• Journal of information and communication convergence engineering
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• v.8 no.3
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• pp.281-288
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• 2010

This paper investigates the performance of IEEE 802.11p wireless access in vehicular environments (WAVE) system in small-scale fading models reported by Georgia Institute of Technology (Georgia Tech). We redesign the small-scale fading models to be applied to the computer simulation and develop the IEEE 802.11p WAVE physical layer simulator to provide the bit error rate and packet error rate performances. Moreover, a new channel estimator using decision directed channel estimation and linear minimum mean square error smoothing is proposed in order to improve the performance of the conventional least square channel estimator using two identical long training symbols. The simulation results are satisfactorily coincident with the scenarios of Georgia Tech report, and the proposed channel estimator significantly outperforms the conventional channel estimator.

• International Journal of Air-Conditioning and Refrigeration
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• v.11 no.4
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• pp.159-169
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• 2003

Five two-phase closed loop thermosyphons (TLTs) specially designed and constructed for the present study are one small scale loop, two medium scale loops (MSLI and MSLII) and two large scale loops (LSLI and LSLII). Two simulation models based on thermal resistance network, lumped and sectorial, are presented. In the Lumped model, the evaporator section is dealt as one lumped boiling section. Whereas, in the Sectorial model, all possible phenomena which would occur in the evaporator section due to the two-phase boiling process are considered in detail. Flow regimes, the flow transitions between flow regimes and other two-phase parameters involved in two-phase flows are carefully analyzed. In the present study, the results of two different simulation models are compared with experimental results. The comparisons showed that the simulation results by the Lumped model and by the Sectorial model did not show any partiality for the model used for the simulation. The simulation results according to the correlations show the various results in the large different range.

• Ocean Systems Engineering
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• v.12 no.2
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• pp.143-157
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• 2022

In this study, the nominal wake field of a semi-displacement type high-speed vessel was computed at full scale by using CFD (Computational Fluid Dynamics) and GEOSIM-based approaches. A scale effect investigation on nominal wake field of benchmark Athena vessel was performed with two models which have different model lengths. The members of the model family have the same Fr number but different Re numbers. The spatial components of nominal wake field have been analyzed by considering the axial, radial and tangential velocities for models at different scales. A linear feature has been found for radial and tangential components while a nonlinear change has been obtained for axial velocity. Taylor wake fraction formulation was also computed by using the axial wake velocities and an extrapolation technique was carried out to get the nonlinear fit of nominal wake fraction. This provides not only to observe the change of nominal wake fraction versus scale ratios but also to estimate accurately the wake fraction at full-scale. Extrapolated full-scale nominal wake fractions by GEOSIM-based approach were compared with the full-scale CFD result, and a very good agreement was achieved. It can be noted that the GEOSIM-based extrapolation method can be applied for estimation of the nominal wake fraction of semi-displacement type high-speed vessels.

• Structural Engineering and Mechanics
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• v.6 no.5
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• pp.571-582
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• 1998

Testing of steel roof purlins is usually performed on full scale models in large vacuum test rigs. To undertake a comparison between web cleat connected purlins and flange bolted purlins a series of tests were performed on a 1:4 small scale model vacuum test rig. Various modelling issues need to be addressed to ensure reasonable comparison with actual constructed roof framing methods but still be suitable for an economical comparison between the connection methods. Model test results were supported by, and found to be in reasonable agreement with, deflection predictions from computer models based on finite element methods. This paper discusses the testing methods adopted and the value of small scale model testing programs as a means of obtaining comparisons between framing options.

• Nuclear Engineering and Technology
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• v.50 no.1
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• pp.54-67
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• 2018

There have been recent efforts to establish methods for high-fidelity and multi-physics simulation with coupled thermal-hydraulic (T/H) and neutronics codes for the entire core of a light water reactor under accident conditions. Considering the computing power necessary for a pin-by-pin analysis of the entire core, subchannel-scale T/H analysis is considered appropriate to achieve acceptable accuracy in an optimal computational time. In the present study, the applicability of in-house code CUPID of the Korea Atomic Energy Research Institute was extended to the subchannel-scale T/H analysis. CUPID is a component-scale T/H analysis code, which uses three-dimensional two-fluid models with various closure models and incorporates a highly parallelized numerical solver. In this study, key models required for a subchannel-scale T/H analysis were implemented in CUPID. Afterward, the code was validated against four subchannel experiments under unheated and heated single-phase incompressible flow conditions. Thereafter, a subchannel-scale T/H analysis of the entire core for an Advanced Power Reactor 1400 reactor core was carried out. For the high-fidelity simulation, detailed geometrical features and individual rod power distributions were considered in this demonstration. In this study, CUPID shows its capability of reproducing key phenomena in a subchannel and dealing with the subchannel-scale whole core T/H analysis.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.5
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• pp.666-674
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• 2013

This paper studied about the design and implementation of tactical communication network simulator in order to obtain tactical communication network parameter, such as link capacity and routing plan, and a number of exceptional cases that may occur during actual deployment by conducting simulation of a large-scale tactical communication networks. This tactical communication network simulator provides equipment models and link models of commercial OPNET simulator for tactical communication network. In addition, 6 types of simulation scenario writings convenience functions and traffic generation models that may occur in situations of tactical communication network environment were implemented in order to enhance user friendliness. By taking advantages of SITL(System-In-The-Loop) function of OPNET, the tactical communication network simulator allows users to perform interoperability test between M&S models and actual equipment in operating simulation of tactical communication network, which is run on software. In order to confirm the functions and performance of the simulator, small-scale of tactical communication network was configured to make sure interoperability between SITL-based equipment and a large-scale tactical communication network was simulated and checked how to cope with traffic generated for each network node. As the results, we were able to confirm that the simulator is operated properly.

