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In this paper, based on the discrete Wirtinger inequality, a novel summation inequality is established which extends the Jensen inequality. By the technique of the novel inequality, a sufficient criterion on asymptotic stability of discrete neural networks with time-varying delays is obtained. A numerical example is given to demonstrate the effectiveness of the inequality. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural network tracking control is studied for a class of uncertain nonlinear systems. The studied systems are in discrete-time form and unknown time-delay is considered here. Up to now, the research works on nonlinear discrete-time main focus on systems without time-delay, so the problem of the unknown time-delay will be solved in this paper. Based on the Lipschitz or norm-boundedness assumption of the unknown nonlinearities, the mean-value theorem is utilized to solve the unknown time-delay problem. In order to overcome the noncausal problem, the strict-feedback systems will be transformed into a special form. The radial basis functions neural networks (RBFNN) are utilized to approximate the unknown functions of the systems, the adaptation laws and the controllers are designed based on the transformed systems. By using the Lyapunov analysis, it is proven that the closed-loop system is stable in the sense that semi-globally uniformly ultimately bounded (SGUUB) and the output tracking errors converge to a bounded compact set. A simulation example is used to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Umbilical cables are essential components of subsea production systems for deep sea oil and gas exploration. Umbilical cables are often set to specific configurations that compensate for the motion of the floating body resulting from harsh environmental loads during operation to ensure structural safety and proper functioning. However, the optimization design of the umbilical cable configuration is a challenging problem owing to highly nonlinear relationship between loads and structural responses as well as the huge computational cost of evaluating numerous sea state combinations. This study proposes a deep learning-driven optimization design framework based on the residual artificial neural network to address the configuration design of lazy-wave umbilical cables. A parallel accelerated computation method based on Adam optimizer was established for the generation of the database. The transfer learning technique was also introduced into the deep learning model to build a configuration design prediction model based on small samples, which significantly reduced the cost of model building and training. Based on the above optimization design framework, a combination of tensile and bending optimization for a lazy-wave umbilical cable configuration was achieved, verifying the effectiveness and practicality of the proposed approach. This study provides an important reference for the rapid engineering design of umbilical cables. [ABSTRACT FROM AUTHOR]

Realizing accurate measurement for thermal contact conductance (TCC) is difficult and needs many additional measurements. In this paper, we design a three-layer back propagation artificial neural network (ANN) model to retrieve TCC of two contact solid specimens. The model can retrieve TCC only according to the measured temperatures and loading pressures, and the effects of loading pressure, temperature and surface roughness on TCC are considered. Besides, thermal conductivity, heat flux and the parameter of the TCC empirical fitting formula can be obtained simultaneously. The retrieved results are validated by experimental values. The results show that for two contact pairs with different surface roughness, the retrieved parameters of TCC empirical fitting formula are clear reasonable and can be explained conceptually compared with the published studies. [ABSTRACT FROM AUTHOR]

• Designs workflow for personalized prediction of response to first-line chemotherapy. • Performs systematic metabolomic analysis of lung cancer fresh tumor core biopsies. • Machine learning analyses differentiate between treatment responses and disease stage. • Predicts response and identifies metabolites associated with patient classifications. Despite extensive effort, the search for clinically-relevant metabolite biomarkers for early detection, disease monitoring, and outcome prediction in lung cancer remains unfulfilled. Although biofluid evaluation has been explored, the complexity inherent in metabolite data and the dynamic discrepancy between metabolites in biofluids vs. tumor tissue have prevented conclusive results. This proof-of-concept study explored models predictive of staging and chemotherapy response based on metabolomic analysis of fresh, patient-derived non-small cell lung cancer (NSCLC) core biopsies. Samples (n = 36) were evaluated with high-resolution 2DLC-MS/MS and 13C-glucose enrichment, and the data were comprehensively analyzed with machine learning techniques. Patients were categorized as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) in terms of first-line chemotherapy. Four major types of learning methods (partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), artificial neural networks, and random forests (RF)) were applied to differentiate between positive (DC and CR/PR) and poor (PD and SD/PD) responses, and between stage I/II/III and stage IV disease. Models were trained with forward feature selection based on variable importance and tested on validation subsets. The models predicted patient classifications in the validation subsets with AUC (95 % CI): DC vs. PD (SVM), 0.970(0.961–0.979); CR/PR vs. SD/PD (PLS-DA), 0.880(0.865–0.895); stage I/II/III vs. IV (SVM), 0.902(0.880–0.924). Highest performing model was SVM for DC vs. PD (balanced accuracy = 0.92; kappa = 0.74). This study illustrates a comprehensive evaluation of patient tumor-specific metabolic profiles, with the potential to identify disease stage and predict response to first-line chemotherapy. [ABSTRACT FROM AUTHOR]

A Long Short-Term Memory (LSTM) neural network model is developed to provide a real-time calculation tool for monitoring the mooring line responses under the operating condition by using the vessel motion as the only input. A feature extraction method based on first order moment and second order center moment is proposed in the data pre-processing to improve the characteristics of fluctuation information of input data. The effective judging standard of the scope of prediction accuracy regarding the proposed neural network model is defined. The impact of normalization method, neural number, and training sets length on the predicting accuracy are studied. The feasibility of the established LSTM neural network model is verified considering different data relevance between the training sets and validating sets. The results indicate that the mooring line responses can be predicted with high precision based on the vessel motion as input by using the established LSTM neural network model. •A LSTM neural network model is developed topredict the mooring line responses only using the vessel motion as input. • A feature extraction method based on first order moment and second order center moment is proposed in the data pre-processing. • The influence of normalization method, neural number and training sets length on the predicting accuracy of LSTM neural network model are studied. [ABSTRACT FROM AUTHOR]