Optimizing Directional Reader Antennas Deployment in UHF RFID Localization System by Using a MPCSO Algorithm

Shi, Weiguang; Guo, Yang; Yan, Shuxia; Yu, Yang; Luo, Peng; Li, Jianxiong

doi:10.1109/jsen.2018.2832216

Cited by 25 publications

(29 citation statements)

References 31 publications

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“…Algorithm 7 presents well-structured pseudocode of CSo for optimized feature selection. Based on performance of CSO, researchers have successfully solved and optimized engineering and science problems, Directional Reader Antennas Optimization [61], Community detection in social networks [62], parameters Optimization of a fuzzy logic system [63].…”

Section: Other Optimization Algorithmsmentioning

confidence: 99%

Evolutionary Computation, Optimization, and Learning Algorithms for Data Science

Mohammadi

Amini

Arabnia

2020

Advances in Intelligent Systems and Computing

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purpose. This motivates researchers to think about optimization and apply nature inspired algorithms, such as meta-heuristic and evolutionary algorithms (EAs) to solve large-scale optimization problems. Building on the strategies of these algorithms, researchers solve large-scale engineering and computational problems with innovative solutions. Although these algorithms look un-deterministic, they are robust enough to reach an optimal solution. To that end, researchers try to run their algorithms more than usually suggested, around 20 or 30 times, then they compute the mean of result and report only the average of 20 / 30 runs' result. This high number of runs becomes necessary because EAs, based on their randomness initialization, converge the best result, which would not be correct if only relying on one specific run. Certainly, researchers do not adopt evolutionary algorithms unless they face a problem which is suffering from placement in local optimal solution, rather than global optimal solution. In this chapter, we first develop a clear and formal definition of the CoD problem, next we focus on feature extraction techniques and categories, then we provide a general overview of meta-heuristic algorithms, its terminology, and desirable properties of evolutionary algorithms.A large number of engineering, science and computational problems have yet to be solved in a more computationally efficient way. One of the emerging challenges is the evolving technologies and how they enhance towards autonomy. This leads to collection of large amount of data from various sensing and measurement technologies, such as cameras, smart phones, health sensors, and environment sensors. Hence, generation, manipulation and illustration of data grow significantly. Meanwhile, data is structured purposefully through different representations, such as large-scale networks and graphs. Therefore, data plays a pivotal role in technologies by introducing several challenges: how to present, what to present, why to present. Researchers explored various approaches to implement a comprehensive solution to express their results in every particular domain, such that the solution enhances the performance and

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Section: Other Optimization Algorithmsmentioning

confidence: 99%

Evolutionary Computation, Optimization, and Learning Algorithms for Data Science

Mohammadi

Amini

Arabnia

2020

Advances in Intelligent Systems and Computing

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“…In this study, the parameters of the SVM are optimized by the improved chicken swarm optimization algorithm (ICSO). Shi et al [19] introduced two strategies in the chicken swarm algorithm and propose the modified parallel cat swarm optimization (MPCSO) algorithm. The two strategies are monomers turbulence in rooster (MTR) and particle renovation in hen (PRH).…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

Li²,

Lin

et al. 2019

Sustainability

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Renewable energy technologies are essential contributors to sustainable energy including renewable energy sources. Wind energy is one of the important renewable energy resources. Therefore, efficient and consistent utilization of wind energy has been an important issue. The wind speed has the characteristics of intermittence and instability. If the wind power is directly connected to the grid, it will impact the voltage and frequency of the power system. Short-term wind power prediction can reduce the impact of wind power on the power grid and the stability of power system operation is guaranteed. In this study, the improved chicken swarm algorithm optimization support vector machine (ICSO-SVM) model is proposed to predict the wind power. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum when solving high-dimensional problems due to its own characteristics. So the CSO algorithm is improved and the ICSO algorithm is developed. In order to verify the validity of the ICSO-SVM model, the following work has been done. (1) The particle swarm optimization (PSO), ICSO, CSO and differential evolution algorithm (DE) are tested respectively by four standard testing functions, and the results are compared. (2) The ICSO-SVM and CSO-SVM models are tested respectively by two sets of wind power data. This study draws the following conclusions: (1) the PSO, CSO, DE and ICSO algorithms are tested by the four standard test functions and the test data are analyzed. By comparing it with the other three optimization algorithms, the ICSO algorithm has the best convergence effect. (2) The number of training samples has an obvious impact on the prediction results. The average relative error percentage and root mean square error (RMSE) values of the ICSO model are smaller than those of CSO-SVM model. Therefore, the ICSO-SVM model can efficiently provide credible short-term predictions for wind power forecasting.

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“…The RF-Detector consists of a robot and the RFID. We install the RFID reader and antenna on the robot, and it scans the tags as the robot moves [10,11]. The RF-Detector can realize automatic scanning and detection, reducing manpower consumption greatly, and it is of low cost and easy to deploy, which makes it applicable in practice.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Management of Chemical Warehouses with RFID Systems

Zhao

Xue

2019

Sensors

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At present, most chemical warehouses rely on human management, which is a time-consuming and laborious process. Therefore, it is very meaningful to use radio frequency identification (RFID) systems for the intelligent management of chemicals. Detecting the remaining amount of chemicals is an important process in the management of a chemical warehouse. It helps managers find the chemicals that are going to run out and replenish them in time. However, in a traditional chemical warehouse, managers usually inspect each chemical on the shelf in turn manually, which is a waste of time and labor. Although some solutions using RFID technology have been proposed, they are expensive and difficult to deploy in a real environment. In order to solve this problem, we propose an intelligent system called the RF-Detector in this paper, which combines robotics and RFID technology. An RFID reader and an antenna are installed on the robot, which achieves automatic scanning of the chemicals. The RF-Detector can achieve two functions: One function is to detect the remaining amount of chemicals using the changes in received signal strength indication (RSSI) and read rate, and the other is to locate chemicals using the phase curve, so that managers can quickly find the chemicals with an insufficient amount remaining. In this paper we implement the RF-Detector and evaluate its performance. The experimental results show that the RF-Detector achieves about 93% detection accuracy and 92% positioning accuracy for chemicals.

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Optimizing Directional Reader Antennas Deployment in UHF RFID Localization System by Using a MPCSO Algorithm

Cited by 25 publications

References 31 publications

Evolutionary Computation, Optimization, and Learning Algorithms for Data Science

Evolutionary Computation, Optimization, and Learning Algorithms for Data Science

Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

Intelligent Management of Chemical Warehouses with RFID Systems

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