Within computer vision, Facial Expression Recognition (FER) is a challenging task involving bottlenecks such as obtaining well-separated and compact expression embeddings that are invariant of identity. This study introduces AGILE (Attribute-GuidedIdentity-IndependentLearning), an innovative approach to enhance FER by distilling identity information and promoting discriminative features. Initially, an adaptive$\beta$Variational Autoencoder (VAE) is proposed from a fixed$\beta$-VAE architecture leveraging the theory of single-dimensional Kalman filter. This enhances disentangled feature learning without compromising the reconstruction quality. Now, to achieve the desired FER objective, we design a two-stage modular scheme built within the framework of adaptive$\beta$-VAE. In the first stage, an expression-driven identity modeling is proposed where an identity encoder is trained with a novel training loss to embed the most likely state corresponding to every subject in latent representation. In the next stage, keeping the identity encoder fixed, an expression encoder is trained with explicit guidance for the latent variables using an adversarial excitation and inhibition mechanism. This form of supervision enhances the transparency and interpretability of the expression space and helps to capture discriminative expression embeddings required for the downstream classification task. Experimental evaluations demonstrate that AGILE outperforms existing methods in identity and expression separability in the latent space and achieves superior performance over state-of-the-art methods on both lab-controlled and in-the-wild datasets, with recognition accuracies of 99.00% on CK+, 90.00% on Oulu-CASIA, 89.01% on MMI, 67.20% on Aff-Wild2, and 68.97% on AFEW.

This work proposes a novel event-triggered exponential supertwisting algorithm (ESTA) for path tracking of a mobile robot. The proposed work is divided into three parts. In the first part, a fractional-order sliding surface-based exponential supertwisting event-triggered controller has been proposed. Fractional-order sliding surface improves the transient response, and the exponential supertwisting reaching law reduces the reaching phase time and eliminates the chattering. The event-triggering condition is derived using the Lipschitz method for minimum actuator utilization, and the interexecution time between two events is derived. In the second part, a fault estimator is designed to estimate the actuator fault using the Lyapunov stability theory. Furthermore, it is shown that in the presence of matched and unmatched uncertainty, event-trigger-based controller performance degrades. Hence, in the third part, an integral sliding-mode controller (ISMC) has been clubbed with the event-trigger ESTA for filtering of the uncertainties. It is also shown that when fault estimator-based ESTA is clubbed with ISMC, then the robustness of the controller increases, and the tracking performance improves. This novel technique is robust toward uncertainty and fault, offers finite-time convergence, reduces chattering, and offers minimum resource utilization. Simulations and experimental studies are carried out to validate the advantages of the proposed controller over the existing methods.

This article providesa systematic approach for the design of an interval type-3 (IT3) Takagi–Sugeno (T–S) fuzzy logic system (FLS) using$\alpha $- plane representation. An IT3 FLS is designed with the baseline of the general type-2 (GT2) FLS in a similar manner as an IT2 FLS was designed from the baseline of type-1 FLS. Hence, IT3 FLS evolved as a successor of GT2 FLS, where secondary membership function is an interval type-2 fuzzy set (FS), and values of tertiary membership are unity over the footprint of uncertainty of secondary membership. This extra degree of freedom in IT3 FLS provides better modeling capability as compared to GT2 FLS in the presence of a high degree of uncertainty and vagueness. The proposed system will be more appealing while dealing with uncertain information or data, which is supposed to be generated from uncertain sources; i.e., there exist uncertainties even in the presence of uncertainty. The computations needed for the design of IT3 FLS are derived using IT2 FS and GT2 FS mathematics. The design algorithms adopted for the baseline IT2 T–S fuzzy system are as per the modified interval type-2 fuzzy c-regression model clustering algorithm and hyper-plane-shaped membership function. The proposed methodology is applied to several benchmark examples and obtained results are compared with recently developed fuzzy modeling methods having a comparable number of rule bases. The proposed IT3 T–S FLS shows good performance in terms of accuracy when data is corrupted by noise and uncertainties related to missing or unvarying data exist. The computational cost is linear with design parameters and by optimum choice of$\alpha $-planes, it is still bearable considering advantages and nature of applications.

This article proposes an online stochastic dynamic event-based near-optimal controller for formation in the networked multirobot system. The system is prone to network uncertainties, such as packet loss and transmission delay, that introduce stochasticity in the system. The multirobot formation problem poses a nonzero-sum game scenario. The near-optimal control inputs/policies based on proposed event-based methodology attain a Nash equilibrium achieving the desired formation in the system. These policies are generated online only at events using actor-critic neural network architecture whose weights are updated too at the same instants. The approach ensures system stability by deriving the ultimate boundedness of estimation errors of actor-critic weights and the event-based closed-loop formation error. The efficacy of the proposed approach has been validated in real-time using three Pioneer P3-Dx mobile robots in a multirobot framework. The control update instants are minimized to as low as 20% and 18% for the two follower robots.

