scholarly journals A particle swarm optimization approach using adaptive entropy-based fitness quantification of expert knowledge for high-level, real-time cognitive robotic control

2019 ◽  
Vol 1 (12) ◽  
Author(s):  
Deon de Jager ◽  
Yahya Zweiri ◽  
Dimitrios Makris
Keyword(s):  
Real Time ◽  
Flight Control ◽  
Autonomous Robots ◽  
Expert Knowledge ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Optimization Approach ◽  
Autonomous Flight ◽  
Challenge Control ◽  
High Level

AbstractHigh-level, real-time mission control of semi-autonomous robots, deployed in remote and dynamic environments, remains a challenge. Control models, learnt from a knowledgebase, quickly become obsolete when the environment or the knowledgebase changes. This research study introduces a cognitive reasoning process, to select the optimal action, using the most relevant knowledge from the knowledgebase, subject to observed evidence. The approach in this study introduces an adaptive entropy-based set-based particle swarm algorithm (AE-SPSO) and a novel, adaptive entropy-based fitness quantification (AEFQ) algorithm for evidence-based optimization of the knowledge. The performance of the AE-SPSO and AEFQ algorithms are experimentally evaluated with two unmanned aerial vehicle (UAV) benchmark missions: (1) relocating the UAV to a charging station and (2) collecting and delivering a package. Performance is measured by inspecting the success and completeness of the mission and the accuracy of autonomous flight control. The results show that the AE-SPSO/AEFQ approach successfully finds the optimal state-transition for each mission task and that autonomous flight control is successfully achieved.

Hybrid Optimization Approach for Gas-Assisted Injection Molding Based on Metamodeling and Particle Swarm Algorithm

2010 ◽  
Vol 97-101 ◽  
pp. 3353-3356
Author(s):  
Wei Chen ◽  
Xian Hong Han ◽  
Xiong Hui Zhou ◽  
Xue Wei Ge
Keyword(s):  
Particle Swarm Optimization ◽  
Injection Molding ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Hybrid Optimization ◽  
Particle Swarm Algorithm ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Plastic Process

As a new plastic process technique, Gas-assisted injection molding has many advantages comparing to the traditional injection molding. Meanwhile, Optimization of Gas-assisted injection molding is more complex since many additional parameters have been introduced to the process. In this paper, a hybrid optimization approach based on metamodeling and particle swarm optimization algorithm is proposed and applied for Gas-assisted injection molding. Moreover, the validation of the approach will be illustrated through the optimization process of a real panel.

Real-time sensor validation for autonomous flight control

1997 ◽  
Author(s):  
R. Bickford ◽  
T. Bickmore ◽  
V. Caluori ◽  
R. Bickford ◽  
T. Bickmore ◽  
...  
Keyword(s):  
Real Time ◽  
Flight Control ◽  
Autonomous Flight ◽  
Sensor Validation

scholarly journals An Improved Particle Swarm Optimization-Powered Adaptive Classification and Migration Visualization for Music Style

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiahan Liu
Keyword(s):  
Real Time ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Learning Performance ◽  
Practical Significance ◽  
Particle Swarm Algorithm ◽  
Specific Content ◽  
Style Transfer ◽  
Music Visualization ◽  
And Migration

Based on the adaptive particle swarm algorithm and error backpropagation neural network, this paper proposes methods for different styles of music classification and migration visualization. This method has the advantages of simple structure, mature algorithm, and accurate optimization. It can find better network weights and thresholds so that particles can jump out of the local optimal solutions previously searched and search in a larger space. The global search uses the gradient method to accelerate the optimization and control the real-time generation effect of the music style transfer, thereby improving the learning performance and convergence performance of the entire network, ultimately improving the recognition rate of the entire system, and visualizing the musical perception. This kind of real-time information visualization is an artistic expression form, in which artificial intelligence imitates human synesthesia, and it is also a kind of performance art. Combining traditional music visualization and image style transfer adds specific content expression to music visualization and time sequence expression to image style transfer. This visual effect can help users generate unique and personalized portraits with music; it can also be widely used by artists to express the relationship between music and vision. The simulation results show that the method has better classification performance and has certain practical significance and reference value.

scholarly journals ParticleChromo3D: A Particle Swarm Optimization Algorithm for Chromosome and Genome 3D Structure Prediction from Hi-C Data

2021 ◽  
Author(s):  
David Vadnais ◽  
Michael Middleton ◽  
Oluwatosin Oluwadare
Keyword(s):  
Particle Swarm Optimization ◽  
Global Solution ◽  
Genome Structure ◽  
3D Structure ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Candidate Solution ◽  
Structure Reconstruction

