Tan Wee Choon
Thumbnail Image
Preferred name
Tan Wee Choon
Official Name
Tan, Wee Choon
Alternative Name
Choon, Tan Wee
Tan, W. C.
Main Affiliation
Scopus Author ID
54891879100

Research Output

Filters

2
1
2
Reset filters

Settings
Now showing 1 - 2 of 2
  • Publication
    MICROWAVE ABSORPTION ANALYSIS ON HEATED EDIBLE SPIRULINA WITH VARIOUS TEMPERATURES
    ( 2023-05-01)
    Cheng Ee Meng
    ;
    Tan Wei Hong
    ;
    Tan Wee Choon
    ;
    Lim Eng Aik
    This paper discusses the microwave absorption analysis of edible Spirulina by using WR62 and WR90 rectangular waveguides in conjunction with Agilent P-series Vector Network Analyzer (PNA). Heat might lead to the degradation of spirulina. This phenomenon involves the chemical and physical reaction that is associated with the variation of dielectric properties. These properties determine the propagation mechanism of microwaves within the sample or material. Hence, an assessment method to detect a nutrient change in spirulina due to heat is necessary. In this context, a microwave absorption measurement system was developed to study the reflection coefficient, transmission coefficient, and absorption coefficient of Spirulina tablets over temperature. The transmission/Reflection line method is well-known because it is non-destructive and rapid in analyzing chemical and physical properties. In this work, Spirulina tablet is used since it is a popular food supplement that is believed to be able to treat diseases is and good for health. The reflection, transmission, and absorption measurements were conducted on Spirulina from 12.4GHz to 18GHz.
  • Publication
    K-means Algorithm Based on Flower Pollination Algorithm and Calinski-Harabasz Index
    ( 2023-01-01)
    Lim Eng Aik
    ;
    Tan Wee Choon
    ;
    Mohd Syafarudy Abu
    Aiming at the problems that the Flower Pollination (FP) algorithm is easy to fall into the local optimum, the searchability is weak, and the k-means algorithm is easily affected by the selection of the initial clustering centre, a k-means algorithm based on the FP algorithm is proposed. Six benchmark functions test the improved FP algorithm. The effectiveness of the k-means algorithm based on the improved FP algorithm was tested and verified with UCI machine learning and artificial datasets. The verification results showed that the improved FP algorithm improved based on ensuring a faster convergence speed. Compared with other algorithms, the performance of this algorithm has been significantly improved in all aspects.