Practical Research on Project-Based Learning (PBL) in Film and Television Production in Xiamen Vocational Education

The film and television industry plays a crucial role in the development of the global cultural sector. In recent years, vocational education in the field of film and television has experienced rapid growth in China. However, the current talent training model for this profession fails to meet the demands of the fast-paced industry development and lacks effective support for its advancement. Project-based learning is a student-centered teaching approach that employs authentic projects as the primary medium for learning. This study presents an empirical investigation conducted in a vocational college in Xiamen, where project-based learning was incorporated into the film and television production courses to assess its effectiveness. The findings of this research demonstrate that the implementation of project-based learning in the context of film and television production is viable. In comparison to traditional didactic instruction, project-based learning significantly enhances students' motivation to learn, practical skills, critical thinking abilities, and teamwork abilities. Consequently, it holds significant value in cultivating applied talents

Solving continuous trajectory and forward kinematics simultaneously based on ANN

Robot movement can be predicted by incorporating Forward Kinematics (FK) and trajectory planning techniques. However, the calculations will become complicated and hard to be solved if the number of specific via points is increased. Thus, back-propagation artificial neural network is proposed in this paper to overcome this drawback due to its ability in learning pattern solutions. A virtual 4-degree of freedom manipulator is exploited as an example and the theoretical results are compared with the proposed method

Solving Continuous Trajectory and Forward Kinematics Simultaneously Based on ANN

Robot movement can be predicted by incorporating Forward Kinematics(FK) and trajectory planning techniques. However, the calculations will becomecomplicated and hard to be solved if the number of specific via points is increased.Thus, back-propagation artificial neural network is proposed in this paper to overcomethis drawback due to its ability in learning pattern solutions. A virtual 4-degreeof freedom manipulator is exploited as an example and the theoretical results arecompared with the proposed method

Optimization and selection of maintenance policies in an electrical gas turbine generator based on the hybrid reliability-centered maintenance (RCM) model

The electrical generation industry is looking for techniques to precisely determine the proper maintenance policy and schedule of their assets. Reliability-centered maintenance (RCM) is a methodology for choosing what maintenance activities have to be performed to keep the asset working within its designed function. Current developments in RCM models are struggling to solve the drawbacks of traditional RCM with regards to optimization and strategy selection; for instance, traditional RCM handles each failure mode individually with a simple yes or no safety question in which question has the possibility of major error and missing the effect of a combinational failure mode. Hence, in the present study, a hybrid RCM model was proposed to fill these gaps and find the optimal maintenance policies and scheduling by a combination of hybrid linguistic-failure mode and effect analysis (HL-FMEA), the co-evolutionary multi-objective particle swarm optimization (CMPSO) algorithm, an analytic network process (ANP), and developed maintenance decision tree (DMDT). To demonstrate the effectiveness and efficiencies of the proposed RCM model, a case study on the maintenance of an electrical generator was conducted at a Yemeni oil and gas processing plant. The results confirm that, compared with previous studies, the proposed model gave the optimal maintenance policies and scheduling for the electrical generator in a well-structured plan, economically and effectively

A reactive collision avoidance approach for mobile robot in dynamic environments

This paper describes a novel reactive obstacle avoidance approach for mobile robot navigation in unknown and dynamic environment. This approach is developed based on the “situated-activity paradigm” and a “divide and conquer” strategy which steers the robot to move among unknown obstacles and towards a target without collision. The proposed approach entitled the Virtual Semi-Circles(VSC). The VSC approach lies in integration of 4 modules: division, evaluation, decision and motion generation. The VSC proposes a comprehensive obstacle avoidance approach for robust and reliable mobile robot navigation in cluttered, dense and complex unknown environments. The simulation result shows the feasibility and effectiveness of the proposed approach

Mobile based Automated Complete Blood Count (Auto-CBC) Analysis System from Blood Smeared Image

Blood cells diagnosis is becoming essential to ensure a proper treatment can be proposed to a blood related disease patient. In current research trending, automated complete blood count analysis system is required for pathologists or researchers to count the blood cells from the blood smeared images. Hence, a portable mobile-based complete blood count (CBC) analysis framework with the aid of microscope is proposed, and the smartphone camera is mounted to the viewing port of the light microscope by adding a smartphone support. Initially, the blood smeared image is acquired from a light microscope with objective zoom of 100X magnifications view the eyepiece zoom of 10X magnification, then captured by the smartphone camera. Next, the areas constitute to the WBC and RBC are extracted using combination of color space analysis, threshold and Otsu procedure. Then, the number of corresponding cells are counted using topological structural analysis, and the cells in clumped region is estimated using Hough Circle Transform (HCT) procedure. After that, the analysis results are saved in the database, and shown in the user interface of the smartphone application. Experimental results show the developed system can gain 92.93% accuracy for counting the RBC whereas 100% for counting the WBC