This article focuses on developing an adaptive fast terminal sliding-mode controller (AFTSMC) with power rate proportional reaching law for the position and altitude tracking of a quadrotor in the presence of parametric uncertainties and bounded external disturbance. A nonlinear fast terminal sliding surface is proposed for the fast and finite-time convergence of the tracking error despite having the system states far away from the equilibrium point. Also, a power rate proportional reaching law has been proposed that ensures fast and finite-time convergence of the sliding manifold while attenuating the chattering phenomena in the sliding phase. To avoid the problem associated with over-estimation of the unknown disturbance bound, which eventually leads to chattering, an adaptive tuning law for gain adaptation is developed based on the Lyapunov’s stability theory that completely eradicates the necessity of knowing the upper bound of the disturbancea priori. The finite-time stability of a closed-loop system is investigated using the Lyapunov theory. The effectiveness of the proposed scheme is compared with an adaptive sliding-mode controller (ASMC) using extensive simulation and validated on the DJI Matrice 100 quadrotor as a proof of concept on the hardware platform.

Traditional machine learning methods assume that training and testing data must be from the same machine running condition (MRC) and drawn from the same distribution. However, in several real-time industrial applications, this assumption does not hold. The traditional methods work satisfactorily in steady-state conditions but fail in time-varying conditions. In order to utilize time-varying data in variable MRCs, this article proposes a novel low-level knowledge transfer framework using a deep neural network (DNN) model for condition monitoring of machines in variable running conditions. The low-level features have been extracted in time, frequency, and time–frequency domains. These features are extracted from the source data to train the DNN. The trained DNN-based parameters are then transferred to another DNN, which is modified according to the low-level features extracted from the target data. The proposed approach is validated through three case studies on: 1) the air compressor acoustic data set; 2) the Case Western Reserve University bearing data set; and 3) the intelligent maintenance system bearing data set. The prediction accuracy obtained for the above case studies is as high as 100%, 93.07%, and 100%, respectively, with fivefold cross-validation. These real-time results show considerable improvement in the prediction performance using the proposed approach.Note to Practitioners—Condition-based monitoring schemes are widely applicable to rotating machines in various industries since they operate in tough working situations, and consequently, unpredicted failures occur. These unpredicted failures may cause perilous accidents in the industries. CBM systems prevent such failures, which results in the reduction of equipment damage and, hence, increases machinery lifetime. Modern industries are so complex and generating huge data, and these data can be collected using sensors, but placing a large number of sensors is difficult and expensive for different but similar kinds of faults in industries. This also increases the cost due to additional sensors and circuits. In this article, the authors have proposed a novel low-level knowledge transfer framework using the deep neural network (DNN)-based method for condition monitoring of machines in variable running conditions. Low-level features have been extracted to reduce the computations of DNN drastically with improved performance. This article also considered additional faults in the target domain, which is more practical in real-time applications. The proposed scheme has been validated with three case studies on acoustic and vibration signatures.

In this paper, a novel robust adaptive barrier Lyapunov function (BLF)-based backstepping controller has been proposed for a class of interconnected, multi-input-multi-output (MIMO) unknown nonaffine nonlinear systems with asymmetric time-varying (ATV) state constraints. The design involves a neural-network-based online approximator to cope with uncertain dynamics of the system. To tune its weights, a novel adaptive law is proposed based on the Hadamard product. A theorem has also been proposed to have the bounds on virtual control signals beforehand. This theorem eliminates the need for tedious offline computation for the feasibility condition on the virtual controller in BLF-based controller design. To overcome the problem of unknown control gain in the nonaffine system, Nussbaum gain has been used during the design. A simulation study on the robot manipulator in task space has been performed to illustrate the effectiveness of the proposed methodology.

This article proposes a real-time event-triggered near-optimal controller for the nonlinear discrete-time interconnected system. The interconnected system has a number of subsystems/agents, which pose a nonzero-sum game scenario. The control inputs/policies based on proposed event-based controller methodology attain a Nash equilibrium fulfilling the desired goal of the system. The near-optimal control policies are generated online only at events using actor-critic neural network architecture whose weights are updated too at the same instants. The approach ensures stability as the event-triggering condition for agents is derived using Lyapunov stability analysis. The lower bound on interevent time, boundedness of closed-loop parameters, and optimality of the proposed controller are also guaranteed. The efficacy of the proposed approach has been validated on a practical heating, ventilation, and air-conditioning system for achieving the desired temperature set in four zones of a building. The control update instants are minimized to as low as 27% for the desired temperature set.

