• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.12
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• pp.935-942
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• 2002

When designing fluid mounts, design parameters can be varied in order to obtain a desired notch frequency and notch depth. The notch frequency is a function of the mount parameters and is typically selected by the designer to occur at the vibration disturbance frequency. Since the process of choosing these parameters can involve some trial and error, it seems to be a great application for obtaining optimal performance of the mount. Many combinations of parameters are possible to give us the desired notch frequency, but the question is which combination provides the lowest depth. Therefore. an automatic optimal technique is needed to optimize the performance of the fluid mount. In this study. the enhanced genetic algorithm (EGA) is applied to minimizing transmissibility of a fluid mount at the desired notch frequency, and at the notch and resonant frequencies. The EGA is modified genetic algorithm to search global and local optimal solutions of multi-modal function optimization. Furthermore. to reduce the searching time as compare to conventional genetic algorithm and Increase the precision of the solutions, the modified simplex method is combined with the algorithm. The results show that the performance of the optimized mount by using the hybrid algorithm is better than that of the conventional fluid mount.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

• Steel and Composite Structures
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• v.45 no.2
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• pp.281-291
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• 2022

Surmounting complexities in analyzing the mechanical parameters of concrete entails selecting an appropriate methodology. This study integrates a novel metaheuristic technique, namely satin bowerbird optimizer (SBO) with artificial neural network (ANN) for predicting uniaxial compressive strength (UCS) of concrete. For this purpose, the created hybrid is trained and tested using a relatively large dataset collected from the published literature. Three other new algorithms, namely Henry gas solubility optimization (HGSO), sunflower optimization (SFO), and vortex search algorithm (VSA) are also used as benchmarks. After attaining a proper population size for all algorithms, the Utilizing various accuracy indicators, it was shown that the proposed ANN-SBO not only can excellently analyze the UCS behavior, but also outperforms all three benchmark hybrids (i.e., ANN-HGSO, ANN-SFO, and ANN-VSA). In the prediction phase, the correlation indices of 0.87394, 0.87936, 0.95329, and 0.95663, as well as mean absolute percentage errors of 15.9719, 15.3845, 9.4970, and 8.0629%, calculated for the ANN-HGSO, ANN-SFO, ANN-VSA, and ANN-SBO, respectively, manifested the best prediction performance for the proposed model. Also, the ANN-VSA achieved reliable results as well. In short, the ANN-SBO can be used by engineers as an efficient non-destructive method for predicting the UCS of concrete.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.1-9
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• 2023

For optimal image quality of computer tomography angiography (CTA), different iodine concentrations and scan parameters were applied to quantitatively evaluate the image quality characteristics of filtered back projection (FBP), hybrid-iterative reconstruction (hybrid-IR), and deep learning reconstruction (DLR). A 320-row-detector CT scanner scanned a phantom with various iodine concentrations (1.2, 2.9, 4.9, 6.9, 10.4, 14.3, 18.4, and 25.9 mg/mL) located at the edge of a cylindrical water phantom with a diameter of 19 cm. Data obtained using each reconstruction technique was analyzed through noise, coefficient of variation (COV), and root mean square error (RMSE). As the iodine concentration increased, the CT number value increased, but the noise change did not show any special characteristics. COV decreased with increasing iodine concentration for FBP, adaptive iterative dose reduction (AIDR) 3D, and advanced intelligent clear-IQ engine (AiCE) at various tube voltages and tube currents. In addition, when the iodine concentration was low, there was a slight difference in COV between the reconstitution techniques, but there was little difference as the iodine concentration increased. AiCE showed the characteristic that RMSE decreased as the iodine concentration increased but rather increased after a specific concentration (4.9 mg/mL). Therefore, the user will have to consider the characteristics of scan parameters such as tube current and tube voltage as well as iodine concentration according to the reconstruction technique for optimal CTA image acquisition.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.131-139
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• 2013

