• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.10-20
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• 2024

In recent automated manufacturing systems, compressed air-based pneumatic cylinders have been widely used for basic perpetration including picking up and moving a target object. They are relatively categorized as small machines, but many linear or rotary cylinders play an important role in discrete manufacturing systems. Therefore, sudden operation stop or interruption due to a fault occurrence in pneumatic cylinders leads to a decrease in repair costs and production and even threatens the safety of workers. In this regard, this study proposed a fault detection technique by developing a time-variant deep learning model from multivariate sensor data analysis for estimating a current health state as four levels. In addition, it aims to establish a real-time fault detection system that allows workers to immediately identify and manage the cylinder's status in either an actual shop floor or a remote management situation. To validate and verify the performance of the proposed system, we collected multivariate sensor signals from a rotary cylinder and it was successful in detecting the health state of the pneumatic cylinder with four severity levels. Furthermore, the optimal sensor location and signal type were analyzed through statistical inferences.

• Journal of Advanced Navigation Technology
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• v.28 no.5
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• pp.650-656
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• 2024

This study proposes an AI-based image restoration technique to reduce image distortion caused by lighting and noise in nighttime environments and improve the performance of infrared detection systems. A synthetic image dataset was constructed using visible light images under various lighting conditions and ISO settings, and deep learning models (AutoEncoder and U-Net) were trained to assess image restoration performance. Experimental results show that the Multi-ISO model (9-channel) outperforms the Single-ISO model (3-channel), especially when utilizing input data with multiple ISO values. This study demonstrates that AI models can be effectively trained using synthetic data, even when real data collection is challenging, and can be applied to image restoration tasks. These findings are expected to contribute to enhancing the performance of optical detection systems through AI-based technology.

• Parasites, Hosts and Diseases
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• v.47 no.3
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• pp.227-233
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• 2009

Local malaria transmission in the United Arab Emirates (UAE) came to an end in 1997. Nevertheless, UAE has been subjected to substantial importation of malaria cases from abroad, concerning both UAE nationals and immigrants from malarious countries with a total number of 2,119 cases in 2007. To evaluate a new DNA extraction technique using nested PCR, blood samples were collected from 132 individuals who presented to Infectious Diseases Department in Rashid Hospital, Dubai, and Central Department of Malaria Control with fever and persistent headache. Giemsa-stained blood films and ELISA test for malaria antibodies were carried out for detection of Plasmodium infection. Plasmodium infections were identified with the genus-specific primer set and species differentiation using nested PCR. A rapid procedure for diagnosis of malaria infections directly from dried blood spots using for the first time DNA extract from FTA Elute cards was evaluated in contrast to extraction techniques using FTA classic cards and rapid boiling technique. Our new simple technique for DNA extraction using FTA Elute cards was very sensitive giving a sensitivity of 100% compared to 94% using FTA classic cards and 62% in the rapid boiling technique. No complex preparation of blood samples was required prior to the amplification. The production cost of DNA isolation in our PCR assay was much less incomparable to that of other DNA extraction protocols. The nested PCR detected plasmodial infection and could differentiate P. falciparum from P. vivax, and also detected the mixed infection.

• Journal of Korea Multimedia Society
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• v.8 no.10
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• pp.1314-1321
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• 2005

In video surveillance and monitoring (VSAM), extracting foreground by detecting moving regions is the most fundamental step. The foreground extracted, however, includes not only objects in motion but also their shadows, which may cause errors in following video image processing steps. To remove the shadows, this paper presents a new technique to determine shadow pixels in the foreground image of a VSAM camera system. The proposed technique utilizes a fact that the effect of shadowing to each pixel is different defending on its brightness in a background image when determining shadow pixels unlike existing techniques where unified decision criteria are used to all pixels. Such an approach can easily accommodate local features in an image and hold consistent Performance even in changing environment. In real experiments, the proposed technique showed better results compared with an existing technique.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.11
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• pp.853-864
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• 2017

