• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9B
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• pp.1092-1097
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• 2011

An anti-collision protocol which tries to minimize the collision probability and identification time is the most important factor in all identification technologies. This paper focuses on methods to improve the efficiency of tag's process in identification systems. Our scheme, Binary Slotted Query Tree (BSQT) algorithm, is a memoryless protocol that identifies an object's ID more efficiently by removing the unnecessary prefixes of the traditional Query Tree (QT) algorithm. With enhanced QT algorithm, the reader will broadcast 1 bit and wait the response from the tags but the difference in this scheme is the reader will listen in 2 slots (slot 1 is for 0 bit Tags and slot 2 is for 1 bit Tags). Base on the responses the reader will decide next broadcasted bit. This will help for the reader to remove some unnecessary broadcasted bits which no tags will response. Numerical and simulation results show that the proposed scheme decreases the tag identification time by reducing the overall number of request.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.891-902
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• 2020

The Re-Identification(Re-ID) is one of the most popular researches in the field of computer vision due to a variety of applications. To achieve a high-level re-identification performance, recently other methods have developed the deep learning based networks that are specialized for only person or vehicle. However, most of the current methods are difficult to be used in real-world applications that require re-identification of both person and vehicle at the same time. To overcome this limitation, this paper proposes a deep neural network learning method that combines triplet and softmax loss to improve performance and re-identify people and vehicles simultaneously. It's possible to learn the detailed difference between the identities(IDs) by combining the softmax loss with the triplet loss. In addition, weights are devised to avoid bias in one-side loss when combining. We used Market-1501 and DukeMTMC-reID datasets, which are frequently used to evaluate person re-identification experiments. Moreover, the vehicle re-identification experiment was evaluated by using VeRi-776 and VehicleID datasets. Since the proposed method does not designed for a neural network specialized for a specific object, it can re-identify simultaneously both person and vehicle. To demonstrate this, an experiment was performed by using a person and vehicle re-identification dataset together.

• The KIPS Transactions:PartB
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• v.9B no.1
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• pp.67-76
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• 2002

MP3 audio format has good quality and high compression rate ; therefore, the use of MP3 format increases. The requirement of keeping the extra information such as author and lyrics in MP3 files increases. And the tagging systems designed to meet this requirement are suggested. ID3 vl tag and Lyrics3 v2 tag are two most widely used tagging systems. But ID3 vl tag and Lyrics3 v2 tag are the last things to arrive when the file is being streamed. Therefore, users cannot get the tag information until the entire audio file is downloaded. Moreover information synchronized with audio stream may lose its feature. In this paper, a system searching and playing audio files based on tag information in MP3 files is implemented. This system solves the problem that the tag information is ignored when an MP3 files is played on internet. An audio object is described in an XML document, and timing and synchronization between elements in that In document is provided in HTML+TIME style using XSL.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.5
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• pp.548-556
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• 2013

A tapered production tubing with two different inner diameters has been suggested to increase production rates. In this research, various tapered tubing combinations are taken into account and possible tubing combinations are proposed to satisfy each objective. In previous studies, production enhancement was the main goal. However, this research also considers flow stability by analyzing tubing pressure traverse, liquid holdup, and operating conditions. For a reservoir assumed in this research, a tapered tubing of, 4.5 inch inner diameter(ID) and 2000 ft in length in the lower part and 5.5 inch ID and 8000 ft in the upper part, shows the highest net present value. Compared to a mono tubing, tapered tubings enable various tubing designs because they have smaller differences in frictional pressure loss. It is important to maintain low liquid holdup to prevent liquid loading. Smaller ID of tapered tubing in the lower part enables to achieve the object. In conclusion, it is identified that various tubing designs are achievable from the analyses of overall production operations depending on purposes specified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.131-147
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• 2020

