• Journal of Electrical Engineering and Technology
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• v.3 no.2
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• pp.285-291
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• 2008

Active Queue Management(AQM) has been widely used for congestion avoidance in Transmission Control Protocol(TCP) networks. Although numerous AQM schemes have been proposed to regulate a queue size close to a reference level, most of them are incapable of adequately adapting to TCP network dynamics due to TCP's non-linearity and time-varying stochastic properties. To alleviate these problems, we introduce an AQM technique based on a dynamic neural network using the Back-Propagation(BP) algorithm. The dynamic neural network is designed to perform as a robust adaptive feedback controller for TCP dynamics after an adequate training period. We evaluate the performances of the proposed neural network AQM approach using simulation experiments. The proposed approach yields superior performance with faster transient time, larger throughput, and higher link utilization compared to two existing schemes: Random Early Detection(RED) and Proportional-Integral(PI)-based AQM. The neural AQM outperformed PI control and RED, especially in transient state and TCP dynamics variation.

• Journal of the Korea institute for structural maintenance and inspection
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• v.2 no.4
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• pp.137-147
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• 1998

The neural net application was tried to develop the technique for monitoring the health status of a steel truss bridge which was scaled down to 1/15 of the real bridge for the laboratory experiments. The damage scenarios were chosen as 7 cases. The dynamic behavior, which was changed due to the breakage of the members, of the bridge was investigated by finite element analysis. The bridge consists of single spam, and eight (8) main structural subsystems. The loading vehicle, which weighs as 100 kgf, was operated by the servo-motor controller. The accelerometers were bonded on the surface of 7 cross-beams to measure the dynamic behavior induced by the abnormal structural condition. Artificial neural network technique was used to determine the severity of the damage. At first, the neural net was learnt by the results of finite element analysis, and also, the maximum detection error was 3.65 percents. Another neural net was also learnt, and verified by the experimental results, and in this case, the maximum detection error was 1.05 percents. In future study, neural net is necessary to be learnt and verified by various data from the real bridge.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.142-147
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• 1999

This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle.

• The Journal of Korean Physical Therapy
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• v.32 no.6
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• pp.359-364
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• 2020

Purpose: This study was undertaken to compare the efficacy of instrument assisted soft tissue mobilization (IASTM) and a neural dynamic technique (NDYT). As an intervention to treat spastic lower limb muscle tone, stiffness, and static balance in stroke patients. Methods: Totally, 26 participants were assigned randomly to two groups: the IASTM (n=13) and NDYT (n=13) groups. Both groups were subjected to their respective technique for 15 minutes, 5 times a week, for 6 weeks. Muscle tone, stiffness, and static balance were evaluated before and after training, to compare both group changes. Results: IASTM group showed significant decrease in the gastrocnemius medial region and semitendinosus muscle tone and stiffness (p<0.05) compare to NDYT group; however, no significant different was observed in static balance between groups (p>0.05). Conclusion: The results suggest that IASTM is an effective method for decreasing the muscle tone and stiffness in acute stroke patients.

• Journal of International Academy of Physical Therapy Research
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• v.9 no.2
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• pp.1447-1454
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• 2018

The objective of this study was to investigate the dynamic neural mobilization program on the changes in muscle activity and nerve conduction velocity (NVC) in stroke patients. The participants were sampled and randomly divided into experimental group I (n=12) who underwent arm neural mobilization and experimental group II (n=13) who underwent arm dynamic neural mobilization. As the pretest, peripheral NVC of the radial, median, and ulnar nerves were measured using the Viking Quest; the biceps brachii, brachioradialis, flexor carpi radialis, and extensor carpi radialis activities were measured with sEMG. Each intervention program consisted of 10 trials per set and three sets per session. The intervention programs were performed once daily for four weeks (four days/week). Posttest measurements were taken equally as the pretest measurements. Significant differences in peripheral NVC in all sections of the radial and median nerves and wristbelow elbow and below elbow-above elbow areas of the ulnar nerve, as well as in muscle activity of all muscles except the biceps brachii. These findings indicate that dynamic neural mobilization was effective in increasing peripheral NVC and altering the muscle activity.

• Wind and Structures
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• v.8 no.3
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• pp.147-161
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• 2005

Two identical tall building models with square cross-sections are experimentally studied in a wind tunnel with high-frequency-force-balance (HFFB) technique to investigate the interference effects on wind loads and dynamic responses of the interfered building. Another wind tunnel test, in which the interfered model is an aeroelastic one, is also carried out to further study the interference effects. The results from the two kinds of tests are compared with each other. Then the influences of turbulence in oncoming wind on dynamic interference factors are analyzed. At last the artificial neural networks method is used to deal with the experimental data and the along-wind and across-wind dynamic interference factor $IF_{dx}$ & $IF_{dy}$ contour maps are obtained, which could be used as references for wind load codes of buildings.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.3
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• pp.170-176
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• 2004

MR dampers show highly nonlinear and histeretic dynamic behavior. Therefore, for a vehicle dynamic simulation with MR dampers, this dynamic characteristics should be accurately reflected in the damper model. In this paper, an artificial neural network technique was developed for modeling MR dampers. This MR damper model was successfully verified through a random input forcing test. This MR damper model can be used for semi-active suspension vehicle dynamics and control simulations with practical accuracy.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.133-139
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• 1997

This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is propored a learning controller consisting of two neural network-fuzzy based on independent resoning and a connection net with fixed weights to simply the neural network-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.3
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• pp.254-262
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• 2008

In this paper, we propose an adaptive neural dynamic surface control (DSC) approach with $H_{\infty}$ tracking performance for full dynamics of nonlinear flight systems. It is assumed that the model uncertainties such as structured and unstrutured uncertainties, and external disturbances influence the nonlinear aircraft model. In our control system, self recurrent wavelet neural networks (SRWNNs) are used to compensate the model uncertainties of nonlinear flight systems, and an adaptive DSC technique is extended for the disturbance attenuation of nonlinear flight systems. All weights of SRWNNs are trained on-line by the smooth projection algorithm. From Lyapunov stability theorem, it is shown that $H_{\infty}$ performance nom external disturbances can be obtained. Finally, we present the simulation results for a nonlinear six-degree-of-freedom F-16 aircraft model to confirm the effectiveness of the proposed control system.

• Journal of the Ergonomics Society of Korea
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• v.14 no.2
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• pp.1-14
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• 1995

Neuromuscular difference between normal subjects and low-back pain patients has been identified in terms of neural excitation signal measured by Electromyography (EMG) under the dynamic flexion/extension trunk motion. Ten healthy subjects and ten low-back pain patients were recruited for this study. New parameters and normalization technique were introduced to quantify the muscle excitation pattern among the flexor-extensor pairs of muscles : rectus abdominis (RA)-erector spinae (ES at L1 and L5 level), external oblique (EO)-internal oblique (IO), rectus femoris (quadricep : QUD)-biceps femoris( hamstring : HAM), and tibialis anterior (TA)-gastrocnemius (GAS). Results indicated that the temporal EMG pattern such as peak timing difference between the hip flexor (QUD) and extensor (HAM) and the duration of coexcitation between ES at L5 and RA muscle pairs showed a statistically significant difference between normal subjects and low-back pain patients. Improtantly, this study presented a new technique to identify the dynamic muscle excitation pattern that canb be least affected by EMG-length-velocity relationship. Further study can performed to validate this method for clinical application to quantitatively identify the low-back pain patients in the future.