• Nuclear Engineering and Technology
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• v.40 no.3
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• pp.175-182
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• 2008

The results of process development for the blending of waste salt from the electrorefining of spent fuel with zeolite-A are presented. This blending is a key step in the ceramic waste process being used for treatment of EBR-II spent fuel and is accomplished using a high-temperature v-blender. A labscale system was used with non-radioactive surrogate salts to determine optimal particle size distributions and time at temperature. An engineering-scale system was then installed in the Hot Fuel Examination Facility hot cell and used to demonstrate blending of actual electrorefiner salt with zeolite. In those tests, it was shown that the results are still favorable with actinide-loaded salt and that batch size of this v-blender could be increased to a level consistent with efficient production operations for EBR-II spent fuel treatment. One technical challenge that remains for this technology is to mitigate the problem of material retention in the v-blender due to formation of caked patches of salt/zeolite on the inner v-blender walls.

• YAKHAK HOEJI
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• v.21 no.2
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• pp.95-100
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• 1977

The effects of the particle size on the homogeneity of mixing and segregation of caffeine-lactose (1:99) mixture were studied. Using the two kinds of caffein, milled and unmilled caffein, V-type blender, the degree of mixing according to the particle size was predicted and the experiment on the change of mixedness by mixing time was carried out by the method derived from mixing index theory by Poole et al. and Hersey. the results could be summarized as follows; (1) The homogeneity of mixing was greatly affected by the particle size and the particle size should be reduced to the adepuate level to attain the desirable mixedness. (2) The homogeneity was not proportional to the mixing time and optimum mixing time for caffein-lactose was about 10 minutes. (3) Segregation tendency was increased by the particle size increase and greatly affected by the flow time in the segregation cell.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.4
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• pp.387-394
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• 2016

This paper presents a fully digital delay locked loop (DLL) that can acquire lock in four clock cycles with a resolution of a 1/4 NAND-delay. The proposed DLL with a multi-dither-free phase detector acquires the initial lock in four clock cycles with 1/2 NAND-delay. Then, it utilizes a multi-dither-free phase detector, a region accumulator, and phase blenders, to improve the resolution to a 1/4 NAND-delay. The region accumulator which continuously steers the control registers and the phase blender, adaptively controls the tracking bandwidth depending on the amount of jitter, and effectively suppresses the dithering jitter. Fabricated in a 65 nm CMOS process, the proposed DLL occupies $0.0432mm^2$, and consumes 3.7 mW from a 1.2-V supply at 2 GHz.

• Analytical Science and Technology
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• v.16 no.1
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• pp.70-77
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• 2003

The optimum conditions for the residue analysis of hydroxyanilide fungicide fenhexamid on cucumber, strawberry and grape were investigated by gas chromatography equipped with electron capture detector (GC-ECD) and the residual amount was determined by sprayed days before harvest. Each samples were extracted with acetone, filtered and concentrated to 50 mL. The concentrated extracts were transferred to dichloromethane and then thoroughly concentrated. The concentrated phase was loaded on the filtration column stuffed with silica gel and purified with acetone:hexane (5:95, 15:85, v/v) mixed solvent. The regression equation and linearity of the standard calibration curves between 0.05~2.00 ng were as follows : cucumber; Y=312.40X+10.26, $R^2=0.9996$, strawberry; Y=313.33X+5.54, $R^2=0.9998$, grape; Y=253.27X-2.23, $R^2=0.9994$. From the standard additional experiments with 0.10 mg/L and 0.40 mg/L, the average recoveries of cucumber, strawberry and grape were 94.8%, 88.1% and 93.7%, respectively and the detection limits were all the same as 0.01 mg/L. Residual amounts in crops were ranged from 0.01 to 0.58 mg/L.

• Journal of the Korean Society of Food Science and Nutrition
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• v.11 no.3
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• pp.29-33
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• 1982

Saengran, one of the traditional Korean foods, is made from hot taste-removed ginger. It's taste depends upon the recipe and maker's skill however, no standard recipe is established in the literature. In this study, the recipes in the literature were evaluated to find out the best recipe for Saengran. The best method for removing hot taste of ginger was the extraction of ginger in a boiling water for 20 minutes The best formular for Sangran was hot taste removed ginger 100g, sugar 77g, honey 57g, and water 154g. The optimum boiling time was 55 minutes with an electric heater. (100V, 600W)

• Smart Structures and Systems
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• v.31 no.5
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• pp.469-483
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• 2023

The dynamic characteristics of wind turbine blades are usually monitored by contact sensors with the disadvantages of high cost, difficult installation, easy damage to the structure, and difficult signal transmission. In view of the above problems, based on computer vision technology and the improved YOLOv5 (You Only Look Once v5) deep learning model, a non-contact dynamic characteristic monitoring method for wind turbine blade is proposed. First, the original YOLOv5l model of the CSP (Cross Stage Partial) structure is improved by introducing the CSP2_2 structure, which reduce the number of residual components to better the network training speed. On this basis, combined with the Deep sort algorithm, the accuracy of structural displacement monitoring is mended. Secondly, for the disadvantage that the deep learning sample dataset is difficult to collect, the blender software is used to model the wind turbine structure with conditions, illuminations and other practical engineering similar environments changed. In addition, incorporated with the image expansion technology, a modeling-based dataset augmentation method is proposed. Finally, the feasibility of the proposed algorithm is verified by experiments followed by the analytical procedure about the influence of YOLOv5 models, lighting conditions and angles on the recognition results. The results show that the improved YOLOv5 deep learning model not only perform well compared with many other YOLOv5 models, but also has high accuracy in vibration monitoring in different environments. The method can accurately identify the dynamic characteristics of wind turbine blades, and therefore can provide a reference for evaluating the condition of wind turbine blades.