• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.4
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• pp.344-359
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• 2016

Due to the increasing occurrence of maritime accidents and high-level requirements and modernization of naval wars, the concept of ship environment adaptability becomes more and more important. Therefore, it is of great importance to carry out an evaluation system for ship environment adaptability, which contributes to both ship design and classification. This paper develops a comprehensive evaluation system for ship environment adaptability based on fuzzy mathematics theory. An evaluation index system for ship environment adaptability is elaborately summarized first. Then the analytic hierarchy process (AHP) and entropy weighting methods are applied to aggregate the evaluations of criteria weights for each criterion and the corresponding subcriteria. Next, the multilevel fuzzy comprehensive evaluation method is applied to assess the ship integrative environment adaptability. Finally, in order to verify the proposed approach, an illustrative example for optimization and evaluation of five ship alternatives is adopted. Moreover, the influence of criteria weights, membership functions and fuzzy operators on the results is also analyzed.

• International Journal of Automotive Technology
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• v.7 no.1
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• pp.43-49
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• 2006

In this paper, an index for the evaluation of a vehicle intake booming noise and intake sound quality were developed through a correlation analysis and a multiple factor regression analysis of objective measurement and subjective evaluation data. At first, an intake orifice noise was measured at the wide-open throttle test condition. And then, an acoustic transfer function between intake orifice noise and interior noise at the steady state condition was estimated. Simultaneously, subjective evaluation was carried out with a 10-scale score by 8 intake noise and vibration expert evaluators. Next, the correlation analysis between the psychoacoustic parameters derived from the measured data and the subjective evaluation was performed. The most critical factor was determined and the corresponding index for intake booming noise and sound quality are obtained from the multiple factor regression method. And, the optimal design of intake system was studied using the booming noise and the sound quality evaluation index for expectation performance of intake system. Conclusively, the optimal designing parameters of intake system from noise level and sound quality whose point of view were extracted by adapting comparative weighting between the booming noise and sound quality evaluation index, which optimized the process. These work could be represented guideline to system engineers, designers and test engineers about optimization procedure of system performance by considering both of noise level and sound quality.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2627-2629
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• 2003

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN). The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized networks, and to be much more flexible and preferable neural network than the conventional SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented with using nonlinear system data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2188-2203
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• 2021

With the rapid development of mobile Internet, smart phones have been widely popularized, among which Android platform dominates. Due to it is open source, malware on the Android platform is rampant. In order to improve the efficiency of malware detection, this paper proposes deep learning Android malicious detection system based on behavior features. First of all, the detection system adopts the static analysis method to extract different types of behavior features from Android applications, and extract sensitive behavior features through Term frequency-inverse Document Frequency algorithm for each extracted behavior feature to construct detection features through unified abstract expression. Secondly, Long Short-Term Memory neural network model is established to select and learn from the extracted attributes and the learned attributes are used to detect Android malicious applications, Analysis and further optimization of the application behavior parameters, so as to build a deep learning Android malicious detection method based on feature analysis. We use different types of features to evaluate our method and compare it with various machine learning-based methods. Study shows that it outperforms most existing machine learning based approaches and detects 95.31% of the malware.

• Journal of the Korean Society of Mechanical Technology
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• v.20 no.6
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• pp.809-817
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• 2018

The model predictive controller performance of the mobile robot is set to an arbitrary value because it is difficult to select an accurate value with respect to the controller parameter. The general model predictive control uses a quadratic cost function to minimize the difference between the reference tracking error and the predicted trajectory error of the actual robot. In this study, we construct a predictive controller by transforming it into a quadratic programming problem considering velocity and acceleration constraints. The control parameters of the predictive controller, which determines the control performance of the mobile robot, are used a simple weighting matrix Q, R without the reference model matrix $A_r$ by applying a quadratic cost function from which the reference tracking error vector is removed. Therefore, we designed the predictive controller 1 and 2 of the mobile robot considering the constraints, and optimized the controller parameters of the predictive controller using a genetic algorithm with excellent optimization capability.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5A
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• pp.399-406
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• 2010

In this paper, the optimization of frictional bearings that applied to improve the seismic performance of conventional bridges were conducted. The nonlinear dynamic analysis of steel bridges and concrete bridges are carried out with the El Centro and artificial earthquake motions, and the reponses of the bridges were optimized by genetic algorithm. The object functions were considered with two parameters, such as shear forces and displacements at bearing, and the optimum object functions were searched by varying the weighting factors of the two parameters. As results, in case of the steel bridges, the optimum results were obtained when larger weight factor was imposed to the shear force. However, in case of concrete bridges, larger weight factor was need to the displacement for optimum results.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.5
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• pp.179-189
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• 2008

The object of this study is to develope a program with proposed numerical techniques for an optimal seismic control of structures using active tendon systems. Ricatti closed-loop algorithm has been applied to control the active tendon systems with time-delay problem. The optimal control is formulated as an optimization problem which is finding optimal weighting matrices by minimizing the quadratic performance index by SUMT. In order to find the optimal location of active tendons in structures, controllability index has been introduced. From numerical examples, the current optimal control technique with optimal location of tendons was suitable to control the seismic response of structures.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.155-170
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• 2018

