• International journal of advanced smart convergence
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• v.7 no.1
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• pp.42-47
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• 2018

Recently, as the demand for limited resources continues to rise and problems of resource depletion rise worldwide, the importance of renewable energy is gradually increasing. In order to solve these problems, various methods such as energy conservation and alternative energy development have been suggested, and biogas, which can utilize the gas produced from biomass as fuel, is also receiving attention as the next generation of innovative renewable energy. New and renewable energy using biogas is an energy production method that is expected to be possible in large scale because it can supply energy with high efficiency in compliance with energy supply method of recycling conventional resources. In order to more efficiently produce and manage these biogas, a biogas plant has emerged. In recent years, a large number of biogas plants have been installed and operated in various locations. Organic wastes corresponding to biogas production resources in a biogas plant exist in a wide variety of types, and each of the incoming raw materials is processed in different processes. Because such a process is required, the case where the biogas plant process is inefficiently operated is continuously occurring, and the economic cost consumed for the operation of the biogas production relative to the generated biogas production is further increased. In order to solve such problems, various attempts such as process analysis and feedback based on the feedstock have been continued but it is a passive method and very limited to operate a medium/large scale biogas plant. In this paper, we propose "CNN-based production yield prediction algorithm for increasing process efficiency of biogas plant" for efficient operation of biogas plant process. Based on CNN-based production yield forecasting, which is one of the deep-leaning technologies, it enables mechanical analysis of the process operation process and provides a solution for optimal process operation due to process-related accumulated data analyzed by the automated process.

• PNF and Movement
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• v.19 no.2
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• pp.261-267
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• 2021

Purpose: This study aimed to investigate changes in gait variables depending on whether a task was performed using a smartphone while walking on a ramp. Methods: The participants of this study were 41 college students attending U University located in Gyeongju City, Gyeongsangbuk-do. In this study, gait variables were measured during ramp gait while using a smartphone to perform a task and during ramp gait without performing such tasks. In other words, four walking conditions were used: 1) walking up a ramp, 2) walking up a ramp while using a smartphone to perform a task, 3) walking down a ramp, and 4) walking down a ramp while using a smart phone to perform a task. Gait variables were measured using a gait analysis tool (Legsys; BioSensics, USA), and stride time, stride length, stride velocity, cadence, and double support were analyzed. The order of measurements was randomized to control for order effects due to repeated measurements. Results: The comparative analysis of gait variables according to the presence or absence of smartphone use during ramp gait showed that there were significant differences in stride time, stride length, and stride velocity during both ramp ascent and ramp descent (p < 0.05). In both ramp ascent and ramp descent, stride time increased when walking using a smartphone, compared to when walking without using a smartphone (p < 0.05). However, in both ramp ascent and ramp descent, stride length and stride velocity were decreased when walking using a smartphone compared to when walking without using a smartphone (p < 0.05). Conclusion: The study results showed that the use of a smartphone during walking can affect safety. Therefore, it is necessary to improve the awareness of risks associated with walking while using a smartphone, and further research needs to be conducted in various environments and with different ramps.

• Smart Structures and Systems
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• v.21 no.5
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• pp.695-703
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• 2018

Based on monitoring data collected from the Nanjing Dashengguan Bridge over the last five years, this paper systematically investigates the effects of temperature field and train loadings on the structural responses of this long-span high-speed railway bridge, and establishes the early warning thresholds for various structural responses. Then, some lessons drawn from the structural health monitoring system of this bridge are summarized. The main context includes: (1) Polynomial regression models are established for monitoring temperature effects on modal frequencies of the main girder and hangers, longitudinal displacements of the bearings, and static strains of the truss members; (2) The correlation between structural vibration accelerations and train speeds is investigated, focusing on the resonance characteristics of the bridge at the specific train speeds; (3) With regard to various static and dynamic responses of the bridge, early warning thresholds are established by using mean control chart analysis and probabilistic analysis; (4) Two lessons are drawn from the experiences in the bridge operation, which involves the lacks of the health monitoring for telescopic devices on the beam-end and bolt fractures in key members of the main truss.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.255-266
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• 2022

