• 한국컴퓨터정보학회논문지
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• 제24권3호
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• pp.77-86
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• 2019

The advent of Internet of Things (IoT) makes common nowadays computing environments involving programming not a single computer but several heterogeneous distributed computers together. Developing programs separately, one for each computer, increases programmer burden and testing all the programs become more complex. To address the challenge, this paper proposes an RPC-based unified programming language, UniPy, for development of MGC (eMbedded, Gateway, and Cloud) applications in IoT systems configured with popular computers such as Arduino, Raspberry Pi, and Web-based DB server. UniPy offers programmers a view of classes as locations and a very simple form of remote procedure call mechanism. Our UniPy compiler automatically splits a UniPy program into small pieces of the program at different locations supporting the necessary RPC mechanism. An advantage of UniPy programs is to permit programmers to write local codes the same as for a single computer requiring no extra knowledge due to having unified programming models, which is very different from the existing research works such as Fabryq and Ravel. Also, the structure of UniPy programs allows programmers to test them by directly executing them before splitting, which is a feature that has never been emphasized yet.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2129-2147
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• 2019

This paper introduces an adaptive face recognition method based on a Novel Incremental Kernel Nonparametric Discriminant Analysis (IKNDA) that is able to learn through time. More precisely, the IKNDA has the advantage of incrementally reducing data dimension, in a discriminative manner, as new samples are added asynchronously. Thus, it handles dynamic and large data in a better way. In order to perform face recognition effectively, we combine the Gabor features and the ordinal measures to extract the facial features that are coded across local parts, as visual primitives. The variegated ordinal measures are extraught from Gabor filtering responses. Then, the histogram of these primitives, across a variety of facial zones, is intermingled to procure a feature vector. This latter's dimension is slimmed down using PCA. Finally, the latter is treated as a facial vector input for the advanced IKNDA. A comparative evaluation of the IKNDA is performed for face recognition, besides, for other classification endeavors, in a decontextualized evaluation schemes. In such a scheme, we compare the IKNDA model to some relevant state-of-the-art incremental and batch discriminant models. Experimental results show that the IKNDA outperforms these discriminant models and is better tool to improve face recognition performance.

• 대한구강악안면병리학회지
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• 제42권6호
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• pp.183-187
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• 2018

The supernumerary tooth which is extra tooth in comparison to normal dentition is one of the developmental problems. The most common type of supernumerary tooth is mesiodens which may cause several complications like delayed eruption, crowding, spacing et al. Moral Incisor Hypomineralization (MIH) describes the clinical appearance of enamel hypomineralization of systemic origin affecting one or more permanent first molars that associated frequently with affected incisors. We report a case of a 6 - year - old boy who visited our clinic for removal of mesiodentes. The patient was diagnosed by mesiodentes and MIH by clinical examination and radiographic examination. Under local anesthesia, Mesiodentes were removed surgically. The demarcated opacities, a feature of MIH, were observed in the removed mesiodentes. After removal of mesiodentes, the maxillary central incisors erupted normally and in order to manage the teeth affected MIH, follow-up and fluoride varnish application were done every 3 months.

• 대기
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• 제28권4호
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• pp.383-392
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• 2018

This study investigates synoptic characteristics of cold surges over South Korea during winter season (December-February). A total of 63 cold events are selected by quantile regression analysis using daily mean temperature observations from 11 KMA stations for 38 years (1979/80-2016/17). Large-scale pressure pattern during the cold surges is well characterized by high over Siberia and low over Aleutian regions, which elucidates cold advection over the Korean peninsula. However, the large-scale pattern cannot successfully explain the observed sudden decrease of temperature during the cold surges. Composite analyses reveal that a synoptic-scale cyclone developing over the northern Japan is a key feature that significantly contribute to the enhancement of cold advection by increasing pressure gradient over the Korean peninsula. Enhanced sensible and latent heat fluxes are observed over the southern ocean of Korea and Japan during the cold surges due to temperature and humidity differences between the near surface and the lower atmosphere over the ocean. The evaporated water vapor transported toward the center of the surface cyclone and condenses in the lower-to-middle troposphere. The released energy likely promotes the development of the surface cyclone by inducing positive PV near the surface of the heating region.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.6000-6017
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• 2018

This paper presents a new descriptor, named Histograms of Prominent Edge Directions (HPED), for the recognition of facial expressions in a person-independent environment. In this paper, we raise the issue of sampling error in generating the code-histogram from spatial regions of the face image, as observed in the existing descriptors. HPED describes facial appearance changes based on the statistical distribution of the top two prominent edge directions (i.e., primary and secondary direction) captured over small spatial regions of the face. Compared to existing descriptors, HPED uses a smaller number of code-bins to describe the spatial regions, which helps avoid sampling error despite having fewer samples while preserving the valuable spatial information. In contrast to the existing Histogram of Oriented Gradients (HOG) that uses the histogram of the primary edge direction (i.e., gradient orientation) only, we additionally consider the histogram of the secondary edge direction, which provides more meaningful shape information related to the local texture. Experiments on popular facial expression datasets demonstrate the superior performance of the proposed HPED against existing descriptors in a person-independent environment.

