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Reducing fiber diameter inconsistencies has been the focus of much research in the fiber manufacturing industry. However, the structure of the fiber drawing machine (FDM) leads to time delays in traditional diameter control methods, thereby making it difficult to achieve satisfactory results. Moreover, traditional methods apply only a single adjustment to control the diameter precision, i.e., drawing velocity, and ignore the crucial impact of furnace temperature on diameter. To address these issues and improve the control precision, we propose a novel hybrid diameter control model that uses an artificial neural network (ANN) as the main framework and integrates bidirectional gated recurrent units (BiGRUs) and a new mechanism known as selective weight optimization (SWO). Specifically, due to the irreplaceable role of diameter prediction in the control task, we adopt BiGRU to predict fiber wire diameter and utilize the results as a supplemental data input to the control policy network. Due to the high complexity of the input data, the ANN-based network is introduced as the policy network to analyze the inherent relevance of the data and provide two separate control strategies, i.e., drawing velocity adjustment and furnace temperature adjustment. Additionally, a selective weight optimization (SWO) mechanism that combines these two strategies and selectively optimizes the weights is proposed to further improve diameter control precision. The effectiveness of the BiGRU-ANN-SWO is extensively evaluated using four various real-world experiments. Results indicate that the BiGRU-ANN-SWO achieves considerably more precise and robust control of fiber diameter compared with existing approaches. [ABSTRACT FROM AUTHOR]

A formation tracking control problem for uncertainty traffic cone robots (TCRs) is considered. In this paper, the TCRs can not only perform formation behaviors but also track placement targets under environmental constraints (i.e., the position constraints in the y -direction.). To deal with the environmental constraints, a differential homeomorphism transformation is selected to eliminate the constraints. Moreover, the uncertainty of the TCRs is bounded, including matching portions and mismatching portions. For the matching uncertainty, a series of adaptive parameters are introduced to estimate the real-time information of the uncertainty. The mismatching uncertainty is tackled by a geometric decomposition, which is orthogonal to the range space of the formation. At the same time, a fuzzy uncertainty optimization design problem is presented. The global solution to the resulting optimization problem always exists and is unique. Using adaptive robust control, TCRs' performance is deterministically and fuzzy-optimized. The system performance is demonstrated by a range of numerical simulations and real experiments with three TCRs. • An adaptive robust formation tracking control design method is proposed. • The artificial potential field function has the property of "repulsion at near and attraction at far". • The uncertainty of the TCR system is divided into a matching part and a mismatching part. • A constrained optimization problem based on uncertain fuzzy information is presented. [ABSTRACT FROM AUTHOR]

The portfolio optimization (PO) model with semi-mean absolute deviation (SMAD) risk measure has been commonly applied to construct optimal portfolios due to the ease of solving the corresponding linear programming model. We propose a robust PO model with SMAD that considers the uncertainty associated with asset expected returns. This uncertainty is dealt by adopting a data-driven approach that captures the uncertain asset returns in a convex uncertainty set through support vector clustering. The proposed model involves solving a quadratic programming problem to identify support vectors and a robust linear PO model. The ability of the proposed technique to control the conservatism and the computational ease associated with it makes it a practical approach to yield robust optimal portfolios. The effectiveness of the model is demonstrated by constructing optimal portfolios with the constituents of four global market indices, namely Dow Jones Industrial Average (USA), DAX 30 (Germany), Nifty 50 (India), and EURO STOXX 50 (Europe). The out-of-sample statistics generated from the robust portfolios are compared with the optimal portfolios obtained from its nominal counterpart, naive 1 / n strategy, and other robust technique available in the literature. We find that the proposed model consistently performs well in most data sets over several performance measures like average returns, risk measured by standard deviation, value at risk, conditional value at risk and various reward-risk ratios. Comparative analysis of these models in different market phases of EURO STOXX 50 demonstrates the effectiveness of the developed robust technique, especially during the bearish phase. [ABSTRACT FROM AUTHOR]

Highlights • We propose novel DEA efficiency scores based on a robust viewpoint. • The scores give a universal tool for comparing DMU's. • We show many remarkable properties of the scores and its interpretation. • We present extensions for uncertain data, additional DMU's and inputs/outputs. Abstract We propose a novel DEA method for computing efficiency scores. The method is based on a robust optimization viewpoint: the higher scores for those decision making units (DMU's) that remain efficient even for larger simultaneous and independent variations of all data and vice versa. Moreover, the value of each score itself gives the distance to inefficiency (or the distance to efficiency for inefficient units), so it provides a decision maker with an additional useful information on how stable the DMU is. The efficiency scores can be computed by solving generalized linear fractional programming problems, but we also present a tight linear programming approximation that preserves the order of rankings. We show many remarkable properties of our approach: It preserves the order of rankings compared to the classical approach, and it is unit invariant. It is naturally normalized, so it can be used for computing universal scores of DMU's of unrelated models. It gives scores not only for inefficient, but also for efficient decision making units. It can also be easily extended to generalized or alternative models, for instance to deal with interval data. We present several examples confirming the desirable properties of the method. [ABSTRACT FROM AUTHOR]

Highlights • Acoustic event spectrograms are segmented using left singular vector. • Segmented acoustic event spectrograms are characterized. • Robustness of the proposed approach is evaluated in noisy conditions. • Recognition performance is compared with other methods. Abstract The traditional frame-based speech features such as Mel-frequency cepstral coefficients (MFCCs) are specifically developed for speech/speaker recognition tasks. Speech is different from acoustic events, when one considers its phonetic structure. Hence, frame-based speech features may not be suitable for Acoustic Event Classification (AEC). In this paper, a novel method is proposed for the extraction of robust acoustic event specific features from the spectrogram using a left singular vector for AEC. It consists of two main stages: segmentation and characterization of acoustic event spectrograms. In the first stage, symmetric Laplacian matrix of an acoustic event spectrogram is decomposed into singular values and vectors. Then, reliable region (spectral shape) of an acoustic from the spectrogram is segmented using a left singular vector. The selected prominent values of a left singular vector using the proposed threshold, automatically segment the reliable region of an acoustic event from the spectrogram. In the second stage, the segmented region of the spectrogram is used as a feature vector for AEC. Characteristics of values of singular vector belonging to reliable (event) and unreliable (non-event) regions of the spectrogram are determined. To evaluate the proposed approach, different categories of 'home' acoustic events are considered from the Freiburg-106 dataset. The results show that the significantly improved performance of acoustic event segmentation and classification. A singular vector effectively segments the reliable region of the acoustic event from spectrogram for Support Vector Machine (SVM) based AEC system. The proposed AEC system is robust to noise and achieves higher recognition rate in clean and noisy conditions compared to the traditional speech feature based systems. [ABSTRACT FROM AUTHOR]

