• 한국초등과학교육학회지:초등과학교육
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• 제31권3호
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• pp.298-310
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• 2012

The purpose of this study was to analyze the characteristics of elementary school students' prediction changes by the suggestion types in a multiple anomalous situation. We investigated the responses, the rate and time of changing prediction, and cognitive conflicts of the students when repeated anomalous situation was suggested in experimental or logical way in science classes focused on buoyancy. As the anomalous situation was repeated, the students to change the prediction increased in number and also the rates to choose the correct prediction became higher. The group who was exposed in experimental way changed their prediction more than in logical way. In addition, when we classified the students to change the prediction by types, the group in experimental way showed higher rate of NM, MM type and FFT type. With anomalous situation repeated, cognitive conflicts of the students has been gradually declining in both groups. But it seemed that the group in experimental way experienced higher mental conflicts. In particular, as students changed the prediction more and arrived at the correct answer after changing their prediction, all the more so. It is concluded that the degree of students' changing prediction and experiencing cognitive conflict can be different according to the suggestion types for situation. Therefore the correlation with cognitive conflict factors can be also observed with the types of students' reactions.

• Current Optics and Photonics
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• 제4권3호
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• pp.210-220
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• 2020

Aiming at the problem that the Local Sparse Difference Index algorithm has low accuracy and low efficiency when detecting target anomalies in a hyperspectral image, this paper proposes a Weighted Collaborative Representation and Sparse Difference-Based Hyperspectral Anomaly Detection algorithm, to improve detection accuracy for a hyperspectral image. First, the band subspace is divided according to the band correlation coefficient, which avoids the situation in which there are multiple solutions of the sparse coefficient vector caused by too many bands. Then, the appropriate double-window model is selected, and the background dictionary constructed and weighted according to Euclidean distance, which reduces the influence of mixing anomalous components of the background on the solution of the sparse coefficient vector. Finally, the sparse coefficient vector is solved by the collaborative representation method, and the sparse difference index is calculated to complete the anomaly detection. To prove the effectiveness, the proposed algorithm is compared with the RX, LRX, and LSD algorithms in simulating and analyzing two AVIRIS hyperspectral images. The results show that the proposed algorithm has higher accuracy and a lower false-alarm rate, and yields better results.