• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.361-366
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• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.7-10
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• 2005

Ad hoc MAC protocols using directional antennas can be used to improve the network capacity. Directional antennas have a Deafness problem and decrease throughput of the network caused by increasing backoff duration. Dual channel protocols have been proposed to mitigate the Deafness and hidden node problems. In this paper, we propose a dual channel MAC protocol using a directional antenna to mitigate the Deafness problem and increase the network capacity. The performance of the proposed Ad hoc MAC protocol is confirmed by computer simulations using Qualnet ver. 3.8 simulator.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.43
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• pp.67-79
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• 1997

In this paper we develop the integrated software that can compare algorithms of the minimum cost flow problem using PC. The chosen algorithms are the network simplex method, dual network simplex method, and out-of-kilter method, which methods correspond to primal, dual, and primal-dual approach respectively. We also present the improved methods obtaining the initial solution to increase the efficiency of algorithms, and experiment results shown the difference between the entering(dropping) selection rules.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.1
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• pp.79-86
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• 2012

This paper proposes PID and RIC (Robust Internal-loop Compensator) based motion controller using dual learning algorithm for position control of linear synchronous motor respectively. Its gains are auto-tuned by using two learning algorithms, reinforcement learning and neural network. The feedback controller gains are tuned by reinforcement learning, and then the feedforward controller gains are tuned by neural network. Experiments prove the validity of dual learning algorithm. The RIC controller has better performance than does the PID-feedforward controller in reducing tracking error and disturbance rejection. Neural network shows its ability to decrease tracking error and to reject disturbance in the stop range of the target position and home.

• IEMEK Journal of Embedded Systems and Applications
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• v.2 no.4
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• pp.260-273
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• 2007

The wireless sensor network (WSN) nodes are required to operate for several months with the limited system resource such as memory and power. The hardware platform of WSN has 128Kbyte program memory and 8Kbytes data memory. Also, WSN node is required to operate for several months with the two AA size batteries. The MAC, Network protocol, and small application must be operated in this WSN platform. We look around the problem of memory and power for WSN requirements. Then, we propose a new computing model of system software for WSN node. It is the Atomic Object Model (AOM) with Dual Priority Scheduling. For the verification of model, we design and implement IEEE 802.15.4 MAC protocol with the proposed model.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.7
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• pp.753-759
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• 2009

As many systems depend on electronics in an intelligent vehicle, concern for fault tolerance is growing rapidly. For example, a car with its braking controlled by electronics and no mechanical linkage from brake pedal to calipers of front tires(brake-by-wire system) should be fault tolerant because a failure can come without any warning and its effect is devastating. In general, fault tolerance is usually designed by placing redundant components that duplicate the functions of the original module. In this way a fault can be isolated, and safe operation is guaranteed by replacing the faulty module with its redundant and normal module within a predefined interval. In order to make in-vehicle network fault tolerant, this paper presents the concept and design methodology of an IEEE 1451 based dual CAN module. In addition, feasibility of the dual CAN network was evaluated by implementing the dual CAN module.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2063-2081
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• 2018

A resource allocation algorithm is proposed in this paper to simultaneously minimize the total system power consumption and maximize the system throughput for the downlink of multi-user multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. With the Lagrange dual decomposition method, we transform the original problem to its convex dual problem and prove that the duality gap between the two problems is zero, which means the optimal solution of the original problem can be obtained by solving its dual problem. Then, we use convex optimization method to solve the dual problem and utilize bisection method to obtain the optimal dual variable. The numerical results show that the proposed algorithm is superior to traditional single-objective optimization method in both the system throughput and the system energy consumption.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.10
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• pp.1018-1023
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• 2010

The CAN (Controller Area Network) system is the most dominant protocol for in-vehicle networking system because it provides bounded transmission delay among ECUs (Electronic Control Units) at data rates between 125Kbps and 1Mbps. And, many automotive companies have chosen the CAN protocol for their in-vehicle networking system such as chassis network system because of its excellent communication characteristics. However, the increasing number of ECUs and the need for more intelligent functions such as ADASs (Advanced Driver Assistance Systems) or IVISs (In-Vehicle Information Systems) require a network with more network capacity and the real-time QoS (Quality-of-Service). As one approach to enhancing the network capacity of a CAN system, this paper introduces a CAN system with dual communication channel. And, this paper presents a distributed message allocation method that allocates messages to the more appropriate channel using forecast traffic of each channel. Finally, an experimental testbed using commercial off-the-shelf microcontrollers with two CAN protocol controllers was used to demonstrate the feasibility of the CAN system with dual communication channel using the distributed message allocation method.

• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.337-338
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• 2016

This paper introduces a novel dual Full-Bridge LLC(FBLLC)resonant converter for CC/CV Charge of the Battery for Electric Vehicles. One full-bridge LLC resonant converter operates with a fixed-resonant network and the other operates with a variable-resonant network for CC and CV mode operations. The proposed converter can achieve ZVS for all the primary switches and exhibits a highefficiency characteristics like aconventional single FBLLC resonant converter. In addition, the variable-resonant network helps minimize the switching-frequency variation. The dual structure makes the proposed converter possible to achieve ZVS and nearly ZCS for all the primary switches in CC mode operation. Since the proposed converter can operate at a fixed frequency in CV mode, it can minimize the circulating current and achieve nearly ZCS. A 6.6 kW prototype converter is implemented to verify the validity of proposed converter and the maximum efficiency of 98.3% was achieved.

• Korean Management Science Review
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• v.29 no.3
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• pp.81-89
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• 2012

Since introduced by Vining and Myers in 1990, the concept of dual response approach based on response surface methodology has widely been investigated and adopted for the purpose of robust design. Separately estimating mean and variance responses, dual response approach may take advantages of optimization modeling for finding optimum settings of input factors. Explicitly assuming functional relationship between responses and input factors, however, it may not work well enough especially when the behavior of responses are poorly represented. A sufficient number of experimentations are required to improve the precision of estimations. This study proposes an alternative to dual response approach in which additional experiments are not required. An artificial neural network has been applied to model relationships between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Training, validating, and testing a neural network with empirical process data, an artificial data based on the neural network may be generated and used to estimate response functions without performing real experimentations. A drug formulation example from pharmaceutical industry has been investigated to demonstrate the procedures and applicability of the proposed approach.