The rapid growth of the elderly population has led to an increased demand for effective and personalized eldercare solutions. In this paper, the design and development of an eldercare robot is presented. This robot is specifically tailored to meet the two specific challenges faced by the elderly. The first is the continuous indoor tracking of the elder, while the second is the fall detection. A comprehensive overview of the hardware and software components, as well as the control architecture of the robot is presented. The hardware design of the robot incorporates a range of features, including a perception system comprising a 2D Lidar, IMU, and camera for environment mapping, localization, and fall detection. The software stack of the robot is explained as consisting of layers for perception, mapping, and localization. The robot is tested experimentally to validate its path planning capability by using Hector SLAM and the RRT* technique. Experimental path planning has shown a positioning accuracy of 93.8% on average. Elderly fall detection is achieved by using the YOLOv7 algorithm at a percentage of 96%. Experimental results have been discussed and evaluated.

In numerous industries, especially in automation and industrial processes, the linear servo mechanism is used. However, the parameters of the friction and backlash models are frequently unknown for servomechanism systems, resulting in system uncertainty. High steady-state inaccuracy is caused by friction, whereas undesired vibration is caused by blowback. In servomechanism systems, friction is an issue that is still not sufficiently addressed by a realistic model. To address these challenges, this research on the linear servo system is controlled by a proportional-integral (PI-Cascaded) controller, which enables systems to respond more rapidly, reduce or reject disturbance, and arrive at a steady state more quickly. Moreover, the controller’s parameters are crucial to getting the best performance from a particular controller. As a result, the controller settings were adjusted using four different meta-heuristic optimization algorithms: Surrogate Based Optimization (SBO), Hybrid Genetic Pattern Search Algorithm (HGSPA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) with four objective functions: Integral Square Error (ISE), Integral Absolute Error (IAE), Integral Time Square Error (ITSE), and Integral Time Absolute Error (ITAE). Throughout the system’s experimental testing, 50 cm was employed as the reference input. Negligible overshoot, quick rise and settling times, and excellent responsiveness are all characteristics of the PSO algorithm with ITSE objective function. Moreover, to assess the system’s robustness. A 50 N force was applied to the system, and a sine wave signal is input into the system. The system shows remarkable stability and resilience throughout the 50 N load experimentation test.

Wind turbines are complicated non-linear systems with certain random disruptions. The pitch control system is a commonly employed method for regulating the electricity generated by a wind turbine. Many researchers have observed developments in the pitch control field during the last few decades. Traditional PID controllers have the drawback of being slow or imprecise when wind and pitch angles suddenly change. These drawbacks can be solved with artificial intelligent algorithms. However, the algorithms' design and implementation are highly complex. A new pitch-regulated variable-speed control strategy for wind turbines to address their nonlinear properties is presented. To manage the pitch system's control mechanisms with disturbances, this research evolved a mathematical model that illustrates HAWT's pitch angle control system and applied a proposed Simple Optimal Intelligent PID Controller (SOI-PID). Under various operating conditions, the proposed SOI-PID controller was tested with the Traditional PID, Fuzzy Logic Controller (FLC), and Fuzzy-Adaptive-PID controller. For system simulation, the MATLAB/Simulink software was used. According to simulation results, compared to PID, FLC, and Fuzzy-Adaptive-PID controllers, the proposed SOI-PID controller responds faster and has a better rise and settling time. Other benefits of the SOI-PID controller are its simplicity of implementation and design, distinguishing it from other intelligent algorithms.

Cyclone separators play a pivotal role in removing particulate matter from gas streams. These elegant devices find applications across diverse industries, including cement production, petroleum refineries, and food processing. This computational study focuses on the effects of adding fined surfaces to the cylindrical part of cyclone separators on the flow field and the performance parameters. Different fins are used at different depths, namely, 0.004 m, 0.008 m, 0.01 m, different pitches, namely, 0.025 m, 0.04 m, 0.05 m and different helix lengths, namely, 0.2 m, 0.25 m, 0.3 m. The results indicated that optimal fin adhesion reduces the pressure drop, helix length, fin depth and fin pitch affect pressure drop increases 6%, 4%, 2.5% respectively. at x50 (cut-off diameter) found the efficiency increases with fin depth 10%, fin pitch by 17%, helix length by 18% over the conventional cyclone, Cyclone with the longest helix length, demonstrates superior performance, especially for smaller particles. Overall, the addition of fins enhances cyclone performance, with optimal configurations significantly improving collection efficiency, underscoring the importance of fin design parameters like pitch and helix length.

Decision Planning (DP) is a critical module in achieving level 5 autonomy for Autonomous Vehicles (AVs). Existing state-of-the-art DP methods each have distinct advantages and limitations. This paper presents a hybrid Decision-Making (DM) framework that integrates classical and deep learning methodologies to emulate human-like behavior in diverse driving scenarios. The proposed architecture includes three main components: (1) a dual-stream Convolutional Neural Network (CNN) that processes front- and rear-view camera inputs to classify driving scenarios as either simple (highway) or complex (urban), (2) a Hierarchical Finite State Machine (HFSM) for handling simple scenarios, and (3) a Deep Learning (DL) module for managing complex environments. The system was evaluated in the CARLA simulator using diverse traffic scenarios. The human driver baseline was derived from a survey of 50 drivers, who assessed the optimal decisions for simulated scenes. The hybrid model outperformed baselines, achieving a 30% improvement in decision optimality over human drivers and a 45% improvement over a standalone DL approach. It also maintained a safety rate of 92%, an efficiency of 66%, and an 85.5% lane idleness rate. These results demonstrate the potential of the proposed framework to enhance safety, generalization, and performance in fully autonomous driving systems.