• 대한화학회지
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• 제59권4호
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• pp.308-319
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• 2015

As a follow-up study to identify references for threshold concepts in science, 20 high school chemistry teachers were interviewed. Seven concepts were identified as threshold concepts. The data revealed that teachers overcome the thresholds while they are teaching as well as learning during their school years. This explains that the mastery experience of threshold concepts involve not only the process of creating subject matter knowledge of a learner but also the reflection on or preparation for teaching. Hence, the current study proposes that a strong relationship exists between the mastery experience of threshold concepts and the development of teachers’ pedagogical content knowledge (PCK). In this regard, findings from this study will provide valuable information to understand the nature of threshold concepts and suggests the value of mastery experience of threshold concepts in terms of PCK development.

• 정보처리학회논문지C
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• 제9C권6호
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• pp.841-846
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• 2002

이 논문에서는 네트워크의 QoS에 가장 큰 영향을 미치는 네트워크 선로 이용률의과거 데이터를 기반으로 단기간 예측과 계절성(seasonality) 예측에 적합한 계절자기회귀이동평균(SARIMA : seasonal ARIMA) 모형을 적용하여 네트워크 특성을 고려한 동적인 임계값을 학습하는 알고리즘을 제시하였다. 이 기법을 통해 선로 이용률의 임계값은 네트워크환경과 시간에 따라 동적으로 변경되며, 확률을 근거로 그 신뢰성을 제공할 수 있다. 또한,실제 환경을 통하여 제시한 모델의 적합성 여부를 평가하였으며, 알고리즘의 성능을 실험하였다. 네트워크 관리자들은 이 알고리즘을 통하여 고정 임계값이 가지는 단점을 극복할 수있을 것이며, 관리 행위의 효율성을 높일 수 있을 것이다.

• 디지털산업정보학회논문지
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• 제13권4호
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• pp.231-245
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• 2017

The purpose of this study is to analyze the trend line through scatter diagram analysis on correlation between SNS addiction tendencies and learning attitudes, figure out the knee point influencing learning attitudes negatively in detail, and examine influence among subareas. To address the goal, study questions are formulated as follows. First, this author did screening on the data of variables measured and analyzed descriptive statistics. Second, this researcher produced the trend line by drawing a scatter diagram in order to analyze correlation between SNS addiction tendencies, withdrawal symptoms, excessive communication, and excessive time wasting, and learning attitudes exploratorily. Third, to explore correlation between self-evaluation, learning participation, and developmental attitudes, the subfactors of learning attitudes related to SNS addiction tendencies, this author drew a scatter diagram and analyzed the threshold of positive and negative correlation. To verify the study questions, the SNS addiction tendency scale and learning attitude scale were applied to 301 university students in Chungcheong area. According to the study results, first, their learning attitudes are influenced by SNS addiction tendencies, excessive communication and excessive time wasting, and they are not influenced by withdrawal symptoms that much. Second, excessive communication, a factor of SNS addiction tendencies, and self-evaluation and developmental attitudes, factors of learning attitudes, show positive correlation to some extent and indicate negative correlation after the threshold. However, excessive communication and learning participation are found to show no correlation. Third, according to the results of examining correlation with learning attitudes by dividing them into excessive communication and excessive time wasting groups with the knee point of 1.40, as the symptom of excessive communication is found more, it influences self-evaluation, learning participation, developmental attitudes, and learning attitudes more negatively in general. The result of this study is expected to provide foundational material necessary to develop educational programs to prevent undergraduates' excessive SNS use and SNS addiction which can be used in the scenes of counseling or education.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.604-607
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• 2003

Most recommendation systems recommend the products or other information satisfying preferences of users on the basis of the users' previous profile information and other information related to product searches and purchase of users visiting web sites. This study aims to apply these application categories to e-mail more necessary to users. The E-Mail System has the strong personality so that there will be some problems even if e-mails are automatically classified by category through the learning on the basis of the personal rules. In consideration with this aspect, we need the semi-automatic system enabling both automatic classification and recommendation method to enhance the satisfaction of users. Accordingly, this paper uses two approaches as the solution against the misclassification that the users consider as the accuracy of classification itself using the dynamic threshold in Bayesian Learning Algorithm and the second one is the methodological approach using the recommendation agent enabling the users to make the final decision.

• 전기전자학회논문지
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• 제24권4호
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• pp.1011-1016
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• 2020

We propose a framework called Stacked Gated Recurrent Unit - Infrequent Residual Analysis (SG-IRA) that detects anomalies in time-series data that can be trained on streams of raw sensor data without any pre-labeled dataset. To enable such unsupervised learning, SG-IRA includes an estimation model that uses a stacked Gated Recurrent Unit (GRU) structure and an analysis method that detects anomalies based on the difference between the estimated value and the actual measurement (residual). SG-IRA's residual analysis method dynamically adapts the detection threshold from the population using frequency analysis, unlike the baseline model that relies on a constant threshold. In this paper, SG-IRA is evaluated using the industrial control systems (ICS) datasets. SG-IRA improves the detection performance (F1 score) by 5.9% compared to the baseline model.