RFID (Radio Frequency Identification) identifies a specific object by radio signals. As the tag provides a unique ID for the purpose of identification, RFID technology effectively solves the ambiguity and occlusion problem that challenges the laser or camera-based approach. This paper proposes an approach to track a moving object based on the integration of RFID and laser ranging information using a particle filter. To be precise, we split laser scan points into different clusters which contain the potential moving objects and calculate the radial velocity of each cluster. The velocity information is compared with the radial velocity estimated from RFID phase difference. In order to achieve the positioning of the moving object, we select a number of K best matching clusters to update the weights of the particle filter. To further improve the positioning accuracy, we incorporate RFID signal strength information into the particle filter using a pre-trained sensor model. The proposed approach is tested on a SCITOS service robot under different types of tags and various human velocities. The results show that fusion of signal strength and laser ranging information has significantly increased the positioning accuracy when compared to radial velocity matching-based or signal strength-based approaches. The proposed approach provides a solution for human machine interaction and object tracking, which has potential applications in many fields for example supermarkets, libraries, shopping malls, and exhibitions.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.40-50
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• 2007

In this paper, we propose an efficient method for detecting and tracking multiple moving objects based on morphological region merging from real-time video sequences. The proposed approach consists of adaptive threshold extraction, morphological region merging and detecting and tracking of objects. Firstly, input frame is separated into moving regions and static regions using the difference of images between two consecutive frames. Secondly, objects are segmented with a reference background image and adaptive threshold values, then, the segmentation result is refined by morphological region merge algorithm. Lastly, each object segmented in a previous step is assigned a consistent identification over time, based on its spatio-temporal information. The experimental results show that a proposed method is efficient and useful in terms of real-time multiple objects detecting and tracking.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.828-835
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• 2008

We present a novel method for eye location by means of a two-level classifier scheme. Locating the eye by machine-inspection of an image or video is an important problem for Computer Vision and is of particular value to applications in biomedical imaging. Our method aims to overcome the significant challenge of an eye-location that is able to maintain high accuracy by disregarding highly variable changes in the environment. A first level of computational analysis processes this image context. This is followed by object detection by means of a two-class discrimination classifier(second algorithmic level).We have tested our eye location system using FERET and BioID database. We compare the performance of two-level classifier with that of non-level classifier, and found it's better performance.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.35 no.1
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• pp.50-53
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• 1999

In this paper, the mechanical movement of lip seal-ring which plays the most important function in stern-tube sealing system and the possibility of leakage caused by pressure fluctuation are studied by theory and experiment. By the finite element method for axial symmetry object which receives the torsional bending load, the displacement and stress analysis of the seal-rings are executed for products of several representative manufacturers of seal-rings, and also the possibility of crack occurance are checked by theoretical analysis. A sample seal-ring id designed and manufactured using the program of displacement and stress analysis developed in this study and made an experimental apparatus to test the sampling seal-ring. The sampling seal-ring functioned excellently, but it had its durability and this problem may be solved by using the Viton instead of NBR.

• Review of KIISC
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• v.8 no.3
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• pp.5-18
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• 1998

인터넷을 통한 상거래가 21세기에 중요한 상거래 수단이 될 전망이다. 인터넷 상거래 시스템은 공개키 암호시스템에 바탕을 두고 실현되고 있다. 공개키 암호 시스템의 폭넓은 사용은 사용자 공개키가 신뢰성이 있어야 안전성을 보장받을 수 있다. 공개키 기반 구조는 이를 위하여 구축되어야 한다. 따라서 안전하고 신뢰성 있게 공개키를 관리하고 공표하기 위한 공개키 기반 구조는 인터넷전자상거래 시스템뿐만 아니라 정부 기간 통신망에서도 매우 중요한 역할을 수행할 것이다. 공개키 기반 구조는 기본적으로 공개키를 CA의 서명용 비밀키로 서명한 공개키 인즌서를 활용한다. 본 고에서는 공개키 기반 구조를 정의하고, 공개키 기반 구조에서 사용될 보안 알고리듬을 살펴보며, X.509 인증서 구조와 종류, 보안 알고리듬 및 보안 정책을 식별하기위한 OID(Object ID) 와 통신 개체를 확인하기 위한 X.500 DN(Distinguished Name)를 정의하고, 믿음의 연결 고리인 인증경로, 인증 경로의 검증 방안, 새로운 요구사항인 인증 경로의 제한, 인증서 발행 및 발급을 위한 인증서 정책, 그리고 인증서 관리 방법 등을 인터넷 공개키 구조인 PKIX문서를 중심으로 기술한다. 또한 공개키 기반 구조를 위한 규격을 제시한다.