• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.405-412
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• 2023

With the development of drone and ICT convergence technology, the use of underwater drones such as leisure underwater drones such as underwater exploration for fishing and industrial drones such as bridge piers is increasing. Existing motor controllers are suitable for aerial drones and these can increase the completeness of underwater drones and their reliability in motor control by developing BLDC motor controllers dedicated to underwater drones. By developing a battery management system (BMS) exclusively for underwater drones, battery stability was ensured by checking the state of charge, checking the state of discharge, adjusting cell balancing, and implementing high/voltage protection functions.

• Journal of ILASS-Korea
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• v.28 no.4
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• pp.191-197
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• 2023

LPG direct injection (LPDi) technology is a method of improving the weaknesses of existing LPG vehicles by directly injection into the combustion chamber. This study was conducted on the comparison of emissions and fuel efficiency performance of the engine and vehicle by applying LPDi technology. The LPDi hybrid engine's maximum output and maximum torque were measured at an equivalent level of less than 1% compared to conventional gasoline fuel. The fuel amount was corrected using the LCU controller, and the THC, CO, and NOx emissions were reduced to 90% in the operating range of the three-way catalyst through air-fuel ratio control. The analysis of THC+NOx and CO emissions in FTP-75 (CVS-75) driving mode satisfied the US LEV III SULEV30 regulation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.3
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• pp.249-255
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• 2017

The finger vein recognition technology is a method to acquire a finger vein image by illuminating infrared light to the finger and to authenticate a person through processes such as feature extraction and matching. In order to recognize a finger vein, a 2D mask-based two-dimensional convolution method can be used to detect a finger edge but it takes too much computation time when it is applied to a low cost micro-processor or micro-controller. To solve this problem and improve the recognition rate, this study proposed an extraction method for the region of interest based on virtual core points and moving average filtering based on the threshold and absolute value of difference between pixels without using 2D convolution and 2D masks. To evaluate the performance of the proposed method, 600 finger vein images were used to compare the edge extraction speed and accuracy of ROI extraction between the proposed method and existing methods. The comparison result showed that a processing speed of the proposed method was at least twice faster than those of the existing methods and the accuracy of ROI extraction was 6% higher than those of the existing methods. From the results, the proposed method is expected to have high processing speed and high recognition rate when it is applied to inexpensive microprocessors.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.5
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• pp.25-37
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• 2010

This study is aimed at enhancing mobility rights for the transportation underprivileged that has been made light of relatively compared to normal people. In order to do this, we've suggested having ITS (Intelligent Traffic System) built and improving satisfaction through the test operation of its main system. The existing sound signal device for the visually handicapped has one problem with managing it. Because, the people in charge of it had to visit each problematic site directly to maintain and fix some problems every time it was out of order. Moreover, it couldn't provide sustainable services about voice guidance and the visually handicapped had to control it by either confirming the location of buttons that were installed on the pillar of traffic light and then pressing one of them or using a remote controller on their own. In order to improve such inconveniences, we have created a new typed sound signal device for the visually handicapped by applying the cutting-edge wireless technology based on ergonomics considering actual road situations. Such technology enables it report the status of signal device and light to them by using its voice guidance system automatically every time they have access to it. Additionally, we've already introduced it to a couple of test areas and then known the fact that they recognized traffic situation more conveniently and safely compared to the existing sound signal device. That is above average in terms of satisfaction. In addition to that, we've provided LTS (Location Tracking System - Location-based service intended for elementary students) by utilizing the existing wireless infrastructure and founded the fact that about 87% of their parents were satisfied with the service based on LTS.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.110-120
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• 2016

A power-efficient neuromodulator is needed for implantable systems. In spite of their stimulation signal's simplicity of wave shape and waiting time of MCU(micro controller unit) much longer than execution time, there is no consideration for low-power design. In this paper, we propose a novel of low-power algorithm based on the characteristics of stimulation signals. Then, we designed and implement a neuromodulation software that we call NMS(neuro modulation simulation). In order to implement low-power algorithm, first, we analyze running time of every function in existing NMS. Then, we calculate execution time and waiting time for these functions. Subsequently, we estimate the transition time between active mode (AM) and low-power mode (LPM). By using these results, we redesign the architecture of NMS in the proposed low-power algorithm: a stimulation signal divided into a number of segments by using characteristics of the signal from which AM or LPM segments are defined for determining the MCU power reduces to turn off or not. Our experimental results indicate that NMS with low-power algorithm reducing current consumption of MCU by 76.31 percent compared to NMS without low-power algorithm.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.8-14
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• 2020

