• Journal of the Korean Operations Research and Management Science Society
• /
• v.34 no.3
• /
• pp.125-136
• /
• 2009

The container packing problem Is one of the traditional optimization problems, which is very related to the knapsack problem and the bin packing problem. In this paper, we deal with the quadratic multiple container picking problem (QMCPP) and it Is known as a NP-hard problem. Thus, It seems to be natural to use a heuristic approach such as evolutionary algorithms for solving the QMCPP. Until now, only a few researchers have studied on this problem and some evolutionary algorithms have been proposed. This paper introduces a new efficient evolutionary algorithm for the QMCPP. The proposed algorithm is devised by improving the original network random key method, which is employed as an encoding method in evolutionary algorithms. And we also propose local search algorithms and incorporate them with the proposed evolutionary algorithm. Finally we compare the proposed algorithm with the previous algorithms and show the proposed algorithm finds the new best results in most of the benchmark instances.

• The Journal of the Acoustical Society of Korea
• /
• v.21 no.3E
• /
• pp.105-111
• /
• 2002

The parabolic equation technique provides an excellent model to describe the wave phenomena when there exists a predominant direction of propagation. The model handles the square root wave number operator in paraxial direction. Realization of the pseudo-differential square root operator is the essential part of the parabolic equation method for its numerical accuracy. The wide-angled approximation of the operator is made based on the Pade series expansion, where the branch line rotation scheme can be combined with the original Pade approximation to stabilize its computational performance for complex modes. The Galerkin integration has been employed to discretize the depth-dependent operator. The benchmark tests involving the half-infinite space, the range independent and dependent environment will validate the implemented numerical model.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.34C no.5
• /
• pp.1-17
• /
• 1997

With the increasing performance and density of VLSI scircuits as well as the popularity of portable devices such as personal digital assitance, power consumption has emerged as an important issue in the design of electronic systems. Low power design techniqeus have been pursued at all design levels. However, it is more effective to attempt to reduce power dissipation at higher levels of abstraction which allow wider view. In this paper, we propose a simultaneous scheduling and binding scheme which increases the correlation between cosecutive inputs to an operation so that the switched capacitance of execution units is reduced in datapath-dominated circuits. The proposed method is implemented and integrated into the scheduling and assignment part of HYPER synthesis environment. Compared with original HYPER synthesis system, average power saving of 23.0% in execution units and 14.2% in the whole circuits, ar eobtained for a set of benchmark examples.

• Proceedings of the IEEK Conference
• /
• 1999.11a
• /
• pp.1011-1014
• /
• 1999

In this paper, a new algorithm has been presented that helps to preserve diversity as well as to enhance the convergence speed of the evolutionary programming. This algorithm is based on the cell partitioning of search region for preserving the diversity. Until now, the greater part of researches is not concerned about preserving the diversity of individuals in a population but improving convergence speed. Although these evolutions are started from multi-point search at the early phase, but at the end those search points are swarming about a one-point, the strong candidate. These evolutions vary from the original idea in some points such as multi-point search. In most case we want to find the only one point of the best solution not several points in the vicinity of that. That is why the cell partitioning of search region has been used. By restricting the search area of each individual, the diversity of individual in solution space is preserved and the convergence speed is enhanced. The efficiency of the proposed algorithm has been verified through benchmark test functions.

• Journal of Broadcast Engineering
• /
• v.20 no.6
• /
• pp.938-949
• /
• 2015

In this paper, we propose a robust speed-limit sign recognition system which is durable to any sign changes caused by exterior damage or color contrast due to light direction. For recognition of speed-limit sign, we apply CNN which is showing an outstanding performance in pattern recognition field. However, original CNN uses multiple hidden layers to extract features and uses fully-connected method with MLP(Multi-layer perceptron) on the result. Therefore, the major demerit of conventional CNN is to require a long time for training and testing. In this paper, we apply randomly-connected classifier instead of fully-connected classifier by combining random forest with output of 2 layers of CNN. We prove that the recognition results of CNN with random forest show best performance than recognition results of CNN with SVM (Support Vector Machine) or MLP classifier when we use eight speed-limit signs of GTSRB (German Traffic Sign Recognition Benchmark).

• Structural Engineering and Mechanics
• /
• v.66 no.6
• /
• pp.677-691
• /
• 2018

Reliability assessment of complex structures using simulation methods is time-consuming. Thus, surrogate models are usually employed to reduce computational cost. AK-MCS is a surrogate-based Active learning method combining Kriging and Monte-Carlo Simulation for structural reliability analysis. This paper proposes three modifications of the AK-MCS method to reduce the number of calls to the performance function. The first modification is related to the definition of an initial Design of Experiments (DoE). In the original AK-MCS method, an initial DoE is created by a random selection of samples among the Monte Carlo population. Therefore, samples in the failure region have fewer chances to be selected, because a small number of samples are usually located in the failure region compared to the safe region. The proposed method in this paper is based on a uniform selection of samples in the predefined domain, so more samples may be selected from the failure region. Another important parameter in the AK-MCS method is the size of the initial DoE. The algorithm may not predict the exact limit state surface with an insufficient number of initial samples. Thus, the second modification of the AK-MCS method is proposed to overcome this problem. The third modification is relevant to the type of regression trend in the AK-MCS method. The original AK-MCS method uses an ordinary Kriging model, so the regression part of Kriging model is an unknown constant value. In this paper, the effect of regression trend in the AK-MCS method is investigated for a benchmark problem, and it is shown that the appropriate choice of regression type could reduce the number of calls to the performance function. A stepwise approach is also presented to select a suitable trend of the Kriging model. The numerical results show the effectiveness of the proposed modifications.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.11 no.2
• /
• pp.97-105
• /
• 2016

