Current Issue : April - June Volume : 2016 Issue Number : 2 Articles : 5 Articles
Soft computing is an important computational paradigm, and it provides the capability of flexible information\nprocessing to solve real world problems. Agricultural data classification is one of the important applications of computing\ntechnologies in agriculture, and it has become a hot topic because of the enormous growth of agricultural data available.\nSupport vector machine is a powerful soft computing technique and it realizes the idea of structural risk minimization principle\nto find a partition hyperplane that can satisfy the class requirement. Rough set theory is another famous soft computing\ntechnique to deal with vague and uncertain data. Ensemble learning is an effective method to learn multiple learners and\ncombine their decisions for achieving much higher prediction accuracy. In this study, the support vector machine, rough set\nand ensemble learning were incorporated to construct a hybrid soft computing approach to classify the agricultural data. An\nexperimental evaluation of different methods was conducted on public agricultural datasets. The experimental results\nindicated that the proposed algorithm improves the performance of classification effectively....
Global optimization for mining complexes aims to generate a production schedule for the various mines\nand processing streams that maximizes the economic value of the enterprise as a whole. Aside from the\nlarge scale of the optimization models, one of the major challenges associated with optimizing mining\ncomplexes is related to the blending and non-linear geo-metallurgical interactions in the processing\nstreams as materials are transformed from bulk material to refined products. This work proposes a\nnew two-stage stochastic global optimization model for the production scheduling of open pit mining\ncomplexes with uncertainty. Three combinations of metaheuristics, including simulated annealing, particle\nswarm optimization and differential evolution, are tested to assess the performance of the solver.\nExperimental results for a copper-gold mining complex demonstrate that the optimizer is capable of\ngenerating designs that reduce the risk of not meeting production targets, have 6.6% higher expected\nnet present value than the deterministic-equivalent design and 22.6% higher net present value than an\nindustry-standard deterministic mine planning software....
In this paper, we present an efficient transmission scheme for multiple-input multiple-output (MIMO) systems, i.e.,\ncoded spatial modulation (SM) systems with soft-decision aided detector. To exploit the powerful error correction of\nchannel coding, the key challenge of coded SM systems is on designing a reliable but low-complexity soft-output\ndetector. Fighting against this problem, we first propose two soft-output detection algorithms by exploiting the\nfeatures of M-phase-shift keying (PSK) and M-quadrature amplitude modulation (QAM) constellations, namely,\nPSK-based soft-output detector (PBSD) and QAM-based soft-output detector (QBSD). Furthermore, to further enhance\nthe performance of the two algorithms, we propose another two soft-output detection algorithms taking into\naccount of counterpart maximum-likelihood (ML) estimate, namely, improved PSK-based soft-output detector (IPBSD)\nand improved QAM-based soft-output detector (IQBSD). The findings of this paper demonstrate that: (1) The\ncomputational complexity of PBSD and QBSD algorithms are much lower than that of Max-Log-LLR algorithm at the\nexpense of error performance. (2) Both the IPBSD and IQBSD algorithms achieve the same performance as\nMax-Log-LLR algorithm with reduced computational complexity. In addition, a comprehensive performance and\ncomputational complexity comparison between the proposed algorithms and the Max-Log-LLR algorithm is provided\nto verify our proposed low-complexity soft-output detectors....
Modern films, games and virtual reality applications are dependent on convincing computer graphics. Highly complex\nmodels are a requirement for the successful delivery of many scenes and environments. While workflows such as\nrendering, compositing and animation have been streamlined to accommodate increasing demands, modelling complex\nmodels is still a laborious task. This paper introduces the computational benefits of an Interactive Genetic Algorithm\n(IGA) to computer graphics modelling while compensating the effects of user fatigue, a common issue with Interactive\nEvolutionary Computation. An intelligent agent is used in conjunction with an IGA that offers the potential to reduce the\neffects of user fatigue by learning from the choices made by the human designer and directing the search accordingly. This\nworkflow accelerates the layout and distribution of basic elements to form complex models. It captures the designer�s\nintent through interaction, and encourages playful discovery....
Most Software Reliability Growth Models (SRGMs) based on the Nonhomogeneous Poisson Process (NHPP) generally assume\nperfect or imperfect debugging. However, environmental factors introduce great uncertainty for SRGMs in the development and\ntesting phase.We propose a novel NHPP model based on partial differential equation (PDE), to quantify the uncertainties associated\nwith perfect or imperfect debugging process. We represent the environmental uncertainties collectively as a noise of arbitrary\ncorrelation. Under the new stochastic framework, one could compute the full statistical information of the debugging process, for\nexample, its probabilistic density function (PDF). Through a number of comparisons with historical data and existing methods,\nsuch as the classic NHPP model, the proposed model exhibits a closer fitting to observation. In addition to conventional focus\non the mean value of fault detection, the newly derived full statistical information could further help software developers make\ndecisions on system maintenance and risk assessment....
Loading....