Regression between headmaster leadership, task load and job satisfaction of special education integration program teacher

Managing school is a daunting task for a headmaster. This responsibility is exacerbated when it involves the Special Education Integration Program (SEIP). This situation requires appropriate and effective leadership in addressing some of the issues that are currently taking place at SEIP such as task load and job satisfaction. This study aimed to identify the influence of headmaster leadership on task load and teacher job satisfaction at SEIP. This quantitative study was conducted by distributing 400 sets of randomized questionnaires to SEIP teachers across Malaysia through google form. The data obtained were then analyzed using Structural Equation Modeling (SEM) and AMOS software. The results show that there is a significant positive effect on the leadership of the headmaster and the task load of the teacher. Likewise, the construct of task load and teacher job satisfaction has a significant positive effect. However, for the construct of headmaster leadership and teacher job satisfaction, there was no significant positive relationship. This finding is very important as a reference to the school administration re-evaluating their leadership so as not to burden SEIP teachers and to give them job satisfaction. In addition, the findings of this study can also serve as a guide for SEIP teachers to increase awareness of the importance of managing their tasks. This study also focused on education leadership in general and more specifically on special education leadership

Design Space Exploration in Cyber-Physical Systems

Cyber physical systems (CPS) integrate a variety of engineering areas such as control, mechanical and computer engineering in a holistic design effort. While interdependencies between the different disciplines are key attributes of CPS design science, little is known about the impact of design decisions of the cyber part on the overall system qualities. To investigate these interdependencies, this paper proposes a simulation-based Design Space Exploration (DSE) framework that considers detailed cyber system parameters such as cache size, bus width, and voltage levels in addition to physical and control parameters of the CPS. We propose an exploration algorithm that surfs the parameter configurations in the cyber physical sub-systems, in order to approximate the Pareto-optimal design points with regards to the trade-os among the design objectives, such as energy consumption and control stability. We apply the proposed framework to a network control system for an inverted-pendulum application. The presented holistic evaluation of the identified Pareto-points reveals the presence of non-trivial trade-os, which are imposed by the control, physical, and detailed cyber parameters. For instance the identified energy and control optimal design points comprise configurations with a wide range of CPU speeds, sample times and cache configuration following non-trivial zig-zag patterns. The proposed framework could identify and manage those trade-os and, as a result, is an imperative rst step to automate the search for superior CSP configurations

Robust Balancing for Bipedal Robot via Model Predictive Control

Robust balancing controllers are essential for bipedal robots to safely operate in real-world applications where human-robot interactions are a common practice. While the balancing controllers being developed are effective, they struggle when adjusting to untested motions and environments. Popular controllers commonly rely on heuristic techniques, and simplified models of the intended system, and are optimized to compute applicable joint inputs quickly. What they sacrifice in robustness, they often make up for in computational efficiency and speed. Here, the triple pendulum model is used as a unique method of simulating the dynamics of a bipedal robot in the 2-D saggital plane. The goal of this research is to develop a control architecture which can stabilize the triple pendulum in real time using the linear center of mass dynamics, and when introduced to random initial conditions, fluctuating stance heights and external disturbances. These objectives will be achieved via a model predictive control architecture, supplemented by the angular linear inverted pendulum model and an inverse dynamics function which computes the applicable low-level joint torques. Various optimization algorithms, most notably the nonlinear Newton's optimization and the nonlinear gradient descent algorithm, will also be tested with the intent of running in real-time. The initial algorithm design stage was completed in MATLAB and Python, before being implemented in the MuJoCo simulation system in Python for final testing. Most notably, the simplified model could be simulated for a prediction horizon of length 20 with a time-step of 0.05[s] (1[s] of look ahead time) with an average calculation time of 363.13[ms]. As was expected, the largest drawback to implementing the discussed control system is the computation time required for each call of the optimization program. That said, results show that implementing the MPC system would result in more stable overall performance, and creates a system which can enter new environments with little-to-no tuning while maintaining stability.No embargoAcademic Major: Mechanical Engineerin

A Reactive and Efficient Walking Pattern Generator for Robust Bipedal Locomotion

Available possibilities to prevent a biped robot from falling down in the presence of severe disturbances are mainly Center of Pressure (CoP) modulation, step location and timing adjustment, and angular momentum regulation. In this paper, we aim at designing a walking pattern generator which employs an optimal combination of these tools to generate robust gaits. In this approach, first, the next step location and timing are decided consistent with the commanded walking velocity and based on the Divergent Component of Motion (DCM) measurement. This stage which is done by a very small-size Quadratic Program (QP) uses the Linear Inverted Pendulum Model (LIPM) dynamics to adapt the switching contact location and time. Then, consistent with the first stage, the LIPM with flywheel dynamics is used to regenerate the DCM and angular momentum trajectories at each control cycle. This is done by modulating the CoP and Centroidal Momentum Pivot (CMP) to realize a desired DCM at the end of current step. Simulation results show the merit of this reactive approach in generating robust and dynamically consistent walking patterns