• Journal of Computing Science and Engineering
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• v.2 no.2
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• pp.200-219
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• 2008

The effective management of uncertainty is one of the most fundamental problems in medical decision making. According to the literatures review, most medical decision models rely on point estimates for input parameters. However, it is natural that they should be interested in the relationship between changes in those values and subsequent changes in model output. Therefore, the purpose of this study is to identify the ranges of numerical values for which each option will be most efficient with respect to the input parameters. The Nonhomogeneous Poisson Process(NHPP) was used for describing the behavior of aging chronic diseases. Three kind of failure models (linear, exponential, and power law) were considered, and each of these failure models was studied under the assumptions of unknown scale factor and known aging rate, known scale factor and unknown aging rate, and unknown scale factor and unknown aging rate, respectively. In addition, this study illustrated developed method with an analysis of data from a trial of immunotherapy in the treatment of chronic Granulomatous disease. Finally, the proposed design of Bayesian value of information analysis facilitates the effective use of the computing capability of computers and provides a systematic way to integrate the expert's opinions and the sampling information which will furnish decision makers with valuable support for quality medical decision making.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3923-3942
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• 2022

Environment, price, regulation, and other factors influence the price of agricultural products, which is a social signal of product supply and demand. The price of many agricultural products fluctuates greatly due to the asymmetry between production and marketing details. Horticultural goods are particularly price sensitive because they cannot be stored for long periods of time. It is very important and helpful to forecast the price of horticultural products which is crucial in designing a cropping plan. The proposed method guides the farmers in agricultural product production and harvesting plans. Farmers can benefit from long-term forecasting since it helps them plan their planting and harvesting schedules. Customers can also profit from daily average price estimates for the short term. This paper study the time series models such as ARIMA, SARIMA, and neural network models such as BPN, LSTM and are used for wheat cost prediction in India. A large scale available data set is collected and tested. The results shows that since ARIMA and SARIMA models are well suited for small-scale, continuous, and periodic data, the BPN and LSTM provide more accurate and faster results for predicting well weekly and monthly trends of price fluctuation.

• Phonetics and Speech Sciences
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• v.15 no.3
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• pp.83-88
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• 2023

This study investigates the fine-tuning of large-scale Automatic Speech Recognition (ASR) models, specifically OpenAI's Whisper model, for domain-specific applications using the KsponSpeech dataset. The primary research questions address the effectiveness of targeted lexical item emphasis during fine-tuning, its impact on domain-specific performance, and whether the fine-tuned model can maintain generalization capabilities across different languages and environments. Experiments were conducted using two fine-tuning datasets: Set A, a small subset emphasizing specific lexical items, and Set B, consisting of the entire KsponSpeech dataset. Results showed that fine-tuning with targeted lexical items increased recognition accuracy and improved domain-specific performance, with generalization capabilities maintained when fine-tuned with a smaller dataset. For noisier environments, a trade-off between specificity and generalization capabilities was observed. This study highlights the potential of fine-tuning using minimal domain-specific data to achieve satisfactory results, emphasizing the importance of balancing specialization and generalization for ASR models. Future research could explore different fine-tuning strategies and novel technologies such as prompting to further enhance large-scale ASR models' domain-specific performance.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.149-158
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• 2024

Traditional manual identification of crop leaf diseases is challenging. Owing to the limitations in manpower and resources, it is challenging to explore crop diseases on a large scale. The emergence of artificial intelligence technologies, particularly the extensive application of deep learning technologies, is expected to overcome these challenges and greatly improve the accuracy and efficiency of crop disease identification. Crop leaf disease identification models have been designed and trained using large-scale training data, enabling them to predict different categories of diseases from unlabeled crop leaves. However, these models, which possess strong feature representation capabilities, require substantial training data, and there is often a shortage of such datasets in practical farming scenarios. To address this issue and improve the feature learning abilities of models, this study proposes a deep transfer learning adaptation strategy. The novel proposed method aims to transfer the weights and parameters from pre-trained models in similar large-scale training datasets, such as ImageNet. ImageNet pre-trained weights are adopted and fine-tuned with the features of crop leaf diseases to improve prediction ability. In this study, we collected 16,060 crop leaf disease images, spanning 12 categories, for training. The experimental results demonstrate that an impressive accuracy of 98% is achieved using the proposed method on the transferred ResNet-50 model, thereby confirming the effectiveness of our transfer learning approach.