This paper introduces a novel pooling method namely fuzzy based pooling for image classification. Herein, a pooling method for bolstering the performance of conventional convolutional neural network (CNN) has been proposed. Conventional architecture of CNN uses pooling operation for dimension reduction which sometimes results in the loss of information. In this paper, a novel pooling method using fuzzy logic is introduced for dimension reduction. The proposed pooling method reduces the spatial size of convolved features in two steps. In the first step, the convolved features within a window to be pooled are processed using type-2 fuzzy logic for identifying the dominant features. Then, type-1 fuzzy logic with a weighted average of the dominant features within a window is used to reduce the spatial size. The proposed method is bench marked against conventional pooling techniques for MNIST dataset of handwritten digits recognition and CIFAR-10 dataset of RGB images. The accuracy shows that the proposed fuzzy based pooling performs better than the standard pooling techniques such as max and average pooling which helps to improve the performance of CNN.

Feature engineering plays a vital role in selecting relevant features that have maximum predictive value. In this paper, we have proposed a Gaussian fuzzy score function to rank the features in descending order of their score values. The mean and variance of the Gaussian fuzzy score function are determined using mean of k-middle. The mean of k-middle plays an important role to determine the complementary information of the features in the dataset. The features selected using proposed feature ranking method are fed to four widely used classifiers, i.e., linear kernel support vector machine, radial basis function kernel support vector machine, random forest and softmax classifier respectively. To show the effectiveness of the proposed approach, we compared its performance with that of state-of-the-art methods on five large-scale gene expression datasets.

This paper proposes a fast terminal sliding mode super twisting controller (FTSMSTC) design for quadrotor position and altitude tracking in the presence of bounded disturbances. A nonlinear fast terminal sliding manifold has been proposed for fast convergence of the tracking error to zero in finite time unlike the conventional sliding mode control (CSMC) that guarantee only asymptotic convergence of the tracking error. The super twisting reaching law has been proposed to deal with the chattering phenomena, which is inherent in the CSMC. The finite time stability of the complete closed loop system is investigated using Lyapunov stability theory and an analytical expression for the convergence time has also been derived. The effectiveness of the designed controller is checked against the CSMC using MATLAB simulation. The controller has been experimentally validated using the DJI Matrice M100 as a proof of utility in real time applications.

This paper proposes a vision-based sliding mode control technique for autonomous tracking of a moving vehicle by a quadrotor. The proposed vision algorithm estimates the quadrotor's position relative to moving vehicle using an on-board monocular camera. The relative position is provided as an input to a Fractional Order Sliding mode Controller (FOSMC) which ensures the convergence of the relative position between the moving vehicle and the quadrotor thereby enabling it to track the vehicle effectively. In addition, the proposed controller guarantees robustness towards bounded external disturbances and modelling uncertainties. The proposed vision-based control scheme is implemented using numerical simulations and validated in real-time on the DJI Matrice 100. Theses validations help in gaining into the maximum allowable speed of the moving target for the quadrotor to successfully track the object. This plays a vital role in surveillance operations and intruder chase.

In this letter, a novel method has been proposed to estimate noisiness of a pixel in an image. The proposed noisiness is based on a robust noisiness measure, which is evaluated using noise and edge information of locality of the pixel. Furthermore, this noisiness is used to develop a fuzzy peer group for removing mixed Gaussian and impulse noises from color images. Also, an iterative approach to the proposed method is presented. The proposed approach is then compared with the state-of-the-art algorithms. Results shows that the proposed method outperform existing algorithms significantly.

The emergence of deep learning has impacted numerous machine learning based applications and research. The reason for its success lies in two main advantages: 1) it provides the ability to learn very complex non-linear relationships between features and 2) it allows one to leverage information from unlabeled data that does not belong to the problem being handled. This paper presents a transfer learning procedure for cancer classification, which uses feature selection and normalization techniques in conjunction with s sparse auto-encoders on gene expression data. While classifying any two tumor types, data of other tumor types were used in unsupervised manner to improve the feature representation. The performance of our algorithm was tested on 36 two-class benchmark datasets from the GEMLeR repository. On performing statistical tests, it is clearly ascertained that our algorithm statistically outperforms several generally used cancer classification approaches. The deep learning based molecular disease classification can be used to guide decisions made on the diagnosis and treatment of diseases, and therefore may have important applications in precision medicine.

