• Journal of Software Assessment and Valuation
• /
• v.17 no.2
• /
• pp.117-124
• /
• 2021

Livestock behavioral analysis is a factor that has a great influence on livestock health management and agricultural productivity increase. However, most digital devices introduced for behavioral analysis of livestock do not provide raw data and also provide limited analysis results. Such a closed system makes it more difficult to integrate data and build big data, which are essential for the introduction of advanced IT technologies. Therefore, it is necessary to supply farm-scale data collection devices that can be easily used at low cost. This study presents a data collection system for analyzing the behavior of livestock. The system consists of a number of miniature computing units that operate wirelessly, and collects livestock body temperature and acceleration data, location information, and livestock environment data. In addition, this study presents an algorithm for estimating the behavior of livestock based on the collected acceleration data. For the experiment, a system was built in a Korean cattle farm in Icheon, Gyeonggi-do, and data were collected for 20 Korean cattle, and based on this, the empirical and analysis results were presented.

• Smart Structures and Systems
• /
• v.15 no.4
• /
• pp.1019-1039
• /
• 2015

Shanghai Tower is a composite structure building with a height of 632 m. In order to verify the structural properties and behaviors in construction and operation, a structural health monitoring project was conducted by Tongji University. The monitoring system includes sensor system, data acquisition system and a monitoring software system. Focusing on the health monitoring in construction, this paper introduced the monitoring parameters in construction, the data acquisition strategy and an integration structural health monitoring (SHM) software. The integration software - Structural Monitoring/ Analysis/ Evaluation System (SMAE) is designed based on integration and modular design idea, which includes on-line data acquisition, finite elements and dynamic property analysis functions. With the integration and modular design idea, this SHM system can realize the data exchange and results comparison from on-site monitoring and FEM effectively. The analysis of the monitoring data collected during the process of construction shows that the system works stably, realize data acquirement and analysis effectively, and also provides measured basis for understanding the structural state of the construction. Meanwhile, references are provided for the future automates construction monitoring and implementation of high-rise building structures.

• The Journal of the Korea Contents Association
• /
• v.19 no.2
• /
• pp.460-468
• /
• 2019

Recently, analysis techniques to extract new meanings using big data analysis and various services using these analysis techniques have been developed. Among them, the transport is one of the most important areas that can be utilized about big data. However, the existing traffic route guidance system can not recommend the optimal traffic route because they use only the traffic information when the user search the route. In this paper, we propose a realtime optimal traffic route guidance system using big data analysis. The proposed system considers the realtime traffic information and results of big data analysis using historical traffic data. And, the proposed system show the warning message to the user when the user need to change the traffic route.

• International journal of advanced smart convergence
• /
• v.13 no.3
• /
• pp.150-155
• /
• 2024

Genomic data plays a transformative role in medicine, biology, and forensic science, offering insights that drive advancements in clinical diagnosis, personalized medicine, and crime scene investigation. Despite its potential, the integration and analysis of diverse genomic datasets remain challenging due to compatibility issues and the specialized nature of existing tools. This paper presents the GenomeSync system, designed to overcome these limitations by utilizing the Hadoop framework for large-scale data handling and integration. GenomeSync enhances data accessibility and analysis through SQL-based search capabilities and machine learning techniques, facilitating the identification of genetic traits and the resolution of forensic cases. By pre-processing DNA profiles from crime scenes, the system calculates similarity scores to identify and aggregate related genomic data, enabling accurate prediction models and personalized treatment recommendations. GenomeSync offers greater flexibility and scalability, supporting complex analytical needs across industries. Its robust cloud-based infrastructure ensures data integrity and high performance, positioning GenomeSync as a crucial tool for reliable, data-driven decision-making in the genomic era.

• Atmosphere
• /
• v.17 no.4
• /
• pp.435-453
• /
• 2007

KEOP (Korea Enhanced Observing Period)-2004 intensive summer observation was carried out from 20 June to 5 July 2004 over the Southwestern part of the Korean peninsula. In this study, the effects of KEOP-2004 intensive observation data on the simulation of precipitation system are investigated using KLAPS (Korea Local Analysis and Prediction System) and PSU/NCAR MM5. Three precipitation cases during the intensive observation are selected for detailed analysis. In addition to the control experiments using the traditional data for its initial and boundary conditions, two sensitivity experiments using KEOP data with and without Jindo radar are performed. Although it is hard to find a clear and consistent improvement in the verification score (threat score), it is found that the KEOP data play a role in improving the position and intensity of the simulated precipitation system. The experiments started at 00 and 12 UTC show more positive effect than those of 06 and 18 UTC. The effect of Jindo radar is dependent on the case. It plays a significant role in the heavy rain cases related to a mesoscale low over Changma front and the landing of a Typhoon. KEOP data produce more strong difference in the 06/18 UTC experiments than in 00/12 UTC, but give more positive effects in 00/12 UTC experiments. One of the possible explanations for this is that : KEOP data could properly correct the atmosphere around them when there are certain amounts of data, while gives excessive effect to the atmospheric field when there are few data. CRA analysis supports this reasoning. According to the CRA (Contiguous Rain Area) analysis, KEOP data in 00/12 UTC experiments improve only the surrounding area, resulting in essentially same precipitation system so the effects remain only in each convective cell rather than the system itself. On the other hand, KEOP data modify the precipitation system itself in 06/18 UTC experiments. Therefore the effects become amplified with time integration.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.56.6-56
• /
• 2001

