• Korean Journal of Remote Sensing
• /
• v.39 no.5_2
• /
• pp.811-825
• /
• 2023

In accordance with the government's stance of actively promoting intelligent administrative service policies through data utilization, in the disaster and safety management field, it also is proceeding with disaster and safety management policies utilizing data and constructing systems for responding efficiently to new and complex disasters and establishing scientific and systematic safety policies. However, it is difficult to quickly and accurately grasp the on-site situation in the event of a disaster, and there are still limitations in providing information necessary for situation judgment and response only by displaying vast data. This paper focuses on deriving specific needs to make disaster situation management work more intelligent and efficient by utilizing intelligent information technology. Through individual interviews with workers at the Central Disaster and Safety Status Control Center, we investigated the scope of disaster situation management work and the main functions and usability of the geographic information system (GIS)-based integrated situation management system by practitioners in this process. In addition, the data built in the system was reclassified according to purpose and characteristics to check the status of data in the GIS-based integrated situation management system. To derive needed to make disaster situation management more intelligent and efficient by utilizing intelligent information technology, 3 strategies were established to quickly and accurately identify on-site situations, make data-based situation judgments, and support efficient situation management tasks, and implementation tasks were defined and task priorities were determined based on the importance of implementation tasks through analytic hierarchy process (AHP) analysis. As a result, 24 implementation tasks were derived, and to make situation management efficient, it is analyzed that the use of intelligent information technology is necessary for collecting, analyzing, and managing video and sensor data and tasks that can take a lot of time of be prone to errors when performed by humans, that is, collecting situation-related data and reporting tasks. We have a conclusion that among situation management intelligence strategies, we can perform to develop technologies for strategies being high important score, that is, quickly and accurately identifying on-site situations and efficient situation management work support.

• Korean Journal of Remote Sensing
• /
• v.36 no.3
• /
• pp.461-474
• /
• 2020

Korea launched Geostationary Environmental Monitoring Satellite (GEMS), a UV/visible spectrometer that measure pollution gases on 18 February 2020. Because satellite retrieval is an ill-posed inverse solving process, the validation with ground-based measurements or other satellite measurements is essential to obtain reliable products. For this purpose, satellite-based OMI and OMPS total column ozone (TCO), and ground-based Pandora TCO in Busan and Seoul were selected for future GEMS validation. First of all, the goal of this study is to validate the ground ozone data using characteristics that satellite data provide coherent ozone measurements on a global basis, although satellite data have a larger error than the ground-based measurements. In the cross validation between Pandora and OMI TCO, we have found abnormal deviation in ozone time series from Pandora #29 observed in Seoul. This shows that it is possible to perform inverse validation of ground data using satellite data. Then OMPS TCO was compared with verified Pandora TCO. Both data shows a correlation coefficient of 0.97, an RMSE of less than 2 DU and the OMPS-Pandora relative mean difference of >4%. The result also shows the OMPS-Pandora relative mean difference with SZA, TCO, cross-track position and season have insignificant dependence on those variables.In addition, we showed that appropriate thresholds depending on the spatial resolution of each satellite sensor are required to eliminate the impact of the cloud on Pandora TCO.

• The KIPS Transactions:PartC
• /
• v.15C no.4
• /
• pp.297-302
• /
• 2008

In case of display a lot of information from mobile devices, those systems are being developed that display the information from mobile devices on remote devices such as TV using the mobile devices as remote controllers because it is difficult to display a lot of information on mobile devices due to their limited bandwidth and small screen sizes. A lot of cost is required to design and develop interfaces for these systems corresponding to each of remote display devices. In this paper, a mobile environment based remote display system for displays at real times is proposed for continuous monitoring of status data for unique 'Mote IDs'. Also, remote data are collected and monitored through sensor network devices such as ZigbeX by applying status perception based remote displays at real times through processing ubiquitous computing environment data, and remote display applications at real times are implemented through PDA wireless mobiles. The system proposed in this paper consists of a PDA for remote display and control, mote embedded applications programming for data collections and radio frequency, server modules to analyze and process collected data and virtual prototyping for monitoring and controls by virtual machines. The result of the implementations indicates that this system not only provides a good mobility from a human oriented viewpoint and a good usability of accesses to information but also transmits data efficiently.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.8 no.4
• /
• pp.14-27
• /
• 2009