The importance of remotely placed controller in a cyber space with sensor-controller-actuator network has increased significantly in industrial, defense, and surveillance sector. Such network has large amount of sensor and controller data. A time-triggered control technique may generate redundant control signals and put unnecessary data on network. Therefore, an event-triggered adaptive controller that generates control action at required instants using state- and error-based conditions has been developed in this paper. A data transmission framework has also been designed in this paper that addresses network delay and packet losses. The proposed controller-communication methodology has been validated through two case studies, first, temperature tracking for heating ventilation and air conditioning system and, second, real-time path tracking by automated guided vehicle. The proposed methodology has also been duly compared with its time-triggered counterpart. The control updates are reduced to approximately 41% and 64% in the two case studies, respectively. The experimental results also prove the designed controller to be efficient when compared with event-triggered incremental PID controller using the same data transmission framework.

This paper proposes a vision-based guidance strategy for safe navigation of a nonholonomic mobile robot in unknown indoor environments. The proposed switching-based sliding mode control (SMC) law makes the robot follow the desired trajectory as given by the guidance law. The guidance strategy uses the centroid of the depth map of an obstacle as obtained from the Red Green Blue -Depth (RGB-D) sensor to generate the desired angular velocity. The fuzzy rule-based guidance is developed to generate the desired linear velocity command. The analysis of guidance strategy is done for an infinite length obstacle. The proposed SMC is shown to be asymptotically stable using Krasovskii Method. The finite time convergence of robot navigation has been shown using Poincare Map method. The stability of the proposed SMC under burst losses has also been established. Experiments on the Pioneer P3-DX robot in different obstacle scenarios show that the robot safely navigates in presence of communication channel burst losses.

As you open this January issue of the IEEE Transactions on Neural Networks and Learning Systems (TNNLS), I hope everyone enjoyed a great holiday season and is excited for the new year of 2019. I am very delighted and honored to report several key metrics of IEEE TNNLS to the community.

Applying machine learning algorithms directly on high dimensional datasets, like those encountered in transcriptome analysis, may lead to high time complexity and low performance of learning models, especially when the number of samples is small compared to the dimensionality. Selecting the optimal set of features then becomes an essential task for such datasets. Filter methods are one of the main class of techniques used for feature selection wherein a score is assigned to features based on criteria such as information gain, statistical measures or similarity based measures and then selects the best scored features. Using filter methods on the complete dataset results in features that have good performance over the dataset but might perform poorly in certain regions of the data, which affects accuracy for data points of those regions. To overcome this degradation in performance, we propose two novel methods to assign a robust score by using the fuzzy combination of the region-specific optimal feature subsets obtained using a standard feature selection algorithm (we use mRMR for this paper).We compare the result with state-of-the-art feature selection algorithm, mRMR (Minimum Redundancy Maximum Relevance) in the terms of accuracy on certain standard datasets.

Object recognition in hazy conditions is quite difficult due to illumination variance. The challenge arises in finding out features from such images. Herein, we have proposed the method to deal with such images. The input image needs to be dehazed before applying the recognition algorithms. On the other hand, dehazing a non-hazy image makes it dark resulting in loss of features. Hence, a decision is to be made whether or not the image should be dehazed before recognition. Also, for a very dense haze, even dehazing doesn't help in object recognition. In order to tackle this issue, this paper presents a novel method to quantitatively estimate the amount of haze in the image - also termed as haze degree - using dark channel prior of the input images. We compared our values with the existing method using FRIDA dataset. The estimated haze degree is used to decide whether input image need to be dehazed or not. We use DehazeNet and Faster RCNN for dehazing and recognition, respectively. We test our method on real time hazy images to set a threshold on haze degree to classify the image as light, moderate or densely hazed. We used the Static Scenes dataset from Color Hazy Images for Comparison (CHIC) database to obtain the threshold values.

In this letter, a novel method has been proposed, which includes formulation of similarity function and a fuzzy-based method for filtering mixed noise. The similarity function is adaptive to local noise level and edge information, and it is used to detect similarity among pixels in a peer group. Based on the peer group, color image corrupted with mixed Gaussian and impulse noise is filtered. The novel method for filtering is an adaptive weighted average of different sized filters. The weights of different sized filters are adaptive to local noise and edge information. The proposed work has been compared with some state-of-the-art techniques. The results show proposed approach is better in preserving edge and color information than others.

