• International Journal of Aeronautical and Space Sciences
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• v.18 no.3
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• pp.565-573
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• 2017

This paper presents a multistage in-flight alignment (MIFA) method for a strapdown inertial navigation system (SDINS) suitable for moving vehicles with no initial attitude references. A SDINS mounted on a moving vehicle frequently loses attitude information for many reasons, and it makes solving navigation equations impossible because the true motion is coupled with an undefined vehicle attitude. To determine the attitude in such a situation, MIFA consists of three stages: a coarse horizontal attitude, coarse heading, and fine attitude with adaptive Kalman navigation filter (AKNF) in order. In the coarse horizontal alignment, the pitch and roll are coarsely estimated from the second order damping loop with an input of acceleration differences between the SDINS and GPS. To enhance estimation accuracy, the acceleration is smoothed by a scalar filter to reflect the true dynamics of a vehicle, and the effects of the scalar filter gains are analyzed. Then the coarse heading is determined from the GPS tracking angle and yaw increment of the SDINS. The attitude from these two stages is fed back to the initial values of the AKNF. To reduce the estimated bias errors of inertial sensors, special emphasis is given to the timing synchronization effects for the measurement of AKNF. With various real flight tests using an UH60 helicopter, it is proved that MIFA provides a dramatic position error improvement compared to the conventional gyro compass alignment.

• ETRI Journal
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• v.32 no.2
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• pp.333-335
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• 2010

This letter proposes a non-coherent blind time-of-arrival (TOA) estimation scheme for impulse radio ultra-wideband systems. The TOA estimation is performed in two consecutive phases: the Rayleigh-quotient theorem-based coarse-signal acquisition (CSA) and the iterative-threshold-test-based fine time estimation (FTE). The proposed scheme serves in a blind manner without demanding any a priori knowledge of the channel and the noise. Analysis and simulations demonstrate that the proposed scheme significantly increases the signal detection probability in CSA and ameliorates the TOA estimation accuracy in FTE.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2480-2495
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• 2014

This paper describes a hierarchical method for image-based age estimation that combines age group classification and age value estimation. The proposed method uses a coarse-to-fine strategy with different appearance features to describe facial shape and texture. Considering the damage to continuity between neighboring groups caused by fixed divisions during age group classification, a dynamic grouping technique is employed to allow non-fixed groups. Based on the given group, an ordinal hyperplane ranking (OHRank) model is employed to transform age estimation into a series of binary enquiry problems that can take advantage of the intrinsic correlation and ordinal information of age. A set of experiments on FG-NET are presented and the results demonstrate the validity of our solution.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2007.10a
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• pp.418-419
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• 2007

• Steel and Composite Structures
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• v.49 no.6
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• pp.645-666
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• 2023

Due to the steadily declining supply of natural coarse aggregates, the concrete industry has shifted to substituting coarse aggregates generated from byproducts and industrial waste. Oil palm shell is a substantial waste product created during the production of palm oil (OPS). When considering the usage of OPSC, building engineers must consider its uniaxial compressive strength (UCS). Obtaining UCS is expensive and time-consuming, machine learning may help. This research established five innovative hybrid AI algorithms to predict UCS. Aquila optimizer (AO) is used with methods to discover optimum model parameters. Considered models are artificial neural network (AO - ANN), adaptive neuro-fuzzy inference system (AO - ANFIS), support vector regression (AO - SVR), random forest (AO - RF), and extreme gradient boosting (AO - XGB). To achieve this goal, a dataset of OPS-produced concrete specimens was compiled. The outputs depict that all five developed models have justifiable accuracy in UCS estimation process, showing the remarkable correlation between measured and estimated UCS and models' usefulness. All in all, findings depict that the proposed AO - XGB model performed more suitable than others in predicting UCS of OPSC (with R², RMSE, MAE, VAF and A_15-index at 0.9678, 1.4595, 1.1527, 97.6469, and 0.9077). The proposed model could be utilized in construction engineering to ensure enough mechanical workability of lightweight concrete and permit its safe usage for construction aims.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• ETRI Journal
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• v.42 no.6
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• pp.815-826
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• 2020

