• Journal of Drive and Control
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• v.14 no.3
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• pp.25-31
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• 2017

This study presents a methodology for simulating a unified approach that controls interaction force between tool and objective by using a synthesis method of robot interacting control law for stabilizing the transient process of motion. Root locus is used to analyze stabilization of motion deviation characteristics. Based on responses of motion deviation, contact force is derived to satisfy exponential stability and we generate control input with respect to motion trajectories and interaction force. Moreover, simulation is applied to experimental application of a Cartesian robot driven by two stepper motors, and the noise of feedback signals is considered as presence of system inaccuracies, and the unified approach of interaction force control is examined precisely.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.672-674
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• 1998

Java virtual machine has the garbage collector that automate memory management. Mark-compact algorithm is one of the garbage collection algorithm that operating in 2 phases, marking and sweeping. One is Marking is marking live objects reachable from root object set. Sweeping is sweeping unmarked object from memory(return to free memory pool). This algorithm is easy to implement but cause a memory fragmentation. So compacting memory, before memory defragmentation become serious. When compacting memory, all other processes are suspended. It is critical for embedded system that must guarantee real-time processing. This paper introduce enhanced mark-compact garbage collection algorithm. Grouping the objects by their size that minimize memory fragmentation. Then apply smart algorithm to the grouped objects when allocating objects and compacting memory.

• Smart Media Journal
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• v.7 no.1
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• pp.16-23
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• 2018

In this paper, we used the Deep Neural Network (DNN) to predict the number of daily spectators of Gwangju - KIA Champions Field in order to provide marketing data for the team and related businesses and for managing the inventories of the facilities in the stadium. In this study, the DNN model, which is based on an artificial neural network (ANN), was used, and four kinds of DNN model were designed along with dropout and batch normalization model to prevent overfitting. Each of four models consists of 10 DNNs, and we added extra models with ensemble model. Each model was evaluated by Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The learning data from the model randomly selected 80% of the collected data from 2008 to 2017, and the other 20% were used as test data. With the result of 100 data selection, model configuration, and learning and prediction, we concluded that the predictive power of the DNN model with ensemble model is the best, and RMSE and MAPE are 15.17% and 14.34% higher, correspondingly, than the prediction value of the multiple linear regression model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.580-586
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• 2021

The CAT methodology is a numerical analysis technique using CAE. Recently, a methodology of applying artificial intelligence techniques to a simulation has been studied. A previous study compared the deformation results according to the injection molding process using a machine learning technique. Although MLP has excellent prediction performance, it lacks an explanation of the decision process and is like a black box. In this study, data was generated using Autodesk Moldflow 2018, an injection molding analysis software. Several Machine Learning Algorithms models were developed using RapidMiner version 9.5, a machine learning platform software, and the root mean square error was compared. The decision-tree showed better prediction performance than other machine learning techniques with the RMSE values. The classification criterion can be increased according to the Maximal Depth that determines the size of the Decision-tree, but the complexity also increases. The simulation showed that by selecting an intermediate value that satisfies the constraint based on the changed position, there was 7.7% improvement compared to the previous simulation.

• Journal of Biosystems Engineering
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• v.41 no.4
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• pp.357-364
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• 2016

Purpose: This study was performed to define the drying characteristics of sorghum by developing thin layer drying equations and evaluating various grain drying equations. Thin layer drying equations lay the foundation characteristics to establish the thick layer drying equations, which can be adopted to determine the design conditions for an agricultural dryer. Methods: The drying rate of sorghum was measured under three levels of drying temperature ($40^{\circ}C$, $50^{\circ}C$, and $60^{\circ}C$) and relative humidity (30%, 40%, and 50%) to analyze the drying process and investigate the drying conditions. The drying experiment was performed until the weight of sorghum became constant. The experimental constants of four thin layer drying models were determined by developing a non-linear regression model along with the drying experiment results. Result: The half response time (moisture ratio = 0.5) of drying, which is an index of the drying rate, was increased as the drying temperature was high and relative humidity was low. When the drying temperature was $40^{\circ}C$ at a relative humidity (RH) of 50%, the maximum half response time of drying was 2.8 h. Contrastingly, the maximum half response time of drying was 1.2 h when the drying temperature was $60^{\circ}C$ at 30% RH. The coefficient of determination for the Lewis model, simplified diffusion model, Page model, and Thompson model was respectively 0.9976, 0.9977, 0.9340, and 0.9783. The Lewis model and the simplified diffusion model satisfied the drying conditions by showing the average coefficient of determination of the experimental constants and predicted values of the model as 0.9976 and Root Mean Square Error (RMSE) of 0.0236. Conclusion: The simplified diffusion model was the most suitable for every drying condition of drying temperature and relative humidity, and the model for the thin layer drying is expected to be useful to develop the thick layer drying model.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.5
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• pp.227-234
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• 2018

