Notes

Early conjecture associated with prostate cancer biochemical recurrence and also recognition associated with ailment perseverance employing PSA isoforms along with individual kallikrein-2.
Moreover, the CNN branches as base learners are combined into the optimal classifier via the proposed two-stage selective ensemble approach based on both accuracy and diversity criteria. Extensive experiments on CIFAR-10 benchmark and two specific medical image datasets illustrate that our approach achieves better performance in terms of accuracy, sensitivity, specificity, and F1 score measurement.This article investigates the adaptive fuzzy control algorithm for a class of large-scale switched fractional-order nonlinear nonstrict feedback systems. In this algorithm, we utilize fuzzy-logic systems (FLSs) to approximate the complicated unknown nonlinear functions. Based on the fractional Lyapunov stability rules, a virtual control law is presented. A fuzzy adaptive decentralized control method is developed under the technique of the Lyapunov function. Under the operation of the proposed algorithm, the stability of the proposed systems and the control performance can be guaranteed. selleck Finally, simulation results are presented to illustrate the feasibility and effectiveness of the proposed method.Recently, canonical correlation analysis (CCA) has been explored to address the fault detection (FD) problem for industrial systems. However, most of the CCA-based FD methods assume both Gaussianity of measurement signals and linear relationships among variables. These assumptions may be improper in some practical scenarios so that direct applications of these CCA-based FD strategies are arguably not optimal. With the aid of neural networks, this work proposes a new nonlinear counterpart called a single-side CCA (SsCCA) to enhance FD performance. The contributions of this work are four-fold 1) an objective function for the nonlinear CCA is first reformulated, based on which a generalized solution is presented; 2) for the practical implementation, a particular solution of SsCCA is developed; 3) an SsCCA-based FD algorithm is designed for nonlinear systems, whose optimal FD ability is illustrated via theoretical analysis; and 4) based on the difference in FD results between two test statistics, fault diagnosis can be directly achieved. The studies on a nonlinear three-tank system are carried out to verify the effectiveness of the proposed SsCCA method.This article studies a fully distributed optimal coordinated control problem with the global cost function for networked Euler-Lagrange (EL) systems subject to unknown model parameters. In particular, the global cost function is the sum of all the local cost functions assigned to each agent and only available to itself. The objective is to minimize the global cost function in a distributed manner while achieving a consensus on its optimal solution. Since the model parameters of the considered EL systems are not available, a new auxiliary system is introduced as a reference model, and its outputs exponentially converge the optimal solution of the global cost function. A fully distributed optimal control algorithm without requiring global information is first proposed. Then, an alternative distributed optimal algorithm via the event-triggered mechanism is proposed to reduce the communication cost. In particular, by combining an edge-based adaptive gain method, the proposed event-triggered optimal algorithm is also fully distributed. Finally, numerical simulation is carried out to validate the theoretical results.This article is concerned with both consensus and coordinated path-following control for multiple nonholonomic wheeled mobile robots. In the design, the path-following control is decoupled into the longitudinal control (speed control) and the lateral control (heading control) for the convenience of implementation. Different from coordinated trajectory tracking control schemes, the proposed control scheme removes the temporal constraint, which greatly improves the coordination robustness. In particular, two new coordinated error variables describing a chasing-and-waiting strategy are introduced in the proposed coordinated path-following control for injective paths and circular paths, respectively. All the closed-loop signals have proved to be asymptotically stable in the Lyapunov sense. Finally, simulation results under three typical paths are presented to verify the proposed coordination controllers.This article addresses the finite-time event-triggered adaptive neural control for fractional-order nonlinear systems. Based on the backstepping technique, a novel adaptive event-triggered control scheme is proposed, and finite-time stability criteria are introduced with the aim to ensure that the tracking error enters into a small region around the origin in finite time. Finally, the stability of the closed-loop system is ensured via a fractional Lyapunov function theory and two simulation examples were used to prove the validity of the designed control scheme.In recent years, single modality-based gait recognition has been extensively explored in the analysis of medical images or other sensory data, and it is recognized that each of the established approaches has different strengths and weaknesses. As an important motor symptom, gait disturbance is usually used for diagnosis and evaluation of diseases; moreover, the use of multimodality analysis of the patient's walking pattern compensates for the one-sidedness of single modality gait recognition methods that only learn gait changes in a single measurement dimension. The fusion of multiple measurement resources has demonstrated promising performance in the identification of gait patterns associated with individual diseases. In this article, as a useful tool, we propose a novel hybrid model to learn the gait differences between three neurodegenerative diseases, between patients with different severity levels of Parkinson's disease, and between healthy individuals and patients, by fusing and aggregating data from multiple sensors. A spatial feature extractor (SFE) is applied to generating representative features of images or signals. In order to capture temporal information from the two modality data, a new correlative memory neural network (CorrMNN) architecture is designed for extracting temporal features. Afterward, we embed a multiswitch discriminator to associate the observations with individual state estimations. Compared with several state-of-the-art techniques, our proposed framework shows more accurate classification results.
Website: https://www.selleckchem.com/products/ABT-737.html