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An Enhanced Approach for Image Edge Detection Using Histogram Equalization (BBHE) and Bacterial Foraging Optimization (BFO)

Kumar Praveen, Jindal Tanvi, Raj Balwinder

International Journal of Electronics and Telecommunications

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2022

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Vol. 68. No. 4

875--880

EN

The Edge detection is a customarily task. Edge detection is the main task to perform as it gives clear information about the images. It is a tremendous device in photograph processing gadgets and computer imaginative and prescient. Previous research has been done on moving window approach and genetic algorithms. In this research paper new technique, Bacterial Foraging Optimization (BFO) is applied which is galvanized through the social foraging conduct of Escherichia coli (E.coli). The Bacterial Foraging Optimization (BFO) has been practice by analysts for clarifying real world optimization problems arising in different areas of engineering and application domains, due to its efficiency. The Brightness preserving bi-histogram equalization (BHEE) is another technique that is used for edge enhancement. The BFO is applied on the low level characteristics on the images to find the pixels of natural images and the values of F-measures, recall(r) and precision (p) are calculated and compared with the previous technique. The enhancement technique i.e. BBHE is carried out to improve the information about the pictures.

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Zastosowanie technik i narzędzi z zakresu sztucznej inteligencji do problemów logistycznych

Praczyk T.

Gospodarka Materiałowa i Logistyka

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2016

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nr 12

658--683

PL

Artykuł prezentuje przegląd wybranych algorytmów, narzędzi i technik z zakresu sztucznej inteligencji które mogą być zastosowane do problemów logistycznych. W artykule zastosowano konwencję zgodnie z którą poszczególne techniki sztucznej inteligencji prezentowane są razem z przykładowymi problemami które mogą być za ich pomocą rozwiązane. W kolejności przedstawiono następujące algorytmy, techniki i narzędzia: Algorytmy Ewolucyjne (EA – ang. Evolutionary Algorithms), Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), Ant Systems (AS), Sztuczne Systemy Immunologiczne (AIS – ang. Artificial Immune Systems) i Sieci Neuronowe (NN – ang. Neural Networks).

EN

The paper presents the survey of selected algorithms, tools and techniques from the field of artificial intelligence that can be applied to logistic problems. In the paper, the convention is used according to which each artificial intelligence tool is given along with example problems to which it can be applied. The following algorithms, tools and techniques are presented: Evolutionary Algorithms (EA), Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), Ant Systems (AS), Artificial Immune Systems (AIS) and Neural Networks (NN).

3

Setpoint weighted PID controller tuning for unstable system using heuristic algorithm

Rajinikanth V., Latha K.

Archives of Control Sciences

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2012

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Vol. 22, no. 4

481-505

EN

Most of the real time chemical process loops are unstable in nature and designing a suitable controller for such systems are difficult than open loop stable processes. In this work, an attempt is made with a two degree of freedom setpoint weighted PID controller tuning procedure for a class of unstable systems using the recent heuristic algorithms such as Particle Swarm Optimization and Bacterial Foraging Optimization. The problem considered in this study is to aptly tune the controller in order to enhance the overall closed loop performance. A novel objective function proposed in this study is used to monitor the heuristic algorithms in order to get the optimal controller parameters like Kp, Ki, Kd, and alpha with minimized iteration number. The proposed method is validated with a simulation study and this helps to accomplish enhanced system performance such as smooth reference tracking, satisfactory disturbance rejection, and error minimization for a class of unstable systems.