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51.
52.
Autonomous use of legged robots in unstructured, outdoor settings requires dynamically dexterous behaviors to achieve sufficient
speed and agility without overly complex and fragile mechanics and actuation. Among such behaviors is the relatively under-studied
pronking (aka. stotting), a dynamic gait in which all legs are used in synchrony, usually resulting in relatively slow speeds but
long flight phases and large jumping heights. Instantiations of this gait for robotic systems have been mostly limited to
open-loop strategies, suffering from severe pitch instability for underactuated designs due to the lack of active feedback.
However, both the kinematic simplicity of this gait and its dynamic nature suggest that the Spring-Loaded Inverted Pendulum
model (SLIP) would be a good basis for the implementation of a more robust feedback controller for pronking. In this paper,
we describe how template-based control, a controller structure based on the embedding of a simple dynamical “template” within a more complex “anchor” system, can
be used to achieve very stable pronking for a planar, underactuated hexapod robot. In this context, high-level control of
the gait is regulated through speed and height commands to the SLIP template, while the embedding controller ensures the stability
of the remaining degrees of freedom. We use simulation studies to show that unlike existing open-loop alternatives, the resulting
control structure provides explicit gait control authority and significant robustness against sensor and actuator noise. 相似文献
53.
In this paper, a novel classification rule extraction algorithm which has been recently proposed by authors is employed to
determine the causes of quality defects in a fabric production facility in terms of predetermined parameters like machine
type, warp type etc. The proposed rule extraction algorithm works on the trained artificial neural networks in order to discover
the hidden information which is available in the form of connection weights in them. The proposed algorithm is mainly based
on a swarm intelligence metaheuristic which is known as Touring Ant Colony Optimization (TACO). The algorithm has a hierarchical
structure with two levels. In the first level, a multilayer perceptron type neural network is trained and its weights are
extracted. After obtaining the weights, in the second level, the TACO-based algorithm is applied to extract classification
rules. The main purpose of the present work is to determine and analyze the most effective parameters on the quality defects
in fabric production. The parameters and their levels which give the best quality results are tried to be discovered and evaluated
by making use of the proposed algorithm. It is also aimed to compare the accuracy of proposed algorithm with several other
rule-based algorithms in order to present its competitiveness. 相似文献
54.
This paper targets at the problem of automatic semantic indexing of news videos by presenting a video annotation and retrieval system which is able to perform automatic semantic annotation of news video archives and provide access to the archives via these annotations. The presented system relies on the video texts as the information source and exploits several information extraction techniques on these texts to arrive at representative semantic information regarding the underlying videos. These techniques include named entity recognition, person entity extraction, coreference resolution, and semantic event extraction. Apart from the information extraction components, the proposed system also encompasses modules for news story segmentation, text extraction, and video retrieval along with a news video database to make it a full-fledged system to be employed in practical settings. The proposed system is a generic one employing a wide range of techniques to automate the semantic video indexing process and to bridge the semantic gap between what can be automatically extracted from videos and what people perceive as the video semantics. Based on the proposed system, a novel automatic semantic annotation and retrieval system is built for Turkish and evaluated on a broadcast news video collection, providing evidence for its feasibility and convenience for news videos with a satisfactory overall performance. 相似文献
55.
Tarık Çakar 《Journal of Intelligent Manufacturing》2011,22(4):481-490
A neuro-dominance rule (NDR) for single machine total weighted tardiness problem with unequal release date is presented by
the author. To obtain the NDR, backpropagation artificial neural network (BPANN) has been trained using 10,000 data and also
tested using 10,000 another data. Inputs of the trained BPANN are starting date of the first job (t), processing times (pi and pj), due dates (di and dj), weights of the jobs (wi and wj) and ri and rj release dates of the jobs. Output of the BPANN is a decision of which job should precede. Training set and test set have
been obtained using Adjusted Pairwise Interchange method. The proposed NDR provides a sufficient condition for local optimality.
It has been proved that if any sequence violates the NDR then violating jobs are switched according to the total weighted
tardiness criterion. The proposed NDR is compared to a number of competing heuristics (ATC, COVERT, EDD, SPT, LPT, WDD, WSPT,
WPD, CR, FCFS) and meta heuristics (simulated annealing and genetic algorithms) for a set of randomly generated problems.
