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Enhancing long-term stability of photoacoustic gas sensor using an extremum-seeking control algorithm

INFRARED PHYSICS & TECHNOLOGY 2023年第1期133卷

作者： Bednyakova, Anastasia Erushin, Evgenii Miroshnichenko, Ilya Kostyukova, Nadezhda Boyko, Andrey Redyuk, Alexey Novosibirsk State Univ Pirogova str 2 Novosibirsk 630090 Russia RAS Inst Laser Phys SB Lavrentyev Ave 15b Novosibirsk 630090 Russia Novosibirsk State Tech Univ K Marksa Ave 20 Novosibirsk 630073 Russia

Smart sensor systems have gained increasing importance in various fields, including healthcare, environmental monitoring, industrial automation, and security. Photoacoustic gas sensors are an emerging type of optical sensor used in various applications due to its enhanced performance characteristics. However, the accuracy and reliability of gas concentration measurements from photoacoustic gas sensors may be impacted by several known limitations, including drift of the gas cell resonant frequency over extended periods of time. Researchers have proposed various solutions, including optimization methods and signal processing algorithms, to address this and others issues. In this paper, we propose a novel solution using an extremum-seeking control algorithm to manage the laser modulation frequency of photoacoustic gas sensors. By tracking the changing resonant frequency of the gas cell, long-term stability can be achieved, making it suitable for environmental monitoring, petroleum exploration, and industrial process control. Our approach has the potential to improve the accuracy and reliability of long-term measurements obtained from photoacoustic gas sensors, providing a stable and reliable method for gas concentration estimation.

关键词： Gas sensing Gas analysis Photoacoustics Absorption Spectroscopy Long-term stability Resonator frequency drift Concentration methane monitoring Extremum-seeking control optimization algorithm

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Robust optimal control of nonlinear fractional systems

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CHAOS SOLITONS & FRACTALS 2023年第Part1期175卷

作者： Liu, Chongyang Zhou, Tuo Gong, Zhaohua Yi, Xiaopeng Teo, Kok Lay Wang, Song Shandong Technol & Business Univ Sch Math & Informat Sci Yantai 264005 Peoples R China Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai 264005 Peoples R China Sunway Univ Sch Math Sci Kuala Lumpur 47500 Malaysia Curtin Univ Sch Elect Engn Comp & Math Sci Perth 6845 Australia

Parameter uncertainty is inevitably found in almost every system and, if is ignored, it could jeopardize the effectiveness of control method. Motivated by this issue, we consider a robust optimal control problem governed by a nonlinear fractional system with uncertain parameters, where the system sensitivity with respect to the uncertain parameters is explicitly included in the cost functional. For this problem, we first prove that the system sensitivity can be expressed as the solution of an auxiliary fractional system. Then, we propose a numerical scheme for discretizing both the original and auxiliary fractional systems, resulting in a finite dimensional optimization problem. Furthermore, a numerical solution algorithm based on gradients of the cost functional is developed for the resulting optimization problem. Finally, numerical results for two example problems are given to demonstrate the validity and applicability of the proposed algorithm.

关键词： Robust optimal control Fractional system Discretization scheme optimization algorithm

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A parametric approach for performance optimization of residential building design in Beijing

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建筑模拟（英文版） 2020年第2期13卷 223-235页

作者： Jingyu Zhang Nianxiong Liu Shanshan Wang School of Architecture Tsinghua University Beijing 100084 China

During the early design stage of green residential buildings, there are tremendous potential of using parametric optimization to achieve preferable green performance, such as building energy consumption efficiency, daylighting, ventilation and thermal comfort. Taking residential design features into consideration, this paper presents an optimization workflow and effects based on a case study of a residential building project in Beijing. Firstly, 27 design parameters related to residential spatial form and building envelope were selected for the optimization. The simulation results of the cooling and heating load were taken as the optimization objects. Secondly, optimized schemes were obtained from 6246 simulation results, with 1925 verified simulation results proving that the optimized result is reliable. Finally, analysis was performed to establish the correlations between design parameters and performance in order to create the easy access for architects to determine design parameters depending on the performance sensitivity of each parameter. Analysis results showed that parametric optimization of spatial form and building envelope at the design stage is a feasible approach to reducing energy consumption in residential building design.

关键词： residential building design performance optimization parametric design optimization algorithm residential energy conservation

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A novel meta-heuristic optimization method based on golden ratio in nature

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SOFT COMPUTING 2020年第2期24卷 1117-1151页

作者： Nematollahi, Amin Foroughi Rahiminejad, Abolfazl Vahidi, Behrooz Amirkabir Univ Technol Dept Elect Engn Tehran *** Iran Esfarayen Univ Technol Dept Elect & Comp Sci Esfarayen *** North Khorasan Iran Amirkabir Univ Technol Dept Elect Engn Tehran ***1 Iran

