찾고 현장실습 기간 동안 해결책에 대한 시제품제작을 진행한다. 어려운 난제가 있을 경우 전담 교수의 도움으로 해결하는 연계 모형이 경북 Y대 전자공학과 사례의 운영을 통해 대학과 산업체가 상생하는 산학협력 교육의 하나의 좋은 모형이라고 판단된다.

• 간호행정학회지
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• 제20권5호
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• pp.587-598
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• 2014

Purpose:This study was conducted to identify the effects of an action learning based creative problem-solving (CPS) course on problem solving, creativity and team-member exchange in nursing students. Methods: A quasi-experimental study applying a non-equivalent control group pre-post design was employed. Sophomore nursing students (32 in the experimental group and 33 in the control group) were recruited from a university in G-city, Korea. Problem solving, creativity and team-member exchange were measured for the pretest and posttest using self-report questionnaires. Kolmogorov-Smirnov test, Chi-square, Fisher's exact test, t-test, and ANCOVA with SPSS/Win 20.0 program were used to analyze the data. Results: The scores for problem solving, creativity and team-member exchange in the experimental group were significantly higher than those of the control group. Conclusion: Results of this study indicate that an action learning based CPS course is an effective teaching method to improve nursing students' competencies. In the future longitudinal studies are needed to assess the long term effects of the course.

• Smart Structures and Systems
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• 제29권4호
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• pp.589-598
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• 2022

Timber structures are susceptible to structural damages caused by variations in moisture content (MC), inducing severe durability deterioration and safety issues. Therefore, it is of great significance to detect MC levels in timber structures. Compared to current methods for timber MC detection, which are time-consuming and require bulky equipment deployment, Lead Zirconate Titanate (PZT)-enabled stress wave sensing combined with statistic machine learning classification proposed in this paper show the advantage of the portable device and ease of operation. First, stress wave signals from different MC cases are excited and received by PZT sensors through active sensing. Subsequently, two non-baseline features are extracted from these stress wave signals. Finally, these features are fed to a statistic machine learning classifier (i.e., naïve Bayesian classification) to achieve MC detection of timber structures. Numerical simulations validate the feasibility of PZT-enabled sensing to perceive MC variations. Tests referring to five MC cases are conducted to verify the effectiveness of the proposed method. Results present high accuracy for timber MC detection, showing a great potential to conduct rapid and long-term monitoring of the MC level of timber structures in future field applications.

• International Journal of Internet, Broadcasting and Communication
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• 제14권1호
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• pp.136-141
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• 2022

A very complex task in deep learning such as image classification must be solved with the help of neural networks and activation functions. The backpropagation algorithm advances backward from the output layer towards the input layer, the gradients often get smaller and smaller and approach zero which eventually leaves the weights of the initial or lower layers nearly unchanged, as a result, the gradient descent never converges to the optimum. We propose a two-factor non-saturating activation functions known as Bea-Mish for machine learning applications in deep neural networks. Our method uses two factors, beta (𝛽) and alpha (𝛼), to normalize the area below the boundary in the Mish activation function and we regard these elements as Bea. Bea-Mish provide a clear understanding of the behaviors and conditions governing this regularization term can lead to a more principled approach for constructing better performing activation functions. We evaluate Bea-Mish results against Mish and Swish activation functions in various models and data sets. Empirical results show that our approach (Bea-Mish) outperforms native Mish using SqueezeNet backbone with an average precision (AP50val) of 2.51% in CIFAR-10 and top-1accuracy in ResNet-50 on ImageNet-1k. shows an improvement of 1.20%.

• 전기전자학회논문지
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• 제24권2호
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• pp.632-637
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• 2020

비간섭적 개별 전기 기기 부하 식별(NILM)은 단일 미터기에서 측정한 총 소비 전력을 사용하여 가정이나 회사에서 개별 전기 기기의 소비 전력과 사용 시간을 효율적으로 모니터링할 수 있는 방법이다. 본 논문에서는 스마트팜의 소비 전력 데이터 취득 시스템에서 LTE 모뎀을 통해 서버로 수집된 총 소비 전력량, 개별 전기 기기의 전력량을 HDF5 형태로 변환하고 NILM 분석을 수행하였다. NILM 분석은 오픈소스를 사용하여 잡음제거 오토인코더(Denoising Autoencoder), 장단기 메모리(Long Short-Term Memory), 게이트 순환 유닛(Gated Recurrent Unit), 시퀀스-투-포인트(sequence-to-point) 학습 방법을 사용하였다.