• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.2B
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• pp.107-118
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• 2009

In this paper, we present design and implementation for identification and location tracking techniques for vehicle information terminals in high-speed mobile environment using IEEE 802.11 wireless LAN technology. RSU be installed at a specific point. RSU recognizes it and then aware identification and location tracking for target vehicle when target vehicle that is equipped with information terminals pass near point. Issue of quick recognition using IEEE 802.11 access management frame, issue of weak wireless LAN security and tracking a range of restrictions be raised and we present solutions. Vehicle information terminal, RSU and a server-side GUI-based management program be implemented based on proposed solution. Implementation system be performed experiment at actual road environment and effectiveness be verified.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.3
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• pp.140-151
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• 2020

It is important to identify the location of ship hull blocks with exact block identification number when scheduling the shipbuilding process. The wrong information on the location and identification number of some hull block can cause low productivity by spending time to find where the exact hull block is. In order to solve this problem, it is necessary to equip the system to track the location of the blocks and to identify the identification numbers of the blocks automatically. There were a lot of researches of location tracking system for the hull blocks on the stockyard. However there has been no research to identify the hull blocks on the stockyard. This study compares the performance of 5 Convolutional Neural Network (CNN) models with multi-view image set on the classification of the hull blocks to identify the blocks on the stockyard. The CNN models are open algorithms of ImageNet Large-Scale Visual Recognition Competition (ILSVRC). Four scaled hull block models are used to acquire the images of ship hull blocks. Learning and transfer learning of the CNN models with original training data and augmented data of the original training data were done. 20 tests and predictions in consideration of five CNN models and four cases of training conditions are performed. In order to compare the classification performance of the CNN models, accuracy and average F1-Score from confusion matrix are adopted as the performance measures. As a result of the comparison, Resnet-152v2 model shows the highest accuracy and average F1-Score with full block prediction image set and with cropped block prediction image set.

• Proceedings of the Acoustical Society of Korea Conference
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• 1992.06a
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• pp.147-152
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• 1992

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2000.08a
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• pp.137-153
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• 2000

• Spatial Information Research
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• v.2 no.1
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• pp.85-92
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• 1994

This study examines the unique characteristics of the parcel identification number especially in developing the Korean land information system(KLIS). There are several limitations in establishing KLIS in terms of the location identification. Therefore, it is necessary to develop the appropriate parcel identification number system in Korea including the road identification systym and the three dimensional parcel identification system. The exact parcel bounary also needs accurate surveying and registration.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.120-123
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• 2005

A dynamic system identification technique based on the topology optimization method is developed. The specific problem in consideration is the damage location identification of a plate structure using the Frequency Response Function (FRF) of a damaged structure. In this work, the identification problem is formulated as a topology optimization problem. The importance of using anti-resonance information in addition to using resonance information is addressed. Though a simple problem was considered here, the possibility of using the topology optimization for damage identification is investigated lot the first time.

• Structural Engineering and Mechanics
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• v.54 no.4
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• pp.711-723
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• 2015

This study investigates the identification of added mass and its location in the glass fiber reinforced polymer (GFRP) beam structures. The main emphasis of this paper is to ascertain the importance of inclusion of rotational degrees of freedom (dofs) in the introduction of added mass or damage identification. Two identification indices that include the rotational dofs have been introduced in this paper: the modal force index (MFI) and the modal rotational curvature index (MRCI). The MFI amplifies damage signature using undamaged numerical stiffness matrix which is related to changes in the altered mode shapes from the original mode shapes. The MRCI is obtained by using a higher derivative of rotational mode shapes. Experimental and numerical results are compared with the existing methods leading to a conclusion that the contributions of the rotational modes play a key role in the identification of added mass. The authors believe that the similar results are likely in the case of damage identification also.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.97-104
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• 2017

Location prediction has been successfully utilized to provide high quality of location-based services to customers in many applications. In its usual form, the conventional type of location prediction is to predict future locations based on user's past movement history. However, as location prediction needs are expanded into much complicated cases, it becomes necessary quite frequently to make inference on the locations that target user visited in the past. Typical cases include the identification of locations that infectious disease carriers may have visited before, and crime suspects may have dropped by on a certain day at a specific time-band. Therefore, primary goal of this study is to predict locations that users visited in the past. Information used for this purpose include user's demographic information and movement histories. Data mining classifiers such as Bayesian network, neural network, support vector machine, decision tree were adopted to analyze 6868 contextual dataset and compare classifiers' performance. Results show that general Bayesian network is the most robust classifier.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.9-18
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• 2013

In this paper, we design and implement an intelligent system for finding objects with RFID(Radio Frequency IDentification) tag in which an mobile robot can do. The system we developed is a learning system of artificial neural network that uses RSSI(Received Signal Strength Indicator) value as input and absolute coordination value as target. Although a passive RFID is used for location estimation, we consider an active RFID for expansion of recognition distance. We design the proposed system and construct the environment for indoor location estimation. The designed system is implemented with software and the result related learning is shown at test bed. We show various experiment results with similar environment of real one from earning data generation to real time location estimation. The accuracy of location estimation is verified by simulating the proposed method with allowable error. We prepare local test bed for indoor experiments and build a mobile robot that can find the objects user want.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10A
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• pp.796-806
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• 2009

This paper presents the method of emergency warning system operation based on T-DMB and the design of T-DMB AEAS receiver model. The proposed receiver model compares the geographical location of emergency with the location of DMB transmitting station from T-DMB broadcasting signal and classifies the receiver location into alert region, neighboring region and non-alert region and transmits the emergency alert message according to each region. The geographical location of emergency can be obtained from FIG 5/2 EWS data field for AEAS message and the location of DMB transmitting station can be estimated from either the latitude and the longitude in main identifier and sub identifier in FIG 0/22 data filed for TII(Transmitter Identification Information) or TII distribution database. In our experiment, we implemented the proposed receiver model with display section, storage section, DMB module for receiving broadcasting signal and control section and performed test emergency alert broadcasting using T-DMB signal generator.