• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.17-26
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• 2011

We have developed a wearable device that can convert sensor data into real-time step counts. Sensor data on gait were acquired using a triaxial accelerometer. A test was performed according to a test protocol for different walking speeds, e.g., slow walking, walking, fast walking, slow running, running, and fast running. Each test was carried out for 36 min on a treadmill with the participant wearing an Actical device, and the device developed in this study. The signal vector magnitude (SVM) was used to process the X, Y, and Z values output by the triaxial accelerometer into one representative value. In addition, for accurate step-count detection, we used three algorithms: an heuristic algorithm (HA), the adaptive threshold algorithm (ATA), and the adaptive locking period algorithm (ALPA). The recognition rate of our algorithm was 97.34% better than that of the Actical device(91.74%) by 5.6%.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.1
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• pp.39-45
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• 2007

In this paper, we present a new postprocessing algorithm using interpolation and signal adaptive filter according to the each block characteristic which is acquired in block classification process. We applied blocking artifact reduction algorithm for four neighbor low frequency block and ringing artifacts is removed with preserving edges by applying a signal adaptive filter in high frequency block based on edge map. The computer simulation results confirmed a better performance by the proposed method in both the subjective and objective image qualities.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.127-133
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• 2013

This paper compare the performance of CCA (Compact Constellation Algorithm) adaptive equalization algorithm by effect of the compact slice weighting value for minimization of the intersymbol interference in the communication channel. The CCA combines the conventional DDA and RCA algorithm, it uses the constant modulus of the transmission signal and the considering the output of decision device by the power of compact slice weighting value in order to improving the initial convergence characteristics and the equalization noise by misadjustment in the steady state. In this process, it is confirmed by computer simulation that the compact slice weight affects the performance of CCA adaptive equalization algorithm. The performance index includes the output signal constellation, the residual isi and maximum distortion and MSE that is for the convergence characteristics, the SER according to the signal and noise power ratio at the channel is used. As a result of computer, it shows that the large weighting value gives more good in every performance index. But in SER performance, it is known that the small values gives more good in low SNR and the large values gives more good in high SNR.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.8
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• pp.495-497
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• 2005

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a neural network used for identification of the process dynamics of s signal input and signal output system and it was shown that this method offered superior capability over the conventional back propagation algorithm. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal estimate input signal about controller using neural networks.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.18-20
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• 2005

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a neural network used for identification of the process dynamics of s signal input and signal output system and it was shown that this method offered superior capability over the conventional back propagation algorithm. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal estimate input signal about controller using neural networks.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.735-738
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• 1997

The traffic congestion caused by the exploding increase of vehicles became one of the severest social problems. Among the various approaches to solve this problem, controlling the length of traffic signals appropriately according to the individual traffic situation would be the most plausible and cost-effective method. To design a traffic signal controller which has such a property as adaptive decision-making process, we adopt fuzzy logic control method(fuzzy traffic signal controller), Moreover, using genetic algorithms we obtain an optimized fuzzy traffic signal controller (GA-fuzzy traffic signal controller). To evaluate and validate the proposed fuzzy and GA-fuzzy traffic signal controller, simulation results are presented.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.421-428
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• 2007

This paper discusses a fitness of the control on intersection network using fuzzy analytic hierachy process. The validity of control of traffic signal on intersection is phase, cycle of intersection. But the aigent of traffic intersection network is relative of intersection article. Therefor the aigent of traffic network intersection is before intersection passing vechile number, delayed vechile number. Fuzzy analytic hierachy process clears the grade of validity of the fixed cycle time controller and adaptive fixed cycle time and fuzzy traffic controller and proposes a new control type a traffic signal by this fuzzy analytic hierachy process.

• Proceedings of the KWS Conference
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• 1996.10a
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• pp.139-141
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• 1996

Among various welding parameters, the welding current which is inversely proportional to the tip-to-workpiece distance in GMAW is an essential parameter to monitor the GMAW process of horizontal fillet joints. For the case of weld defect such as overlap in horizontal fillet welding, therefore, the signal processing for process monitoring or automatic seam tracking should be modified by considering the weld pool surface geometry including the corresponding weld defect. In other words, the adequate signal processing algorithm is indispensible to improve the performance of the arc sensor. However, arc sensor algorithm already developed usually focus on weld seam tracing but do not considering the weld qualities. In this paper, various experiments were carried out to investigate the tendencies of the weld defects when weaving motion is added, and the experimental method based on 2$^n$ factorial design was proposed for deriving the mathematical model between the leg length and the various welding conditions. Moreover, a signal processing method based on the artificial neural network(Adaptive Resonance Theory) was proposed far discriminating the current signal of sound weld beads from that of weld beads with overlap. Finally, the algorithm for weld seam tracking combined with the mathematical modeling and the signal processing method was carried out to track the weld line in conjunction with the improvement of the weld qualities. The reliability of the proposed algorithms were evaluated through various experiments, which showed that the proposed algorithms could be effectively used for arc welding automation.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1832-1833
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• 2006

This paper presents a design method of the wavelet neural network(WNN) controller based on a direct adaptive control scheme for the intelligent control of Autonomous Underwater Vehicle(AUV) steering systems. The neural network is constructed by the wavelet orthogonal decomposition to form a wavelet neural network that can overcome nonlinearities and uncertainty. In our control method, the control signals are directly obtained by minimizing the difference between the reference track and original signal of AUV model that is controlled through a wavelet neural network. The control process is a dynamic on-line process that uses the wavelet neural network trained by gradient-descent method. Through computer simulations, we demonstrate the effectiveness of the proposed control method.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.1
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• pp.8-15
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• 2011

This paper presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving. The proposed method detects vehicles based on detecting vehicle headlights and taillights using techniques of image segmentation and clustering. First, in order to effectively extract spotlight of interest, a pre-signal-processing process based on camera lens filter and labeling method is applied on road-scene images. Second, to spatial clustering vehicle of detecting lamps, a grouping process use light tracking method and locating vehicle lighting patterns. For simulation, we are implemented through Da-vinci 7437 DSP board with visible light mono-camera and tested it in urban and rural roads. Through the test, classification performances are above 89% of precision rate and 94% of recall rate evaluated on real-time environment.