• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.414-421
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• 2007

This paper presents a engine/brake integrated VDC(Vehicle Dynamic Control) system using neural network algorithm methods for wheel slip and yaw rate control. For stable performance of vehicle, not only is the lateral motion control(wheel slip control) important but the yaw motion control of the vehicle is crucial. The proposed NNPI(Neural Network Proportional-Integral) controller operates at throttle angle to improve the performance of wheel slip. Also, the suggested NNPID controller performs at brake system to improve steering performance. The proposed controller consists of multi-hidden layer neural network structure and PID control strategy for self-learning of gain scheduling. Computer Simulation have been performed to verify the proposed neural network based control scheme of 17 dof vehicle dynamic model which is implemented in MATLAB Simulink.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.596-606
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• 2008

In this paper, we present a co-design methodology of dynamic optimal network-bandwidth allocation (ONBA) and adaptive control for networked control systems (NCSs) to optimize overall control performance and reduce total network-bandwidth usage. The proposed dynamic co-design strategy integrates adaptive feedback control with real-time scheduling. As part of this co-design methodology, a "closed-loop" ONBA algorithm for NCSs with communication constraints is presented. Network-bandwidth is dynamically assigned to each control loop according to the quality of performance (QoP) information of each control loop. As another part of the co-design methodology, a network quality of service (QoS)-adaptive control design approach is also presented. The idea is based on calculating new control values with reference to the network QoS parameters such as time delays and packet losses measured online. Simulation results show that this co-design approach significantly improves overall control performance and utilizes less bandwidth compared to static strategies.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.77-87
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• 2021

In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6C
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• pp.571-577
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• 2009

This paper proposes some new improved versions of node-wise VCRBP algorithm for low-density parity-check (LDPC) codes, called forced-convergence node-wise VCRBP algorithm and sign based node-wise VCRBP, both of which significantly reduce the decoding complexity and latency, with only negligible deterioration in error correcting performance.

• Korean Management Science Review
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• v.17 no.2
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• pp.125-134
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• 2000

Keeping the promised delivery date for a customer order is crucial for a company to promote customer satisfaction and generate further businesses. For this, a company should be able to quote the delivery date that can be achieved with the capacity available on the shop floor. In a dynamic make-to-order manufacturing environment, the problem of determining a delivery date for an incoming order with consideration of resource capacity, workload, and finished-product inventory can hardly be solved by an analytical solution procedure. This paper considers a situation in which a delivery date for a customer order is determined based on a job schedule, and presents the SimTriD algorithm that provides the best scheduling for determining a delivery date of customer order through the job schedule that efficiently utilizes manufacturing resources with consideration of interacting factors such as resource utilization, finished-product inventory, and due date.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.1
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• pp.46-53
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• 2013

Recently, intensive research has been performed for reducing video decoder energy consumption, especially based on DVFS (Dynamic Voltage and Frequency Scaling) technique. Our previous work [1] has proposed the optimal DVFS algorithm for energy reduction in video decoders. In spite of the mathematical optimality of the algorithm, the precondition of known frame decoding cycle/complexity limits its application to some realistic scenarios. This paper overcomes this limitation by frame data size-based estimation of frame decoding complexity. The proposed decoding complexity estimation method shows over 90% accuracy. And with this estimation method and buffer underflow margin of around 20% of frame size, almost same power consumption reduction performance as the optimal algorithm can be achieved.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.5C
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• pp.486-495
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• 2002

In this paper, we propose a delay reduction method for frame-based packet transmit scheduling algorithm. A high-speed network such as ATM network has to provide some performance guarantees such as bandwidth and delay bound. Framing strategy naturally guarantees bandwidth and enables simple rate-control while having the inherently bad delay characteristics. The proposed delay reduction method uses the same hierarchical frame structure as HRR (Hierarchical Round-Robin) but does not use the static priority scheme such as round-robin. Instead, we use a dynamic priority change scheme so that the delay unfairness between wide bandwidth connection and narrow bandwidth connection can be eliminated. That is, we use FIFO (First-In First-Out) concept to effectively reduce the occurrence of worst-case delay and to enhance delay distribution. We compare the performance for the proposed algorithm with that of HRR. The analytic and simulation results show that HFIFO inherits almost all merits of HRR with fairly better delay characteristics.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.1
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• pp.78-85
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• 2008

Most researches for power management have focused on increasing the utilization of system performance by scaling operating frequency or operating voltage. If operating frequency is changed frequently, it reduces the real system performance. To reduce power consumption, alternative approaches use the limited number of operating frequencies or set the smoothing frequencies during execution to increase the system performance, but they are not suitable for real time applications. To reduce power consumption and increase system performance for real time applications, this paper proposes a new power-aware schedule method by allocating operating frequencies and by setting smoothing frequencies. The algorithm predicts so that frequencies with continuous interval are mapped into discrete operating frequencies. The frequency smoothing reduces overheads of systems caused by changing operating frequencies frequently as well as power consumption caused by the frequency mismatch at a wide frequency interval. The simulation results show that the proposed algorithm reduces the power consumption up to 40% at maximum and 15% on average compared to the CC RT-DVS.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.6A
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• pp.803-819
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• 1999

We introduces a wireless multimedia CDMA system configuring multiple transmission links between a user and radio ports. We propose a centralized reservation access control scheme with transmission scheduling and dynamic allocation (CRMA/TSDA) to support the diverse multimedia traffic in the introduced CDMA system. We propose two types of transmission allocation algorithms: slot and link allocation algorithms with local information and global information. The transmission allocation algorithm proposed in this paper allocates a set of ports configuring multiple radio links and transmission slot/power to each of scheduled transmission requests. We perform simulations for the proposed system and algorithms. Through the simulation, we show that the performance of the algorithm with local information stands comparison with that of the quasi-optimum algorithm with global information. Also, the two algorithms in the system has shown to have better performance than the conventional CDMA system with a distributed random transmission method.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.2
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• pp.187-200
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• 1998

This paper presents an original approach for solving short-term power scheduling in extended power system with two fuels in a unit and a limited fuel using Lagrangian relaxations. The underlying model incorporates the full set of costs and constraints including setup, production, ramping, and operational status, and takes the form of a mixed integer nonlinear control problem. Moreover, the mathematical model developed includes two fuels in a unit and a limited fuel, regulation reserve requirements of prespecified group of units. Lagrangian relaxation is used to disaggregate the model by generator into separate subproblems which are then solved with a nested dynamic program including empirical knowledges. The strength of the methodology lies partially in its ability to construct good feasible solutions from information provided by the dual. Thus, the need for branch-and-bound is eliminated. In addition, the inclusion of two fuels in a unit and a limited fuel provides new insight into the limitations of current techniques. Computational experience with the proposed algorithm indicates that Problems containing up to 23 units including 8 unit used two fuels and 24 time periods can be readily solved in reasonable times. Duality gaps of less than 4％ were achieved.