• Industrial Engineering and Management Systems
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• v.9 no.1
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• pp.10-19
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• 2010

This paper presents an extended computing procedure for the global optimization of the triple response system (TRS) where the response functions are nonconvex (nonconcave) quadratics and the input factors satisfy a radial region of interest. The TRS arising from response surface modeling can be approximated using a nonlinear mathematical program involving one primary (objective) function and two secondary (constraints) functions. An optimization algorithm named triple response surface algorithm (TRSALG) is proposed to determine the global optimum for the nondegenerate TRS. In TRSALG, the Lagrange multipliers of target (secondary) functions are computed by using the Hooke-Jeeves search method, and the Lagrange multiplier of the radial constraint is located by using the trust region (TR) method at the same time. To ensure global optimality that can be attained by TRSALG, included is the means for detecting the degenerate case. In the field of numerical optimization, as the family of TR approach always exhibits excellent mathematical properties during optimization steps, thus the proposed algorithm can guarantee the global optimal solution where the optimality conditions are satisfied for the nondegenerate TRS. The computing procedure is illustrated in terms of examples found in the quality literature where the comparison results with a gradient-based method are used to calibrate TRSALG.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.377-382
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• 2007

This paper proposes a training algorithm for global optimization of the parameters of radial basis function networks. Since conventional training algorithms usually perform only local optimization, the performance of the network is limited and the final network significantly depends on the initial network parameters. The proposed hybrid simulated annealing algorithm performs global optimization of the network parameters by combining global search capability of simulated annealing and local optimization capability of gradient-based algorithms. Via experiments for function approximation problems, we demonstrate that the proposed algorithm can find networks showing better training and test performance and reduce effects of the initial network parameters on the final results.

• Journal of information and communication convergence engineering
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• v.4 no.3
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• pp.123-129
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• 2006

The limited number of roads and the increasing number of vehicles demand the automatic regulation of overspeed vehicles, illegal vehicles, and overloaded vehicles and the automatic charge calculation depending on the type of the vehicle. To meet such requirements, it is important to remove the shadow of the vehicle as processing and recognizing an image captured by a camera. The shadow of the vehicle is likely to cause misclassification of the vehicle type due to diverse errors and mistakes occurring when detecting geometrical properties of the vehicle. In case that shadows of two different vehicles are overlapped, not only the type of the vehicles may be misclassified but also it is difficult to accurately identify the type of the vehicles. In this paper, we propose a robust algorithm to remove the shadow of a vehicle by calculating the luminance, the chrominance, the gradient density of the cast shadow from information acquired using the image subtraction of the background, and to recognize the substantial vehicle figure. Even when it is hard to detect and split a target vehicle from its shadow as shadows of vehicles are attached to each other, our robust algorithm can detect the vehicle figure only. We implemented our system with a general camera and conducted experiments on various vehicles on general roads to find out our vehicle shade removal algorithm is efficient when detecting and recognizing vehicles.

• Computers and Concrete
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• v.24 no.4
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• pp.313-327
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• 2019

The purpose of the present study is to present a new approach to designing and selecting the details of multidimensional continuous RC beam by applying all strength, serviceability, ductility and other constraints based on ACI318-14 using Teaching Learning Based Optimization (TLBO) algorithm. The optimum reinforcement detailing of longitudinal bars is done in two steps. in the first stage, only the dimensions of the beam in each span are considered as the variables of the optimization algorithm. in the second stage, the optimal design of the longitudinal bars of the beam is made according to the first step inputs. In the optimum shear reinforcement, using gradient-based methods, the most optimal possible mode is selected based on the existing assumptions. The objective function in this study is a cost function that includes the cost of concrete, formwork and reinforcing steel bars. The steel used in the objective function is the sum of longitudinal and shear bars. The use of a catalog list consisting of all existing patterns of longitudinal bars based on the minimum rules of the regulation in the second stage, leads to a sharp reduction in the volume of calculations and the achievement of the best solution. Three example with varying degrees of complexity, have been selected in order to investigate the optimal design of the longitudinal and shear reinforcement of continuous beam.

• Computers and Concrete
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• v.21 no.6
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• pp.697-703
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• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• Current Optics and Photonics
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• v.2 no.6
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• pp.612-622
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• 2018

In wave-front-sensor-less adaptive optics (WFS-less AO) systems, the Jacopo Antonello (JA) method belongs to the model-based class and requires few iterations to achieve acceptable distortion correction. However, this method needs a lot of measurements, especially when it deals with moderate or severe aberration, which is undesired in free-space optical communication (FSOC). On the contrary, the stochastic parallel gradient descent (SPGD) algorithm only requires three time measurements in each iteration, and is widely applied in WFS-less AO systems, even though plenty of iterations are necessary. For better and faster compensation, we propose a WFS-less hybrid approach, borrowing from the JA method to compensate for low-order wave front and from the SPGD algorithm to compensate for residual low-order wave front and high-order wave front. The correction results for this proposed method are provided by simulations to show its superior performance, through comparison of both the Strehl ratio and the convergence speed of the WFS-less hybrid approach to those of the JA method and SPGD algorithm.

