Design, Fabrication, and Optimization of a Printed Ag Nanoparticle-Based Flexible Capacitive Sensor for Automotive IVI Bezel Display Applications.

Since printed capacitive sensors provide better sensing performance, they can be used in automotive bezel applications. It is necessary to fabricate such sensors and apply an optimization approach for choosing the optimal sensor pattern. In the present work, an effort was made to formulate interdigitated pattern-printed Silver (Ag) electrode flexible sensors and adopt the Taguchi Grey Relational (TGR)-based optimization approach to enhance the flexible sensor's panel for enhanced automobile infotainment applications. The optimization technique was performed to derive better design considerations and analyze the influence of the sensor's parameters on change in capacitance when touched and production cost. The fabricated flexible printed sensors can provide better sensing properties. A design pattern which integrates an overlap of 15 mm, an electrode line width of 0.8 mm, and an electrode gap 0.8 mm can produce a higher change in capacitance and achieve a lower weight. The overlap has a greater influence on sensor performance owing to its optimization of spatial interpolation.

Adaptive Fixed-Time Neural Network Tracking Control of Nonlinear Interconnected Systems.

In this article, a novel adaptive fixed-time neural network tracking control scheme for nonlinear interconnected systems is proposed. An adaptive backstepping technique is used to address unknown system uncertainties in the fixed-time settings. Neural networks are used to identify the unknown uncertainties. The study shows that, under the proposed control scheme, each state in the system can converge into small regions near zero with fixed-time convergence time via Lyapunov stability analysis. Finally, the simulation example is presented to demonstrate the effectiveness of the proposed approach. A step-by-step procedure for engineers in industry process applications is proposed.

Adaptive Fixed-Time Control of Strict-Feedback High-Order Nonlinear Systems.

This paper examines the adaptive control of high-order nonlinear systems with strict-feedback form. An adaptive fixed-time control scheme is designed for nonlinear systems with unknown uncertainties. In the design process of a backstepping controller, the Lyapunov function, an effective controller, and adaptive law are constructed. Combined with the fixed-time Lyapunov stability criterion, it is proved that the proposed control scheme can ensure the stability of the error system in finite time, and the convergence time is independent of the initial condition. Finally, simulation results verify the effectiveness of the proposed control strategy.

Tensor Decomposition for Spatial-Temporal Traffic Flow Prediction with Sparse Data.

Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. This paper presents a transport traffic prediction method that leverages the spatial and temporal correlation of transportation traffic to tackle this problem. We first propose to model the traffic flow using a fourth-order tensor, which incorporates the location, the time of day, the day of the week, and the week of the month. Based on the constructed traffic flow tensor, we either propose a model to estimate the correlation in each dimension of the tensor. Furthermore, we utilize the gradient descent strategy to design a traffic flow prediction algorithm that is capable of tackling the data sparsity problem from the spatial and temporal perspectives of the traffic pattern. To validate the proposed traffic prediction method, case studies using real-work datasets are constructed, and the results demonstrate that the prediction accuracy of our proposed method outperforms the baselines. The accuracy decreases the least with the percentage of missing data increasing, including the situation of data being missing on neighboring roads in one or continuous multi-days. This certifies that the proposed prediction method can be utilized for sparse data-based transportation traffic surveillance.

Unsupervised Learning for Monaural Source Separation Using Maximization⁻Minimization Algorithm with Time⁻Frequency Deconvolution.

This paper presents an unsupervised learning algorithm for sparse nonnegative matrix factor time⁻frequency deconvolution with optimized fractional ß-divergence. The ß-divergence is a group of cost functions parametrized by a single parameter ß. The Itakura⁻Saito divergence, Kullback⁻Leibler divergence and Least Square distance are special cases that correspond to ß=0, 1, 2, respectively. This paper presents a generalized algorithm that uses a flexible range of ß that includes fractional values. It describes a maximization⁻minimization (MM) algorithm leading to the development of a fast convergence multiplicative update algorithm with guaranteed convergence. The proposed model operates in the time⁻frequency domain and decomposes an information-bearing matrix into two-dimensional deconvolution of factor matrices that represent the spectral dictionary and temporal codes. The deconvolution process has been optimized to yield sparse temporal codes through maximizing the likelihood of the observations. The paper also presents a method to estimate the fractional ß value. The method is demonstrated on separating audio mixtures recorded from a single channel. The paper shows that the extraction of the spectral dictionary and temporal codes is significantly more efficient by using the proposed algorithm and subsequently leads to better source separation performance. Experimental tests and comparisons with other factorization methods have been conducted to verify its efficacy.