Highlights • Constraint handling techniques (CHTs) are classified according to the search biases. • CHTs are discussed on five state-of-the-art evolutionary algorithms respectively. • The dynamic fuzzy search biases of individuals are defined by fuzzy theory. Abstract Constraint handling technique (CHT) is a critical task for the evolutionary algorithms (EAs) on solving the constrained optimization problems (COPs). In the CHTs development, the definition of the search bias in the search process of an EA is one of the important issues. Based on the different search bias definitions, this study divides the CHTs into two categories: deterministic and non-deterministic techniques. The deterministic techniques define the search bias with an artificial rule. These techniques generally perform well for several scenarios. However, they may have poor performance for some others. This is mainly due to the difficulty to establish an accurate mapping between the complex search scenarios and the uncertain search biases. In contrast with the deterministic techniques, the non-deterministic techniques apply stochastic based methods to escape this mapping establishment which will improve the robustness of the algorithm. However, the probability-based blind random selection can affect negatively on the search efficiency of the algorithm. To compensate the deficiencies of these two categories, this paper proposes a fuzzy search bias guided CHT, namely FSB. The search bias in the FSB is guided by a membership function. It is used to meet the fuzzy knowledge that a solution with large constraint violation biases the search toward solutions with smaller constraint violation and a solution with small constraint violation biases the search toward solutions with better objective function value. The experimental results show that FSB is more robust and more efficient than the other compared techniques. [ABSTRACT FROM AUTHOR]

Highlights • A new group decision making methodology is proposed to solve outranking problems. • ELECTRE I is extended with VIKOR to provide complete ranking under intuitionistic fuzzy environment. • The methodology employs entropy method to identify the weights of criteria and decision makers. • Weighted distance approach is utilized to determine the weights of concordance sets objectively. • Different solutions and rankings are proposed according to different decision making strategies. Abstract This study proposes a new group decision making (GDM) methodology to solve outranking problems efficiently under uncertainty integrating multi criteria decision making (MCDM) and intuitionistic fuzzy set (IFS) theory. To this aim, ELECTRE I, an outranking based MCDM approach is extended with VIKOR method, an ideal solution based approach, under intuitionistic fuzzy environment. The methodology utilizes pairwise comparison and outranking relation concepts of ELECTRE methods to construct different concordance and discordance sets in order to reflect preferences of decision makers effectively. By the help of similarities between concordance and discordance characteristics of ELECTRE and maximum group utility and minimum individual regret characteristics of VIKOR, the methodology enables the intuitionistic fuzzy ELECTRE to overcome its weakness in ranking process and presents complete ranking. In addition, this study employs entropy method to identify the weights of criteria and decision makers, and weighted distance approach to determine the weights of different concordance sets. Utilizing these methods leads to specifying these weights objectively. The applicability of the proposed approach is explored by an illustrative example on supplier selection problem by explaining the method step by step. Furthermore, four computational experiments are conducted in order to demonstrate its applicability to the other areas and the results are compared. [ABSTRACT FROM AUTHOR]

Highlights • The proposed method works on face recognition as well as facial expression recognition system. • The method recognizes noisy face images also. • Log-ICA II performs better than Log-ICA I. Abstract By reducing the gaussianity, Independent Component Analysis (ICA) behaves robustly in segregating individual signals of non-skewed characteristic from a mixed composite signal. In this article, we present a next-generation variant of ICA, especially applicable in the skewed composite signal scenario, applying the Logarithmic transformation on basic ICA, named as Log-ICA. This approach is capable of decreasing overlapping probability densities of the composite signal, which, in turn, extracts more independent components because of reduced gaussianity. Here also we use two different architectures Log-ICA I and Log-ICA II corresponding to two variants of ICA architecture (ICA I and ICA II). We justify the effectiveness of the proposed technique on five separate benchmark face datasets using five classifiers. Out of five face datasets, two datasets contain both visible and thermal face images. Experimental results show that Log-ICA II performs better than Log-ICA I and two variants of ICA for original face images and noise-induced face images. [ABSTRACT FROM AUTHOR]

Highlights • Multimodal biometric system based on Iris, finger vein and fingerprint. • Performance optimization using Backtracking Search Optimization (BSA). • Optimal score level fusion model resolves conflicts among classifier scores. • The results exhibits low equal error rate and high accuracy. Abstract Multimodal biometric systems fuse information from multiple modalities to overcome limitations of individual classifiers. Score level fusion of multiple classifiers can effectively combine information from different modalities. However, most of the multimodal biometric systems are impaired by conflicting classifier scores under dynamic environment, which results in degradation in system's robustness and reliability. To address this, we propose a multimodal biometric system based on an optimal score level fusion model. The key idea of this work is to optimally integrate three complementary biometric traits namely iris, finger vein and fingerprint. For this, individual classifier performance is optimized using evolutionary Backtracking Search Optimization Algorithm (BSA). In addition, conflicting beliefs from individual classifiers are resolved using proportional conflict redistribution rules (PCR-6) to obtain a concurrent solution. The system exhibits optimal behaviour under dynamic environment through boosting or suppression of concurrent classifiers and resolving conflicts among discordant classifiers. The proposed biometric system is evaluated over chimeric multimodal datasets created from benchmark images. On an average, we achieve an accuracy of 98.43% and an EER of 1.57%. The proposed biometric system not only outperforms state-of-the-art techniques but also shows directions towards development of an expert multimodal biometric system. [ABSTRACT FROM AUTHOR]

In this paper, a secure blind image watermarking scheme is proposed to improve efficiency of hybrid Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) based schemes. The main contribution of this research is using two-level singular value decomposition in hybrid DWT + SVD watermarking schemes results to high improvement in both imperceptibility and robustness. Also, security is ensured in this scheme by proposing a two-level authentication system for watermark extraction in which not only the false positive problem is eliminated, when the attacks are very severe, the false negative effect is also detected and prevented. In the proposed scheme, the cover image is initially transformed to DWT and the high frequency sub bands are selected. Subsequently, High (HH) sub band is divided into 8 × 8 non overlapping blocks and the SVD transform is applied to each of them. In SVD transform, the highest energy compaction is in the first row and the first column of the matrix of singular values. These singular values for each block will be collected in a separate matrix and then the second SVD transform will be exerted on the produced matrix. The singular values of the watermark image after exerting one level DWT, is embedded into the singular values of this matrix. Experimental result shows when the scheme is subjected to ten most common types of signal processing and geometric attacks, using the second level of SVD makes it highly robust and imperceptible while it is independent from type of image. So, the proposed scheme is tested and certified for both medical and normal images. This scheme is compared to a range of hybrid schemes using DWT and one level SVD, and experimental results show high improvements in imperceptibility with the average of 46.6950 db and robustness more than 99% and proven efficiency in security. [ABSTRACT FROM AUTHOR]

Automatic language identification (LID) in practical environments is gaining a lot of scientific attention due to rapid developments in multilingual speech processing applications. When an LID is operated in noisy environments a degradation in the performance can be observed and it can be majorly attributed to mismatch between the training and operating environments. This work is aimed towards developing an LID system that can robustly operate in clean and noisy environments. Traditionally, to reduce the mismatch between training and operating environments, noise is synthetically induced to the training corpus and these models are termed as multi-SNR models. In this work, various curriculum learning strategies are explored to train multi-SNR models, such that the trained models have better generalization in performance over varying background environments. I-vector, Deep neural networks (DNN) and DNN With Attention (DNN-WA) architectures are used in this work for developing LID systems. Experimental verification of the proposed approach is carried out using IIIT-H Indian database and AP17-OLR database. The performance of LID system is tested at different signal-to-noise ratio (SNR) levels using white and vehicular noises from NOISEX dataset. In comparison to multi-SNR models, the LID systems trained with curriculum learning have performed better in terms of equal error rate (EER) and generalization in EER across varying background environments. The degradation in the performance of LID systems due to environmental noise has been effectively reduced by training multi-SNR models using curriculum learning. [ABSTRACT FROM AUTHOR]