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.88-93
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• 2020

Based on the capture-emission energy (CEE) maps of CMOS devices, a physics-informed machine learning model for the bias temperature instability (BTI)-induced threshold voltage shifts and low frequency noise is presented. In order to incorporate physics theories into the machine learning model, the integration of artificial neural network (IANN) is employed for the computation of the threshold voltage shifts and low frequency noise. The model combines the computational efficiency of IANN with the optimal estimation of Gaussian mixture model (GMM) with soft clustering. It enables full lifetime prediction of BTI under various stress and recovery conditions and provides accurate prediction of the dynamic behavior of the original measured data.

• 대한임베디드공학회논문지
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• 제18권2호
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• pp.59-65
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• 2023

Currently, polysomnography is essential to diagnose sleep-related breathing disorders. However, there are several disadvantages to polysomnography, such as the requirement for multiple sensors and a long reading time. In this paper, we propose a system for predicting the severity of sleep-related breathing disorders at home utilizing measurable elements in a wearable device. To predict severity, the variables were refined through a three-step variable selection process, and the refined variables were used as inputs into three machine-learning models. As a result of the study, random forest models showed excellent prediction performance throughout. The best performance of the model in terms of F1 scores for the three threshold criteria of 5, 15, and 30 classified as the AHI index was about 87.3%, 90.7%, and 90.8%, respectively, and the maximum performance of the model for the three threshold criteria classified as the RDI index was approx 79.8%, 90.2%, and 90.1%, respectively.

• Structural Monitoring and Maintenance
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• 제5권1호
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• pp.51-65
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• 2018

A number of sensing techniques have been implemented for detecting defects in civil infrastructures instead of onsite human inspections in structural health monitoring. However, the issue of faults in sensors has not received much attention. This issue may lead to incorrect interpretation of data and false alarms. To overcome these challenges, this article presents a deep learning-based method with a new architecture of Stateful Long Short Term Memory Neural Networks (S-LSTM NN) for detecting sensor fault without going into details of the fault features. As LSTMs are capable of learning data features automatically, and the proposed method works without an accurate mathematical model. The detection of four types of sensor faults are studied in this paper. Non-stationary acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured bridge data with estimation to detect the sensor fault. Another set of sensor output data is used to supervise the network parameters and backpropagation algorithm to fine tune the parameters to establish a deep self-coding network model. The response residuals between the true value and the predicted value of the deep S-LSTM network was statistically analyzed to determine the fault threshold of sensor. Experimental study with a cable-stayed bridge further indicated that the proposed method is robust in the detection of the sensor fault.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.3867-3886
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• 2015

This article investigates the problem of opportunistic spectrum access in dynamic environment, in which the signal-to-noise ratio (SNR) is time-varying. Different from existing work on continuous feedback, we consider more practical scenarios in which the transmitter receives an Acknowledgment (ACK) if the received SNR is larger than the required threshold, and otherwise a Non-Acknowledgment (NACK). That is, the feedback is discrete. Several applications with different threshold values are also considered in this work. The channel selection problem is formulated as a non-cooperative game, and subsequently it is proved to be a potential game, which has at least one pure strategy Nash equilibrium. Following this, a multi-agent Q-learning algorithm is proposed to converge to Nash equilibria of the game. Furthermore, opportunistic spectrum access with multiple discrete feedbacks is also investigated. Finally, the simulation results verify that the proposed multi-agent Q-learning algorithm is applicable to both situations with binary feedback and multiple discrete feedbacks.

• 한국산업보건학회지
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• 제25권4호
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• pp.573-583
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• 2015

Objectives: This study was carried out to evaluate the effect of noise exposure and aging on changes in hearing threshold level and the relationship between age and noise. Materials: The author selected 274 male shipyard and assembly line workers as the noise exposed group and 582 males not exposed to noise as the general population group. Data were collected from five years of consecutive annual audiometric tests performed from 2008 to 2012. Results: In the general population and noise exposed groups, there was a reverse phenomenon that hearing threshold level for 2009 was lower than that of 2008, which seemed to be due to the learning effect, but from 2010 hearing threshold level increased. In the noise exposed group, the mean hearing threshold level in the left ear was significantly higher than that for right ear. In the general population group, the older was the age, the higher was the hearing threshold level, especially at 4000 Hz. In the general population and noise exposed groups, frequency, age group and noise exposure independently affected hearing threshold level, and there was no relationship between age and noise exposure. Over all frequencies, the change of hearing threshold level was larger in the noise exposed group than in the general population group. In the noise exposed group below thirty years old, the change at 4000 Hz was remarkable. Conclusions: Age and noise exposure seem to affect hearing threshold level independently and contribute to an additive effect on hearing threshold level.