Microcontrollers (MCUs) for endpoint smart sensor devices of internet-of-thing (IoT) are being implemented as system-on-chip (SoC) with on-chip instruction flash memory, in which user firmware is embedded. MCUs directly fetch binary code-based instructions through bit-line sense amplifier (S/A) integrated with on-chip flash memory. The S/A compares bit cell current with reference current to identify which data are programmed. The S/A in reading '0' (erased) cell data consumes a large sink current, which is greater than off-current for '1' (programmed) cell data. The main motivation of our approach is to reduce the number of accesses of erased cells by binary code level transformation. This paper proposes a built-in write/read path architecture using binary code inversion method based on hot-spot region detection of instruction code access to reduce sensing current in S/A. From the profiling result of instruction access patterns, hot-spot region of an original compiled binary code is conditionally inverted with the proposed bit-inversion techniques. The de-inversion hardware only consumes small logic current instead of analog sink current in S/A and it is integrated with the conventional S/A to restore original binary instructions. The proposed techniques are applied to the fully-custom designed MCU with ARM Cortex-M0$^{TM}$ using 0.18um Magnachip Flash-embedded CMOS process and the benefits in terms of power consumption reduction are evaluated for Dhrystone$^{TM}$ benchmark. The profiling environment of instruction code executions is implemented by extending commercial ARM KEIL$^{TM}$ MDK (MCU Development Kit) with our custom-designed access analyzer.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.7
• /
• pp.1759-1769
• /
• 1997

The Error BackPropagation (EBP) algorithm for multi-layered neural networks is widely used in various areas such as associative memory, speech recognition, pattern recognition and robotics, etc. Nevertheless, many researchers have continuously published papers about improvements over the original EBP algorithm. The main reason for this research activity is that EBP is exceeding slow when the number of neurons and the size of training set is large. In this study, we developed new learning speed acceleration methods using variable learning rate, variable momentum rate and variable slope for the sigmoid function. During the learning process, these parameters should be adjusted continuously according to the total error of network, and it has been shown that these methods significantly reduced learning time over the original EBP. In order to show the efficiency of the proposed methods, first we have used binary data which are made by random number generator and showed the vast improvements in terms of epoch. Also, we have applied our methods to the binary-valued Monk's data, 4, 5, 6, 7-bit parity checker and real-valued Iris data which are famous benchmark training sets for machine learning.

• Smart Structures and Systems
• /
• v.25 no.3
• /
• pp.369-384
• /
• 2020

A wavefield sparse reconstruction technique based on compressed sensing is developed in this work to dramatically reduce the number of measurements. Firstly, a severely underdetermined representation of guided wavefield at a snapshot is established in the spatial domain. Secondly, an optimal compressed sensing model of guided wavefield sparse reconstruction is established based on l₁-norm penalty, where a suite of discrete cosine functions is selected as the dictionary to promote the sparsity. The regular, random and jittered undersampling schemes are compared and selected as the undersampling matrix of compressed sensing. Thirdly, a gradient projection method is employed to solve the compressed sensing model of wavefield sparse reconstruction from highly incomplete measurements. Finally, experiments with different excitation frequencies are conducted on an aluminum plate to verify the effectiveness of the proposed sparse reconstruction method, where a scanning laser Doppler vibrometer as the true benchmark is used to measure the original wavefield in a given inspection region. Experiments demonstrate that the missing wavefield data can be accurately reconstructed from less than 12% of the original measurements; The reconstruction accuracy of the jittered undersampling scheme is slightly higher than that of the random undersampling scheme in high probability, but the regular undersampling scheme fails to reconstruct the wavefield image; A quantified mapping relationship between the sparsity ratio and the recovery error over a special interval is established with respect to statistical modeling and analysis.

• Smart Structures and Systems
• /
• v.18 no.6
• /
• pp.1251-1267
• /
• 2016

In this paper, Simple Adaptive Control (SAC) is used to enhance the seismic response of nonlinear tall buildings based on acceleration feedback. Semi-active MR dampers are employed as control actuator due to their reliability and well-known dynamic models. Acceleration feedback is used because of availability, cost-efficiency and reliable measurements of acceleration sensors. However, using acceleration feedback in the control loop causes the structure not to apparently meet some requirements of the SAC algorithm. In addition to defining an appropriate SAC reference model and using inherently stable MR dampers, a modification in the original structure of the SAC is proposed in order to improve its adaptability to the situation in which the plant does not satisfy the algorithm's stability requirements. To investigate the performance of the developed control system, a numerical study is conducted on the benchmark 20-story nonlinear building and the responses of the SAC-controlled structure are compared to an $H_2/LQG$ clipped-optimal controller under the effect of different seismic excitations. As indicated by the results, SAC controller effectively reduces the story drifts and hence the seismically-induced damage throughout the structural members despite its simplicity, independence of structural parameters and while using fewer number of dampers in contrast with the $H_2/LQG$ clipped-optimal controller.