Semi-Supervised Growing Self Organizing Map (SSGSOM) is one of the best methods for online classification with partial labeled data. Many parameters can affect the performance of this method. The structure of GSOM network, activation degree and learning approach are the most important factors in SSGSOM. In this paper, a comprehensive robust mathematical formulation of the problem is proposed and then half quadratic (HQ) is used to solve it. Furthermore, an adaptive method is proposed to adjust activation degree optimally to improve the performance of SSGSOM. The results are reported on a variety of synthetic and UCI datasets and in the noisy conditions, which show superiority and robustness of the proposed method compared with the state of the art approaches. [ABSTRACT FROM AUTHOR]

When investing in the financial market, determining a trading signal that can fulfill the financial performance demands of an investor is a difficult task and a very popular research topic in the financial investment area. This paper presents an approach combining the principal component analysis (PCA) with the NeuroEvolution of Augmenting Topologies (NEAT) to generate a trading signal capable of achieving high returns and daily profits with low associated risk. The proposed approach is tested with real daily data from four financial markets of different sectors and with very different characteristics. Three different fitness functions are considered in the NEAT algorithm and the most robust results are produced by a fitness function that measures the mean daily profit obtained by the generated trading signal. The results achieved show that this approach outperforms the Buy and Hold (B&H) strategy in the markets tested (in the S&P 500 index this system achieves a rate of return of 18.89% while the B&H achieves 15.71% and in the Brent Crude futures contract this system achieves a rate of return of 37.91% while the B&H achieves −9.94%). Furthermore, it’s concluded that the PCA method is vital for the good performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Teaching-Learning-Based Optimisation (TLBO) is one of the more recently developed metaheuristics and has been successfully applied to solve various optimisation problems. However, TLBO has not been academically reported for solving the robust machine layout design (MLD) problems with dynamic demand. Considering internal logistics activities, shortening material flow distance within a manufacturing area can lead to efficient productivity and a decrease in related costs. The robust machine layout is concerned with determining the efficient arrangement of machines/facilities located on a manufacturing shop floor under future demand fluctuation. A robust designed layout is essential for a company to maintain a high productivity rate through multiple time-periods of demand uncertainty with minimum effects related to the re-layout time and cost, manufacturing disruption, and the movement of monument machines. The objectives of this paper were to: i) describe the development of a computer aided layout designing tool for minimising the total material flow distance under dynamic demand scenario, ii) investigate the appropriate setting of TLBO parameters, and iii) propose four TLBO modifications for improving its performance. The modified TLBOs were inspired by multiple teachers with two types of classes and two approaches to teacher selection. The numerical experiments were designed and conducted using eleven MLD benchmarking datasets. Statistical analyses on the experimental results showed a superior performance for the proposed modifications. [ABSTRACT FROM AUTHOR]

In the past few decades, credit scoring has become an increasing concern for financial institutions and is currently a popular topic of research. This study aims to generate a novel ensemble model for credit scoring, to obtain superior performance and high robustness, adapting to different imbalance ratio datasets. First, according to the credit scoring data characteristics, the proposed model extends the BalanceCascade approach to generate adjustable balanced subsets based on the imbalance ratios of training data. Further, it reduces the negative effect of imbalanced data and improves the comprehensive performance of the predictive model. Second, the proposed model adopts two kinds of tree-based classifiers, random forest and extreme gradient boosting, as the base classifiers for a three-stage ensemble model. This includes the use of stacking to generate predicted results of the former layer as new explanatory features in the latter layer, and the use of a particle swarm optimization algorithm for parameters optimization of the base classifiers. Finally, the results indicate that the average performance of the proposed model is superior to other comparative algorithms as reflected in most evaluation measures for different datasets. It demonstrates that the proposed model is robust and represents a positive development in credit scoring. [ABSTRACT FROM AUTHOR]

Given a classification task, an approach to improve accuracy relies on the use of abstaining classifiers. These classifiers are trained to reject observations for which predicted values are not reliable enough: these rejected observations belong to an abstaining area in the feature space. Two equivalent methods exist to theoretically compute the optimal abstaining area for a given classification problem. The first one is based on the posterior probability computed by the model and the other is based on the derivative of the ROC function of the model. Although the second method has proved to give the best results, in small-sample settings such as the one found in omics data, the estimation of posterior probabilities and derivative of ROC curve are both lacking of precision leading to far from optimal abstaining areas. As a consequence none of the two methods bring the expected improvements in accuracy. We propose five alternative algorithms to compute the abstaining area adapted to small-sample problems. The idea of these algorithms is to compute an accurate and robust estimation of the ROC curve and its derivatives. These estimation are mainly based on the assumption that the distribution of the output of the classifier for each class is normal or mixture of normal distributions. These distributions are estimated by a kernel density estimator or Bayesian semiparametric estimator. Another method works on the approximation of the convex hull of the ROC curve. Once the derivative of the ROC curve are estimated, the optimal abstaining area can be directly computed. The performance of our algorithms are directly related to their capacity to compute an accurate estimation of the ROC curve. A sensitivity analysis of our methods to the dataset size and rejection cost has been done on a set of experiments. We show that our methods improve the performances of the abstaining classifiers on several real datasets and for different learning algorithms. [ABSTRACT FROM AUTHOR]

Machine learning classification algorithms are widely applied to different malware analysis problems because of their proven abilities to learn from examples and perform relatively well with little human input. Use cases include the labelling of malicious samples according to families during triage of suspected malware. However, automated algorithms are vulnerable to attacks. An attacker could carefully manipulate the sample to force the algorithm to produce a particular output. In this paper we discuss one such attack on Android malware classifiers. We design and implement a prototype tool, called IagoDroid, that takes as input a malware sample and a target family, and modifies the sample to cause it to be classified as belonging to this family while preserving its original semantics. Our technique relies on a search process that generates variants of the original sample without modifying their semantics. We tested IagoDroid against RevealDroid, a recent, open source, Android malware classifier based on a variety of static features. IagoDroid successfully forces misclassification for 28 of the 29 representative malware families present in the DREBIN dataset. Remarkably, it does so by modifying just a single feature of the original malware. On average, it finds the first evasive sample in the first search iteration, and converges to a 100% evasive population within 4 iterations. Finally, we introduce RevealDroid*, a more robust classifier that implements several techniques proposed in other adversarial learning domains. Our experiments suggest that RevealDroid* can correctly detect up to 99% of the variants generated by IagoDroid. [ABSTRACT FROM AUTHOR]

This paper proposes a robust structure identification method (RSIM) based on incremental partitioning learning. RSIM starts with an open region (initial domain) that covers all input samples. The initial region starts with one fuzzy rule without fuzzy terms and then evolves through incremental partitioning learning, which creates many subregions for system error minimization. The three major contributions of the proposed RSIM are as follows: It locates sufficient splitting points provided through a robust partitioning technique, determines the optimum trade-off between accuracy and complexity through a novel partition-selection technique, minimizes global error through global least square optimization. These contributions offer many remarkable advantages. First, RSIM provides a solution for the curse of dimensionality. Second, RSIM can also be applied to low-dimensional problems. Third, RSIM seeks to produce few rules with low number of conditions to improve system readability. Fourth, RSIM minimizes the number of fired rules. Therefore, RSIM can achieve low-level complexity systems. Three low-dimension and six high-dimension and real-life benchmarks are used to evaluate the performance of RSIM with state-of-the art methods. Although RSIM has high interpretability, the results prove that RSIM exhibits greater accuracy than other existing methods. [ABSTRACT FROM AUTHOR]

