• The Research Journal of the Costume Culture
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• v.18 no.5
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• pp.942-959
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• 2010

The Realization of Ubiquitous is achieved by magic mirror and it is required more concrete study to realize it's functionality. Especially its function of professional fashion co-ordinator for managing the appearance could be of further use. The Objective of this Study is to establish RFID chip data base to put into a computer for making use of the functionality of the magic mirror aiming at suggesting the available information on the total fashion co-ordination. I sought firstly the code with binary system determining the criteria of accessories to be input in a RFID chip. Secondly, as the image with cloth is an important element for the total fashion co-ordination, desired co-ordination among the emphasis, harmony, character, season and accent can be made selectable classifying into a limit element and a common element to extract the codes. Thirdly, necessary conditions were given to the generated codes using Visual C++ program of Microsoft and the extracted codes as per groups were compared and analyzed.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.7
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• pp.70-82
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• 1993

In this paper, employing the 3-dimensional discrete cosine transform (DCT) for the utilization of the temporal correlation, an adaptive motion sequence coding technique is proposed. The energy distribution in a 3-D DCT block, due to the nonstationary nature of the image data, varies along the veritical, horizontal and temporal directions. Thus, aiming an adaptive system to local variations, adaptive procedures, such as the 3-D classification, the classified linear scanning technique and the VLC table selection scheme, have been implemented in our approach. Also, a hybrid structure which adaptively combines inter-frame coding is presented, and it is found that the adaptive hybrid frame coding technique shows a significant performance gain for a moving sequence which contains a relatively small moving area. Through an intensive computer simulation, it is demonstrated that, the performance of the proposed 3-D transform coding technique shows a close relation with the temporal variation of the sequence to be code. And the proposed technique has the advantages of skipping the computationally complex motion compensation procedure and improving the performance over the 2-D motion compensated transform coding technique for rates in the range of 0.5 ~ 1.0 bpp.

• Journal of the Semiconductor & Display Technology
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• v.17 no.4
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• pp.1-5
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• 2018

Atomic force microscopy (AFM) simulation for Si (001) surface defects was conducted by using density functional theory (DFT). Three major defects on the Si (001) surface are difficult to analyze due to external noises that are always present in the images obtained by AFM. Noise-free surface defects obtained by simulation can help identify the real surface defects on AFM images. The surface defects were first optimized by using a DFT code. The AFM tip was designed by using five carbon atoms and positioned on the surface to calculate the system's energy. Forces between tip and surface were calculated from the energy data and converted into an AFM image. The simulated AFM images are noise-free and, therefore, can help evaluate the real surface defects present on the measured AFM images.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.127-132
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• 2024

With the rapid evolution of the Internet, the application of artificial intelligence fields is more and more extensive, and the era of AI has come. At the same time, adversarial attacks in the AI field are also frequent. Therefore, the research into adversarial attack security is extremely urgent. An increasing number of researchers are working in this field. We provide a comprehensive review of the theories and methods that enable researchers to enter the field of adversarial attack. This article is according to the "Why? → What? → How?" research line for elaboration. Firstly, we explain the significance of adversarial attack. Then, we introduce the concepts, types, and hazards of adversarial attack. Finally, we review the typical attack algorithms and defense techniques in each application area. Facing the increasingly complex neural network model, this paper focuses on the fields of image, text, and malicious code and focuses on the adversarial attack classifications and methods of these three data types, so that researchers can quickly find their own type of study. At the end of this review, we also raised some discussions and open issues and compared them with other similar reviews.

• Journal of Broadcast Engineering
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• v.25 no.4
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• pp.609-619
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• 2020

In accordance with a demand for high quality video streaming, it needs high data rate in limited bandwidth and more traffic congestion occurs. In particular, when providing real time video service, packet loss rate and bit error probability increase significantly. To solve these problems, a raptor code, which is one of FEC(Forward Error Correction) techniques, is pervasively used in the application layers as a method for improving real-time service quality. In this paper, we propose a method of determining image transmission parameters based on various deep neural networks to increase transmission efficiency at a similar level of image quality by using raptor codes. The proposed neural network uses the packet loss rate, video encoding rate and data rate as inputs, and outputs raptor FEC parameters and packet sizes. The results of the proposed method present that the throughput is 1.2% higher than that of the existing multimedia transmission technique by optimizing the transmission efficiency at a PSNR(Peak Signal-to-Noise Ratio) level similar to that of the existing technique.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.232-237
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• 2020

