• Journal of applied mathematics & informatics
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• v.9 no.1
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• pp.61-76
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• 2002

The robust non-fragile guaranteed cost control problem is studied in this paper for class of uncertain linear large-scale systems with time-varying delays in subsystem interconnections and given quadratic cost functions. The uncertainty in the system is assumed to be norm-hounded arid time-varying. Also, the state-feedback gains for subsystems of the large-scale system are assumed to have norm-bounded controller gain variations. The problem is to design state feedback control laws such that the closed-loop system is asymptotically stable and the closed-loop cost function value is not more than a specified upper bound far all admissible uncertainties. Sufficient conditions for the existence of such controllers are derived based on the linear matrix inequality (LMI) approach combined with the Lyapunov method. A parameterized characterization of the robust non-fragile guaranteed cost contrellers is 7iven in terms of the feasible solution to a certain LMI. Finally, in order to show the application of the proposed method, a numerical example is included.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.501-512
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• 2012

Social welfare maximization in a double-sided auction market is performed by implementing an aggregation-based particle swarm optimization (CAPSO) algorithm for optimal placement and sizing of one Static Synchronous Series Compensator (SSSC) device. Dallied simulation results (without/with line flow constraints and without/with SSSC) are generated to demonstrate the impact of SSSC on the congestion levels of the modified IEEE 14-bus test system. The proposed CAPSO algorithm employs conventional quadratic smooth and augmented quadratic nonsmooth generator cost curves with sine components to improve the accurate of the model by incorporating the valve loading effects. CAPSO also employs quadratic smooth consumer benefit functions. The proposed approach relies on particle swarm optimization to capture the near-optimal GenCos and DisCos, as well as the location and rating of SSSC while the Newton based load flow solution minimizes the mismatch equations. Simulation results of the proposed CAPSO algorithm are compared to solutions obtained by sequential quadratic programming (SQP) and a recently implemented Fuzzy based genetic algorithm (Fuzzy-GA). The main contributions are inclusion of customer benefit in the congestion management objective function, consideration of nonsmooth generator characteristics and the utilization of a coordinated aggregation-based PSO for locating/sizing of SSSC.

• Journal of Korean Institute of Industrial Engineers
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• v.2 no.1
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• pp.85-92
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• 1976

In this paper, the employment planning model is developed which is a decision-making model for determining the optimum employment level with respect to varying net manpower requirement for each planing period such that total cost in a planning horizon is minimized. It is constructed as a nonlinear programming model and a dynamic programming model on the basis of studies in the areas of production smoothing and manpower scheduling. Costs for a planning period are categorized into regular wage cost, hiring cost, and overtime cost. The first is a linear function. The other two cost functions are of quadratic nature. The planning horizon of this planning model is intermediate range (five years) for which a fair planning accuracy can be guaranteed. The model considers learning period for each job class. It is simple and an optimum solution can be easily obtained by direct search techniques.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.4
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• pp.517-526
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• 1999

This paper deals with two economic optimal designs of constant-stress accelerated life test(ALT) where failure distribution follows one of location-scale family, i. e., exponential, Weibull, and lognormal distributions which have been ones of the popular choices of failure distributions. Two optimization criteria to develop ALT plans are the statistical efficiency per unit expected cost which consists of the fixed test cost, cost being proportional to the number of test units, and variable test cost depending on test period and stress level, and the expected loss which combines Taguchi's quadratic loss function and expected test cost. Optimum plan determines the low stress level, test units allocated to each stress, and censoring times at two stress levels under Type I censoring. The proposed ALT plans are illustrated with a numerical example and sensitivity analyses are conducted to study effects of pre-estimates of design parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3519-3533
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• 2020