A novel parameter-free meta-heuristic optimization algorithm known as the golden ratio optimization method (GROM) is proposed. The proposed algorithm is inspired by the golden ratio of plant and animal growth which is formulated by the well-known mathematician Fibonacci. He introduced a series of numbers in which a number (except the first two numbers) is equal to the sum of the two previous numbers. In this series, the ratio of two consecutive numbers is almost the same for all the numbers and is known as golden ratio. This ratio can be extensively found in nature such as snail lacquer part and foliage growth of trees. The proposed approach employed this golden ratio to update the solutions in an optimization algorithm. In the proposed method, the solutions are updated in two different phases to achieve the global best answer. There is no need for any parameter tuning, and the implementation of the proposed method is very simple. In order to evaluate the proposed method, 29 well-known benchmark test functions and also 5 classical engineering optimization problems including 4 mechanical engineering problems and 1 electrical engineering problem are employed. Using several test functions, the performance of the proposed method in solving different problems including discrete, continuous, high dimension, and high constraints problems is testified. The results of the proposed method are compared with those of 11 well-regarded state-of-the-art optimization algorithms. The comparisons are made from different aspects such as the final obtained answer, the speed and behavior of convergence, and CPU time consumption. Superiority of the purposed method from different points of views can be concluded by means of comparisons.

关键词： Meta-heuristic Golden ratio optimization method optimization algorithm Constrained optimization optimization

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Analysis of thermal distribution in two end pumping Nd:YAG laser rod using bacterial foraging optimization algorithm

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PRAMANA-JOURNAL OF PHYSICS 2013年第3期80卷 487-505页

作者： Minshed, Mohammed A. Univ Technol Baghdad Laser & Optoelect Engn Dept Baghdad Iraq

There is a strong need for the optimized management of the thermal problem in Nd:YAG laser rod and for a powerful, fast, and accurate modelling tool capable of treating the heat source distribution very close to what it actually is. In this paper, a new optimization algorithm called bacterial foraging optimization algorithm (BFOA) is proposed for simulation of the radial heat distribution. A BFOA discloses a simulation method which delivers the exact temperature distribution in a circularly cylindrical structure with a circularly symmetrical, longitudinally, and transversally non-uniform heat source distribution and circularly symmetrical cooling means. The output power is obtained and compared with previously published experimental measurements for different pump power and a good agreement has been found.

关键词： Solid-state lasers thermal distribution bacterial foraging optimization algorithm

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Reconfiguration Error Correction Model for an FBG Shape Sensor Based on the Sparrow Search algorithm

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SENSORS 2023年第16期23卷 7052-7052页

作者： Shang, Qiufeng Liu, Feng North China Elect Power Univ Dept Elect & Commun Engn Baoding 071003 Peoples R China North China Elect Power Univ Dept Elect & Commun Engn Hebei Key Lab Power Internet Things Technol Baoding 071003 Peoples R China North China Elect Power Univ Baoding Key Lab Opt Fiber Sensing & Opt Commun Baoding 071003 Peoples R China

A reconfiguration error correction model for an FBG shape sensor (FSS) is proposed. The model includes curvature, bending direction error correction, and the self-correction of the FBG placement angle and calibration error based on an improved sparrow search algorithm (SSA). SSA could automatically correct the placement angle and calibration direction of the FBG, and then use the corrected placement angle and calibration direction to correct the curvature and bending direction of the FSS, thereby improving the accuracy of shape reconfiguration. After error correction, the tail point reconfiguration errors of different shapes were reduced from 2.56% and 4.96% to 1.12% and 2.45%, respectively. This paper provides a new reconfiguration error correction method for FSS that does not require a complicated experimental calibration process, is simpler, more efficient, and more operable than traditional methods, and has great potential in FSS application scenarios.

关键词： shape sensor FBG error correction curvature optimization algorithm

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An Effective Method for Segmentation of MR Brain Images Using the Ant Colony optimization algorithm

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JOURNAL OF DIGITAL IMAGING 2013年第6期26卷 1116-1123页

作者： Taherdangkoo, Mohammad Bagheri, Mohammad Hadi Yazdi, Mehran Andriole, Katherine P. Taba Med Imaging Ctr Shiraz Iran Harvard Univ Sch Med Ctr Evidence Based Imaging Dept RadiolBrigham & Womens Hosp Brookline MA 02446 USA Shiraz Univ Fac Elect & Comp Engn Dept Commun & Elect Shiraz Iran Shiraz Univ Med Sci Med Imaging Res Ctr Shiraz Iran

Since segmentation of magnetic resonance images is one of the most important initial steps in brain magnetic resonance image processing, success in this part has a great influence on the quality of outcomes of subsequent steps. In the past few decades, numerous methods have been introduced for classification of such images, but typically they perform well only on a specific subset of images, do not generalize well to other image sets, and have poor computational performance. In this study, we provided a method for segmentation of magnetic resonance images of the brain that despite its simplicity has a high accuracy. We compare the performance of our proposed algorithm with similar evolutionary algorithms on a pixel-by-pixel basis. Our algorithm is tested across varying sets of magnetic resonance images and demonstrates high speed and accuracy. It should be noted that in initial steps, the algorithm is computationally intensive requiring a large number of calculations;however, in subsequent steps of the search process, the number is reduced with the segmentation focused only in the target area.