This paper proposes a false alarm rate reduction technique for detection of small targets in a terrestrial environment. CFAR algorithm is useful in homogeneous background, but it is not easy to detect targets in non-homogeneous background. In particular, when the clutter power is not significantly different from the target signal, it is difficult to detect the target due to high false alarm rate. To solve these difficulties, this study presents the false alarm rate reduction technique based on CFAR algorithm, matched filter and binary integration technique. The parameters are studied through the theoretical analysis and the validity of the proposed study is examined by the test results.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.5
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• pp.149-162
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• 2018

It is necessary to select the appropriate global climate model (GCM) to take into account the impacts of climate change on integrated water management. The objective of this study was to develop the selection technique of representative GCMs for uncertainty in climate change scenario. The selection technique which set priorities of GCMs consisted of two steps. First step was evaluating original GCMs by comparing with grid-based observational data for the past period. Second step was evaluating whether the statistical downscaled data reflect characteristics for the historical period. Spatial Disaggregation Quantile Delta Mapping (SDQDM), one of the statistical downscaling methods, was used for the downscaled data. The way of evaluating was using explanatory power, the stepwise ratio of the entire GCMs by Expert Team on Climate Change Detection and Indices (ETCCDI) basis. We used 26 GCMs based on CMIP5 data. The Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios were selected for this study. The period for evaluating reproducibility of historical period was 30 years from 1976 to 2005. Precipitation, maximum temperature, and minimum temperature were used as collected climate variables. As a result, we suggested representative 13 GCMs among 26 GCMs by using the selection technique developed in this research. Furthermore, this result can be utilized as a basic data for integrated water management.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.6
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• pp.863-871
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• 2013

Recently application of open protocols and external network linkage to the national critical infrastructure has been growing with the development of information and communication technologies. This trend could mean that the national critical infrastructure is exposed to cyber attacks and can be seriously jeopardized when it gets remotely operated or controlled by viruses, crackers, or cyber terrorists. In this paper virtual Honeynet model which can reduce installation and operation resource problems of Honeynet system is proposed. It maintains the merits of Honeynet system and adapts the virtualization technology. Also, virtual Honeynet model that can minimize operating cost is proposed with data analysis and collecting technique based on the verification of attack intention and focus-oriented analysis technique. With the proposed model, new type of attack detection system based on virtual Honeynet, that is Cybertrap, is designed and implemented with the host and data collecting technique based on the verification of attack intention and the network attack pattern visualization technique. To test proposed system we establish test-bed and evaluate the functionality and performance through series of experiments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.611-617
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• 2018

The application of health monitoring, including a fault detection technique, is needed to secure the structural safety of large structures. A 2-step crack identification method for detecting the crack location and size of the beam structure is presented. First, a crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape obtained from the distributed local strain data. The crack location and size were then identified based on the natural frequencies obtained from the acceleration data and the neural network technique for the pre-estimated crack occurrence region. The natural frequencies of a cracked beam were calculated based on an equivalent bending stiffness induced by the energy method, and used to generate the training patterns of the neural network. An experimental study was carried out on an aluminum cantilever beam to verify the present method for crack identification. Cracks were produced on the beam, and free vibration tests were performed. A crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape, and the crack location and size were assessed using the natural frequencies and neural network technique. The identified crack occurrence region agrees well with the exact one, and the accuracy of the estimation results for the crack location and size could be enhanced considerably for 3 damage cases. The presented method could be applied effectively to the structural health monitoring of large structures.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.11
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• pp.6432-6439
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• 2014

