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Exploring the effects of future technological skills on students’ achievement: a bibliometric analysis
No full textHumanity is facing global technological, social, and educational transformations, which has forced educators and educational policy leaders to define future skills for students’ success in school, life and work. Therefore, educational institutions must realize the role of these skills in improving academic achievement and integrating them into the curricula .Therefore, this research comprehensively examines future skills and students’ achievement by conducting a bibliometric analysis. This study expanded all research from 2007 to 2021 by utilizing the similarities visualization software (Vosviewer). A sum of 2433 publications were analyzed as documented in the Scopus database in August 2022, identifying the most compelling subjects covered by the journal. Findings demonstrate several significant research concerns (Basic skills, learning skills (4CS), technical skills and social skills. Several emerging topics have been identified (critical thinking skills, leadership The research presents a roadmap for potential researchers, concentrating on critical areas where success is possible
Hub angle control of flexible manipulator based on bacterial foraging optimization
No full textFlexible manipulator offers industry with less material requirement, lighter in weight thus transportable, consuming less power, require smaller actuators, less control complexity while being able to operate in higher payload to weight. But, due to high flexibility of the flexible manipulator, excessive vibration can be found if the system is implemented. This study aims to simulate an accurate model system using system identification (SI) technique via Bacterial Foraging Optimization (BFO) for control of the hub angle of the flexible manipulator system in simulation environment. It is vital to model the system that represents actual characteristics of the flexible manipulator before precisely control the hub angle of the flexible manipulator’s movement. The experimental data obtained from the flexible manipulator system’s hub are utilised to construct a model of the system using an auto-regressive with exogenous (ARX) structure. Bacterial Foraging Optimization (BFO) is used to develop the modelling by SI technique to obtain the mathematical models. The generated model’s performance is assessed using three methods: minimum mean square error (MSE), correlation tests, and stability test in pole-zero diagram. The model of hub angle constructed using BFO has a minimum mean square error of 1.9694,10-5, a high degree of stability, and strong correlation tests. The model of hub angle constructed using BFO has a minimum mean square error of 1.9694,10-5, a high degree of stability, and strong correlation results. Following that, a PID controller is designed and heuristically tuned to provide accurate hub angle positioning with a short settling time using the BFO model. It is also worth noting that BFO’s model successfully regulated the hub angle’s positioning with a 0.8% overshoot and a 0.5242 s settling time in the presence of single disturbances
A Nonlinear Autoregressive Exogenous Neural Network (NARX) model for the prediction of the pH neutralization process for Palm Oil Mill Effluent
No full textThis paper introduces a Nonlinear Autoregressive Exogenous Neural Network (NARX) to predict the pH value of the Palm Oil Mill Effluent (POME). NARX is a computing tool that is widely used for nonlinear time series problems, the techniques that can predict efficient and good performance. In this paper, the pH neutralization process is a MISO (Multiple Input Single Output) systems, the inputs of which are the dosing stroke rates of acid and base, and the output value is the pH value. The neural network was built and trained using the experimental data collected in an open-loop test. The neural network structure for modeling the pH neutralization was identified and the training and validation of the neural network structure were analyzed. The result showed that the NARX modeling was able to predict the pH based on the acid and base dosing stroke rate with an overall regression of 0.9934 and MSE values of 0.000924197
Exploring the needs of alternative curriculum among Bajau pupils in Semporna, Sabah
No full textThere is no way for pupils to fall behind in school. The difference in growth in these pupils’ lives will determine the future development of a nation. Bajau are an unidentified group of people who still live a maritime and nomadic lifestyle and have no access to formal education. They differ primarily in terms of socioeconomic status, place of residence, culture, historical origin, and citizenship. They have no chance of obtaining the government-provided education and spend their days on a boat or at the beach. This study is aimed to explore the needs of alternative curriculum among Bajau pupils in Semporna, Sabah. This research applied qualitative research by conducted focus group discussion with pupils from a national school in Semporna, Sabah. 15 pupils who meet the criteria as sample are purposely picked to participate in this research. All of the participants were able to express their perspectives and understanding. The data gathered demonstrated participants' enthusiasm for alternate curriculum. It is claimed that an entertaining alternative curriculum will entice students to attend school. As a result, there is an immediate need for us to create a fun learning alternative curriculum in order to reduce primary school dropout rates in Semporna, Sabah
Analysis of space optimization of three-dimensional container loading problem
No full textThe container loading problem (CLP) has been studied for maximising container space utilization as a method of lowering costs and increasing supply chain efficiency. This paper presents an approach to the CLP, in which a container is to be filled with a selection of cargoes from an available set so that the volume utilization of the container is maximized. By minimizing the outer volume of all the cargoes placed, tight arrangement of the cargoes can be achieved. Genetic algorithm (GA) with adaptive chromosome length formation has been used to find the optimum solution for the arranged cargoes to be placed with minimum space utilization. Two experiments have been conducted, to simulate the space optimization with fixed and unfixed number of cargoes. Our findings show that the GA with adaptive chromosome length proposes better arrangements of cargoes to give optimum space utilization for the container with 93.4% fitness difference percentage than the first generation’s fitness value
Data-driven model for human tracking and prediction using Kalman filter with particle swarm optimization