This paper proposes a novel adaptive Type-2 fuzzy filter for removing salt and pepper noise from the images. The filter removes noise in two steps. In the first step, the pixels are categorized as good or bad based on their primary membership function (MF) values in the respective filter window. In this paper, two approaches have been proposed for finding threshold between good or bad pixels by designing primary MFs. a) MFs with distinct Means and same Variance and b) MFs with distinct Means and distinct Variances. The primary MFs of the Type-2 fuzzy set is chosen as Gaussian membership functions. Whereas, in the second step, the pixels categorized as bad are denoised. For denoising, a novel Type-1 fuzzy approach based on a weighted mean of good pixels is presented in the paper. The proposed filter is validated for several standard images with the noise level as low as 20% to as high as 99%. The results show that the proposed filter performs better in terms of peak signal-noise-ratio values compared to other state-of-the-art algorithms.

The heating, ventilation, and air conditioning system is an important component for achieving desired thermal condition in rooms or spaces in buildings, office complex, or airports. This paper proposes a real-time event-triggered adaptive critic controller for generating near optimal control actions to achieve desired temperatures. The desired temperatures may have variable or fixed values over time. The real-time controller is designed in two phases. Initially event-triggered control actions are generated by linear quadratic regulator for small period while the actor-critic network of controller is trained. Later, adaptive critic controller takes over for event-based actions. Hence, the event triggering conditions for both general linear and nonlinear discrete time systems using Lyapunov stability analysis are derived in this paper. The event-based actor-critic network weight update formulation and ultimate boundedness of parameters are also presented in this paper. The proposed approach has been validated for different and common temperature sets for four zones, where the control execution events are minimized to 20% and 26%, respectively.

The state-of-art algorithms in computational intelligence have become better than human intelligence in some of pattern recognition areas. Most of these state-of-art algorithms have been developed from the concept of multi-layered artificial neural networks. Large amount of numerical and linguistic rule data has been created in recent years. Fuzzy sets are useful in modeling uncertainty due to vagueness, ambiguity and imprecision. Fuzzy inference systems incorporate linguistic rules intelligible to human beings. Many attempts have been made to combine assets of fuzzy sets, fuzzy inference systems and artificial neural networks. Use of a single fuzzy inference system limits the performance. In this paper, we propose a generic architecture of multi-layered network developed from Takagi Sugeno fuzzy inference systems as basic units. This generic architecture is called “Takagi Sugeno Deep Fuzzy Network”. Multiple distinct fuzzy inference structures can be identified using proposed architecture. A general three layered TS deep fuzzy network is explained in detail in this paper. The generic algorithm for identification of all network parameters of three layered deep fuzzy network using error backpropagation is presented in the paper. The proposed architecture as well as its identification procedure are validated using two experimental case studies. The performance of proposed architecture is evaluated in normal, imprecise and vague situations and it is compared with performance of artificial neural network with same architecture. The results illustrate that the proposed architecture eclipses over three layered feedforward artificial neural network in all situations.

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Fuzzy support vector machine (FSVM) is accepted as a significant addition over soft margin SVM like C-SVM, because the latter gives suboptimal results in the presence of outliers. FSVM's ability to absorb outliers strongly depends on how well the training samples are assigned fuzzy membership values (MVs). Traditionally, the membership functions (MFs) used for the FSVM were custom made for applications, and MFs used for one could, in general, not be used for others. To overcome this, general purpose membership functions (GPMFs) are defined in this paper as those MFs that can universally be used for multiple applications and statistically perform better than C-SVM. This paper contributes to the GPMF literature in two stages. This paper first with the help of convex hulls presents limitations that the FSVM faces while treating all samples of a class with a single MF, and recommends differential treatment to data by dividing them into two fuzzy sets: one containing possible nonoutliers and the other containing possible outliers. While possible outliers are modeled with a normal MF, possible nonoutliers are recommended to have a constant MV of “1.” Subsequently, this paper introduces novel GPMFs that use clustering techniques to recognize possible outliers, and use set measures like the Hausdorff distance and pt-set distance for defining new MF heuristics. To establish conclusions, the introduced GPMFs are thoroughly evaluated and statistically compared with earlier GPMFs on 15 real-world benchmark datasets. The results were very encouraging, and showed that the proposed GPMFs not only perform significantly better in treating class noise, but also execute with efficient run time complexity.

Classification algorithms have been traditionally designed to simultaneously reduce errors caused by bias as well by variance. However, there occur many situations where low generalization error becomes extremely crucial to getting tangible classification solutions, and even slight overfitting causes serious consequences in the test results. In such situations, classifiers with low Vapnik-Chervonenkis (VC) dimension can bring out positive differences due to two main advantages: 1) the classifier manages to keep the test error close to training error and 2) the classifier learns effectively with small number of samples. This paper shows that a class of classifiers named majority vote point (MVP) classifiers, on account of very low VC dimension, can exhibit a generalization error that is even lower than that of linear classifiers. This paper proceeds by theoretically formulating an upper bound on the VC dimension of the MVP classifier. Later, through empirical analysis, the trend of exact values of VC dimension is estimated. Finally, case studies on machine fault diagnosis problems and prostate tumor detection problem revalidate the fact that an MVP classifier can achieve a lower generalization error than most other classifiers.

