• The Journal of Korea Robotics Society
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• v.15 no.2
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• pp.169-176
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• 2020

This paper studied the collision detection of robot manipulators for safe collaboration in human-robot interaction. Based on sensor-based collision detection, external torque is detached from subtracting robot dynamics. To detect collision using joint torque sensor data, a comparative study was conducted using data-based machine learning algorithm. Data was collected from the actual 3 degree-of-freedom (DOF) robot manipulator, and the data was labeled by threshold and handwork. Using support vector machine (SVM), decision tree and k-nearest neighbors KNN method, we derive the optimal parameters of each algorithm and compare the collision classification performance. The simulation results are analyzed for each method, and we confirmed that by an optimal collision status detection model with high prediction accuracy.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.870-881
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• 2020

This paper presents cell ID (cell identity) detection schemes using PSS/SSS (primary synchronization signal/secondary synchronization signal) for 5G NR (new radio) system and evaluates the detection performance. In this paper, we consider two cell ID detection schemes, i.e. two-stage detection and joint detection schemes. The two-stage detection scheme consists of two stages which estimate a channel gain between a transmitter and receiver and detect the PSS and SSS sequences. The joint detection scheme jointly detects the PSS and SSS sequences. In addition, this paper presents coherent and non-coherent combining schemes. The coherent scheme calculates the correlation value for the total length of the given PSS and SSS sequences, and the non-coherent combining scheme calculates the correlation within each group by dividing the total length of the sequence into several groups and then combines them non-coherently. For the detection schemes considered in this paper, the detection error rates of PSS, SSS and overall cell ID are evaluated and compared through computer simulations. The simulation results show that the joint detection scheme outperforms the two-stage detection scheme for both coherent and non-coherent combining schemes, but the two-stage detection scheme can greatly reduce the computational complexity compared to the joint detection scheme. In addition, the non-coherent combining detection scheme shows better performance under the additive white Gaussian noise (AWGN), fixed, and mobile environments.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1044-1050
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• 2011

The quality of brazing joints is one of the most important factors that have an effect on the performance of the brazing joint-based heat exchangers with the growing use in industry recently. Therefore, it is necessary to inspect the brazing joints in order to guarantee the performance of the heat exchangers. This paper presents a non-destructive method to inspect the brazing joints of the heat exchangers using X-ray. Firstly, X-ray cross-sectional images of the brazing joints are obtained by using CT (Computerized Tomography) technology. Cross-sectional image from CT is more useful to detect the inner defects than the traditional transmitted X-ray image. Secondly, the acquired images are processed by an algorithm proposed for the defect detection of brazing joint. Finally, two types of brazing joint are examined in a series of experiments to detect the defects in brazing joints. The experimental results show that the proposed algorithm is effective for defect detection of the brazing joints in heat exchangers.

• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.1909-1918
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• 2016

This paper presents a unified framework for joint Convolutional Neural Network (CNN) based vehicle detection by leveraging multi-spectral image pairs. With the observation that under challenging environments such as night vision and limited light source, vehicle detection in a single color image can be more tractable by using additional far-infrared (FIR) image, we design joint CNN architecture for both RGB and FIR image pairs. We assume that a score map from joint CNN applied to overall image can be considered as confidence of vehicle existence. To deal with various scale ratios of vehicle candidates, multi-scale images are first generated scaling an image according to possible scale ratio of vehicles. The vehicle candidates are then detected on local maximal on each score maps. The generation of overlapped candidates is prevented with non-maximal suppression on multi-scale score maps. The experimental results show that our framework have superior performance than conventional methods with a joint framework of multi-spectral image pairs reducing false positive generated by conventional vehicle detection framework using only single color image.

• Tunnel and Underground Space
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• v.19 no.1
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• pp.1-10
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• 2009

Site characterization for structural stability in rock masses mainly involves the collection of joint property data, and in the current practice, much of this data is collected by hand directly at exposed slopes and outcrops. There are many issues with the collection of this data in the field, including issues of safety, slope access, field time, lack of data quantity, reusability of data and human bias. It is shown that information on joint orientation, spacing and roughness in rock masses, can be automatically extracted from LIDAR (light detection and ranging) point floods using the currently available Split-FX point cloud processing software, thereby reducing processing time, safety and human bias issues.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1795-1811
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• 2019