• ETRI Journal
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• v.44 no.5
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• pp.746-758
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• 2022

This paper focuses on a mobile edge-computing-enabled heterogeneous network. A battery level-aware task-scheduling framework is proposed to improve the energy efficiency and prolong the operating hours of battery-powered mobile devices. The formulated optimization problem is a typical mixed-integer nonlinear programming problem. To solve this nondeterministic polynomial (NP)-hard problem, a decomposition-based task-scheduling algorithm is proposed. Using an alternating optimization technology, the original problem is divided into three subproblems. In the outer loop, task offloading decisions are yielded using a pruning search algorithm for the task offloading subproblem. In the inner loop, closed-form solutions for computational resource allocation subproblems are derived using the Lagrangian multiplier method. Then, it is proven that the transmitted power-allocation subproblem is a unimodal problem; this subproblem is solved using a gradient-based bisection search algorithm. The simulation results demonstrate that the proposed framework achieves better energy efficiency than other frameworks. Additionally, the impact of the battery level-aware scheme on the operating hours of battery-powered mobile devices is also investigated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.233-262
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• 2024

Due to the rapid evolution of vehicular ad hoc networks (VANETs), effective communication and security are now essential components in providing secure and reliable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. However, due to their dynamic nature and potential threats, VANETs need to have strong security mechanisms. This paper presents a novel approach to improve VANET security by combining the Vehicular Delay-Tolerant Network (VDTN) protocol with the Deep Reinforcement Learning (DRL) technique known as the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. A store-carry-forward method is used by the VDTN protocol to resolve the problems caused by inconsistent connectivity and disturbances in VANETs. The TD3 algorithm is employed for capturing and detecting Worm Hole Attack (WHA) behaviors in VANETs, thereby enhancing security measures. By combining these components, it is possible to create trustworthy and effective communication channels as well as successfully detect and stop rushing attacks inside the VANET. Extensive evaluations and simulations demonstrate the effectiveness of the proposed approach, enhancing both security and communication efficiency.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.1 s.307
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• pp.9-16
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• 2006

This Paper proposes an adaptive error diffusioThis paper proposes an adaptive error diffusion algorithm for text enhancement followed by an efficient text segmentation that uses the maximum gradient difference (MGD). The gradients are calculated along with scan lines, and the MGD values are filled within a local window to merge the potential text segments. Isolated segments are then eliminated in the non-text region filtering process. After the left segmentation, a conventional error diffusion method is applied to the background, while the edge enhancement error diffusion is used for the text. Since it is inevitable that visually objectionable artifacts are generated when using two different halftoning algorithms, the gradual dilation is proposed to minimize the boundary artifacts in the segmented text blocks before halftoning. Sharpening based on the gradually dilated text region (GDTR) prevents the printing of successive dots around the text region boundaries. The error diffusion algorithm with edge enhancement is extended to halftone color images to sharpen the tort regions. The proposed adaptive error diffusion algorithm involves color halftoning that controls the amount of edge enhancement using a general error filter. The multiplicative edge enhancement parameters are selected based on the amount of edge sharpening and color difference. Plus, the additional error factor is introduced to reduce the dot elimination artifact generated by the edge enhancement error diffusion. By using the proposed algorithm, the text of a scanned image is sharper than that with a conventional error diffusion without changing background.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.12
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• pp.85-96
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• 1999

In a number of predictive video compression standards, the motion is compensated by the block-based motion compensation (BMC). The effective motion field used for the prediction by the BMC is obviously discontinuous since one motion vector is used for the entire macro-block. The usage of discontinuous motion field for the prediction causes the blocky artifacts and one obvious approach for eliminating such artifacts is to use a smoothed motion field. The optimal procedure will depend on the type of motion within the video. In this paper, several procedures for the motion vectors are considered. For any interpolation or approaches, however, the motion vectors as provided by the block matching algorithm(BMA) are no longer optimal. The optimum motion vectors(still one per macro-block) must minimize the of the displaced frame difference (DFD). We propose a unified algorithm that computes the optimum motion vectors to minimize the of the DFD using a conjugate gradient search. The proposed algorithm has been implemented and tested for the affine transformation based motion compensation (ATMC), the bilinear transformation based motion compensation (BTMC) and our own filtered motion compensation(FMC). The performance of these different approaches will be compared against the BMC.