• Journal of the Society of Naval Architects of Korea
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• v.46 no.1
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• pp.87-95
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• 2009

The applicability of optimization techniques for hull surface fitting has been important in the ship design process. In this research, the Genetic Algorithm has been used as a searching technique for solving surface fitting problem and minimizing errors between B-spline surface and the ship's offset data. The encoded design variables are the location of the vertex points and parametric values. The sufficient accuracy in surface fitting implies not only various techniques for computer-aided design, but also the future production design.

• Journal of KSNVE
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• v.8 no.4
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• pp.632-642
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• 1998

A method based on finite element discretization is developed for optimizing the polarization profile of PVDF film to create the modal transducer for specific modes. Using this concept, one can design the modal transducer in two-dimensional structure having arbitrary geometry and boundary conditions. As a practical means for implementing this polarization profile without repoling the PVDF film the polarization profile is approximated by optimizing electrode patterns, lamination angles, and poling directions of the multi-layered PVDF transducer. This corresponds to the approximation of a continuous function using discrete values. The electrode pattern of each PVDF layer is optimized by deciding the electrode of each finite element to be used or not. Genetic algorithm, suitable for discrete problems, is used as an optimization scheme. For the optimization of each layers lamination angle, the continuous lamination angle is encoded into discrete value using binary 5 bit string. For the experimental demonstration, a modal sensor for first and second modes of cantilevered composite plate is designed using two layers of PVDF films. The actuator is designed based on the criterion of minimizing the system energy in the control modes under a given initial condition. Experimental results show that the signals from residual modes are successfully reduced using the optimized multi-layered PVDF sensor. Using discrete LQG control law, the modal peaks of first and second modes are reduced in the amount of 12 dB and 4 dB, resepctively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.946-949
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• 2005

It is very a time-consuming and error-prone process to obtain the optimal injection condition, which can produce good injection molding products in some operational variation of facilities, from a seed injection condition. This study proposes a new approach to search the optimal injection molding condition using a neural network and a genetic algorithm. To estimate the defect type of unknown injection conditions, this study forces the neural network into learning iteratively from the injection molding conditions collected. Major two parameters of the injection molding condition - injection pressure and velocity are encoded in a binary value to apply to the genetic algorithm. The optimal injection condition is obtained through the selection, cross-over, and mutation process of the genetic algorithm. Finally, this study compares the optimal injection condition searched using the proposed approach. with the other ones obtained by heuristic algorithms and design of experiment technique. The comparison result shows the usability of the approach proposed.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.4
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• pp.35-41
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• 2008

When memory devices are exposed to a space environment. they suffer various effects such as SEU(Single Event Upset). For these reasons, memory systems for space applications are generally equipped with error detection and correction(EDAC) logics against SEUs. In this paper, the error detection and correction strategy in the Mass Memory Unit(MMU) of the STSAT-3 is discussed. The probability equation of un-recoverable SEUs in the mass memory system is derived when the whole memory is encoded and decoded by the RS(10,8) Reed-Solomon code. Also the probability value is analyzed for various occurrence rates of SEUs which the STSAT-3 possibly suffers. The analyzed results can be used to determine the period of scrubbing the whole memory, which is one of the important parameters in the design of the MMU.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.377-379
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• 2020

The hologram data is having a dependence on the pixel pitch of the SLM (spatial light modulator) and the wavelength of light, and the quality of the digital hologram is proportional to the unit pixel pitch and the total resolution. In addition, since each pixel has a complex value, the amount of data in the digital hologram also increases exponentially, and the size is bound to be very large. Therefore, in order to efficiently handle digital hologram files, it is essential to reduce the file size through a codec and store it. Recently, research on enhancing image quality damaged by the codec is actively underway. In this paper, the hologram image of JPEG Pleno, which is the standard hologram data, was used, and the image quality damage that occurs whenthe holographic image is encoded and decoded through the JPEG2000, AVC, and HEVC codec is enhanced with a deep learning network to find out whether the image quality can be improved. we also compare and quantitatively find out the degree of improvement in image quality.

• Proceedings of the Optical Society of Korea Conference
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• 2003.07a
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• pp.60-61
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• 2003

