• Asian Pacific Journal of Cancer Prevention
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• 제13권12호
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• pp.6533-6537
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• 2012

Cancers are significant contributors to the mortality and health care expenditures. Cancer can be reduced and monitored by new information technology. Radio frequency identification or RFID is a wireless identification technology. The use of this technology can be employed for identifying and tracking clinical staff, patients, supplies, medications and equipments. RFID can trace and manage chemotherapy drugs. There are different types of RFID. Implantable RFID allowing a chip to be embedded under the skin and that store the cancer patient's identifier. These are concerns about applications of RFID. Privacy, security and legal issues are key problems. This paper describes capabilities, benefits and confidentiality aspects in radio frequency identification systems and solutions for overcoming challenges.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2000년도 추계학술대회 논문집
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• pp.571-577
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• 2000

This paper is designed for the identification system of the train using RFID(radio Frequency Identification) in the 134.2㎑. The reader of transmitter transmits an interrogation signal to the tag, then the tag of receiver retransmits the defined data to the reader after receiving the interrogation signal from it. The system is also applied to the reflection communication as a backscatter modulation and selected the passive Pouter system which does not need an active electrical power device. It is useful that ore can effectively manage and control of the information of train such as materials loaded in the train. We make the reader of the identification system for the train which can communicate with the tag installed under the train.

• Structural Engineering and Mechanics
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• 제60권4호
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• pp.729-747
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• 2016

This study aims to present a novel optimization algorithm known as gravitational search algorithm (GSA) for structural damage detection. An objective function for damage detection is established based on structural vibration data in frequency domain, i.e., natural frequencies and mode shapes. The feasibility and efficiency of the GSA are testified on three different structures, i.e., a beam, a truss and a plate. Results show that the proposed strategy is efficient for determining the locations and the extents of structural damages using the first several modal data of the structure. Multiple damages cases in different types of structures are studied and good identification results can be obtained. The effect of measurement noise on the identification results is investigated.

• 한국음향학회지
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• 제27권5호
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• pp.252-255
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• 2008

본 논문에서는 성별 인식 시스템의 성능향상을 위해 변별적 가중치 학습 (discriminative weight training) 기반의 최적화된 SVM (support vector machine)을 제안한다. MCE (minimum classification error)방법을 도입하여, 각각의 MFCC (mel-frequency cepstral coefficients) 특징벡터 차수별로 다른 가중치를 가지는 SVM을 제안한다. 제안된 알고리즘은 기존의 동일 가중치를 가지는 SVM 기반의 성별인식 시스템과 비교하였으며, 우수한 성능을 보인다.

• Journal of Power Electronics
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• 제15권6호
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• pp.1567-1576
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• 2015

Offline inductance identification of a permanent magnet synchronous motor (PMSM) is essential for the design of the closed-loop controller and position observer in sensorless vector controlled drives. On the base of the offline inductance identification method combining direct current (DC) offset and high frequency (HF) voltage injection which is fulfilled at standstill, this paper investigates the inverter nonlinearity effects on the inductance identification while considering harmonics in the induced HF current. The negative effects on d-q axis inductance identifications using HF signal injection are analyzed after self-learning of the inverter nonlinearity characteristics. Then, both the voltage error and the harmonic current can be described. In addition, different cases of voltage error distribution with different injection conditions are classified. The effects of inverter nonlinearities on the offline inductance identification using HF injection are validated on a 2.2 kW interior PMSM drive.

• Structural Engineering and Mechanics
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• 제14권2호
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• pp.151-170
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• 2002

Moving force identification is a very important inverse problem in structural dynamics. Most of the identification methods are eventually converted to a linear algebraic equation set. Different ways to solve the equation set may lead to solutions with completely different levels of accuracy. Based on the measured bending moment responses of the bridge made in laboratory, this paper presented the time domain method (TDM) and frequency-time domain method (FTDM) for identifying the two moving wheel loads of a vehicle moving across a bridge. Directly calculating pseudo-inverse (PI) matrix and using the singular value decomposition (SVD) technique are adopted as means for solving the over-determined system equation in the TDM and FTDM. The effects of bridge and vehicle parameters on the TDM and FTDM are also investigated. Assessment results show that the SVD technique can effectively improve identification accuracy when using the TDM and FTDM, particularly in the case of the FTDM. This improved accuracy makes the TDM and FTDM more feasible and acceptable as methods for moving force identification.

