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Prediction of lipid oxidation and polycyclic aromatic hydrocarbons in grilled sausages based on optimized back propagation neural network and computer vision

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JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2025年第5期19卷 3384-3402页

作者： Mei, Hebao Xing, Wei Hu, Manzi Zhou, Hui Chen, Gongwei Cai, Kezhou Xu, Baocai Hefei Univ Technol Key Lab Anim Food Green Mfg & Resource Min Anhui P Hefei 230009 Peoples R China Anhui Agr Univ Sch Anim Sci & Technol Hefei 230036 Peoples R China Hefei Univ Technol Engn Res Ctr Bioproc 420 Feicui Rd Hefei 230601 Anhui Peoples R China

This study developed a model to predict multiple quality parameters of grilled sausages using a computer vision system (CVS) integrated with a back propagation neural network (BP-NN) optimized by particle swarm optimization (PSO). The non-contact characteristic of CVS allowed for the quick and non-destructive acquisition of color information, which was used as input variables. Peroxide value (POV), 2-thiobarbituric acid reactive substances (TBARS), and polycyclic aromatic hydrocarbons (PAHs) were used as output layer parameters. The TBARS, POV, and PAH4 models outperformed other prediction models in terms of accuracy, with prediction error under 10%, a maximum mean squared error (MSE) of 0.037, and correlation coefficients (R) above 0.9. In contrast, the PAH15 prediction model had a prediction error under 16%, an MSE of 0.083, and correlation coefficients ranging from 0.76 to 0.83. Furthermore, sensitivity analysis revealed the a-value as the most influential parameter for predicting lipid oxidation and PAH4 levels in grilled sausages. Overall, these results suggest that the color parameters extracted by the CVS combined with the PSO-BP-NN model have great potential for predicting lipid oxidation and PAHs formation during grilled sausage processing in a rapid and non-destructive manner.

关键词： Computer vision system Artificial neural network particle swarm algorithm Lipid oxidation Polycyclic aromatic hydrocarbons

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Congestion management and energy optimization in multi-market electricity systems

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JOURNAL OF ENERGY STORAGE 2025年 112卷

作者： Xie, Xiaomin Liu, Hui Du, Xuanfu Anhui Vocat & Tech Coll Intelligent Mfg Coll Hefei 230011 Peoples R China

The increasing importance of energy exchange between electricity markets across regions, commonly referred to as "market integration", has emerged as a critical challenge in modern power networks. This study introduces a novel framework for managing congestion in transmission networks and optimizing energy and reserve settlement in interconnected electricity markets. The framework allows market participants to simultaneously bid in multiple markets and exchange energy through interconnecting transmission lines. Each market independently manages energy generation and storage, while a coordinating entity ensures optimal allocation of transmission network capacity to maintain system security. The study examines three methods for simultaneous energy and reserve settlement, each designed to enhance efficiency and minimize energy waste in multi-market systems. The framework is validated through simulations on a 3-region, 15-bus system, and the IEEE RTS-96 system, demonstrating its applicability and performance across diverse market configurations. The particle swarm optimization with time-varying acceleration coefficients (PSO-TVAC) metaheuristic optimization algorithm is employed to solve the problem, effectively managing congestion and optimizing market operations. Simulation results using the IEEE RTS-96 system showed that integrating market interactions reduced congestion by 100 % across critical transmission lines within five iterations. The third reserve settlement method reduced overall market costs by 8.3 % compared to the second method, with a 6.2 % cost reduction in Market C. Furthermore, storage price reductions of up to 53.3 % were achieved compared to conventional methods, showcasing the economic and operational benefits of the proposed approach.

关键词： Multiple electricity markets Energy market Storage market Market coupling Congestion management particle swarm algorithm

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particle swarm optimization service composition algorithm based on prior knowledge

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JOURNAL OF INTELLIGENT MANUFACTURING 2024年第1期35卷 35-53页

作者： Wang, Hongbin Ding, Yang Xu, Hanchuan Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Yunnan Peoples R China Harbin Inst Technol Fac Comp Harbin 150001 Heilongjiang Peoples R China

In order to quickly find an appropriate composition of services that meet the individual user's requirements in the Internet big data, this paper proposes an improved particle swarm service composition method based on prior knowledge. This method firstly mines the service composition partial segments with certain frequencies of usage from a large number of historical service composition solutions, i.e. the service pattern. While receiving the user's service composition requirement, this method uses the service pattern matching algorithm proposed in this paper to match the corresponding service patterns as a partial solution of this composition requirement. Then the method proposes an improved particle swarm algorithm for the part that do not successfully match the corresponding service patterns. This improved particle swarm algorithm has a mechanism to escape from the local optima. Finally, the method integrates the partial solutions of the two aspects into a complete solution, i.e. a complete service composition solution. This paper compares the optimality, time complexity and convergence with other related service composition optimization algorithms through simulation experiments. According to the analysis of the experimental results, the method proposed in this paper shows good performance in three aspects: optimality, time complexity and convergence.

