Membrane separation is a promising method to separate CO2 and H-2 from hydrogen-rich gases. This simultaneously achieves H-2 recovery and CO2 enrichment. The latter is conducive to subsequent carbon capture and storag...
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Membrane separation is a promising method to separate CO2 and H-2 from hydrogen-rich gases. This simultaneously achieves H-2 recovery and CO2 enrichment. The latter is conducive to subsequent carbon capture and storage, and thereby the development of negative emission technologies. In this study, single-, double-, triple-, and quadruple-tube systems with palladium (Pd) membranes and cross-flow configuration are considered, while the Reynolds number (Re) is in the range of 1-50. To maximize H-2 recovery and CO2 enrichment in the systems, the systems are designed using a two-stage optimization in which the parametric sweep technology followed by the evolutionary computation of the Nelder-Mead simplex method is applied to find the best configuration and the exit H2 concentration is chosen as the objective function. On account of the scavenging waves stemming from the upstream tubes, the goals of the optimization is to diminish the concentration polarization effect of the upstream tubes upon the downstream ones. The predictions indicate that an increase in the number of tubes raises the optimization efficiency. Compared to the tubes in tandem, the optimized configuration at Re = 10 can improve the hydrogen recovery up to 12.2%, while the CO2 enrichment can be intensified by up to 7%.
Interactive evolutionary computation (EC) creates media contents that fit on a user;however, user's fatigue problem still remains. Takagi et al. proposed a framework of EC that evaluates media contents based on a ...
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Interactive evolutionary computation (EC) creates media contents that fit on a user;however, user's fatigue problem still remains. Takagi et al. proposed a framework of EC that evaluates media contents based on a user's physiological indices, and this technique is expected to solve the problem. In this study, we constructed an EC system that creates musical melody using listener's heartbeat intervals. Based on the constructed system, we proposed a method that creates media contents and presents them to a user automatically using blank. The constructed system and the proposed method have the possibility to reduce user's fatigue. (C) 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
This paper develops an evolutionary method that learns inductively to recognize the makeup and the position of very short consensus sequences, cis-acting sites, which are a typical feature of promoters in genomes. The...
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This paper develops an evolutionary method that learns inductively to recognize the makeup and the position of very short consensus sequences, cis-acting sites, which are a typical feature of promoters in genomes. The method combines a Finite State Automata (FSA) and Genetic Programming (GP) to discover candidate promoter sequences in primary sequence data. An experiment measures the success of the method for promoter prediction in the human genome. This class of method can take large base pair jumps and this may enable it to process very long genomic sequences to discover gene specific cis-acting sites, and genes which are regulated together. (C) 2003 Elsevier Ireland Ltd. All rights reserved.
In the realm of software testing various organizations wish to predict the faults in their software systems prior to their deployment. This improves the delivered quality and also reduces the maintenance effort. A mul...
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In the realm of software testing various organizations wish to predict the faults in their software systems prior to their deployment. This improves the delivered quality and also reduces the maintenance effort. A multitude of software metrics and statistical models have been developed to solve this problem and one such method is called defect prediction. Defect prediction is the process of identifying the defects in the software program prior to its deployment. In recent times, a class of learners called evolutionary computation (EC) techniques has emerged. These EC techniques apply the Darwinian principle of 'survival of the fittest'. This study performs an empirical assessment of the performance of various EC techniques in the prediction of software defects over multiple data sets. An empirical assessment compares and assesses the performance capability of 16 EC techniques for evaluating the relationship between object-oriented metrics and defect prediction. The developed models are validated using 7 data sets obtained from open source software systems developed by the Software Foundation. On investigating their predictive capabilities and comparative performance, it was found that a majority of EC techniques proved to be highly effective. DTG (a hybridized algorithm) was observed to be the best performing technique. The work done in the current study shows that EC techniques are very effective and can be highly beneficial to testers in the realm of defect prediction in the future.
evolutionary computation has been used with great success for the solution of hard optimization problems. Theoretical analysis, although important in its own right, e.g. for understanding underlying phenomena and char...