Identification and prioritization of risk factors in electrical generator based on hybrid FMEA framework

The oil and gas industry is looking for ways to accurately identify and prioritize the failure modes (FMs) of the equipment. Failure mode and effect analysis (FMEA) is the most important tool used in the maintenance approach for the prevention of malfunctioning of the equipment. Current developments in the FMEA technique are mainly focused on addressing the drawbacks of the conventional risk priority number calculations, but the group effects and interrelationships of FMs on other measurements are neglected. In the present study, a hybrid distribution risk assessment framework was proposed to fill these gaps based on the combination of modified linguistic FMEA (LFMEA), Analytic Network Process (ANP), and Decision Making Trial and Evaluation Laboratory (DEMATEL) techniques. The hybrid framework of FMEA was conducted in a hazardous environment at a power generation unit in an oil and gas plant located in Yemen. The results show that mechanical and gas leakage FM in electrical generators posed a greater risk, which critically affects other FMs within the plant. It was observed that the suggested framework produced a precise ranking of FMs, with a clear relationship among FMs. Also, the comparisons of the proposed framework with previous studies demonstrated the multidisciplinary applications of the present framework

Development of an artificial neural network topology for generating the motion of robotic manipulator

Motion planning is an important issue in robot industry. Without an appropriate motion planning, a robot may be colliding with obstacles or passing through undesirable points. In order to control the motion of a robot manipulator, a person has to possess the knowledge of kinematics, dynamics, and trajectory planning. However, there are two main problems in using conventional methods. Firstly, the equations are hard to be derived and the calculations are complex. Secondly, the characteristics of different trajectories are different and there is no mathematical solution for unknown trajectory. Hence, the first objective in this research is to simplify the complex calculations in terms of solving kinematics and trajectory planning issues simultaneously. Another objective of this research was to help in computing the motion of a manipulator even though the characteristic of the trajectory is unknown. In order to achieve these research goals, artificial neural network (ANN) was proposed as a solution. In the early stage, a virtual manipulator was developed and subjected to different primitive trajectories. In order to examine the ability of ANN in tracking the motion of a robot manipulator, a primitive ANN would be used to track the moving path of the virtual robot manipulator’s end effector in the virtual environment. This ANN was developed based on the fundamental of back-propagation neural network (BPNN) topology. The topology of ANN would be modified for reducing the errors and deviations. Eventually, the developed ANN would be validated through a real time 5 catalyst robot. Besides, obstacle avoidance planning would be integrated into the developed ANN. Virtual obstacles would be allocated within the robot’s workspace randomly and the performances of developed ANN would be observed through simulation experiments. The results indicated that ANN possessed ability in tracking the motion of a robot manipulator in terms of solving kinematics and trajectory planning issues simultaneously and it was able to compute the motion of a manipulator even though the characteristic of the trajectory was unknown. Obstacle avoidance planning was integrated into the architecture of developed ANN for better performances and the results were satisfactory. With this developed method, a person is able to compute a safe path for a robot manipulator to avoid obstacles (objects which enclosed in a sphere)

Open the box : video drama

This report is a companion and comprehensive guide to our short feature film, Open The Box.Bachelor of Communication Studie

Classification of Faults in Grid-Connected Photovoltaic System based on Wavelet Packet Transform and an Equilibrium Optimization Algorithm-Extreme Learning Machine

A novel intelligent scheme using the wavelet packet transform (WPT) and extreme learning machine (ELM) is proposed for fault event classification in the grid-connected photovoltaic (PV) system. The WPT is applied for preprocessing the cycle of the post-fault voltage samples at the point of common coupling (PCC) measurement to get the normalized logarithmic energy entropy (NLEE). The ELM is applied to classify the different fault cases. To enhance the performance of ELM for faults classification, a hybrid optimization mechanism based on an equilibrium optimization algorithm (EOA) is proposed to optimize the selection of input feature subset and the number of ELM hidden nodes. Furthermore, to evaluate the proposed scheme\u27s performance, a comprehensive evaluation was conducted on a 250 kW grid-connected photovoltaic system. From simulation, the classification accuracy is recorded to be 100% under the no-noise condition, while at the signal-to-noise ratios (SNR) of 30, 35, and 40 dB, the accuracies are 98.96, 99.04, and 99.36%, respectively. Moreover, the practical performance of the EOA-ELM classifier is validated using IEEE 34 bus system. The obtained results validate the effectiveness of the proposed scheme in terms of robustness against measurement noise, computation time, and detection accuracy