• International Journal of Computer Science & Network Security
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• 제21권8호
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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.

• 한국산업융합학회 논문집
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• 제23권6_2호
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• pp.1025-1032
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• 2020

In this paper, we propose a method to classify road weather conditions into rain, fog, and sun using a SVM (Support Vector Machine) classifier after extracting weather features from images acquired in real time using an optical sensor installed on a roadside post. A multi-dimensional weather feature vector consisting of factors such as image sharpeness, image entropy, Michelson contrast, MSCN (Mean Subtraction and Contrast Normalization), dark channel prior, image colorfulness, and local binary pattern as global features of weather-related images was extracted from road images, and then a road weather classifier was created by performing machine learning on 700 sun images, 2,000 rain images, and 1,000 fog images. Finally, the classification performance was tested for 140 sun images, 510 rain images, and 240 fog images. Overall classification performance is assessed to be applicable in real road services and can be enhanced further with optimization along with year-round data collection and training.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2356-2376
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• 2021

In this paper, we proposed a novel modulation recognition method based on quantum elephant herding algorithm (QEHA) evolving neural network under impulse noise environment. We use the adaptive weight myriad filter to preprocess the received digital modulation signals which passing through the impulsive noise channel, and then the instantaneous characteristics and high order cumulant features of digital modulation signals are extracted as classification feature set, finally, the BP neural network (BPNN) model as a classifier for automatic digital modulation recognition. Besides, based on the elephant herding optimization (EHO) algorithm and quantum computing mechanism, we design a quantum elephant herding algorithm (QEHA) to optimize the initial thresholds and weights of the BPNN, which solves the problem that traditional BPNN is easy into local minimum values and poor robustness. The experimental results prove that the adaptive weight myriad filter we used can remove the impulsive noise effectively, and the proposed QEHA-BPNN classifier has better recognition performance than other conventional pattern recognition classifiers. Compared with other global optimization algorithms, the QEHA designed in this paper has a faster convergence speed and higher convergence accuracy. Furthermore, the effect of symbol shape has been considered, which can satisfy the need for engineering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1448-1463
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• 2021

With the continuous development of cloud storage, plenty of redundant data exists in cloud storage, especially multimedia data such as images and videos. Data deduplication is a data reduction technology that significantly reduces storage requirements and increases bandwidth efficiency. To ensure data security, users typically encrypt data before uploading it. However, there is a contradiction between data encryption and deduplication. Existing deduplication methods for regular files cannot be applied to image deduplication because images need to be detected based on visual content. In this paper, we propose a secure image deduplication scheme based on hashing and clustering, which combines a novel perceptual hash algorithm based on Local Binary Pattern. In this scheme, the hash value of the image is used as the fingerprint to perform deduplication, and the image is transmitted in an encrypted form. Images are clustered to reduce the time complexity of deduplication. The proposed scheme can ensure the security of images and improve deduplication accuracy. The comparison with other image deduplication schemes demonstrates that our scheme has somewhat better performance.

• 4차산업연구
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• 제1권1호
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• pp.31-40
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• 2021

Purpose - According to the importance of business simulation learning as a new type of business learning tool, this study reviews the dimensions of business education and a brief history of business education simulation. At the end Capsim strategic management simulation program is introduce with its feature. Research design, data, and methodology - This study has been analyzed in a way that reviews at previous literature on simulation learning and looks at examples and features of Capsim simulation, online business simulation tools which has been used in the global market. Result - Capsim simulations are designed to offer focused opportunities for deep practice. That's why they are often more effective than passive tools such as textbooks, videos, or lectures. By the way, 'deep practice' is very different from 'ordinary practice'. After commuters who drive to school or work can accumulate thousands of hours of driving, but that doesn't make them expert drivers. The key to deep practice is self-awareness. That is, paying attention to what you are doing well and not so well. This is so important to learn that scientists use a specific term for it: 'metacognition', or thinking about the way you think and learn. Conclusion - The use of business simulation learning, such as Capsim, which is a given case, can create similar local systems by potentially engaging a large number of users in the virtual market. It could also be used as an individual to complete business training for students and those who are active in the business field of business.