• 열병합발전
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• s.14
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• pp.20-26
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• 2000

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1658-1662
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• 2007

The tire uneven wear has been an ongoing concern for a long time, and one of customer's complaints too. This paper deals with uneven wear improvement of passenger car tires, to have tested the tire wear levels by each wheel alignment set (according to changing toe and camber) using taxis. The pre-set wheel alignments on test vehicle were gained by energy friction simulation of tire. The result of this experiment was as follows : First, verified the effects of initial wheel alignment (adjusted at Curb Vehicle Weight) to minimize tire uneven wear. Second, tire uneven wear makes tire life much shorter than even wear does.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10a
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• pp.244-247
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• 2006

본 논문에서는 검사장비에 제어용 라이브러리를 연구, 개발을 하였다. 개발된 라이브러리는 OPC 클라이어트, 스테이지 구동, 스위치 구동, 현미경 구동, 화상처리, 얼라이먼트, 태스크 관리로 나뉘며, 라이브러리 테스트 프로그램을 개발하여 모든 라이브러리의 기능 구현 테스트를 실시하고 있다. 제어 시스템 분야에서 세계 표준인OPC 개념을 도입하여, 검사 장비의 PLC 프로그램과 네트워크를 통해 데이터를 주고, 받을 수 있도록 OPC 서버를 사용하여 데이터를 관리하였다. 실제 검사장비에 탑재하기 전에 성능테스트를 위해PLC에 테스트 프로그램을 입력하여 실험을 하였다.

• Journal of the KSME
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• v.29 no.3
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• pp.317-325
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• 1989

광학시 비접촉 미소 변위 계측 장치의 응용예는 앞서 소개한 응용 외에도 여러 분야에서 많이 활용되고 있다. 즉, 주행 중인 차량 바퀴의 얼라이먼트(alignment) 계측에 이용되기도 하며, 파괴 역학 분야에서 크랙 개구단부(COD)의 변위량 측정에도 이용되며, 충격 역학 분야에서는 변위의 출력 응답 특성이 경시적으로 대단히 복잡하게 변하는 동적 파괴 현상의 원인 규명의 목적으 로도 많이 활용되고 있다. 더욱이 눈부신 발전을 거듭하고 있는 재료 요소 기술 및 센서 기술의 도움으로 앞으로는 이러한 비접촉식 미소 변위 측정 시스템이 보다 더욱 다양하게 사용되어질 것으로 기대되며, 또한 점차 성능 및 가격적 측면에서도 고성능의 계측 시스템을 손쉽게 큰 부 담이 없이 구입 가능할 수 있으리라 믿는다. 그러나 한편으로는 고정도의 물리량의 계측은 꼭 최신의 고가의 장비로만 되는 것이 아니라는 점과, 이러한 계측 시스템의 활용 시에는 기본 검출 원리 및 특성의 이해와 더불어 각 시험 경우에 따른 측정치에 대한 검증 작업을 게을리 하지 말아야 된다는 점도 아울러 당부하고자 한다.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.145-154
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• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.1084-1087
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• 1997

In this paper, we developed the micro NCT system for punching the thin plates, which is driven is driven by the standalone type microprocessor. In order to adjust the alignment between the punch and die in-situ punching procedures, the non-contact type laser sensor for measuring the burr and micro-driving system for punching die with using the differential screw are developed. The height of burr in four directions in the punched hole of test specimen are measured, and the measured data are transferred to the personal computer by RS232C serial communication technology. In the personal computer, by using the graphic user interface type monitoring program and data handling procedures which includes the filtering algorithms, the direction and length of movement of the die position is decided and these data are transferred back to the microprocessor. The microprocessor drives the micro positioning stage based on these data. Even if this method is not a perfect solution for the in-situ alignment in micro punching, but this alignment methodology is accomplished in the same stage just after the punching that we hope to solve the alignment problem in the punching system based on this technology.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.7
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• pp.726-735
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• 2009

In this paper, we propose an approach to signal model-based fault detection and diagnosis system for induction motors. The current fault detection techniques used in the industry are limit checking techniques, which are simple but cannot predict the types of faults and the initiation of the faults. The system consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, the system extracts the significant features from sound signals using combination of variance, cross-correlation and wavelet. Consequently, the pattern classification technique is applied to the fault diagnosis process to recognize the system faults based on faulty symptoms. The sounds generated from different kinds of typical motor's faults such as motor unbalance, bearing misalignment and bearing loose are examined. We propose two approaches for fault detection and diagnosis system that are waveletand-variance-based and wavelet-and-crosscorrelation-based approaches. The results of our experiment show more than 95 and 78 percent accuracy for fault classification, respectively.