• Journal of Information Processing Systems
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• v.12 no.1
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• pp.73-82
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• 2016

In cognitive radio networks (CRNs), the performance of the transmission control protocol (TCP) at the secondary user (SU) severely drops due to the mistrigger of congestion control. A long disruption is caused by the transmission of primary user, leading to the mistrigger. In this paper, we propose a cross-layer approach, called a CR-aware scheme that enhances TCP performance at the SU. The scheme is a sender side addition to the standard TCP (i.e., TCP-NewReno), and utilizes an explicit cross-layer signal delivered from a physical (or link) layer and the signal gives an indication of detecting the primary transmission (i.e., transmission of the primary user). We evaluated our scheme by implementing it onto a software radio platform, the Universal Software Radio Peripheral (USRP), where many parts of lower layer operations (i.e., operations in a link or physical layer) run as user processes. In our implementation, we ran our CR-aware scheme over IEEE 802.15.4. Furthermore, for the purpose of comparison, we implemented a selective ACK-based local recovery scheme that helps TCP isolate congestive loss from a random loss in a wireless section.

• 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 Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.9-12
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• 2000

In this paper, we carry out a performance study related to the Advanced Peer-to-Peer Network(APPN). For this particular network, it has been proposed to use the leaky bucket as a way of controlling congestion within the network. On the top of leaky bucket type rate based congestion control scheme for high speed networks, a user will typically operate an error control scheme for retransmitting lost and erroneous packets. We propose a Perform ance model in order to study the Interaction between a user's error control scheme and the leaky bucket congestion control scheme for high speed networks. Simulation results show that parameters such as the window size and the token generation rate in the leaky bucket are key factors affecting the end-to-end delay.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.5B no.4
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• pp.358-365
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• 2005

In induction machine drive without a speed sensor, the estimation of the motor flux and speed often becomes deteriorated at low speeds with low back EMF. Our analysis shows that, in addition to the state resistance variation, the estimated value of field orientation angle is often corrupted by accumulative errors from the integration of voltage variables at motor terminals that have low signal/noise ratio at low frequencies. A repetitive loop path of integration in the feedback can amplify this type of error, thus speeding up the degradation process. The control system runs into information starvation due to the loss of correct field orientation. The machine's spiral vectors are controlled only in a reduced dimension in this situation. A novel control scheme is developed to improve the control performance of motor's current, torque and speed at low frequencies. The scheme gains a full-dimensional vector control and is less sensitive to the combined effect of the error sources at the low frequencies. Experimental tests demonstrate promising performances are achievable even below 0.5 Hz.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.197-200
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• 1996

This paper considers feedback error learning neural networks for robot manipulator control. Feedback error learning proposed by Kawato [2,3,5] is a useful learning control scheme, if nonlinear subsystems (or basis functions) consisting of the robot dynamic equation are known exactly. However, in practice, unmodeled uncertainties and disturbances deteriorate the control performance. Hence, we presents a robust feedback error learning scheme which add robustifying control signal to overcome such effects. After the learning rule is derived, the stability is analyzed using Lyapunov method.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.7
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• pp.1112-1120
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• 1994

This paper presents a servo control scheme for the surface-mounted permanent-magnet synchronous motor(SPMSM) which essentially uses vector control algorithm. The control system is composed of the PI controller for speed control and the current controller using space voltage vector PWM technique. The high-speed calculation and processing for vector control is carried out by TMS320C31 digital signal processor and IGBT module. The proposed scheme is verified through digital simulations and experiments for 2.2kW SPMSM and shows good dynamic performance.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.356-361
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• 2005

In this paper, a new scheme of current programmed control for three phase PWM AC-AC converter is presented. Compared to duty-ratio voltage control, current programmed control has several advantages such as reduction of system order and inherent current protection. By considering only the magnitude components, the same scheme in the DC-DC converter can be extended to the three phase PWM AC-AC converter. Sensing the output voltage and the inductor current, an error signal will be generated by comparing the output voltage magnitude with a reference value. Then the error signal will be processed by a PI compensator to generate the current command. The converter switches will the change the state by a periodic clock pulse or at the instants when the inductor current magnitude reaches the current command. As an example case, the buck converter is treated. The converter analysis is carried out by applying the complex DQ circuit transformation to obtain basic physical insight into the converter operation and to establish some important characteristic equations for control purpose. The simulation with Simplorer shows the validity of the proposed scheme and the experimental results support the verification of the design.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.15-24
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• 2022

At present, there are few studies on signal control of pedestrian traffic flow and non-motor traffic flow at intersections. Research on the optimization scheme of mixed traffic flow signal control can coordinate and control the overall traffic flow of pedestrians, non-motor vehicles and motor vehicles, which is of great significance to improve the congestion at intersections. For the traffic optimization of intersections, this paper starts from two aspects: channelization optimization and phase design, and reduces the number of conflict points at intersections from spatial and temporal right-of-way allocation respectively. Taking the classical signal timing method as the theoretical basis, and aiming at ensuring the safety and time benefit of traffic travelers, a channelization optimization and signal control scheme of the intersection is proposed. The channelization and phase design methods of intersections with motor vehicles, non-motor vehicles and pedestrians as objects are discussed, and measures to improve the channelization optimization of intersections are proposed. A multi-objective optimization model of intersection signal control was established, and the model was solved based on NSGA-II algorithm.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.609-614
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• 1998

A new small signal modeling of an average current mode control is proposed. In order to analyze the characteristics of the control scheme, the discrete and continuous time small signal models are derived. The derivation are mainly come from the analysis of the sampling effect presented in the current control loop. By the mathematical interpretation of practical sampler representing the sampling effect of a current control loop, the small signal models of an average current mode control can be easily derived. The instability of the current control loop, which gives rise to the subharmonic oscillation, can be identified by the proposed models. To show the usefulness of the proposed models, the simulation and experiment are carried out. The results show that the predicted results by the proposed model are much better agreed with the measured ones than that of the conventional model, even though the high gain of the compensation network of a current control loop is employed.