Alongkorn Pirayawaraporn

After harvesting and drying, areca nut (betel nut) processing requires shell cracking and separation of the kernel from the shell. Conventional processing often relies on manual sorting or additional separation machines after husking. To address this limitation, this study developed a mechanically simple machine that integrates husking and separation of full nuts, broken nuts, and shells into a single processing unit without cutting blades or complex control systems. The proposed machine employs all-terrain vehicle (ATV) tires as husking elements, providing a compliant and high-friction contact surface to promote shear-dominant shell detachment in combination with concave metal sieves. Dried areca nuts are fed through a hopper into the shearing zone formed between the rotating ATV tires and stationary concave sieves, where shells are detached through compressive and tangential forces. The husked material is then conveyed to an integrated separating system for in-line classification. Experimental results showed that, under selected operating conditions (tire pressure of 138 kPa, wheel-sieve clearance of 20 mm, husking wheel speed of 442 rpm, and separating system speed of 337 rpm), the machine achieved 80.2% fully husked nuts, 13.8% unhusked nuts, and 6.0% broken nuts. The results demonstrate the feasibility of a shear-based, integrated husking-and-separating approach for dried areca nut processing.

This study presents a modular battery charging system based on a DC–DC buck converter with proportional–integral (PI) control, developed to support hands-on learning in power electronics education. In response to the need for flexible experimental platforms, the system is designed to bridge theoretical concepts of power conversion and control with practical implementation. The proposed setup employs cascaded current and voltage control loops to achieve constant current (CC) and constant voltage (CV) charging modes, while its modular hardware architecture allows modification of key parameters such as inductance, capacitance, and circuit topology. The control algorithms are implemented on a microcontroller, and real-time data acquisition is integrated using the ThingSpeak platform for monitoring system behaviour. Experimental results show that the current control loop recovers to its reference value within approximately 6 ms under abrupt load variations, whereas the voltage control loop settles within approximately 15 ms, demonstrating stable closed-loop performance. In addition, the system successfully charges a 12 V lead-acid battery following a standard CC–CV charging profile. Overall, the proposed experiment kit provides an effective educational platform and a practical basis for further exploration of battery charging strategies and power converter control.

After arecanuts (or betel nut) are harvested and dried, the processing of arecanut involves cracking the shell and separating the kernel seed from the shell. In this process, traditional machines required full nuts separation by hands or other machines. For this reason, this study developed a low-cost machine to husk and separate full nuts, broken nuts, and shells simultaneously. In addition, this study also aimed to decrease the number of broken nuts from husking by reduction the power of the husking motor. Once dry betel nuts were fed through the hopper into the spaces between concave sieves and ATV (All-Terrain Vehicle) wheels. The shells of betel nuts were peeled by the shearing force of the tire’s surfaces and the sieves before being consecutively passed through the separating hopper to sort out full nuts, broken nuts, and shells. The experimental results presented the testing performance of 500 betel nuts that can simultaneously husk and separate for 80.2% of full nuts, 13.8% of unhusked nuts, and 6% of broken nuts based on the optimum setting (20 psi of tire pressure, 20 mm of the gap between the tires’ surface and the sieves, 442 rpm for husking tires, and 337 rpm for the separating system).

Maximizing solar energy capture requires the continuous alignment of photovoltaic panels perpendicular to incoming solar radiation. However, traditional sensor‐based tracking systems face challenges such as calibration issues, installation complexity, and environmental susceptibility, while sensorless tracking systems often require extensive datasets and incur high computational costs. To address these limitations, this study proposes a sensorless dual‐axis solar tracking system based on the whale optimization algorithm (WOA) to enhance energy generation while reducing computational complexity and tracking energy consumption. The WOA‐based tracking system is tested over 35 days (October 2023 to April 2024) at King Mongkut's University of Technology North Bangkok, Rayong Campus, Thailand. It is benchmarked against two systems: a particle filter (PF) system and a fixed system. The results indicate that the WOA‐based system achieves improvements in average daily net energy gain of 16% and 42% compared to the PF and fixed flat‐plate systems, respectively. Additionally, it reduces average time and energy consumption per tracking cycle by 13% and 14%, respectively, and memory footprint by 24% compared to the PF‐based system.

Attention capability is an essential component of human–robot interaction. Several robot attention models have been proposed which aim to enable a robot to identify the attentiveness of the humans with which it communicates and gives them its attention accordingly. However, previous proposed models are often susceptible to noisy observations and result in the robot’s frequent and undesired shifts in attention. Furthermore, most approaches have difficulty adapting to change in the number of participants. To address these limitations, a novel attentiveness determination algorithm is proposed for determining the most attentive person, as well as prioritizing people based on attentiveness. The proposed algorithm, which is based on relevance theory, is named the Scalable Hidden Markov Model (Scalable HMM). The Scalable HMM allows effective computation and contributes an adaptation approach for human attentiveness; unlike conventional HMMs, Scalable HMM has a scalable number of states and observations and online adaptability for state transition probabilities, in terms of changes in the current number of states, i.e., the number of participants in a robot’s view. The proposed approach was successfully tested on image sequences (7567 frames) of individuals exhibiting a variety of actions (speaking, walking, turning head, and entering or leaving a robot’s view). From these experimental results, Scalable HMM showed a detection rate of 76% in determining the most attentive person and over 75% in prioritizing people’s attention with variation in the number of participants. Compared to recent attention approaches, Scalable HMM’s performance in people attention prioritization presents an approximately 20% improvement.

In the field of robot navigation, the odometric parameters, such as wheel radii and wheelbase length, and the relative pose of the optical sensing camera with respect to the robot are very important criteria for accurate operation. Hence, these parameters are necessary to be estimated for more precise operation. However, the odometric and head-eye parameters are typically estimated separately, which is an inconvenience and requires longer calibration time. Even though several researchers have proposed simultaneous calibration methods that obtain both odometric and head-eye parameters simultaneously to reduce the calibration time, they are only applicable to a mobile robot with a fixed camera mounted, not for mobile robots equipped with a pan-tilt motorized camera systems, which is a very common configuration and widely used for wide view. Previous approaches could not provide the z-axis translation parameter between head-eye coordinate systems on mobile robots equipped with a pan-tilt camera. In this paper, we present a full simultaneous mobile robot calibration of head–eye and odometric parameters, which is appropriate for a mobile robot equipped with a camera mounted on the pan-tilt motorized device. After a set of visual features obtained from a chessboard or natural scene and the odometry measurements are synchronized and received, both odometric and head-eye parameters are iteratively adjusted until convergence prior to using a nonlinear optimization method for more accuracy.