• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.205-211
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• 2021

Plants are important for humanity. They intervene in several areas of human life: medicine, nutrition, cosmetics, decoration, etc. The large number of varieties of these plants requires an efficient solution to identify them for proper use. The ease of recognition of these plants undoubtedly depends on the classification of these species into family; however, finding the relevant characteristics to achieve better automatic classification is still a huge challenge for researchers in the field. In this paper, we have developed a new automatic plant classification technique based on artificial neural networks. Our model uses leaf texture characteristics as parameters for plant family identification. The results of our model gave a perfect classification of three plant families of the Ivorian flora, with a determination coefficient (R2) of 0.99; an error rate (RMSE) of 1.348e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and an accuracy (Accuracy) of 100%. The same technique was applied on Flavia: the international basis of plants and showed a perfect identification regression (R2) of 0.98, an error rate (RMSE) of 1.136e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and a trueness (Accuracy) of 100%. These results show that our technique is efficient and can guide the botanist to establish a model for many plants to avoid identification problems.

• Journal of Animal Science and Technology
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• v.65 no.6
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• pp.1254-1269
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• 2023

This study aims to predict the change in corn share according to the grazing of 20 gestational sows in a mature corn field by taking images with a camera-equipped unmanned air vehicle (UAV). Deep learning based on convolutional neural networks (CNNs) has been verified for its performance in various areas. It has also demonstrated high recognition accuracy and detection time in agricultural applications such as pest and disease diagnosis and prediction. A large amount of data is required to train CNNs effectively. Still, since UAVs capture only a limited number of images, we propose a data augmentation method that can effectively increase data. And most occupancy prediction predicts occupancy by designing a CNN-based object detector for an image and counting the number of recognized objects or calculating the number of pixels occupied by an object. These methods require complex occupancy rate calculations; the accuracy depends on whether the object features of interest are visible in the image. However, in this study, CNN is not approached as a corn object detection and classification problem but as a function approximation and regression problem so that the occupancy rate of corn objects in an image can be represented as the CNN output. The proposed method effectively estimates occupancy for a limited number of cornfield photos, shows excellent prediction accuracy, and confirms the potential and scalability of deep learning.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.2
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• pp.57-64
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• 2015

At the case of peak threshold algorithm, 3-axes data should sample step data over 20 Hz to get sufficient accuracy. But most of the digital sensors like 3-axes accelerometer have very low sampling rate caused by low data communication speed on limited SPI or $I^2C$ bandwidth of the low-cost MPU for ubiquitous devices. If the data transfer rate of the 3-axes accelerometer is getting slow, the sampling rate also slows down and it finally degrades the data accuracy. In this study, we proved there is a distinct functional relation between the sampling rate and threshold on the peak threshold step detection algorithm under the 20Hz frequency, and made a threshold function through the experiments. As a result of experiments, when we apply threshold value from the threshold function instead of fixed threshold value, the step detection error rate can be lessen about 1.2% or under. Therefore, we can suggest a peak threshold based new step detection algorithm with threshold function and it can enhance the accuracy of step detection and step count. This algorithm not only can be applied on a digital step counter design, but also can be adopted any other low-cost ubiquitous sensor devices subjected on low sampling rate.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.565-575
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• 2022

Recently, in the field of water resource engineering, interest in predicting time series water levels and flow rates using deep learning technology that has rapidly developed along with the Fourth Industrial Revolution is increasing. In addition, although water-level and flow-rate prediction have been performed using the Long Short-Term Memory (LSTM) model and Gated Recurrent Unit (GRU) model that can predict time-series data, the accuracy of flow-rate prediction in rivers with rapid temporal fluctuations was predicted to be very low compared to that of water-level prediction. In this study, the Paldang Bridge Station of the Han River, which has a large flow-rate fluctuation and little influence from tidal waves in the estuary, was selected. In addition, time-series data with large flow fluctuations were selected to collect water-level and flow-rate data for 2 years and 7 months, which are relatively short in data length, to be used as training and prediction data for the LSTM and GRU models. When learning time-series water levels with very high time fluctuation in two models, the predicted water-level results in both models secured appropriate accuracy compared to observation water levels, but when training rapidly temporal fluctuation flow rates directly in two models, the predicted flow rates deteriorated significantly. Therefore, in this study, in order to accurately predict the rapidly changing flow rate, the water-level data predicted by the two models could be used as input data for the rating curve to significantly improve the prediction accuracy of the flow rates. Finally, the results of this study are expected to be sufficiently used as the data of flood warning system in urban rivers where the observation length of hydrological data is not relatively long and the flow-rate changes rapidly.

