This research aimed to optimize the camera calibration process by identifying the optimal distance and angle for capturing checkered board images, with a specific focus on understanding the factors that influence the reprojection error (${ϵ}_{RP}$). The objective was to improve calibration efficiency by exploring the impacts of distance and orientation factors and the feasibility of independently manipulating these factors. The study employed Zhang’s camera calibration method, along with the 2^k full-factorial analysis method and the Latin Hypercube Sampling (LHS) method, to identify the optimal calibration parameters. Three calibration methods were devised: calibration with distance factors (D, H, V), orientation factors (R, P, Y), and the combined two influential factors from both sets of factors. The calibration study was carried out with three different stereo cameras. The results indicate that D is the most influential factor, while H and V are nearly equally influential for method A; P and R are the two most influential orientation factors for method B. Compared to Zhang’s method alone, on average, methods A, B, and C reduce ${ϵ}_{RP}$ by 25%, 24%, and 34%, respectively. However, method C requires about 10% more calibration images than methods A and B combined. For applications where lower value of ${ϵ}_{RP}$ is required, method C is recommended. This study provides valuable insights into the factors affecting ${ϵ}_{RP}$ in calibration processes. The proposed methods can be used to improve the calibration accuracy for stereo cameras for the applications in object detection and ranging. The findings expand our understanding of camera calibration, particularly the influence of distance and orientation factors, making significant contributions to camera calibration procedures.

Electricity demand is also known as load in electric power system. This article presents a Long-Term Load Forecasting (LTLF) approach for Malaysia. An Artificial Neural Network (ANN) of 5-layer Multi-Layered Perceptron (MLP) structure has been designed and tested for this purpose. Uncertainties of input variables and ANN model were introduced to obtain the prediction for years 2022 to 2030. Pearson correlation was used to examine the input variables for model construction. The analysis indicates that Primary Energy Supply (PES), population, Gross Domestic Product (GDP) and temperature are strongly correlated. The forecast results by the proposed method (henceforth referred to as UQ-SNN) were compared with the results obtained by a conventional Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model. The R2 scores for UQ-SNN and SARIMA are 0.9994 and 0.9787, respectively, indicating that UQ-SNN is more accurate in capturing the non-linearity and the underlying relationships between the input and output variables. The proposed method can be easily extended to include other input variables to increase the model complexity and is suitable for LTLF. With the available input data, UQ-SNN predicts Malaysia will consume 207.22 TWh of electricity, with standard deviation (SD) of 6.10 TWh by 2030.