The quality control of coarse aggregate among aggregates, which are the main ingredients of concrete, is currently carried out by SPC(Statistical Process Control) method through sampling. We construct a smart factory for manufacturing innovation by changing the quality control of coarse aggregates to inspect the coarse aggregates based on this image by acquired images through the camera instead of the current sieve analysis. First, obtained images were preprocessed, and HED(Hollistically-nested Edge Detection) which is the filter learned by deep learning segment each object. After analyzing each aggregate by image processing the segmentation result, fineness modulus and the aggregate shape rate are determined by analyzing result. The quality of aggregate obtained through the video was examined by calculate fineness modulus and aggregate shape rate and the accuracy of the algorithm was more than 90% accurate compared to that of aggregates through the sieve analysis. Furthermore, the aggregate shape rate could not be examined by conventional methods, but the content of this paper also allowed the measurement of the aggregate shape rate. For the aggregate shape rate, it was verified with the length of models, which showed a difference of ±4.5%. In the case of measuring the length of the aggregate, the algorithm result and actual length of the aggregate showed a ±6% difference. Analyzing the actual three-dimensional data in a two-dimensional video made a difference from the actual data, which requires further research.

• The Journal of Bigdata
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• v.8 no.1
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• pp.1-10
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• 2023

This paper presents the design process of an agricultural data sharing platform intended to address major challenges faced by the domestic agricultural industry. The platform was designed with a user interface that prioritizes user requirements for ease of use and offers various analysis techniques to provide growth prediction for field environment, growth, management, and control data. Additionally, the platform supports File to DB and DB to DB linkage methods to ensure seamless linkage between the platform and farmhouses. The UI design process utilized HTML/CSS-based languages, JavaScript, and React to provide a comprehensive user experience from platform login to data upload, analysis, and detailed inquiry visualization. The study is expected to contribute to the development of Korean smart farm models and provide reliable data sets to agricultural industry sites and researchers.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.4
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• pp.241-255
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• 2015

Recent network environment has been rapidly evolved to cloud computing environment based on the development of the Internet technologies. Furthermore there is an increasing demand on mobile cloud computing due to explosive growth of smart devices and wide deployment of LTE-based cellular networks. Thus mobile Desktop-as-a-Service(DaaS) could be a pervasive service for nomadic users. In addition, video streaming traffic is currently more than two-thirds of mobile traffic and ever increasing. All such trends enable that video streaming in mobile DaaS could be an important concern for mobile cloud computing. It should be noted that the performance of the Transmission Control Protocol(TCP) on cloud host servers greatly affects Quality of Service(QoS) of video streams for mobile users. With widely deployed Linux server platforms for cloud computing, in this paper, we conduct an experimental analysis of the twelve Linux TCP variants in mobile DaaS environments. The results of our experiments show that the TCP Illinois outperforms the other TCP variants in terms of wide range of packet loss rate and propagation delay over LTE-based wireless links between cloud servers and mobile devices, even though TCP CUBIC is usually used in default in the current Linux systems.

• Journal of Drive and Control
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• v.21 no.3
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• pp.46-55
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• 2024

In the agricultural machinery field, load analysis is mostly done through field tests. However, field tests are time-consuming and costly. There are also limitations in test conditions due to weather conditions. To overcome these environmental limitations, research on load analysis through simulation in a virtual environment is actively being conducted. This study aimed to select the most appropriate soil particle size for modeling by analyzing the effect of soil particle size on the prediction of draft force of the implement using simulation and soil particle modeling in a virtual environment with the discrete element method (DEM) software. The accuracy was verified by simulating the draft force for the same moving speed by soil particle size. For soil particle modeling, DEM soil modeling was performed by designing soil property measurement procedure. Soil particle correction was performed with a virtual vane shear test. Average DEM simulation results showed an error of 27.39% (19.43~40.66%) compared to actual measured data. The possibility of improvement was confirmed through additional research. Results of this study provide useful information for selecting soil particle size in soil modeling using DEM from the perspective of agricultural machinery research.

• Journal of Korea Game Society
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• v.16 no.5
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• pp.69-78
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• 2016

A sense of oneness between a player and an avatar is more emphasized than role-playing itself in Korean RPG playing. Because of this tendency, the main contents of smart phone-based mobile RPG are mainly focus on combat. As PvP occurs more frequently in mobile RPG, playing experiences are diversified from sensitive control to simple repetition. Also, the auto-combat function offered in game system ingenerates funs from testing a mental model that a player statistically sets before playing. This paper analyzes the process of generating fun in mobile RPG based on Ralph Koster's Game Grammar theory. is selected as subject of this analysis. The pattern of mobile RPG playing is dichotomized : automation of producing materials and concentration on raid and PvP contents.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.129-134
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• 2018

Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.175-185
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• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.