• Korean Journal of Environmental Agriculture
• /
• v.35 no.1
• /
• pp.6-14
• /
• 2016

BACKGROUND: Residual organochlorine pesticides (OCPs) are chemical substances that persist in the environment, bioaccumulate through the food web, and pose a risk of causing adverse effect to human health and the environment. They were designated as persistent organic pollutants (POPs) by Stockholm Convention. Greenhouse strawberry is economic crop in agriculture, and its cultivation area and yield has been increased. Therefore, we tried to investigate the POPs residue in greenhouse soil and strawberry.METHODS AND RESULTS: Extraction and clean-up method for the quantitative analysis of OCPs was developed and validated by gas chromatography (GC) with electron capture detector (ECD). The clean-up method was established using the modified quick, easy, cheap, effective, rugged, and safe(QuEChERS) method for OCPs in soil and strawberry. Limit of quantitation (LOQ) and recovery rates of OCPs in greenhouse soil and strawberry were 0.9-6.0 and 0.6-0.9 μg/kg, 74.4-115.6 and 75.6-88.4%, respectively. The precision was reliable sincerelative standard deviation (RSD) percentage (0.5-3.7 and 2.9-5.2%) was below 20, which was the normal percent value. The residue of OCPs in greenhouse soil was analyzed by the developed method, and dieldrin, β-endosulfan and endosulfan sulfate were detected at 1.6-23, 2.2-28.4 and 1.8-118.6 μg/kg, respectively. Those in strawberry were not detected in all samples.CONCLUSION: Dieldrin, β-endosulfan and endosulfan sulfate in a part of investigated greenhouse soil were detected. But those were not detected in investigated greenhouse strawberry. These results showed that the residue in greenhouse soil were lower level than bioaccumulation occurring.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.27 no.10
• /
• pp.24-33
• /
• 1990

Since a number of welding points are distributed in an automobile part, the number of welding points alloted to each robot are large. So, there is an increasing need of an optimal sequence planning to minimize the total welding time. In this paper, an off-line programming scheme for effective teaching of a spot welding robot is presented. A collision free, optimal welding sequence planning is done through applying the modified Traveling Salesman Problem algorithm. Also, a data extraction method from an existing general CAD system is presented for reuse of the existing exact model data produced by a model designer and easy constructing the world model data base. The result show that the proposed system could enhance the efficiency of spot welding robot in automobile industry.

• The KIPS Transactions:PartB
• /
• v.10B no.7
• /
• pp.815-822
• /
• 2003

Fingerprint image enhancement and minutiae matching are two key steps in an automatic fingerprint identification system. In this paper, we propose a fingerprint recognition technique by using minutiae linking information. Recognition process have three steps ; preprocessing, minutiae extraction, matching step based on minutiae pairing. After extracting minutiae of a fingerprint from its thinned image for accuracy, we introduce matching process using minutiae linking information. Introduction of linking information into the minutiae matching process is a simple but accurate way, which solves the problem of reference minutiae pair selection with low cost in comparison stage of two fingerprints. This algorithm is invariable to translation and rotation of fingerprint. The matching algorithm was tested on 500 images from the semiconductor chip style scanner, experimental result revealed the false acceptance rate is decreased and genuine acceptance rate is increased than existing method.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.25 no.1
• /
• pp.27-35
• /
• 2005

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature spare. This paper describes an alternative approach which uses the least mean square (LMS) method and exportation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximiBation (SAGE) algorithm ill conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor. Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances.

• Journal of the Institute of Convergence Signal Processing
• /
• v.4 no.1
• /
• pp.15-26
• /
• 2003

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature space. This paper describes an alternative approach which uses the least mean square (LMS) method and expectation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximization (SAGE) algorithm In conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.46 no.2
• /
• pp.210-219
• /
• 2017

This study aimed to establish an optimal extraction process and high-performance liquid chromatography (HPLC)-photodiode array (PDA) analytical method for determination of marker compounds, dihydrokaempferol (DHK) and 3-O-methylquercetin (3-MeQ), as a part of materials standardization for the development of health functional foods from stems of Opuntia ficus-indica var. saboten (OFS). The quantitative determination method of marker compounds was optimized by HPLC analysis, and the correlation coefficient for the calibration curve showed very good linearity. The HPLC-PDA method was applied successfully to quantification of marker compounds in OFS after validation of the method in terms of linearity, accuracy, and precision. Ethanolic extracts from stems of O. ficus-indica var. saboten (OFSEs) were evaluated by reflux extraction at 70 and $80^{\circ}C$ with 50, 70, and 80% ethanol for 3, 4, 5, and 6 h. Among OFSEs, OFS70E at $80^{\circ}C$ showed the highest contents of DHK and 3-MeQ of $26.42{\pm}0.65$ and $3.88{\pm}0.29mg/OFS100g$, respectively. Furthermore, OFSEs were determined for their antioxidant activities by measuring 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging and lipid peroxidation (LPO) inhibitory activities in rat liver homogenate. OFS70E at $70^{\circ}C$ showed the most potent antioxidant activities with $IC_{50}$ values of $1.19{\pm}0.11$ and $0.89{\pm}0.09mg/mL$ in the DPPH radical scavenging and LPO inhibitory assays, respectively. To identify active components of OFS, various chromatographic separation of OFS70E led to isolation of 11 flavonoids: dihydrokaempferol, dihydroquercetin, 3-O-methylquercetin, quercetin, isorhamnetin 3-O-glucoside, isorhamnetin 3-O-galactoside, narcissin, kaempferol 7-O-glucoside, quercetin 3-O-galactoside, isorhamnetin, and kaempferol 3-O-rutinoside. The results suggest that standardization of DHK in OFSEs using HPLC-PDA analysis would be an acceptable method for the development of health functional foods.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.45 no.11
• /
• pp.1610-1616
• /
• 2016