In order to achieve rapid and accurate detection of vehicle objects in complex traffic conditions, we propose a novel vehicle detection method. Firstly, more contextual and small-object vehicle information can be obtained by our Joint Feature Network (JFN). Secondly, our Evolved Region Proposal Network (EPRN) generates initial anchor boxes by adding an improved version of the region proposal network in this network, and at the same time filters out a large number of false vehicle boxes by soft-Non Maximum Suppression (NMS). Then, our Mask Network (MaskN) generates an example that includes the vehicle occlusion, the generator and discriminator can learn from each other in order to further improve the vehicle object detection capability. Finally, these candidate vehicle detection boxes are optimized to obtain the final vehicle detection boxes by the Fine-Tuning Network(FTN). Through the evaluation experiment on the DETRAC benchmark dataset, we find that in terms of mAP, our method exceeds Faster-RCNN by 11.15%, YOLO by 11.88%, and EB by 1.64%. Besides, our algorithm also has achieved top2 comaring with MS-CNN, YOLO-v3, RefineNet, RetinaNet, Faster-rcnn, DSSD and YOLO-v2 of vehicle category in KITTI dataset.

• The Journal of Korea Robotics Society
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• v.13 no.3
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• pp.157-163
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• 2018

This paper deals with the development and application of control algorithms for series elastic relief robots for rescue operations in harsh environment like disasters or battlefield. The joint controller applied in this paper has a cascade structure combining inner loop for torque control and outer loop for position control. The torque loop contains feedforward and feedback controller and disturbance observer for independent, decentralized joint control. The effect of the elastic component and motor dynamics are treated as the nonlinear disturbance and compensated with the disturbance observer of torque controller. For the collision detection, Band Designed Disturbance Observer is configured to recognize/respond to external disturbance robustly in the continuously changing environment. The controller is applied to a 7-dof series elastic manipulator to evaluate the torque tracking and collision detection/response performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.601-617
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• 2014

In digital radio broadcasting systems, long delays are incurred in service start time when tuning to a particular frequency because several synchronization steps, such as symbol timing synchronization, frame synchronization, and carrier frequency offset and sampling frequency offset compensation are necessary. Therefore, the operation of the synchronization blocks causes delays ranging from several hundred milliseconds to a few seconds until the start of the radio service after frequency tuning. Furthermore, if spectrum inversed signals are transmitted in digital radio broadcasting systems, the receivers are unable to decode them, even though most receivers can demodulate the spectral inversed signals in analog radio broadcasting systems. Accordingly, fast synchronization techniques and a method for spectral inversion detection are required in digital radio broadcasting systems that are to replace the analog radio systems. This paper presents a joint detection method of frame, integer carrier frequency offset, and spectrum inversion for DRM Plus digital broadcasting systems. The proposed scheme can detect the frame and determine whether the signal is normal or spectral inversed without any carrier frequency offset and sampling frequency offset compensation, enabling fast frame synchronization. The proposed method shows outstanding performance in environments where symbol timing offsets and sampling frequency offsets exist.

• ETRI Journal
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• v.26 no.5
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• pp.384-390
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• 2004

We propose a new bandwidth-efficient technique that achieves high data rates over a wideband wireless channel. This new scheme is targeted for a multiple-input multiple- output orthogonal frequency-division multiplexing (MIMO-OFDM) system that achieves transmit diversity through a space frequency block code and capacity enhancement through the iterative joint processing of zero-forcing detection and maximum a posteriori (MAP) decoding. Furthermore, the proposed scheme is compared to the coded Bell Labs Layered Space-Time OFDM (BLAST-OFDM) scheme.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.11
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• pp.1-8
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• 2008

In this paper, we analyze the symbol error rate (SER) performance of maximum likelihood (ML) joint detection in downlink multiple-input multiple-output (MIMO) multicarrier code division multiple access (MC-CDMA) systems by deriving a tight union bound on the symbol error rate (SER). The union bound for ML joint detection is utilized to demonstrate the performance of MIMO MC-CDMA systems quantitatively in multiuser and frequency selective Rayleigh fading environments. An analysis of the diversity order of the systems shows the effects of multiple users, spread subcarriers, and multiple antennas on the ML joint detection performance. Furthermore, the analysis shows that MIMO MC-CDMA systems without full loading can achieve more diversify than MIMO orthogonal frequency division multiplexing (OFDM) systems.