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Subject: search.filters.subject.design of experiment

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    Solving the incomplete data problem in Greco-Latin square experimental design by exact-scheme analysis of variance without data imputation
    (2024-01-01)
    Sirikasemsuk, Kittiwat
    ;
    Wongsriya, Sirilak
    ;
    Leerojanaprapa, Kanogkan
    This study introduced a novel exact-scheme analysis of variance to tackle the challenge of incomplete data within the Greco-Latin square experimental design (GLSED), specifically for scenarios with a single missing observation across any treatment and block level, thus eliminating the need for conventional data imputation methods. This approach innovatively addresses and mitigates the bias in the treatment sum of squares, a significant drawback of traditional missing plot techniques, by providing a precise, exact-scheme-based formula for calculating the treatment sum of squares in fixed-effect GLSED contexts with unrecorded values. Moreover, it offers a method for correcting biased treatment sum of squares values, presenting an adjustment mechanism for instances where the least squares method was previously employed to estimate missing values. This comprehensive strategy not only enhances the methodological accuracy and integrity of GLSED studies but also contributes significantly to the field by offering a solution to navigate the complexities of incomplete datasets without resorting to data imputation, thus improving the rigor and validity of experimental designs in the face of missing data challenges.
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    Multiple Performance Optimization for Microstrip Patch Antenna Improvement
    (2023-05-01)
    Chen, Ja Hao
    ;
    Cheng, Chen Yang
    ;
    Chien, Chuan Min
    ;
    Yuangyai, Chumpol
    ;
    Chen, Ting Hua
    As the Internet of Things (IOT) becomes more widely used in our everyday lives, an increasing number of wireless communication devices are required, meaning that an increasing number of signals are transmitted and received through antennas. Thus, the performance of antennas plays an important role in IOT applications, and increasing the efficiency of antenna design has become a crucial topic. Antenna designers have often optimized antennas by using an EM simulation tool. Although this method is feasible, a great deal of time is often spent on designing the antenna. To improve the efficiency of antenna optimization, this paper proposes a design of experiments (DOE) method for antenna optimization. The antenna length and area in each direction were the experimental parameters, and the response variables were antenna gain and return loss. Response surface methodology was used to obtain optimal parameters for the layout of the antenna. Finally, we utilized antenna simulation software to verify the optimal parameters for antenna optimization, showing how the DOE method can increase the efficiency of antenna optimization. The antenna optimized by DOE was implemented, and its measured results show that the antenna gain and return loss were 2.65 dBi and 11.2 dB, respectively.
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    Regression sum of squares of symmetric balanced incomplete block design consisting of another one missing observation by accident
    (2017-07-02)
    Sirikasemsuk, Kittiwat
    ;
    Leerojanaprapa, Kanogkan
    A balanced incomplete block design (BIBD) is the effective way to help analyze a treatment variable and one block variable under the condition where experimental units are limited. This paper considered the symmetric balanced incomplete block design (SBIBD) with t treatments and t blocks of size t-1. The trouble of analysis is caused if another one missing value unintentionally occurs in the experiments. The SBIBD with another one missing value was analyzed by means of the exact approach, i.e., the general regression significance testing procedure. There was no ready-made formula in the past. Hence, the paper provided the mathematical formulae for the fitted parameters and the regression sum of squares for the full effect model of experimental data.
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    One Missing Value Problem in Latin Square Design of Any Order: Regression Sum of Squares
    (2016-12-28)
    Sirikasemsuk, Kittiwat
    This paper introduced an incomplete Latin square design that leads to difficulties in the analysis of variance (ANOVA). The aim was to determine the explicit and mathematical formulae for the regression sum of squares for the full model of experimental data by means of the exact approach. From an overall review of the literature, the gap was a lack of definite formula for the ANOVA table for the cases of the incomplete Latin square designs. However, in this paper, the least square normal equations for the one-missing-value Latin square design were built to determine the estimates of model parameters and the regression sum of squares of the full effect model. It is noted that the regression sum of squares of the full effect model is one part of the analysis of variance with the exact approach.