• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.79-94
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• 2013

Recently, the high value added business is steadily growing in the culture and art area. To generated high value from a performance, the satisfaction of audience is necessary. The flow in a critical factor for satisfaction, and it should be induced from audience and measures. To evaluate interest and emotion of audience on contents, producers or investors need a kind of index for the measurement of the flow. But it is neither easy to define the flow quantitatively, nor to collect audience's reaction immediately. The previous studies of the group flow were evaluated by the sum of the average value of each person's reaction. The flow or "good feeling" from each audience was extracted from his face, especially, the change of his (or her) expression and body movement. But it was not easy to handle the large amount of real-time data from each sensor signals. And also it was difficult to set experimental devices, in terms of economic and environmental problems. Because, all participants should have their own personal sensor to check their physical signal. Also each camera should be located in front of their head to catch their looks. Therefore we need more simple system to analyze group flow. This study provides the method for measurement of audiences flow with group synchronization at same time and place. To measure the synchronization, we made real-time processing system using the Differential Image and Group Emotion Analysis (GEA) system. Differential Image was obtained from camera and by the previous frame was subtracted from present frame. So the movement variation on audience's reaction was obtained. And then we developed a program, GEX(Group Emotion Analysis), for flow judgment model. After the measurement of the audience's reaction, the synchronization is divided as Dynamic State Synchronization and Static State Synchronization. The Dynamic State Synchronization accompanies audience's active reaction, while the Static State Synchronization means to movement of audience. The Dynamic State Synchronization can be caused by the audience's surprise action such as scary, creepy or reversal scene. And the Static State Synchronization was triggered by impressed or sad scene. Therefore we showed them several short movies containing various scenes mentioned previously. And these kind of scenes made them sad, clap, and creepy, etc. To check the movement of audience, we defined the critical point, ${\alpha}$and ${\beta}$. Dynamic State Synchronization was meaningful when the movement value was over critical point ${\beta}$, while Static State Synchronization was effective under critical point ${\alpha}$. ${\beta}$ is made by audience' clapping movement of 10 teams in stead of using average number of movement. After checking the reactive movement of audience, the percentage(%) ratio was calculated from the division of "people having reaction" by "total people". Total 37 teams were made in "2012 Seoul DMC Culture Open" and they involved the experiments. First, they followed induction to clap by staff. Second, basic scene for neutralize emotion of audience. Third, flow scene was displayed to audience. Forth, the reversal scene was introduced. And then 24 teams of them were provided with amuse and creepy scenes. And the other 10 teams were exposed with the sad scene. There were clapping and laughing action of audience on the amuse scene with shaking their head or hid with closing eyes. And also the sad or touching scene made them silent. If the results were over about 80%, the group could be judged as the synchronization and the flow were achieved. As a result, the audience showed similar reactions about similar stimulation at same time and place. Once we get an additional normalization and experiment, we can obtain find the flow factor through the synchronization on a much bigger group and this should be useful for planning contents.

• Journal of Advanced Navigation Technology
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• v.16 no.6
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• pp.927-934
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• 2012

In this paper, we present relative positioning algorithm for moving land vehicle using GPS, MEMS IMU and B-CDMA module. This algorithm does not calculate precise absolute position but calculates relative position directly, so additional infrastructure and I2V communication device are not required. Proposed algorithm has several steps. Firstly, unbiased relative position is calculated using pseudorange difference between two vehicles. Simultaneously, the algorithm estimates position of each vehicle using GPS/INS integration. Secondly, proposed algorithm performs filtering and finally estimates relative position and relative velocity. Using proposed algorithm, we can obtain more precise relative position for moving land vehicles with short time interval as IMU sensor has. The simulation is performed to evaluate this algorithm and the several field tests are performed with real time program and miniature vehicles for verifying performance of proposed algorithm.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.317-323
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• 2016

This paper suggests a system that can detect malfunctions of the temperature testing equipment, and then notify this information to surroundings using arduino. Precision electronics need a test under extremely high/low temperature using temperature called a chamber. If this chamber have a malfunction situation, the precision electronics under test is damaged and scrapped. Especially when the temperature test is automatically conducted at night with no representative, this system monitors the actual temperature of the tested product in real-time by attaching a temperature sensor to the inside of the test equipment. In case when it is out of the temperature range set up by the tester, the damage to high-priced products can be prevented in the condition of extremely high/low temperature, by turning off the power of the temperature testing equipment, and also notifying this information to the worker at night-time. Regardless of the equipment manufacturers, proposed system in this paper can be applied to all kind of temperature testing equipments, and it can be also produced for low cost.

• Economic and Environmental Geology
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• v.50 no.2
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• pp.159-170
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• 2017

The temporal and spatial temperature distribution of the heat storage mortar made of porous feldspar was measured and the thermal properties and electricity consumption were analyzed. For the experiment, two real size chambers (control model and test model) with hot water pipes were constructed. Two large scale models with hot water pipes were constructed. The surface temperature change of the heat storage layer was remotely monitored during the heating and cooling process using infrared thermal imaging camera and temperature sensor. The temperature increased from $20^{\circ}C$ to $30^{\circ}C$ under the heating condition. The temperature of the heat storage layer of the test model was $2.0-3.5^{\circ}C$ higher than the control model and the time to reach the target temperature was shortened. As the distance from the hot water pipe increased, the temperature gap increased from $4.0^{\circ}C$ to $4.8^{\circ}C$. The power consumed until the surface temperature of the heat storage layer reached $30^{\circ}C$ was 2.2 times that of the control model. From the heating experiment, the stepwise temperature and electricity consumption were calculated, and the electricity consumption of the heat storage layer of the test model was reduced by 66%. In the cooling experiment, the surface temperature of the heat storage layer of the test model was maintained $2^{\circ}C$ higher than that of the control model. The heat storage effect of the porous feldspar mortar was confirmed by the temperature experiment. With considering that the time to reheat the heat storage layer is extended, the energy efficiency will be increased.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.263-267
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• 2015

