• The KIPS Transactions:PartC
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• v.15C no.2
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• pp.93-102
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• 2008

The network weather map and bandwidth time-series graph are popularly used to understand the current and past traffic condition of NSP, ISP, and enterprise networks. These systems collect traffic performance data from a SNMP agent running on the network devices such as routers and switches, store the gathered information into a DB, and display the network performance status in the form of a time-series graph or a network weather map using Web user interface. Most of current enterprise networks are constructed in the form of a hierarchical tree-like structure with multi-Gbps Ethernet links, which is quietly different from the national or world-wide backbone network structure. This paper focuses on the network weather map for current enterprise network. We start with the considering points in developing a network weather map system suitable for enterprise network. Based on these considerings, this paper proposes the best way of using SNMP in constructing a network weather map system. To prove our idea, we designed and developed a network weather map system for our campus network, which is also described in detail.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.37-45
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• 2020

The development process of deep learning is an iterative task that requires a lot of manual work. Among the steps in the development process, pre-processing of learning data is a very costly task, and is a step that significantly affects the learning results. In the early days of AI's algorithm research, learning data in the form of public DB provided mainly by data scientists were used. The learning data collected in the real environment is mostly the operational data of the sensors and inevitably contains various noises. Accordingly, various data cleaning frameworks and methods for removing noises have been studied. In this paper, we proposed a method for detecting and removing noises from time-series data, such as sensor data, that can occur in the IoT environment. In this method, the linear regression method is used so that the system repeatedly finds noises and provides data that can replace them to clean the learning data. In order to verify the effectiveness of the proposed method, a simulation method was proposed, and a method of determining factors for obtaining optimal cleaning results was proposed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.887-892
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• 2019

Many researchers have developed a series of vision-based technologies to monitor construction sites automatically. To achieve high performance of vision-based technologies, it is essential to build a large amount and high quality of training image database (DB). To do that, researchers usually visit construction sites, install cameras at the jobsites, and collect images for training DB. However, such human and site-dependent approach requires a huge amount of time and costs, and it would be difficult to represent a range of characteristics of different construction sites and resources. To address these problems, this paper proposes a framework that automatically constructs a training image DB using web crawling techniques. For the validation, the authors conducted two different experiments with the automatically generated DB: construction work type classification and equipment classification. The results showed that the method could successfully build the training image DB for the two classification problems, and the findings of this study can be used to reduce the time and efforts for developing a vision-based technology on construction sites.

• Journal of Platform Technology
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• v.11 no.5
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• pp.49-58
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• 2023

Factory energy management system is rapidly growing and evolving due to factors such as the 3rd Basic Energy Plan and global energy cost increases, as well as environmental issues. However, implementing an essential data collection system for energy management in factory settings, which have limited space and unique characteristics, presents spatial, environmental, and energy-related challenges. This paper endeavors to mitigate these challenges by devising a data collection system implemented through an edge-based lightweight platform. A comparison and evaluation of database operation on edge devices are conducted. To conduct the evaluation, a benchmarking tool called CDI Benchmark is developed, utilizing the characteristics of existing factories involved in practical applications. The evaluation results revealed that RDBMS systems like MySQL encountered errors in the database due to high data insertion loads, making them inoperable. On the other hand, InfluxDB, thanks to its highly efficient compression algorithm, demonstrated compression rates about 6 times higher than MyRocks according to the evaluation. However, it was observed that MyRocks outperformed InfluxDB by a significant margin, recording a maximum processing time approximately 80 times faster compared to InfluxDB.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.52-56
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• 2004

The Information of classification and estimate about KOSPI200 index`s up and down in the stock market becomes an important standard of decision-making in designing portofolio in futures and option market. Because the coming trend of time series patterns, an economic indicator, is very subordinate to the most recent economic pattern, it is necessary to study the recent patterns most preferentially. This paper compares classification and estimated performance of SVM(Support Vector Machine) and Fuzzy SVM model that are getting into the spotlight in time series analyses, neural net models and various fields. Specially, it proves that Fuzzy SVM is superior by presenting the most suitable dimension to fuzzy membership function that has time series attribute in accordance with learning Data Base.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.11-13
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• 2021

본 논문에서는 시계열 데이터 분류를 통한 음성 감정 인식을 제안한다. mel-spectrogram을 사용하여 음성파일에서 특징을 뽑아내 다변수 시계열 데이터로 변환한다. 이를 Conv1D, GRU, Transformer를 결합한 딥러닝 모델에 학습시킨다. 위의 딥러닝 모델에 음성 감정 인식 데이터 세트인 TESS, SAVEE, RAVDESS, EmoDB에 적용하여 각각의 데이터 세트에서 기존의 모델 보다 높은 정확도의 음성 감정 분류 결과를 얻을 수 있었다. 정확도는 99.60%, 99.32%, 97.28%, 99.86%를 얻었다.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.7
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• pp.116-124
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• 2002

The planning of part feeder and other manufacturing automation equipments is almost always underestimated. Planning ahead for those crucial pitfalls can permit steps to take to minimize heir impacts, especially if the problems can be discovered in the planning phase, not on the shop floor. Planning process is an engineering process, namely a series of trade-offs. The effective trade-offs in the shortest amount of time can be possible with the help of a computer-aided ngineering (CAE) technique. The main parts of CAE fur part feeder are database system of fabricated workpiece parts, part feeders, part feeder components. In this study, a planning process of part feeder is presented. Especially, a systematic analysis of workpiece parts and part feeders is performed for the design of databases of CAE system.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.803-809
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• 2023

This paper analyzes and evaluates to optimally build a data collection system essential for factory energy management systems on an edge-based lightweight platform. A "Sensor/OPC-UA simulator" was developed based on sensors in an actual food factory and used to evaluate the storage engine of edge devices. The performance of storage engines in edge devices was evaluated to suggest the optimal storage engine. The experimental results show that when using the RocksDB storage engine, it has less than half the memory and database size compared to using InnoDB, and has a 3.01 times faster processing time. This study enables the selection of advantageous storage engines for managing time-series data on devices with limited resources and contributes to further research in this field through the sensor/OPC simulator.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.463-466
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• 2009

Some strategies for the integrated water management system based on GIS was suggested for the comprehensive and systematic management on the watershed. Contents of database and thematic layers for the related elements with GIS was indicated to estimate the quantity of total pollution loads and to simulate the water quality. Also, functions for the information providing was suggested on the connection with spatial data and attribute data, the search for collected data, the analysis for time series, and the visual presentations. Finally, it was suggested to integrate the existing systems and database structures, and to construct of data warehouse.

• Spatial Information Research
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• v.19 no.1
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• pp.29-40
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• 2011

GOCI the world first Ocean Color Imager in Geostationary Orbit, which could obtain total 8 images of the same region a day, however, its spatial resolution(500m) is not enough to use for the accurate land application, Super Resolution(SR), reconstructing the high resolution(HR) image from multiple low resolution(LR) images introduced by computer vision field. could be applied to the time-series remotely sensed images such as GOCI data, and the higher resolution image could be reconstructed from multiple images by the SR, and also the cloud masked area of images could be recovered. As the precedent study for developing the efficient SR method for GOCI images, on this research, it reproduced the simulated data under the acquisition process of the remote sensed data, and then the simulated images arc applied to the proposed algorithm. From the proposed algorithm result of the simulated data, it turned out that low resolution(LR) images could be registered in sub-pixel accuracy, and the reconstructed HR image including RMSE, PSNR, SSIM Index value compared with original HR image were 0.5763, 52.9183 db, 0.9486, could be obtained.