Many intelligent systems, such as assistive robots, augmented reality trainers or unmanned vehicles, need to know their physical location in the environment in order to fulfill their task. While relying exclusively on natural landmarks for that task is the preferred option, their use is somewhat limited because the proposed methods are complex, require high computational power, and are not reliable in all environments. On the other hand, artificial landmarks can be placed in order to alleviate these problems. In particular, square fiducial markers are one of the most popular tools for camera pose estimation due to their high performance and precision. However, the state-of-the-art methods still perform poorly under difficult image conditions, such as camera defocus, motion blur, small scale or non-uniform lighting. This paper proposes a method to robustly detect this type of landmarks under challenging image conditions present in realistic scenarios. To do so, we re-define the marker identification problem as a classification one based on state-of-the-art machine learning techniques. Second, we propose a procedure to create a training dataset of synthetically generated images affected by several challenging transformations. Third, we show that, in this problem, a classifier can be trained using exclusively synthetic data, performing well in real and challenging conditions. Different types of classifiers have been tested to prove the validity of our proposal (namely, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN) and Support Vector Machine (SVM)), and statistical analyses have been performed in order to determine the best approach for our problem. Finally, the obtained classifiers have been compared to the ArUco and AprilTags fiducial marker systems in challenging video sequences. The results obtained show that the proposed method performs significantly better than previous approaches, making the use of this technology more reliable in a wider range of realistic scenarios such as outdoor scenes or fast moving cameras. [ABSTRACT FROM AUTHOR]

This paper introduces a novel proposal to discover the best associative classification rules through studying the influence of the attributes used in robust catalogues. Notice that a catalogue is defined as a dataset free of duplicate records. Moreover, a robust catalogue is obtained when incomplete records and those with uncertainty are eliminated from a catalogue. Therefore, a robust catalogue is a collection of association rules with 100% confidence and unitary support. In this paper we demonstrate that robust catalogues contain the same association rules as the datasets from which they were obtained, but can be managed in memory without eliminating any data from the analysis. In fact, the experiments performed show that all robust catalogues contained in a classification dataset are easily obtained, providing millions of associative classification rules with 100% confidence to the expert researcher in data mining. [ABSTRACT FROM AUTHOR]

Many transportation systems for routing flows between several origin-destination pairs of demand nodes have been widely designed as hub-and-spoke networks. To improve the provided service level of these networks, service time requirements are here considered during modeling, giving rise to a multiple allocation incomplete hub location problem with service time requirements. The problem consists of designing a hub and spoke network by locating hubs, establishing inter-hub arcs, and routing origin-destination demand flows at minimal cost while meeting some service time requirements. As travel times are usually uncertain for most real cases, the problem is approached via a binary linear programming robust optimization model, which is solved by two specialized Benders decomposition algorithms. The devised Benders decomposition framework outperforms a general purpose optimization solver on solving benchmark instances of the hub location literature. The achieved results also show how the probability of violating the travel time requirements decreases with the prescribed protection level, at the expense of the higher costs of the optimal solution for the robust optimization model. [ABSTRACT FROM AUTHOR]

The increasing number of video surveillance cameras is challenging video control systems. Monitoring centers require tools to guide the process of supervision. Different video analysis methods have effectively met the main requirements from the industry of perimeter protection. High accuracy detection systems are able to process real time video on affordable hardware. However some problematic environments cause a massive number of false alerts. Many approaches in the literature do not consider this kind of environments while others use metrics that dilute their impact on results. An intelligent video solution for perimeter protection must select and show the cameras which are more likely witnessing a relevant event but systems based only on background modeling tend to give importance to problematic situations no matter if an intrusion is taking place or not. We propose to add a module based on machine learning and global features, bringing adaptability to the video surveillance solution, so that problematic situations can be recognized and given the right priority. Tests with thousands of hours of video show how good an intruder detector can perform but also how a simple fault in a camera can flood a monitoring center with alerts. The new proposal is able to learn and recognize events such that alerts from problematic environments can be properly handled. [ABSTRACT FROM AUTHOR]

Data clustering is a very useful data mining technique to find groups of similar objects present in the dataset. Scalability to handle immense volumes, robustness to intrinsic outlier data and validity of clustering results are the main challenges of any data clustering approach. In order to address these challenges, a fuzzy weighted clustering approach which is comprehensibly parallel and distributed in every phase, is proposed in this research. Although the proposed method can be used for various data clustering purposes, it has been applied in gene expression clustering to reveal functional relationships of genes in a biological process. Conforming to MapReduce, the proposed method also presents a novel similarity measure which benefits from combining ordered weighted averaging and Spearman correlation coefficient. In the proposed method, density reachable genes were joined to establish subclusters. Afterwards, final cluster results were obtained by merging these subclusters. A voting system detects the best weights and consequently the most valid clusters among all possible results for each distinct dataset. The whole algorithm is implemented on a distributed processing platform and it is scalable to process any size of data stored in cloud infrastructures. Precision of resulting clusters were evaluated using some of the well-known cluster validity indexes in the literature. Also, the efficiency of the proposed method in scalability and robustness was compared with recently published similar researches. In all the mentioned comparisons, the proposed method outperformed recent works on the same datasets. [ABSTRACT FROM AUTHOR]

In this paper we propose robust efficiency scores for the scenario in which the specification of the inputs/outputs to be included in the DEA model is modelled with a probability distribution. This probabilistic approach allows us to obtain three different robust efficiency scores: the Conditional Expected Score, the Unconditional Expected Score and the Expected score under the assumption of Maximum Entropy principle. The calculation of the three efficiency scores involves the resolution of an exponential number of linear problems. The algorithm presented in this paper allows to solve over 200 millions of linear problems in an affordable time when considering up 20 inputs/outputs and 200 DMUs. The approach proposed is illustrated with an application to the assessment of professional tennis players. [ABSTRACT FROM AUTHOR]

Sparse representation methods based on l 1 and/or l 2 regularization have shown promising performance in different applications. Previous studies show that the l 1 regularization based representation has more sparse property, while the l 2 regularization based representation is much simpler and faster. However, when dealing with noisy data, both naive l 1 and l 2 regularization suffer from the issue of unsatisfactory robustness. In this paper, we explore the method to implement an antinoise sparse representation method for robust face recognition based on a joint version of l 1 and l 2 regularization. The contributions of this paper are mainly shown in the following aspects. First, a novel objective function combining both l 1 and l 2 regularization is proposed to implement an antinoise sparse representation. An iterative fitting operation via l 1 regularization is integrated with l 2 norm minimization, to obtain an antinoise classification. Second, the rationale how the proposed method produces promising discriminative and antinoise performance for face recognition is analyzed. The l 2 regularization enhances robustness and runs fast, and l 1 regularization helps cope with the noisy data. Third, the classification robustness of the proposed method is demonstrated by extensive experiments on several benchmark facial datasets. The method can be considered as an option for the expert systems for biometrics and other recognition problems facing unstable and noisy data. [ABSTRACT FROM AUTHOR]

