• Journal of Life Science
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• v.26 no.5
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• pp.503-508
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• 2016

Biosensors have been used as first-step monitoring tools to detect on-site samples in a simple and cost-effective manner. Numerous recombinant microbial biosensors have been exploited for monitoring on-site toxic chemicals and biological signals. Herein, a recombinant microbial biosensor was constructed for monitoring cadmium. The cadmium responding cadC regulatory gene and it’s promoter from Staphylococcus aureus was amplified through PCR, fused with the lacZ gene, and transformed into Escherichia coli BL21 (DE3) cells. In the presence of cadmium, the biosensor cells express β-galactosidase showing red color development with chlorophenol red β-galactopyranoside (CPRG) as the enzymatic substrate. The biosensor cells showed the best β-galactosidase activity after 3 hr induction with cadmium at pH 5 and a detection range from 0.01 μM to 10 mM cadmium with a linearity from 0.01 to 0.1 μM cadmium (y = 0.98 x + 0.142, R² = 0.98). Among the heavy metals, cadmium and lead showed good responses, tin and cobalt showed medium responses, and mercury and copper showed no responses. The biosensor cells showed good responses to several waste waters similar to buffer solution, all spiked with cadmium. The biosensor described herein could be applied for on-site cadmium monitoring in a simple and cost-effective manner without sample pretreatments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.185-195
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• 2018

As machines have been automated in the field of industries in recent years, it is a paramount importance to manage and maintain the automation machines. When a fault occurs in sensors attached to the machine, the machine may malfunction and further, a huge damage will be caused in the process line. To prevent the situation, the fault of sensors should be monitored, diagnosed and classified in a proper way. In the paper, we propose a sensor fault detection scheme based on SVM and CNN to detect and classify typical sensor errors such as erratic, drift, hard-over, spike, and stuck faults. Time-domain statistical features are utilized for the learning and testing in the proposed scheme, and the genetic algorithm is utilized to select the subset of optimal features. To classify multiple sensor faults, a multi-layer SVM is utilized, and ensemble technique is used for CNN. As a result, the SVM that utilizes a subset of features selected by the genetic algorithm provides better performance than the SVM that utilizes all the features. However, the performance of CNN is superior to that of the SVM.

• Korean Journal of Food Science and Technology
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• v.46 no.3
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• pp.269-275
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• 2014

We developed a competitive indirect enzyme-linked immunosorbent assay (ciELISA) for determining the buckwheat content in processed foods by using rabbit polyclonal antibodies against buckwheat proteins (BWP). The detection limit of this assay was $0.05-100{\mu}g/mL$. The cross-reactivities of the anti-BWP antibodies toward BWP, buckwheat flour, whole buckwheat, and cereals (wheat flour, whole wheat, black bean, mung bean, red bean, brack rice, brown rice, glutinous rice, white rice, millet, African millet, nonglutinous millet, adlay, and rye) were 100, 17.9, 11.8, and 0%, respectively. Thus, the antibodies were found to be specific for buckwheat only. When buckwheat flour was heated for 30 min, the mean assay recoveries of BWP were 83.0% at $60-90^{\circ}C$ and 44.5% at $100^{\circ}C$. The spike test showed that the mean assay recoveries of buckwheat from raw noodle, boiled noodle, starch gel, and cereal flour were 99.1, 98.6, 81.1, and 104%, respectively. For the 22 commercial items tested, the qualitative coincidence ratio of assay result and the corresponding value indicated on the item's package label was 100%. However, the average quantitative coincidence ratios from 12 commercial items were 31.6%. Thus, the results suggest that ciELISA is an efficient tool to detect buckwheat in processed foods.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.1
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• pp.39-49
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• 2002

In this study, limits of detection (LOD), accuracy and precision of four sampling/ analytical methods were evaluated and compared for the determination of airborne hexavalent chromium, Cr (VI). The methods include : (1) a combination of the National Institute for Occupational Safety and Health (NIOSH) Method 7600/U. S. Environmental Protection Agency (EPA) Method 218.6 (NIOSH/EPA Method) proposed by Shin and Paik, 2) two impinger methods using 2% NaOH/3% Na$_2$CO$_3$. (3) same as (2) but with 0.02 N NaHCO$_3$absorbing solution, and (4) the Occupational Safety and Health (OSHA) Method ID-215. An ion chromatograph/visible absorbance detector was used for the analysis of Cr (VI) in sample solution. Limit of detection (LOD) , analytical accuracy, and precision were also tested using Cr (VI) spike samples. Recoveries (as index of accuracy) and coefficient of variation (CV) (as a index of precision) were determined. Two-way ANOVA and Turkey's test were performed to test the significance in differences among recoveries and CVs of the methods. In all the methods, the peaks of Cr (VI) were separated sharply on chromatograms and exhibited a strong linearity with Cr (VI) concentrations in solution. The correlation coefficients of calibration curves typically ranged from 0.9997 to 0.9999, and the analytical LODs from 0.025 to 0.1$\mu\textrm{g}$/sample. All the method had good sensitivities and linearities between Cr (VI) levels and peak areas. The accuracies (% mean recoveries) of the methods ranged from 80.1 to 104.2%, while the precisions (pooled coefficient of variation) ranged from 3.16 to 4.43%. The impinger methods showed higher recoveries ( > 95%) than those of the PVC filter methods (the OSHA Method and the NIOSH/EPA Method). It was assumed that Cr (VI) on PVC filter was exposed to air and reduced to trivalent chromium, Cr (III), whereas it was stabilized in alkali solution contained in impinger. Thus, a special treatment of Cr (VI) samples collected on PVC filters may be required.

