Computer modelling of single sludge systems for the computer aided design and control of activated sludge processes

Sanders, David; Hudson, Adam; Cawte, Howard; Fenske, O.; Poland, G. A.; Tewkesbury, Giles

doi:10.1016/0165-6074(94)90057-4

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…Using an integrated programming environment with access to high‐level editing and de‐bugging tools assisted in the creation of the systems. A modular structure was adopted to simplify program construction and minimise duplication of code (Sanders and Rasol, 2001; Sanders et al , 2001, 1994).…”

Section: The Expert Systemmentioning

confidence: 99%

Expert system to interpret hand tremor and provide joystick position signals for powered wheelchairs with ultrasonic sensor systems

Sanders

Stott

Graham-Jones

et al. 2011

Industrial Robot: An International Journal

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PurposeThe purpose of this paper is to investigate how to make powered‐wheelchair driving easier using simple expert systems to interpret joystick and ultrasonic sensor data. The expert systems interpret shaky joystick movement and identify potentially hazardous situations and then recommend safe courses of action.Design/methodology/approachThe way that a human user interacts with a powered‐wheelchair is investigated. Some simple expert systems are presented that interpret hand tremor and provide joystick position signals for an ultrasonic sensor system. Results are presented from a series of timed tasks completed by users using a joystick to control a powered‐wheelchair. Effect on the efficiency of driving a powered‐wheelchair is measured using the times to drive through progressively more complicated courses. Drivers completed tests both with and without sensors and the most recently published systems are used to compare results.FindingsThe new expert systems consistently out‐performed the most recently published systems. A minor secondary result was that in simple environments, wheelchair drivers tended to perform better without any sensor system to assist them but in more complicated environments then they performed better with the sensor systems.Research limitations/implicationsThe time taken for a powered‐wheelchair to move from one place to another partly depends on how a human user interacts with the powered‐wheelchair. Wheelchair driving relies heavily on visual feedback and the experience of the drivers. Although attempts were made to remove variation in skill levels by using sets of data associated with each driver and then using paired statistical tests on those sets, some variation must still be present.Practical implicationsThe paper presents new systems that could allow more people to use powered‐wheelchairs and also suggests that the amount of sensor support should be varied depending on circumstances.Originality/valueThe new systems described in the paper consistently performed driving tasks more quickly than the most recently published systems.

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Section: The Expert Systemmentioning

confidence: 99%

Expert system to interpret hand tremor and provide joystick position signals for powered wheelchairs with ultrasonic sensor systems

Sanders

Stott

Graham-Jones

et al. 2011

Industrial Robot: An International Journal

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“…These will be investigated with automatic programming (Hudson et al, 1997). Hudson et al (1996), Tewkesbury and Sanders (1994) and Sanders et al (1994) and artificial neural networks (Sanders et al, 1996).…”

Section: Future Workmentioning

confidence: 99%

Comparing ability to complete simple tele‐operated rescue or maintenance mobile‐robot tasks with and without a sensor system

Sanders

2010

Sensor Review

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The effect on completion of mobile-robot tasks is investigated depending on how a human tele-operator interacts with a sensor system and a mobile-robot. Design/methodology/approach: Interaction is investigated using two mobile-robot systems, three different ways of interacting with the robots and several different environments of increasing complexity. In each case, the operation is investigated with and without sensor systems to assist an operator to move a robot through narrower and narrower gaps and in completing progressively more complicated driving tasks. Tele-operators used a joystick and either watched the robot while operating it, or sat at a computer and viewed scenes remotely on a screen. Cameras were either mounted on the robot to view the space ahead of the robot or mounted remotely so that they viewed both the environment and robot. Every test was compared with sensor systems engaged and with them disconnected. Findings: A main conclusion is that human tele-operators perform better without the assistance of sensor systems in simple environments and in those cases it may be better to switch off the sensor systems or reduce their effect. In addition, tele-operators sometimes performed better with a camera mounted on the robot compared with pre-mounted cameras observing the environment (but that depended on tasks being performed). Research limitations/implications: Tele-operators completed tests both with and without sensors. One robot system used an Umbilical Cable and one used a radio link. Practical implications: The paper quantifies the difference between tele-operation control and sensor assisted control when a robot passes through narrow passages. This could be useful information when system designers decide if a system should be tel-operated, automatic or sensorassisted. The paper suggests that in simple environments then the amount of sensor support should be small but in more complicated environments then more sensor support needs to be provided. Originality/value: The paper investigates the effect of completing mobile-robot tasks depending on whether a human tele-operator uses a sensor system or not and how they interact with the sensor system and the mobile-robot. The paper presents the results from investigations using two mobilerobot systems, three different ways of interacting with the robots and several different environments of increasing complexity. The change in the ability of a human operator to complete progressively more complicated driving tasks with and without a sensor system is presented and the human teleoperators performed better without the assistance of sensor systems in simple environments.

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“…Machine Intelligence combines a wide variety of advanced technologies to give machines an ability to learn, adapt, make decisions and display new behaviours [1,3] . This is achieved using technologies such as neural networks [10][11][12][13][14] , expert systems [15][16][17][18] , self-organizing maps [2,19] , fuzzy logic [3,20] and genetic algorithms [1,21] and that machine intelligence technology has been developed through its application to many areas, such as: assembly [3,13,22,23] , building modelling [3,24,25] , computer vision [13,[26][27][28][29][30] , environmental engineering [2,[31][32][33][34][35] , human-computer interaction [12,14,[36][37][38] , internet use [39,40] , powered-wheelchair assistance [41][42][43][44] , maintenance and inspection ...…”

mentioning

confidence: 99%

Introducing AI into MEMS can lead us to brain-computer interfaces and super-human intelligence

Sanders¹

2009

Assembly Automation

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Last year I spoke about the progress being made in machine intelligence [1] and with sensors and networks of sensors [2] . Earlier this year (in this journal) I spoke about ambient-intelligence, rapidprototyping and the role of humans in the factories of the future [3] . I addressed new applications and technologies such as merging machines with human beings, micro-electromechanics, electromechanical systems that can be personalized, smarter than human intelligence and swarms of smart sensors. Although the research to get us to all that will include human-machine interfaces, sensors, Artificial Intelligence (AI) and Ambient Intelligence (AmI), there is one technology above all others that has the potential to get us there fast… and that is the creation and development of intelligent micro-electromechanical machines (MEMS).

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Computer modelling of single sludge systems for the computer aided design and control of activated sludge processes

Cited by 6 publications

References 3 publications

Expert system to interpret hand tremor and provide joystick position signals for powered wheelchairs with ultrasonic sensor systems

Expert system to interpret hand tremor and provide joystick position signals for powered wheelchairs with ultrasonic sensor systems

Comparing ability to complete simple tele‐operated rescue or maintenance mobile‐robot tasks with and without a sensor system

Introducing AI into MEMS can lead us to brain-computer interfaces and super-human intelligence

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