Journal of Korean Tunnelling and Underground Space Association
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v.19
no.6
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pp.1029-1044
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2017
The Discrete Element Method (DEM) is one of the useful numerical methods to analyze the behavior of the ground formation by computing the motion and interaction using particles. The DEM has not been applied in civil engineering but also a wide range of industrial fields, such as chemical engineering, pharmacy, material science, food engineering, etc. In this study, to review a performance of the spoke-type earth pressure balance (EPB) shield TBM (Tunnel Boring Machine), the commercial software based on the DEM technology was used. An analysis of the TBM during excavation was conducted according to two pre-defined excavation conditions with the different rotation speed of a cutterhead. During the analysis, the resistant torque at the face of the cutterhead, the compressive force at the cutterhead and shield surface, the muck discharge at the screw auger were measured and compared. Upon the two kinds of excavation conditions, the applicability of the DEM analysis was reviewed as a modelling method for the TBM.
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.
The Journal of Korea Institute of Information, Electronics, and Communication Technology
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v.9
no.3
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pp.285-290
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2016
In rural area, our farmers confront decreasing benefits owing to imported crops and increased cost. Recently, the government encourage the 6th Industry that merges farming, rural resources, and information and communication technology. Therefor the government makes an investment in supplying 'smart greenhouse' in which a farmer monitor growing crops and environment information to control growing condition. The objective of this study is developing an Moving Monitor and Control System for crops in green House. This system includes a movable sensing unit, a controlling unit, and a server PC unit. The movable sensing unit contains high resolution IP camera, temperature and humidity sensor and WiFi repeater. It rolls on a rail hanging beneath the ceiling of a green house. The controlling unit contains embedded PC, PLC module, WiFi router, and BLDC motor to drive the movable sensing unit. And the server PC unit contains a integrated farm management software and home pages and databases in which the images of crops and environment informations. The movable sensing unit moves widely in a green house and gathers lots of information. The server saves these informations and provides them to customers with the direct commercing web page. This system will help farmers to control house environment and sales their crops in online market. Eventually It will be helpful for farmers to increase their benefits.
Park, Jaebeom;Kim, Byeonggi;Song, Seokhwan;Rho, Daeseok
Journal of the Korea Academia-Industrial cooperation Society
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v.14
no.1
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pp.369-377
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2013
This paper deals with the state of charge(SOC) and life cycle evaluation algorithm for lead-acid battery, which is essential factor of the electric vehicle(EV) and the stabilization of renewable energy in the smart grid. In order to perform the effective operation of the lead-acid battery, SOC and life cycle evaluation algorithm is required. Specific gravity with the change of electrolyte temperature inside battery case should be obtained to evaluate the SOC of lead-acid battery, however it is difficult to measure the electrolyte temperature of sealed type lead-acid battery. To overcome this problem, this paper proposes the equation of thermal transmission to compensate internal temperature of the lead-acid battery. Also, it is difficult to exactly evaluate the life cycle of battery, depending on the operation conditions of lead-acid battery such as charging and discharging state, self discharging rate and environmental issue. In order to solve the problem, this paper presents the concept for gravity accumulation of charge and discharge cycle, which is the value converted at $20^{\circ}C$. By using the proposed algorithm, this paper propose the test device based on the Labview software. The simulation results show that it is a practical tool for the maintenance of lead-acid battery in the field of industry.
KIPS Transactions on Software and Data Engineering
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v.5
no.7
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pp.345-350
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2016
This research suggests the keyword filtering about disaster and the method of detecting area in real-time event detecting system by analyzing contents of twitter. The diffusion of smart-mobile has lead to a fast spread of SNS and nowadays, various researches based on studying SNS are being processed. Among SNS, the twitter has a characteristic of fast diffusion since it is written in 140 words of short paragraph. Therefore, the tweets that are written by twitter users are able to perform a role of sensor. By using these features the research has been constructed which detects the events that have been occurred. However, people became reluctant to open their information of location because it is reported that private information leakage are increasing. Also, problems associated with accuracy are occurred in process of analyzing the tweet contents that do not follow the spelling rule. Therefore, additional designing keyword filtering and the method of area detection on detecting real-time event process were required in order to develop the accuracy. This research suggests the method of keyword filtering about disaster and two methods of detecting area. One is the method of removing area noise which removes the noise that occurred in the local name words. And the other one is the method of determinating the area which confirms local name words by using landmarks. By applying the method of keyword filtering about disaster and two methods of detecting area, the accuracy has improved. It has improved 49% to 78% by using the method of removing area noise and the other accuracy has improved 49% to 89% by using the method of determinating the area.
