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Big data analysis has been pervasively adopted as a method to analyze the tremendous amount of daily generated high throughput data in an efficient and accurate manner. Among the series of tools available in the field of big biomedical data, correlation networks are one of the most powerful tools for modelling gene expression, which is important in the study of disease and ageing. With the help of...
Recent studies of biological networks show that these networks are robust against the random or selective deletion of network nodes and / or edges. Ability to maintain performance of network under mutations is a key feature of live systems that has long been recognized. However, the molecular and cellular basis of this stability has just begun to be understood. Robustness is a key to understanding...
A nonlinear optimal H-infinity control approach is proposed for bioreactors aiming at improved biofuels production. The dynamic model of the bioprocess taking place in the bioreactor undergoes approximate linearization round temporary equilibria which are recomputed at each iteration of the control method. The linearization makes use of Taylor series expansion and of the computation of the system's...
Human action recognition has been extensively studied with a lot of real life application. Many methods have been proposed and achieved promising results when the input video captured from the same viewpoints. However, their accuracy decreases significantly under viewpoint changing. The reason is that action appearance is quite different when looking from a different angle. To overcome this problem,...
The Chemical Master Equation (CME) is used to stochastically model biochemical reaction networks, under the Markovian assumption. The low-order statistical moments induced by the CME are often the key quantities that one is interested in. However, in most cases, the moments equation is not closed; in the sense that the first n moments depend on the higher order moments, for any positive integer n...
In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. However, in the pursuit of ever increasing tracking performance, their characteristic speed and real-time capability have gradually faded. Further, the increasingly complex models, with massive number of trainable parameters, have introduced the risk of severe over-fitting...
Large-scale infrastructures are critical to economic and social development, and hence their continued performance and security are of high national importance. Such an infrastructure often is a system of systems, and its functionality critically depends on the inherent robustness of its constituent systems and its defense strategy for countering attacks. Additionally, interdependencies between the...
The severity of global magnetic disturbances in Near-Earth space can crucially affect human life. These geomagnetic disturbances are often indicated by a Kp index, which is derived from magnetic field data from ground stations, and is known to be correlated with solar wind observations. Forecasting of Kp index is important for understanding the dynamic relationship between the magnetosphere and solar...
Membrane computing also called P system, seeks to discover new computational models from the study of cellular membranes. In this study, we reported our initial efforts to classify Macao visitor expenditure profile using a membrane computing approach. Specifically, we designed a novel P system including specific membrane structure and membrane rules to realize an improved k-medoids clustering algorithm...
In this paper, we integrate two independent multiplicative uncertainties (uncertain demand and cost) and facility disruptions together, introduce budget uncertainty set to capture uncertain parameters. Based on the underlying deterministic model, we propose a two-stage robust facility location model, incorporating facility disruptions in recourse stage, which is an nonlinear problem resulted by two...
We propose a novel paradigm of neural computation based on synfire rings, i.e., synfire chains that loop back in on themselves. We show that any finite state automaton can be simulated by a Boolean recurrent neural network made up of synfire rings. More precisely, if the given automaton and its corresponding network are run in parallel on a same input stream, then the successive computational states...
We prove stability and robustness results for chemostat models with one substrate, an arbitrary number of species, a constant dilution rate, and constant inputs of the species. Unlike all previous works, we prove input-to-state stability under uncertainties in important cases where the controls are the input nutrient concentration and the species inputs. Our assumptions ensure global asymptotic stability...
In this paper, we develop a novel metric, which we call biometric permanence, to characterize the stability of biometric features. First, we define permanence in terms of the change in false non-match ratio (FNMR) over a repeated sequence of enrolment and verification events for a given population. We consider how such a measure may be experimentally determined. Since changes in FNMR, for most biometric...
Robustness analysis method is proposed for rotorcraft pilot coupling with helicopter flight control system in loop. Combining with the lateral identification model of BO-105 helicopter, McRuer's pilot model, and the designed stability augmentation system, frequency domain model is established for rotorcraft pilot coupling analysis. μ analysis method and performance specifications in ADS-33E are adopted...
This work tackles the L2–L∞ filter design problem for the discrete-time Takagi-Sugeno (T-S) fuzzy stochastic systems. The approach based on the (T-S) fuzzy systems, and the objective is to provide a new design with sufficient condition via LMI procedure. Less conservative results are obtained through the use of the projection lemma to introduce additional free matrices. These tools allow to obtain...
Analysis of a system and its control design can be greatly simplified while working with lower-order models as compared to high order models. In this paper we analyze a minimal realization biomechanical model of reflexive movement of human finger when the little finger is bent. This is a 6th order minimal state space realization model for movement simulation of two fingers. We developed an H2 robust...
Classifier fusion is a well-studied problem in which decisions from multiple classifiers are combined at the score, rank, or decision level to obtain better results than a single classifier. Subsequently, various techniques for combining classifiers at each of these levels have been proposed in the literature. Many popular methods entail scaling and normalizing the scores obtained by each classifier...
A lot of feature-based correspondence matching methods have been proposed in the field of computer vision, image processing and pattern recognition. These methods are also effective for biometric recognition. In general, in the case of feature-based matching methods, the matching score is calculated as a ratio between the number of feature points and corresponding points. These methods need to normalize...
This paper presents an effective approach for multi-task control of the underwater biomimetic vehicle-manipulator system. The main idea of this approach lies in organizing and combining the tasks by priorities, while decomposes the coupled relations among the tasks by null-space mapping consecutively avoiding interaction effects. A direct kinematic model is firstly built to describe the motions and...
Several authors have ton the importance of aggregating the results of different feature selection methods in order to improve the solutions obtained. To the best of our knowledge, the consensus rankings obtained in all of these proposals do not allow that some variables are tied. This paper studies the advantages of allowing ties in the consensus ranking obtained from aggregating several features...
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