• Agricultural and Biosystems Engineering
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• 제4권1호
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• pp.16-21
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• 2003

In the agricultural field, a machine vision system has been widely used to automate most inspection processes especially in quality grading. Though machine vision system was very effective in quantifying geometrical quality factors, it had a deficiency in quantifying color information. This study was conducted to evaluate color of beef using machine vision system. Though measuring color of a beef using machine vision system had an advantage of covering whole lean tissue area at a time compared to a colorimeter, it revealed the problem of sensitivity depending on the system components such as types of camera, lighting conditions, and so on. The effect of color balancing control of a camera was investigated and multi-layer BP neural network based color calibration process was developed. Color calibration network model was trained using reference color patches and showed the high correlation with L*a*b* coordinates of a colorimeter. The proposed calibration process showed the successful adaptability to various measurement environments such as different types of cameras and light sources. Compared results with the proposed calibration process and MLR based calibration were also presented. Color calibration network was also successfully applied to measure the color of the beef. However, it was suggested that reflectance properties of reference materials for calibration and test materials should be considered to achieve more accurate color measurement.

• Fisheries and Aquatic Sciences
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• 제13권4호
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• pp.263-271
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• 2010

G protein-coupled receptors (GPCR) constitute the largest superfamily of cell membrane receptors, mediating diverse signal-transduction pathways. The melanocortin 4 receptor (MC4R) has been of interest for its physiological role and size, one of the smallest among the GPCRs, which makes it a good model system for the structural study of GPCRs. To study the molecular structure and tissue-specific expression of MC4R in olive flounder (Paralichthys olivaceus), the full-length MC4R gene was obtained using PCR amplification of genomic DNA as well as cDNA synthesis. Sequence analysis of the gene indicates that 978 bp of the MC4R gene encodes 325 amino acids without introns. Sequence alignment with the MC4Rs from other fish shows the highest degree of identity (96%) between Paralichthys olivaceous and Verasper moseri, followed by Takifugu rubripes and Tetraodon nigroviridis (89%). RNA was isolated from various tissues to examine the tissue distribution of MC4R by using RT-PCR. The results showed major expression of MC4R in the liver, brain, and eye, which is consistent with the expression pattern in other fish belonging to the order Pleuronectiformes.

• 한국정밀공학회지
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• 제9권4호
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• pp.22-35
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• 1992

In highly automated machining centers, intelligent sensor fddeback systems are indispensable on order to monitor their operations, to ensure efficient metal removal, and to initate remedial action in the event of accident. In this study, an on-line tool wear detection system for thrning operations is developed, and experimentally evaluated. The system employs multiple sensors and the signals from these sensors are processed using a multichannel autoegressive (AR) series model. The resulting output from the signal processing block is then fed to a previously tranied artificial neural network (multiayered perceptron) to make a final decision on the state of the cutting tool. To learn the necessary input/output mapping for tool wear detection, the weithts and thresholds of the network are adjusted according to the back propagation (BP) method during off-line training. The results of experimental evaluation show that the system works well over a wide range of cutting conditions, and the ability of the system to detect tool wear is improved due to the generalization, fault-tolearant and self-ofganizing properties of the neural network.

• Fisheries and Aquatic Sciences
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• 제10권1호
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• pp.8-15
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• 2007

Rhodopsin, a dim-light receptor, is a model system for the study of G protein-coupled receptors that transduce extracellular signals into cells. To study the molecular mechanisms of visual systems in fish, the rod opsin gene of olive flounder Paralichthys olivaceus was characterized. The full-length P. olivaceus opsin gene was obtained by PCR amplification of genomic DNA, as well as cDNA synthesis. A comparison of clones obtained from both methods indicated that the olive flounder rod opsin gene lacks introns. Sequence analysis of the opsin gene indicated that it contains a 1,056-bp open reading frame encoding 352 amino acids. The deduced amino acid sequence contains features of typical rod opsins, such as sites for Schiff's base formation (K296) and its counterion (E113), disulfide formation (C110 and C187), and palmitoylation (C322 and C323). An opsin sequence alignment showed the highest similarity between P. olivaceus and Solea solea (95.1%), followed by Hippoglossus hippoglossus (94.5%). An opsin phylogenetic tree revealed a close relationship between olive flounder and teleost rod opsins.

