On periodically pendulum-diven systems for underactuated locomotion: a viscoelastic jointed model

This paper investigates the locomotion principles and nonlinear dynamics of the periodically pendulum-driven (PD) systems using the case of a 2-DOF viscoelastic jointed model. As a mechanical system with underactuation degree one, the proposed system has strongly coupled nonlinearities and can be utilized as a potential benchmark for studying complicated PD systems. By mathematical modeling and non-dimensionalization of the physical system, an insight is obtained to the global system dynamics. The proposed 2-DOF viscoelastic jointed model establishes a commendable interconnection between the system dynamics and the periodically actuated force. Subsequently, the periodic locomotion principles of the actuated subsystem are elaborately studied and synthesized with the characteristic of viscoelastic element. Then the analysis of qualitative changes is conducted respectively under the varying excitation amplitude and frequency. Simulation results validate the efficiency and performance of the proposed system comparing with the conventional system

Modelling and control of an elastically joint-actuated cart-pole underactuated system

This paper investigates the modelling and closedloop tracking control issues of a novel elastic underactuated multibody system. A torsional inverted pendulum cart-pole system with a single rotary actuator at the pivot of the cart is proposed. The system dynamics which incorporates with motion planning is firstly described. An optimization procedure is then discussed to plan the feasible trajectories that not just meet the performance requirements but also obtain optimality with respect to the cart displacement and average velocity. A closed-loop tracking controller is designed under collocated partial feedback linearization (CPFL). Subsequent presentation of simulation demonstrates that the proposed system is promising as compared to the previous work. The paper concludes with the application of our novel scheme to the design and control of autonomous robot systems

Modelling and analysis of dynamic frictional interactions of vibro-driven capsule systems with viscoelastic property

This paper studies the dynamic frictional interactions of the underactuated vibro-driven capsule systems with the viscoelastic property. Frictional dynamics of the capsule systems is an active research domain, while the online implementation and control of the friction models are still intractable tasks. This paper investigates the frictional characteristics of the capsule systems in the dynamic regime, including particularly the non-reversible drooping and hysteresis. Firstly, the frictional interaction dynamics is modelled and characterized using a combined physics-based and analytical-based approach. Subsequently, the qualitative changes in the capsule dynamics and friction-induced vibrational responses that triggered by multiple control parameters are discussed. It is found that the capsule dynamics is mainly periodic, and the motion velocity of the capsule systems can be controlled by appropriate tuning of the control parameters around the identified control points. Simulation results have a good agreement with the experimentally observed frictional characteristics. The effectiveness of the proposed method is verified in terms of satisfaction of the energy requirements and quenching of the friction-induced vibrations. It is also found that the frictional interaction dynamics of the capsule systems can be predicted for a wide range of vibrational behaviours. Finally, the importance of a concrete understanding and accurate description of the dynamic friction at the sliding substrate is highlighted

Robot-assisted smart firefighting and interdisciplinary perspectives

Urbanization and changes in modern infrastructure have introduced new challenges to current firefighting practices. The current manual operations and training including fire investigation, hazardous chemicals detection, fire and rescue are insufficient to protect the firefighter's safety and life. The firefighting and rescue functions of the existing equipment and apparatus and their dexterity are limited, particularly in the harsh firefighting environments. It is well-established that data and informatics are key factors for efficient and smart firefighting operation. This paper provides a review on the robot-assisted firefighting systems with interdisciplinary perspectives to identify the needs, requirements, challenges as well as future trends to facilitate smart and efficient operations. The needs and challenges of robot-assisted firefighting systems are firstly investigated and identified. Subsequently, prevailing firefighting robotic platforms in literature as well as in practices are elaborately scrutinized and discussed, followed by investigation of localization and navigation support methods. Finally, conclusions and future trends outlook are provided

A relational study of supply chain agility, competitiveness and business performance in the oil and gas industry

This paper assesses the link between dimensions of agile supply chain, competitive objectives and business performance in the UK North Sea upstream oil and gas industry. A questionnaire was designed and administered covering important criteria of agility identified from the literature. The questionnaire was sent to a sample of 880 supply chain managers within the UK oil and gas industry and a net response rate of 17.8% was achieved. Statistical tests for validity and reliability were carried out. Also, the KS statistical test for normality was undertaken on the data. All the tests affirm that the data came from a normal distribution. Non-response bias analysis was conducted through wave analysis using one-way ANOVA and no statistically significant difference was revealed by the t-test result. By examining the whole supply chain associated with agile practices in an important sector, the paper identifies the most important dimensions and attributes of supply chain agility and provides a deeper insight into those characteristics of agility that are most relevant within the oil and gas industry

A combination selection algorithm on forecasting

It is widely accepted in forecasting that a combination model can improve forecasting accuracy. One important challenge is how to select the optimal subset of individual models from all available models without having to try all possible combinations of these models. This paper proposes an optimal subset selection algorithm from all individual models using information theory. The experimental results in tourism demand forecasting demonstrate that the combination of the individual models from the selected optimal subset significantly outperforms the combination of all available individual models. The proposed optimal subset selection algorithm provides a theoretical approach rather than experimental assessments which dominate literature. © 2013 Elsevier B.V. All rights reserved

A survey on wearable sensor modality centred human activity recognition in health care

Increased life expectancy coupled with declining birth rates is leading to an aging population structure. Aging-caused changes, such as physical or cognitive decline, could affect people's quality of life, result in injuries, mental health or the lack of physical activity. Sensor-based human activity recognition (HAR) is one of the most promising assistive technologies to support older people's daily life, which has enabled enormous potential in human-centred applications. Recent surveys in HAR either only focus on the deep learning approaches or one specific sensor modality. This survey aims to provide a more comprehensive introduction for newcomers and researchers to HAR. We first introduce the state-of-art sensor modalities in HAR. We look more into the techniques involved in each step of wearable sensor modality centred HAR in terms of sensors, activities, data pre-processing, feature learning and classification, including both conventional approaches and deep learning methods. In the feature learning section, we focus on both hand-crafted features and automatically learned features using deep networks. We also present the ambient-sensor-based HAR, including camera-based systems, and the systems which combine the wearable and ambient sensors. Finally, we identify the corresponding challenges in HAR to pose research problems for further improvement in HAR