• International Journal of Highway Engineering
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• v.10 no.2
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• pp.1-13
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• 2008

This study presents a case study of condition evaluation of various asphalt pavement sections to estimate performance lives. The pavement surface conditions including cracking and rutting are first evaluated using a automatic pavement analyzer, ARAN. HPCI(Highway Pavement Condition Index) values are estimated using the pavement surface distress data. It is observed from the pavement distress survey that the major distress type of the sections is top-down cracking. The modulus value of each pavement layer is back-calculated from the defection data obtained from a FWD(Falling Weight Deflectometer) and compared with the laboratory measured dynamic modulus values. Remaining lives of the various pavement sections are estimated based on a mechanistic-empirical approach and AAHTO 1993 design guide. The structural capacities of the all pavement sections based on the two approaches are strong enough to maintain the pavement sections for the rest of design life. Since the major distress type is top-down cracking, the remaining lives of the pavement sections are estimated based on HPCI and existing performance database of highway pavements. To evaluate the causes of premature pavement distress, various material properties, such as air void, asphalt binder content, aggregate gradation, dynamic modulus and fatigue resistance, are measured from the field cores. It is impossible to accurately estimate the binder contents of field samples using the ignition method. It is concluded from the laboratory tests that the premature top down cracking is mainly due to insufficient compaction and inadequate aggregate gradation.

• Journal of Advanced Marine Engineering and Technology
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• v.35 no.4
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• pp.436-442
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• 2011

To obtain the engine output correctly is basically very important factor for estimating a engine performance. But, it has been reported that the IHP measured from electronic indicator such as MIPS(Mean Indication Pressure System) has a deviation compared to mechanical indicator. It was reported by authors that the uncertainty of crank angle for TDC position could be one of the reasons. In this paper, the uncertainty of crank angle for TDC position and its influence to engine output were investigated respectively about M/E and G/E for marine diesel engines. For the purpose, two sampling methods of pressure in cylinder were considered which were 'angle base sampling' and 'time base sampling'. Angle base sampling is real crank angle acquired from angle encoder which is attached to crank shaft and time base sampling is crank angle calculated by detected revolution with Z-pluse of encoder. Time base sampling is same method of MIPS. This paper concluded that time base sampling method is not suitable for obtaining the output of marine diesel engine on board because of instantaneous speed variation and load fluctuation. Also it is verified that the variation of engine speed by load fluctuation should be one of reasons additionally in case of M/E.

• Journal of Korean Society of Steel Construction
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• v.25 no.5
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• pp.555-567
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• 2013

Slim floor system using deep decks has been developed and employed in Europe to reduce the floor height of steel structures. Although long span buildings involving the issue of reducing floor height are being increasingly built in Korea, employing deep decks in more than 7m long span structures is likely to cause problems associated with excessive deflection. This study is applied to the long-span concrete casting of the deep deck plate usability of deflection due to bending and torsional instability of open cross-section, as a way to improve the problem of cap plates are suggested, and the optimum length of reinforcement and location are derived from theoretic estimation. The cap plates are placed on the deep decks with regular intervals to overcome the instability of open sections, improve the stiffness of the sections and control the deflection at the centers. The improvement in flexural capacity associated with the location of the cap plates and the length of reinforcement are verified through analysis and test.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.43-54
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• 2018

The accurate estimation of reference crop evapotranspiration ($ET_o$) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of $ET_o$ has resulted in many direct and indirect methods to approximate its value and include pan evaporation, meteorological-based estimations, lysimetry, soil moisture depletion, and soil water balance equations. Artificial neural networks (ANNs) have been intensively implemented for process-based hydrologic modeling due to their superior performance using nonlinear modeling, pattern recognition, and classification. This study adapted two well-known ANN algorithms, Backpropagation neural network (BPNN) and Generalized regression neural network (GRNN), to evaluate their capability to accurately predict $ET_o$ using daily meteorological data. All data were obtained from two automated weather stations (Chupungryeong and Jangsu) located in the Yeongdong-gun (2002-2017) and Jangsu-gun (1988-2017), respectively. Daily $ET_o$ was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of $ET_o$ and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against $ET_o$ calculated from the benchmark method and multiple linear regression (MLR) model. The overall results showed that the BPNN algorithm performed best followed by the MLR and GRNN in a statistical sense and this could contribute to provide valuable information to farmers, water managers and policy makers for effective agricultural water governance.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.10
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• pp.1395-1403
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• 2008