To overcome the limitations in performance and power consumption of traditional electrical interconnection based network-on-chips (NoCs), a hybrid optical network-on-chip (HONoC) architecture using optical interconnects is emerging. However, the HONoC architecture should use circuit-switching scheme owing to the overhead by optical devices, which worsens the latency unfairness problem caused by frequent path collisions. This resultingly exert a bad influence in overall performance of the system. In this paper, we propose a new task mapping algorithm for optimizing latency by reducing path collisions. The proposed algorithm allocates a task to a certain processing element (PE) for the purpose of minimizing path collisions and worst case latencies. Compared to the random mapping technique and the bandwidth-constrained mapping technique, simulation results show the reduction in latency by 43% and 61% in average for each $4{\times}4$ and $8{\times}8$ mesh topology, respectively.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.10
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• pp.595-602
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• 2003

This Paper presents an efficient hybrid techniques for a standard cell placement. The prototype tool adopts a middle-down methodology in which an n${\times}$m grid is imposed over the layout area and cells are assigned to bins forming a global placement. The optimization technique applied in this phase is based on the Relaxation-Based Local Search (RBLS) framework [12］in which a combinatorial search mechanism is driven by an analytical engine. This enables a more global view of the problem and results in complex modifications of the placement in a single search“move.”Details of this approach including a novel placement legalization procedure are presented. When a global placement converges, a detailed placement is formed and further optimized by the optimal interleaving technique[13］. Experimental results on MCNC benchmarking circuits are presented and compared with the Feng Shui's results in［14］. Solution Qualifies are almost the same as the Feng Shui's results.

• Proceedings of the Korean Geotechical Society Conference
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• 1999.10a
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• pp.415-422
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• 1999

This paper describes a new optimum design approach for piled raft foundations using the genetic algorithm. The objective function considered is the cost-based total weight of raft and piles. The genetic algorithm is a search or optimization technique based on nature selection. Successive generation evolves more fit individuals on the basis of the Darwinism survival of the fittest. In formulating the genetic algorithm-based optimum design procedure, the analysis of piled raft foundations is peformed based on the 'hybrid'approach developed by Clancy(1993), and also the simple genetic algorithm proposed by the Goldberg(1989) is used. To evaluate a validity of the optimum design procedure proposed based on the genetic algorithm, comparisons regarding optimal pile placement for minimizing differential settlements by Kim et at.(1999) are made. In addition using proposed design procedure, design examples are presented.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.1061-1064
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• 2009

The objective of this study is to find the optimal operational planning of the hybrid cooling system, which is combined by ice storage system and the absorption chiller. The optimization technique used in this study is dynamic programming. The objective function is summed cost during a day including charge and discharge periods of ice storage system and operation time of absorption chiller. Assuming that initially ice storage tank is stored fully and the cooling load is perfectly predicted for the operational planning. This method provides the most efficient and economic combination of equipment operational planning for cooling with respect to energy consumption cost.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.2
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• pp.247-254
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• 2003

The paper presents the influence of spring dynamics and friction on dynamic responses in a spring-driven cam system. The characteristics of the friction on the camshaft are analyzed using the nonlinear pendulum experiment while the parameters of the friction model are estimated using the optimization technique. The analysis reveals that the friction of the camshaft depends on stick-slip, Stribeck effect and viscous damping. Spring elements are found to have much influence on the dynamic characteristics. Hence, they are modeled as four-degree-of-freedom lumped masses with equivalent springs. The appropriateness of the derived friction model and spring model is verified by its application to a vacuum circuit breaker mechanism of the cam-follower type.

• Journal of Electrical Engineering and Technology
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• v.8 no.2
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• pp.238-243
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• 2013

In this paper, a novel approach for optimized installation and operations of battery energy storage system (BESS) and electric double layer capacitor (EDLC) modules for the renewable energy based intermittent generation is presented for them to be connected with an electric power grid. In order to make use of not merely the high energy density of battery but also the high power density of EDLC modules, it is very useful to devise the hybrid system which combines BESS and EDLC modules. The proposed method adopts the linear programming to calculate the optimized capacity as well as the quadratic programming to transmit the optimal operational signals to BESS and EDLC modules. The efficiency of this methodology will be demonstrated in the experimental study with the real data of wind speed in Texas.