关键词： Image processing Segmentation optimization algorithm Ant colony optimization

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Web page prediction using adaptive deer hunting with chicken swarm optimization based neural network model

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INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING 2022年第6期13卷

作者： Gangurde, Roshan Anant Ajeenkya DY Patil Univ Sch Engn DY Patil Knowledge City Rd Pune 412105 Maharashtra India

The world wide web acts as the dominant tool for data transmission due to access such as data retrieving and data transactions. The retrieval of data from the web is a complex procedure due to the large volume of web domain. The basic uses of the websites are described through web usage mining, which mines the weblog records to identify the pattern of accessing the web pages through the user. The web page prediction assists the web users in finding the plot and obtains the information as to their requirements. Several effective algorithms have been developed to mine association rules that make the predictive model more appropriate for web prediction. They can be commonly revised to ensure the changing feature of web access patterns. The Apriori algorithm involves extracting the recurrent itemset and interrelation rule that learns the relational data is commonly utilized for web page prediction. The Apriori algorithm remains the standard model for deriving the patterns and rules from the datasets in co-operative rule extraction. The Apriori algorithm thus generates large mines associated rules for web page prediction. Hence, to select the best rule, the proposed deer hunting rooster-based chicken swarm optimization algorithm is used by integrating the cockerel search agents' dominating social search creatures' hunting habits and their traits of looking for food. Further, the neural network (NN) is employed in this research for the prediction of web pages with minimum error. The trained NN is a technique of unsupervised learning that analyzes a dataset of input to produce the desired result, in which the effectiveness of the NN is enhanced by optimal tuning of weight by the adaptive deer hunting rooster-based chicken swarm optimization algorithm. The experimental analysis illustrates that the proposed adaptive deer hunting rooster-based chicken swarm optimization frameworks inherit lower error measures such as mean deviation = 139.89 and symmetric mean absolute percen

关键词： Deer hunting rooster-based chicken swarm optimization algorithm optimization algorithm Apriori algorithm web content mining world wide web

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A New Discretization Approach of Bat and K-Means

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第1期12卷 510-516页

作者： Mohamed, Rozlini Samsudin, Noor Azah Univ Tun Hussein Onn Malaysia UTHM Fac Comp Sci & Informat Technol Pant Raja Johor Malaysia

Bat algorithm is one of the optimization techniques that mimic the behavior of bat. Bat algorithm is a powerful algorithm in finding the optimum feature data collection. Classification is one of the data mining tasks that useful in knowledge representation. But, the high dimensional data become the issue in the classification that interrupt classification accuracy. From the literature, feature selection and discretization able to overcome the problem. Therefore, this study aims to show Bat algorithm is potential as a discretization approach and as a feature selection to improve classification accuracy. In this paper, a new hybrid Bat-K-Mean algorithm refer as hBA is proposed to convert continuous data into discrete data called as optimize discrete dataset. Then, Bat is used as feature selection to select the optimum feature from the optimized discrete dataset in order to reduce the dimension of data. The experiment is conducted by using k-Nearest Neighbor to evaluate the effectiveness of discretization and feature selection in classification by comparing with continuous dataset without feature selection, discrete dataset without feature selection, and continuous dataset without discretization and feature selection. Also, to show Bat is potential as a discretization approach and feature selection method. The experiments were carried out using a number of benchmark datasets from the UCI machine learning repository. The results show the classification accuracy is improved with the Bat-K-Means optimized discretization and Bat optimized feature selection.

关键词： Classification discretization feature selection optimization algorithm bat algorithm

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A comparison of methods for determining performance based employee deployment in production systems

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PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT 2021年第3-4期15卷 335-342页

作者： Ast, Jonas Wasseghi, Raed Nyhuis, Peter Leibniz Univ Hannover Inst Prod Syst & Logist Univ 2 D-30823 Garbsen Germany

Employee deployment is a crucial process in production systems. Based on qualification and individual performance of employees, deployment decisions can lead to ambiguous outcomes. This paper first reviews the state of the art and further compares two methods based on combinatorial analysis for employee deployment. Therefore, this paper emphasizes the costs and benefits of a Brute Force and an alternative Greedy method. When considering the qualification and individual performance of each employee, both algorithms provide working solutions. In direct comparison, the outcome of the alternative Greedy algorithm is more efficient in terms of calculation time whereas the Brute Force method provides the combination with the global optimum. This means calculation time as well as quality of outcome differ. The exponential growth of employee allocation possibilities depends on the amount of employees and leads to high calculation times, when using a Brute Force method. The comparison of both methods reveal that the proposed alternative Greedy algorithm reaches nearly as high outcomes as the Brute Force does, with significantly less calculation time. Furthermore, this paper offers an insight into the impact of deployment decisions within production systems.

关键词： optimization algorithm Employee deployment Operational planning Production management Task assignment

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