In recent years, research on machine learning techniques that can be integrated with existing suspension control algorithms for enhanced control effects has advanced considerably. Machine learning, especially involving neural networks, often requires many samples, which makes maintaining robust performance in diverse, changing environments challenging. The present study applied reinforcement learning, which can generalize complex situations not previously encountered, to overcome this obstacle and is crucial for suspension control under varying road conditions. The effectiveness of the proposed control method was evaluated on different road conditions using the quarter-vehicle model. The impact of training data was assessed by comparing models trained under two distinct road conditions. In addition, a validation exercise on the performance of the control method that utilizes reinforcement learning demonstrated its potential for enhancing the adaptability and efficiency of suspension systems under various road conditions.

In this paper, a prosthetic robot hand was designed and fabricated and experimental evaluation of the realization of basic gripping motions was performed. As a first step, a robot finger was designed with same structural configuration of the human hand and the movement of the finger was evaluated via kinematic analysis. Electromyogram (EMG) signals for hand motions were measured using commercial wearable EMG sensors and classification of hand motions was achieved by applying the artificial neural network (ANN) algorithm. After training and testing for three kinds of gripping motions via ANN, it was observed that high classification accuracy can be obtained. A prototype of the proposed robot hand is manufactured through 3D printing and servomotors are included for position control of fingers. It was demonstrated that effective realization of gripping motions of the proposed prosthetic robot hand can be achieved by using EMG measurement and machine learning-based classification under a real-time environment.

In this work, a theoretical investigation on the energy harvesting is undertaken using one of potential smart materials; piezoelectric material. The energy equations for both square and circular types of the piezoelectric material are derived, and the energy generated from two commercially available products: PZT (Lead/Zirconium/Titanium: Pb(Zr,Ti)O₃) and PVDF (polyvinylidene fluoride) are investigated in terms of the thickness and area. In addition, a finite element analysis (FEA) is undertaken to obtain the generated energy due to the uniform pressure applied on the surface of the piezoelectric materials. A comparative work between the theory and the FEA is made followed by the brief discussion on the usage of the harvested energy for Bio MEMS.