关键词： Service composition Service pattern particle swarm algorithm Quality of service

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A Rapidly Dispatchable Energy Strategy Utilizing Electric Vehicles with Energy Storage Systems (EV-ESS) to Enhance Grid Redundancy

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SMART GRIDS AND SUSTAINABLE ENERGY 2025年第1期10卷 1-9页

作者： Ajao, Qasim Sadeeq, Lanre Natl Inst Technol Dept Engn West Afr Chapter Lagos Nigeria Microsoft Corp Dept Engn Lagos Nigeria

This paper presents a rapid and dispatchable energy storage strategy that integrates electric vehicles (EVs) with energy storage systems (ESS) into smart grids to reduce load, minimize costs, and optimize energy management during periods of varying demand. The mobility and dynamic usage patterns of EVs present challenges in managing energy flow within smart grids. To address these challenges and enhance EV participation in smart grid operations, this paper introduces an improved particle swarm optimization (PSO) algorithm. The algorithm incorporates an adjustable weight factor, enhancing its ability to efficiently search for optimal solutions. Simulations confirm that this strategy significantly improves the coordination of EVs, leveraging their energy storage potential to boost the overall efficiency of smart grid operations. Additionally, the improved PSO algorithm reduces operational costs by up to 22% compared to conventional methods, demonstrating its practicality and cost-effectiveness.

关键词： Electric vehicles (EVs) Energy storage systems (ESS) Energy dispatch particle swarm algorithm Smart grid

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A Hybrid Composite Differential Evolution and Multiobjective particle swarm Optimization Evolutionary algorithm and Its Application

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IEEE ACCESS 2024年 12卷 74417-74431页

作者： Shang, Jin Li, Guiying Wuxi Inst Technol Control Technol Inst Wuxi 214121 Peoples R China Heilongjiang Univ Sch Mech & Elect Engn Harbin 150080 Peoples R China

The current multi-objective particle swarm algorithms excel in convergence speed for solving complex problems but often suffer from a loss of population diversity. Conversely, composite differential evolution algorithms maintain superior solution distribution but lag in convergence efficiency. This research introduces an improved hybrid algorithm, CoDE-MOPSO, which integrates multi-objective particle swarm optimization with composite differential evolution based on clustering technology. The clustering algorithm is used for all individual clusters to analyze the distribution constructs of populations, which determines whether the new solutions come from global or local populations at a mating restriction probability. The mating restriction probability is updated at each generation. To adapt the balance between the population solution diversities and the convergence speed of the algorithm, at each generation, the control probability is adjusted by a developed adaptive strategy according to the reproduction utility of the two mechanisms of generating new solutions over the last certain generations. This research introduces the CoDE-MOPSO algorithm, designed to transcend existing multi-objective optimization methods' limitations by optimally balancing exploration and exploitation. Our approach significantly advances evolutionary multi-objective optimization, demonstrating superior performance through lower Inverse Generational Distance and higher Hypervolume metrics, indicating enhanced efficiency in solving complex MOPs across various fields. In practical scenarios like gear reducer optimization, CoDE-MOPSO showcases remarkable effectiveness, highlighting its value in engineering applications and setting a foundation for sophisticated optimization strategies that combine speed with solution quality.

关键词： Statistics Social factors Optimization Convergence Clustering algorithms particle swarm optimization Interconnected systems Pareto optimization Clustering algorithm composite differential evolution gear reducer multiobjective optimization particle swarm algorithm

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Parameter fitting of variogram based on hybrid algorithm of particle swarm and artificial fish swarm

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 116卷 265-274页

作者： Zhang, Xialin Lian, Lingkun Zhu, Fukang China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Peoples R China Engn Technol Innovat Ctr Mineral Resources Explor Minist Nat Resources Wuhan 550081 Peoples R China Intelligent Geol Resources & Environm Technol Hub Wuhan 430074 Peoples R China

Variation function is an important tool for describing the spatial correlation characteristics of regionalized variables in geostatistical methods. Variation function modeling is an important part of kriging interpolation and will directly affect the accuracy of the final interpolation result. The purpose of this work is to address the shortcomings of traditional variogram fitting methods, introduce particle swarm algorithm and artificial fish swarm algorithm under swarm intelligence framework, and design a variogram parameter fitting based on the hybrid algorithm of particle swarm and artificial fish swarm method. With this method, the minimum difference between the variation function fitting model and the given experimental variation value is utilized as the optimization goal. An appropriate objective function is set to convert it into a minimum problem. The hybrid algorithm has a strong search ability and convergence, as well as the ability to obtain the satisfactory fitness values. By comparing the results of the VARFIT fitting and the results of the optimization algorithm, it can be concluded that the absolute deviation of the fitting results of the optimization algorithm is 3.39 lower than the results of the VARFIT fitting. Compared with the traditional variogram modeling approach, this method has a strong optimization ability and high precision, and can effectively realize the automatic fitting of variogram parameters. (C) 2020 Published by Elsevier B.V.

