• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.703-710
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• 2007

This paper presents an attitude determination scheme for the Science and Technology Satellite-2(STSAT-2) using Fine Digital Sun Sensor(FDSS). The FDSS has been developed for STSAT-2, and exhibits the accuracy of 0.032degree in $1{\sigma}$. To be specific, the attitude information from the sensor is exploited to compensate for Fiber Optic Gyro(FOG) mounted on STSAT-2, and Kalman filter model is derived and implemented. To show the effectiveness of the present compensation scheme, computer simulations have been carried out resulting in the attitude errors within a bound.

• Journal of Power System Engineering
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• v.19 no.3
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• pp.36-41
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• 2015

The true north-finding equipment utilizing gyro sensor is used for INS(Inertial Navigation System) and has an increasing preference for gyro compass system due to compact size, lightening, low power and price. The purpose of this experiment is to research gyro compass system providing high performance in the field of manned or unmanned gyro compass system which proposes the accuracy of 1% utilizing experimented equipment at true north of the earth. Unlike the conventional system, the proposed gyro sensor system indicates to be applied in the various and specific equipment using multiple technique and method.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.339-341
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• 2006

This paper proposes a method of container position measurement using automatic landing system that is estimated by a laser range finder. In the most of container position measurement methods, CCD cameras or laser scanners have been used to get the source data. However those sensors are not only weak for disturbances, for examples, the light, fog, and rain, but also the system cost is high. When the spreader arrives the goal position, it is still swung by inertia or by wind effect. In this paper, the spreader swung data have been used to find the container position. The laser range finder is equipped in the front side of spreader. It can measure distance and relative position between spreader and container. This laser range finder can be rotated as desired by a motor. And a tilt sensor is equipped on the spreader to measure spreader sway. We estimate the relative position information between the spreader and a container using the laser range finder and tilt sensor through the geometrical analysis.

• Proceedings of the KSR Conference
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• 1999.05a
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• pp.307-314
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• 1999

The potential use of ranging sensors for reducing the occurrence of accidents in real environment is explored by many companies and laboratories. Most of the sensors under investigation utilize the FMCW(Frequency Modulated Continuous Wave) waveforms. The automotive environment presents to the FMCW radar sensor a multitude of moving and fixed targets and the sensor must detect and track only the targets which may pose a threat of collision or passengers accident. The sensor must function accurately in the presence of background echoes generated by moving and fixed targets, ground reflections, atmospheric noises, including rains, fog, and, snow and noise generated within the receiver. False detection of the desired target in this environment may issue false alarms. That may be dangerous to the passenger and the vehicle. A high false alarm rate is totally unacceptable. The false alarm mechanism consists of noise peaks, crossing the threshold and the undesired response of the system to off lane targets which are not potentially hazardous to the radar equipped vehicle. This paper presents an improve technique safety performance for driver-less operation using FMCW radar sensors.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.1368-1370
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• 2000

The automotive environment presents to the FMCW radar sensor a multitude of moving and fixed targets and the sensor must detect and track only the targets which may pose a threat of collision or passengers accident. The sensor must function accurately in the presence of background echoes generated by moving and fixed targets, ground reflections, atmospheric noises, including rains, fog, and, snow and noise generated within the receiver. False detection of the desired target in this environment may issue false alarms. That may be dangerous to the passenger and the vehicle. A high false alarm rate is totally unacceptable. The false alarm mechanism consists of noise peaks, crossing the threshold and the undesired response of the system to off lane targets which are not potentially hazardous to the radar equipped vehicle. This paper presents an improve technique safety performance for driver-less operation using FMCW radar sensors.

• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.6
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• pp.643-648
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• 2013

In this paper, we developed a system which can detect and can alarm about the sailing provocative climate by using a photo. The research on domestic shipbuilding industry and in IT fusion technology is under construction, but a reliable safety device which can alarm a sailor about the circumstances of the fog and rain during ship operation as soon as possible due to the constant state in domestic. In this paper, a compact, for system low-power transceiver and data processing equipment for sensing were developed, also a performance evaluation got accomplished with simulation analysis. In results, it is operating normally at least 32.36[dB] and maximum values f 89.20[dB] in the domestic, and 32.55 to 60.66[dB] in the outdoors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.165-170
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• 2021

Machine learning has recently been applied to research in most areas. This is because the results of machine learning are not determined, but the learning of input data creates the objective function, which enables the determination of new data. In addition, the increase in accumulated data affects the accuracy of machine learning results. The data collected here is an important factor in machine learning. The proposed system is a convergence system of cloud systems and local fog systems for service delivery. Thus, the cloud system provides machine learning and infrastructure for services, while the fog system is located in the middle of the cloud and the user to collect and refine data. The data for this application shall be based on the Sensitive data generated by smart devices. The machine learning technique applied to this system uses SVM algorithm for classification and RNN algorithm for status recognition.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.39-45
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• 2007

This paper proposes a method of container position measurement using automatic landing system that is estimated by a laser range finder. In the most of container position measurement methods, CCD cameras or laser scanners have been used to get the source data. However those sensors are not only weak for disturbances, for examples, the light, fog, and rain, but also the system cost is high. When the spreader arrives at the goal position, it is still swung by inertia or by wind effect. In this paper, the spreader swung data have been used to find the container position. The laser range finder is equipped in the front side of spreader. It can measure distance and relative position between spreader and container. This laser range finder can be rotated as desired by a motor. And a tilt sensor is equipped on the spreader to measure spreader sway. The relative position information between the spreader and a container using the laser range finder and tilt sensor is estimated through the geometrical analysis.

• Journal of IKEEE
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• v.24 no.1
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• pp.232-237
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• 2020

In this paper, we propose an efficient sensor fusion method for autonomous vehicle recognition and distance measurement. Typical sensors used in autonomous vehicles are radar, lidar and camera. Among these, the lidar sensor is used to create a map around the vehicle. This has the disadvantage, however, of poor performance in weather conditions and the high cost of the sensor. In this paper, to compensate for these shortcomings, the distance is measured with a radar sensor that is relatively inexpensive and free of snow, rain and fog. The camera sensor with excellent object recognition rate is fused to measure object distance. The converged video is transmitted to a smartphone in real time through an IP server and can be used for an autonomous driving assistance system that determines the current vehicle situation from inside and outside.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4865-4885
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• 2019

We propose a FPGA based design that performs real-time power-efficient analysis of heterogeneous sensor data using adaptive ANN on edge gateway of smart military wearables. In this work, four independent ANN classifiers are developed with optimum topologies. Out of which human activity, BP and toxic gas classifier are multiclass and ECG classifier is binary. These classifiers are later integrated into a single adaptive ANN hardware with a select line(s) that switches the hardware architecture as per the sensor type. Five versions of adaptive ANN with different precisions have been synthesized into IP cores. These IP cores are implemented and tested on Xilinx Artix-7 FPGA using Microblaze test system and LabVIEW based sensor simulators. The hardware analysis shows that the adaptive ANN even with 8-bit precision is the most efficient IP core in terms of hardware resource utilization and power consumption without compromising much on classification accuracy. This IP core requires only 31 microseconds for classification by consuming only 12 milliwatts of power. The proposed adaptive ANN design saves 61% to 97% of different FPGA resources and 44% of power as compared with the independent implementations. In addition, 96.87% to 98.75% of data throughput reduction is achieved by this edge gateway.