Predicting a company's financial bankruptcy is traditionally one of the most crucial forecasting problems in business analytics. In previous studies, prediction models have been proposed by applying or combining statistical and machine learning-based techniques. In this paper, we propose a novel intelligent prediction model based on the simulated annealing which is one of the well-known optimization techniques. The simulated annealing is known to have comparable optimization performance to the genetic algorithms. Nevertheless, since there has been little research on the prediction and classification of business decision-making problems using the simulated annealing, it is meaningful to confirm the usefulness of the proposed model in business analytics. In this study, we use the combined model of simulated annealing and machine learning to select the input features of the bankruptcy prediction model. Typical types of combining optimization and machine learning techniques are feature selection, feature weighting, and instance selection. This study proposes a combining model for feature selection, which has been studied the most. In order to confirm the superiority of the proposed model in this study, we apply the real-world financial data of the Korean companies and analyze the results. The results show that the predictive accuracy of the proposed model is better than that of the naïve model. Notably, the performance is significantly improved as compared with the traditional decision tree, random forests, artificial neural network, SVM, and logistic regression analysis.

• Radiation Oncology Journal
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• v.19 no.1
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• pp.53-65
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• 2001

Purpose : To improve the local control of patients with nasopharyngeal cancer, we have implemented 3-D conformal radiotherapy and forward intensity modulated radiation therapy (IMRT) to used of compensating filters. Three dimension conformal radiotherapy with intensity modulation is a new modality for cancer treatments. We designed 3-D treatment planning with 3-D RTP (radiation treatment planning system) and evaluation dose distribution with tumor control probability (TCP) and normal tissue complication probability (NTCP). Material and Methods : We have developed a treatment plan consisting four intensity modulated photon fields that are delivered through the compensating tilters and block transmission for critical organs. We get a full size CT imaging including head and neck as 3 mm slices, and delineating PTV (planning target volume) and surrounding critical organs, and reconstructed 3D imaging on the computer windows. In the planning stage, the planner specifies the number of beams and their directions including non-coplanar, and the prescribed doses for the target volume and the permissible dose of normal organs and the overlap regions. We designed compensating filter according to tissue deficit and PTV volume shape also dose weighting for each field to obtain adequate dose distribution, and shielding blocks weighting for transmission. Therapeutic gains were evaluated by numerical equation of tumor control probability and normal tissue complication probability. The TCP and NTCP by DVH (dose volume histogram) were compared with the 3-D conformal radiotherapy and forward intensity modulated conformal radiotherapy by compensator and blocks weighting. Optimization for the weight distribution was peformed iteration with initial guess weight or the even weight distribution. The TCP and NTCP by DVH were compared with the 3-D conformal radiotherapy and intensitiy modulated conformal radiotherapy by compensator and blocks weighting. Results : Using a four field IMRT plan, we have customized dose distribution to conform and deliver sufficient dose to the PTV. In addition, in the overlap regions between the PTV and the normal organs (spinal cord, salivary grand, pituitary, optic nerves), the dose is kept within the tolerance of the respective organs. We evaluated to obtain sufficient TCP value and acceptable NTCP using compensating filters. Quality assurance checks show acceptable agreement between the planned and the implemented MLC(multi-leaf collimator). Conclusion : IMRT provides a powerful and efficient solution for complex planning problems where the surrounding normal tissues place severe constraints on the prescription dose. The intensity modulated fields can be efficaciously and accurately delivered using compensating filters.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.44 no.4
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• pp.423-430
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• 2011

The most satisfactory split-beam transducer for fish sizing maintains a wide bearing angle region for correct fish tracking without interference from side lobes and lower sensitivity to fish echoes outside of the main lobe region to correctly measure the angular location of free-swimming fishes in the sound beam. To evaluate the performance of an experimentally developed 50 kHz split-beam transducer, the angular location of a target was derived from the electrical phase difference between the resultant signals for the pair of transducer quadrants in the horizontal and vertical planes consisting of 32 transducer elements. The electrical phase difference was calculated by cross-spectral density analysis for the signals from the pair of receiving transducer quadrants, and the directivity correction factor for a developed split-beam transducer was estimated as the fourth-order polynomial of the off-axis beam angle for the angular location of the target. The experimental results demonstrate that the distance between the acoustic centers for the pair of receiving transducer quadrants can be controlled to less than one wavelength by optimization with amplitude-weighting transformers, and a smaller center spacing provides a range of greater angular location for tracking of a fish target. In particular, a side lobe level of -25.2 dB and an intercenter spacing of $0.96\lambda$($\lambda$= wavelength) obtained in this study suggest that the angular location of fish targets distributing within a range of approximately ${\pm}28^{\circ}$ without interference from side lobes can be measured.