Fuzzy logic controllers suffer from the curse of dimensionality problem since the number of rules in a standard fuzzy system increases exponentially with the number of input and output variables. One way to overcome t...
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ISBN:
(纸本)9781538647769
Fuzzy logic controllers suffer from the curse of dimensionality problem since the number of rules in a standard fuzzy system increases exponentially with the number of input and output variables. One way to overcome this problem is to decide the utilized linguistic variables, partitioning the linguistic variable and the rule base together, in order to only evolve very simple, but still accurate models. In order to accomplish these, we propose a novel 2-tuple linguistic fuzzy model and linguistic fuzzy partitioning technique. Our propose 2-tuple linguistic fuzzy logic controller tackles the curse of dimensionality problem in high-dimensional regression problems when the number of input and output variables becomes high. The linguistic information can be expressed by means of 2-tuples (S,α), where S is the linguistic term and α is the numeric value between [-0.5, 0.5]. Here, tackle 2-tuple fuzzy linguistic model capable for making processes of computing with words (CW) without loss of information. In order to validate our proposed method, we solve the high dimensional regression problem: length and maintenance cost estimation of low and medium voltage line respectively. We present extensive simulation results in order to demonstrate the simplicity and superiority of the proposed technique while comparing with other methods.
As is known, the reactive power of the signal can be determined by the phase shift of one of the signals to pi/2 radians and the subsequent application of the active power measurement algorithm. The time delay units, ...
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In order to overcome the weakness of the traditional topic detection clustering strategy and realize hot topic auto-discovery, we re-examined density-based clustering algorithms, and then put forward a sub-cluster rel...
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In order to overcome the weakness of the traditional topic detection clustering strategy and realize hot topic auto-discovery, we re-examined density-based clustering algorithms, and then put forward a sub-cluster relation-based and multi-resolution density clustering algorithm(SRBMRClustering) which considers both adjacency information of sub-clusters and relative density concept. And in the meanwhile, in order to reduce the computational complexity, we proposed a Web structure-based text feature weight calculation method and a concept-feature extraction method and used feature-based news text vector representation method to improve the textual representation and shrink the dimension of feature space. Finally, we used Chinese news corpus of June-July 2012 to verify our algorithm. The experimental results show that the algorithm's performance and clustering quality are improved to a notable extent.
Find the main driver in the train cab and keep tracking him is the primary step for driver activity analysis in real *** long-term robust human tracking is still a challenging task due to many practical *** template-b...
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Find the main driver in the train cab and keep tracking him is the primary step for driver activity analysis in real *** long-term robust human tracking is still a challenging task due to many practical *** template-based tracking algorithms of templates often fail when encountering occlusion,pose variation and motion *** this paper,we propose a real-time algorithm based on correlation filters for long-term human *** restore the false model caused by occlusion,we construct a conditional target re-detection *** adopted a high-confidence model update strategy to ensure the correct detection and avoid model drift *** experimental results indicate that the proposed algorithm is robust and accurate to variations in pose and heavy occlusion while runs at speed in real-time.
Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a ...
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Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator. However, standard imitation learning methods assume that the agent receives examples of observation-action tuples that could be provided, for instance, to a supervised learning algorithm. This stands in contrast to how humans and animals imitate: we observe another person performing some behavior and then figure out which actions will realize that behavior, compensating for changes in viewpoint, surroundings, object positions and types, and other factors. We term this kind of imitation learning "imitation-from-observation," and propose an imitation learning method based on video prediction with context translation and deep reinforcement learning. This lifts the assumption in imitation learning that the demonstration should consist of observations in the same environment configuration, and enables a variety of interesting applications, including learning robotic skills that involve tool use simply by observing videos of human tool use. Our experimental results show the effectiveness of our approach in learning a wide range of real-world robotic tasks modeled after common household chores from videos of a human demonstrator, including sweeping, ladling almonds, pushing objects as well as a number of tasks in simulation.
Virtual electric traction machine (VETM) is a power electronic converter based system capable of emulating behavior of an electric motor. The widely used VETM systems in industrial drives testing environment, makes us...
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ISBN:
(纸本)9781538693179;9781538693162
Virtual electric traction machine (VETM) is a power electronic converter based system capable of emulating behavior of an electric motor. The widely used VETM systems in industrial drives testing environment, makes use of traditional two-stage AC-DC-AC converter. However, this multistage conversion requires DC link energy storage device as well as individual control algorithm. Hence, to simplify control and reduce number of converter stages, this paper proposes a three-phase AC-DC converter topology suitable for four-quadrant operation of VETM system. Unlike twelve switches in traditional AC-DC-AC converter, the proposed topology uses ten power semiconductor switches reducing switching losses. In this paper, a detailed operation of proposed converter is presented in along with simulation and experimental results for a 2 kW PMSM motor emulator.
Mixture generalized gamma distribution is a combination of two distributions -- Generalized gamma distribution and length biased generalized gamma distribution. This distribution is presented by Suksaengrakcharoen and...
