• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2701-2717
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• 2023

In this paper, a hybrid fuzzy-based method is suggested for determining India's best system for power generation. This suggested approach was created using a fuzzy-based combination of the Giza Pyramids Construction (GPC) and Recalling-Enhanced Recurrent Neural Network (RERNN). GPC is a meta-heuristic algorithm that deals with solutions for many groups of problems, whereas RERNN has selective memory properties. The evaluation of the current load requirements and production profile information system is the main objective of the suggested method. The Central Electricity Authority database, the Indian National Load Dispatch Centre, regional load dispatching centers, and annual reports of India were some of the sources used to compile the data regarding profiles of electricity loads, capacity factors, power plant generation, and transmission limits. The RERNN approach makes advantage of the ability to analyze the ideal power generation from energy data, however the optimization of RERNN factor necessitates the employment of a GPC technique. The proposed method was tested using MATLAB, and the findings indicate that it is effective in terms of accuracy, feasibility, and computing efficiency. The suggested hybrid system outperformed conventional models, achieving the top result of 93% accuracy with a shorter computation time of 6814 seconds.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.806-807
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• 2011

This paper presents a new sensorless controller used with both the classical sliding mode observer(SMO) and the rate of current change in order to a reduced torque ripple for switched reluctance motor (SRM) sensorless drives. The new sensorless scheme consists of a sliding mode observer (SMO)-based position sensorless approach for high speeds along with a low-resolution discrete the rate of current change for low speeds and standstill. The new position estimation resets between the SMO and the low-resolution of current change according to the speed sign and the position error difference between the SMO and the low-resolution rate of current change. The simulation results show the robustness of this new high performance sensorless control approach with the hybrid sensorless control topology.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.17 no.4
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• pp.221-237
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• 2013

In this paper an asymptotic numerical method named as Initial Value Method (IVM) is suggested to solve the singularly perturbed weakly coupled system of reaction-diffusion type second order ordinary differential equations with negative shift (delay) terms. In this method, the original problem of solving the second order system of equations is reduced to solving eight first order singularly perturbed differential equations without delay and one system of difference equations. These singularly perturbed problems are solved by the second order hybrid finite difference scheme. An error estimate for this method is derived by using supremum norm and it is of almost second order. Numerical results are provided to illustrate the theoretical results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5723-5743
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• 2018

The evolving internet-based services demand high-speed data transmission in conjunction with scalability. The next generation optical network has to exploit artificial intelligence and cognitive techniques to cope with the emerging requirements. This work proposes a novel way to solve the dynamic provisioning problem in optical network. The provisioning in optical network involves the computation of routes and the reservation of wavelenghs (Routing and Wavelength assignment-RWA). This is an extensively studied multi-objective optimization problem and its complexity is known to be NP-Complete. As the exact algorithms incurs more running time, the heuristic based approaches have been widely preferred to solve this problem. Recently the software-defined networking has impacted the way the optical pipes are configured and monitored. This work proposes the dynamic selection of path computation algorithms in response to the changing service requirements and network scenarios. A software-defined controller mechanism with a novel packet matching feature was proposed to dynamically match the traffic demands with the appropriate algorithm. A software-defined controller with Path Computation Element-PCE was created in the ONOS tool. A simulation study was performed with the case study of dynamic path establishment in ONOS-Open Network Operating System based software defined controller environment. A java based NOX controller was configured with a parent path computation element. The child path computation elements were configured with different path computation algorithms under the control of the parent path computation element. The use case of dynamic bulk path creation was considered. The algorithm selection method is compared with the existing single algorithm based method and the results are analyzed.

• Bulletin of the Korean Chemical Society
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• v.31 no.8
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• pp.2322-2328
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• 2010

A multiple solid-phase extraction (SPE) method was used with liquid chromatography, coupled with mass spectrometry (LC/MS), for the analysis of heterocyclic amines (HCAs) in human urine. Separation efficiencies based on the pH of the mobile phase and the types of columns were compared. An amide column showed better baseline separation and narrower HCA peak widths at pH 5.0 for the mobile phase than a $C_8$ column. Each SPE step, HLB, MCX, and HybridSPE, was optimized by controlling the pH conditions. The combined method with the three SPEs effectively removed interfering species that cause ion-suppression during HCA detection. Validation of the method, performed with SIM and SRM detection, showed correlation coefficients above 0.991 in the range 0.3 - 16.7 ng/mL. Recovery rates were 45.4 - 97.3% on the $C_8$ column and 71.8 - 101.4% on the amide column, and method detection limits were 0.11 - 0.65 ng/mL on the $C_8$ column and 0.12 - 0.48 ng/mL on the amide column. This method using multiple SPEs offers significant benefits for high-throughput determination of HCAs in urine.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.682-684
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• 2015

A 12/14 bearingless switched reluctance motor (BLSRM) with hybrid stator poles has been proposed due to the outstanding decoupling characteristics between the torque and suspending force. However, the motor is a two-phase motor. The output torque of the motor has torque dead zone and high torque ripple. Hence, the motor cannot self-start at some rotor positions. To solve the self-starting problems and reduce the torque ripple, a stepped rotor is proposed in this paper. Then, the motor with the stepped rotor is optimally designed. In the new designed motor, the majority parameters are kept the same with those of original motor; only the torque pole arc and rotor pole shape are optimally designed. The characteristics of the redesigned motor, such as inductance, torque and suspending force, are analyzed and compared with those in the original motor. Finally, the effectiveness of the proposed method is verified by the simulation results.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.