Tuberculosis is a chronic and delayed infection which is easily experienced by young people. According to the statistics of the World Health Organization (WHO), there are nearly ten million fell ill with tuberculosis and a total of 1.5 million people died from tuberculosis in 2018 (including 251000 people with HIV). Tuberculosis is the largest single infectious pathogen that leads to death. In order to help doctors with tuberculosis diagnosis, we compare the tuberculosis classification abilities of six popular convolutional neural network (CNN) models in the same data set to find the best model. Before training, we optimize three parts of CNN to achieve better results. We employ sigmoid function to replace the step function as the activation function. What's more, we use binary cross entropy function as the cost function to replace traditional quadratic cost function. Finally, we choose stochastic gradient descent (SGD) as gradient descent algorithm. From the results of our experiments, we find that Densenet121 is most suitable for tuberculosis diagnosis and achieve a highest accuracy of 0.835. The optimization and expansion depend on the increase of data set and the improvements of Densenet121.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.5
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• pp.155-162
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• 2015

This paper proposes a deterministic optimization algorithm to solve economic load dispatch problem with quadratic convex fuel cost function. The proposed algorithm primarily partitions a generator with prohibited zones into multiple generators so as to place them afield the prohibited zone. It then sets initial values to $P_i{\leftarrow}P_i^{max}$ and reduces power generation costs of those incurring the maximum unit power cost. It finally employs a swap optimization process of $P_i{\leftarrow}P_i-{\beta}$, $P_j{\leftarrow}P_j+{\beta}$ where $_{max}\{F(P_i)-F(P_i-{\beta})\}$ > $_{min}\{F(P_j+{\beta})-F(P_j)\}$, $i{\neq}j$, ${\beta}=1.0,0.1,0.01,0.001$. When applied to 3 different 15-generator cases, the proposed algorithm has consistently yielded optimized results compared to those of heuristic algorithms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.4
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• pp.219-224
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• 2017

In facing the lack of a deterministic algorithm for economic load dispatch optimization problem, only non-deterministic heuristic algorithms have been suggested. Worse still, there is a near deficiency of research devoted to real-time load dispatch optimization algorithm. In this paper, therefore, I devise a shut-off and swap algorithm to solve real-time load dispatch optimization problem. With this algorithm in place, generators with maximum cost-per-unit generation power are to be shut off. The proposed shut-off criteria use only quadratic function in power generation cost function without valve effect nonlinear absolute function. When applied to the most prevalent economic load dispatch benchmark data, the proposed algorithm is proven to largely reduce the power cost of known algorithms.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.2
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• pp.40-46
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• 2015

The modular assembly system can make it possible for the variety of products to be assembled in a short lead time. In this system, necessary components are assembled to optional components tailor to customers' orders. Budget for inventory investments composed of inventory and purchasing costs are practically limited and the purchasing cost is often paid when an order is arrived. Service cost is assumed to be proportional to service level and it is included in budget constraint. We develop a heuristic procedure to find a good solution for a continuous review inventory system of the modular assembly system with a budget constraint. A regression analysis using a quadratic function based on the exponential function is applied to the cumulative density function of a normal distribution. With the regression result, an efficient heuristics is proposed by using an approximation for some complex functions that are composed of exponential functions only. A simple problem is introduced to illustrate the proposed heuristics.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.65-68
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• 2000

It is well-known that the decision rule in the mini-mum mean-squares-error decision feedback equalizer(MMSE-DFE) is biased, and therefore suboptimum with respect to error probability. We present a new family of algorithms that solve the bias problem in the adaptive DFE. A novel constraint, called the constant-norm con-straint, is introduced unifying the quadratic constraint and the monic one. A new cost function based on the constant-norm constraint and Lagrange multiplier is defined. Minimizing the cost function gives birth to a new family of unbiased adaptive DFE. The simula-tion results demonstrate that the proposed method in-deed produce unbiased solution in the presence of noise while keeping very simple both in computation and im-plementation.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.333-336
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• 1992

In this paper, We propose a PID-type of self-tuning algorithm which is based on the parameter estimation and the minimization of the cost function. We use the CARIMA model for parameter estimation and determine the discrete PID controller parameters by minimizing the cost function which considers the quadratic deviations of the predicted output over the set-point as well as the control efforts. Also, The algorithm is extended by incorporating constraints of the control signal. Simulations are performed to illustrate the efficiency of the proposed method.