Condition-based monitoring (CBM), with its tremendous scope in improving operational cost efficiency, has become an important component in most industrial machine maintenance frameworks. While many strategies already exist for CBM, this paper presents a novel pattern analysis framework, which focuses on improving reliability of real-time fault detection. The introduced framework is based upon a novel feature selection method, which selects good, reliable, and consistent features. Graphical indices are proposed in this paper which try and quantify the goodness of features and accordingly rank them for the feature selection procedure. The presented algorithm also takes into account the possibility of corrupt data creeping in during data collection, and takes necessary steps to discard them. Additionally, novel methods for organizing training data and a method for sensitive position identification for placing sensor(s) have also been introduced, for further improving the quality of selected features. To validate the framework, experiments have been performed on an air compressor for real-time detection of leakage inlet valve fault and leakage outlet valve fault, and also on an induction motor for detecting presence of faulty bearings. The findings clearly show substantial improvement in fault detection performance and confirm the effectiveness of proposed framework.

Transcriptome data has been proved to be very valuable for clinical applications, such as diagnosis and prognosis of various cancers. In this paper, we present layer-wise feature selection in conjunction with stacked sparse auto-encoders (SSAE), a deep learning strategy for tumor classification with gene expression data. While SSAE learns high-level features from data, performing feature selection in every layer is a heuristic to obtain relevant features at every stage and also to assist in reducing the computation during fine-tuning procedure. The data in the new feature representation is finally used by classifier(s) to perform Tumor detection. The algorithm was tested on 36 datasets from the GEMLeR repository and w.r.t. AUC (Area under ROC curve) performance, it was found to outperform the GEMLeR benchmark results on 35 datasets (tied on the other dataset).

Heating, ventilation and air-conditioning(HVAC) system is a very important component in designing a Smart Home. The HVAC system itself is a Cyber-Physical system(CPS) and comes under Industry 4.0. Being connected to network it requires an integrated architecture of communication and control for its smooth operation. Heterogeneous nature of control and cyber domains is a great challenge in dealing with CPS development. An intelligent controller design coupled in a communication framework is presented in this paper for performance improvements in HVAC system. The control and communication architecture considers relevant system objectives based on system states and actuator actions. The HVAC system regulates the flow of conditioned air for various desired temperatures in different thermal zones. The formulated problem has been solved through real time optimization approach using learning based Proportional-Integral(PI) controller methodology following the communication protocol. The gradient descent algorithm updates the parameters of PI controller which in turn generates online control actions for achieving desired states. The algorithm helps in obtaining optimal control actions for the actuators and shows a fast convergence to the different desired temperature sets.

The Disturbance observer is becoming very popular and is being mainly used in high speed motion control applications where high precision is required. This paper focus on the problem of designing a nonlinear disturbance observer for good estimation of external disturbances acting on the body of quadcopter during flying for attitude, altitude as well as position regulation control problem. The proposed controller is based on composite controller scheme, consists of a nonlinear disturbance observer and a Backstepping controller.The stability analysis of the nonlinear disturbance observer is successfully done using Lyapunov stability theory. The effectiveness of the proposed disturbance observer is investigated by MATLAB simulation. The simulation results shows that Backstepping controller with nonlinear disturbance observer has good tracking ability in the presence of external disturbance.

