Sharif Digital Repository

This paper is concerned with the design and simulation of an intelligent stop and go cruise control system in an automated vehicle. In this paper Active learning method is used to extract driver's behavior and to derive control rules for cruise control system. First, there is a brief introduction to ALM (Active Learning Method) and its specifications. Then a one-line space for driving is assumed and its parameters are extracted. By using IDS, the processing engine of ALM, effective parameters in controller are derived. A simulation program is written to produce learning samples and also to evaluate controller's parameters. To apply controller's output, appropriate acceleration of the... 

In this paper the Unsupervised Active Learning Method (UALM), a novel clustering method based on the Active Learning Method (ALM) is introduced. ALM is an adaptive recursive fuzzy learning algorithm inspired by some behavioral features of human brain functionality. UALM is a density-based clustering algorithm that relies on discovering densely connected components of data, where it can find clusters of arbitrary shapes. This approach is a noise-robust clustering method. The algorithm first blurs the data points as ink drop patterns, then summarizes the effects of all data points, and finally puts a threshold on the resulting pattern. It uses the connected-component algorithm for finding... 

This paper presents a new and simple algorithm for partitioning the input domain for implementation of Active Learning Method (ALM) algorithm. ALM is a pattern-based algorithm for soft computing which uses the Ink Drop Spread (IDS) algorithm as its main engine for feature extraction. In this paper a simple algorithm is introduced with a few computation cost. In order to evaluate the performance of the proposed algorithm, it is applied to two applications, system modeling and pattern recognition. Simulation results show the effectiveness of our algorithm in specifying the appropriate points for dividing the inputs domains  

This paper introduces an innovative adaptive controller based on the actor-critic method. The proposed approach employs the ink drop spread (IDS) method as its main engine. The IDS method is a new trend in softcomputing approaches that is a universal fuzzy modeling technique and has been also used as a supervised controller. Its process is very similar to the processing system of the human brain. The proposed actor-critic method uses an IDS structure as an actor and a 2-dimensional plane, representing control variable states, as a critic that estimates the lifetime goodness of each state. This method is fast, simple, and away from mathematical complexity. The proposed method uses the... 

In this study Active Learning Method (ALM) as a novel fuzzy modeling approach is compared with optimized Support Vector Machine (SVM) using simple Genetic Algorithm (GA), as a well known datadriven model for long term simulation of daily streamflow in Karoon River. The daily discharge data from 1991 to 1996 and from 1996 to 1999 were utilized for training and testing of the models, respectively. Values of the Nash-Sutcliffe, Bias, R 2, MPAE and PTVE of ALM model with 16 fuzzy rules were 0.81, 5.5 m 3 s -1, 0.81, 12.9%, and 1.9%, respectively. Following the same order of parameters, these criteria for optimized SVM model were 0.8, -10.7 m 3 s -1, 0.81, 7.3%, and -3.6%, respectively. The... 

In this paper a novel fuzzy approach exploiting Active Learning Method is employed in order to estimate the immeasurable states required to control a non-observable double inverted pendulum. Active Learning Method (ALM) is a fuzzy modeling method which exploits Ink Drop Spread (IDS) as its main engine. IDS is a universal fuzzy modeling technique which is very similar to the way human brain processes different phenomena. The ALM system is trained by the data obtained from Linear Quadratic Regulator (LQR) controller. LQR uses an optimal control approach which under certain conditions guarantees robustness. Instead of an expert's knowledge, the LQR controller output is used as a priori... 

This paper presents a high performance direct torque control (DTC) theme for the induction motor (IM). To solve those problems associated with conventional DTC, such as flux and torque ripple, variable switching frequency, inaccuracy in motor model and other parts of system. The Active Learning Method (ALM) is implemented on the DTC. In the Active Learning Method for information modeling, a method known as Ink Drop Spread (IDS) is used. The simulation results of DTC system based on ALM and the comparison of motor performance under the proposed control system with respect to those obtained under conventional DTC confirms its effectiveness and accuracy  

In this paper, we propose a novel density-based fuzzy clustering algorithm based on Active Learning Method (ALM), which is a methodology of soft computing inspired by some hypotheses claiming that human brain interprets information in pattern-like images rather than numerical quantities. The proposed clustering algorithm, Fuzzy Unsupervised Active Learning Method (FUALM), is performed in two main phases. First, each data point spreads in the feature space just like an ink drop that spreads on a sheet of paper. As a result of this process, densely connected ink patterns are formed that represent clusters. In the second phase, a fuzzifying process is applied in order to summarize the effects... 

In this paper, we propose a novel density-based fuzzy clustering algorithm based on Active Learning Method (ALM), which is a methodology of soft computing inspired by some hypotheses claiming that human brain interprets information in pattern-like images rather than numerical quantities. The proposed clustering algorithm, Fuzzy Unsupervised Active Learning Method (FUALM), is performed in two main phases. First, each data point spreads in the feature space just like an ink drop that spreads on a sheet of paper. As a result of this process, densely connected ink patterns are formed that represent clusters. In the second phase, a fuzzifying process is applied in order to summarize the effects... 