The smart irrigation system using ICT technology is crucial for stable production of upland crops. The objective of this study was to develop a smart irrigation system that can control soil water, depending on irrigation methods, in order to improve crop production. In surface irrigation, three irrigation methods (sprinkler irrigation (SI), surface drip irrigation (SDI), and fountain irrigation (FI)) were installed on a crop field. The soil water contents were measured at 10, 20, 30, and 40 cm depth, and an automatic irrigation system controls a valve to maintain the soil water content at 10 cm to be 30%. In subsurface drip irrigation (SSDI), the drip lines were installed at a depth of 20 cm. Controlled drainage system (CDS) was managed with two ground water level (30 cm and 60 cm). The seasonal irrigation amounts were 96.4 ton/10a (SDI), 119.5 ton/10a (FI), and 113 ton/10a (SI), respectively. Since SDI system supplied water near the root zone of plants, the water was saved by 23.9% and 17.3%, compared with FI and SI, respectively. In SSDI, the mean soil water content was 38.8%, which was 10.8% higher than the value at the control treatment. In CDS, the water contents were greatly affected by the ground water level; the water contents at the surface zone with 30 cm ground water level was 9.4% higher than the values with 60 cm ground water level. In conclusion, this smart irrigation system can reduce production costs of upland crops.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.9-24
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• 2022

Currently, a vast amount of hydrologic data is accumulated in real-time through automatic water level measuring instruments in agricultural reservoirs. At the same time, false and missing data points are also increasing. The applicability and reliability of quality control of hydrological data must be secured for efficient agricultural water management through calculation of water supply and disaster management. Considering the characteristics of irregularities in hydrological data caused by irrigation water usage and rainfall pattern, the Korea Rural Community Corporation is currently applying the Hampel filter as a water level data quality management method. This method uses window size as a key parameter, and if window size is large, distortion of data may occur and if window size is small, many outliers are not removed which reduces the reliability of the corrected data. Thus, selection of the optimal window size for individual reservoir is required. To ensure reliability, we compared and analyzed the RMSE (Root Mean Square Error) and NSE (Nash-Sutcliffe model efficiency coefficient) of the corrected data and the daily water level of the RIMS (Rural Infrastructure Management System) data, and the automatic outlier detection standards used by the Ministry of Environment. To select the optimal window size, we used the classification performance evaluation index of the error matrix and the rainfall data of the irrigation period, showing the optimal values at 3 h. The efficient reservoir automatic calibration technique can reduce manpower and time required for manual calibration, and is expected to improve the reliability of water level data and the value of water resources.

• Smart Structures and Systems
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• v.15 no.4
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• pp.997-1018
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• 2015

Structural health monitoring along with damage detection and assessment of its severity level in non-accessible reinforced concrete members using piezoelectric materials becomes essential since engineers often face the problem of detecting hidden damage. In this study, the potential of the detection of flexural damage state in the lower part of the mid-span area of a simply supported reinforced concrete beam using piezoelectric sensors is analytically investigated. Two common severity levels of flexural damage are examined: (i) cracking of concrete that extends from the external lower fiber of concrete up to the steel reinforcement and (ii) yielding of reinforcing bars that occurs for higher levels of bending moment and after the flexural cracking. The purpose of this investigation is to apply finite element modeling using admittance based signature data to analyze its accuracy and to check the potential use of this technique to monitor structural damage in real-time. It has been indicated that damage detection capability greatly depends on the frequency selection rather than on the level of the harmonic excitation loading. This way, the excitation loading sequence can have a level low enough that the technique may be considered as applicable and effective for real structures. Further, it is concluded that the closest applied piezoelectric sensor to the flexural damage demonstrates higher overall sensitivity to structural damage in the entire frequency band for both damage states with respect to the other used sensors. However, the observed sensitivity of the other sensors becomes comparatively high in the peak values of the root mean square deviation index.

• Korean Journal of Environmental Agriculture
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• v.41 no.2
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• pp.101-107
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• 2022

BACKGROUND: Soil amendment was necessary applied for the sand that had been used to root zone of green ground in golf course because of its low water retention power and cation exchangeable capacity. This study was conducted to evaluate the effect of the mixed ratio of peat moss and coconut coir as soil amendment materials on the soil physicochemical properties applied to rootzone based on sand. METHODS AND RESULTS: The soil amendments were blended at 0, 3, 5, 7 and 10% by soil volume. The pH in the peat moss treatment was lower than that of control (0% soil amendment), and pH and electrical conductivity (EC) in the coconut coir were higher. The blending ratio of peat moss was negatively correlated with pH of rootzone soil (p<0.01), and that of coconut coir positively with EC (p<0.01). As compared with control, capillary porosity, the physical factors such as air-filled porosity, total porosity, and hydraulic conductivity of rootzone soil were increased by applying peat moss and coconut coir. For correlation coefficients between percentage of soil amendments and soil physical factors, peat moss and coconut coir were positively correlated with porosity and hydraulic conductivity (p<0.01). CONCLUSION(S): These results indicated that the application of peat moss and coconut coir affected on the change of physicochemical properties of rootzone soil, and improved soil porosity and hydraulic conductivity.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.11-18
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• 2023

Recently, in the field of Speech Emotion Recognition (SER), many studies have been conducted to improve accuracy using voice features and modeling. In addition to modeling studies to improve the accuracy of existing voice emotion recognition, various studies using voice features are being conducted. This paper, voice files are separated by time interval in a time series method, focusing on the fact that voice emotions are related to time flow. After voice file separation, we propose a model for classifying emotions of speech data by extracting speech features Mel, Chroma, zero-crossing rate (ZCR), root mean square (RMS), and mel-frequency cepstrum coefficients (MFCC) and applying them to a recurrent neural network model used for sequential data processing. As proposed method, voice features were extracted from all files using 'librosa' library and applied to neural network models. The experimental method compared and analyzed the performance of models of recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU) using the Interactive emotional dyadic motion capture Interactive Emotional Dyadic Motion Capture (IEMOCAP) english dataset.