• International Journal of Industrial Entomology and Biomaterials
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• v.6 no.1
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• pp.15-19
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• 2003

The study evaluated the influence of feeding different maturity leaves i.e., tender, medium, coarse and mixed leaves of three different maturity during late larval stages of new bivoltine race $(CSR_{3} {\times} CSR_{6})$. The data were compared with shoot feeding and control batches in which conventional feeding method was followed. The most of the larval and cocoon characters were recorded significantly higher in tender leaves fed batches followed by T4 batch (2 times tender and 1 time coarse leaves). Lowest melting percentage (1.494%) was recorded in T4 and highest (4.69%) was recorded in coarse leaf (T3) fed batches. Significantly higher post cocoon parameters viz., average filament length, non-breakable filament length, renditta and raw silk percentage were recorded in tender loaves fed batches.

• Journal of Communications and Networks
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• v.11 no.1
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• pp.42-46
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• 2009

Superimposed training (SIT) design for estimating of time-varying multipath channels is investigated for mobile orthogonal frequency division multiplexing (OFDM) systems. The design of optimum SIT consists of two parts: The optimal SIT sequence is derived by minimizing the channel estimates' mean square error (MSE); the optimal power allocation between training and information data is developed by maximizing the averaged signal to interference plus noise ratio (SINR) under the condition of equal powered paths. The theoretical analysis is verified by simulations. For the metric of the averaged SINR against signal to noise ratio (SNR), the theoretical result matches the simulation result perfectly. In contrast to an interpolated frequency-multiplexing training (FMT) scheme or an SIT scheme with random pilot sequence, the SIT scheme with proposed optimal sequence achieves higher SINR. The analytical solution of the optimal power allocation is demonstrated by the simulation as well.

• Nuclear Engineering and Technology
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• v.42 no.2
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• pp.151-162
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• 2010

A severe accident training simulator that can simulate important severe accident phenomena and nuclear plant behaviors is developed. The simulator also provides several interactive control devices, which are helpful to assess results of a particular accident management behavior. A simple and direct dynamic linked library (DLL) data communication method is used for the development of the simulator. Using the DLL method, various control devices were implemented to provide an interactive control function during simulation. Finally, a training model is suggested for accident mitigation training and its performance is verified through application runs.

• English Language & Literature Teaching
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• v.17 no.4
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• pp.395-411
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• 2011

The purpose of this study was to explore the elementary English conversation instructors' perception on their professionalism and the needs of teacher training program. The survey data were attained from 136 elementary English conversation instructors. Descriptive statistics were employed to discuss the result of the survey response. The results of this study revealed that the elementary English conversation instructors perceived the need of in-service training program for their professionalism improvement, especially in teaching techniques for four language skills. The result also revealed that the instructors need to be more equipped with the knowledge of elementary learners' developmental psychology and L2 learning process. The study concludes with several suggestions for elementary English conversation instructors' improvement of teaching professionalism and in-service training program.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1075-1086
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• 2018

In this paper a cross-validation algorithm for training probabilistic neural networks (PNNs) is presented in order to be applied to automatic face identification. Actually, standard PNNs perform pretty well for small and medium sized databases but they suffer from serious problems when it comes to using them with large databases like those encountered in biometrics applications. To address this issue, we proposed in this work a new training algorithm for PNNs to reduce the hidden layer's size and avoid over-fitting at the same time. The proposed training algorithm generates networks with a smaller hidden layer which contains only representative examples in the training data set. Moreover, adding new classes or samples after training does not require retraining, which is one of the main characteristics of this solution. Results presented in this work show a great improvement both in the processing speed and generalization of the proposed classifier. This improvement is mainly caused by reducing significantly the size of the hidden layer.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.365-374
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• 1998

Control Chart is a powerful tool to detect that process is in control or out of control. CIM can have real effect when CIM involve automated quality control. A neural network approach is used for unnatural pattern detecting of control chart. The previous moving window method uses all unnatural pattern that is detected as moving time window. Therefore, It trains a large number of unnatural pattern and takes training time long. In this paper, the proposed method tests a small number of training unnatural pattern which modifies test data without repeating time. We shows that the proposed method has differences In training time and identification rate on the previous moving windows method. As results, we reduced training time and obtain the same identification rate.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.17 no.4
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• pp.48-54
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• 2009

We implemented the Aural Cueing System(ACS) system of the reconfigurable tactical trainer(RTT) for th rotor aircraft. RTT provides a collective training system to meet aviation training requirements and supports organizational training for aviations units in combined arms collective training and mission rehearsal. ACS handles the volume, pitch and repetition of the digitally stored sounds based on commands it receives from an UDP/IP. In this paper, we explained and implemented the conceptual and detail design the ACS system for the rotor aircraft such as AH-1H(Iroquios), UH-60(Blackhwak), AH-1(Cobra) etc. The conceptual design composed of the sound cueing data analysis, sound modelling which is inner, outer, weapon and warn environment of rotor aircraft, sound synthesis and replay.

• Industrial Engineering and Management Systems
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• v.11 no.1
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• pp.26-29
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• 2012

In order to deal with the classification problems of type-2 fuzzy training samples on generalized credibility space. Firstly the type-2 fuzzy training samples are reduced to ordinary fuzzy samples by the mean reduction method. Secondly the definition of strong fuzzy linear separable data for type-2 fuzzy samples on generalized credibility space is introduced. Further, by utilizing fuzzy chance-constrained programming and classic support vector machine, a support vector machine based on type-2 fuzzy training samples and established on generalized credibility space is given. An example shows the efficiency of the support vector machine.

• Journal of Engineering Education Research
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• v.21 no.3
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• pp.38-45
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• 2018

The purpose of this study is to analyze the change of perceptions and images of teachers about engineering, according to practical training in laboratories of engineering research institutes. For this purpose, 149 elementary and secondary school teachers were surveyed before and after visiting the engineering research institutes and examined the perception of engineers and engineering. Through this teacher training program, perceptions and images of most teachers are changed positively and they can understand practically about engineering, and related fields. The results of this study can be used as basic data for the development, operation, and expansion of teacher training using advanced science and technology research institutes with excellent human and material resources.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.3
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• pp.346-354
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• 2024

Deep learning models require high computing power due to a substantial amount of computation. It is difficult to use them in devices with limited computing environments, such as coastal surveillance equipments. In this study, a lightweight model is constructed by analyzing the weight changes of the convolutional layers during the training process based on MobileNet and then pruning the layers that affects the model less. The performance comparison results show that the lightweight model maintains performance while reducing computational load, parameters, model size, and data processing speed. As a result of this study, an effective pruning method for constructing lightweight deep learning models and the possibility of using equipment resources efficiently through lightweight models in limited computing environments such as coastal surveillance equipments are presented.