• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.3106-3106
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• 2001

Imaging spectrometry or hyperspectral imaging is a recent development that makes possible quantitative and qualitative measurement for food quality and safety. This paper presents the research results that a hyperspectral imaging system can be used effectively for detecting fecal (from duodenum, cecum, and colon) and ingesta contamination on poultry carcasses from the different feed meals (wheat, mile, and corn with soybean) for poultry safety inspection. A hyperspectral imaging system has been developed and tested for the identification of fecal and ingesta surface contamination on poultry carcasses. Hypercube image data including both spectral and spatial domains between 430 and 900 nm were acquired from poultry carcasses with fecal and ingesta contamination. A transportable hyperspectral imaging system including fiber optically fabricated line lights, motorized lens control for line scans, and hypercube image data from contaminated carcasses with different feeds are presented. Calibration method of a hyperspectral imaging system is demonstrated using different lighting sources and reflectance panels. Principal Component and Minimum Noise Fraction transformations will be discussed to characterize hyperspectral images and further image processing algorithms such as image band ratio of dual-wavelength images and its histogram stretching with thresholding process will be demonstrated to identify fecal and ingesta materials on poultry carcasses. This algorithm could be further applied for real-time classification of fecal and ingesta contamination on poultry carcasses in the poultry processing line.

• 한국환경농학회:학술대회논문집
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• 2009.07a
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• pp.119-126
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• 2009

In recent years, research at the Environmental Microbial and Food Safety Laboratory (EMFSL), Agricultural Research Service (ARS) has focused on the development of novel image-based sensing technologies to address agro-food safety concerns, and transformation of these novel technologies into practical instrumentation for industrial implementations. The line-scan-based hyperspectral imaging techniques have often served as a research tool to develop rapid multispectral methods based on only a few spectral bands for rapid online applications. We developed a newer line-scan hyperspectral imaging platform for high-speed inspection on high-throughput processing lines, capable of simultaneous multiple inspection algorithms for different agro-food safety problems such as poultry carcass inspection for wholesomeness and apple inspection for fecal contamination and defect detection. In addition, portable imaging devices were developed for in situ identification of contamination sites and for use by agrofood producer and processor operations for cleaning and sanitation inspection of food processing surfaces. The aim of this presentation is to illustrate recent advances in the above agro.food safety sensing technologies.

• KIEE International Transactions on Power Engineering
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• v.11A no.4
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• pp.15-20
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• 2001

Power system stability is correlated with system structure, disturbances and operating conditions, and power flows on transmission lines are closely related with those conditions. This paper proposes a methodology to identify correlative power flows for power system transient and small-signal stability prediction. In transient stability sense, the Critical Clearing Time is used to select some dominant contingencies, and Transient Stability Prediction index is proposed for the quantitative comparison. For small-signal stability discusses a methodology to identify crucial transmission lines for stability prediction by introducing a sensitivity factor based on eigenvalue sensitivity technique. On-line monitoring of the selected lines enables to predict system stability in real-time. Also, a procedure to make a priority list of monitored transmission lines is proposed. The procedure is applied to a test system, and it shows capabilities of the proposed method.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.135-142
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• 2004

Two-step identification approach for effective bridge health monitoring is proposed to alleviate the issues associated with many unknown parameters faced in the real structures and to improve the accuracy in the estimate results. It is suitable for on-line monitoring scheme, since the damage assessment is not always needed to be carried out whereas the alarming for damages is to be continuously monitored. In the first step for screening potential damaged members, damage indicator method based on modal strain energy, probabilistic neural networks and the conventional neural networks using grouping technique are used and then the conventional neural network technique is utilized for damage assessment on the screened members in the second step. The proposed methods are verified through a field test on the northern-most span of old Hannam Grand Bridge.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.971-974
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• 2001

In this study, the static and dynamic characteristics of endmilling process was modelled and the analytic realization of chatter mechanism was discussed. In this regard, We have discussed on the comparative assessment of recursive time series modeling algorithms that can represent the machining process and detect the abnormal machining behaviors in precision endmilling operation. In this study, simulation and experimental work were performed to show the malfunctional behaviors. For this purpose, new recursive(RLSM) were adopted for the on-line system identification and monitoring of a machining process, we can apply these new algorithms in real process for detection of abnormal chatter. Also, the stability lobe of chatter was analysed by varying parameter of cutting dynamices in regenerative chatter mechanics.