I1 Proceedings of the Fifteenth Annual UT- KBRIN Bioinformatics Summit 2016 Eric C. Rouchka, Julia H. Chariker, Benjamin J. Harrison, Juw Won Park P1 CC-PROMISE: Projection onto the Most Interesting Statistical Evidence (PROMISE) with Canonical Correlation to integrate gene expression and methylation data with multiple pharmacologic and clinical endpoints Xueyuan Cao, Stanley Pounds, Susana Raimondi, James Downing, Raul Ribeiro, Jeffery Rubnitz, Jatinder Lamba P2 Integration of microRNA-mRNA interaction networks with gene expression data to increase experimental power Bernie J Daigle, Jr. P3 Designing and writing software for in silico subtractive hybridization of large eukaryotic genomes Deborah Burgess, Stephanie Gehrlich, John C Carmen P4 Tracking the molecular evolution of Pax gene Nicholas Johnson; Chandrakanth Emani P5 Identifying genetic differences in thermally dimorphic and state specific fungi using in silico genomic comparison Stephanie Gehrlich, Deborah Burgess, John C Carmen P6 Identification of conserved genomic regions and variation therein amongst Cetartiodactyla species using next generation sequencing Kalpani De Silva, Michael P Heaton, Theodore S Kalbfleisch P7 Mining physiological data to identify patients with similar medical events and phenotypes Teeradache Viangteeravat, Rahul Mudunuri, Oluwaseun Ajayi, Fatih Şen, Eunice Y Huang P8 Smart brief for home health monitoring Mohammad Mohebbi, Luaire Florian, Douglas J Jackson, John F Naber P9 Side-effect term matching for computational adverse drug reaction predictions AKM Sabbir, Sally R Ellingson P10 Enrichment vs robustness: A comparison of transcriptomic data clustering metrics Yuping Lu, Charles A Phillips, Michael A Langston P11 Deep neural networks for transcriptome-based cancer classification Rahul K Sevakula, Raghuveer Thirukovalluru, Nishchal K. Verma, Yan Cui P12 Motif discovery using K-means clustering Mohammed Sayed, Juw Won Park P13 Large scale discovery of active enhancers from nascent RNA sequencing Jing Wang, Qi Liu, Yu Shyr P14 Computationally characterizing genomic pipelines and benchmarking results using GATK best practices on the high performance computing cluster at the University of Kentucky Xiaofei Zhang, Sally R Ellingson P15 Development of approaches enabling the identification of abnormal gene expression from RNA-Seq in personalized oncology Naresh Prodduturi, Gavin R Oliver, Diane Grill, Jie Na, Jeanette Eckel-Passow, Eric W Klee P16 Processing RNA-Seq data of plants infected with coffee ringspot virus Michael M Goodin, Mark Farman, Harrison Inocencio, Chanyong Jang, Jerzy W Jaromczyk, Neil Moore, Kelly Sovacool P17 Comparative transcriptomics of three Acinetobacter baumanii clinical isolates with different antibiotic resistance patterns Leon Dent, Mike Izban, Sammed Mandape, Shruti Sakhare, Siddharth Pratap, Dana Marshall P18 Metagenomic assessment of possible microbial contamination in the equine reference genome assembly M Scotty DePriest, James N MacLeod, Theodore S Kalbfleisch P19 Molecular evolution of cancer driver genes Chandrakanth Emani, Hanady Adam, Ethan Blandford, Joel Campbell, Joshua Castlen, Brittany Dixon, Ginger Gilbert, Aaron Hall, Philip Kreisle, Jessica Lasher, Bethany Oakes, Allison Speer, Maximilian Valentine P20 Biorepository Laboratory Information Management System Naga Satya V Rao Nagisetty, Rony Jose, Teeradache Viangteeravat, Robert Rooney, David Hains

Intelligent fault diagnosis of machines for early recognition of faults saves industry from heavy losses occurring due to machine breakdowns. This paper proposes a process with a generic data mining model that can be used for developing acoustic signal-based fault diagnosis systems for reciprocating air compressors. The process includes details of data acquisition, sensitive position analysis for deciding suitable sensor locations, signal pre-processing, feature extraction, feature selection, and a classification approach. This process was validated by developing a real time fault diagnosis system on a reciprocating type air compressor having 8 designated states, including one healthy state, and 7 faulty states. The system was able to accurately detect all the faults by analyzing acoustic recordings taken from just a single position. Additionally, thorough analysis has been presented where performance of the system is compared while varying feature selection techniques, the number of selected features, and multiclass decomposition algorithms meant for binary classifiers.

Biclustering is a commonly used technique for extracting local patterns from microarray data, for which several algorithms have been proposed. Hence it is important to define metrics that compare the various algorithms. In this paper, we have defined novel measures of hausdorff distance between biclusters and global silhouette index for estimating the quality of biclusters extracted by the existing algorithms. We have also compared these measures with the standard measures such as the proportion of enriched biclusters for benchmark biological datasets. Our experimental results show almost similar variation of all these metrics for most of the datasets. The computation of these metrics for a given dataset for all the existing algorithms gives the most suited algorithm for the considered dataset.