The FDAS(Flight Data Analysis & Animation System) is a comprehensive analysis system designed for the improvement of flight safety. FDAS provides decoding, analysis and animation tools that can be used for investigation of data origination primarily from Flight Data Recoders(FDRs), Quick Access Recoders(QARs), Using FOQA(Flight Operation Quality Assurance) Data Analysis, an analyst can perform a variety of functions including data smoothing, interpolation, differentiation, integration, calculator function, flight path generation, performance routines, as well as user-programmed functions. Utilizing data captured and processed by our FDAS software module, FDAS provides high-fidelity 3-D aircraft views and instruments views. Multiple windows enable you to view the situation from a variety of perspectives, including out-of-window, chase plane ...

• 한국데이터정보과학회:학술대회논문집
• /
• 2005.10a
• /
• pp.35-45
• /
• 2005

Data fusion is method to combination data. The purpose of this study is to design and implementation for street fashion information analysis system using data fusion. It can offer variety and actually information because it can fuse image data and survey data for street fashion. Data fusion method exists exact matching method, judgemental matching method, probability matching method, statistical matching method, data linking method, etc. In this study, we use exact matching method. Our system can be visual information analysis of customer's viewpoint because it can analyze both each data and fused data for image data and survey data.

• Journal of Korea Water Resources Association
• /
• v.39 no.10 s.171
• /
• pp.855-866
• /
• 2006

In this paper, the Hydrologic Data Management System (HDMS) was developed for the efficient management of hydrologic data. The applicability of the system was demonstrated using the hydrologic data of study watershed located in the southwest from Suwon city MySQL 5.0, relational database management system, and MS Visual Basic 6.0 were used for the development of MS windows based HDMS. The primary components of the HDMS are data search system, data management system, and data analysis system. Data search and management systems provide basic functions for the efficient data search, storage, update and export. Data analysis system enables the users to get the further and diverse hydrologic statistical information from the stored data. Furthermore, the accuracy and quality of hydrologic data was analyzed and evaluated through data analysis system.

• Computers and Concrete
• /
• v.20 no.5
• /
• pp.555-562
• /
• 2017

The accuracy and integrity of stress data acquired by bridge heath monitoring system is of significant importance for bridge safety assessment. However, the missing and abnormal data are inevitably existed in a realistic monitoring system. This paper presents a data reconstruction approach for bridge heath monitoring based on the wavelet multi-resolution analysis and support vector machine (SVM). The proposed method has been applied for data imputation based on the recorded data by the structural health monitoring (SHM) system instrumented on a prestressed concrete cable-stayed bridge. The effectiveness and accuracy of the proposed wavelet-based SVM prediction method is examined by comparing with the traditional autoregression moving average (ARMA) method and SVM prediction method without wavelet multi-resolution analysis in accordance with the prediction errors. The data reconstruction analysis based on 5-day and 1-day continuous stress history data with obvious preternatural signals is performed to examine the effect of sample size on the accuracy of data reconstruction. The results indicate that the proposed data reconstruction approach based on wavelet multi-resolution analysis and SVM is an effective tool for missing data imputation or preternatural signal replacement, which can serve as a solid foundation for the purpose of accurately evaluating the safety of bridge structures.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.13 no.6
• /
• pp.305-312
• /
• 2018

Software has been used to develop many functions of the modern weapon systems which has a high mission criticality. Weapon system software must consider multi-threaded processing to satisfy growing performance requirement. However, developing multi-threaded programs are difficult because of concurrency faults, such as unintended data races. Especially, it is important to prepare analysis for debugging the data races, because the weapon system software may cause personal injury. In this paper, we present an efficient framework of analysis, called ConDeWS, which is designed to determine the scope of dynamic analysis through using the result of static analysis and fault analysis. As a result of applying the implemented framework to the target software, we have detected unintended data races that were not detected in the static analysis.