The objective of the present study was to estimate genetic parameters for economic traits in Markhoz goats. Data collected from 1993 to 2006 by the Markhoz goat Performance Testing Station in Sanandaj, Iran, were analyzed. The traits recorded as body weight performance at birth (BW), weaning (WW), six month (6MW), nine month (9MW), yearling (YW) and yearling fleece weight (YFW) were investigated. Least square analyses were used for estimation of environmental effects. Genetic parameters were estimated with single and multi trait analysis using restricted maximum likelihood (REML) procedures, under animal models. By ignoring or including maternal additive genetic effects and maternal permanent environmental effects, five different models were fitted for each trait. The effects of sex, type of birth, age of dam and year of birth on the all body weights were significant (p<0.01), but had no effects on YFW except year of birth. Age of kids had significant influences on WW and 6MW (p<0.01). A log likelihood ratio test was carried out for choosing the most suitable model for each trait. Total heritability estimates for YFW and growth traits varied from 0.16 for YFW and WW to 0.41 for YW. For all traits, maternal heritability was lower than direct heritability, ranging from 0.06 for BW to 0.01 for 6MW and 9MW. The magnitude of $c^2$ was more substantial for BW than the others, and relative importance was reduced from 0.12 for BW to 0.04 for 9MW. The direct additive genetic correlations estimates were positive and varied from 0.21 between BW-YW to 0.96 between WW-6MW. Direct additive genetic correlations between YFW and body weight traits were positive and ranged from 0.14 between BW-YFW to 0.67 between 6MW-YFW. For all traits, the corresponding estimates for phenotypic correlation were positive and lower than genetic correlations. The maternal additive genetic correlations between various traits were varied and ranged from -0.19 between 9MW-YFW to 0.96 between 6MW-9MW. The estimates of the maternal permanent environmental correlations between various traits were positive and ranged from 0.33 between WW-YFW to 0.93 between WW-6MW. Also, the environmental correlations between various traits ranged from 0.01 between BW-YFW and WW-YFW to 0.70 between 9MW-YW. Estimates of genetic parameters for various traits in this study confirm that selection should be applied on WW for genetic improvement in Markhoz goats.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.144-153
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• 2009

Recently, as the necessity of a light-weighted video encoding technique has been rising for applications such as UCC(User Created Contents) or Multiview Video, Distributed Video Coding(DVC) where a decoder, not an encoder, performs the motion estimation/compensation taking most of computational complexity has been vigorously investigated. Wyner-Ziv coding reconstructs an image by eliminating the noise on side information which is decoder-side prediction of original image using channel code. Generally the side information of Wyner-Ziv coding is generated by using frame interpolation between key frames. The channel code such as Turbo code or LDPC code which shows a performance close to the Shannon's limit is employed. The noise model of Wyner-Ziv coding for channel decoding is called Virtual Channel Noise and is generally modeled by Laplacian or Gaussian distribution. In this paper, we propose a Wyner-Ziv coding method based on the frequency-adaptive channel noise modeling in transform domain. The experimental results with various sequences prove that the proposed method makes the channel noise model more accurate compared to the conventional scheme, resulting in improvement of the rate-distortion performance by up to 0.52dB.

