A distance based clustering method for arbitrary shaped clusters in large datasets

Clustering has been widely used in different fields of science, technology, social science, etc. Naturally, clusters are in arbitrary (non-convex) shapes in a dataset. One important class of clustering is distance based method. However, distance based clustering methods usually find clusters of convex shapes. Classical single-link is a distance based clustering method, which can find arbitrary shaped clusters. It scans dataset multiple times and has time requirement of O(n²), where n is the size of the dataset. This is potentially a severe problem for a large dataset. In this paper, we propose a distance based clustering method, l-SL to find arbitrary shaped clusters in a large dataset. In this method, first leaders clustering method is applied to a dataset to derive a set of leaders; subsequently single-link method (with distance stopping criteria) is applied to the leaders set to obtain final clustering. The l-SL method produces a flat clustering. It is considerably faster than the single-link method applied to dataset directly. Clustering result of the l-SL may deviate nominally from final clustering of the single-link method (distance stopping criteria) applied to dataset directly. To compensate deviation of the l-SL, an improvement method is also proposed. Experiments are conducted with standard real world and synthetic datasets. Experimental results show the effectiveness of the proposed clustering methods for large datasets.     

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.     

Significance of duration modification for speaker verification under mismatch speech tempo condition

This work explores the scope of duration modification for speaker verification (SV) under mismatch speech tempo condition. The SV performance is found to depend on speaking rate of a speaker. The mismatch in the speaking rate can degrade the performance of a system and is crucial from the perspective of deployable systems. In this work, an analysis of SV performance is carried out by varying the speaking rate of train and test speech. Based on the studies, a framework is proposed to compensate the mismatch in speech tempo. The framework changes the duration of test speech in terms of speaking rate according to the derived mismatch factor between train and test speech. This in turn matches speech tempo of the test speech to that of the claimed speaker model. The proposed approach is found to have significant impact on SV performance while comparing the performance under mismatch conditions. A set of practical data having mismatch in speech tempo is also used to cross-validate the framework.     

Multi-style speaker recognition database in practical conditions

This work describes the process of collection and organization of a multi-style database for speaker recognition. The multi-style database organization is based on three different categories of speaker recognition: voice-password, text-dependent and text-independent framework. Three Indian institutes collaborated for the collection of the database at respective sites. The database is collected over an online telephone network that is deployed for speech based student attendance system. This enables the collection of data for a longer period from different speakers having session variabilities, which is useful for speaker verification (SV) studies in practical scenario. The database contains data of 923 speakers for the three different modes of SV and hence termed as multi-style speaker recognition database. This database is useful for session variability, multi-style speaker recognition and short utterance based SV studies. Initial results are reported over the database for the three different modes of SV. A copy of the database can be obtained by contacting the authors.     

Automatic syllabification of speech signal using short time energy and vowel onset points

This paper describes a language independent method for automatic syllabification of speech signal. This method utilizes the valleys in short time energy (STE) contour and location of vowel onset points (VOP) for marking the syllable boundaries. In the proposed method, automatic syllabification is performed in three steps. First, long silence/pause regions are marked with the help of speech/non-speech detection. Then VOPs are located from the Hilbert Envelope of LP residual. The existence of more than one VOP in a continuous speech region (identified using speech/non-speech detection in the first step) is an indication of syllable boundaries within the region. Location with minimum energy in the STE contour between two consecutive VOP is identified as the syllable boundary. Since automatic VOP detection algorithm fails to detect some of the VOPs, certain syllable boundaries will be missed. Therefore, at the third step, additional syllable boundaries are detected from STE contour by fixing a valley threshold which is equal to the mean value of STE corresponding to each speech region between two consecutive syllable boundaries. This method is evaluated for 50 sentences each in read, extempore and conversational mode speech of Malayalam and Bengali languages. Overall accuracy of 80% is obtained with ± 50 ms tolerance with reference to manually marked syllable boundaries for this database. Method also shows good accuracy in case of TIMIT and NTIMIT data without tuning of thresholds and other parameters. This method is useful for applications that do not require exact syllable boundaries, rather a meaningful separation of syllables. Application of this technique for prosody based emotion recognition is illustrated using Emo-DB German emotional database.     

