Showing papers in "Engineering Applications of Artificial Intelligence in 2011"

TL;DR: The primary objective of this paper is to serve as a glossary for interested researchers to have an overall picture on the current time series data mining development and identify their potential research direction to further investigation.

TL;DR: This paper is devoted to the presentation of a new linear and nonlinear filter modeling based on a gravitational search algorithm (GSA) where unknown filter parameters are considered as a vector to be optimized.

TL;DR: The numerical results demonstrate that constrained blended BBO outperforms SGA and performs similarly to SPSO 07 for constrained single-objective optimization problems.

TL;DR: An efficient hybrid evolutionary optimization algorithm based on combining Modify Imperialist Competitive Algorithm and K-means, which is called K-MICA, for optimum clustering N objects into K clusters is presented.

TL;DR: The test results show that the feature vector extraction method and neural networks can be used successfully for isolated word recognition and this system is flexible and open for future extension.

TL;DR: The findings affirmed the robustness, fast convergence and proficiency of the proposed MBF over other existing techniques, and the Otsu based optimization method converges quickly as compared with Kapur's method.

TL;DR: Artificial bee colony (ABC) algorithm is employed to search out the optimal combinations of different operating parameters for three widely used NTM processes, i.e. electrochemical machining, electrochemical discharge machining and electrochemical micromachining processes, which prove the applicability and suitability of the ABC algorithm in enhancing the performance measures of the considered N TM processes.

TL;DR: Three chaotic differential evolution methods are proposed based on the Tent equation to solve DED problem with valve-point effects and, compared with DE and those other methods reported in literatures recently, the proposed CDE methods are capable of obtaining better quality solutions with higher efficiency.

TL;DR: A hybrid genetic algorithm is proposed to solve mixed model assembly line balancing problem of type I (MMALBP-I) by sequentially hybridizing the three well known heuristics, Kilbridge & Wester Heuristic, Phase-I of Moodie & Young Method, and Ranked Positional Weight Technique with genetic algorithm.

TL;DR: The application of the proposed methodology to predict bus travel time over four bus route sections in Melbourne, Australia, leads to quantitative decomposition of total prediction uncertainty into the component sources.

TL;DR: A fuzzy TOPSIS based methodology along with a mechanism for determination of fuzzy linguistic value of each attribute is proposed in this article to address the imprecision of suppliers or decision makers in formulating the preference value of various attributes in MCDM.

TL;DR: A novel hybrid method coupling genetic programming and orthogonal least squares, called GP/OLS, was employed to derive new ground-motion prediction equations (GMPEs), which are remarkably simple and straightforward and can be used for the pre-design purposes.

TL;DR: Quantitative comparisons of the image segmentation system with the other methods on real brain MR images using Tanimoto similarity index demonstrate that the system shows better segmentation performance for the gray matter while it gives average results for white matter.

TL;DR: The EADDE provides better results compared to classical DE and other methods recently reported in the literature as demonstrated by simulation results.

TL;DR: On testing 28,800 fault cases with varying fault resistance, fault inception angle, fault distance, load angle, percentage compensation level and source impedance, the performance of the proposedWT-ELM technique is found to be quite promising and the results indicate that the proposed method is robust to wide variation in system and operating conditions.

TL;DR: In the proposed algorithm, several strategies are employed to avoid falling into local optimum, improve the diversity and achieve better solution, and good performances of MSCPSO in solving the complex multimodal functions are demonstrated.

TL;DR: New models are developed, based on evolutionary polynomial regression (EPR), for assessment of liquefaction potential and lateral spreading, and are compared to those obtained from neural network and linear regression based techniques.

TL;DR: The present paper mainly focuses on the impact on forecast accuracy of various parameters, related with the discrete wavelet transform, such as both the wavelet order and the decomposition level, and the topology of the neural networks used.

TL;DR: Sensitivity analysis suggests the importance of depth of flow and pier width in predicting the scour depth when using support vector regression based modeling approach, and Comparisons of results with four predictive equations suggest an improved performance by supportvector regression.

TL;DR: The proposed multi-agent signal control was found to produce a significant improvement in the traffic conditions of the road network reducing the total travel time experienced by vehicles simulated under dual and multiple peak traffic scenarios.

TL;DR: To speed up the matching process and to control the misclassification error, a combined approach called the adaptive asymmetrical support vector machines (AASVMs) are applied in order to improve the overall generalization performance.

TL;DR: The research findings show that the proposed model has a high accuracy, and the resulting outcomes are significant both theoretically and practically.

TL;DR: A structure damage diagnosis method combining the wavelet packet decomposition, multi-sensor feature fusion theory and neural network pattern classification was presented and a much more precise and reliable diagnosis information is obtained and the diagnosis accuracy is improved.

TL;DR: This paper compares two artificial intelligence based methods, artificial neural networks (ANN) and support vector machines (SVM), utilizing a reduced set of weather parameters, to predict forest fire occurrence by reducing the number of monitored features, and eliminating the need for weather prediction mechanisms.

TL;DR: An improved PSO (IPSO) algorithm is used to enhance global search ability and convergence speed of algorithm and when the change in fitness value is smaller than a predefined value, the searching process is switched to SQP to accelerate the search process and find an accurate solution.

TL;DR: This contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience.

TL;DR: A wearable inertial sensor system for the acquisition of gait features provides a quantitative assessment of mobility state of the impaired subject and may be helpful to the clinician in the identification of pathological gait impairments, prescribe treatment, and assess the improvements in response to therapeutic intervention.

TL;DR: This paper presents an application of MAPS, an agent framework for wireless sensor networks based on the Java-programmable Sun SPOT sensor platform, for the development of a real-time WBSN-based system for human activity monitoring.

TL;DR: The results of these experiments demonstrate the feasibility and benefits of the proposed control approach compared to traditional PID control strategies and the good performance of the MGA-based inverse N ARX fuzzy model in the proposed hybrid inverse NARX fuzzy-PID position control of the PAM robot arm.

TL;DR: This contribution presents a ConceptBase formal specification of PRONTO that focuses on the structural hierarchy of products, and includes mechanisms to infer structural information from the explicit knowledge represented at each of the AH levels: Family, VariantSet and Product.