Collection of invaluable real-time traffic data becomes available under ubiquitous transportation sensor networks (UTSN). Various research efforts have been made to utilize such useful data for deriving more accurate and reliable traffic information. This study presented a novel concept of decentralized traffic information and method to process traffic data which are obtained from inter-vehicle communications under the UTSN. In addition, an experimental evaluation to investigate the feasibility of the proposed method using probe vehicle data. Predictive travel times were estimated and evaluated for the feasibility investigation. Technical issues were derived and discussed to fully implement the proposed system. The outcomes of this study would be used as a guideline in designing better next-generation traffic information systems.

• Journal of Broadcast Engineering
• /
• v.22 no.6
• /
• pp.713-723
• /
• 2017

Since the panoramic image can overcome the limitation of the viewing angle of the camera and have a wide field of view, it has been studied effectively in the fields of computer vision and stereo camera. In order to generate a panoramic image, stitching images taken by a plurality of general cameras instead of using a wide-angle camera, which is distorted, is widely used because it can reduce image distortion. The image stitching technique creates descriptors of feature points extracted from multiple images, compares the similarities of feature points, and links them together into one image. Each feature point has several hundreds of dimensions of information, and data processing time increases as more images are stitched. In particular, when a panorama is generated on the basis of an image photographed by a plurality of unspecified cameras with respect to an object, the extraction processing time of the overlapping feature points for similar images becomes longer. In this paper, we propose a preprocessing process to efficiently process stitching based on an image obtained from a number of unspecified cameras for one object or environment. In this way, the data processing time can be reduced by pre-grouping images based on camera sensor information and reducing the number of images to be stitched at one time. Later, stitching is done hierarchically to create one large panorama. Through the grouping preprocessing proposed in this paper, we confirmed that the stitching time for a large number of images is greatly reduced by experimental results.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.4
• /
• pp.1982-1987
• /
• 2011

In this study, we proposed a new method that can estimate viscosity curves of unknown samples or high viscous resins like LFT(Long Fiber Thermoplastics). First, we built the system that could detect the pressure of melt during filling the cavity in a mold. It consists of both pressure sensors which are installed in a mold and the Kit which can convert analog signal to digital signal. The kit measures the melt pressure in mold cavity. We could also simulate the cavity pressure during filling process with commercialized CAE softwares(ex, Moldflow). If the viscosity data in CAE Database were correct, the simulated pressure profile coincided with the measured one. According to our proposed algorithm, we obtained correct viscosity data by iterating the process of comparing the simulated profile with the measured one until both coincided each other. In order to verify this algorithm, we selected well-defined PP resin and concluded that the experimental profile comply with the CAE profile. We could also estimate the optimized viscosity curves for PP-LFT by applying our method.

• Journal of the Korean Society for Marine Environment & Energy
• /
• v.18 no.4
• /
• pp.304-309
• /
• 2015

