• School Mathematics
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• v.7 no.3
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• pp.269-286
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• 2005

The purpose of this paper is to study the spatial sense that is introduced for the first time in our 7th mathematics curriculum. For this purpose, we first investigated the factors of the spatial sense ability and with this factors, we analyze the errors those was founded in elementary school students' carrying out tasks related to the spatial sense, and the contents of elementary mathematics textbook. From the analysis, we found that the teaching topics in the spatial sense is disagreed with the students' learning level and for each similar topics is cut off into not adjacent grades, connecting these topics to each other and to the other traditional geo-metric topics is not easy. we must consider this findings in the future revision of mathematics curriculum.

• Journal of Fisheries and Marine Sciences Education
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• v.6 no.2
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• pp.198-216
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• 1994

The problem of promoting instructional effect using reorganizing the content of textbook is one of the major concerns of many education theorists and teachers. The results of many researches about above problem reveal that reorganizing the content of textbook promotes the ability of recall and problem solving of learners. The content structure of current navigation textbook revealed a categorical structure as its basic framework, though it seems to be a poor one. A categorical structure is known as providing an inferior information processing mechanism for learners than a learning hierarchy content structure is. Furthermore current content structure hasn't given any considerations to navigation in practice, spatial contexts and sequential events of ships from a harbor to another harbor. The learning hierarchy content structure has an advantage of giving learners more systematic and stronger knowledge networks than a categorical structure.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.73-88
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• 2023

Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.

• Journal of the Korean Geographical Society
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• v.39 no.6 s.105
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• pp.971-983
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• 2004

It is increasingly clear that student mastery of concepts and skills in geographic education is based on a complex set of variables. Sex and gender are the key variables. Much has been written about biological sex differences in learning, but less attention has been paid to the impacts of socio-cultural gender on learning geography. As such, the aims of this paper are two-fold. First, to examine theories which seek to explain why males and females might differ in their geographic and spatial knowledge or skill. Second, to examine the extent of sex differences and gender traits in the geographic learning. The results of study illustrate clearly that there are more similarities than differences between the sexes. Therefore, there are significant gender differences between the preferences of regions, contents, activities in the secondary geographic learning. The results also provide insights into improving contents and method of geographic education.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.

• Journal of Elementary Mathematics Education in Korea
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• v.20 no.4
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• pp.737-760
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• 2016

This study analyzed the 2009 revised curriculum 6th grade math geometric domain and developed virtual operation tool contents based RME theory. These materials were examine to find out how to effect on the spatial sense and mathematical attitudes by applying it to teach the 6th grade students. The results were as follows. First, it is more effective for improving spatial sense to study mathematics based RME theory with virtual operation tool contents than normal one. This means that mathematics study based RME theory with virtual operation contents overcomes the limitations of flat learning environment. And it is great educational and effective method for students to improve their spatial sense. Second, it is more effective for improving mathematical attitudes to study mathematics based RME theory with virtual operation tool contents than normal one. This means that Mathematics study based RME theory with virtual operation contents makes student more participate learning actively. It helps the students who have passive learning habits to have self-directed learning habits, ability to cooperation and communicate. The results of this study suggest that mathematics study based RME theory is very helpful for student to improve their spatial sense and have positive effect on self-concept in mathematics, attitudes toward mathematics and improving study habits in mathematical attitudes.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Journal of Information Technology Services
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• v.14 no.1
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• pp.99-113
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• 2015

Adaptive bitrate (ABR) streaming technology has become an important and prevalent feature in many multimedia delivery systems, with content providers such as Netflix and Amazon using ABR streaming to increase bandwidth efficiency and provide the maximum user experience when channel conditions are not ideal. Where such systems could see improvement is in the delivery of live video with a closed loop cognitive control of video encoding. In this paper, we present streaming camera system which provides spatially and temporally adaptive video streams, learning the user's preferences in order to make intelligent scaling decisions. The system employs a hardware based H.264/AVC encoder for video compression. The encoding parameters can be configured by the user or by the cognitive system on behalf of the user when the bandwidth changes. A cognitive video client developed in this study learns the user's preferences (i.e. video size over frame rate) over time and intelligently adapts encoding parameters when the channel conditions change. It has been demonstrated that the cognitive decision system developed has the ability to control video bandwidth by altering the spatial and temporal resolution, as well as the ability to make scaling decisions

• Journal of Wellbeing Management and Applied Psychology
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• v.5 no.2
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• pp.15-19
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• 2022

Purpose: This pilot study aimed to clarify features of motor module during walking in exercise experts who experienced lately repeated training for sports skill. To identify motor modules, autoencoder machine learning algorithm was used, and modules were extracted from muscle activities of lower extremities. Research design, data and methodology: A total of 10 university students were participated. 5 students did not experience any sports training before, and 5 students did experience sports training more than 5 years. Eight muscle activities of dominant lower extremity were measured. After modules were extracted by autoencoder, the numbers of modules and spatial muscle weight values were compared between two groups. Results: There was no significant difference in the minimal number of motor modules that explain more than 90% of original data between groups. However, in similarity analysis, three motor modules were shown high similarity (r>0.8) while one module was shown low similarity (r<0.5). Conclusions: This study found not only common motor modules between exercise novice and expert during walking, but also found that a specific motor module, which would be associated with high motor control ability to distinguish the level of motor performance in the field of sports.

• The Korean Journal of Pain
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• v.35 no.3
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• pp.261-270
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• 2022

Background: The rostral ventromedial medulla (RVM) is a critical region for the management of nociception. The RVM is also involved in learning and memory processes due to its relationship with the hippocampus. The purpose of the present study was to investigate the molecular mechanisms behind orexin-A signaling in the RVM and hippocampus's effects on capsaicin-induced pulpal nociception and cognitive impairments in rats. Methods: Capsaicin (100 g) was applied intradentally to male Wistar rats to induce inflammatory pulpal nociception. Orexin-A and an orexin-1 receptor antagonist (SB-334867) were then microinjected into the RVM. Immunoblotting and immunofluorescence staining were used to check the levels of cyclooxygenase-2 (COX-2) and brain-derived neurotrophic factor (BDNF) in the RVM and hippocampus. Results: Interdental capsaicin treatment resulted in nociceptive responses as well as a reduction in spatial learning and memory. Additionally, it resulted in decreased BDNF and increased COX-2 expression levels. Orexin-A administration (50 pmol/1 µL/rat) could reverse such molecular changes. SB-334867 microinjection (80 nM/1 µL/rat) suppressed orexin's effects. Conclusions: Orexin-A signaling in the RVM and hippocampus modulates capsaicin-induced pulpal nociception in male rats by increasing BDNF expression and decreasing COX-2 expression.