AbstractThe three-dimensional (3D) structure of chromatin has a massive effect on its function. Because of this, it is desirable to have an understanding of the 3D structural organization of chromatin. To gain greater insight into the spatial organization of chromosomes and genomes and the functions they perform, chromosome conformation capture techniques, particularly Hi-C, have been developed. The Hi-C technology is widely used and well-known because of its ability to profile interactions for all read pairs in an entire genome. The advent of Hi-C has greatly expanded our understanding of the 3D genome, genome folding, gene regulation and has enabled the development of many 3D chromosome structure reconstruction methods. Here, we propose a novel approach for 3D chromosome and genome structure reconstruction from Hi-C data using Particle Swarm Optimization approach called ParticleChromo3D. This algorithm begins with a grouping of candidate solution locations for each chromosome bin, according to the particle swarm algorithm, and then iterates its position towards a global best candidate solution. While moving towards the optimal global solution, each candidate solution or particle uses its own local best information and a randomizer to choose its path. Using several metrics to validate our results, we show that ParticleChromo3D produces a robust and rigorous representation of the 3D structure for input Hi-C data. We evaluated our algorithm on simulated and real Hi-C data in this work. Our results show that ParticleChromo3D is more accurate than most of the existing algorithms for 3D structure reconstruction. Our results also show that constructed ParticleChromo3D structures are very consistent, hence indicating that it will always arrive at the global solution at every iteration. The source code for ParticleChromo3D, the simulated and real Hi-C datasets, and the models generated for these datasets are available here: https://github.com/OluwadareLab/ParticleChromo3D

scholarly journals Real-Time Application Optimization Control Algorithm for Energy Management Strategy of the Hybrid Power System Based on Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yanying Ma ◽  
Qiang Liu
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Fuel Consumption ◽  
Energy Management ◽  
Management Strategy ◽  
Lithium Battery ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Energy Management Strategy ◽  
Hybrid Power

In recent years, due to the strengthening of our country’s comprehensive strength, the rapid development of science and technology and artificial intelligence has also attracted people’s attention. Artificial intelligence is a highly applicable subject, which has very good applications in power systems. In the experiment, the open circuit voltage method and the ampere-hour integration method are used to estimate the SOC of the lithium battery and the particle swarm energy management algorithm is used to allocate the output power of the fuel cell and the lithium battery. The particle swarm algorithm module calls the dual source hybrid power system module through the sim function to convert the actual value input in the system into a fuzzy quantity suitable for fuzzy control. The energy management strategy based on particle swarm optimization and fuzzy control was tested based on working conditions under the comprehensive test bench. Finally, the matching of the hybrid system is analyzed from the structure, component parameters, control strategy, and driving cycle of the vehicle. The experimental data show that the total fuel consumption of the three sets of experiments is averaged to get a fuel consumption rate of 26.3 m3/100 km for the hybrid city bus under the optimized energy management strategy. The results show that the real-time energy management strategy based on particle swarm algorithm can significantly improve the real-time performance of traditional instantaneous energy management strategies while reducing fuel consumption.

scholarly journals Optimizing the High-Level Maintenance Planning Problem of the Electric Multiple Unit Train Using a Modified Particle Swarm Optimization Algorithm

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 349 ◽  
Author(s):  
Jianping Wu ◽  
Boliang Lin ◽  
Hui Wang ◽  
Xuhui Zhang ◽  
Zhongkai Wang ◽  
...  
Keyword(s):  
Programming Model ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Planning Problem ◽  
Discrete Problem ◽  
Maintenance Planning ◽  
Transportation Demand ◽  
Multiple Unit ◽  
High Level

Electric multiple unit (EMU) trains’ high-level maintenance planning is a discrete problem in mathematics. The high-level maintenance process of the EMU trains consumes plenty of time. When the process is undertaken during peak periods of the passenger flow, the transportation demand may not be fully satisfied due to the insufficient supply of trains. In contrast, if the process is undergone in advance, extra costs will be incurred. Based on the practical requirements of high-level maintenance, a 0–1 programming model is proposed. To simplify the description of the model, candidate sets of delivery dates, i.e., time windows, are generated according to the historical data and maintenance regulations. The constraints of the model include maintenance regulations, the passenger transportation demand, and capacities of workshop. The objective function is to minimize the mileage losses of all EMU trains. Moreover, a modified particle swarm algorithm is developed for solving the problem. Finally, a real-world case study of Shanghai Railway is conducted to demonstrate the proposed method. Computational results indicate that the (approximate) optimal solution can be obtained successfully by our method and the proposed method significantly reduces the solution time to 500 s.

GPU-Based PSO Application in Multiuser Detection and Trajectory Parameter Estimation

2013 ◽  
Vol 340 ◽  
pp. 829-832
Author(s):  
Lei Sun ◽  
Han Tao Zhang ◽  
Xiao Ping Zhou
Keyword(s):  
Parameter Estimation ◽  
Real Time ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
System Capacity ◽  
Particle Swarm Algorithm ◽  
Processing Unit ◽  
Trajectory Parameter ◽  
Multiuser Detector ◽  
Swarm Algorithm

The parallel character of particle swarm algorithm (PSO) and the Graphic Processing Unit (GPU) technology of Compute United Device Architecture (CUDA) from NVIDIA are analyzed. Two methods of the realization of PSO based on GPU are discussed. One method is using the module of open source particle swarm algorithm supporting the GPU, with the application of multiuser detector (MUD). The other method is using the module of MATLAB supporting the GPU with the application of the moving parameter estimation. The test results show that the PSO algorithm based on GPU technology can significantly improve the speed of system capacity, to solve the problem of multi-dimensional global optimization, with the poor real-time performance. It can be widely used in the project of high real-time requirements.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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