To evaluate the degree of contamination caused by oil spill accident in the sea, the in-situ sensors which are based on the scientific method are needed in the real site. The sensors which are based on the fluorescence detection theory can provide the useful data, such as the concentration of oil. However these kinds of sensors commonly are composed of the ultraviolet (UV) light source such as UV mercury lamp, the multiple excitation/emission filters and the optical sensor which is mainly photomultiplier tube (PMT) type. Therefore, the size of the total sensing platform is large not suitable to be handled in the oil spill field and also the total price of it is extremely expensive. To overcome these drawbacks, we designed the fluorimeter for the oil spill detection which has compact size and cost effectiveness. Before the detail design process, we conducted the experiments to measure the excitation and emission spectrum of oils using five different kinds of crude oils and three different kinds of processed oils. And the fluorescence spectrometer were used to analyze the excitation and emission spectrum of oil samples. We have compared the spectrum results and drawn the each common spectrum regions of excitation and emission. In the experiments, we can see that the average gap between maximum excitation and emission peak wavelengths is near 50 nm for the every case. In the experiment which were fixed by the excitation wavelength of 365 nm and 405 nm, we can find out that the intensity of emission was weaker than that of 280 nm and 325 nm. So, if the light sources having the wavelength of 365 nm or 405 nm are used in the design process of fluorimeter, the optical sensor needs to have the sensitivity which can cover the weak light intensity. Through the results which were derived by the experiment, we can define the important factors which can be useful to select the effective wavelengths of light source, photo detector and filters.

• KIPS Transactions on Software and Data Engineering
• /
• v.2 no.8
• /
• pp.543-554
• /
• 2013

Model-Driven Development(MDD) helps developers verify requirements and design issues of a software system in the early stage of development process by taking advantage of a software model which is the most highly abstracted form of a software system. In practice, however, many software systems have been developed through a code-centric method that builds a software system bottom-up rather than top-down. So, without support of appropriate tools, it is not easy to introduce MDD to real development process. Although there are many researches about extracting a model from code to help developers introduce MDD to code-centrically developed system, most of them only extracted base-level models. However, using concept of abstract component one can continuously extract higher level model from base-level model. In this paper we propose a practical method for automatic extraction of base level abstract component from source code, which is the first stage of continuous extraction process of abstract component, and validate the method by implementing an extraction tool based on the method. Target code chosen is the source code of TinyOS, an operating system for wireless sensor networks. The tool is applied to the source code of TinyOS, written in nesC language.

• Journal of Appropriate Technology
• /
• v.5 no.2
• /
• pp.70-81
• /
• 2019

The garment industry is one of the most labor-intensive manufacturing industries, with its sewing process relying almost entirely on manual labor. Its costs highly depend on the efficiency of this production line and thus is crucial to determine the production rate in real-time for line balancing. However, current production tracking methods are costly and make it difficult for many Small and Medium-sized Enterprises (SMEs) to implement them. As a result, their reliance on manual counting of finished products is both time consuming and prone to error, leading to high manufacturing costs and inefficiencies. In this paper, a production tracking system that uses the sewing machines' energy consumption data to track and count the total number of sewing tasks completed through Convolutional Neural Network (CNN) classifiers is proposed. This system was tested on two target sewing tasks, with a resulting maximum classification accuracy of 98.6%; all sewing tasks were detected. In the developing countries, the garment sewing industry is a very important industry, but the use of a lot of capital is very limited, such as applying expensive high technology to solve the above problem. Applied with the appropriate technology, this system is expected to be of great help to the garment industry in developing countries.

• Journal of Animal Environmental Science
• /
• v.8 no.3
• /
• pp.199-208
• /
• 2002

This paper introduces the method to make management network about milking cow farm tasks. The object of this research was to design of biological measuring system and managing network system in a livestock farm. This auto-management system provides informations about individual cows' temperature, conductivity of milk and weight for efficient management of feeding, and milking works by a micro-processor and RS -485 type serial COM. ports. And measured bio-data which are basic informations for remote raising management are saved to user PC by serial communication between the PLC and user PC. Milking cow farm is divided into three working place to each measurement work and feed. The first working place is milking station which has two thermometers, a conduct meter and a scale set. The second working place is feeding station, and the third place is cattle cage. These are combined by network system and the PLC which is used to drive network and sub-modules. Sub-modules have a micro-process to control the sensor and to interface with network. The PLC which drive network and control sequence has two serial communication port to be linked with user PC for sending the measured data and for receiving data. Above all, in this study tells the sequence operating method by the driving scenario of breeding milk cow for livestock auto-management using the PLC and network system.