The problem sizes have been taken as 50, 70, 100. NDR is applied 270,000 randomly generated problems. Computational results
indicate that the NDR dominates the heuristics and meta heuristics in all runs. Therefore, the NDR can improve the upper and
lower bounding schemes. 相似文献
56.
Abdulnasir Yildiz Mehmet Akın Mustafa Poyraz 《Expert systems with applications》2011,38(10):12880-12890
Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder. The traditional diagnosis methods of the disorder are cumbersome and expensive. The ability to automatically identify OSA from electrocardiogram (ECG) recordings is important for clinical diagnosis and treatment. In this study, we proposed an expert system based on discrete wavelet transform (DWT), fast-Fourier transform (FFT) and least squares support vector machine (LS-SVM) for the automatic recognition of patients with OSA from nocturnal ECG recordings. Thirty ECG recordings collected from normal subjects and subjects with sleep apnea, each of approximately 8 h in duration, were used throughout the study. The proposed OSA recognition system comprises three stages. In the first stage, an algorithm based on DWT was used to analyze ECG recordings for the detection of heart rate variability (HRV) and ECG-derived respiration (EDR) changes. In the second stage, an FFT based power spectral density (PSD) method was used for feature extraction from HRV and EDR changes. Then, a hill-climbing feature selection algorithm was used to identify the best features that improve classification performance. In the third stage, the obtained features were used as input patterns of the LS-SVM classifier. Using the cross-validation method, the accuracy of the developed system was found to be 100% for using a subset of selected combination of HRV and EDR features. The results confirmed that the proposed expert system has potential for recognition of patients with suspected OSA by using ECG recordings. 相似文献
57.
Hub location problems deal with finding the location of hub facilities and with the allocation of demand nodes to these located hub facilities. In this paper, we study the single allocation hub covering problem over incomplete hub networks and propose an integer programming formulation to this end. The aim of our model is to find the location of hubs, the hub links to be established between the located hubs, and the allocation of non-hub nodes to the located hub nodes such that the travel time between any origin–destination pair is within a given time bound. We present an efficient heuristic based on tabu search and test the performance of our heuristic on the CAB data set and on the Turkish network. 相似文献
58.
Nilgün Fığlalı Celal Özkale Orhan Engin Alpaslan Fığlalı 《Computers & Industrial Engineering》2009,56(2):538-559
In recent years, one of the most important and promising research fields has been metaheuristics to find optimal or near-optimal solutions for NP-hard combinatorial optimization problems. Improving the quality of the solution or the solution time is basic research area on metaheuristics. Modifications of the existing ones or creation of hybrid approaches are the focus of these efforts. Another area of improving the solution quality of metaheuristics is finding the optimal combination of algorithm control parameters. This is usually done by design of experiments or one-at-a-time approach in genetic algorithms, simulated annealing and similar metaheuristics. We observe that, in studies which use Ant Colonies Optimization (ACO) as an optimization technique; the levels of control parameters are determined by some non-systematic initial experiments and the interactions of the parameters are not studied yet.In this study, the parameters of Ant System have been investigated on different sized and randomly generated job-shop scheduling problems by using design of experiments. The effects and interactions of the parameters have been interpreted with the outputs of the experiments. Referring to the statistical analysis it is observed that none of the interactions between the Ant System parameters has a significant effect on makespan value. A specific fractional experimental design is suggested instead of the full factorial design. Depending on the findings from the benchmark problems it will be a reliable approach to use the suggested design for saving time and effort in experiments without sacrificing the solution quality. 相似文献
59.
This paper presents a new hybrid optimization approach based on immune algorithm and hill climbing local search algorithm. The purpose of the present research is to develop a new optimization approach for solving design and manufacturing optimization problems. This research is the first application of immune algorithm to the optimization of machining parameters in the literature. In order to evaluate the proposed optimization approach, single objective test problem, multi-objective I-beam and machine-tool optimization problems taken from the literature are solved. Finally, the hybrid approach is applied to a case study for milling operations to show its effectiveness in machining operations. The results of the hybrid approach for the case study are compared with those of genetic algorithm, the feasible direction method and handbook recommendation. 相似文献
60.