Anti-swing control is a well-known term in gantry crane control. It is designed to move the payload of gantry crane as fast as possible while the payload swing angle should be kept as small as possible at the final position. A number of studies have proposed anti-swing control using the well-known proportional, integral, derivative (PID) control method. However, PID controllers cannot always effectively control systems with changing parameters. Some studies have also proposed intelligent-based control including fuzzy control. However, the designers often have to face the problem of tuning many parameters during the design to obtain optimum performance. Thus, a lot of effort has to be taken in the design stage. In this paper Fuzzy-tuned PID controller design for anti-swing gantry crane control is presented. The objective is to design a practical anti-swing control which is simple in the design and also robust. The proposed Fuzzy-tuned PID utilizes fuzzy system as PID gain tuners to achieve robust performance to parameters’ variations in the gantry crane. A complex dynamic analysis of the system is not needed. PID controller is firstly optimized in MATLAB using a rough model dynamic of the system which is identified by conducting a simple open-loop experiment. Then, the PID gains are used to guide the range of the fuzzy outputs of the Fuzzy-tuned PID controllers. The experimental results show that the proposed anti-swing controller has satisfactory performance. In addition, the proposed method is straightforward in the design.

Effective fault detection and classification play essential roles in reducing the hazards such as electric shocks and fire in photovoltaic (PV) systems. However, the issues of interest in fault detection and classification for PV systems remain an open-ended challenge due to manual and time-consuming processes that require the relevant domain knowledge and experience of fault diagnoses. This paper proposes a hybrid deep-learning (DL) model-based combined architectures as the novel DL approaches to achieve the real-time automatic fault detection and classification of a PV system. This research employed the wavelet packet transform (WPT) as a data preprocessing technique to handle the PV voltage signal collected and feeding them as the inputs for combined DL architectures that consist of the equilibrium optimizer algorithm (EOA) and long short-term memory (LSTM-SAE) approaches. The combined DL architectures are able to extract the fault features automatically from the preprocessed data without requiring any previous knowledge, therefore can override the traditional shortages of manual feature extraction and manual selection of optimal features from the extracted fault features. These desirable features are anticipated to speed up the fault detection and classification capability of the proposed DL model with higher accuracy. In order to determine the performance of the proposed fault model, we carried out a comprehensive evaluation study on a 250-kW grid-connected PV system. In this paper, symmetrical and asymmetrical faults have been studied involving all the phases and ground faults such as single phase to ground, phases to phase, phase to phase to ground, and three-phase to ground. The simulation results validate the efficacy of the proposed model in terms of computation time, accuracy of fault detection, and noise robustness. Comprehensive comparisons between the simulation results and previous studies demonstrate the multidisciplinary applications of the present study.

Maritime objects frequently exhibit low-quality and insufficient feature information, particularly in complex maritime environments characterized by challenges such as small objects, waves, and reflections. This situation poses significant challenges to the development of reliable object detection including the strategies of loss function and the feature understanding capabilities in common YOLOv8 (You Only Look Once) detectors. Furthermore, the widespread adoption and unmanned operation of intelligent ships have generated increasing demands on the computational efficiency and cost of object detection hardware, necessitating the development of more lightweight network architectures. This study proposes the EL-YOLO (Efficient Lightweight You Only Look Once) algorithm based on YOLOv8, designed specifically for intelligent ship object detection. EL-YOLO incorporates novel features, including adequate wise IoU (AWIoU) for improved bounding box regression, shortcut multi-fuse neck (SMFN) for a comprehensive analysis of features, and greedy-driven filter pruning (GDFP) to achieve a streamlined and lightweight network design. The findings of this study demonstrate notable advancements in both detection accuracy and lightweight characteristics across diverse maritime scenarios. EL-YOLO exhibits superior performance in intelligent ship object detection using RGB cameras, showcasing a significant improvement compared to standard YOLOv8 models.

The article presents a comprehensive summary of Intelligent Ship Marine Object Detection (ISMOD) based on the RGB Camera. Marine object detection plays a pivotal role in enabling intelligent ships to acquire crucial data and security assurances for autonomous navigation. Among the various detection sensors, the RGB Camera is an informative and cost‐effective tool with a wide range of civil applications. In the beginning, the ISMOD metrics based on the RGB camera is analyzed from three significant aspects, namely accuracy, speed, and robustness. Subsequently, the latest research status and comparative overview are presented, encompassing three mainstream detection methods: traditional detection, deep learning detection, and sensor fusion detection. Finally, the existing challenges of ISMOD are discussed and future development trends are recommended. The results demonstrate that forthcoming development will predominantly concentrate on deep learning approaches, complemented by other techniques. It is imperative to advance detection performance in domains such as deep fusion, multi‐feature extraction, multi‐fusion technology, and lightweight detection architecture.

Optimization appears in many aspects of engineering problems. There are quite numbers of modern optimization algorithms proposed in the last two decades to solve optimization problems. Particle swarm optimization (PSO) and differential evolution (DE) are among the well-known modern optimization algorithms. This paper presents a comparative study for min-max constrained optimization using PSO and DE. Here, the constrained optimization is represented by some selected standard benchmark functions. A new constraint handling and stopping criterion technique is also adopted in the optimization algorithm. Generally, in terms of repeatability and the quality of the obtained solutions, DE outperforms PSO

Robot for painting is one of the earliest applications for industrial robot, however, the precision and finishing for the painting is an important issue for any painting job. Accordingly, the aim of this project is utilize an industrial robot (ABB robot model IRB1410) for painting applications. The robot was programmed to paint alphabets using its Flexpendant. The FlexPendant was used to manually teach the robot how to follow the paths for specific targets of letters. The robot End Effector (painting tool) was chosen and mounted on the robot to perform an effective painting task. It was programmed based on its functionality. Finally suitable painting environment was designing. Two software packages were used in this project. The Computer Aided Design (CAD) of the system work-objects and end effector was programmed based on Solidworks software. Robotstudio Software used to program the paths and target of the alphabets to be painted by the IRB1400 Robot which generate a RAPID GUI code used for robot interfacing. The final results demonstrate that implementation such system helps to boost the quality of painting, reduce paint consumption and improve safety