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Heidelberg, Germany

Tang L.-L.,HITSZ | Pan J.-S.,HITSZ | Luo H.,ZJU | Li J.,HIT
IEICE Transactions on Communications | Year: 2012

A novel watermarked MDC system based on the SFQ algorithm and the sub-sampling method is proposed in this paper. Subsampling algorithm is applied onto the transformed image to introduce some redundancy between different channels. Secret information is embedded into the preprocessed sub-images. Good performance of the new system to defense the noise and the compression attacks is shown in the experimental results. Copyright © 2012 The Institute of Electronics, Information and Communication Engineers. Source

Pal A.K.,HIT | Mudi R.K.,Jadavpur University | De Maity R.R.,DBCREC
Advances in Intelligent Systems and Computing | Year: 2013

A non-fuzzy self-tuning scheme is proposed for Fuzzy PD controller in this paper. To eliminate the design complexity, output scaling factor (SF) of the proposed fuzzy controller is updated according to the process trend by a gain modification factor, which is determined by the normalized change of error of the system and its number of fuzzy partitions. The proposed non-fuzzy self-tuning fuzzy PD controller (NFST-FPDC) is demonstrated on a laboratory scale overhead crane. Moving a suspended load along a pre-specified path is not an easy task when strict specifications on the swing angle and transfer time need to be satisfied. In this study, twin NFST-FPDC are designed to control the trolley position of the crane and swing angle of the load. The proposed non-fuzzy gain tuning scheme guarantees a fast and precise load transfer and the swing suppression during load movement, despite of model uncertainties. © 2013 Springer-Verlag. Source

Sarkar S.,Jadavpur University | Sekh M.,HIT | Mitra S.,Jadavpur University | Bhattacharyya B.,Jadavpur University
Precision Engineering | Year: 2011

Wire bending due to gap force is a major cause of imprecision in WEDM applications. To achieve higher precision and accuracy the knowledge of gap force and wire lag is extremely essential. In the present research, an in depth study on wire lag phenomenon has been carried out. A novel method to measure gap force intensity and wire lag under any given machining condition has been proposed by developing an analytical model. Experiments were carried out to verify the proposed model. Beside this, the impact of wire deflection on profile accuracy during cutting cylindrical job has been investigated. Based upon the developed analytical model an effective method has been proposed to eliminate this inaccuracy using wire lag compensation technique. The research finding will lead to better understanding of the gap force phenomena and will promote significant development in the domain of high precision WEDM. © 2011 Elsevier Inc. All rights reserved. Source

He Z.,Harbin Institute of Technology | He Z.,Sun Yat Sen University | Li J.,HIT
Expert Systems with Applications | Year: 2014

Kernel-based learning strategies have recently emerged as powerful tools for hyperspectral classification. However, designing optimal kernels is still a challenging issue that needs to be further investigated. In this paper, we propose a multiple data-dependent kernel (MDK) for classification of HSI. Core ideas of the MDK are twofold: (1) optimizing the combination of multiple basic kernels in merit of centered kernel alignment (CKA), which can evaluate the degree of agreement between a kernel and a learning task; (2) optimizing the coefficients of data-dependent kernel (DK) by virtue of Fisher's discriminant analysis (FDA), which can measure the between-class and within-class separability of the data simultaneously. Furthermore, we apply the proposed MDK to two state-of-the-art classifiers, i.e. support vector machine (SVM) and sparse representation classifier (SRC). Experimental results conducted on three benchmark HSIs with different spectral and spatial resolutions validate the feasibility of the proposed methods. © 2014 Elsevier Ltd. All rights reserved. Source

Liu M.,HIT | Chen L.,BNUZ | Liu B.,HIT | Wang X.,HIT
IJCAI International Joint Conference on Artificial Intelligence | Year: 2015

There are lots of texts appearing in the web every day. This fact enables the amount of texts in the web to explode. Therefore, how to deal with large-scale text collection becomes more and more important. Clustering is a generally acceptable solution for text organization. Via its unsupervised characteristic, users can easily dig the useful information that they desired. However, traditional clustering algorithms can only deal with small-scale text collection. When it enlarges, they lose their performances. The main reason attributes to the high-dimensional vectors generated from texts. Therefore, to cluster texts in large amount, this paper proposes a novel clustering algorithm, where only the features that can represent cluster are preserved in cluster's vector. In this algorithm, clustering process is separated into two parts. In one part, feature's weight is fine-tuned to make cluster partition meet an optimization function. In the other part, features are reordered and only the useful features that can represent cluster are kept in cluster's vector. Experimental results demonstrate that our algorithm obtains high performance on both small-scale and large-scale text collections. Source

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