• Journal of Korean Society of Forest Science
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• v.109 no.1
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• pp.72-80
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• 2020

Two strategies for calculating economic feasibility are the machine rate and cash-flow methods. This study used the cash-flow method to evaluate the economic feasibility of the HAM300 yarding operation for extracting tree length logs in Larix kaempferi forest stands. In financial analysis based on 7-year cash-flow, the net present value and pay-back period method were used. We analyzed two scenarios: operating opportunities (50, 100, 150, and 200 days per year) and productivity change yield (7.5 and 10.5 ㎥/scheduled machine hour: SMH). The analysis indicated that high rates of return on extraction activity investment can be achieved when machines are used for >150 days per year. In addition, improved productivity (10.5 ㎥/SMH) increased financial feasibility compared to current productivity (7.5 ㎥/SMH) when machines were operated for 100 days per year. These results suggest that the appropriateness of HAM300 harvesting depends on the number of annual operating days and productivity.

• Restorative Dentistry and Endodontics
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• v.12 no.1
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• pp.131-147
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• 1986

The purpose of this study was to observe the color matching of lining or filling materials according to the remaining tooth material. Twenty-seven freshly extracted human central incisors were used in this experiments. The teeth were stored in saline solution at room temperature after extraction. All teeth were cut parallel to the tangent to height of contour on labial surface from the lingual surface until the pulp were completely removed. Then 27 teeth were devided into 0.5mm, 1.0mm and 1.5mm reduction groups according to the thickness of cutting the lingual surfaces of teeth. The specimens of control group were three teeth of 27 teeth with cutting the lingual surface same mode as above described. In the specimens of experimental groups, 8 kinds of lining and filling materials; FUJI IONOMER TYPE II (G-C Co. Japan), LINING CEMENT (G-C Co. Japan), Dycal (Caulk, U.S.A.), CLEARFIL F II (Kuraray Co. Japan), Crown Bridge & Inlay Cement (G-C Co. Japan), Copalite (Harry J. Bosworth Co. U.S.A.), HY-BOND (G-C Co. Japan) and LIV-CENERA (G-C Co. Japan); applied on the back of 24 teeth with 0.5mm, 1.0mm and 1.5mm cut thickness of lingual surfaces. Three teeth of control group did not applied linging or filling materials on the back of 3 kinds of different thickness of cutting the lingual surfaces. The absorbances of total 27 specimens were obtained by reflection spectrophotometer. (Cary 17 D, Varian Co, U.S.A.) The following conclusions were drawn from above the results; 1. The absorbance patterns in both experiment and control groups were gradually decreased with increasing wavelength of spectra. 2. The absorbance patterns were not decreased in relation to the kinds of lining or filling materials, but the amount of the remaining tooth materials. 3. In 0.5mm reduction group, FUJI IONOMER TYPE II, LINING CEMENT, LIV-CENERA and Copalite applied on the back of cut lingual surface showed similar absorbance patterns as control group. 4. The specimens which were reduced up to 1.0mm thickness and lined with FUJI IONOMER TYPE II and LINING CEMENT showed the comparable absorbance patterns to the control group. 5. In case of HY-BOND application after 1.5mm reduction were observed the similar absorbance pattern as compared with control group. 6. When Dycal, CLEARFIL and Crown Bridge & Inlay Cement were applied to cut teeth surfaces, there were much differences of absorbance between control groups and experimental groups.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.147-156
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• 2011

This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.

• Journal of Applied Biological Chemistry
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• v.56 no.2
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• pp.95-100
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• 2013

Soil microbes are important integral components of soil ecosystem which have significant and diverse role in organic matter decomposition, nitrogen cycling, and nitrogen fixation. In this study an effective denaturing gradient gel electrophoresis (DGGE) method was employed for paddy soil microbial diversity survey. For optimum paddy soil microbial DNA extraction, different methods such as Lysis buffer, skim milk bead, sodium phosphate buffer, Epicentre Soil Master DNA extraction kit (Epicentre, USA) and Mo Bio Power Soil DNA kit (MO BIO, USA) methods were utilized. Among all the method, using Mo Bio Power Soil kit was most effective. DGGE analysis of Bacteria was carried out at 6% polyacylamide gel and 45-60% denaturing gradient in the optimal conditions. Whereas DGGE analysis of fungi was done at 6% polyacrylamide gel and 45-80% denaturing gradient in the optimal conditions. By applying the above assay, it was found that variation within the microbial community of paddy soil occurs by a factor of time. DGGE assay used in this study through for a variety of soil microbial analysis suggests the potential use of this method.