• Journal of Microbiology and Biotechnology
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• 제28권6호
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• pp.902-908
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• 2018

Optimization of the culture medium to maximize menaquinone-7 (MK-7) production by Bacillus subtilis strain KCTC 12392BP in static culture was carried out using statistical experimental methods, including one factor at a time, fractional factorial design, and response surface methodology (RSM). Maltose (carbon source), tryptone (nitrogen source), and glycerol (activator) were identified as the key medium components for MK-7 synthesis by the fractional factorial design, and were selected for statistical optimization by RSM. The statistical analysis indicated that, in the range that was studied, maltose, tryptone, and glycerol were all critical factors having profound effects on the production of MK-7, with their coefficients for linear and quadratic all significant at the p < 0.05 level. The established model was efficient and feasible, with a determination coefficient ($R^2$) of 0.9419. The predicted concentrations of maltose, tryptone, and glycerol in the optimal medium were determined as 36.78, 62.76, and 58.90 g/l, respectively. In this optimized medium, the maximum yield of MK-7 reached a remarkably high level of $71.95{\pm}1.00{\mu}g/ml$ after 9 days of static fermentation, which further verified the practicability of this optimized strategy.

• 한국식물학회:학술대회논문집
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• 한국식물학회 1987년도 식물생명공학 심포지움 논문집 Proceedings of Symposia on Plant Biotechnology
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• pp.391-401
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• 1987

Plant chloroplast DNA exists as an unique circular structure in which large single copy(LSC) region and small single copy (SSC) region are separated by large inverted repeat sequences (IRS). It has been known that the unique existence of inverted repeat sequences in chloroplast DNA has no relation with the stability of the chloroplast DNA, but causes the inversion between inverted repeat its biological significance has not been understood so far. In rice, several gene clusters have been cloned and sequenced which contain ribulose-5-biophosphate car-boxylase large subunit (rbcL). Especially, one rbcL gene is linked with rp12 gene which is located in the IRS region in one of the gene clusters. By comparison of nucleotide sequence, the two genes are found to be linked through 151 bp repeat sequence which is homologous to the rp123 gene in IRS region. The repeat sequence is found to be located 3' downstream of rfcL gene and near psbA gene in LSC region. The existence of these repeat sequences and the presence of gene clusters caused by the gene rearrangement thorough the repeat sequence provide a possible which is found to be dispersed chloroplast DNA provide the model system to explaine the heterogeneity of the chloroplast DNA in rice in term of gene rearrangement.

• 대한전자공학회논문지
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• 제27권11호
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• pp.144-151
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• 1990

본 연구에서는, Ohotomo 등이 모음의 학습과 인식을 위해 구성한 BP 구조 신경망의 학습을 위해 사용하였던 입력 패턴의 방법을 보완하여, 포만트 주파수의 대역폭을 고려한 측면값을 학습용 입력패턴에 두어 수렵 속도와 인식율을 높이고자 한다. 본 연구에서 제안한 방법이 오인식율에서는 $30{\%}$정도의 감소와 수렴 속도며에서는 $7{\%}$의 증가를 컴퓨터 시뮬레이션을 통하여 알 수 있었다.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2007년도 하계학술대회 논문집 Vol.8
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• pp.464-465
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• 2007

Solid insulation exposed to voltage is degraded by electrical tree process. And the degradation of the insulation is accelerated by voltage application. For this experimental, specimen of electrical tree model is made by XLPE (cross-linked polyethylene). And the size of the specimen is $7*5*7\;mm^3$. Distance of needle and plane is 2 mm. Voltages applied for acceleration test are 12 kV to 15 kV. And distribution characteristic of degraded stage is studied too. As a PD detecting and data process, discharge data acquire from PD detecting system (Biddle instrument). The system presents statistical distribution as phase resolved. Moreover the processing time of electrical tree is recorded to know the speed of degradation according to voltage.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2004년도 하계학술대회 논문집 Vol.5 No.1
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• pp.33-36
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• 2004

The process modeling for the growth rate in pulsed laser deposition(PLD)-grown ZnO thin films is investigated using neural networks(NNets) and the process recipes is optimized via genetic algorithms(GAs). D-optimal design is carried out and the growth rate is characterized by NNets based on the back-propagation(BP) algorithm. GAs is then used to search the desired recipes for the desired growth rate. The statistical analysis is used to verify the fitness of the nonlinear process model. This process modeling and optimization algorithms can explain the characteristics of the desired responses varying with process conditions.

• Computers and Concrete
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• 제20권6호
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• pp.627-634
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• 2017

Predicting the compressive strength of concrete is important to assess the load-carrying capacity of a structure. However, the use of blended cements to accrue the technical, economic and environmental benefits has increased the complexity of prediction models. Artificial Neural Networks (ANNs) have been used for predicting the compressive strength of ordinary Portland cement concrete, i.e., concrete produced without the addition of supplementary cementing materials. In this study, models to predict the compressive strength of blended cement concrete prepared with a natural pozzolan were developed using regression models and single- and 2-phase learning ANNs. Back-propagation (BP), Levenberg-Marquardt (LM) and Conjugate Gradient Descent (CGD) methods were used for training the ANNs. A 2-phase learning algorithm is proposed for the first time in this study for predictive modeling of the compressive strength of blended cement concrete. The output of these predictive models indicates that the use of a 2-phase learning algorithm will provide better results than the linear regression model or the traditional single-phase ANN models.