• Korean Journal of Veterinary Research
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• v.34 no.4
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• pp.843-850
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• 1994

The study was carried out to determine the residues of sulfonamides in swine edible tissues with high performance thin layer chromatography. For this purpose, the Rf values of sulfonamides in various solvent systems and the recovery rate of sulfameathazine from sampike saples were obtained. Thirty-four samples collected from meat market in Seoul were analyzed. The results obtained from the present study were followings: 1. The average recovery rate of sulfamathazine residues from spiked tissues 0.05, 0.1, 0.5 and 1mg/kg sample weight was 85%. 2. Two of 34 samples of pork for domestic consumption were reported to have been exceeded 0.05 ppm in sulfamethazine residues degree. 3. On the basis of the results, the degree of residues of sulfamethazine in swine meat for domestic consumption is seemed not to be dangerous for public health.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.4
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• pp.93-99
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• 2011

This paper proposes a novel network jitter estimation algorithm for robust VoIP communication system. The proposed method computes the current network environment mode using the differences of arrival time and generation time from sequential received packets. According to the current network environment mode, the jitter variance weights is adjusted to minimize the error for estimating the network jitter. The jitter average and variance are calculated by the autoregressive estimated algorithm, and then the network jitter is estimated by applying the jitter variance weights.

• Proceedings of the KIEE Conference
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• 2002.11a
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• pp.257-259
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• 2002

In investigating the characteristics of a neural network, the use of planar microelectrode array shows several advantages over normal intracellular recording[1]. A transparent indium tin oxide(ITO) multi-electrode array(MEA) was fabricated and its top surface was insulated with photodefinable polyimide(HD-8001) except the exposed area for interfacing between the ITO electrodes and the neuronal cells. The exposed ITO electrodes were platinized in order to reduce the impedance between the electrodes and electrolyte. The one-minute platinization with $0.99nA/{\mu}m^2$ current density reduced the average impedance of the electrodes from $2.5M\Omega\;to\;90k\Omega$ at 1kHz in normal ringer solution. Cardiac cells were cultured on this MEA as a pilot study before neuron culture. The signals detected by the platinized electrodes had larger amplitudes and improved signal to noise ratio(SNR) compared to non-platinized electrodes. It is clear that microelectrodes need to have lower impedance to make reliable extracellular recordings, and thus platinization is essential part of MEA fabrication. Burst spike of cultured olfactory bulb was also detected with the MEA having platinized electrodes.

• Korean Journal of Food Science and Technology
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• v.47 no.3
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• pp.401-404
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• 2015

A sandwich ELISA (sELISA) to detect pork in processed foods was developed using goat anti-pig IgG antibodies. From the sELISA standard curve, the detection range of pork was $3-1,000{\mu}g/mL$. The cross-reactivity between the pig IgG antibodies, pork, and other meats (beef, chicken, fish, and crustaceas) was 100, 0.18, and 0%, respectively. When pork was heated for 10 min, the mean assay recoveries of pig-IgG were 79-32% at $60-70^{\circ}C$ and less than 0.11% at $80^{\circ}C$ or higher. When pork was spiked into cream soup, weaning food, fish paste, and sauce, the mean assay recoveries were 8.8, 45, 36, and 39%, respectively. In 12 commercial processed foods, the assay results coincided qualitatively with the food labels on the packages.

• Korean Journal of Food Science and Technology
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• v.46 no.5
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• pp.538-543
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• 2014

A sandwich enzyme-linked immunosorbent assay method (sELISA) for detecting the presence of shrimp in processed foods was developed using rabbit polyclonal antibodies against tropomyosin produced by black tiger prawns (shrimp). Based on the standard curve derived using this method, the detection range of shrimp was determined to be $1-100{\mu}g/mL$. The cross-reactivity of these antibodies toward black tiger prawns, fleshy prawns, cocktail prawns, lobster, and blue crab was 100, 73, 155, 18, and 0%, respectively. When shrimp was heated for 10 min, the mean assay recovery of tropomyosin was 121-221% at $70-100^{\circ}C$ and 7.8% at $121^{\circ}C$. When shrimp was added to cream soup, weaning food, sausage, fish paste, and sauce, the mean assay recovery was 397, 639, 168, 234, and 0%, respectively. In sample tests involving 14 commercial items, the coincidence ratio of assay results and reference was 79%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.163-169
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• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.