• International Journal of Concrete Structures and Materials
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• v.9 no.2
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• pp.159-172
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• 2015

Estimation of the residual strength of corroded reinforced concrete beams has been studied from experimental and theoretical perspectives. The former is arduous as it involves casting beams of various sizes, which are then subjected to various degrees of corrosion damage. The latter are static; hence cannot be generalized as new coefficients need to be re-generated for new cases. This calls for dynamic models that are adaptive to new cases and offer efficient generalization capability. Computational intelligence techniques have been applied in Construction Engineering modeling problems. However, these techniques have not been adequately applied to the problem addressed in this paper. This study extends the empirical model proposed by Azad et al. (Mag Concr Res 62(6):405-414, 2010), which considered all the adverse effects of corrosion on steel. We proposed four artificial neural networks (ANN) models to predict the residual flexural strength of corroded RC beams using the same data from Azad et al. (2010). We employed two modes of prediction: through the correction factor ($C_f$) and through the residual strength ($M_{res}$). For each mode, we studied the effect of fixed and random data stratification on the performance of the models. The results of the ANN models were found to be in good agreement with experimental values. When compared with the results of Azad et al. (2010), the ANN model with randomized data stratification gave a $C_f$-based prediction with up to 49 % improvement in correlation coefficient and 92 % error reduction. This confirms the reliability of ANN over the empirical models.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.280-285
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• 2008

ZigBee is Low Power and Low Data Rate Wireless Communication protocol. It apply to much Ubiquitous Sensor Network. ZigBee PAN is two type PAN. One is Beacon Enabled PAN, the other is Non Beacon Enabled PAN. To support Low Power in Non Beacon Enabled PAN, End-Device enter Active status at End-Device's wishing time and send a data. So, Router does not know End-Device sends a data time. To solving this problem, Router must always exist to Active status. In this case, Router receive a power supply always in Non Beacon Enabled PAN. But Router does not receive a power supply always, Router can not normal operation, such as Router use a battery. To solve this problem, Router will be support low power. In this paper, we will present Router's Low Power Algorithm. And we suggest 'PAN Time'. Device use 'PAN Time' for PAN synchronous. Router using Low Power Algorithm can be enter to inactive status. So Non Beacon Enabled PAN of Router support the low power mode Therefore Router does not receive a power supply always, Router can normal operation.

• Journal of Advanced Navigation Technology
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• v.20 no.5
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• pp.433-439
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• 2016

Several low-cost global navigation satellite system (GNSS) receivers do not support general range-domain correction, and DGNSS-CP (differential GNSS) method had been suggested to solve this problem. It improves its position accuracy by projecting range-domain corrections to the position-domain and then differentiating the stand-alone position by the projected correction. To project the range-domain correction, line-of-sight vectors from the receiver to each satellite should be calculated. The line-of-sight vectors can be obtained from GNSS broadcast ephemeris data or satellite direction information, and this paper shows positioning performance for the two methods. Stand-alone positioning result provided from Septentrio PolaRx4 Pro receiver was used to show the difference. The satellite direction information can reduce the computing load for the DGNSS-CP by 1/15, even though its root mean square(RMS) of position error is bigger than that of ephemeris data by 0.1m.

• Journal of Korea Water Resources Association
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• v.33 no.1
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• pp.61-72
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• 2000

The purpose of this study is to improve the method of evaluating groundwater recharge by using grid-based soil moisture routing technique. A model which predicts temporal variation and spatial distribution of soil moisture on a daily time step was developed. The model uses ASCII-formatted map data supported by the irregular gridded map of the GRASS(Geographic Resources Analysis Support System)-GIS and can generate daily and monthly spatial distribution map of surface runoff, soil moisture content, evapotranspiration within the watershed. The model was applied to Ipyunggyo watershed($75.6\;\textrm{km}^2$) located in the upstream of Bocheongchun watershed. Seven maps; DEM(Digital Elevation Mode]), stream, flow path, soil, land use, Thiessen network and free groundwater level, were used for input data. Predicted streamflows resulting from two years (l995, 1996) daily data were compared with the observed values at the watershed outlet. The results of temporal variations and spatial distributions of soil moisture are presented by using GRASS GIS. As a final result, the monthly predicted groundwater recharge was presented.sented.

• Journal of Korea Game Society
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• v.18 no.1
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• pp.105-114
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• 2018

Various data analysis methods used for customer type analysis are very important for game companies to understand their type and characteristics in an attempt to plan customized content for our customers and to provide more convenient services. In this paper, we propose a k-mode cluster analysis algorithm that uses information uncertainty by extending information entropy to reduce information loss. Therefore, the measurement of the similarity of attributes is considered in two aspects. One is to measure the uncertainty between each attribute on the center of each partition and the other is to measure the uncertainty about the probability distribution of the uncertainty of each property. In particular, the uncertainty in attributes is taken into account in the non-probabilistic and probabilistic scales because the entropy of the attribute is transformed into probabilistic information to measure the uncertainty. The accuracy of the algorithm is observable to the result of cluster analysis based on the optimal initial value through extensive performance analysis and various indexes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3694-3722
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• 2022

