• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.2
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• pp.212-226
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• 2014

This paper presents the design and implementation of a pipelined architecture and a method for real-time human detection using depth image from a Time-of-Flight (ToF) camera. In the proposed method, we use Euclidean Distance Transform (EDT) in order to extract human body location, and we then use the 1D, 2D scanning window in order to extract human joint location. The EDT-based human extraction method is robust against noise. In addition, the 1D, 2D scanning window helps extracting human joint locations easily from a distance image. The proposed method is designed using Verilog HDL (Hardware Description Language) as the dedicated hardware architecture based on pipeline architecture. We implement the dedicated hardware architecture on a Xilinx Virtex6 LX750 Field Programmable Gate Arrays (FPGA). The FPGA implementation can run 80 MHz of maximum operating frequency and show over 60fps of processing performance in the QVGA ($320{\times}240$) resolution depth image.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.8
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• pp.374-379
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• 2002

In this study, we developed a new human arm movement detection system using electrical bio-impedance method with several skin-electrodes. The correlation coefficients of the joint angle and the impedance change from human arm movement was obtained using a goniometer and impedance measurement system developed in this study. The correlation coefficients of the wrist and the elbow movements were 0.94 and -0.99, respectively. This system was applied to control a robotic arm by converting the measured impedance to joint angle to confirm the validity of the proposed system. In conclusion, we confirmed that this system can control the robotic arm according to arm movement without any limitation of movement. This system showed possibility that upper arm movement could be easily measured by impedance measurement system with a few skin-electrodes.

• Journal of Welding and Joining
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• v.32 no.5
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• pp.50-57
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• 2014

Friction Stir Welding (FSW) is a complex process with several mutually interdependent parameters. A slight difference from known settings may lead to imperfections in the stirred zone. These inhomogeneities affect on the mechanical properties of the FSWed joints. In order to prevent the failure of the welded joint it is necessary to detect the most critical defects non-destructive. Especially critical defects are wormhole and lack of penetration (LOP), because of the difficulty of detection. Online thermography is used process-accompanying for defect detecting. A thermographic camera with a fixed position relating to the welding tool measures the heating-up and the cool down of the welding process. Lap joints with sound weld seam surfaces are manufactured and monitored. Different methods of evaluation of heat distribution and intensity profiles are introduced. It can be demonstrated, that it is possible to detect wormhole and lack of penetration as well as surface defects by analyzing the welding and the cooling process of friction stir welding by passive online thermography measurement. Effects of these defects on mechanical properties are shown by tensile testing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2525-2532
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• 2009

In this paper, a fast sphere decoder is proposed for the joint detection of phase-shift keying (PSK) signals in uncoded Vertical Bell Laboratories Layered Space Time (V-BLAST) systems. The proposed decoder, PSD, consists of preprocessing stage and search stage. The search stage of PSD relies on the depth-first branch-and-bound (BB) algorithm with "best-first" orders stored in lookup tables. Simulation results show that the PSD is able to provide the system with the maximum likelihood (ML) performance at low complexity.

• Smart Structures and Systems
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• v.5 no.4
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• pp.329-344
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• 2009

Ultrasonic chaotic excitations combined with sensor prediction algorithms have shown the ability to identify incipient damage (loss of preload) in a bolted joint. In this study we examine a physical experiment on a single-bolt aluminum lap joint as well as a three-dimensional physics-based simulation designed to model the behavior of guided ultrasonic waves through a similarly configured joint. A multiple bolt frame structure is also experimentally examined. In the physical experiment each signal is imparted to the structure through a macro-fiber composite (MFC) patch on one side of the lap joint and sensed using an equivalent MFC patch on the opposite side of the joint. The model applies the waveform via direct nodal displacement and 'senses' the resulting displacement using an average of the nodal strain over an area equivalent to the MFC patch. A novel statistical classification feature is developed from information theory concepts of cross-prediction and interdependence. This damage detection algorithm is used to evaluate multiple damage levels and locations.