• A new Imp-GRNN controller applied to uncertain plants with unknown disturbances. • Imp-GRNN adds a new input-output layer including adaptable updated weights. • Its input-hidden layer weights are adaptively auto-tuning instead of being static. • Smoothed σ parameter used to eradicate the need to set it prior or adapt it online. • Imp-GRNN better than GRNN and other advanced controllers via two benchmark tests. In this paper, a novel adaptive Improved Global Regressive Neural Networks (Imp-GRNN) controller is developed, which provides novel improvement to the basic GRNN neural model. Proposed Imp-GRNN is designed to handle the multivariable uncertain nonlinear systems. The significant improvements of Imp-GRNN include the addition of a new output layer connected directly with the input layer via newly adaptable weights. Also, the Imp-GRNN input-hidden weights are designed to be adaptively auto-tuning instead of being static, as in most of the precedent adaptive neural controllers. Besides, a new smoothen coefficient σ is introduced to eradicate the requirement to set it beforehand or to adapt it online. The superiority of proposed Imp-GRNN method is proven quite perfect compared of GRNN and other advanced neural controllers as demonstrated via the two numerical benchmark test results. [ABSTRACT FROM AUTHOR]

Uncertainty is frequently present in logistics and transportation, where vehicle routing problems play a crucial role. However, due to the complexity inherent in dealing with uncertainty, most research has been devoted to deterministic problems. This paper considers a robust version of the vehicle routing problem with hard time windows, in which travel times are uncertain. A budget polytope uncertainty set describes the travel times, to limit the maximum number of sailing legs that can be delayed. This makes sure that improbable scenarios are not considered, while making sure that solutions are immune to delays on a given number of sailing legs. Existing exact methods are only able to solve small instances of the problem and can be computationally demanding. With the aim of solving large instances with reduced running times, this paper proposes an efficient heuristic based on adaptive large neighborhood search. The computational study performed on instances with different uncertainty levels compares and analyzes the performance of four versions of the heuristic and shows how good quality solutions can be obtained within short computational times. [ABSTRACT FROM AUTHOR]

In Data Mining, during the preprocessing step, there is a considerable diversity of candidate algorithms to select important features, according to some criteria. This broad availability of algorithms that perform the Feature Selection task gives rise to the difficulty of choosing, a priori , between the algorithms at hand, the most promising one for a particular problem. In this paper, we present the proposal and evaluation of a new architecture for the recommendation of Feature Selection algorithms based on the use of Metalearning. Our framework is very flexible since the user can adapt it to its proper needs. This flexibility is one of the main advantages of our proposal over other approaches in the literature, which involve steps that cannot be adapted to the user’s local requirements. Furthermore, it combines several concepts of intelligent systems, including Machine Learning and Data Mining, with topics derived from expert systems, as user and data-driven knowledge, with meta-knowledge. This set of solutions coupled with leading-edge technologies allows our architecture to be integrated into any information system, which impact on the automation of services and in reducing human effort during the process. Regarding the Metalearning process, our framework considers several types of properties inherent to the data sets, as well as, Feature Selection algorithms based on many information, distance, dependence and consistency measures. The quality of the methods for Feature Selection was estimated according to a multicriteria performance measure, which guided the ranking process of these algorithms for the construction of data metabases. Proposed by the authors of this work, this multicriteria performance measure combines any three measurements on a single one, creating an interesting and powerful tool to evaluate not only FS algorithms but also to assess any context where it is necessary a combination to maximize a measure or minimize it. The recommendation models, represented by decision trees and induced from the training metabases, allowed us to see in what circumstances a Feature Selection algorithm outperforms the other and what aspects of the data present greater influence in determining the performance of these algorithms. Nevertheless, if the user wishes, any other learning algorithm may be used to induce the recommendation model. This versatility is another strong point of this proposal. Results show that with the characterization of data, through statistical, information and complexity measures, it is possible to reach an accuracy higher than 90%. Besides yielding recommendation models that are interpretable and robust to overfitting, the developed architecture is less computationally expensive than approaches recently proposed in the literature. [ABSTRACT FROM AUTHOR]

Robotic manipulators are a multi-input multi-output, dynamically coupled, highly time-varying, complex and highly nonlinear systems wherein the external disturbances, parameter variations, and random noise adversely affects the performance of the robotic system. Therefore, in order to deal with such complexities, however, an intriguing task for control researchers, these systems require an efficient and robust controller. In this paper, a novel application of genetic algorithms (GA) optimization approach to optimize the scaling factors of interval type-2 fuzzy proportional derivative plus integral (IT2FPD+I) controllers is proposed for 5-DOF redundant robot manipulator for trajectory tracking task. All five controllers' parameters are optimized simultaneously. Further, a procedure for selecting appropriate initial search space is also demonstrated. In order to make a fair comparison between different controllers, the tuning of each of the controllers' parameters is done with GA. This optimization technique uses the time domain optimal tuning while minimizing the fitness function as the sum of integral of multiplication of time with square error (ITSE) for each joint. To ascertain the effectiveness of IT2FPID controller, it is compared against type-1 fuzzy PID (T1FPID) and conventional PID controllers. Furthermore, robustness testing of developed IT2FPID controller for external disturbances, parameter variations, and random noise rejection is also investigated. Finally, the experimental study leads us to claim that our proposed controller can not only assure best trajectory tracking in joint and Cartesian space, but also improves the robustness of the systems for external disturbances, parameter variations, and random noise. [ABSTRACT FROM AUTHOR]

Achieving robustness with a limited distortion level is a challenging design problem for watermarking systems in multimedia applications with a guaranteed quality requirement. In this paper, we provide an intelligent system for watermarking through incorporating a meta-heuristic technique along with an embedding method to achieve an optimized performance. The optimization objective is to provide the maximum possible robustness without exceeding a predetermined distortion limit. Hence, the quality level of the watermarking method could be guaranteed through that constraint optimization. A new fitness function is defined to provide the required convergence toward the optimum solution for the defined optimization problem. The fitness function is based on dividing its applied solution population into two groups, where each group is ranked according to a different objective. Thus, the multi-objectives in the problem are decoupled and solved through two single-objective sub-problems. Unlike existing watermarking optimization techniques, the proposed work does not require weighting factors. To illustrate the effectiveness of the proposed approach, we employ a recent watermarking technique, and then use it as the embedding method to be optimized. The Artificial Bee Colony is selected as the meta-heuristic optimization method in which the proposed fitness function is used. Experimental results show that the imposed quality constraint is satisfied, and that the proposed method provides enhanced robustness under different attacks for various quality thresholds. The presented approach offers a robust solution that can be applied to numerous multimedia applications such as film industry, intelligent surveillance and security systems. [ABSTRACT FROM AUTHOR]

Robust mixture models approaches, which use non-normal distributions have recently been upgraded to accommodate asymmetric data. In this article we propose a new method based on the General Split Gaussian distribution (GSG) and Cross–Entropy Clustering (CEC). The GSG is a flexible density with reasonably small number of parameters which are easy to estimate. We combine the model with a clustering method which allows to treat groups separately and estimate parameters individually in each cluster. Consequently, we introduce an effective clustering algorithm which deals with non-normal data. [ABSTRACT FROM AUTHOR]

