Fuzzy Control: Theory and Practice (Advances in Intelligent and Soft Computing)
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Speech recognition technology development is measured by the ratio of incorrectly recognized words to the number of words spoken, known as the word error rate. While the word error rate can be affected by a number of factors such as the size of the vocabulary, the speaking style of the user, whether the query is open or task-oriented, whether the system is trained for more than one user, the channel quality of the system, and background noise, the rate is not affected by the language chosen.
Applications of artificial intelligence in mining. For many years, AI tools have been in use in various mining-related applications. Bandopadhyay and Venkatasubramanian developed a fault diagnostic expert system for the longwall shearer. Schofield developed MINDER, a decision support system capable of assisting the mine planner in the complex task of selecting the optimum surface mining equipment. One of the major objectives of this program was to enable integration with other mining software packages.
Denby and Kizil describe the development of an advanced computer system for the assessment of geotechnical risk in surface coal mines. The authors review the ESDS, an expert system for slope stability assessment. The paper concludes by presenting an example that illustrates how ESDS may be utilized as a decision support system at the design stage of a UK surface coal mine.
There are numerous examples of work done in the area of mapping minerals. Moore and Sattar developed a knowledge-based system to assist in the modelling and economic assessment of potential mineral deposits in Queensland, Australia. The system was able to be readily tailored to address specific mineral commodities and environments. Bearman and Milne reviewed the opportunities for expert systems in the minerals industry, and explored potential future developments and applications for the industry.
Romans provides examples of application of knowledge-based systems in the minerals industry leading to substantial savings. They mentioned Waller and Rowsell's work on the development of a system named 'Intelligent Drilling Control using AI in petroleum industry. The system aimed to optimize the drilling process, and measure the real-time pick consumption and calculate the drilling costs. Toll discusses the application of AI systems for geotechnical applications. Morin discusses the integration of support elements such as expert systems, numerical models, data analysis and visualization tools, and simulation to bring added functionality and intelligence to the mine design and planning system.
According to the author, the integration of these elements, if feasible, would form an intelligent design system with decision-support capabilities that exceed anything currently available on the market. This study mentions the use of AI tools for exploration and reserve estimation, geophysics, rock engineering, mineral processing, remote sensing, process control and optimization, and equipment selection.
They were able to promote effective and meaningful learning of scientific observation in the area of Earth Science. According to Knobloch et al. Based on a 'self-learning' process, this Al technology can be used to interpret almost any geoscientfic data for generation of both qualitative prediction of locations and quantitative prediction of locations, grades, tonnages mineral predictive maps.
By analysing the footprints of known mineralization in theframework of available geoscientfic data, the approach generates trained ANNs that are further used to generate predictive maps. Traditional science is centred on the binary status view that a statement is either entirely true, or entirely false. However, according to Kosko , ''fuzz' includes statements that are only partially true in order to define vague terms that have entered human language.
This way of thinking has generally not been accepted by modern science. A set is a binary structure to which objects belong. An object either belongs to a set or it does not; sets do not allow for partial membership. Depending on whether or not they belong to a set, objects are represented by a 1 or 0. However, a fuzzy set allows for partial membership. Fuzzy logic systems use fuzzy sets to convert inputs into the correct outputs. Like a human expert, a fuzzy logic system uses rules of thumb to determine what action must be taken in a certain situation.
These are called Fuzzy If-Then rules and they follow the form ifx is A then y is B where A and B are linguistic values defined by the fuzzy sets on the universe of disclosure X and Y Castillo and Melin, In the above rule, x is A is defined as the antecedent or premise while y is B is called the consequent or conclusion. The curse of dimensionality says that there will always be a limit to the number of rules that can be used due to the memory limits of computer chips.
A membership function maps the object values within a set and can take a number of forms. Bezdek lists the following five basic operations that are used to manipulate fuzzy sets: Fuzzy logic began in Ancient Greece with the philosopher Zeno. According to Kosko , Zeno posed the question: The fuzzy answer is that the pile leaves the set of piles of sand as smoothly as the individual grains are taken away from it. At the turn of the 20th century, Bertrand Russell stated that 'everything is vague to a degree you do not realise it until you try to make it precise'. Russell said that during the transition phase, objects would spend the majority of their time in a mix of the two states.
In the s Jan Lukasiwicz looked at these theories as an extension of binary logic. In , Max Black drew the first graph of a fuzzy set. However, the philosophical community largely ignored these views due to their attitude towards logic at the time. The term 'Fuzzy' was introduced by a paper by Lofti Zadeh, the chairman of the electrical engineering department at the University of California, in However, it was decades before his work received recognition in the form of a medal of honour from the Institute of Electrical Engineers in According to Kosko , one of the first devices to use fuzzy logic was a kiln for F.
The coal feed rate was reduced if both temperature and oxygen levels were high within the kiln. One of the most notable developments in the use of this technology was the Sendai subway railway system developed by Hitachi in The system replaced train drivers along the Many of the earliest applications of fuzzy logic were designed to be used by organizations. The first home appliance to use fuzzy logic was a washing machine developed in Wakami et al.
The optimum washing time was determined through outputs from a washing sensor and fuzzy logic technology.