关键词： Variation function particle swarm algorithm Artificial fish swarm algorithm Parameter fitting

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A GA and particle swarm Optimization Based Hybrid algorithm

A GA and Particle Swarm Optimization Based Hybrid Algorithm

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IEEE Congress on Evolutionary Computation

作者： Nie Ru Yue Jianhua

ISBN: (纸本)9781424418220

In this paper an improved particle swarm algorithm is presented firstly and then a hybrid method combining Genetic algorithm(GA) and particle swarm Optimization(PSO) is proposed. This hybrid technique incorporates concepts from GA and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by mechanisms of PSO. It can solve the problem of local minimum of the particle swarm optimization and has higher efficiency of search. Simulation results show that the proposed method is effective for the optimization problems.

关键词： particle swarm optimization Gallium parametric subharmonic oscillator particle swarm algorithm Local Minimum HYBRID Hybrid electric vehicles

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Image Segmentation Based on Modified particle swarm Optimization and Fuzzy C-Means Clustering algorithm

Image Segmentation Based on Modified Particle Swarm Optimiza...

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2nd International Conference on Intelligent Computation Technology and Automation

作者： Zhou Xian-cheng Hunan Univ Commerce Sch Comp & Elect Engn Changsha Hunan Peoples R China

ISBN: (纸本)9780769538044

In order to solve the problems of the fuzzy C-means (FCM) clustering algorithm when it is applied to the image segmentation such as making itself easily traps into local optimum and huge calculation, an image segmentation algorithm based on the modified particle swarm optimization(MPSO) and FCM clustering algorithm is proposed. The simulation results and the comparison between the proposed algorithm and FCM algorithm indicate that the proposed algorithm can obtain better segmentation effects and excel the existing FCM algorithm in several performance, such as the average dispersion, the maximum intra-distance between pixel and their cluster center, and the minimum inter-distance between any pair of clusters.

关键词： particle swarm algorithm fuzzy C-means clustering image segmentation

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Fault Diagnosis Based on particle swarm Fuzzy Clustering algorithm

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International Conference on Mechanical Engineering, Industry and Manufacturing Engineering (MEIME 2011)

作者： Wang Zhixiao Niu Qiang China Univ Min & Technol Coll Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China

ISBN: (纸本)9783037851371

Fuzzy c-means clustering algorithm (FCM) is sensitive to noise and less effective when handling high dimensional data set. Given that particle swarm optimization algorithm (PSO) has strong global search capability and efficient performance, a new PSO based fuzzy clustering algorithm is proposed. particles in the new algorithm are encoded by membership in FCM. The new algorithm adopts a new strategy to meet the constraints of FCM, so as to optimize the clustering effect of FCM. Finally, this algorithm is applied to motor fault diagnosis. Experiment shows that the new algorithm made up for the shortcomings of FCM, improved the efficiency and accuracy of clustering and bettered fault diagnosis results.

关键词： fault diagnosis clustering particle swarm algorithm fuzzy c means

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Thermal Layout Optimization of Stacked Chips Based on Hybrid algorithm of Simulated Annealing and particle swarm

Thermal Layout Optimization of Stacked Chips Based on Hybrid...

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2nd International Conference on Computer Science and Network Technology (ICCSNT)

作者： Zang, Mingxiang Wang, Meng Lai, Xinquan He, Huisen Xidian Univ Sch Comp Sci & Technol Xian Peoples R China

ISBN: (纸本)9781467329644;9781467329637

As the density of chip package increases, the thermal layout optimization of stacked chips has become more and more important. The thermal layout optimization of stacked chips is studied in this paper. Firstly, a hybrid algorithm of simulated annealing and particle swarm (SA-PSO) is proposed for thermal layout optimization. Secondly, the fitness function is obtained based on thermal superposition model and heat conduction equation. Then the temperature of chips is chosen as the evaluation index and the corresponding optimization scheme is achieved. Finally, finite element analysis (FEA) software is used to evaluate the thermal field distribution of stacked chips and the result shows the temperatures of stacked chips are lowered after optimization. Thus, we can prove that the chip layout optimization algorithm proposed in this paper is effective and robust in thermal layout optimization of stacked chips.

关键词： stacked chips simulated annealing algorithm particle swarm algorithm thermal layout optimization finite element analysis

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