This study aimed to establish an optimal extraction process and high performance liquid chromatography-ultraviolet (HPLC-UV) analytical method for determination of 3,5-dicaffeoylquinic acid (3,5-DCQA) as a part of materials standardization for the development of a xanthine oxidase inhibitor as a health functional food. The quantitative determination method of 3,5-DCQA as a marker compound was optimized by HPLC analysis using a Luna RP-18 column, and the correlation coefficient for the calibration curve showed good linearity of more than 0.9999 using a gradient eluent of water (1% acetic acid) and methanol as the mobile phase at a flow rate of 1.0 mL/min and a detection wavelength of 320 nm. The HPLC-UV method was applied successfully to quantification of the marker compound (3,5-DCQA) in Aster glehni extracts after validation of the method with linearity, accuracy, and precision. Ethanolic extracts of A. glehni (AGEs) were evaluated by reflux extraction at 70 and $80^{\circ}C$ with 30, 50, 70, and 80% ethanol for 3, 4, 5, and 6 h, respectively. Among AGEs, 70% AGE at $70^{\circ}C$ showed the highest content of 3,5-DCQA of $52.59{\pm}3.45mg/100g$ A. glehni. Furthermore, AGEs were analyzed for their inhibitory activities on uric acid production by the xanthine/xanthine oxidase system. The 70% AGE at $70^{\circ}C$ showed the most potent inhibitory activity with $IC_{50}$ values of $77.01{\pm}3.13{\sim}89.96{\pm}3.08{\mu}g/mL$. The results suggest that standardization of 3,5-DCQA in AGEs using HPLC-UV analysis would be an acceptable method for the development of health functional foods.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.17 no.2
• /
• pp.15-23
• /
• 2017

The person Re-identification is the most challenging part of computer vision due to the significant changes in human pose and background clutter with occlusions. The picture from non-overlapping cameras enhance the difficulty to distinguish some person from the other. To reach a better performance match, most methods use feature selection and distance metrics separately to get discriminative representations and proper distance to describe the similarity between person and kind of ignoring some significant features. This situation has encouraged us to consider a novel method to deal with this problem. In this paper, we proposed an enhanced recurrent neural network with three-tier hierarchical network for person re-identification. Specifically, the proposed recurrent neural network (RNN) model contain an iterative expectation maximum (EM) algorithm and three-tier Hierarchical network to jointly learn both the discriminative features and metrics distance. The iterative EM algorithm can fully use of the feature extraction ability of convolutional neural network (CNN) which is in series before the RNN. By unsupervised learning, the EM framework can change the labels of the patches and train larger datasets. Through the three-tier hierarchical network, the convolutional neural network, recurrent network and pooling layer can jointly be a feature extractor to better train the network. The experimental result shows that comparing with other researchers' approaches in this field, this method also can get a competitive accuracy. The influence of different component of this method will be analyzed and evaluated in the future research.

• The KIPS Transactions:PartB
• /
• v.10B no.4
• /
• pp.443-450
• /
• 2003

In this paper, we proposed the novel hierarchical algorithm for the recognition of English calling cards that processes multiresolution images of calling cards hierarchically to extract individual characters and recognizes the extracted characters by using the enhanced neural network method. The hierarchical recognition algorithm generates multiresolution images of calling cards, and each processing step in the algorithm selects and processes the image with suitable resolution for lower processing overhead and improved output. That is, first, the image of 1/3 times resolution, to which the horizontal smearing method is applied, is used to extract the areas including only characters from the calling card image, and next, by applying the vertical smearing and the contour tracking masking, the image of a half time resolution is used to extract individual characters from the character string areas. Lastly, the original image is used in the recognition step, because the image includes the morphological information of characters accurately. And for the recognition of characters with diverse font types and various sizes, the enhanced RBF network that improves the middle layer based on the ART1 was proposed and applied. The results of experiments on a large number of calling card images showed that the proposed algorithm is greatly improved in the performance of character extraction and recognition compared with the traditional recognition algorithms.

• The KIPS Transactions:PartB
• /
• v.14B no.5
• /
• pp.383-390
• /
• 2007

Interactions between proteins and genes are often considered essential in the description of biomolecular phenomena and networks of interactions are considered as an entre for a Systems Biology approach. Recently, many works try to extract information by analyzing biomolecular text using natural language processing technology. Previous researches insist that linguistic information is useful to improve the performance in detecting gene interactions. However, previous systems do not show reasonable performance because of low recall. To improve recall without sacrificing precision, this paper proposes a new method for detection of gene interactions based on syntactic relations. Without biomolecular knowledge, our method shows reasonable performance using only small size of training data. Using the format of LLL05(ICML05 Workshop on Learning Language in Logic) data we detect the agent gene and its target gene that interact with each other. In the 1st phase, we detect encapsulation types for each agent and target candidate. In the 2nd phase, we construct verb lists that indicate the interaction information between two genes. In the last phase, to detect which of two genes is an agent or a target, we learn direction information. In the experimental results using LLL05 data, our proposed method showed F-measure of 88% for training data, and 70.4% for test data. This performance significantly outperformed previous methods. We also describe the contribution rate of each phase to the performance, and demonstrate that the first phase contributes to the improvement of recall and the second and last phases contribute to the improvement of precision.