The washing sensor used a light-emitting diode and a phototransistor to measure the transmittance of the water. The rate at which the light transmittance decreased indicated whether the dirt in the machine is muddy or oily, which in turn indicated whether there was a light or heavy amount of dirt in the washing machine. Fuzzy logic used these inputs to determine the optimal washing time. Fuzzy logic technology has also been used to stabilize videos shot with amateur video cameras.
As video cameras became smaller and lighter, the effect of hand jitters became more noticeable. The image stabilizing system consists of a motion detection chip, interpolation processing chip, field memory, and microprocessor to hold the fuzzy interference. The image is sent to the field memory while also being processed by the motion detection chip.
The motion detection chip looks for correlation between images to detect the amount and direction of movement. The field memory then repositions the image and uses the electronic zoom function of the camera to enlarge the compensated image. Fuzzy logic is used to distinguish between a shaking video and a moving subject. This is determined by whether all objects within the image are moving in the same direction or if they are moving in different directions. Fuzzy logic technology, when combined with an infrared ray detector, can be used to assist an air conditioner in efficiently cooling the occupants of a room through knowledge of the current temperature and the number of occupants and their positions.
The rotating infrared ray detector creates a two-dimensional thermal image with each element given a temperature value. The fuzzy logic algorithm then performs three tasks - removing the background, identifying each occupant, and expanding the region of each occupant. In order to isolate the occupants from the background, the algorithm identifies areas where the average temperature for that group of elements is higher than a set value.
Given that a peak temperature would represent each occupant, the algorithm identifies elements whose temperature is higher than the eight elements surrounding it and assigns each of these peaks a number to identify the number of occupants. The algorithm then expands the area covered by each occupant by lowering the threshold temperature in order to expand the area covered without increasing the number of peaks.
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Fairhurst and Lin discuss the application of fuzzy methodology in tunnel support design. According to the authors, 'a decision system for tunnel support design allows questions to be posed and answered in relation to the information stored in a rafael design knowledge base: Such systems will be successful depending on their ability to extract information from geology, rock mechanics, and tunnel technology and translate it into a form or forms that help the user to make a more intelligent decision for tunnel design.
The study presents a preliminary discussion of approaches to the development of such systems.
Advances in Intelligent Systems and Computing
Fuzzy logic technology can be used to compensate for friction in machinery. Friction is based on both the position and the velocity of an object and is difficult to predict with an accurate model Liu et al. However, due to the large impact of friction at low velocities, developing such a model is important.
Experts designed a fuzzy logic control to approximate such phenomena. A fuzzy logic system has also been used to identify suitable advisors for call centre customers.
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According to Shah et al. It is therefore important to match the correct advisor to each customer. There are five behaviour dimensions of the advisor that can influence customer satisfaction; these include mutual understanding, authenticity, extra attention, competence, and meeting minimum standards. Different customers would react in different ways to each of these, so it is important to know which will work best for each demographic of customer. Fuzzy logic can be used to infer the goals of the users. To develop such a system, the company must collect data, cluster and analyse the data, identify the separate categories, categorize both consumers and advisors, identify the critical factors and derive their membership functions, develop if-then rules, implement the fuzzy interference process, test the system in a real-world environment, and validate the system from feedback received.
Beynon discusses the use of fuzzy logic in decision trees.
Decision trees allow for greater interpretation of an analysis by humans. The root node of each tree is split into leaf nodes to further classify objects. Each leaf node is created using fuzzy if-then rules. Decision trees increase the understanding of complicated situations and can be applied to a variety of scenarios. Beynon applies a fuzzy decision tree to the complex situation of company audit fee evaluation. The VR system offers many benefits to its users, including flexibility in time and place, and the rate and privacy of the learning experience.
The system has a variety of uses, including the development of understanding and the retention of learning, customized on-the-job training, fault finding, easy communication of complex data, evaluation of the consequences of poor decision-making, trainee assessment, identification of flaws in training programmes, and accident identification and reconstruction. The modules developed at the School can immensely benefit from the use of both AI and fuzzy logic technologies.
In order to improve the interactive feature of the system, the user must be able to control the system in a more natural way. However, speech and gesture are the most natural way in which humans interact with others. According to iCinema , a motion capture function that can recognize the gestures of up to five people at once is currently offered by the AVIE system.
However, the current mining simulations do not utilize this feature. Integrating this feature will definitely improve the interaction in the AVIE facility and will add more realism required specifically for training effectiveness in this environment. To reduce the word error rate for this new function, the control could be initially limited to a number of key phrases. Using a similar technology as in air conditioners, infrared detectors can track the movement of people in this environment and then through the use of AI make changes to the way the module operates.
As an example, different actions by a group of trainees can lead to different outcomes. This module involves decision-making based on input from the user. An overview of the historical and current uses of AI and fuzzy logic technologies, as well as how each form of computing works, has been conducted, and some applications of both AI and fuzzy logic provided. From this study, it can be seen that there is a lot of opportunity for applying AI and fuzzy logic technologies to the current VR technology used at the School in order to benefit learning and teaching.
Further detailed studies will look into the specifics of how these technologies could be integrated into the current system. A pilot study will initially be conducted on one of the modules to test the feasibility of this integrated technology. Mine ventilation expert system. Applied Artificial Intelligence, vol.
Foundations of Fuzzy Systems
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