A Procedure to Design Fault-Tolerant Wide-Area Damping Controllers

Bento, Murilo E. C.; Dotta, Daniel; Kuiava, Rôman; Ramos, Rodrigo A.

doi:10.1109/access.2018.2828609

Cited by 66 publications

(25 citation statements)

References 43 publications

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“…Some popular and new methods are particle swarm optimization (PSO) [65], [66], ant colony optimization (ACO) [67], bacterial foraging optimization (BFO) [68]- [70], teaching-learning based optimizer (TLBO) [71], gray wolf optimizer (GWO) [45], [72], moth-flame optimization (MFO) [73]- [75], moth search algorithm (MSA) [76], grasshopper optimization algorithm (GOA) [77]- [79], whale optimization algorithm (WOA) [27], [80]- [82], fruit fly optimization algorithm (FOA) [83]- [85] and Harris hawks optimizer (HHO) [86]. Owing to its strong global optimization capability, these MAs have applied in many scenarios, including medical diagnosis, machine learning, engineering design, energy management, job scheduling, and pharmaceutical industry [87]- [98]. As a new member of MAs, SCA has a simple structure and can be implemented easily.…”

Section: Proposed Coscamentioning

confidence: 99%

Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine

et al. 2020

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This study proposes an effective intelligent predictive model for prediction of cervical hyperextension injury. The prediction model is constructed by combing an improved sine cosine algorithm (SCA) with support vector machines (SVM), which is named COSCA-SVM. The core of the developed model is the COSCA method that combines the opposition-based learning mechanism and covariance mechanism to boost and recover the exploratory competence of SCA. The proposed COSCA approach is utilized to optimize the two critical parameters of the SVM, and it is also employed to catch the optimal feature subset. Based on the optimal parameter combination and feature subset, COSCA-SVM is able to make self-directed prediction of cervical hyperextension injury. The proposed COSCA was compared with other well-known and effective methods using 23 benchmark problems. Simulation results verify that the proposed COSCA is significantly superior to studied methods in dealing with majority of benchmark problems. Meanwhile, the proposed COSCA-SVM is compared with six other machine learning approaches considering a reallife dataset. Results have shown that the proposed COSCA-SVM can achieve better classification routine and higher stability on all four indicators. Therefore, we can expect that COSCA-SVM can be a promising building block for predicting cervical hyperextension injury.

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Section: Proposed Coscamentioning

confidence: 99%

Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine

et al. 2020

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“…Genetic algorithm is a randomized search method and has a certain global optimization capability. In literature [11], a wide-area robust damping controller design method based on genetic algorithm is proposed, which has certain guidance for power system damping design methods. Meaning, it provides technical support for control design technology for other key issues in the power system field.…”

Section: A Related Workmentioning

confidence: 99%

“…represented the length of the jth segment on the ith route. The total energy cost of the ith path could be approximated via the following formula: (11) Maximum benefits of attack missions: Regarding the attack mission threat weight as the income weight, the mission income of attacking base k was (12) where n was the number of aircraft sent to enemy base i. (13) In the present paper, the flight cost was the weighted sum of the UAV track length and the cost of the track threatened by radar.…”

Section: B Model Establishment 1) Route Cost Functionmentioning

confidence: 99%

Three-Dimensional Mountain Complex Terrain and Heterogeneous Multi-UAV Cooperative Combat Mission Planning

et al. 2020

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Most research regarding multi-target, multi-base, and multi-unmanned aerial vehicle (UAV) coordinated combat mission planning faces the problems of ignoring heterogeneous UAVs, as well as poor task allocation and trajectory planning coupling. To solve these problems, based on air maneuver combat mission backgrounds, the present paper provided a heterogeneous multi-UAV cooperative mission planning method in the complex three-dimensional (3D) mountain environment. In the present paper, based on the Life-cycle Swarm Optimization (LSO) algorithm, varying the number of individuals in the population was utilized to improve the algorithm and further combined with the Rapidly exploring Random Tree (RRT) algorithm to obtain an optimized path. Then, an improved algorithm was utilized for task allocation and trajectory optimization, and the number and speed of drones dispatched by each base were determined regarding time coordination. Finally, a simulation experiment was conducted. Numerical simulation results showed that the following algorithm was compared with the Particle Swarm Optimization (PSO) algorithm and the Whale Optimization Algorithm (WOA) when considering radar threats and solid obstacle areas. This has good approximation and high convergence accuracy, and it was effectively utilized in the planning of UAV collaborative missions in 3D complex terrain environments.

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“…Heuristic information is used for navigating the search space for potential individuals, and this can achieve globally optimal solutions. Since then, there have been many works that used GAs in practice [7][8][9][10][11].…”

Section: Related Workmentioning

confidence: 99%

Multiswarm Multiobjective Particle Swarm Optimization with Simulated Annealing for Extracting Multiple Tests

Bui

Nguyen

Huynh

et al. 2020

Scientific Programming

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Education is mandatory, and much research has been invested in this sector. An important aspect of education is how to evaluate the learners’ progress. Multiple-choice tests are widely used for this purpose. The tests for learners in the same exam should come in equal difficulties for fair judgment. Thus, this requirement leads to the problem of generating tests with equal difficulties, which is also known as the specific case of generating tests with a single objective. However, in practice, multiple requirements (objectives) are enforced while making tests. For example, teachers may require the generated tests to have the same difficulty and the same test duration. In this paper, we propose the use of Multiswarm Multiobjective Particle Swarm Optimization (MMPSO) for generating k tests with multiple objectives in a single run. Additionally, we also incorporate Simulated Annealing (SA) to improve the diversity of tests and the accuracy of solutions. The experimental results with various criteria show that our approaches are effective and efficient for the problem of generating multiple tests.

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A Procedure to Design Fault-Tolerant Wide-Area Damping Controllers

Cited by 66 publications

References 43 publications

Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine

Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine

Three-Dimensional Mountain Complex Terrain and Heterogeneous Multi-UAV Cooperative Combat Mission Planning

Multiswarm Multiobjective Particle Swarm Optimization with Simulated Annealing for Extracting Multiple Tests

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