• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.399-406
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• 2021

With the steady growth of the content industry, the need for research that automatically recommending content suitable for individual tastes is increasing. In order to improve the accuracy of automatic content recommendation, it is needed to fuse existing recommendation techniques using users' preference history for contents along with recommendation techniques using content metadata or features extracted from the content itself. In this work, we propose a new graph-based music recommendation method which learns an LSTM-based classification model to automatically extract appropriate tagging words from sound data and apply the extracted tagging words together with the users' preferred music lists and music metadata to graph-based music recommendation. Experimental results show that the proposed method outperforms existing recommendation methods in terms of the recommendation accuracy.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1270-1277
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• 2019

A problem with virtual synchronous generator (VSG) systems is that they are difficult to operate stably with photovoltaic (PV) power as the DC side. With this problem in mind, a PV-VSG control strategy considering the dynamic characteristics of the DC side is proposed after an in-depth analysis of the dynamic characteristics of photovoltaic power with a parallel energy-storage capacitor. The proposed PV-VSG automatically introduces DC side voltage control for the VSG when the PV enters into an unstable working interval, which avoids the phenomenon where an inverter fails to work due to a DC voltage sag. The stability of the original VSG and the proposed PV-VSG were compared by a root locus analysis. It is found that the stability of the PV-VSG is more sensitive to the inertia coefficient J than the VSG, and that a serious power oscillation may occur. According to this, a new rotor model is designed to make the inertial coefficient automatically change to adapt to the operating state. Experimental results show that the PV-VSG control strategy can achieve stable operation and maximum power output when the PV output power is insufficient.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.31-39
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• 2019

A robot usually adopts ANN (artificial neural network)-based object detection and instance segmentation algorithms to recognize objects but creating datasets for these algorithms requires high labeling costs because the dataset should be manually labeled. In order to lower the labeling cost, a new scheme is proposed that can automatically generate a training images and label them for specific objects. This scheme uses an instance segmentation algorithm trained to give the masks of unknown objects, so that they can be obtained in a simple environment. The RGB images of objects can be obtained by using these masks, and it is necessary to label the classes of objects through a human supervision. After obtaining object images, they are synthesized with various background images to create new images. Labeling the synthesized images is performed automatically using the masks and previously input object classes. In addition, human intervention is further reduced by using the robot arm to collect object images. The experiments show that the performance of instance segmentation trained through the proposed method is equivalent to that of the real dataset and that the time required to generate the dataset can be significantly reduced.

• Journal of the Korea Society of Computer and Information
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• v.24 no.2
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• pp.127-137
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• 2019

In this study, we have developed a calculation system for BIM-based quantities, 4D process, and 5D construction costs, by integrating object shape attributes and the standard classification system which consist of Cost Breakdown System(CBS), Object Breakdown System(OBS) and Work Breakdown System(WBS) in order to use for the 4 dimensional process control of roads and rivers. First, a new BIM library database connected with the BIM library shape objects was built according to the CBS/OBS/WBS standard classification system of the civil engineering field, and a integrated database system of BIM-based quantities, process(4D), and construction costs(5D) for roads and rivers was constructed. Nextly, the process classification system and the cost classification system were automatically disassembled to the BIM objects consisting of the Revit-family style elements. Finally, we added functions for automatically generating four dimensional activities and generating a automatic cost statement according to the combination of WBS and CBS classification system The ultimate goal of this study was to extend the integrated quantities, process(4D), and construction costs(5D) system for new roads and rivers, enabling the integrated use of process(4D) and construction costs(5D) in the design and construction stage, based on the tasks described above.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.368-370
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• 2016

The purpose of the cultivated crops have been changes in the aim of improving quality production. In recent years, as people's attention on health, the demand for healthy crops such as mushrooms gradually increased. Farmers use plastic greenhouse cultivation mode more and more in order to reduce the impact of outdoor environment on crop cultivation, which requires farmers to adjust the greenhouse temperature at any time. But the majority of farmers still use a thermometer to measure temperature. This paper constructs an environment that can automatically adjust the temperature, so as to measuring temperature in real time, improving the efficiency of the farm work, and reducing unnecessary labor.

• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.69-76
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• 2019

Recent Enterprise System has component driven real-time distributed architecture (RDA) and this kind of architecture should performed with satisfying strict constraints on life cycle of object and response time such as synchronization, transaction and so on. Microsoft's .NET platform supports RDA and is able to implement services including before mentioned time restriction and security service by only specifying attribute code and maximizing advantages of OMG's Model Driven Architecture (MDA). In this study, a method to automatically generate an extended model of essential elements in an enterprise-system-based RDA as well as the platform specific model (PSM) for Microsoft's .NET platform are proposed. To realize these ideas, the functionalities that should be considered in enterprise system development are specified and defined in a meta-model and an extended UML profile. In addition, after defining the UML profile for .NET specification, these are developed and applied as plug-ins of the open source MDA tool, and extended models are automatically generated using this tool. Accordingly, by using the proposed specification technology, the profile and tools can easily and quickly generate a reusable extended model even without detailed coding-level information about the functionalities considered in the .NET platform and RDA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1284-1297
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• 2019

The network environment is presently becoming very increased. Accordingly, the study of traffic classification for network management is becoming difficult. Automatic signature extraction system is a hot topic in the field of traffic classification research. However, existing automatic payload signature generation systems suffer problems such as semi-automatic system, generating of disposable signatures, generating of false-positive signatures and signatures are not kept up to date. Therefore, we provide a fully automatic signature update system that automatically performs all the processes, such as traffic collection, signature generation, signature management and signature verification. The step of traffic collection automatically collects ground-truth traffic through the traffic measurement agent (TMA) and traffic management server (TMS). The step of signature management removes unnecessary signatures. The step of signature generation generates new signatures. Finally, the step of signature verification removes the false-positive signatures. The proposed system can solve the problems of existing systems. The result of this system to a campus network showed that, in the case of four applications, high recall values and low false-positive rates can be maintained.

• Journal of Digital Convergence
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• v.17 no.10
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• pp.243-249
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• 2019

In this paper, we have developed a device to reduce the standby power consumption that is unnecessarily consumed in unused electrical appliances. The Doppler sensor is used to automatically power off and power off the outlet depending on whether or not a person is present near the outlet. The Doppler sensor uses a coaxial cable trap to design a transmitting antenna and emits a 10 GHz band RF signal and receives a reflected wave signal whose wavelength is reflected from the target object to the receiver to detect an object and recognize human approach. It automatically cuts off and restores standby power to prevent unnecessary power consumption, saving energy and developing a standby power automatic shutdown and recovery device that can prevent the risk of large fires caused by leakage current.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.40-50
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• 2021

Science museum are dealing with exhibits on field of changing science and technology, and previous research suggested that exhibits replacement should carried out at least every 5 years. In order to efficiently replace exhibits within a limited budget, various studies analyzed visitors' preferences to exhibits. Recently, studies use various technologies to collect the data on visitors' preferences automatically, but almost of studies had a high dependency on their visitors such as visitors needed to carry specific sub-devices in the museums for gathering data. As complementing the limitations of previous research, this study introduces the improved method which is able to automatically collect and quantify visitors' preferences to exhibits using TensorFlow, a deep learning technology. By the proposed analysis method, it was possible to collect 2,520 data of visitors' experience on exhibits in totality. Based on collected data, attraction power and holding power indicating the preference of visitors on exhibits were able to be calculated. The result also confirmed antecedent research conclusion that the attraction power and holding power of the exhibit which consists of 3 dimensional structures work are higher than other exhibits. As a conclusion, the proposed method will provide more convenient data collection method for detecting visitors' preference.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.396-405
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• 2021

In surgery to remove pancreatic cancer, it is important to figure out the shape of a patient's pancreas. However, previous studies have a limit to detect a pancreas automatically in abdominal CT images, because the pancreas varies in shape, size and location by patient. Therefore, in this paper, we propose a method of learning various shapes of pancreas according to the patients and adjacent slices using Faster R-CNN based on Inception V2, and automatically detecting the pancreas from abdominal CT images. Model training and testing were performed using the NIH Pancreas-CT Dataset, and intensity normalization was applied to all data to improve pancreatic detection accuracy. Additionally, according to the shape of the pancreas, the test dataset was classified into top, middle, and bottom slices to evaluate the model's performance on each data. The results show that the top data's mAP@.50IoU achieved 91.7% and the bottom data's mAP@.50IoU achieved 95.4%, and the highest performance was the middle data's mAP@.50IoU, 98.5%. Thus, we have confirmed that the model can accurately detect the pancreas in CT images.