Flame Image Segmentation Based on the Bee Colony Algorithm with Characteristics of Levy Flights

Zhang, Xiaolin; Yang, Tao; Cui, Ningning

doi:10.1155/2015/805075

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…It can be seen from Figure 4 that the horizontal and vertical coordinates, respectively, represent the K and I values of prior bounding box of flame [ 28 – 32 ]. With the continuous increase of K value, I value also keeps increasing.…”

Section: Related Workmentioning

confidence: 99%

A Real-Time Detection Algorithm of Flame Target Image

Jing

2022

Computational Intelligence and Neuroscience

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In many research tasks, the speed and accuracy of flame detection using supply chain have always been a challenging task for many researchers, especially for flame detection of small objects in supply chain. In view of this, we propose a new real-time target detection algorithm. The first step is to enhance the flame recognition of small objects by strengthening the feature extraction ability of multi-scale fusion. The second step is to introduce the K-means clustering method into the prior bounding box of the algorithm to improve the accuracy of the algorithm. The third step is to use the flame characteristics in YOLO+ algorithm to reject the wrong detection results and increase the detection effect of the algorithm. Compared with the YOLO series algorithms, the accuracy of YOLO+ algorithm is 99.5%, the omission rate is 1.3%, and the detection speed is 72 frames/SEC. It has good performance and is suitable for flame detection tasks.

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Section: Related Workmentioning

confidence: 99%

A Real-Time Detection Algorithm of Flame Target Image

Jing

2022

Computational Intelligence and Neuroscience

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“…At last results of both has been compared against various evaluation parameters. Xiaolin Zhang, Tao Yang et.al ,2015 presented [6], the limitations of older technique that is 2d entropy threshold segmentation. The researcher has proposed a flame data segmentation method that utilize artificial bee colony algorithm having the properties of levy flights.…”

Section: Abdalla Mostafa Etal 2015 Presented[3]mentioning

confidence: 99%

Attack Detection within Network using Hybridization of Bisection TDE

Dogra¹,

Gurdaspur²,

Abstract³

2020

IJEAT

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Today the leading cause of attack is traffic distribution that affects transmission as well as network performance. Traditionally the mechanism is utilized for detecting traffic distribution is not so accurate, user friendly and also they are time consuming. Therefore in this paper a hybrid approach is used that detect attacks by eliminating replicated features. It utilizes TDE with bisection mechanism that has great attack detection rate The result of the proposed system as compared to existing system is better by the margin of 8 to 10%.. The primary reason for the betterment is because of contrast enhancement and scaling factor at pre-processing and objective function overhead consideration at segmentation and classification phase. The results are shown in terms of accuracy, PSNR and MSE.

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“…In this paper, the Mantegna algorithm was used to simulate the Lévy distribution. The specific principles are as follows [37]:…”

Section: Updated Positions Of Individuals Based On Lévy Flightmentioning

confidence: 99%

Multi‐Objective Reentrant Hybrid Flowshop Scheduling with Machines Turning on and off Control Strategy Using Improved Multi‐Verse Optimizer Algorithm

Geng

Cao

et al. 2019

Mathematical Problems in Engineering

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This paper focuses on the multi-objective optimization of the reentrant hybrid flowshop scheduling problem (RHFSP) with machines turning on and off control strategy. RHFSP exhibits significance in many industrial applications, but scheduling with both energy consumption consideration and reentrant concept is relatively unexplored at present. In this study, an improved Multi-Objective Multi-Verse Optimizer (IMOMVO) algorithm is proposed to optimize the RHFSP with objectives of makespan, maximum tardiness, and idle energy consumption. To solve the proposed model more effectively, a series of improved operations are carried out, including population initialization based on Latin hypercube sampling (LHS), individual position updating based on Lévy flight, and chaotic local search based on logical self-mapping. In addition, a right-shift procedure is used to adjust the start time of operations aiming to minimize the idle energy consumption without changing the makespan. Then, Taguchi method is utilized to study the influence of different parameter settings on the scheduling results of the IMOMVO algorithm. Finally, the performance of the proposed IMOMVO algorithm is evaluated by comparing it with MOMVO, MOPSO, MOALO, and NSGA-II on the same benchmark set. The results show that IMOMVO algorithm can solve the RHFSP with machines turning on and off control strategy effectively, and in terms of convergence and diversity of non-dominated solutions, IMOMVO is obviously superior to other algorithms. However, the distribution level of the five algorithms has little difference. Meanwhile, by turning on and off the machine properly, the useless energy consumption in the production process can be reduced effectively.

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Flame Image Segmentation Based on the Bee Colony Algorithm with Characteristics of Levy Flights

Cited by 5 publications

References 16 publications

A Real-Time Detection Algorithm of Flame Target Image

A Real-Time Detection Algorithm of Flame Target Image

Attack Detection within Network using Hybridization of Bisection TDE

Multi‐Objective Reentrant Hybrid Flowshop Scheduling with Machines Turning on and off Control Strategy Using Improved Multi‐Verse Optimizer Algorithm

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