No full textIntelligent monitoring systems have evolved due to technological improvement and innovation in biometric identification technology and protecting lives and property. As a result, intelligent monitoring systems are becoming increasingly prevalent. Consequently, this paper proposes the development of an IoT-based Human Tracking system that incorporates the Kalman Filter (KF) Algorithm. Apart from the deployment of the Kalman Filter, analysis was also done with an optimized Kalman filter with PSO (KF-PSO) using Particle Swarm Optimization. Rather than manually tuning the process noise error, R, and measurement error, Q, which are involved in KF, PSO was also used to adjust the errors to obtain their best values for optimal estimation. As a result, a two-dimensional (2D) Kalman filter is developed. The positions and velocities of the object being tracked (i.e., humans) are estimated in x- and y-directions. The proposed system has been evaluated using ten different human datasets, each consisting of 100 samples. The quality performance of KF and KF-PSO models was also compared using accuracy analysis. In comparison, the KF-PSO model yielded an average Mean-Square error of 17 mm (i.e., 1.7% error), while the conventional KF model gave an average Mean-Square error of 22 mm (i.e., 2.2% error). Hence, the KF-PSO model is a better filter than the conventional KF model because of its higher accuracy
Enhancement of robust control law for active front steering control strategy
No full textIn vehicle lateral dynamic studies, the transient performances of yaw stability control system are essential. Due to uncertainty of cornering stiffness when the road surface is changing, this perturbation may influence the transient performances and affected the handling quality of the vehicle. By designing a yaw rate tracking controller for active front steering control strategy with the enhanced control law using the sliding mode control algorithm, the transient performances are improved. The vehicle lateral dynamic behaviours are described using the linear and nonlinear vehicle models for controller design, simulations and evaluations. To achieve the control objective, the enhanced robust control law is proposed to cater the uncertainty of front wheels cornering stiffness. The proposed control strategy is evaluated using the step steer manoeuvre test for three road surface conditions. The simulation results obtained showed that the transient performances of yaw rate with the enhanced robust control law are better compared to the conventional control law and uncontrolled vehicle especially for wet and snow/icy road surfaces. An enhancement of robust control law that solved the cornering stiffness uncertainty is expected as a knowledge contribution to vehicle lateral dynamic studies
GSM device localization in indoor environment using Received Signal Strength Indicator (RSSI) and Convolutional Neural Networks (CNN)
No full textEavesdropping activities have always been punitive threats to data security for every level of society. With the revolution and advance of technology, the tools to accomplish these have become more sophisticated, smaller yet cheaper. Medium of transmissions for transferring these data have also improved tremendously, including over mobile telephone networks through GSM networks. In order to mitigate this threat, a fast and effective approach to localize the eavesdropping device is called for. Thus, this paper is to propose a reliable localization framework that coordinate and locate GSM eavesdropping devices in indoor environment based on Convolutional Neural Networks (CNN) algorithm and Received Signal Strength Indicator (RSSI). GSM device used in this experiment is planted in the conference rooms to prove the effectiveness of this system. 3D radio images are constructed based on RSSI fingerprints to locate GSM device accurately by determining its location coordinate. The different choice of optimization algorithms, parameters and architecture model was tested in our proposed method to achieve better localization performance. The simulation results proved that RMSProp optimization algorithm with kurtosis provide a better localization accuracy and computational complexity
Image reconstruction enhancement for electrical capacitance tomography using an improved sensitivity map
No full textElectrical Capacitance Tomography (ECT) is a viable application for measuring multi-phase flow. The quality of the reconstructed images is a significant factor for ECT practical application. ECT images are reconstructed by using the data from the sensitivity map to reconstruct an image of the region of interest. The resolution of the reconstructed image is strongly influenced by the sensitivity map computation. This paper focuses on the image reconstruction based on three types of sensitivity maps with changes made to the boundary condition on the finite element method software, specifically the boundary setting for the gap between the electrodes. The boundary settings are (1) ground (2) floating potential and (3) electrical insulator setting. A twelve-copper based ECT system is used to obtain the sensor measurement. From the modelling, the tendency of the electrical potential and electrical fields are presented. The sensitivity maps resulting from all boundary conditions tests are generated using MATLAB. Based on the image reconstructions, the boundary setting for gaps between electrodes of electrical floating potential and electrical insulator are able to produce image reconstruction of multiphase flow. Electrical insulator setting for the gap between electrodes is generally better than floating potential setting because, in addition to producing a reconstruction image of multiphase flow, it also eliminates high potential profiles at the edge of the electrodes
Optimized PID controller of a laboratory-scaled water distribution system via swarm intelligence techniques
No full textIn this paper, an off-line optimized PID controller algorithm, utilizing swarm intelligence is developed, for a laboratory-scaled water distribution system. The swarm intelligence optimization techniques are simulated in MATLAB and Simulink Tools. The two techniques used are Particle Swarm Intelligence (PSO) and Grey Wolf Optimization (GWO). An efficient controller produces a better system, i.e. the attainment of optimized level of the reservoir tank of the system in meeting customers demand. Hence by implementing the optimization techniques, the performance of the PID-controller, is improved, by generating the optimal values of the PID parameters, thus improving the system’s performance. When the optimized parameters are applied to the system, output response of the system using PSO and GWO are compared and analyzed. From the results obtained, it can be observed that GWO produces better results than PSO, specifically in the reduction of the system performance’s overshoot