• The korean journal of orthodontics
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• v.26 no.5 s.58
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• pp.487-495
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• 1996

The purpose of this study was to evaluate the dentofacial characteristics and the fost-treatment dentofacial changes of those treated by four premolar extractions and to investigate the factors affecting extraction decision. The sample consisted of 35 patients (27 females, and 8 males) with no more than 7.0mm crowding, diagnosed as Class I protrusion. Pre-treatment and post-treatment lateral cephalograms were evaluated. Computerized statistical analysis was carried out using SPSS/PC+ program. The results were as follows. 1. There was no significant change in skeletal pattern after treatment while there was significant change in dentoalveolar and soft tissue pattern. 2. In pre-treatment skeletal pattern, a tendency toward vertical discrepancy was found. 3. In pre-treatment dental pattern, interincisal angle was $113.11^{\circ}$, U1 to FH was $117.78^{\circ}$ and L1 to A-Pog was 7.94mm. Pre-treatment upper and lower lip position was 2.88mm and 5.43mm to E line. 4. After treatment, interincisal angle increased $14.46^{\circ}$ and upper and lower lip moved back 2.45mm and 3.2mm to E line.(p<0.001) 5. The EI was 138.71 before treatment and 148.2 after treatment.

• Analytical Science and Technology
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• v.17 no.1
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• pp.1-7
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• 2004

The synergistic solvent extraction of Mn(II) by N-nitroso-N-phenylhydroxylamineammonium salt (cupferron) and tetrabutylammonium ion ($TBA^+$) has been studied. In the presence of $TBA^+$, over 95% Mn(II) was extracted from an aqueous solution into chloroform by the cupferron in the pH range of 4 to 10. But a part of Mn(II) was extracted with only cupferron. The ternary complex of Mn(II) was more efficiently extracted into $CH_2Cl_2$ and $CHCl_3$ than other nonpolar solvents. The extracted Mn(II) was determined in the back-extracted $HNO_3$ solution by GF-AAS. This fixed procedure was applied to the determination of trace Mn(II) in tap water samples of pH 5.0. The detection limit equivalent to 3 times standard deviation of the background absorption was 0.37 ng/mL and Mn(II) was determined with the range of 0.4 to 1.01 ng/mL in our laboratory's tap water. And the recovery was 94 to 107% in samples in which 2.0 ng/mL Mn(II) was spiked. The interferences of common concomitant elements such as Cu(II), Ca(II), Fe(III) and so on were not shown up to $10{\sim}20{\mu}g/mL$. From these results, this procedure could be concluded to be applied for the determination of trace Mn(II) in other environmental water samples.

• ETRI Journal
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• v.42 no.1
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• pp.90-100
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• 2020

Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state-of-the-art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short-term memory and back propagation through time approaches. The proposed models consider both language-dependent features, such as part-of-speech tags, and language-independent features, such as the "context windows" of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f-measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively.

• The KSFM Journal of Fluid Machinery
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• v.5 no.4 s.17
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• pp.47-53
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• 2002

The steam generator feedwater flow-rate in a nuclear power plant was estimated by means of artificial neural networks with the wavelet analysis for enhanced information extraction. The fouling of venturi meters, used for steam generator feedwater flow-rate in pressurized water reactors, may result in unnecessary plant power derating. The back-propagation network was used to generate models of signals for a pressurized water reactor Multiple-input, single-output hetero-associative networks were used for evaluating the feedwater flow rate as a function of a set of related variables. The wavelet was used as a low pass filter eliminating the noise from the raw signals. The results have shown that possible fouling of venturi can be detected by neural networks, and the feedwater flow-rate can be predicted as an alternative to existing methods. The research has also indicated that the decomposition of signals by wavelet transform is a powerful approach to signal analysis for denoising.

• Journal of Korea Multimedia Society
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• v.17 no.6
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• pp.671-680
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• 2014

Recently, with the remarkable increase of social network services, it is necessary to extract interesting information from lots of data about various individual opinions and preferences on SNS(Social Network Service). The sentiment information can be applied to various fields of society such as politics, public opinions, economics, personal services and entertainments. To extract sentiment information, it is necessary to use processing techniques that store a large amount of SNS data, extract meaningful data from them, and search the sentiment information. This paper proposes an efficient method to extract sentiment information from various unstructured big data on social networks using HDFS(Hadoop Distributed File System) platform and MapReduce functions. In experiments, the proposed method collects and stacks data steadily as the number of data is increased. When the proposed functions are applied to sentiment analysis, the system keeps load balancing and the analysis results are very close to the results of manual work.

• International Journal of Contents
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• v.7 no.1
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• pp.14-22
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• 2011

Temporal Sign Language Recognition (TSLR) from hand motion is an active area of gesture recognition research in facilitating efficient communication with deaf people. TSLR systems consist of two stages: a motion sensing step which extracts useful features from signers' motion and a classification process which classifies these features as a performed sign. This work focuses on two of the research problems, namely unknown time varying signal of sign languages in feature extraction stage and computing complexity and time consumption in classification stage due to a very large sign sequences database. In this paper, we propose a combination of Dynamic Time Warping (DTW) and application of the Single hidden Layer Feedforward Neural networks (SLFNs) trained by Extreme Learning Machine (ELM) to cope the limitations. DTW has several advantages over other approaches in that it can align the length of the time series data to a same prior size, while ELM is a useful technique for classifying these warped features. Our experiment demonstrates the efficiency of the proposed method with the recognition accuracy up to 98.67%. The proposed approach can be generalized to more detailed measurements so as to recognize hand gestures, body motion and facial expression.