A new approach to reduce the computation time of genetic algorithm (GA) for making binary phase holograms is described. Synthesized holograms having diffraction efficiency of 75.8% and uniformity of 5.8% are proven in computer simulation and experimentally demonstrated. Recently, computer-generated holograms (CGHs) having high diffraction efficiency and flexibility of design have been widely developed in many applications such as optical information processing, optical computing, optical interconnection, etc. Among proposed optimization methods, GA has become popular due to its capability of reaching nearly global. However, there exits a drawback to consider when we use the genetic algorithm. It is the large amount of computation time to construct desired holograms. One of the major reasons that the GA' s operation may be time intensive results from the expense of computing the cost function that must Fourier transform the parameters encoded on the hologram into the fitness value. In trying to remedy this drawback, Artificial Neural Network (ANN) has been put forward, allowing CGHs to be created easily and quickly (1), but the quality of reconstructed images is not high enough to use in applications of high preciseness. For that, we are in attempt to find a new approach of combiningthe good properties and performance of both the GA and ANN to make CGHs of high diffraction efficiency in a short time. The optimization of CGH using the genetic algorithm is merely a process of iteration, including selection, crossover, and mutation operators [2]. It is worth noting that the evaluation of the cost function with the aim of selecting better holograms plays an important role in the implementation of the GA. However, this evaluation process wastes much time for Fourier transforming the encoded parameters on the hologram into the value to be solved. Depending on the speed of computer, this process can even last up to ten minutes. It will be more effective if instead of merely generating random holograms in the initial process, a set of approximately desired holograms is employed. By doing so, the initial population will contain less trial holograms equivalent to the reduction of the computation time of GA's. Accordingly, a hybrid algorithm that utilizes a trained neural network to initiate the GA's procedure is proposed. Consequently, the initial population contains less random holograms and is compensated by approximately desired holograms. Figure 1 is the flowchart of the hybrid algorithm in comparison with the classical GA. The procedure of synthesizing a hologram on computer is divided into two steps. First the simulation of holograms based on ANN method [1] to acquire approximately desired holograms is carried. With a teaching data set of 9 characters obtained from the classical GA, the number of layer is 3, the number of hidden node is 100, learning rate is 0.3, and momentum is 0.5, the artificial neural network trained enables us to attain the approximately desired holograms, which are fairly good agreement with what we suggested in the theory. The second step, effect of several parameters on the operation of the hybrid algorithm is investigated. In principle, the operation of the hybrid algorithm and GA are the same except the modification of the initial step. Hence, the verified results in Ref [2] of the parameters such as the probability of crossover and mutation, the tournament size, and the crossover block size are remained unchanged, beside of the reduced population size. The reconstructed image of 76.4% diffraction efficiency and 5.4% uniformity is achieved when the population size is 30, the iteration number is 2000, the probability of crossover is 0.75, and the probability of mutation is 0.001. A comparison between the hybrid algorithm and GA in term of diffraction efficiency and computation time is also evaluated as shown in Fig. 2. With a 66.7% reduction in computation time and a 2% increase in diffraction efficiency compared to the GA method, the hybrid algorithm demonstrates its efficient performance. In the optical experiment, the phase holograms were displayed on a programmable phase modulator (model XGA). Figures 3 are pictures of diffracted patterns of the letter "0" from the holograms generated using the hybrid algorithm. Diffraction efficiency of 75.8% and uniformity of 5.8% are measured. We see that the simulation and experiment results are fairly good agreement with each other. In this paper, Genetic Algorithm and Neural Network have been successfully combined in designing CGHs. This method gives a significant reduction in computation time compared to the GA method while still allowing holograms of high diffraction efficiency and uniformity to be achieved. This work was supported by No.mOl-2001-000-00324-0 (2002)) from the Korea Science & Engineering Foundation.

• Food Science and Preservation
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• v.16 no.4
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• pp.472-481
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• 2009

Response surface methodology (RSM) is frequently used for optimization studies. In the present work, RSM was used to determine the antimicrobial activitiesof grapefruit seed extract (GFSE) and a lactic acid mixture (LA) against Staphylococcus aureus, Bacillus cereus, Escherichia coli, Salmonella typhimurium, Pseudomonas fluorescens, and Vibrio parahaemolyticus. A central composite design was used to investigate the effects of independent variables on dependent parameters. One set of antimicrobial preparations included mixtures of 1% (w/w) GFSE and 10% (w/w) LA, in which the relative proportions of component antimicrobials varied between 0 and 100%. In further experiments, the relative proportions were between 20% and 100%. Antimicrobial effects against various microorganisms were mathematically encoded for analysis. The codes are given in parentheses after the bacterial names, and were S. aureus ($Y_1$), B. cereus ($Y_2$), E. coli ($Y_3$), S. typhimurium ($Y_4$), P. fluorescens ($Y_5$), and V. parahaemolyticus ($Y_6$). The optimum antimicrobial activity of the 1% (w/w) GFSE:10% (w/w) LA mixture against each microorganism was obtained by superimposing contour plots ofantimicrobial activities on measures of response obtained under various conditions. The optimum rangesfor maximum antimicrobial activity of a mixture with a ratio of 1:10 (by weight) GFSE and LA were 35.73:64.27 and 56.58:43.42 (v/v), and the optimum mixture ratio was 51.70-100%. Under the tested conditions (a ratio of 1% [w/w] GFSE to 10% [w/w] LA of 40:60, and a concentration of 1% [w/w] GFSE and 10% [w/w] LA, 70% of the highest value tested), and within optimum antimicrobial activity ranges, the antimicrobial activities of the 1% (w/w) GFSE:10% (w/w) LA mixture against S. aureus ($Y_1$), B. cereus ($Y_2$), E. coli ($Y_3$), S. typhimurium ($Y_4$), P. fluorescens ($Y_5$), and V. parahaemolyticus ($Y_6$) were 24.55, 25.22, 20.20, 22.49, 23.89, and 28.04 mm, respectively. The predicted values at optimum conditions were similar to experimental values.