Given the recent pace of development and expansion of Artificial Intelligence (AI) technology, the influence and ripple effects of AI technology on the whole of our lives will be very large and spread rapidly. The National Artificial Intelligence R&D Strategy, published in 2019, emphasizes the importance of artificial intelligence education for K-12 students. It also mentions STEM education, AI convergence curriculum, and budget for supporting the development of teaching materials and tools. However, it is necessary to create a new type of curriculum at a time when artificial intelligence curriculum has never existed before. With many attempts and discussions going very fast in all countries on almost the same starting line. Also, there is no suitable professor for K-12 students, and it is difficult to make K-12 students understand the concept of AI. In particular, it is difficult to teach elementary school students through professional programming in AI education. It is also difficult to learn tools that can teach AI concepts. In this paper, we propose an educational model for elementary school students to improve their understanding of AI through play or experience. This an experiential education model that combineds exploratory learning and discovery learning using multi-intelligence and the PLAY teaching-learning model to undertand the importance of data training or data required for AI education. This educational model is designed to learn how a computer that knows only binary numbers through UA recognizes images. Through code.org, students were trained to learn AI robots and configured to understand data bias like play. In addition, by learning images directly on a computer through TeachableMachine, a tool capable of supervised learning, to understand the concept of dataset, learning process, and accuracy, and proposed the process of AI inference.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.595-603
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• 2015

With the advances of the big-data process technology, acquiring the real-time information from the massive image data taken by a mobile device inside a vehicle will be possible in the near future. Among the information that can be found around the vehicle, the route information is needed for safe driving. In this study, the automatic extraction of route information from the road sign imagery was dealt with. The scope of the route information in this study included the route number, route type, and their relationship with the driving direction. For the recognition of the route number, the modified Tesseract OCR (Optical Character Recognition) engine was used after extracting the rectangular-road-sign area with the Freeman chain code tracing algorithm. The route types (expressway, highway, rural highway, and municipal road) are recognized using the proposed algorithms, which are acquired from colour space analysis. Those road signs provide information about the route number as well as the roads that may be encountered along the way. In this study, such information was called “OTW (on the way)” or “TTW (to the way)” which between the two should be indicated is determined using direction information. Finally, the route number is matched with the direction information. Experiments are carried out with the road sign imagery taken inside a car. As a result, route numbers, route number type, OTW or TTW are successfully recognized, however some errors occurred in the process of matching TTW number with the direction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2599-2613
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• 2015

Multi-index hashing (MIH) is the state-of-the-art method for indexing binary codes, as it di-vides long codes into substrings and builds multiple hash tables. However, MIH is based on the dataset codes uniform distribution assumption, and will lose efficiency in dealing with non-uniformly distributed codes. Besides, there are lots of results sharing the same Hamming distance to a query, which makes the distance measure ambiguous. In this paper, we propose a data-oriented multi-index hashing method (DOMIH). We first compute the covariance ma-trix of bits and learn adaptive projection vector for each binary substring. Instead of using substrings as direct indices into hash tables, we project them with corresponding projection vectors to generate new indices. With adaptive projection, the indices in each hash table are near uniformly distributed. Then with covariance matrix, we propose a ranking method for the binary codes. By assigning different bit-level weights to different bits, the returned bina-ry codes are ranked at a finer-grained binary code level. Experiments conducted on reference large scale datasets show that compared to MIH the time performance of DOMIH can be improved by 36.9%-87.4%, and the search accuracy can be improved by 22.2%. To pinpoint the potential of DOMIH, we further use near-duplicate image retrieval as examples to show the applications and the good performance of our method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.211-220
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• 2018

The standard data link layer encryption provided by CCSDS has a structure that encodes HDLC frame into it using an AES algorithm. However, CCSDS is standard method has a structure in which the receiving side cannot request a re-activation when noise interference occurs over an unstable channel. SES Alarmed has a structure that enables the receiving side to additionally detect errors and perform re-activation requests in an operational structure similar to that of link encryption in CCSDS. The SES Alarmed related paper was intended to identify the optimum range of thresholds and identify data corruption due to channel noise. In this paper, the focus was on reducing the re-activation process if the HDLC frame, excluding the password Sync code, consistently exceeds any threshold levels. The HDLC frame order was changed and the results of using SES Alarmed were proposed and compared.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1581-1587
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• 2021

As embedded system are used widely and variously, multi-edge system, which multiple edges gather and perform complex operations together, is actively operating. In a multi-edge system, it often occurs that an abnormal operation at one edge is transferred to another edge or the entire system goes down. It is necessary to determine and control edge anomalies in order to prevent system down, but this can be a heavy burden on the resource-limited edge. As a solution to this, we use power consumption data to check the state of the edge device and transmit it based on a QRcode to check and control errors at the server. The architecture proposed in this paper is implemented using 'chip-whisperer' to measure the power consumption of the edge and 'Raspberry Pi 3' to implement the server. As a result, the proposed architecture server showed successful data transmission and error determination without additional load appearing at the edge.