Fuzzy Rule based regression, classification and control have found great use in modern applications due to its simplicity, flexibility and capability. A key issue in all such methods is the computation time. Computational complexity of training and testing is linearly dependent on the size of fuzzy rule base and the respective fuzzy rule space is exponentially dependent on data dimensionality. Sparse Auto-Encoders (SAs) have become popular in giving compact feature representations for image, audio and speech data and have helped in giving state of the art pattern recognition performances in most of the domains. These feature representation are learnt in an unsupervised fashion and are found to give higher order building blocks with which the data is seemingly made of. This paper proposes a method where SAs are used for getting compact feature representation of input data and if needed with reduced dimensionality. The regular fuzzy rule based models are then learnt from data in the new feature space. The method was tested for Regression and Classification problems, giving impressive results in both. The method with Regression problem gave comparable performance with almost half the number of rules and with Classification problem it gave improvement in classification accuracy by 2.67% while reducing the size of fuzzy rule base by 11.25 times and 7.5 times by number.

Fuzzy Support Vector Machine (FSVM) has become a handy tool for many classification problems. FSVM provides flexibility of incorporating membership values to individual training samples. Performance of FSVM largely depends on how well these membership values are assigned to the training samples. Recently, a new approach for assigning membership values was proposed, where only possible outliers are allowed to have membership value lower than `l'. For doing the same, first DBSCAN clustering is performed to find the set of possible outliers and such possible outliers were then assigned membership values based on some heuristics. All other remaining samples were assigned a membership value of `l'. This paper extends the same approach by further analyzing the algorithm, introducing Fuzzy C-Means clustering based heuristic for assigning membership values and also comparing two methods of finding optimal parameters for FSVM model. Experiments have been performed over 4 real world datasets for comparing and analyzing the different methods.

Support Vector Machine (SVM) is one of the most widely used classification algorithms. It is found that soft margin SVM like C-SVM, in the presence of outliers and class imbalance, give suboptimal results. Fuzzy SVM (FSVM) and class imbalance learning (CIL) appears to solve these problems of outliers and class imbalance respectively. The strength of FSVM in absorbing the effect of outliers strongly depends on how well we assign fuzzy membership values to the training samples. In this paper, we present a novel method for assigning these fuzzy membership values. The objective of our method is to assign membership values in the range (0, 1) to only those samples which are possibly outliers and not otherwise. For this, first density based clustering is performed to find the probable outliers and then assign them membership values based on one of the two heuristics. The heuristics use Hausdorff distance of the probable outliers from their own class. The proposed method was evaluated on three real world datasets. The proposed method gave consistently good results as compared to older methods; hence the method can be seen as a potential tool for classifying noisy datasets.

We present a novel approach for the development of fuzzy hidden Markov models (FHMMs) by exploiting both additive and nonadditive properties of input fuzzy sets in the fuzzy rules of generalized fuzzy model (GFM). This development utilizes 1) Gaussian mixture model (GMM) to manipulate the mixture parameters for the input fuzzy sets and 2) GFM rules for the inclusion of states in the consequent part to be able to use HMM. Taking the components of Gaussian mixture density conditioned on the past system states and making use of equivalence of GMM with GFM, parameters of the additive and nonadditive FHMMs are estimated using the forward-backward procedure of the Baum-Welch algorithm. The additive and nonadditive FHMMs are validated on three benchmark applications involving time-series prediction, and the results are compared and found to be better than or equal to those of the existing recent fuzzy models.

This paper presents a computational technique for modeling the Earth's magnetic field using the framework of Adaptive Non-Additive Fuzzy System (ANAFS). The defuzzified output constructed from both the premise and consequent parts of the GFM rules takes the form of Choquet integral. Premise and consequent parameters of non-additive fuzzy model are then updated for each online data based on the estimation error to make the adaptive system. The resulting model is applied on the real data, i.e. H-component of the magnetic field obtained from the magnetometer and the training and prediction results are found to be better and can be used for modeling such a complex system whose mathematical or physical modeling is very difficult to obtain. Thus, this work makes an important contribution to the field of fuzzy modeling of real life complex systems.

This paper presents the formulation of nonadditive generalized fuzzy model (GFM) by using the framework of the Gaussian mixture model, which provides the membership functions for the input fuzzy sets. By treating the consequent part as a function of fuzzy measures, we derive its coefficients. The defuzzified output constructed from both the premise and consequent parts of the GFM rules takes the form of Choquet integral. The computational burden involved with the solution of lambda-measure is mitigated using Q-measure. This nonadditive fuzzy model is applied on two benchmark applications, and the results are found to be better than those obtained from the additive fuzzy models.

The cluster-weighted modeling (CWM) is a mixture density estimator around local models. To be specific, the input regions together with output regions are treated to be Gaussian serving as local models. These models are linked by a linear function involving the mixture of densities of local models. A connection between the CWM and generalized fuzzy model (GFM) is established in this work for utilizing the concepts of probability theory in deriving additive and non-additive fuzzy system versions of GFM