Zamin artificial life model is designed to be a general purpose environment for researches on evolution of learning methods, living strategies and complex behaviors and is used in several studies thus far. As a main target for Zamin's design has been its expandability and ease of problem definition, a new agent based structure for this artificial world is introduced in this paper, which is believed to be much easier to use and extend. In this new model, all control and world running processes are done by agents. Therefore, any change in world processes does not require recoding the main engine and can be done just by altering the behavior of one or some agents. To have an easier interface... 

In this work we present a sparse dictionary learning method, specifically tuned to solve the tracking problem. Recently, sparse representation has drawn much attention because of its genuineness and strong mathematical background. In this paper we present an online method for dictionary learning which is desirable for problems such as tracking. Online learning methods are preferable because the whole data are not available at the current time. The presented method tries to use the advantages of the generative and discriminative models to achieve better performance. The experimental results show our method can overcome many tracking challenges  

One of the main elements of a spoken dialogue system is the Spoken Language Understanding (SLU) unit. Hidden Vector State (HVS) is one of the popular statistical methods applied to the SLU component. Extended Hidden Vector State (EHVS) is an enhanced version of the HVS. Although both parsers need only abstract data annotation, it is quiet time consuming and difficult to label the data. Thus, we present a novel active learning method for the EHVS parser to reduce the human labeling effort. The active learner makes use of pattern classification to select the informative data based on four different uncertainty measures. Experiments are done on a Persian dataset, the University Information... 

Mobility is one of the most challenging issues in mobile Ad-Hoc networks which has a significant impact on performance of network protocols. To cope with this issue, the protocol designers should be able to analyze the movement of mobile nodes in a particular wireless network. In this paper, a new framework called Mobility Analyzer has been introduced for analysis and recognition of mobility traces. At first, the Mobility Analyzer acquires some mobility traces collected by GPS or generated with mobility simulators; then it calculates some mobility metrics which represent the movement behavior of the mobile nodes; finally it attempts to classify mobility traces into particular mobility models... 

In this investigation, the idea of Visual Working Memory (VWM) mechanism modeling based on versatile fuzzy method; Active Learning method, is presented. Visual information process; retrieving and learning rely on the use of Ink Drop Spread (IDS) and Center of Gravity (COG) as spatial density convergence operators. IDS modeling is characterized by processing that uses intuitive pattern information instead of complex formulas, and it is capable of stable and fast convergence. Furthermore, because it approves that distortion in retrieving irrelative data is adaptive to avoid storing lots of repetitive external information in daily visualization. Subsequently, this distortion is analyzed via two... 

Recently, the problem of integrating side information about classes has emerged in the learning settings like zero-shot learning. Although using multiple sources of information about the input space has been investigated in the last decade and many multi-view and multi-modal learning methods have already been introduced, the attribute learning for classes (output space) is a new problem that has been attended in the last few years. In this paper, we propose an attribute learning method that can use different sources of descriptions for classes to find new attributes that are more proper to be used as class signatures. Experimental results show that the learned attributes by the proposed... 

Estimation of 3D human pose from a single image is a challenging task because of ambiguities in projection from 3D space to the 2D image plane. A new two-stage deep convolutional neural network-based method is proposed for regressing the distance and angular difference matrices among body joints. Using the angular difference between body joints in addition to the distance between them in articulated objects such as human body can better model the structure of the shapes and increases the modelling capability of the learning method. Experimental results on HumanEva I and Human3.6M datasets show that the proposed method has substantial improvement in the mean per joint position error measure... 

Automatic image annotation (AIA) is an image retrieval mechanism to extract relative semantic tags from visual content. So far, the improvement of accuracy in newly developed such methods have been about 1 or 2% in the F1-score and the architectures seem to have room for improvement. Therefore, the authors designed a more detailed architecture for AIA and suggested new algorithms for its main parts. The proposed architecture has three main parts: feature extraction, learning, and annotation. They designed a novel learning method using machine learning and probability bases. In the annotation part, they suggest a novel method that gains the maximum benefit from the learning part. The... 

This paper describes a genetic-fuzzy system adapted to find efficient partitions on data domains for IDS (Ink Drop Spread). IDS is the engine of Active Learning Method (ALM), a methodology of soft computing. IDS extracts useful information from a system subjected to modeling. Proposed method, called GIDS (Genetic IDS), uses genetic algorithm which optimizes the parameters of membership functions that represent the partitions on data planes. Obtained Results showed that using genetic algorithm to find the partitions has better accuracy than the previous generic IDS methods. © 2008 IEEE  

After the emergence of modern technologies in recent years, learning methods have attained a new form. Using game consoles as educational tools is a new idea which is gained attracted by some researchers. This paper proposes a new method for improving English lab classes using Sony PSP (PlayStation Portable) handheld game consoles especially for Iranian schools. © 2008 IEEE