• Journal of Broadcast Engineering
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• v.17 no.3
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• pp.519-528
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• 2012

Voice activity detection (VAD) is generally conducted by extracting features from the acoustic signal and a decision rule. The performance of such VAD algorithms driven by the input acoustic signal highly depends on the acoustic noise. When video signals are available as well, the performance of VAD can be enhanced by using the visual information which is not affected by the acoustic noise. Previous visual VAD algorithms usually use single visual feature to detect the lip activity, such as active appearance models, optical flow or intensity variation. Based on the analysis of the weakness of each feature, we propose to combine intensity change measure and the optical flow in the mouth region, which can compensate for each other's weakness. In order to minimize the computational complexity, we develop simple measures that avoid statistical estimation or modeling. Specifically, the optical flow is the averaged motion vector of some grid regions and the intensity variation is detected by simple thresholding. To extract the mouth region, we propose a simple algorithm which first detects two eyes and uses the profile of intensity to detect the center of mouth. Experiments show that the proposed combination of two simple measures show higher detection rates for the given false positive rate than the methods that use a single feature.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.6
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• pp.585-597
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• 2014

In this study, the cutter acting forces were measured by 3-directional load cell at two different skew angles and various S/d ratios during a series of linear cutting tests using a slim conical pick. The analysis for cutting performance were carried out after calculating average values of the measured results. The increase of penetration depth results in the decrease of specific energy. And the variations of the cutter acting forces depending on penetration depth in the case of 6 degree skew angle were smaller than in the case of 0 degree skew angle. From this results, 6 degree skew angle is more effective than 0 degree skew angle in designing optimal specifications of cutting head. In addition, $F_c/F_n$ under the setting of 6 degree skew angle was smaller than under the setting of 0 degree skew angle. However, it should be considered that the increase of cutter acting force in the cutting direction accompanied the increase of driving force in the case of the setting for 6 degree skew angle.

• The Journal of the Acoustical Society of Korea
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• v.23 no.2
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• pp.180-186
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• 2004

The presence of noise in speech signals degrades the performance of recognition systems in which there are mismatches between the training and test environments. To make a speech recognizer robust, it is necessary to compensate these mismatches. In this paper, we studied about an improvement of stochastic feature extraction based on band-SNR for robust speech recognition. At first, we proposed a modified version of the multi-band spectral subtraction (MSS) method which adjusts the subtraction level of noise spectrum according to band-SNR. In the proposed method referred as M-MSS, a noise normalization factor was newly introduced to finely control the over-estimation factor depending on the band-SNR. Also, we modified the architecture of the stochastic feature extraction (SFE) method. We could get a better performance when the spectral subtraction was applied in the power spectrum domain than in the mel-scale domain. This method is denoted as M-SFE. Last, we applied the M-MSS method to the modified stochastic feature extraction structure, which is denoted as the MMSS-MSFE method. The proposed methods were evaluated on isolated word recognition under various noise environments. The average error rates of the M-MSS, M-SFE, and MMSS-MSFE methods over the ordinary spectral subtraction (SS) method were reduced by 18.6%, 15.1%, and 33.9%, respectively. From these results, we can conclude that the proposed methods provide good candidates for robust feature extraction in the noisy speech recognition.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.317-322
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• 2010

This paper proposes how to improve performance of the Korean document sentiment-classification system using semantic properties of the sentiment words. A sentiment word means a word with sentiment, and sentiment features are defined by a set of the sentiment words which are important lexical resource for the sentiment classification. Sentiment feature represents different sentiment intensity in general field and in specific domain. In general field, we can estimate the sentiment intensity using a snippet from a search engine, while in specific domain, training data can be used for this estimation. When the sentiment intensity of the sentiment features are estimated, it is called semantic orientation and is used to estimate the sentiment intensity of the sentences in the text documents. After estimating sentiment intensity of the sentences, we apply that to the weights of sentiment features. In this paper, we evaluate our system in three different cases such as general, domain-specific, and general/domain-specific semantic orientation using support vector machine. Our experimental results show the improved performance in all cases, and, especially in general/domain-specific semantic orientation, our proposed method performs 3.1% better than a baseline system indexed by only content words.