• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1259-1270
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• 2019

The upper bound of differential characteristic probability is mainly used to determine the number of rounds when constructing a block cipher. As the number of rounds affects the performance of block cipher, it is critical to evaluate the tight upper bound in the constructing process. In order to calculate the upper bound of differential characteristic probability, the previous searching methods for minimum number of active S-boxes constructed constraint equations for non-linear operations and linear operations, independently. However, in the case of BOGI design strategy, where linear operation is dependent on non-linear operation, the previous methods may present the less tight upper bound. In this paper, we exploit the properties of BOGI strategy to propose a new method to evaluate a tighter upper bound of differential characteristic probability than previous ones. Additionally, we mathematically proved the validity of our method. Our proposed method was applied to GIFT-64 and GIFT-128, which are based on BOGI strategy, and the upper bounds of differential characteristic probability were derived until 9 round. Previously, the upper bounds of differential characteristic probability for 7-round GIFT-64 and 9-round GIFT-128 were 2^-18.395 and 2^-26.885, respectively, while we show that the upper bounds of differential characteristic probability are more tight as 2^-19.81 and 2^-28.3, respectively.

• Korean Journal of Cognitive Science
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• v.32 no.3
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• pp.117-139
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• 2021

The way of change detection in which presence of a different item is determined between memory and test arrays with a brief in-between time interval resembles how visual search is done considering that the different item is searched upon the onset of a test array being compared against the items in memory. According to the resemblance, the present study examined whether varying the probability of change occurrence in a visual short-term memory-based change detection task can influence the aspect of response-decision making (i.e., change prevalence effect). The simple-feature change detection task in the study consisted of a set of four colored boxes followed by another set of four colored boxes between which the participants determined presence or absence of a color change from one box to the other. The change prevalence was varied to 20, 50, or 80% in terms of change occurrences in total trials, and their change detection errors, detection sensitivity, and their subsequent RTs were analyzed. The analyses revealed that as the change prevalence increased, false alarms became more frequent while misses became less frequent, along with delayed correct-rejection responses. The observed change prevalence effect looks very similar to the target prevalence effect varying according to probability of target occurrence in visual search tasks, indicating that the background principles deriving these two effects may resemble each other.

• Smart Media Journal
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• v.7 no.3
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• pp.16-21
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• 2018

This paper presents the design and implementation of a deep learning-based automated weed detector on onion fields. The system is based on a Convolutional Neural Network that specifically selects proposed regions. The detector initiates training with a dataset taken from agricultural onion fields, after which candidate regions with very high probability of suspicion are considered weeds. Non-maximum suppression helps preserving the less overlapped bounding boxes. The dataset collected from different onion farms is evaluated with the proposed classifier. Classification accuracy is about 99% for the dataset, indicating the proposed method's superior performance with regard to weed detection on the onion fields.

• Journal of Korean Elementary Science Education
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• v.36 no.1
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• pp.1-15
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• 2017

The purpose of this study is to analyze the problems of land and sea breeze model experiment that has presented in $5^{th}$ grade curriculum in chapter "Weather and our lives" and makes better model simulation so that learners can have better and more effective way to study it. To survey the opinions from dedicated teachers about land and sea breeze model experiment, we produced the survey through interview with science exclusive teacher from M elementary school. An elementary science education expert, 3 men of science EdD modified and complemented survey and started Delphi survey to 12 science teachers who have career teaching more than 3 years. The problems found in this survey were 'one heat bulb, short heating time, small temperature difference of water and sand, lack of class time, empty space between sand and water, back of transparent boxes, little amount of scent and the location of the it' etc. But the most of all, it is hard to see the successful result of the experiment. Based on these kinds of investigations, and lots of trial and error, redesigned the new model experiment that has the most similarity to the real one and high probability of success. According to this, it was able to see the smoke forms horizontal movement along the sand and the smoke goes in one circulation cycle. through this experiment, we made a conclusion that although those scientific experiments in textbook were developed through lots of considerations of expert, to consider the aspect of consumer, it needs to reach the educational agreement about simulation experiment so that It can lead to successful experiment and high quality education.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.693-698
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• 2018

This paper proposes a vehicle detection method for parking areas using unmanned aerial vehicles (UAVs) and using YOLOv2, which is a recent, known, fast, object-detection real-time algorithm. The YOLOv2 convolutional network algorithm can calculate the probability of each class in an entire image with a one-pass evaluation, and can also predict the location of bounding boxes. It has the advantage of very fast, easy, and optimized-at-detection performance, because the object detection process has a single network. The sliding windows methods and region-based convolutional neural network series detection algorithms use a lot of region proposals and take too much calculation time for each class. So these algorithms have a disadvantage in real-time applications. This research uses the YOLOv2 algorithm to overcome the disadvantage that previous algorithms have in real-time processing problems. Using Darknet, OpenCV, and the Compute Unified Device Architecture as open sources for object detection. a deep learning server is used for the learning and detecting process with each car. In the experiment results, the algorithm could detect cars in a dense area using UAVs, and reduced overhead for object detection. It could be applied in real time.