Modeling the human brain as a network has been widely considered as a powerful approach to investigating the brain's structural and functional systems. However, many previous approaches focused on a single scale o...
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Web-based technologies are often built to capitalize on the flexibility and fluidity that is supported by the internet, with the value of 'access anywhere'underpinning a blurring of boundaries across home and ...
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This paper proposes a pitch angle forecasting model based on the k-nearest neighbor classification. Air temperature, atmosphere pressure, wind direction, wind speed, rotor speed and wind power parameters were represen...
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Most of the parameters proposed for the characterization of the emotion in speech concentrate their attention on phonetic and prosodic features. Our approach goes beyond by trying to relate the biometrical signature o...
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There are some problems in researches on supply chain control of pharmaceutical chain enterprises under dynamic environment. The authors establish a new paradigm of computer simulation analysis and adaptive control, t...
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Information technology (IT) supports companies' business processes and decision making through the use of technologies and tools that are provided by suppliers. Companies that are suppliers and consumers establish...
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This paper compares the use of RGB and HSV histograms during the execution of an Evolutionary Algorithm. This algorithm generates abstract images that try to match the histograms of a target image. Three different fit...
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ISBN:
(纸本)9789898565778
This paper compares the use of RGB and HSV histograms during the execution of an Evolutionary Algorithm. This algorithm generates abstract images that try to match the histograms of a target image. Three different fitness functions have been used to compare: the differences between the individual with the RGB histogram of the test image, the HSV histogram, and an average of the two histograms at the same time. Results show that the HSV fitness also increases the similarities of the RGB (and therefore, the average) more than the other two measures.
This paper investigates the performance and the results of an evolutionary algorithm (EA) specifically designed for evolving the decision engine of a program (which, in this context, is called bot) that plays Plan...
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This paper investigates the performance and the results of an evolutionary algorithm (EA) specifically designed for evolving the decision engine of a program (which, in this context, is called bot) that plays Planet Wars. This game, which was chosen for the Google Artificial Intelligence Challenge in 2010, requires the bot to deal with multiple target planets, while achieving a certain degree of adaptability in order to defeat different opponents in different scenarios. The decision engine of the bot is initially based on a set of rules that have been defined after an empirical study, and a genetic algorithm (GA) is used for tuning the set of constants, weights and probabilities that those rules include, and therefore, the general behaviour of the bot. Then, the bot is supplied with the evolved decision engine and the results obtained when competing with other bots (a bot offered by Google as a sparring partner, and a scripted bot with a pre-established behaviour) are thoroughly analysed. The evaluation of the candidate solutions is based on the result of non-deterministic battles (and environmental interactions) against other bots, whose outcome depends on random draws as well as on the opponents' actions. Therefore, the proposed GA is dealing with a noisy fitness function. After analysing the effects of the noisy fitness, we conclude that tackling randomness via repeated combats and reevaluations reduces this effect and makes the GA a highly valuable approach for solving this problem.
The reduction of the energy consumption in the domain of the embedded systems is becoming the most important design goal due to the increasing use of battery powered consumer devices. Previous research has pointed out...
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The reduction of the energy consumption in the domain of the embedded systems is becoming the most important design goal due to the increasing use of battery powered consumer devices. Previous research has pointed out the instruction memory organisation as one of the major sources of energy consumption of the embedded systems. Due to this fact, the introduction of any enhancement in this component of the system becomes crucial in order to decrease this energy bottleneck. The purpose of this paper is to present a highlevel energy analysis of the loop buffer schemes that exist in the embedded systems. The crucial energy analysis that is presented in this paper not only proposes a method to evaluate different loop buffer schemes for a certain application, but also guides embedded systems designers to make the correct decision in the trade-offs that exist between the energy budget, the required performance, and the area cost of the embedded system. Experimental results used in this analysis show that, the search of energy savings (up to 76%) has to take into account the performance penalty, the area cost, and the impact of the implementation technology in order to choose the most suitable enhancement that has to be introduced in the instruction memory organisation from the point of view of the energy consumption.
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