Using technologies such as Web browser-based programming languages and multimedia tools, the authors are developing a sophisticated control environment for use as a World Wide Web front-end to intelligent control syst...
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Using technologies such as Web browser-based programming languages and multimedia tools, the authors are developing a sophisticated control environment for use as a World Wide Web front-end to intelligent control systems. Cooperative behaviour will be remotely facilitated through a two-way transferral of data-status information from the vehicles to the operator, and commands/parameters from the operator to the vehicles. This paper describes the background and context, the proposed solution at the concept level, and issues concerned with implementation.
A concept for implementation of a colony of cooperative autonomous agents is under development. This concept enables useful and productive work to be accomplished by adaptable and dynamic autonomous agents in real-tim...
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A concept for implementation of a colony of cooperative autonomous agents is under development. This concept enables useful and productive work to be accomplished by adaptable and dynamic autonomous agents in real-time and real-world environments. Three primary issues of cooperative autonomous mobile agents are addressed being interaction, management and sustainability. The focus of this paper is the issue of interaction within a colony of cooperative agents while the issues of management and sustainability are scheduled for future research. The view of the agents as a colony is due to their number and the use of agent support structures. A colony of agents has a defined base of operations consisting of relatively static agents while also containing a number of mobile agents.
Multiple-Valued Logic (MVL) functions are implemented via Boolean multiple-wire arrangements where a careful state assignment methodology is used to ensure efficient implementation regimes. A 'power of N' modu...
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Multiple-Valued Logic (MVL) functions are implemented via Boolean multiple-wire arrangements where a careful state assignment methodology is used to ensure efficient implementation regimes. A 'power of N' module is proposed for GF (2/sup 3/). The method avoids the need to factorize the polynomial and circuits can be realised using a combination of NOT AND and XOR functions. In addition, a novel transform over GF (2/sup 2/) is proposed which shows promise when compared to the Reed-Muller-Fourier transform, in its capacity to produce zero coefficients. A possible implementation strategy, using Field Programmable Gate Arrays (FPGAs) is briefly discussed.
A management strategy for a population of cooperative autonomous agents has been developed. This strategy enables activity behaviour management of a large and dynamically changing population. To make large populations...
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A management strategy for a population of cooperative autonomous agents has been developed. This strategy enables activity behaviour management of a large and dynamically changing population. To make large populations of agents manageable it is necessary to make them autonomous. One of the key criteria for such autonomy is that agent behaviour must be adaptable to a dynamic environment. If the goal is to establish a large scale population of agents away from immediate human contact such that the population is autonomous, then it needs to be adaptable and self-sustaining. Management of an autonomous population is seen as one of the key factors for realisation of this goal. System requirements have been identified and application areas suggested.
The paper examines several methods for improving the generalization capability of the BRAINNE technique. These methods deal with proper training of neural networks and improvements in defining bounds for continuous data.
The paper examines several methods for improving the generalization capability of the BRAINNE technique. These methods deal with proper training of neural networks and improvements in defining bounds for continuous data.
A method for learning disjunctive rules using a combination of two existing neural network schemes is proposed. The hybrid network consists of two layers; the first is an unsupervised network while the second is a sup...
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A method for learning disjunctive rules using a combination of two existing neural network schemes is proposed. The hybrid network consists of two layers; the first is an unsupervised network while the second is a supervised network. The first layer is used for ordering the inputs of training instances into clusters. Initial rules are extracted from this layer using an existing technique called Unsupervised BRAINNE. These rules are then fed into the second layer which is trained using the delta rule. The second layer is then examined to determine which clusters define the output nodes. This method is able to identify disjunctive rules directly rather than utilising a generate and test paradigm as was used in previous supervised versions of BRAINNE.
Genetic algorithms are highly parallel, adaptive search method based on the processes of Darwinian evolution. This paper combines genetic algorithms with simulated annealing algorithms to a new kind of random search a...
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Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are su...
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Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning-based model training (ICPTL) and (2) cluster-level model training (CM). These approaches aim to find a trade-off between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL’s model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model.
The International Conference on intelligentcomputing (ICIC) was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intel- gence, machine learning, pattern recognition, im...
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ISBN:
(数字)9783642148316
ISBN:
(纸本)9783642148309
The International Conference on intelligentcomputing (ICIC) was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intel- gence, machine learning, pattern recognition, image processing, bioinformatics, and computational biology. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the m- tifaceted aspects of intelligentcomputing. ICIC 2010, held in Changsha, China, August 18-21, 2010, constituted the 6th - ternational Conference on intelligentcomputing. It built upon the success of ICIC 2009, ICIC 2008, ICIC 2007, ICIC 2006, and ICIC 2005, that were held in Ulsan, Korea, Shanghai, Qingdao, Kunming and Hefei, China, respectively. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligentcomputing. Its aim was to unify the picture of contemporary intelligentcomputing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was “Advanced intelligentcomputing Technology and Applications.” Papers focusing on this theme were solicited, addressing theories, methodologies, and applications in science and technology.
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