• 한국정보통신학회논문지
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• 제13권10호
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• pp.2019-2029
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• 2009

해양안전 제고를 위해서 국내에서 운영 중인 MCS(Monitoring, Control and Surveillance) 시스템으로는 AIS(Automatic Identification System)와 LRIT(Long-Range Identification and Tracking) 기술에 기반을 둔 VMS(Vessel Monitoring System)와 RFID(Radio Frequency IDentification) 기술을 활용하고 있는 선박프리패스 시스템, 수협중앙회의 어업정보통신이 있다. 본 논문에서는 각 시스템의 기반 기술 현황을 정리하고, 각 시스템들의 역할과 기능을 비교하며, 각 시스템의 진화 방향과 이들을 유기적으로 연동하기 위해 수행되어야 하는 과제들을 정리하였다.

• Structural Engineering and Mechanics
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• 제63권6호
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• pp.761-769
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• 2017

Accurately extracting the axle distribution information of a passing vehicle from bridge dynamic responses experiences a key and challenging step in non-pavement bridge weigh-in-motion (BWIM). In this article, the wavelet transformation is adopted and the wavelet coefficient curve is used as a substitute for dynamic response. The driving frequency is introduced and expanded to multi-axle vehicle, and the wavelet coefficient curve on specific scale corresponding to the driving frequency is confirmed to contain obvious axle information. On this basis, an automatic method for axle distribution information identification is proposed. The specific wavelet scale can be obtained through iterative computing, and the false peaks due to bridge vibration can be eliminated through cross-correlation analysis of the wavelet coefficients of two measure points. The integrand function that corresponds to the maximum value of the cross-correlation function is used to identify the peaks caused by axles. A numerical application of the proposed axle information identification method is carried out. Numerical results demonstrate that this method acquires precise axle information from the responses of an axle-insensitive structure (e.g., girder) and decreases the requirement of sensitivity structure of BWIM. Finally, an experimental study on a full-scale simply supported bridge is also conducted to verify the effectiveness of this method.

• 한국통신학회논문지
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• 제32권4A호
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• pp.358-364
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• 2007

RFID(Radio Frequency IDentification) 기술은 라디오 주파수를 사용하는 비접촉 자동인식 기술이다. 이런 RFID 기술의 확산을 위해서는 리더(reader)가 다수의 태그(tag)를 짧은 시간 안에 인식하는 다중 태그 식별 문제를 해결 해야만 한다. 지금까지 이 문제를 해결하기 위한 충돌 방지(anti-collision) 알고리즘이 많이 개발되었고 이것들은 크게 알로하(ALOHA) 기반 알고리즘과 트리(tree) 기반 알고리즘으로 나뉜다. 본 논문에서는 이 두 가지 방법의 특징을 혼합한 새로운 충돌 방지 알고리즘 2가지를 제안한다. 그리고 대표적인 충돌 방지 알고리즘인 18000-6 Type A, Type B, Type C, query tree 알고리즘과 성능 비교 및 평가를 한다.

• 대한원격탐사학회지
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• 제40권2호
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• pp.219-228
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• 2024

Vessels can operate with their Automatic Identification System (AIS) turned off, prompting the development of strategies to identify them. Among these, utilizing satellites to collect radio frequency (RF) data in the absence of AIS has emerged as the most effective and practical approach. The purpose of this study is to develop a matching algorithm for RF with AIS data and find the RF's applicability to classify a suspected ship. Thus, a matching procedure utilizing three RF datasets and AIS data was employed to identify ships in the Yellow Sea and the Korea Strait. The matching procedure was conducted based on the proximity to AIS points, ensuring accuracy through various distance-based sections, including 2 km, 3 km, and 6 km from the AIS-based estimated points. Within the RF coverage, the matching results from the first RF dataset and AIS data identified a total of 798 ships, with an overall matching rate of 78%. In the cases of the second and third RF datasets, 803 and 825 ships were matched, resulting in an overall matching rate of 84.3% and 74.5%, respectively. The observed results were partially influenced by differences in RF and AIS coverage. Within the overlapped region of RF and AIS data, the matching rate ranged from 80.2% to 98.7%, with an average of 89.3%, with no duplicate matches to the same ship.