In today's globalized world, there is no transparency in exchanging data and information between producers and consumers. However, these tasks experience many challenges, such as administrative barriers, confidential data leakage, and extensive time delays. To overcome these challenges, we propose a decentralized, secured, and verified smart chain framework using Ethereum Smart Contract which employs Inter Planetary File Systems (IPFS) and MongoDB as storage systems to automate the process and exchange information into blocks using the Tendermint algorithm. The proposed work promotes complete traceability of the product, ensures data integrity and transparency in addition to providing security to their personal information using the Lelantos mode of shipping. The Tendermint algorithm helps to speed up the process of validating and authenticating the transaction quickly. More so in this time of pandemic, it is easier to meet the needs of customers through the Ethermint Smart Chain, which increases customer satisfaction, thus boosting their confidence. Moreover, Smart contracts help to exploit more international transaction services and provide an instant block time finality of around 5 sec using Ethermint. The paper concludes with a description of product storage and distribution adopting the Ethermint technique. The proposed system was executed based on the Ethereum-Tendermint Smart chain. Experiments were conducted on variable block sizes and the number of transactions. The experimental results indicate that the proposed system seems to perform better than existing blockchain-based systems. Two configuration files were used, the first one was to describe the storage part, including its topology. The second one was a modified file to include the test rounds that Caliper should execute, including the running time and the workload content. Our findings indicate this is a promising technology for food supply chain storage and distribution.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.222-227
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• 2022

In this paper, we implemented a Korean text generation and classification model based on a deep learning algorithm that can be applied to various industries. It consists of two implemented GAN-based Korean handwriting generation models and CNN-based Korean handwriting classification models. The GAN model consists of a generator model for generating fake Korean handwriting data and a discriminator model for discriminating fake handwritten data. In the case of the CNN model, the model was trained using the 'PHD08' dataset, and the learning result was 92.45. It was confirmed that Korean handwriting was classified with % accuracy. As a result of evaluating the performance of the classification model by integrating the Korean cursive data generated through the implemented GAN model and the training dataset of the existing CNN model, it was confirmed that the classification performance was 96.86%, which was superior to the existing classification performance.

• Smart Structures and Systems
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• v.32 no.5
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• Journal of the Korean earth science society
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• v.21 no.2
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• pp.137-158
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• 2000

The correlation and Empirical Orthogonal Function (EOF) analyses over the globe have been applied to intercompare lower-stratospheric (${\sim}$70hPa) temperature obtained from satellite data and two model reanalyses. The data is the19 years (1980-98) Microwave Sounding Unit (MSU) channel 4 (Ch4) brightness temperature, and the reanalyses are GCM (NCEP, 1980-97; GEOS, 1981-94) outputs. In MSU monthly climatological anomaly, the temperature substantially decreases by ${\sim}$21k in winter over southern polar regions, and its annual cycle over tropics is weak. In October the temperature and total ozone over the area south of Australia remarkably increase together. High correlations (r${\ge}$0.95) between MSU and reanalyses occur in most global areas, but they are lower (r${\sim}$O.75) over the 20-3ON latitudes, northern America and southern Andes mountains. The first mode of MSU and reanalyses for monthly-mean Ch4 temperature shows annual cycle, and the lower-stratospheric warming due to volcanic eruptions. The analyses near the Korean peninsula show that lower-stratospheric temperature, out of phase with that for troposphere, increases in winter and decreases in summer. In the first mode for anomaly over the tropical Pacific, MSU and reanalyses indicate lower-stratospheric warming due to volcanic eruptions. In the second mode MSU and GEOS present Quasi-Biennial Oscillation (QBO) while NCEP, El Ni${\tilde{n}}$o. Volcanic eruption and QBO have more impact on lower-stratospheric thermal state than El Ni${\tilde{n}}$o. The EOF over the tropical Atlantic is similar to that over the Pacific, except a negligible effect of El Ni${\tilde{n}}$o. This study suggests that intercomparison of satellite data with model reanalyses may estimate relative accuracy of both data.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.119-120
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• 2002

The aim is to urge the need of elaborate commissioning of 3D RTP system from the firsthand experience. A 3D RTP system requires so much data such as beam data and patient data. Most data of radiation beam are directly transferred from a 3D dose scanning system, and some other data are input by editing. In the process inputting parameters and/or data, no error should occur. For RTP system using algorithm-bas ed-on beam-modeling, careless beam-data processing could also cause the treatment error. Beam data of 3 different qualities of photon from two linear accelerators, patient data and calculated results were commissioned. For PDD, the doses by Clarkson, convolution, superposition and fast superposition methods at 10 cm for 10${\times}$10 cm field, 100 cm SSD were compared with the measured. An error in the SCD for one quality was input by the service engineer. Whole SCD defined by a physicist is SAD plus d$\sub$max/, the value was just SAD. That resulted in increase of MU by 100${\times}$((1_d$\sub$max//SAD)$^2$-1)%. For 10${\times}$10 cm open field, 1 m SSD and at 10 cm depth in uniform medium of relative electron density (RED) 1, PDDs for 4 algorithms of dose calculation, Clarkson, convolution, superposition and fast-superposition, were compared with the measured. The calculated PDD were similar to the measured. For 10${\times}$10 cm open field, 1 m SSD and at 10 cm depth with 5 cm thick inhomogeneity of RED 0.2 under 2 cm thick RED 1 medium, PDDs for 4 algorithms were compared. PDDs ranged from 72.2% to 77.0% for 4 MV X-ray and from 90.9% to 95.6% for 6 MV X-ray. PDDs were of maximum for convolution and of minimum for superposition. For 15${\times}$15 cm symmetric wedged field, wedge factor was not constant for calculation mode, even though same geometry. The reason is that their wedge factor is considering beam hardness and ray path. Their definition requires their users to change the concept of wedge factor. RTP user should elaborately review beam data and calculation algorithm in commissioning.