• Structural Engineering and Mechanics
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• v.23 no.1
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• pp.75-95
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• 2006

Two-step identification approaches for effective bridge health monitoring are proposed to alleviate the issues associated with many unknown parameters faced in real structures and to improve the accuracy in the estimate results. It is suitable for on-line monitoring scheme, since the damage assessment is not always needed to be carried out whereas the alarming for damages is to be continuously monitored. In the first step for screening potentially damaged members, a damage indicator method based on modal strain energy, probabilistic neural networks and the conventional neural networks using grouping technique are utilized and then the conventional neural networks technique is utilized for damage assessment on the screened members in the second step. The effectiveness of the proposed methods is investigated through a field test on the northern-most span of the old Hannam Grand Bridge over the Han River in Seoul, Korea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8940-8947
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• 2015

Mixed-model assembly line has been widely used in automotive assembly industry to quickly respond the diverse product demands. But, this model can lead to part confusion, which is a source for assembly errors when parts are physically interchangeable in a mixed-model assembly line. With the recent application of new technologies such as radio frequency identification (RFID) and ZigBee wireless sensor network (WSN) to the assembly process, real-time information has become available in this manufacturing systems through IT infrastructures. At first, this paper presents an RFID application for assembly processes, specifically, for a mixed-model assembly line. Thus, to ensure that parts be picked accurately, each cockpit module on the assembly line is attached with a RFID tag and the tag is scanned using a RFID reader and recognizes the vehicle, and each part of the cockpit module is attached with a barcode and the barcode is scanned by a barcode reader and each part is identified correctly for the vehicle. Second, this paper presents a ZigBee wireless sensor network (WSN) protocol-based application for a reconfigurable mixed-model assembly line of cockpit module to reduce the assembly errors and the cost of the change/reconfiguration on the assembly lines due to the various orders and new models from the motor company, avoiding the wiring efforts and inconvenience by wiring between the several RFID devices and the IT server system. Finally, we presents the operation results for several years using this RFID/ZigBee wireless sensor network (WSN) protocol-based cockpit module assembly line.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.550-555
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• 1993

The aim of this paper is to present the development of visual seam tracking system equipped with visual range finder. The visual range finder, which consists of a CCD camera and a diode laser system with line generating optics, developed to recognize the types of weld joints and detect the location of weld joints. In practical applications, however, images of the weld joints are often degraded due to spatters, are flares, surface specularity, and welding smoke. To overcome the problem, this paper proposes a syntactic approach which is a class of artificial intelligence techniques. In the approach, the type of weld joint is inferred based upon the production rules which are linguiques grammars consisting of a set of line and junction primitives of laser strip image projected on weld joint. The production rules eliminate several noisy primitives to create new primitives through the merging process of primitives. After the recognition of weld joint, arc welding is started and the location of weld joints is repeatedly detected using a spring model-based template matching in which the template model is a by-product of the recognition process of weld joint. To show the effectiveness of the proposed approach a series of experiments-identification and robotic tracking-are conducted for four different types of weld joints.

• Journal of information and communication convergence engineering
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• v.9 no.5
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• pp.500-504
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• 2011

In this paper, we propose an equivalent Wiener-Hopf equation. The proposed algorithm can obtain the weight vector of a TDL(tapped-delay-line) filter and the error simultaneously if the inputs are orthogonal to each other. The equivalent Wiener-Hopf equation was analyzed theoretically based on the MMSE(minimum mean square error) method. The results present that the proposed algorithm is equivalent to original Wiener-Hopf equation. The new algorithm was applied into the identification of an unknown system for evaluating the performance of the proposed method. We compared the Wiener-Hopf solution with the equivalent Wiener-Hopf solution. The simulation results were similar to those obtained in the theoretical analysis. In conclusion, our method can find the coefficient of the TDL (tapped-delay-line) filter where a lattice filter is used, and also when the process of Gram-Schmidt orthogonalization is used. Furthermore, a new cost function is suggested which may facilitate research in the adaptive signal processing area.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.354-356
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• 2006

This paper presents a new one-terminal numerical algorithm for fault location estimation and for faults recognition. The proposed algorithm are derived for the case of most frequent single-phase line to ground fault in the time domain. The arc voltage wave shape is modeled numerically on the basis of a great number of arc voltage records obtained by transient recorder. From the calculated arc voltage amplitude it can make a decision whether the fault is permanent of transient. In this paper the algorithm uses a very short data window and enables fast fault detection and classification for real-time transmission line protection. To test the validity of the proposed algorithm the Electro-Magnetic Transient Program(EMTP/ATP) is used.