The current research work presents an investigation into use of the fitting coefficients resulting from the cubic curve fitting of the torque transducer calibration results in one direction to calculate the actual torque in the other torque direction with two methods: one is direct substitution with the nominal torque which gives a propagated linear relative interpolation error and the other is changing the sign of the second coefficient in the cubic function when using in the other torque direction. This proposed modification improves the absolute relative interpolation error by 5 to 16 times in the clockwise and counterclockwise directions based on the torque transducer’s classification.

Accurate prediction of power load plays a crucial role in the power industry and provides economic operation decisions for the power operation department. Due to the unpredictability and periodicity of power load, an improved method to deal with complex nonlinear relation was adopted, and a short-term load forecasting model combining FEW (fuzzy exponential weighting) and IHS (improved harmonic search) algorithms was proposed. Firstly, the domain space was defined, the harmony memory base was initialized, and the fuzzy logic relation was identified. Then the optimal interval length was calculated using the training sample data, and local and global optimum were updated by optimization criteria and judging criteria. Finally, the optimized parameters obtained by an IHS algorithm were applied to the FEW model and the load data of the Huludao region (2013) in Northeast China in May. The accuracy of the proposed model was verified using an evaluation criterion as the fitness function. The results of error analysis show that the model can effectively predict short-term power load data and has high stability and accuracy, which provides a reference for application of short-term prediction in other industrial fields.

Energy consumed by the sensor nodes are more sporadic in a sensor networks. A skilled way to bring down energy consumption and extend maximum life-time of any sensor present can be of evenly and unevenly distributed random area networks. Cluster heads are more responsible for the links between the source and destination. Energy consumption are much compare to member nodes of the network. Re-clustering will take place if the connectivity in the distributed network failure occurs in between the cluster networks that will affects redundancy in the network efficiency. Hence, we propose pragmatic distribution based routing cluster lifetime using fitness function (PDBRC) prototype is better than the existing protocol using MATLAB 2021a simulation tool.