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ISBN:
(纸本)9781450363396
Mixture generalized gamma distribution is a combination of two distributions -- Generalized gamma distribution and length biased generalized gamma distribution. This distribution is presented by Suksaengrakcharoen and Bodhisuwan in 2014. The fmdings showed that probability density function (pdf) had fairly complexities, so it made problems in estimating parameters. The problem occurred in parameter estimation was that we were unable to calculate estimators in the form of critical expression. Thus, we will use numerical estimation to find the estimators. In this study, we presented a new method of the parameter estimation by using the expectation maximization algorithm (EM), the conjugate gradient method, and the quasi -Newton method. The data was generated by acceptance -rejection method which is used for estimating alpha, beta, lambda and p . lambda is the scale parameter, p is the weight parameter, alpha and beta are the shape parameters. We will use Monte Carlo technique to fmd the estimator's performance. Determining the size of sample equals 30, 100 and the simulation were repeated 20 times in each case. We evaluated the effectiveness of the estimators which was introduced by considering values of the mean squared errors and the bias. The findings revealed that the EM -algorithm had proximity to the actual values determined. Also, the maximum likelihood estimators via the conjugate gradient and the quasi Newton method are less precision than the maximum likelihood estimators via the EM -algorithm.
Pub/sub systems form the underlying framework for many distributed applications including large social networking applications. In this paper, we consider the optimization of the end-to-end latency of a pub/sub system...
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ISBN:
(纸本)9781538651407
Pub/sub systems form the underlying framework for many distributed applications including large social networking applications. In this paper, we consider the optimization of the end-to-end latency of a pub/sub system in which the publisher, the broker, and the subscriber are in different administrative domains. While general pub/sub systems provide reliability of message delivery, good end-to-end latency in a multi-domain environment requires that the pub/sub system adapts to workload changes and bottlenecks in the different sub-systems. This study is motivated by two applications. First, a pub/sub based Simple Lookup Service (sLS) that is used in perfSONAR to provide information about network performance in Research and Education (R&E) networks. Second, the pub/sub system that is used to distribute alerts generated in the data pipeline in the Zwicky Transient Factory (ZTF). In this multi-domain pub/sub performance study, we consider a publisher with a multi-threaded architecture that uses batching to coalesce messages over some variable polling period. We propose a control algorithm that auto-tunes the batch-processing parameters namely, the batch size and the polling interval, to the input message load and to broker-side congestion. Using a detailed simulation model, we demonstrate the performance of the control algorithm for different scenarios. We then study the performance using a real-trace obtained from the Simple Lookup Service (sLS). We show that the proposed algorithm quickly adapts to rapid changes in the workload and yields lower mean end-to-end delay performance when compared with delays in the current deployment.
Stochastic rule-based models serve as natural and compact representations for biochemical reactions. The Gillespie stochastic simulationalgorithm and its variants are employed to predict the behavior of biochemical s...
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ISBN:
(纸本)9781538685211
Stochastic rule-based models serve as natural and compact representations for biochemical reactions. The Gillespie stochastic simulationalgorithm and its variants are employed to predict the behavior of biochemical systems modeled by such stochastic rule-based models. However, it is often not feasible to create a complete stochastic rule-based model from first principles. Instead, our knowledge of the biochemical system is used to obtain the set of chemical reactions of the stochastic rule-based model. The lack of knowledge about the rate constants of biochemical reactions is readily modeled by using unknown parameters in stochastic rule-based models.A primary challenge in the use of such a parameterized stochastic rule-based model for predicting the behavior of a biological system is the determination of the parameters of the model from multiple experimental observations. However, the focus of many earlier efforts has been on discovering parameter values of a parameterized stochastic biological model from a single specification written down in a computer-readable language such as probabilistic temporal *** practice, a biological model must satisfy multiple experimental observations made on the biological system being modeled. Hence, it is important to synthesize a single set of parameter values that cause a parameterized stochastic model to satisfy multiple probabilistic temporal logic specifications *** present a new approach for estimating parameter values of stochastic biochemical models so that a single parameterized model satisfies all the given probabilistic temporal logic behavioral specifications simultaneously. Our approach first computes a quantitative metric describing how well a stochastic biochemical model satisfies a given specification. It then utilizes a multiple hypothesis testing based statistical model checking method to simultaneously validate the model against multiple probabilistic temporal logic behavioral specifications.W
Medical image segmentation is a basic step in medical image analysis, especially for medical image sequences such as CT sequences. Automated segmentation of different objects in the medical image sequences is of great...
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Medical image segmentation is a basic step in medical image analysis, especially for medical image sequences such as CT sequences. Automated segmentation of different objects in the medical image sequences is of great significance to the 3D reconstruction of medical images. A novel image recognition method which can be implemented in automated medical image segmentation is introduced. In contrast with other algorithm, HTM(hierarchical temporal memory) is a network using a spatio-temporary hierarchy that works as our neocortex. The algorithm refereed in this paper consists of three main steps. Firstly, a four level hierarchical structure is established. Secondly, create frames by animating gray images to train the HTM network. During the learning phase, the nodes in HTM network build its representations spatial pooler and temporal pooler for inputs. Thirdly, test with dataset to get the inference result for classification. The results show that the proposed method can recognize the "middle slice" for different given objects when process the medical image sequences.
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