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This chapter presents a novel human arm gesture tracking and recognition technique based on fuzzy logic and nonlinear Kalman filtering with applications in crane guidance. A Kinect visual sensor and a Myo armband sensor are jointly utilised to perform data fusion to provide more accurate and reliable information on Euler angles, angular velocity, linear acceleration and electromyography data in real time. Dynamic equations for arm gesture movement are formulated with Newton–Euler equations based on Denavit–Hartenberg parameters. Nonlinear Kalman filtering techniques, including the extended Kalman filter and the unscented Kalman filter, are applied in order to perform reliable sensor fusion, and their tracking accuracies are compared. A Sugeno-type fuzzy inference system is proposed for arm gesture recognition. Hardware experiments have shown the efficacy of the proposed method for crane guidance applications.

The potential benefits of structuring first-year general chemistry courses around an expert decision model (EDM) to help students develop conceptual understanding and sustainable problem-solving strategies are discussed, as is the creation of the EDM and the challenges faced in doing so. The usefulness of categorizing the course material as either process knowledge, conditional knowledge, or conceptual knowledge is also outlined. The EDM created for the course consists of four fundamental content domains branching out with a series of hierarchal questions and actions designed to guide students toward characterizing a question or solving a problem. The key characteristics of each of these domains are described along with aspects of this EDM structure that have been implemented over the past couple of years in the author’s classes and the impact this has had on the students and the author’s teaching.

SD is defined as a failure to sense correctly the attitude, motion, and/or position of the aircraft with respect to the surface of the earth (Benson, 2003). The types of SD are generally thought to be “unrecognized” and “recognized” (Previc & Ercoline, 2004). Although a third type has been reported (incapacitating), this type seems irrelevant to UAV operations. Unrecognized SD occurs when the person at the controls is unaware that a change in the motion/attitude of the aircraft has taken place. The cause is often the result of a combination of sub-threshold motion and inattention. This type of SD is known to be the single most serious human factors reason for aircraft accidents today, accounting for roughly 90% of all known SD-related mishaps (Davenport, 2000). Recognized SD occurs when a noticeable conflict is created between the actual motion/attitude of the aircraft and any one of the common physiological sensory mechanisms (e.g., visual, vestibular, auditory, and tactile). Recognized SD is the most common type of SD, accounting for the remaining SD-related accidents.

The vehicle–track interaction and the resulting dynamic response of the vehicle involve a number of complex nonlinear problems. Large vertical loads act through a small contact patch leading to very high contact pressures. Transverse loads acting through this contact induce a relative velocity between wheel and rail expressed in non-dimensional form as a creepage. The wheel and rail profiles determine the contact patch shape and affect the ability of the vehicle to run stably. If the yaw stiffness of the axles is too low, the vehicle will become unstable at a relatively low speed; conversely, if the yaw stiffness is too high, the curving behaviour will be adversely affected. The vehicle suspension, especially the secondary suspension, also affects the ride comfort of passengers. Finally, it is shown how the speed profiles of accelerating and decelerating trains can be calculated from basic assumptions about the train power, adhesion and rolling resistance.