We propose a two-dimensional (2D) scattering-center-extraction (SCE) method using sparse recovery based on the compressive-sensing theory, even with data missing from the received radar cross-section (RCS) dataset. First, using the proposed method, we generate a 2D grid via adaptive discretization that has a considerably smaller size than a fully sampled fine grid. Subsequently, the coarse estimation of 2D scattering centers is performed using both the method of iteratively reweighted least square and a general peak-finding algorithm. Finally, the fine estimation of 2D scattering centers is performed using the orthogonal matching pursuit (OMP) procedure from an adaptively sampled Fourier dictionary. The measured RCS data, as well as simulation data using the point-scatterer model, are used to evaluate the 2D SCE accuracy of the proposed method. The results indicate that the proposed method can achieve higher SCE accuracy for an incomplete RCS dataset with missing data than that achieved by the conventional OMP, basis pursuit, smoothed L0, and existing discrete spectral estimation techniques.

• Journal of the Korean Geosynthetics Society
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• v.17 no.1
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• pp.65-74
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• 2018

The degree of compaction of embankments is generally measured using the sand replacement method or a soil density gauge. However, these methods include coarse particles, which are relatively large. The degree of compaction is overestimated if the in-situ soil density is simply compared with the density obtained from a Proctor compaction test (KS F 2312, 2001), because the density of coarse particles is higher than that of soil. However, there is no recommended correction for the coarse particle ratio in Korea, thus intentionally increasing the degree of compaction for structures to which large loads are applied or for which compaction is critical. Here, a correction considering the Korean Proctor compaction test and the difference between the maximum allowable particle sizes was recommended after corrections for coarse particle ratios in other countries were collected and analyzed. The degree of compaction was re-estimated by applying the recommended correction to the results of both Proctor compaction and sand replacement tests. The degree of compaction without the correction of coarse particle ratio was overestimated, because the re-estimated degree of compaction decreased as the coarse particle ratio increased. The relatively accurate results obtained from the field application of the correction will offer long-term cost savings due to reduced maintenance fees during operation.

• Advances in concrete construction
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• v.8 no.4
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• pp.321-333
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• 2019

The present study targets to access the consequence of utilization of coarse aggregates retrieved from waste concrete as a substitution of coarse fraction of natural aggregates and silica nano-particles as partial substitution of cement using principles of factorial design. Furthermore, procedures of design of experiments are employed to examine the effect of use of recycled aggregates and nano-silica. In this investigation, compressive strength found after at 7, 28, 90 and 365 days, split and flexural tensile strength, ultrasonic pulse velocity and rebound number and are chosen as responses, whereas the percentages of recycled coarse aggregates (RCA%) and nano-silica (NS(%)) are selected as factors. Analysis of Variance has been conducted on the experimental results for the selected responses with consideration the both factors, which indicates that RCA (%) and NS (%) have substantial impact on the various responses. However, the present analysis depicts that interaction between factors has considerable effect on the chosen parameters of concrete. Furthermore, validation experiments are carried to validate these models for compressive and tensile strength for 100% RCA and 1% NS. The results of comparative study indicates that that the error of the estimation determined using the relevant models are found to be small (±5%) in comparison with the analogous experimental results, which authenticates the calculated models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.421-441
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• 2021

Fruit detection in orchards is one of the most crucial tasks for designing the visual system of an automated harvesting robot. It is the first and foremost tool employed for tasks such as sorting, grading, harvesting, disease control, and yield estimation, etc. Efficient visual systems are crucial for designing an automated robot. However, conventional fruit detection methods always a trade-off with accuracy, real-time response, and extensibility. Therefore, an improved method is proposed based on coarse-to-fine multitask cascaded convolutional networks (MTCNN) with three aspects to enable the practical application. First, the architecture of Fruit-MTCNN was improved to increase its power to discriminate between objects and their backgrounds. Then, with a few manual labels and operations, synthetic images and labels were generated to increase the diversity and the number of image samples. Further, through the online hard example mining (OHEM) strategy during training, the detector retrained hard examples. Finally, the improved detector was tested for its performance that proved superior in predicted accuracy and retaining good performances on portability with the low time cost. Based on performance, it was concluded that the detector could be applied practically in the actual orchard environment.