Fuzzy algorithm implementation in microcontroller for DC motor control

In industrial automation, motor control technique plays the vital role. Motor consists of inductor or electromagnet. Causing inductor or electromagnet, magnetic inductions are produces which resists any change of motor speed. Hence, according to set point, precise speed control is challenging. However, using various control technique can be controls the speed of DC motor. The aim of the present paper is to implement hardware and control the speed of DC motor using embedded fuzzy logic. Set point have been applied externally and recorded the speed of motor through opto-isolator sensor module. In the hardware of DC motor control keypad, 2x16 LCD, DC motor driver and opto-isolator module are interfaced to PIC microcontroller. The Fuzzy algorithm is embedded in the microcontroller wherein input fuzification signals ‘error (Δe)’ and ‘change in error (e(n))’ and output fuzification signal ‘PWM’. The both of inputs of fuzzy algorithm are varied and record output of fuzzy algorithm which is PWM. Moreover, the hardware implementation has been tested for real time control of DC Motor.

Exploring destination image through online reviews: an augmented mining model using latent Dirichlet allocation combined with probabilistic hesitant fuzzy algorithm

PurposeIn the era of information overload, the density of tourism information and the increasingly sophisticated information needs of consumers have created information confusion for tourists and scenic-area managers. The study aims to help scenic-area managers determine the strengths and weaknesses in the development process of scenic areas and to solve the practical problem of tourists' difficulty in quickly and accurately obtaining the destination image of a scenic area and finding a scenic area that meets their needs.Design/methodology/approachThe study uses a variety of machine learning methods, namely, the latent Dirichlet allocation (LDA) theme extraction model, term frequency-inverse document frequency (TF-IDF) weighting method and sentiment analysis. This work also incorporates probabilistic hesitant fuzzy algorithm (PHFA) in multi-attribute decision-making to form an enhanced tourism destination image mining and analysis model based on visitor expression information. The model is intended to help managers and visitors identify the strengths and weaknesses in the development of scenic areas. Jiuzhaigou is used as an example for empirical analysis.FindingsIn the study, a complete model for the mining analysis of tourism destination image was constructed, and 24,222 online reviews on Jiuzhaigou, China were analyzed in text. The results revealed a total of 10 attributes and 100 attribute elements. From the identified attributes, three negative attributes were identified, namely, crowdedness, tourism cost and accommodation environment. The study provides suggestions for tourists to select attractions and offers recommendations and improvement measures for Jiuzhaigou in terms of crowd control and post-disaster reconstruction.Originality/valuePrevious research in this area has used small sample data for qualitative analysis. Thus, the current study fills this gap in the literature by proposing a machine learning method that incorporates PHFA through the combination of the ideas of management and multi-attribute decision theory. In addition, the study considers visitors' emotions and thematic preferences from the perspective of their expressed information, based on which the tourism destination image is analyzed. Optimization strategies are provided to help managers of scenic spots in their decision-making.