• Journal of the Korea Computer Industry Society
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• v.8 no.3
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• pp.173-180
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• 2007

This paper describes the point-detector arrays system to processes the fields of signal and noise of the turbulent atmosphere. By using aboves, the maxmum output of direct-detection shows a little differences between experimental datas and theoretical datas. As a whole the experimental datas are agreed with the joint Gaussian theoretical curves and K-distribution curves.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.2
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• pp.133-138
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• 2016

Force control and collision detection for a robot are usually conducted using a 6-axis force/torque sensor mounted at the end-effector. However, this scheme suffers from high-cost and the inability to detect collisions at the robot body. As an alternative, joint torque sensors embedded in each joint were used, which also suffered from various errors in torque measurement. To resolve this problem, a robot joint module with an improved joint torque sensor is proposed in this study. In the proposed torque sensor, a cross-roller bearing and disk-type coupling are added to prevent the moment load from adversely affecting the measurement of the joint torque under consideration. This joint design also aims to reduce the stress induced during the assembly process of the sensor. The performance of the proposed joint torque sensor was verified through various experiments.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.3
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• pp.577-592
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• 2023

In this study, nanofiber-based textile sensors were developed for motion detection and monitoring. Poly(vinylidene fluoride) (PVDF) nanofibers containing zinc oxide (ZnO) nanoparticles and silver nanowires (AgNW) were fabricated using electrospinning. PVDF was chosen as a piezoelectric polymer, zinc oxide as a piezoelectric ceramic, and AgNW as a metal to improve electric conductivity. The PVDF/ZnO/AgNW nanocomposite fibers were used to develop a textile sensor, which was then incorporated into an elbow band to develop a wearable smart band. Changes in the output voltage and peak-to-peak voltage (V_p-p) generated by the joint's flexion and extension were investigated using a dummy elbow. The β-phase crystallinity of pure PVDF nanofibers was 58% when analyzed using Fourier transform infrared spectroscopy; however, the β-phase crystallinity increased to 70% in PVDF nanofibers containing ZnO and to 78% in PVDF nanocomposite fibers containing both ZnO and AgNW. The textile sensor's output voltage values varied with joint-bending angle; upon increasing the joint angle from 45° to 90° to 150°, the V_p-p value increased from 0.321 V_p-p to 0.542 V_p-p to 0.660 V_p-p respectively. This suggests that the textile sensor can be used to detect and monitor body movements.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.313-322
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• 2020

The recognition of the risk hazards is a vital step to effectively prevent accidents on a construction site. The advanced development in computer vision systems and the availability of the large visual database related to construction site made it possible to take quick action in the event of human error and disaster situations that may occur during management supervision. Therefore, it is necessary to analyze the risk factors that need to be managed at the construction site and review appropriate and effective technical methods for each risk factor. This research focuses on analyzing Occupational Safety and Health Agency (OSHA) related to risk zone identification rules that can be adopted by the image recognition technology and classify their risk factors depending on the effective technical method. Therefore, this research developed a pattern-oriented classification of OSHA rules that can employ a large scale of safety hazard recognition. This research uses joint reasoning of risk zone Identification and numeric input by utilizing a stereo camera integrated with an image detection algorithm such as (YOLOv3) and Pyramid Stereo Matching Network (PSMNet). The research result identifies risk zones and raises alarm if a target object enters this zone. It also determines numerical information of a target, which recognizes the length, spacing, and angle of the target. Applying image detection joint logic algorithms might leverage the speed and accuracy of hazard detection due to merging more than one factor to prevent accidents in the job site.

• ETRI Journal
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• v.41 no.5
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• pp.560-573
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• 2019

Two supervised learning algorithms, a basic neural network and a long short-term memory recurrent neural network, are applied to traffic including DDoS attacks. The joint effects of preprocessing methods and hyperparameters for machine learning on performance are investigated. Values representing attack characteristics are extracted from datasets and preprocessed by two methods. Binary classification and two optimizers are used. Some hyperparameters are obtained exhaustively for fast and accurate detection, while others are fixed with constants to account for performance and data characteristics. An experiment is performed via TensorFlow on three traffic datasets. Three scenarios are considered to investigate the effects of learning former traffic on sequential traffic analysis and the effects of learning one dataset on application to another dataset, and determine whether the algorithms can be used for recent attack traffic. Experimental results show that the used preprocessing methods, neural network architectures and hyperparameters, and the optimizers are appropriate for DDoS attack detection. The obtained results provide a criterion for the detection accuracy of attacks.