• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.685-694
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• 2018

In this paper, we designed and fabricated the frequency mixer, which is the core parts of high frequency head in Ku-band tracking radar. To overcome the problem of single-ended and single-balanced resistive structure, we designed the fine-line structure with balanced mixer, to generate IF signal without distortion in L-band, after receiving the RF signal of the Ku-band. The prototype mixer showed a Noise Figure Max of 6.823dB, Gain of 4.1598~4.676dB and Band Pass of 61MHz in 5 Ku-band samples frequency.

• Advances in nano research
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• v.14 no.5
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• pp.409-419
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• 2023

Tracking the motion of tennis balls is a challenging task in using cameras around the tennis court. The most important instance of the tennis trajectory is the time of impact and touch the court which in some cases could not be detected precisely. In the present study, we aim to present a novel design of tennis balls equipped with nano-sensors to detect the touch of the ball to the court. In the impact instance, tennis ball receives significant acceleration and change in the linear momentum. This large acceleration could deform a small-beam structure with piezoelectric layer to produce voltage. The voltage could further be utilized to produce infrared waves which could be easily detected by infrared detection sensors installed on the same video cameras or separately near the tennis court. Therefore, the exact time of the impact could be achieved with higher accuracy than image analyzing method. A detailed dynamical property of such sensors is discussed using nonlinear beam equations. The results show that within the acceleration range of tennis ball during an impact, the piezoelectric patches of the nano-sensors in the tennis ball could produce enough voltages to propagate infrared waves to be detected by infrared detectors.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.977-985
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• 2009

This paper proposes a real-time algorithm for tracking the fast moving human body. Although Iterative closest point (ICP) algorithm is suitable for real-time tracking due to its efficiency and low computational complexity, ICP often fails to converge when the human body moves fast because the closest point may be mistakenly selected and trapped in a local minimum. To overcome such limitation, we combine a particle filter based on a motion history information with the ICP. The proposed human body motion tracking algorithm reduces the search space for each limb by employing a hierarchical tree structure, and enables tracking of the fast moving human bodies by using the motion prediction based on the motion history. Experimental results show that the proposed human body motion tracking provides accurate tracking performance and fast convergence rate.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.10
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• pp.842-850
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• 2014

This paper presents a trajectory tracking controller for rotorcraft UAVs to improve the tracking performances in the presence of various uncertainties. The proposed tracking method consists of a velocity guidance law based on the relative distance and L1 adaptive augmentation loop for tracking the velocity commands. In the proposed structure, the desired velocity generated by the guidance law is the reference value of the adaptive controller for accurate path tracking. In the guidance law, the desired acceleration is generated based on the relative distance and its derivatives, and then the velocity command of the inner control loop is calculated by integrating the accelerations. $L_1$ augmentation loop supplements the linear controller to guarantee the flight performances such as a tracking accuracy in the presence of the uncertainties. The proposed controller was validated in actual flight tests to successfully demonstrate its capability using a quadrotor UAV.

• Smart Structures and Systems
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• v.24 no.2
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• pp.243-256
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• 2019

Determination of the most meaningful structural modes and gaining insight into how these modes evolve are important issues for long-term structural health monitoring of the long-span bridges. To address this issue, modal parameters identified throughout the life of the bridge need to be compared and linked with each other, which is the process of mode tracking. The modal frequencies for a long-span bridge are typically closely-spaced, sensitive to the environment (e.g., temperature, wind, traffic, etc.), which makes the automated tracking of modal parameters a difficult process, often requiring human intervention. Machine learning methods are well-suited for uncovering complex underlying relationships between processes and thus have the potential to realize accurate and automated modal tracking. In this study, Gaussian mixture model (GMM), a popular unsupervised machine learning method, is employed to automatically determine and update baseline modal properties from the identified unlabeled modal parameters. On this foundation, a new mode tracking method is proposed for automated mode tracking for long-span bridges. Firstly, a numerical example for a three-degree-of-freedom system is employed to validate the feasibility of using GMM to automatically determine the baseline modal properties. Subsequently, the field monitoring data of a long-span bridge are utilized to illustrate the practical usage of GMM for automated determination of the baseline list. Finally, the continuously monitoring bridge acceleration data during strong typhoon events are employed to validate the reliability of proposed method in tracking the changing modal parameters. Results show that the proposed method can automatically track the modal parameters in disastrous scenarios and provide valuable references for condition assessment of the bridge structure.