Incremental maximum margin clustering

This paper proposes incremental maximum margin clustering in which one data point at a time is examined to decide which cluster the new data point belongs. The proposed method adopts the off-line iterative maximum margin clustering method’s alternating optimization algorithm. Accurate online support vector regression is employed in the alternating optimization. To avoid premature convergence, a sequence of decremental unlearning and incremental learning steps is performed. The proposed method is experimentally argued to (i) be scalable and competitive on training time front when compared with iterative maximum margin clustering and (ii) achieve competitive cluster quality compared to the off-line counterpart.     

Experimental Comparison Research on Active Vibration Control for Flexible Piezoelectric Manipulator Using Fuzzy Controller

Space manipulators are flexible structures. Vibration problem will be unavoidable due to motion or external disturbance excitation. Model based control methods will not maintain the required accuracy because of the existence of nonlinear factors and parameter uncertainties. To solve these problems, fuzzy logic control laws with different membership function groups are adopted to suppress vibrations of a flexible smart manipulator using collocated piezoelectric sensor/actuator pair. Also, dual-mode controllers combining fuzzy logic and proportional integral control are designed, for suppressing the lower amplitude vibration near the equilibrium point significantly. Experimental comparison research is conducted, using fuzzy control algorithms and the dual-mode controllers with different membership functions. The experimental results show that the adopted fuzzy control algorithms can substantially suppress the larger amplitude vibration; and the dual-mode controllers can also damp out the lower amplitude vibration significantly. The experimental results demonstrate that the proposed fuzzy controllers and dual-mode controllers can suppress vibration effectively, and the optimal placement is feasible.     

Integral criteria for optimal tuning of PI/PID controllers for integrating processes

Tuning formulas for PI/PID controllers for integrating processes are presented in this paper. The controller parameters are obtained by minimizing various integral performance index. Bacterial Foraging strategy, a new entrant to the family of evolutionary algorithms is used for minimization to avoid the local minima in the optimization procedure. A setpoint filter is used to reduce the large overshoot, and a significant improvement in control performance is obtained when compared to recently reported methods. Simulation results for an assumed perturbation in the plant delay are also given to illustrate the robustness of the proposed controller design method. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Higher-order convergence with fractional-step method for singularly perturbed 2D parabolic convection–diffusion problems on Shishkin mesh

In this article, we propose a second-order uniformly convergent numerical method for a singularly perturbed 2D parabolic convection–diffusion initial–boundary-value problem. First, we use a fractional-step method to discretize the time derivative of the continuous problem on uniform mesh in the temporal direction, which gives a set of two 1D problems. Then, we use the classical finite difference scheme to discretize those 1D problems on a special mesh, which results almost first-order convergence, i.e., $O(N^{?1+\beta }lnN+\Delta t)$. To enhance the order of convergence to $O(N^{?2+\beta }{ln}^{2}N+\Delta t^2)$, we use the Richardson extrapolation technique. In support of the theoretical results, numerical experiments are performed by employing the proposed technique.     

A system for semantic segmentation of TV news broadcast videos

TV news channels present rich and complete experience of various events through audio-visual content. This makes television news an influential medium to affect masses and thus persuaded various social scientists and regulators to monitor and analyze the content of broadcast videos. An organized archive of newscast is a prerequisite for any such analysis. Creating such archive requires segmentation of continuous news videos into suitable logical units. Based on the application, these logical units may be one of channel content obtained after advertisement removal, different shows, news stories or video shots. In this work, we propose an end to end system with software architecture for segmenting the TV broadcast videos at all these four granularities. The videos are segmented into shots. Video shots are used as basic unit for all further processing. Video shots are first subjected to advertisement detection and removal to obtain the non-commercial channel content. This channel content is further processed to identify various program boundaries. We propose to identify three types of shows based on the presentation format viz. news bulletins, interviews and debates. News bulletins so obtained are processed further to obtain news stories. We propose a modular and scalable framework and software architecture for the broadcast segmentation system for deployment on a computation cluster. This involves scheduler based recording module and broadcast segmentation module. We have presented the detailed software architecture for individual modules, automation of entire processing pipeline along with resource and database management systems. We have implemented and verified the software architecture by deploying the proposed system on a cluster of nine desktops and one workstation. The deployed system was used for round the clock processing of three Indian English news channels.