• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.127-136
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• 2021

Real-time human recognition is a challenging task, as the images are captured in an unconstrained environment with different poses, makeups, and styles. This limitation is addressed by generating several facial images with poses, makeup, and styles with a single reference image of a person using Generative Adversarial Networks (GAN). In this paper, we propose deep learning-based human recognition using integration of GAN and Spatial Domain Techniques. A novel concept of human recognition based on face depiction approach by generating several dissimilar face images from single reference face image using Domain Transfer Generative Adversarial Networks (DT-GAN) combined with feature extraction techniques such as Local Binary Pattern (LBP) and Histogram is deliberated. The Euclidean Distance (ED) is used in the matching section for comparison of features to test the performance of the method. A database of millions of people with a single reference face image per person, instead of multiple reference face images, is created and saved on the centralized server, which helps to reduce memory load on the centralized server. It is noticed that the recognition accuracy is 100% for smaller size datasets and a little less accuracy for larger size datasets and also, results are compared with present methods to show the superiority of proposed method.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.719-731
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• 2021

This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.

• Journal of Information Technology Applications and Management
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• v.23 no.3
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• pp.1-12
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• 2016

Visual representations for concepts in business management are quite challenging, especially those abstract concepts in Accountancy discipline. For example, there might not be a consensus on what to use to represent such abstraction as an asset, liability, or owner equity. This is because asset can be property, estate, resources, equipment, or any tangible or non-tangible valuables. Cognitive science concepts and behavior engagement have been used to develop visual representations for financial data. The concepts include spatial processing, big picture thinking, and metaphor. Review of past studies together with a brief research plan to test the usability for learning of four new augmented reality 0visuals are provided in the present paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4799-4818
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• 2015

In this paper, we investigate the problem of achieving global optimization for distributed carrier aggregation (CA) in small cell networks, using a game theoretic solution. To cope with the local interference and the distinct cost of intra-band and inter-band CA, we propose a non-cooperation game which is proved as an exact potential game. Furthermore, we propose a spatial adaptive play learning algorithm with heterogeneous learning parameters to converge towards NE of the game. In this algorithm, heterogeneous learning parameters are introduced to accelerate the convergence speed. It is shown that with the proposed game-theoretic approach, global optimization is achieved with local information exchange. Simulation results validate the effectivity of the proposed game-theoretic CA approach.

• 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.

• The Korean Journal of Ecology
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• v.28 no.6
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• pp.405-415
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• 2005

This paper considers the advantage of Machine Learning (ML) implemented to freshwater ecosystem research. Currently, many studies have been carried out to find the patterns of environmental impact on dynamics of communities in aquatic ecosystems. Ecological models popularly adapted by many researchers have been a means of information processing in dealing with dynamics in various ecosystems. The up-to-date trend in ecological modelling partially turns to the application of ML to explain specific ecological events in complex ecosystems and to overcome the necessity of complicated data manipulation. This paper briefly introduces ML techniques applied to freshwater ecosystems in Korea. The manuscript provides promising information for the ecologists who utilize ML for elucidating complex ecological patterns and undertaking modelling of spatial and temporal dynamics of communities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2359-2376
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• 2022

With the exponential growth of medical image big data represented by high-resolution CT images(CTI), the high-resolution CTI data is of great importance for clinical research and diagnosis. The paper takes lung CTI as an example to study. Retrieving answer CTIs similar to the input one from the large-scale lung CTI database can effectively assist physicians to diagnose. Compared with the conventional content-based image retrieval(CBIR) methods, the CBIR for lung CTIs demands higher retrieval accuracy in both the contour shape and the internal details of the organ. In traditional supervised deep learning networks, the learning of the network relies on the labeling of CTIs which is a very time-consuming task. To address this issue, the paper proposes a Weakly Supervised Similarity Evaluation Network (WSSENet) for efficiently support similarity analysis of lung CTIs. We conducted extensive experiments to verify the effectiveness of the WSSENet based on which the CBIR is performed.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.1
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• pp.48-55
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• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.

• Journal of Gifted/Talented Education
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• v.23 no.5
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• pp.697-713
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• 2013

The research aimed to investigate characteristics of middle school students enrolled in a science gifted education center affiliated with university in terms of multiple intelligence, self-regulated learning and personality traits. The 89 subjects in the study responded to questionnaires of multiple intelligence, self-regulated learning ability and a personality trait in October, 2011. It was found that both science and math gifted students presented intrapersonal intelligence as strength and logical-mathematical intelligence as weakness. While physics and earth science gifted ones showed spatial intelligence as strength, chemistry and biology gifted ones did intrapersonal intelligence. For self-regulated learning ability, both science and mathematics gifted students tend to show higher levels than general students, in particular, cognitive and motivation strategies comparatively higher than meta-cognition and environment condition strategies. Characteristics of personal traits widely distributed across science and mathematics gifted students, showing that each gifted student presented distinct characteristics individually. Those gifted students showing certain intelligence such as spatial, intrapersonal, or natural intelligences as strength also showed different characteristics of self-regulated learning ability and personal traits among students showing same intelligence as strength. It was concluded that science and mathematics gifted students showed various characteristics of multiple intelligences, self-regulated learning ability, and personal traits across science and mathematics areas.

• Journal of Korean Elementary Science Education
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• v.40 no.4
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• pp.480-497
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• 2021

This study intends to explore the content and organization characteristics of elementary school science textbooks in the astronomical domain from the perspective of understanding the astronomical spatial concept. This study analyzed the relevant unit of the achievement criteria in the 2015 revised science curriculum for the 'space' domain in elementary school science textbooks in comparison with that in secondary school textbooks according to the spatial concept analysis criteria by texts, illustrations, and inquiry activities. As a result, elementary school science textbooks were found to be organized around phenomena rather than concepts, targeting observable celestial bodies linked to real life in order to optimize the amount of learning content. However, the learning contents of astronomical observation related to observation and phenomena-centered real life should stimulate the curiosity and imagination of elementary school students and encourage their intellectual participation. Students need to be supported for understanding of the spatial concept to find an answer to the question of 'why'. The content organization for core concepts should be organized around the transferable spatial concept rather than simply focusing on presenting results by accurately describing the phenomena observed by students through scientific practice in order for students to answer various questions that arise in the course of scientific practice.