Foreground detection is one of the most important and fundamental tasks in many computer vision applications such as real-time video surveillance. Although there have been many efforts to find solutions to this problem, many obstacles such as illumination changes, noises, dynamic backgrounds, and computational complexities have prevented them from being used in real surveillance systems. In this paper, to alleviate these inherent limitations of conventional methods, we propose a fast illumination-robust foreground detection (FIFD) system that provides robustness against illumination variations and noises from various real circumstances with an efficient computational scheme. In contrast to the conventional approaches, our method focuses on efficiently formulating the foreground object detection system by leveraging a foreground candidate region detection and hierarchical distribution map. Specifically, our approach consists of three parts. First, for a query image, foreground candidates are detected by fusing multiple methods. The existence and the block size of the foreground object are determined through the use of the foreground continuity. Second, the foreground block is found from the estimated distribution map and then detected from the extracted valid blocks. Finally, with a labeling scheme, the foreground is detected. To intensively evaluate our approach compared to the conventional methods, we use the publicly available I2R and traffic datasets, and we build a novel electron multiplying charge-coupled device foreground detection benchmark taken in an environment with light lower than 10lux. Experimental results show that our approach provides satisfactory performance compared to the state-of-the-art methods even under very challenging circumstances. Furthermore, our approach is very efficient in that it takes only approximately 31 ms per frame. [ABSTRACT FROM AUTHOR]

The discriminative ability of geometric features can be well supported by empirical studies in ear recognition. Recently, a number of methods have been suggested for geometric feature extraction from ear images. However, these methods usually have relatively high feature dimension or are sensitive to rotation and scale variations. In this paper, we propose a novel geometric feature extraction method to address these issues. First, our studies show that the minimum Ear Height Line (EHL) is also helpful to characterize the contour of outer helix, and the combination of maximal EHL and minimum EHL can achieve better recognition performance. Second, we further extract three ratio-based features which are robust to scale variation. Our method has the feature dimension of six, and thus is efficient in matching for real-time ear recognition. Experimental results on two popular databases, i.e. USTB subset1 and IIT Delhi, show that the proposed approach can achieve promising recognition rates of 98.33% and 99.60%, respectively. [ABSTRACT FROM AUTHOR]

An efficient reversible adaptive video watermarking scheme for multiple watermarks based on Bi-directional Associative Memory (BAM) Neural Networks and Fuzzy Inference System namely, Multi-BAM-FUZ scheme is proposed in this paper. The main goal of this paper is to design a robust video watermarking system which facilitates secure video transmission over a communication channel by maintaining a trade-off among imperceptibility, robustness and watermark capacity or payload. The BAM neural network supports creation of weight matrix (formed out of multiple images) and this matrix is embedded into the DWT uncorrelated mid frequency coefficients of all the components (Y, Cb, Cr) of every frames of the video with varying embedding strength ‘α’. This adaptive embedding strength is generated using the Fuzzy Inference System which takes HVS characteristics such as luminance, texture and edge of each frame as an input in the DWT transform. The simulations performed on various test videos demonstrate that the proposed Multi-BAM-FUZ not only outperforms other existing methods with respect to various video degradation processes, but also maintains a satisfactory image quality, robustness and payload. It is noted that, the implementation of the novel adaptive process enhances the visual quality of about 60.97 dB in terms of PSNR and 0.9998 in terms of SSIM, robustness of about nearly 1.0000 and 0.9999 in terms of Normalized Cross Correlation (NCC) value and Bit Correction Rate (BCR) respectively against various attacks. Moreover, the proposed scheme facilitates high level of payload without affecting the imperceptibility and robustness level. [ABSTRACT FROM AUTHOR]

The Economic Load Dispatch (ELD) problem has attracted much attention in the field of electric power system. This paper proposes a novel parallel hybrid optimization methodology aimed at solving ELD problem with various generator constraints. The proposed approach combines the Differential Evolution (DE) and Particle Swarm Optimization (PSO). Initially the whole population (in increasing order of fitness) is divided into three groups - Inferior Group, Mid Group and Superior Group. DE is employed in the inferior and superior groups, whereas PSO is used in the mid-group. The proposed method is called DPD as it uses DE-PSO-DE on a population in parallel manner. Two strategies namely Elitism (to retain the best obtained values so far) and Non-redundant search (to improve the solution quality) have been employed in DPD cycle. Moreover, the suitable mutation strategy for both DEs used in DPD is investigated over a set of 8 popular mutation strategies. Combination of 8 mutation strategies generated 64 different variants of DPD. Top 4 DPDs are investigated through IEEE CEC 2006 functions. Based on the performance analysis, best DPD is reported and further used in solving four different typical test systems of ELD problem. Numerical and graphical results indicate the efficiency, convergence characteristic and robustness of proposed DPD. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive fractional order PID sliding mode controller (AFOPIDSMC) using a Bat algorithm to control of a Caterpillar robot manipulator. A fractional order PID (FOPID) control is applied to improve both trajectory tracking and robustness. Sliding mode controller (SMC) is one of the control methods which provides high robustness and low tracking error. Using hybridization, a new combined control law is proposed for chattering reduction by means of FOPID controller and high trajectory tracking through using SMC. Then, an adaptive controller design motivated from the SMC is applied for updating FOPID parameters. A metaheuristic approach, the Bat search algorithm based on the echolocation behavior of bats is applied for optimal design of the Caterpillar robot in order to tune the parameter AFOPIDSMC controllers (BA-AFOPIDSMC). To study the effectiveness of Bat algorithm, its performance is compared with five other controllers such as PID, FOPID, SMC, AFOPIDSMC and PSO-AFOPIDSMC. The stability of the AFOPIDSMC controller is proved by Lyapunov theory. Numerical simulation results completely indicate the advantage of BA-AFOPIDSMC for trajectory tracking and chattering reduction. [ABSTRACT FROM AUTHOR]

This article solves the tracking trajectory problem of teleoperated robotic systems with different workspaces. The overall robotic system satisfies a regular master–slave structure with a delta-robot as the master device and a planar manipulator attached to a Cartesian robot with five degrees of freedom (DOFs) as the slave device. A forward kinematics analysis describes the workspace for the delta-robot and the five-DOFs manipulator. A coordinate transformation based on sigmoidal functions establishes the relation between the workspaces for both robotic devices. The obtained transformation yields a feasible workspace for the slave robot. Then, an inverse kinematics analysis provides the desired reference trajectories. The tracking trajectory control implements a robust strategy based on Barrier Lyapunov functions. The barrier controller does not allow the slave robot to reach non-attainable configurations. State-dependent gains characterized the obtained controller taking into account the different workspaces for each device. Numerical results describe the suggested controller applied in a virtual teleoperated robot and a real robotic system platform. The obtained results confirm the improvements (measured in terms of the mean square error and the l 2 norm of the applied controls) of the proposed controller against a traditional state feedback realization. Furthermore, the results show that the trajectories of the slave robot do not leave the valid workspace. • Output feedback control for teleoperated manipulators under state constraints. • Adaptive control formulation based on logarithmic barrier Lyapunov functions. • Super-twisting differentiator implementation to obtain the tracking error derivative. • Experimental evaluations on a manipulator (slave) and a Delta robot (master). [ABSTRACT FROM AUTHOR]