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evolutionary computation has been used with great success for the solution of hard optimization problems. Theoretical analysis, although important in its own right, e.g. for understanding underlying phenomena and characteristics of evolutionary search, can only provide upper and/or lower bounds of performance estimation of evolutionary algorithms for hard optimization problems. In practice, empirical analysis is the most important means to assess and compare the performance of algorithms. In order to facilitate this fair and transparent comparison, the evolutionary computation Benchmarking Repository (EvoCoBR) by M. Roberts et al. (2006) has been designed and put into operation in a beta version and trial phase. The aim is to create a central Web-based repository for storing detailed benchmark problem descriptions. However, with EvoCoBR we want to go one step further and archive, along with the problem description, a list of references to previously achieved results and the best result so far. This enables researchers to more easily see how their results compare to results in the literature. EvoCoBR will also invite researchers to submit and archive the programs that produced those results. EvoCcBR's architecture enables the entire evolutionary computation community to contribute and own the Web-based archive. Its contents will be submitted by researchers and practitioners, and openly accessible by all. In other words, the EvoCoBR design defines the framework that needs to be filled by the evolutionary computation community for the evolutionary computation community
Although more than 30 years old, the field of evolutionary computation has experienced a significant growth of interest and activity in the past few years. This has resulted in fresh perspectives and a flurry of new r...
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Although more than 30 years old, the field of evolutionary computation has experienced a significant growth of interest and activity in the past few years. This has resulted in fresh perspectives and a flurry of new results in both theory and applications. This paper will attempt to summarize the current state of the field and suggest promising new directions.
A deterministic switching regressions estimator is evaluated using an evolutionary method based on genetic algorithms. Distinctive aspects of the method include (1) a combination of simple and random chromosomal cross...
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A deterministic switching regressions estimator is evaluated using an evolutionary method based on genetic algorithms. Distinctive aspects of the method include (1) a combination of simple and random chromosomal crossover and (2) extension of the principle of natural selection to internal parameterization. The evolutionary computation duplicates, significantly faster, the results of an existing enumerative method in samples small enough to permit enumeration. It also provides the ability to calculate the estimator in much larger sample sizes than is possible with the enumerative approach. An example problem from the United States gasoline market is given.
Intrusion detection systems protect our infrastructures by monitoring for signs of intrusions. However, intrusion detection systems are themselves susceptible to vulnerabilities, which the attackers take advantage of ...
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Intrusion detection systems protect our infrastructures by monitoring for signs of intrusions. However, intrusion detection systems are themselves susceptible to vulnerabilities, which the attackers take advantage of to evade detection. In particular, we focus on evasion attacks in which the attacker aims to generate a stealthy attack that eliminates or minimizes the likelihood of detection. Attackers achieve stealth by mimicking normal behaviour while achieving the attack goals, hence bypassing the detector. Previous work focused on generating evasion attacks using the internal knowledge of the detectors, hence adopting a 'white-box' access to the detector. On the other hand, we adopt a 'black-box' approach and propose an evolutionary attacker based on Genetic Programming. The access of our 'black-box' approach is limited to the feedback of the detector such as anomaly rates and delays. We compare our 'black-box' approach with various 'white-box' approaches to investigate its effectiveness. In doing so, the impact of anomalies from the break-in stage of the attacks and the delays based on locality frame counts are also discussed. This is particularly important if the performance comparison is to reflect the real capabilities of detectors.
In a previous work, we proposed an evolutionary computation system designed to solve group decision making multiobjective problems for human groups, which is equivalent to obtaining consensus solutions to multiobjecti...
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In a previous work, we proposed an evolutionary computation system designed to solve group decision making multiobjective problems for human groups, which is equivalent to obtaining consensus solutions to multiobjective optimization problems. Multi-human-agentbased evolutionary computation (Mhab-EC) is a primary component of the system, used to obtain converged solutions for multiobjective optimization problems. The other main component is a mechanism that allows owners of simulated human agents to review simulation results thus far and adjust their agents accordingly between successive simulation runs of the Mhab-EC. However, in our previous study, we simply conducted simulations to demonstrate that a single run yielded converged solutions. Consensus solutions were assumed to be obtained through iterations of the Mhab-EC run and agent adjustment. Therefore, in this study, we conducted simulations of the entire system, including the agent adjustment mechanism. For this purpose, we implemented a simple model of agent adjustment by owners to facilitate solution convergence. Simulation results showed that the system indeed yielded converged solutions, which are considered to indicate consensus.
This study examines the psychological research that focuses on road safety in Smart Cities as proposed by the Vulnerable Road Users (VRUs) sphere. It takes into account qualities such as VRUs' personal information...
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ISBN:
(纸本)9783319926391;9783319926384
This study examines the psychological research that focuses on road safety in Smart Cities as proposed by the Vulnerable Road Users (VRUs) sphere. It takes into account qualities such as VRUs' personal information, their habits, environmental measurements and things data. With the goal of seeing VRUs as active and proactive actors with differentiated feelings and behaviours, we are committed to integrating the social factors that characterize each VRU into our social machinery. As a result, we will focus on the development of a VRU Social Machine to assess VRUs' behaviour in order to improve road safety. The formal background will be to use Logic Programming to define its architecture based on a Deep Learning approach to Knowledge Representation and Reasoning, complemented with an evolutionary approach to Computing.
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