Purpose - The ICT market in the EU is lagging behind that of the US; however, algorithm and software development within the EU have grown steadily, and they involve focusing on the creative cultural convergence conceptualized as part of Horizon 2020 and connecting neighboring markets in the EE and the Mediterranean region. It is essential to study the requirements to market the EU's creative ICT development in emerging industrial countries after examining its applicability in these countries. Research design, data, and methodology - This study deals with data pertaining to the EU's creative industry and competitive edge. The global cultural expansion of the EU facilitates a new concept involving not only low-cost IT products to enhance local cultural artifacts through R&D and the construction of efficient infrastructure services, but also information exchange with a realistic commercialization of the technology that can be applied for creative cultural localization. In the European industry, research on algorithms has been applied for the benefit of consumers. We investigated how the process is conducted in the EU. Results - Europe needs to adjust its economic structure to the local culture as part of IT distribution convergence. The convergence has been converted into a production algorithm with IT in the form of low-cost production. This is because there is an attempt to improve the quality of transport infrastructure, workforce availability, and the distribution of the distance to the local industries and consumers, using IT algorithms. Integrated into the manufacturing industry, based on the ICT infrastructure and solutions, smart localized regional clusters are formed with the help of grafting. Europe has own strategy to increase the number of hub-and-spoke cities. Europe is now becoming integrated, with an EPC system for regional cooperation rather than national competition in ICT technology. Europe has also been recognized in this study as changing the step-by-step paradigm for global competitiveness through new creative culture industries. Conclusions - As a result, there are several ways of converging with others through EU R&D intensity; therefore, the EU can be seen as successfully increasing marginal value, which is useful in developing a special industrial cluster or local cultural cities that create converged development by connecting people and objects with IT. In fact, when compared to the US, Europe has a strong culture and the car industries have a tendency to overshadow the IT industries with integration of services in IT distribution. Considering the rapid environmental changes, the convergence of IT services is likely to take place in Europe, similar to the pharmaceutical industry and the automotive industry. This requires a focus on human resources and automated systems management. The trend is to move away from low-wage industries, switched to key personnel centers of the local university-industry. EU emphasizes the creation of IT market demand in Europe involving local cultural convergence for marketing as the second step to strengthen the economic hub-and-spoke areas.
KIPS Transactions on Software and Data Engineering
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v.4
no.10
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pp.447-454
/
2015
This paper proposes geographical name denoising by machine learning of event detection based on twitter. Recently, the increasing number of smart phone users are leading the growing user of SNS. Especially, the functions of short message (less than 140 words) and follow service make twitter has the power of conveying and diffusing the information more quickly. These characteristics and mobile optimised feature make twitter has fast information conveying speed, which can play a role of conveying disasters or events. Related research used the individuals of twitter user as the sensor of event detection to detect events that occur in reality. This research employed geographical name as the keyword by using the characteristic that an event occurs in a specific place. However, it ignored the denoising of relationship between geographical name and homograph, it became an important factor to lower the accuracy of event detection. In this paper, we used removing and forecasting, these two method to applied denoising technique. First after processing the filtering step by using noise related database building, we have determined the existence of geographical name by using the Naive Bayesian classification. Finally by using the experimental data, we earned the probability value of machine learning. On the basis of forecast technique which is proposed in this paper, the reliability of the need for denoising technique has turned out to be 89.6%.
The purpose of this study is to develop an Android smartphone app providing analysis capabilities of remote sensing images, by using mobile browsing open sources of gvSIG, open source remote sensing software of OTB and open source DBMS of PostgreSQL. In this app, five kinds of remote sensing algorithms for filtering, segmentation, or classification are implemented, and the processed results are also stored and managed in image database to retrieve. Smartphone users can easily use their functions through graphical user interfaces of app which are internally linked to application server for image analysis processing and external DBMS. As well, a practical tiling method for smartphone environments is implemented to reduce delay time between user's requests and its processing server responses. Till now, most apps for remotely sensed image data sets are mainly concerned to image visualization, distinguished from this approach providing analysis capabilities. As the smartphone apps with remote sensing analysis functions for general users and experts are widely utilizing, remote sensing images are regarded as information resources being capable of producing actual mobile contents, not potential resources. It is expected that this study could trigger off the technological progresses and other unique attempts to develop the variety of smartphone apps for remote sensing images.
The Journal of Korea Institute of Information, Electronics, and Communication Technology
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v.12
no.3
/
pp.242-250
/
2019
In this paper, we propose a method to predict the failure of industrial robot using Seq2Seq (Sequence to Sequence) model, which is a model for transforming time series data among Artificial Neural Network models. The proposed method uses the data of the joint current and angular value, which can be measured by the robot itself, without additional sensor for fault diagnosis. After preprocessing the measured data for the model to learn, the Seq2Seq model was trained to convert the current to angle. Abnormal degree for fault diagnosis uses RMSE (Root Mean Squared Error) during unit time between predicted angle and actual angle. The performance evaluation of the proposed method was performed using the test data measured under different conditions of normal and defective condition of the robot. When the Abnormal degree exceed the threshold, it was classified as a fault, and the accuracy of the fault diagnosis was 96.67% from the experiment. The proposed method has the merit that it can perform fault prediction without additional sensor, and it has been confirmed from the experiment that high diagnostic performance and efficiency are available without requiring deep expert knowledge of the robot.
The Journal of the Korea institute of electronic communication sciences
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v.14
no.1
/
pp.111-118
/
2019
For the security of a vast amount of information, it has been started to diagnose the site as a way of operating and managing the information owned by a company holding assets, to establish indexes to check the actual status and all kinds of standards to obtain security, and also to classify the information assets based on that. This has been extended to many different areas including policies to operate and manage information assets, services, the management of owned devices as physical assets, and also the management of logical assets for application software and platforms. Some of these information assets are already being operated in reality as new technology in new areas, for example, Internet of Things. Of course, a variety of electronic devices like Smart Home are being used in ordinary families, and unlike in the past, these devices generate a series of information life cycles such as accumulating and processing information. Moreover, as even distribution is now being realized, we are facing a task to secure the stability of information assets and also information that assets are holding. The purpose of this study is to suggest and apply standard security policy by moduling methods for information assets owned by companies and even families and obtain the enhancement of confidentiality as well as integrity.
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