The NAUT (Non-contact Air coupled Ultrasonic Testing) technique is one of the ultrasonic testing methods that enables non-contact ultrasonic testing by compensating for the energy loss caused by the difference in acoustic impedance of air with an ultrasonic pulser receiver, PRE-AMP and high-sensitivity transducer. As the NAUT is performed in a state of steady ultrasonic transmission and reception, testing can be performed on materials of high or low temperatures or specimens with a rough surface or narrow part, which could not have been tested using the conventional contact-type testing technique. For this study, the internal defects of spot weld, which are often applied to auto parts, and CFRP parts, were tested to determine if it is practical to make the NAUT technique commercial. As the spot welded part had a high ultrasonic transmissivity, the result was shown as red. On the other hand, the part with an internal defect had a layer of air and low transmissivity, which was shown as blue. In addition, depending on the PRF (Pulse Repetition Frequency), an important factor that determines the measurement speed, the color sharpness showed differences. With the images obtained from CFRP specimens or an imaging device, it was possible to identify the shape, size and position of the internal defect within a short period of time. In this paper, it was confirmed in the above-described experiment that both internal defect detection and image processing of the defect could be possible using the NAUT technique. Moreover, it was possible to apply NAUT to the detection of internal defects in the spot welded parts or in CFRP parts, and commercialize its practical application to various fields.

• The Korean Journal of Nuclear Medicine
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• v.24 no.1
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• pp.29-36
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• 1990

Most of the diagnostic methods currently used for the detection of neoplastic masses provide indirect evidence. To obtain greater specificity in the interpretation of neoplasias by in vivo methods, the immunological approach appears to be most promising. Two problems that interfered with progress in this field were the lack of tumor specific antigen and the lack of well-defined and reproducible antibodies. To improve the sensitivity and specificity of radioimmunoscintigraphy as a technique for tumor localization, the use of monoclonal antibodies, fragments of antibodies and single photon emission computerized tomography (SPECT) are reasonable. The obvious advantages of monoclonal antibodies are their homogeneity, their specificity for the immunizing antigen and the reaction with a single determinant-thus no large immunecomplexes with antigen are formed. Monoclonal antibody technique has recently provided an opportunity to reevaluate the role of nuclear medicine for the diagnosis of malignant diseases by using the immunological approach. Out first results by means of radioimmunoscintigraphy of CEA and CA 19-9 producing tumors using a cocktail of fragments F $(ab')_2$, of mocolonal antibodies to CA 19-9 and CEA labeled with $^{131}I$ (IMACIS-1) are reported. The aims of this investigation was to evaluate the role of immunoscintigraphy in patients with colorectal and other cancers for diagnosis of local recurrences and metastasis. This report contains results of the first 8 colorectal and pancreas cancer patients with the elevation of the level of serum CEA and/or CA 19-9. IMACIS-1 was injected intravenously during 30 minutes in 100 ml saline solution after skin test. Planar scintigrams were recorded 3, 5 and 7 days after the injection of the IMACIS-1. Anterior, lateral and posterior views of the liver as well as anterior and posterior views of the pelvis were obtained in each patients as an $^{131}I-antibody$ image. We were able to localize exactly the malignant process with the double-nuclide double-compound $^{99m}Tc\;^{131}I$ (Tc+l) scintigrams. In Tc & I double-nuclide scintigraphy, computer subtraction display provided more clear localization of the tumor. We compared the results of radioimmunoscintigraphy with CT, ultrasonograms, conventional scintigrams. The results were as follows: 1) The sensitivity and specificity of radioimmunoscintigraphy using the fragments $F(ab')_2$ of the cocktails of CEA and CA 19-9 monoclonal antibodies were 80% and 100% respectively. 2) Tumor detection rate was not proportionated to the level of serum tumor markets. 3) Second tracer technique was essential for tumor localization as an anatomic landmark using double-nuclide scintigraphy. 4) A slow infusion of the antibodies was necessary to prevent the formation of large immune complexes. 5) Tumor/non-tumor radioactivity was most elevated at 7 days delayed imaging. 6) Using planar scintigraphic technique of $^{131}I$ labeled monoclonal antibodies are possible for imaging most of the tumors.