• Korean Journal of Forensic Psychology
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• v.12 no.2
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• pp.151-169
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• 2021

In the P300-based concealed information test, most commonly used methods to detect whether a subject is lying are the bootstrapped amplitude difference (BAD) and the bootstrap correlation difference (BCD). Previous studies comparing the accuracy of the two methods reported inconsistent results. Most studies showed that the BAD is more accurate than the BCD, but some studies found that the BCD had a higher accuracy rate than the BAD. The purpose of the study is to identify conditions where the each method has higher accuracy compared to the other. In the result of Monte Carlo study, the false alarm rate of the BAD was generally higher than that of the BCD, and the hit rate of the BAD was higher than that of the BCD. Compared to the condition where the P300 latencies of probe and irrelevant were similar, the hit rate of the BCD was decreased when the P300 latency of probe was about 100 ms faster, and the hit rate of the BCD was increased when the P300 latency of probe was about 100 ms slower. When the P300 amplitude of the probe was slightly larger than that of the irrelevant and the P300 latency of probe was longer than that of target, the hit rate of the BCD was higher than that of the BAD. The reason why the false alarm rate of the BAD is higher than that of BCD and why the hit rate of the BCD is affected by the P300 latency of the probe were discussed.

• The Journal of the Korea Contents Association
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• v.12 no.10
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• pp.148-158
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• 2012

In the world, the flow of globalization are causing that many cities in the world are trying to enhance city awareness and city image through the establishment of their identity. Seoul has also preceded various studies in order to establish its identity of the urban landscape in this flow. But, most of studies have only considered some of the problems of the urban landscape and its improvement. Therefore, a study to understand the current built urban landscape and its own identity has not been studied yet. In this study, we propose an analyzing and visualizing framework of the urban landscape through an internet survey using the compare contents based on the differences in Cognition and recognition. The core of the framework which analyses the differences in Cognitions and recognition about the urban landscape was performed by the correct and clear rate. In addition, it was extracted from the answers of the survey and the visualization, was performed by diagrams of accuracy rate that was derived from the correlation between the correct rate and the clear rate.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.106-112
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• 2003

High-speed machining is one of the most effective technology to improve productivity. Because of the high speed and high feed rate, high-speed machining can give great advantages for the machining of dies and molds. This paper describes on the improvement of machining accuracy in high-speed machining and an estimate about machining accuracy of high-speed machining.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2216-2219
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• 2003

Accurate speech region detection and automatic syllable segmentation is important part of speech recognition system. In automatic speech recognition system, they are needed for the purpose of accurate recognition and less computational complexity, In this paper, we Propose improved syllable segmentation method using ramp edge detection method and residual signal Peak energy. These methods were used to ensure accuracy and robustness for endpoint detection and syllable segmentation system. They have almost invariant response to various background noise levels. As experimental results, we obtained the rate of 90.7％ accuracy in syllable segmentation in a condition of accurate endpoint detection environments.

• Journal of the Korean Society of Combustion
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• v.18 no.1
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• pp.1-6
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• 2013

Authors' previous works on thermoacoustic(TA) model development showed good results in predicting combustion instability characteristics in a gas turbine combustor. However, they also suggested there were some limitations in growth rate estimation, which might be related with over-simplification of flame structure. As a first trial for improving the model accuracy, the current paper introduces the modified TA model considering the actual flame location in the combustor. The combustor is divided into the unburned and the burned area before and after the flame location, and then acoustic equations are re-organized. The modified TA model results show a better accuracy in predicting the growth rate of instabilities comparing with the previous results. However, obtained results still overestimate the conditions where the combustor goes unstable. Further researches considering heat release distribution through flames are required.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1201-1206
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• 2003

A measurement device for gas flow rate using hot-wire module is developed for the utilization in low-accuracy industrial applications. The module has three wires of measuring and heating, and a bridge circuit is installed to detect electric current through the wire in the module. An amplification of the signal and conversion to digital output are conducted for the online measurement with a personal computer. In addition, temperature distribution in the module is numerically analyzed to examine the measured outcome from the module experiment. The flow rate of air and carbon dioxide gas is separately measured for the performance examination of the device. The experimental relation of measurement and flow agrees with the prediction from the numerical analysis. The outcome of the performance test indicates that the accuracy and reproducibility of the module is satisfactory for the purpose of industrial applications.