This paper proposes a multi-step procedure that integrates robust methods, clustering analysis and data envelopment analysis (DEA) to identify bank branch managerial clusters and to study efficiency performance. By applying robust techniques based on principal component analysis, we look for (1) the detection of influential branches, i.e., exhibiting extreme operating behaviors, and (2) the clustering of branches based on operating characteristics. Our premise is that influential branches affect both the clustering and the determination of efficiency performance. The application of the procedure yields various aggregate influential-based branch profiles along with cluster profiles. These aggregate profiles provide valuable insights on the determinants of branch efficiency performance and operating patterns. Using the profiles as contextual information, DEA input-oriented slack-based models are applied to study branch efficiency performance from meta-frontier and cluster-frontier perspectives. Branch performance is characterized in terms of influential-based and cluster profiles, and efficiency designations. This allows for the understanding of how efficiency and peer selection are affected by influential branches, and how the profiles can be used to inform design decisions. [ABSTRACT FROM AUTHOR]

ECG Steganography ensures protection of patient data when ECG signals embedded with patient data are transmitted over the internet. Steganography algorithms strive to recover the embedded patient data entirely and to minimize the deterioration in the cover signal caused by the embedding. This paper presents a Continuous Ant Colony Optimization (CACO) based ECG Steganography scheme using Discrete Wavelet Transform and Singular Value Decomposition. Quantization techniques allow embedding the patient data into the ECG signal. The scaling factor in the quantization techniques governs the tradeoff between imperceptibility and robustness. The novelty of the proposed approach is to use CACO in ECG Steganography, to identify Multiple Scaling Factors (MSFs) that will provide a better tradeoff compared to uniform Single Scaling Factor (SSF). The optimal MSFs significantly improve the performance of ECG steganography which is measured by metrics such as Peak Signal to Noise Ratio, Percentage Residual Difference, Kullback–Leibler distance and Bit Error Rate. Performance of the proposed approach is demonstrated on the MIT-BIH database and the results validate that the tradeoff curve obtained through MSFs is better than the tradeoff curve obtained for any SSF. The results also advocate appropriate SSFs for target imperceptibility or robustness. [ABSTRACT FROM AUTHOR]

This paper presents a Robust Genetic Programming approach for discovering profitable trading rules which are used to manage a portfolio of stocks from the Spanish market. The investigated method is used to determine potential buy and sell conditions for stocks, aiming to yield robust solutions able to withstand extreme market conditions, while producing high returns at a minimal risk. One of the biggest challenges GP evolved solutions face is over-fitting. GP trading rules need to have similar performance when tested with new data in order to be deployed in a real situation. We explore a random sampling method (RSFGP) which instead of calculating the fitness over the whole dataset, calculates it on randomly selected segments. This method shows improved robustness and out-of-sample results compared to standard genetic programming (SGP) and a volatility adjusted fitness (VAFGP). Trading strategies (TS) are evolved using financial metrics like the volatility, CAPM alpha and beta, and the Sharpe ratio alongside other Technical Indicators (TI) to find the best investment strategy. These strategies are evaluated using 21 of the most liquid stocks of the Spanish market. The achieved results clearly outperform Buy&Hold, SGP and VAFGP. Additionally, the solutions obtained with the training data during the experiments clearly show during testing robustness to step market declines as seen during the European sovereign debt crisis experienced recently in Spain. In this paper the solutions learned were able to operate for prolonged periods, which demonstrated the validity and robustness of the rules learned, which are able to operate continuously and with minimal human intervention. To sum up, the developed method is able to evolve TSs suitable for all market conditions with promising results, which suggests great potential in the method generalization capabilities. The use of financial metrics alongside popular TI enables the system to increase the stock return while proving resilient through time. The RSFGP system is able to cope with different types of markets achieving a portfolio return of 31.81% for the testing period 2009–2013 in the Spanish market, having the IBEX35 index returned 2.67%. [ABSTRACT FROM AUTHOR]

Among the forms of assessment of materials to use in a particular application there is the measuring systems of Vickers hardness. This measurement is performed manually by experts, being very interpretative and subjective, which usually leads to variability of the Vickers hardness value between observers and even for the same observer. The experience of the skilled in measurement will determine if the material can be used for an application or not. There are some works use traditional methods to perform the measurement of Vickers hardness for Digital Image Processing (DIP). This work’s main objective has been to propose a new methodology capable of determining the Vickers Hardness testing values from indentation images by using the Adaptive Balloon Active Contour Methods. The results of the hardness measurement using the Adaptive Balloon Active Contour Method (ABACM) were significant compared to other methods, by observing the MSE obtained from the measured Vickers hardness, the value by obtained ABACM method is three times lower than the Region Growing method, and five times lower than Watershed method. In addition, the measurement method was carried out in 1.2 ± 0.3 s. The proposed method stands out for not requiring pre-processing and post-processing steps, because its mathematical formulation is robust to noise in this application. It is worth highlighting que Significantly cam close to the two specialists, Demonstrating que can be used to aid in measuring the Vickers hardness. [ABSTRACT FROM AUTHOR]

Seam Carving is a method which aims at retargeting images, i.e., adjusting input images into arbitrary dimensions by removing pixel paths (seams) with minimal visual impact. The standard method is based on an exhaustive searching method for minimal cost seams according to a pixel relevance function also named energy function. In this work, we propose a new method to retarget images based on both Genetic Algorithms (GAs) and a high-performance scale and rotation-invariant interest point descriptor, the well-known method, Speed Up Robust Features (SURF). Along with our proposal, we present a content-aware fitness function, a new individual modeling, as well as a new index for image retargeting quality assessment. According to the simulations carried out, our proposal is very handy for image retargeting and outperforms the Seam Carving in terms of the image quality assessment index in most results. [ABSTRACT FROM AUTHOR]

According to the fact that the intrinsic dynamism of self-organizing systems challenges the existing methods of engineering for modeling reliable complex systems, in this paper, we propose a new formal-based method to model self-organizing systems. The capabilities of the proposed method which are used to address several challenges in design, development and analysis of self-organizing systems are: modularity and robustness, decentralized control and scalability, required adaptation types, flexible and adaptive control mechanism, separation of adaptation and business logic, and safe adaptation. To evaluate the proposed method, we use self-organizing traffic management system as a case study and exploit the proposed method for modeling this dynamic system. Moreover, we propose and employ a novel policy-based runtime verification mechanism to ensure that the safety properties are satisfied by the implementation at runtime. We provide our case study prototype using Java and the Ponder2 toolkit and apply our runtime verification method to show its proper reaction capabilities to the property violations. This benefit is the result of using dynamic policies in our method to control the behavior of systems. [ABSTRACT FROM AUTHOR]