current mine detector can't division the section if it is conducted and it needs too much labor force and time. in addition to, if the user don't move the head of sensor in regular speed or move it too fast, it is hard to detect a mine exactly. according to this, to improve the problem using one direction ultrasonic wave sensing signal, that is made up of human body antenna part, main micro processor unit part, smart glasses part, body equipped LCD monitor part, wireless data transmit part, belt type power supply part, black box type camera, Security Communication headset. the user can equip this at head, body, arm, waist and leg in removable type. so it is able to detect the powder in a 360-degree on(under) the ground whether it is metal or nonmetal and it can express the 2D or 3D film about distance, form and material of the mine. so the battle combats can avoid the mine and move fast. also, through the portable battery and twin self power supply system of the power supply part, combat troops can fight without extra recharge and we can monitoring the battle situation of distant place at the command center server on real-time. and then, it makes able to sharing the information of battle among battle combats one on one. as a result, the purpose of this study is researching a smart wearable mine detector which can establish a smart battle system as if the commander is in the site of the battle.

• Journal of the Korean GEO-environmental Society
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• v.17 no.7
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• pp.5-13
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• 2016

This research is focused on the seepage behavior of embankment which had the weak zone with big permeability. The distributed TDR (Time Domain Reflectometer) and point sensors such as settlement gauge, pore water pressuremeter, vertical total stressmeter, and FDR (Frequency Domain Reflectometer) sensor were used to measure the seepage characteristics and embankment behavior. Also, the measured data were compared to the data of 2-D and 3-D numerical analysis. The dimension of model embankment was 7 m length, 5 m width and 1.5 m height, which is composed of fine-grained sands and the water level of embankment was 1.3 m height. The seepage behavior of measuring and numerical analysis were very similar, it means that the proper sensing system can monitor the real-time safety of embankment. The result by 2-D and 3-D numerical analysis showed similar saturation processing, however in case of weak zone, the phreatic lines of 2-D showed faster movement than that of 3-D analysis, and finally they converged.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.22 no.3
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• pp.57-68
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• 2017

Continuous monitoring of spatial and temporal changes in key marine environmental parameters such as SST (sea surface temperature) near IORS (Ieodo Ocean Research Station) is demanded to investigate the ocean ecosystem, climate change, and sea-air interaction processes. In this study, we aimed to develop the system for continuously measuring SST using a TIR (thermal infrared) sensor mounted at the IORS. New SST algorithm is developed to provide SST of better quality that includes automatic atmospheric correction and emissivity calculation for different oceanic conditions. Then, the TIR-based SST products were validated against in-situ water temperature measurements during May 17-26, 2015 and July 15-18, 2015 at the IORS, yielding the accuracy of 0.72-0.85 R-square, and $0.37-0.90^{\circ}C$ RMSE. This TIR-based SST observing system can be installed easily at similar Ocean Research Stations such as Sinan Gageocho and Ongjin Socheongcho, which provide a vision to be utilized as calibration site for SST remotely sensed from satellites to be launched in future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.1-14
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• 2012

Traffic management centers (TMC) collect real-time traffic data from the field and have powerful databases for analysing, recording, and archiving the data. Recent advanced sensor and communication technologies have been widely applied to intelligent transportation systems (ITS). Regarding sensors, various in-vehicle sensors, in addition to global positioning system (GPS) receiver, are capable of providing high resolution data representing vehicle maneuverings. Regarding communication technologies, advanced wireless communication technologies including vehicle-to-vehicle (V2V) and vehicle-to-vehicle infrastructure (V2I), which are generally referred to as V2X, have been widely used for traffic information and operations (references). The V2X environment considers the transportation system as a network in which each element, such as the vehicles, infrastructure, and drivers, communicates and reacts systematically to acquire information without any time and/or place restrictions. This study is motivated by needs of exploiting aforementioned cutting-edge technologies for developing smarter transportation services. The proposed system has been implemented in the field and discussed in this study. The proposed system is expected to be used effectively to support the development of various traffic information control strategies for the purpose of enhancing traffic safety on highways.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.49-57
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• 2022

Currently, the problem of food shortage is emerging in our society due to climate problems and an increase population in the world. As a solution to this problem, we propose a multi-remote control smart farm that combines artificial intelligence (AI) and information and communication technology (ICT) technologies. The proposed smart farm integrates ICT technology to remotely control and manage crops without restrictions in space and time, and to multi-control the growing environment of crops. In addition, using Arduino and deep-learning technology, a smart farm capable of multiple control through a smart-phone application (APP) was proposed, and Ai technology with various data securing and diagnosis functions while observing crop growth in real-time was included. Various sensors in the smart farm are controlled by using the Arduino, and the data values of the sensors are stored in the built database, so that the user can check the stored data with the APP. For multiple control for multiple crops, each LED, COOLING FAN, and WATER PUMP for two or more growing environments were applied so that the user could control it conveniently. And by implementing an APP that diagnoses the growth stage through the Tensor-Flow framework using deep-learning technology, we developed an application that helps users to easily diagnose the growth status of the current crop.

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