• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.250-260
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• 2023

In this paper, we designed a IoT(Internet of Things) sensor block embedded lightweight house deck structures that can be implemented using Carbon Fiber Reinforced Polymer(CFRP). Deck-Sensor interconnection interface block via IoT connectivity Hub that can mount external environmental sensors such as fire sensors on the Deck body itself was also proposed. Additionally we described the configuration of devices for data acquisition and analysis based on IoT environmental detection sensors that can be commonly installed and used on these deck bodies. On the other hand, received sensing data based monitoring user interface(UI) also developed and used for sensing data analysis for remote monitoring center. Through the implementation of such IoT-based sensor data transmission and collection analysis devices and UI software, this paper confirmed the availability of CFRP based lightweight House deck structure and possibility of CFRP deck-based IoT sensor data networking and analysis functions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1224-1248
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• 2022

In various sensor network applications, such as climate observation organizations, sensor nodes need to collect information from time to time and pass it on to the recipient of information through multiple bounces. According to field tests, this information corresponds to most of the energy use of the sensor hub. Decreasing the measurement of information transmission in sensor networks becomes an important issue.Compression sensing (CS) can reduce the amount of information delivered to the network and reduce traffic load. However, the total number of classification of information delivered using pure CS is still enormous. The hybrid technique for utilizing CS was proposed to diminish the quantity of transmissions in sensor networks.Further the energy productivity is a test task for the sensor nodes. However, in previous studies, a clustering approach using hybrid CS for a sensor network and an explanatory model was used to investigate the relationship between beam size and number of transmissions of hybrid CS technology. It uses efficient data integration techniques for large networks, but leads to clone attacks or attacks. Here, a new algorithm called SBEA (Snowball Endurance Algorithm) was proposed and tested with a bow. Thus, you can extend the battery life of your WSN by running effective copy detection. Often, multiple nodes, called observers, are selected to verify the reliability of the nodes within the network. Personal data from the source centre (e.g. personality and geographical data) is provided to the observer at the optional witness stage. The trust and reputation system is used to find the reliability of data aggregation across the cluster head and cluster nodes. It is also possible to obtain a mechanism to perform sleep and standby procedures to improve the life of the sensor node. The sniffers have been implemented to monitor the energy of the sensor nodes periodically in the sink. The proposed algorithm SBEA (Snowball Endurance Algorithm) is a combination of ERCD protocol and a combined mobility and routing algorithm that can identify the cluster head and adjacent cluster head nodes.This algorithm is used to yield the network life time and the performance of the sensor nodes can be increased.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.600-605
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• 1989

A theoretical study is presented for the end-point holding control of a one-link flexible arm, whose base is subjected to a lateral fluctuation. The arm is clamped on a rigid hub mounted directly on the shaft of d.c. servomoter. The tip position is measured by a gap sensor fixed in space isolated from the system vibration. The arm is controlled so as to make the end point stay precisely at its initial position even if the base is fluctuated.

• Proceedings of the KIEE Conference
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• 2008.10c
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• pp.132-135
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• 2008

This paper presents a PLL(Phase Lock Loop) control method for speed control of high speed miniature BLDCM(Brushless DC Motor) using hall sensor. The Proposed PLL based speed control method uses a only phase shift between reference pulse signal according to speed reference and actual pulse signal from hall sensor. It doesn't use any speed calculation, and calculates a direct current reference from phase shift. The current reference is changed to reduce the phase shift between reference and actual pulse. So the actual speed can keep the reference speed. The proposed control scheme is very simple but effective speed control is possible.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.106-114
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• 2015

Wireless Body Area Network(WBAN) is a network standard around a human body which connects various medical sensor and devices It has to satisfy various demands such as data transmission rate, priority, and delay time. In this paper, considering a data priority and transmission delay time, is proposed to improve efficiency of WBAN service depending on congestion status of network. The proposed algorithm operates with adapted data removal probability according to data priority when the hub buffer is congested than before. And in the case of lower congestion within the hub buffer data is served considering data delay time. Through the comparison with other existing scheduling algorithms, it is confirmed that quality of WBAN service is improved due to lower data loss rate of medical data and less delay time in the proposed algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.3
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• pp.135-152
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• 2018

The purpose of this study is to propose an optimal fusion method of aerial multi - sensor data to improve the accuracy of land cover classification. Recently, in the fields of environmental impact assessment and land monitoring, high-resolution image data has been acquired for many regions for quantitative land management using aerial multi-sensor, but most of them are used only for the purpose of the project. Hyperspectral sensor data, which is mainly used for land cover classification, has the advantage of high classification accuracy, but it is difficult to classify the accurate land cover state because only the visible and near infrared wavelengths are acquired and of low spatial resolution. Therefore, there is a need for research that can improve the accuracy of land cover classification by fusing hyperspectral sensor data with multispectral sensor and aerial laser sensor data. As a fusion method of aerial multisensor, we proposed a pixel ratio adjustment method, a band accumulation method, and a spectral graph adjustment method. Fusion parameters such as fusion rate, band accumulation, spectral graph expansion ratio were selected according to the fusion method, and the fusion data generation and degree of land cover classification accuracy were calculated by applying incremental changes to the fusion variables. Optimal fusion variables for hyperspectral data, multispectral data and aerial laser data were derived by considering the correlation between land cover classification accuracy and fusion variables.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.214-219
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• 2014

