Special Session on Industry 4.0

No abstract available

Innovative Developments in HCI and Future Trends

The recent developments in technology have made noteworthy positive impacts on the human computer interaction (HCI). It is now possible to interact with computers using voice commands, touchscreen, eye movement etc. This paper compiles some of the innovative HCI progresses in the modern desktop and mobile computing and identifies some future research directions

Special Session on Industry 4.0

No abstract available

RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization

Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment

Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms. A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0

Modelling, dynamic analysis and control of capsubot systems with stable propulsion for medical and recovery assistances

The growth of medical robots since the mid-1980s has been striking. From a few initial efforts in stereotactic brain surgery, orthopaedics, endoscopic surgery, microsurgery, and other areas, the field has expanded to include commercially marketed, clinically deployed systems, and a robust and exponentially expanding research community. Obscure gastrointestinal (GI) bleeding, Crohn disease, Celiac disease, small bower tumors, and other disorders that occur in the GI tract have always been challenging to be diagnosed and treated due to the inevitable difficulty in accessing such a complex environment within the human body. Robot-assisted minimally invasive surgery has become an choice

Geometric techniques for trajectory planning and chaos control of a bio-inspired autogenetic capsule robot

Biological systems achieve energy efficient and adaptive behaviours through extensive internal and external compliance interactions. Active dynamic compliance are created and enhanced from musculoskeletal system (joint-space) to external environment (task-space) amongst the underactuated motions. The terminology bio-inspiration implies the understanding of fundamental principles underlying the motion behaviours of animals and humans and transfers these principles into the development of robotic systems. For example, during walking, the muscles constantly change their stiffness and damping when the leg is swinging forward and the foot is put on the ground. This idea enables the exploration in robotic systems with flexible elements—viscoelasticity to mimic the compliant motion of biological muscles. Underactuated systems with viscoelastic actuation are similar to these biological systems, in that their self-organisation and overall tasks must be achieved by coordinating the subsystems and dynamically interacting with the environment

Modelling and Remote Control of an Excavator

This paper reposts the results of an on-going project and investigates modelling and remote control issues of an industry excavator. The details of modelling, communication and control of a remotely controllable excavator are studied. The paper mainly focuses on trajectory tracking control of the excavator base and robust control of the excavator arm. These will provide the fundamental base for our next research step. In addition, extensive simulation results for trajectory tracking of the excavator base and robust control of the excavator arm are given. Finally, conclusions and further work have been identified

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