• Journal of Biomedical Engineering Research
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• v.31 no.6
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• pp.427-433
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• 2010

Although tracking methods are efficient and popular for vessel segmentation, they require a seed to initiate an instance of tracking. In this paper, a new method to detect new seeds for tracking of arterial segments from CT angiography (CTA) and to construct a vascular structure is proposed. The proposed algorithm is based on shape analysis of connected components in a volume of interest around a vessel segment which was already extracted by tracking. The eigenvalues of the covariance matrix are used as the shape features for detection. The experimental results on actual clinical data showed that the results totally revealed the arterial tree not hindered by bone or veins. In visual comparison to a method which combines registration and subtraction of both pre-contrast and post-contrast CT volumes, the proposed method produced comparable results to the reference method and were confirmed of its feasibility for clinical use of reducing the cost and burden of patients.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1359-1375
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• 2015

Energy efficiency is critical for Wireless Sensor Networks (WSNs) since sensor nodes usually have very limited energy supply from battery. Sleep scheduling and nodes cooperation are two of the most efficient methods to achieve energy conservation in WSNs. In this paper, we propose an adaptive energy optimization approach for target tracking applications, called Energy-Efficient Node Coordination (EENC), which is based on the grid structure. EENC provides an unambiguous calculation and analysis for optimal the nodes cooperation theoretically. In EENC, the sleep schedule of sensor nodes is locally synchronized and globally unsynchronized. Locally in each grid, the sleep schedule of all nodes is synchronized by the grid head, while globally the sleep schedule of each grid is independent and is determined by the proposed scheme. For dynamic sleep scheduling in tracking state we propose a multi-level coordination algorithm to find an optimal nodes cooperation of the network to maximize the energy conservation while preserving the tracking performance. Experimental results show that EENC can achieve energy saving of at least 38.2% compared to state-of-the-art approaches.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.6
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• pp.363-372
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• 2015

Recent some SoC FPGA Releases that integrate ARM processor and FPGA fabric show better performance compared to the ASIC SoC used in typical embedded image processing system. In this study, using the above advantages, we implement a SoC FPGA-based Real-Time Object Recognition and Tracking System. In our system, the video input and output, image preprocessing process, and background subtraction processing were implemented in FPGA logics. And the object recognition and tracking processes were implemented in ARM processor-based programs. Our system provides the processing performance of 5.3 fps for the SVGA video input. This is about 79 times faster processing power than software approach based on the Nios II Soft-core processor, and about 4 times faster than approach based the HPS processor. Consequently, if the object recognition and tracking system takes a design structure combined with the FPGA logic and HPS processor-based processes of recent SoC FPGA Releases, then the real-time processing is possible because the processing speed is improved than the system that be handled only by the software approach.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.361-364
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• 2007

A small sized device with computing, communicating, sensing capability is changing our life. It will be deployed in the world and acquire a lot of data from the world. It is used for various applications such as military surveillance, environmental monitoring, structure health monitoring, building management, asset tracking, etc. In this paper we focus on USN middleware for asset tracking. A mobile asset is moving here and there within a specific area. The USN middleware tracks the mobile assets in real-time by using sensor nodes and notify their current positions to a user. To achieve the goal, the USN middleware provides some features related to the positions of mobile assets.. They are storing location data by using 3D indexing method, retrieving them by using spatio-temporal query, making trace of an asset, and retrieving the history data of an asset. In the paper, we developed USN middleware to adapt the requirements of asset tracking. It can help users increase the efficiency of their business related to mobile assets and make a valuable decision.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.5
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• pp.354-361
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• 2016

Parallel robots are usually used for performing pick-and-place motion to increase productivity in high-speed environments. The present study proposes a high-speed parallel robot and a control approach to improve the tracking performance for the purpose of handling a solar cell. However, the target processes are not limited to the solar cell-handling field. Therefore, a delta-type parallel manipulator is designed, and a ball joint structure is specifically proposed to increase the allowed angle that would meet the required workspace. A control algorithm considering the synchronization between multiple joints in a closed-chain mechanism is also suggested to improve the tracking performance, where the tracking and synchronization errors are simultaneously considered. In addition, a prototype machine with the proposed ball joint is implemented. A satisfactory tracking performance is achieved by applying the proposed control algorithm, with a cycle time of 0.3 s for a 0.1 kg payload.