In this paper, a new robot system is designed and manufactured to improve the educational robot. The main processor is realized using the ARM Cortex-M3 and the co-processor is implemented by AVR ATMega2560 to deal with data coming from sensor modules. The processor of sensor modules was developed by the AVR ATMega8. The communication system is composed with IIC communication to alleviate the load of main processor. We have developed the educational robot system adopting the hub module for extension characteristics.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2195-2196
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• 2006

This study is for implementation of PC based Motor fault diagnosis system. By using harmonics and current signals of the motor, this system diagnoses the motor condition by accumulated harmonic contribution rate. In this proposed system that was composed of 5 parts. A sensor, connection box, evaluation board, device server, and main computer are those. There were two types of sensor, one was harmonic sensor the other was current sensors. The signal was acquired by sensor, and transferred to evaluation board. Second one is connection box. Because the output type of sensor and input type of evaluation board is different, connection box was necessary. Third one was evaluation board. The signal from the sensor was converted to digital signal in evaluation board. And this signal was transferred to device server. Fourth one was device server. Device server transferred the data from evaluation board to main computer. And the last one was other parts controlled by main computer. In main computer, there were communication and diagnosis algorithms. The result was derived by main computer. In the result, there were 12 categories and 5 levels of motor conditions. The proposed system had some advantages comparing with stand alone type commercial motor fault diagnosis system. The first, by using remote access it was easier to get the conditions of motor. The second, there was no need to handle the sensors when users measured the motor signals. By this property, no one was necessary at motor location site. The third, this system was less restricted by times and places than commercial stand alone type diagnosis system. Therefore users can operate this system only using the main computer. Once the sensors are installed at the motor, users doesn't need to move to check up the condition of motors. Moreover, if there is ethernet hub, many motors can be not only diagnosed at once but also decreased its cost.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.563-564
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• 2006

This study is for implementation of PC based Motor fault diagnosis system. By using harmonics and current signals of the motor, this system diagnoses the motor condition by accumulated harmonic contribution rate. In this proposed system that was composed of 5 parts. A sensor, connection box, evaluation board, device server, and main computer are those. There were two types of sensor, one was harmonic sensor the other was current sensors. The signal was acquired by sensor, and transferred to evaluation board. Second one is connection box. Because the output type of sensor and input type of evaluation board is different, connection box was necessary. Third one was evaluation board. The signal from the sensor was converted to digital signal in evaluation board. And this signal was transferred to device server Fourth one was device solver. Device server transferred the data from evaluation board to main computer. And the last one was other parts controlled by main computer. In main computer, there were communication and diagnosis algorithms. The result was derived by main computer. In the result, there were 12 categories and 5 levels of motor conditions. The proposed system had some advantages comparing with stand alone type commercial motor fault diagnosis system. The first, by using remote access it was easier to get the conditions of motor. The second, there was no need to handle the sensors when users measured the motor signals. By this Property, no one was necessary at motor location site. The third, this system was less restricted by times and places than commercial stand alone type diagnosis system. Therefore users can operate this system only using the main computer. Once the sensors are installed at the motor, users doesn't need to move to check up the condition of motors. Moreover, if there is ethernet hub, many motors can be not only diagnosed at once but also decreased its cost.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.1229-1230
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• 2006

This study is for implementation of PC based Motor fault diagnosis system. By using harmonics and current signals of the motor, this system diagnoses the motor condition by accumulated harmonic contribution rate. In this proposed system that was composed of 5 parts. A sensor, connection box, evaluation board, device server, and main computer are those. There were two types of sensor, one was harmonic sensor the other was current sensors. The signal was acquired by sensor, and transferred to evaluation board. Second one is connection box. Because the output type of sensor and input type of evaluation board is different, connection box was necessary. Third one was evaluation board. The signal from the sensor was converted to digital signal in evaluation board. And this signal was transferred to device server. Fourth one was device server. Device server transferred the data from evaluation board to main computer. And the last one was other parts controlled by main computer in main computer, there were communication and diagnosis algorithms. The result was derived by main computer. In the result, there were 12 categories and 5 levels of motor conditions. The proposed system had some advantages comparing with stand alone type commercial motor fault diagnosis system. The first, by using remote access it was easier to get the conditions of motor. The second, there was no need to handle the sensors when users measured the motor signals. By this property, no one was necessary at motor location site. The third, this system was less restricted by times and places than commercial stand alone type diagnosis system. Therefore users can operate this system only using the main computer. Once the sensors are installed at the motor, users doesn't need to move to check up the condition of motors. Moreover, if there is ethernet hub, many motors can be not only diagnosed at once but also decreased its cost.