Recently, there was an increasing demand for an integrated access control system which is capable of user recognition, door control, and facility operations control for smart buildings automation. The market available door lock access control solutions need to be improved from the current level security of door locks operations where security is compromised when a password or digital keys are exposed to the strangers. At present, the access control system solution providers focusing on developing an automatic access control system using (RF) based technologies like bluetooth, WiFi, etc. All the existing automatic door access control technologies required an additional hardware interface and always vulnerable security threads. This paper proposes the user identification and authentication solution for automatic door lock control operations using camera based visible light communication (VLC) technology. This proposed approach use the cameras installed in building facility, user smart devices and IoT open source controller based LED light sensors installed in buildings infrastructure. The building facility installed IoT LED light sensors transmit the authorized user and facility information color grid code and the smart device camera decode the user informations and verify with stored user information then indicate the authentication status to the user and send authentication acknowledgement to facility door lock integrated camera to control the door lock operations. The camera based VLC receiver uses the artificial intelligence (AI) methods to decode VLC data to improve the VLC performance. This paper implements the testbed model using IoT open-source based LED light sensor with CCTV camera and user smartphone devices. The experiment results are verified with custom made convolutional neural network (CNN) based AI techniques for VLC deciding method on smart devices and PC based CCTV monitoring solutions. The archived experiment results confirm that proposed door access control solution is effective and robust for automatic door access control.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.54-59
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• 2010

This paper present to study the guidance system as localization technique using sensor fusion and line tracking technique using virtual line for AGV(autonomous guided vehicle). An existing AGV could drive on decided line only. And representative guidance systems of such guidance system are magnet-gyro guidance and wired guidance. However, those have had the high cost of installation and maintenance, and the difficulty of system change according to variation of working environment. To solve such problems, we make the localization system which is fused with a laser navigation and gyro, encoder. The system is robust against noise, and flexible according to working environment through sensor fusion. For line tracking of laser navigation without wire guidance, we set the virtual line in program, and design the driving controller based on difference of angle and distance between AGV's position and decided virtual line. To experiment, we use the AGV which is made by ourselves, and experiment the line tracking repeatedly on same experimental environment. In result, maximum distance error between decided virtual line and AGV's position was less than 49.93mm, and we verified that the proposed system is efficient for line tracking of actual AGV.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.421-426
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• 2018

With regard to LED lighting devices which have currently been commercialized, LED operating sequences are being sold in a fixed state. In such a state, the external environmental factors are not taken into consideration as only the illumination environment application is considered. Currently, it is difficult to create an optimal lighting environment which can adapt to changes in external environmental factors in the ship. Therefore, it was concluded that there is a need to input the external environment value so that the optimal illumination value can be reflected in real time in order to adapt more organically and actively to the change of external environmental factors. In this paper, we used a microprocessor as an integrated management system for environmental data that changes in real time according to existing external environmental factors. In addition, a controller capable of lighting control of RGB LED module by combining fuzzy inference system. For this, a fuzzy control algorithm is designed and a fuzzy control system is constructed. The distance and the illuminance value from the external environment element are input to the sensor, and these values are converted to the optimum illumination value through the fuzzy control algorithm, and are expressed through the dimming control of the RGB LED module and the practical effectiveness of the fuzzy control system is confirmed.

• Journal of Service Research and Studies
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• v.11 no.2
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• pp.118-130
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• 2021

This study designs and validates a greenhouse complex environmental control algorithm with a multi-phase processing scheme that can combine and control actuators according to the degree of change in the greenhouse environment. The composite environmental control system is a system in which the complex environmental controller analyzes the information detected by sensors and operates appropriately actuators to maintain the crop growth environment. A composite environmental controller directs control devices driving actuators through a composite environmental control algorithm, which calculates the values necessary for the operation of the control devices. Most existing algorithms carry out control procedures on a single phase by iteration cycle, which can cause abnormal changes in the greenhouse environment due to errors in output. The proposed algorithm distributes control procedures over multiple phases: environmental control, environmental control, and device operation, and every iteration cycle, detects environmental changes in the environmental control phase first, and then combines control devices that can control the environment in the environmental control phase, and finally, performs the controls to derive the actuators in the device operation phase. The proposed algorithm is designed based on the analysis of the relationship between greenhouse environmental elements and control devices deriving actuators. According to verification analysis, the multi-phase processing scheme provides room to modify or supplement the setting value and enables the control devices to reflect changes in the associated environmental components.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.395-403
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• 2024

Recently, with the advancement of technology, the automotive industry has seen an increase in network connectivity. CAN (Controller Area Network) bus technology enables fast and efficient data communication between various electronic devices and systems within a vehicle, providing a platform that integrates and manages a wide range of functions, from core systems to auxiliary features. However, this increased connectivity raises concerns about network security, as external attackers could potentially gain access to the automotive network, taking control of the vehicle or stealing personal information. This paper analyzed abnormal messages occurring in CAN and confirmed that message occurrence periodicity, frequency, and data changes are important factors in the detection of abnormal messages. Through DBC decoding, the specific meanings of CAN messages were interpreted. Based on this, a model for classifying abnormalities was proposed using the GRU model to analyze the periodicity and trend of message occurrences by measuring the difference (residual) between the predicted and actual messages occurring within a certain period as an abnormality metric. Additionally, for multi-class classification of attack techniques on abnormal messages, a Random Forest model was introduced as a multi-classifier using message occurrence frequency, periodicity, and residuals, achieving improved performance. This model achieved a high accuracy of over 99% in detecting abnormal messages and demonstrated superior performance compared to other existing models.