• Smart Structures and Systems
• /
• v.24 no.6
• /
• pp.733-744
• /
• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Journal of Energy Engineering
• /
• v.25 no.1
• /
• pp.23-28
• /
• 2016

In spite of various merit of algae as biofuel, the production cost of algae is a considerable obstacle for commercialization. The concurrent development of essential technologies is needed for the cultivating, harvesting, extracting and energy transformation. The production cost of algae biofuel has still higher than that of the other commercial biofuel. The major research activity has been focused on the cultivating and the research of other processes has been done with relatively lower activity. It is difficult to separate the algae from water because of the similar magnitude of density each other. The agglomeration and extracting of algae with the hybrid technology using ultrasonic wave is rare effect of environmental hazard and also it is appropriate technology for the next generation energy resources. The present research is investigated for the effective separation of algae from water with the ultrasonics wave. The aim of the present research is focused on the establishment of optimal design of algae agglomeration system. For this purpose, the computational fluid dynamic analysis has been conducted in the flow field with ultrasonic wave and algae flow to clarify the mechanism of algae separation by ultrasonic wave.

• Journal of Satellite, Information and Communications
• /
• v.12 no.4
• /
• pp.100-105
• /
• 2017

In this paper, we study an interference avoidance scenario in the presence of a interferer which can rapidly observe the transmit power of backscatter communications and effectively interrupt backscatter signals. We consider a power control with a sub-channel allocation to avoid interference attacks and a power-splitting ratio for backscattering and RF energy harvesting in sensors. We formulate the problem based on a Stackelberg game theory and compute the optimal transmit power, power-splitting ratio, and sub-channel allocation parameter to maximize a utility function against the interferer. We propose the utility maximization using Lagrangian dual decomposition for the backscatter communications and the interferer to prove the existence of the Stackelberg equilibrium. Numerical results show that the proposed algorithms effectively maximize the utility, compared to that of the algorithm based on the Nash game, so as to overcome a malicious interference in backscatter communications.

• Journal of Convergence for Information Technology
• /
• v.12 no.3
• /
• pp.294-301
• /
• 2022

In this study, a multi-layer cascade (MLC) electrical array configuration method for thermoelectric generator consisting of plural number of thermoelectric modules (TEMs) was proposed to reduce mismatch loss caused by temperature maldistribution on the surfaces of the TEMs. To validate the effect of MLC on the mismatch loss reduction, a numerical model capable of reflecting multi-physics phenomena occuring in the TEMs was developed. MLC can be employed by placing a group of TEMs experiencing relatively low temperature differences in an electric layer with more electrical branches while locating a group of TEMs experiencing relatively high temperature differences in an electric layer with less electrical branches. The TEMs were classified using the temperature distribution obtained by the numerical model. A MLC with an optimal electrical branch ratio showed a 96.5% of electric power generation compared to an ideal case.