• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.3917-3941
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• 2019

The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.

• Journal of Information Processing Systems
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• 제17권6호
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• pp.1057-1070
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• 2021

Case-related news filtering is crucial in legal text mining and divides news into case-related and case-unrelated categories. Because case-related news originates from various fields and has different writing styles, it is difficult to establish complete filtering rules or keywords for data collection. In addition, the labeled corpus for case-related news is sparse; therefore, to train a high-performance classification model, it is necessary to annotate the corpus. To address this challenge, we propose topic-enhanced positive-unlabeled learning, which selects positive and negative samples guided by topics. Specifically, a topic model based on a variational autoencoder (VAE) is trained to extract topics from unlabeled samples. By using these topics in the iterative process of positive-unlabeled (PU) learning, the accuracy of identifying case-related news can be improved. From the experimental results, it can be observed that the F1 value of our method on the test set is 1.8% higher than that of the PU learning baseline model. In addition, our method is more robust with low initial samples and high iterations, and compared with advanced PU learning baselines such as nnPU and I-PU, we obtain a 1.1% higher F1 value, which indicates that our method can effectively identify case-related news.

• Journal of Power Electronics
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• 제12권1호
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• pp.201-207
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• 2012

This paper presents the results and gained experiences from the Project Based Learning (PBL) of magnetic component design within a Power Electronics Course. PBL was applied during the laboratory exercises through a design-project task based on a boost converter test board. The students were asked to calculate the main boost converter's circuit parameters' capacitor C and inductor L, and then additionally required to design and build-up the inductor L, in order to meet the project's goals. The whole PBL process relied on ideas from the CDIO (Conceive, Design, Implement, Operate), where the students are encouraged to consider the whole system's process, in order to obtain hands-on experience. PBL is known to be a motivating and problem-centered teaching method that gives students the ability to transfer their acquired scientific knowledge into industrial practice. It has the potential to help students cope with demanding complexities in the field, and those problems they will face in their future careers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

• 한국환경교육학회지:환경교육
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• 제18권2호
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• pp.101-112
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• 2005

The purpose of this study is to develop environmental inquiry activities for teaching the 10th grade students in science classes of high school. The activities are developed to perform goals of environmental education for sustainable development. In order to this, activities are sequently organized in order of context of laboratory, field, and problem solving in respect of one learning topic. The object of inquiry activities in laboratory context is understanding concepts related environment and environmental pollution. The inquiry activities in field context have an object of attaining good awareness and attitude toward environment. Throughout the activities in probem solving context students are expected to have a mind of participating in environmental issues. The activities are designed to learn and use integrated science knowledge in many domains. Some activities are intended to utilize MBL(Microcomputer-based Laboratory). The ICT materials, lesson plans, instructional sheets for teaching and student' sheets for inquiry were produced to guide these activities. It is expected that this effort will contribute to cultivate environmental literate persons who have not only scientific understanding but also practical will of environmental issues.

• Nuclear Engineering and Technology
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• 제55권2호
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• pp.603-622
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• 2023

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.

• Smart Structures and Systems
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• 제25권5호
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• pp.605-617
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• 2020

The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (nMSEDI) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using nMSEDI to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from nMSEDI are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.

• 정보교육학회논문지
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• 제15권4호
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• pp.625-634
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• 2011

본 연구는 초등학교 과학수업에서 로봇활용 MBL수업을 적용 후 학생들의 과학탐구능력에 미치는 교육적 효과를 조사하였다. 또한 로봇활용 과학수업에 대한 학생들의 인식 조사 및 인터뷰가 수행되었다. 실험 집단은 로봇활용 과학 수업을 통제집단은 교과서, 실험관찰을 활용한 전통적인 과학수업을 실시하였다. 연구결과 탐구능력의 측정, 예상, 추리 세 가지 영역에서 유의미한 차이가 발견되었다(<.05). 그러나 관찰과 분류 요소에서는 유의미한 차이는 발견되지 않았다. 로봇활용 과학실험 수업에 대한 학생들의 인식조사 결과 로봇을 통하여 과학수업에 흥미를 가지게 되었으며 쉽게 학습내용을 이해한 것으로 나타났다.

• Nuclear Engineering and Technology
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• 제56권4호
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• pp.1153-1164
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• 2024

In recent years, unmanned ground vehicles (UGVs) have been used to search for lost or stolen radioactive sources to avoid radiation exposure for operators. To achieve autonomous localization of radioactive sources, the UGVs must have the ability to automatically determine the next radiation measurement location instead of following a predefined path. Also, the radiation field of radioactive sources has to be reconstructed or inverted utilizing discrete measurements to obtain the radiation intensity distribution in the area of interest. In this study, we propose an effective source localization framework and method, in which UGVs are able to autonomously explore in the radiation area to determine the location of radioactive sources through an iterative process: path planning, radiation field reconstruction and estimation of source location. In the search process, the next radiation measurement point of the UGVs is fully predicted by the design path planning algorithm. After obtaining the measurement points and their radiation measurements, the radiation field of radioactive sources is reconstructed by the Gaussian process regression (GPR) model based on machine learning method. Based on the reconstructed radiation field, the locations of radioactive sources can be determined by the peak analysis method. The proposed method is verified through extensive simulation experiments, and the real source localization experiment on a Cs-137 point source shows that the proposed method can accurately locate the radioactive source with an error of approximately 0.30 m. The experimental results reveal the important practicality of our proposed method for source autonomous localization tasks.

• 한국과학교육학회지
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• 제30권3호
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• pp.323-336
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• 2010

The purpose of this study was to evaluate students' written arguments embedded in scientific inquiry investigations using the Science Writing Heuristic (SWH) approach. Argument components defined in this study are questions, claims, questions-claims relationship, evidence, claims-evidence relationship, multiple modal representations, and reflection. A set of criteria for evaluating each argument component was developed to evaluate writing samples of students from college freshman general chemistry laboratory classes. Results indicate that students produced, on average, moderate to powerful questions, claims, and evidence. They also constructed reasonable questions-claims relationship and claims-evidence relationship. Compared to other component scores, the average score for reflection was relatively low. Overall, the average Total Argument score was 21.4 out of a possible 36, that is, the quality of the written arguments using the SWH approach during a series of inquiry-based chemistry laboratory investigations was moderate to powerful. The findings of this study suggest that students, on average, developed reasonable scientific arguments generated as part of scientific inquiry. In other words, students are capable of putting together reasonable arguments as they participate in inquiry-based laboratory classrooms.