This paper presents a multiple hybrid methods combining the lambda iteration and simulated annealing methods (MHLSA) to solve an economic dispatch (ED) problem with smooth cost function characteristics. The constraints of the economic dispatch were load demand and transmission loss.

This paper proposes a system for measuring unknown resistances for metal oxide MQ gas sensors. The circuit configuration is based on the Anderson current loop interface, which connects directly to an Arduino Mega 2560. We analyze errors arising from variations in supply voltage of conventional divider circuits, including those introduced by the Analog-to-Digital Conversion (ADC) of microcontroller. To enhance the accuracy of resistance measurements, a voting technique for selecting the optimal unknown resistances is introduced. In this technique, the digital voltage at each node is analyzed to determine the frequency of occurrence of each level. If a particular voltage level has a frequency of occurrence greater than the reference threshold k , it is selected. If no voltage level meets this criterion, the average of the observed voltage levels is used. From the experimental results, unknown resistances were measured in the range of 362 – 15,397 Ω with a maximum approximated error of 0.55% with k = 90%, while the gas content was measured with a maximum error of approximately 0.24% under conditions of power supply voltage fluctuation from 4.9 to 5.1 V.

This paper introduces a simplified 2-wire measurement system for accurate resistance measurement of resistive sensors, leveraging the Anderson current loop. The proposed configuration features a precision reference resistor (R_ref) and two diodes (D₁ and D₂), directly interfaced with the Analog-to-Digital Converter (ADC) and output pins of a microcontroller, eliminating the need for external stabilization resistors. Utilizing ratiometric voltage measurements and an adaptive tuning factor, the system effectively compensates for lead wire resistance and diode mismatches. A prototype implemented with an ATmega2560 microcontroller demonstrates accurate resistance measurements in the range of 60–320 Ω, corresponding to a Pt100 sensor, achieving an uncertainty error of less than 0.13% for a lead wire resistance of 4.87 Ω and 0.15% for 10.0 Ω. Experimental results confirm the system’s precision and reliability over long lead lengths, offering a cost-effective and robust solution for industrial and remote sensing applications requiring accurate temperature measurements.

To prognosticate the cattle's health, the farmer can observe the cattle activities such as the time period of walking-grazing, standing and sleeping. However, to monitor the cattle's behaviors, it is unable to monitor such behavior all the time and thorough, especially raise many cattle. Therefore, this paper proposes to classify the cattle's behaviors by using the magnitude and standard deviation of accelerometer output signal. The magnitude of each axis is used to classify the behaviors into two groups: 1) walking-grazing and standing and 2) lying down. While the standard deviation of Y-axis is used to notify the behaviors of walking-grazing and standing. The classification results were tested with two cattle and measured precise time of each behavior comparing with human observers. As a result, duration of each behavior is nearby, it has the errors as follows walking-grazing maximum errors 2% standing maximum errors 13% and lying maximum errors 7%.