Spectral clustering is a popular clustering method due to its simplicity and superior performance in the data sets with non-convex clusters. The method is based on the spectral analysis of a similarity graph. Previous studies show that clustering results are sensitive to the selection of the similarity graph and its parameter(s). In particular, when there are data sets with arbitrary shaped clusters and varying density, it is difficult to determine the proper similarity graph and its parameters without a priori information. To address this issue, we propose a parameter-free similarity graph, namely Density Adaptive Neighborhood (DAN). DAN combines distance, density and connectivity information, and it reflects the local characteristics. We test the performance of DAN with a comprehensive experimental study. We compare k -nearest neighbor (KNN), mutual KNN, ε-neighborhood, fully connected graph, minimum spanning tree, Gabriel graph, and DAN in terms of clustering accuracy. We also examine the robustness of DAN to the number of attributes and the transformations such as decimation and distortion. Our experimental study with various artificial and real data sets shows that DAN improves the spectral clustering results, and it is superior to the competing approaches. Moreover, it facilitates the application of spectral clustering to various domains without a priori information. [ABSTRACT FROM AUTHOR]

L 1 adaptive controller has been recognized for having a structure that allows decoupling between robustness and adaption owing to the introduction of a low pass filter with adjustable gain in the feedback loop. The trade-off between performance, fast adaptation and robustness, is the main criteria when selecting the structure or the coefficients of the filter. Several off-line methods with varying levels of complexity exist to help finding bounds or initial values for these coefficients. Such values may require further refinement using trial-and-error procedures upon implementation. Subsequently, these approaches suggest that once implemented these values are kept fixed leading to sub-optimal performance in both speed of adaptation and robustness. In this paper, a new practical approach based on fuzzy rules for online continuous tuning of these coefficients is proposed. The fuzzy controller is optimally tuned using Particle Swarm Optimization (PSO) taking into accounts both the tracking error and the controller output signal range. The simulation of several examples of systems with moderate to severe nonlinearities demonstrate that the proposed approach offers improved control performance when benchmarked to L 1 adaptive controller with fixed filter coefficients. [ABSTRACT FROM AUTHOR]

Expert and intelligent control schemes have recently emerged out as a promising solution with robustness which can efficiently deal with the nonlinearities, along with various types of modelling uncertainties, present in different real world systems e.g. binary distillation column. This paper is an attempt to propose an intelligent control system which takes the form of a fractional order fuzzy proportional–integral–derivative (FOFPID) controller which is investigated as a solution to deal with the complex dynamic nature of the distillation column. The FOFPID controller is an extension of an existing formula based self tuning fuzzy proportional integral controller structure, which varies its gains at run time in accordance with the instantaneous error and rate of change of error. The FOFPID controller is a Takagi–Sugeno (TS) model based fuzzy adaptive controller comprising of non-integer order of integration and differentiation operators used in the controller. It has been observed that inclusion of non-integer order of the integration and differentiation operators made the controller scheme more robust. For the performance evaluation of the proposed scheme, the performance of FOFPID controller is compared with that of its integer order counterpart, a fuzzy proportional–integral–derivative (FPID) controller. The parameters of both the controllers were optimized for minimum integral of absolute error (IAE) using a bio-inspired global optimization algorithm, genetic algorithm (GA). Intensive LabVIEWۛ simulation studies were performed which included setpoint tracking with and without uncertainties, disturbance rejection, and noise suppression investigations. For testing the parameter uncertainty handling capability of the proposed controller, uncertain and time varying relative volatility and uncertain tray hydraulic constant were applied. Also, for the disturbance rejection studies, intensive simulations were conducted, which included two most common causes of disturbance i.e. variation in feed composition and variation in feed flow rate. All the simulation investigations clearly suggested that FOFPID controller provided superior performance over FPID controller for each case study i.e. setpoint tracking, disturbance rejection, noise suppression and parameter uncertainties. [ABSTRACT FROM AUTHOR]

With the aim of designing a more robust digital watermarking scheme against various unintentional and intentional attacks, a significant region (SR) based image watermarking technique is proposed in the present paper using lifting wavelet transform (LWT). While the energy compaction property of LWT provides higher tolerance against image distortion as opposed to conventional wavelet transform, the proposed block selection procedure provides greater security over the existing watermarking approaches. Non-overlapping coefficient blocks from the lowpass subband are selected after applying three levels of LWT and using certain criterion based on minimum coefficient difference and a threshold value. To disguise the intruder completely, secret key based randomization of coefficients, blocks, and watermark bits is incorporated. Maximum coefficients difference of each selected block and the same threshold value are then used for deciding which block to choose for embedding the bit 0 or 1. Performance of the proposed method is analyzed and compared with some of the existing schemes that demonstrates that the proposed scheme not only outperforms other methods with respect to various attacks for most of the cases, but also maintains a satisfactory image quality. [ABSTRACT FROM AUTHOR]

Fingerprint recognition is a reliable solution in user authentication systems. Nevertheless, the security and secrecy of the users data are a concern in today’s biometric systems and most of the security attention is focused in biometric template protection to avoid identity theft. In recent years, several approaches have been presented where the main objective is to have the biometric revocable, but almost all them have failed in verify their security and robustness. In last years, chaotic systems have been proposed in cryptography due they have several properties related with cryptography properties such as extreme sensibility on initial conditions with confusion and ergodicity with diffusion. In this paper, we present a novel fingerprint template protection scheme based on chaotic encryption by using the logistic map and Murillo-Escobar’s algorithm (Murillo-Escobar et al., 2014). In addition, we present a novel implementation of our scheme in a 32 bit microcontroller for secure authentication systems to show its application on embedded expert systems. In contrast with recent approaches presented in literature, we present a complete security analysis in both statistical and implementation level, to justify the proposed scheme in a real application. Based in the results, the proposed embedded authentication system is secure, effective and at low cost, and it could be implemented on real secure access control systems. [ABSTRACT FROM AUTHOR]

A tongue diagnosis system can offer significant information for health condition. To ensure the feasibility and reliability of tongue diagnosis, a robust and accurate tongue segmentation method is a prerequisite. However, both of the common segmentation methods (edge-based or region-based) have respective limitations so that satisfactory results especially for medical use are often out of reach. In this paper, we proposed a robust tongue segmentation method by fusing region-based and edge-based approaches. Before segmentation, ROI (region of interest), which will be used as input for the subsequent segmentation, was extracted by a novel way. Next, we merged adjacent regions utilizing the histogram-based color similarity criterion to get a rough tongue contour. It is essentially a region-based method and hence the results are less sensitive to cracks and fissures on surface of the tongue. Then, we adopted a fast marching method to connect four detected reliable points together to get a close curve, which is based on edge features. Contour obtained by region-based approach was utilized to act as a mask during fast marching process (edge-based) and the mask added limits so that the ultimate contour will be more robust. Qualitative and quantitative comparisons show that the proposed method is superior to the other methods for the segmentation of tongue body in terms of robustness and accuracy. [ABSTRACT FROM AUTHOR]

The studies of behavioral finance show that the cognitive bias plays an important role in investors’ decision-making process. In this paper, based on the robust theory and prospect theory, a robust multi-period portfolio considering investors’ behavioral factors is constructed, which features the reference dependence, loss aversion and diminishing sensitivity. To solve the proposed portfolio model, an improved particle swarm optimization (PSO) algorithm is developed, which incorporates the two-stage initialization strategy, the improved stochastic ranking approach, the aging leader and the multi-frequency vibrational mutation operator. We illustrate the robust model with real market data and show its effectiveness based on the performance of the proposed algorithm. The results show that the proposed algorithm is successful in solving the constrained multi-period portfolio model and the proposed portfolio model provides an effective tool for a real multi-period investment. [ABSTRACT FROM AUTHOR]