Multivariable Adaptive Control of the Rewinding Process of a Roll-to-roll System Governed by Hyperbolic Partial Differential Equations

In this paper, an active control scheme for the rewinding process of a roll-to-roll (R2R) system is investigated. The control objectives are to suppress the transverse vibration of the moving web, to track the desired velocity profile, and to keep the desired radius value of a rewind roller. The bearing coefficient in the rewind shaft is unknown and the rotating elements in the drive motor are various. The moving web is modeled as an axially moving beam system governed by hyperbolic partial differential equations (PDEs). The control scheme utilizes two control inputs: a control force exerted from a hydraulic actuator equipped with a damper, and a control torque applied to the rewind roller. Two adaptation laws are derived to estimate the unknown bearing coefficient and the bound of variations of the rotating elements. The Lyapunov method is employed to prove the robust stability of the rewind section, specifically the uniform and ultimate boundedness of all of the signals. The effectiveness of the proposed control schemes was verified by numerical simulations.     

The binomial decomposition of OWA functions,the 2‐additive and 3‐additive cases in n dimensions

In the context of the binomial decomposition of ordered weighted averaging (OWA) functions, we investigate the constraints associated with the 2‐additive and 3‐additive cases in n dimensions. The 2‐additive case depends on one coefficient whose feasible region does not depend on the dimension n. On the other hand, the feasible region of the 3‐additive case depends on two coefficients and is explicitly dependent on the dimension n. This feasible region is a convex polygon with n vertices and n edges, which is strictly expanding in the dimension n. The orness of the OWA functions within the feasible region is linear in the two coefficients, and the vertices associated with maximum and minimum orness are identified. Finally, we discuss the 3‐additive binomial decomposition in the asymptotic infinite dimensional limit.     

A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam

Horizontal displacement of hydropower dams is a typical nonlinear time-varying behavior that is difficult to forecast with high accuracy. This paper proposes a novel hybrid artificial intelligent approach, namely swarm optimized neural fuzzy inference system (SONFIS), for modeling and forecasting of the horizontal displacement of hydropower dams. In the proposed model, neural fuzzy inference system is used to create a regression model whereas Particle swarm optimization is employed to search the best parameters for the model. In this work, time series monitoring data (horizontal displacement, air temperature, upstream reservoir water level, and dam aging) measured for 11 years (1999–2010) of the Hoa Binh hydropower dam were selected as a case study. The data were then split into a ratio of 70:30 for developing and validating the hybrid model. The performance of the resulting model was assessed using RMSE, MAE, and R ². Experimental results show that the proposed SONFIS model performed well on both the training and validation datasets. The results were then compared with those derived from current state-of-the-art benchmark methods using the same data, such as support vector regression, multilayer perceptron neural networks, Gaussian processes, and Random forests. In addition, results from a Different evolution-based neural fuzzy model are included. Since the performance of the SONFIS model outperforms these benchmark models with the monitoring data at hand, the proposed model, therefore, is a promising tool for modeling horizontal displacement of hydropower dams.

     

RAW Image Reconstruction Using a Self-contained sRGB–JPEG Image with Small Memory Overhead

Most camera images are saved as 8-bit standard RGB (sRGB) compressed JPEGs. Even when JPEG compression is set to its highest quality, the encoded sRGB image has been significantly processed in terms of color and tone manipulation. This makes sRGB–JPEG images undesirable for many computer vision tasks that assume a direct relationship between pixel values and incoming light. For such applications, the RAW image format is preferred, as RAW represents a minimally processed, sensor-specific RGB image that is linear with respect to scene radiance. The drawback with RAW images, however, is that they require large amounts of storage and are not well-supported by many imaging applications. To address this issue, we present a method to encode the necessary data within an sRGB–JPEG image to reconstruct a high-quality RAW image. Our approach requires no calibration of the camera’s colorimetric properties and can reconstruct the original RAW to within 0.5% error with a small memory overhead for the additional data (e.g., 128 KB). More importantly, our output is a fully self-contained 100% compliant sRGB–JPEG file that can be used as-is, not affecting any existing image workflow—the RAW image data can be extracted when needed, or ignored otherwise. We detail our approach and show its effectiveness against competing strategies.     

New look on relay selection strategies for full-duplex multiple-relay NOMA over Nakagami-m fading channels

Wireless Networks - By removing the orthogonal use of radio-resources, non-orthogonal multiple access (NOMA) has been introduced to improve the spectral efficiency of fifth generation (5G) and...     

Atomically Smooth Graphene-Based Hybrid Template for the Epitaxial Growth of Organic Semiconductor Crystals

Atomically thin 2D materials are good templates to grow organic semiconductor thin films with desirable features. However, the 2D materials typically exhibit surface roughness and spatial charge inhomogeneity due to nonuniform doping, which can affect the uniform assembly of organic thin films on the 2D materials. A hybrid template is presented for preparation of highly crystalline small-molecule organic semiconductor thin film that is fabricated by transferring graphene onto a highly ordered self-assembled monolayer. This hybrid graphene template has low surface roughness and spatially uniform doping, and it yields highly crystalline fullerene thin films with grain sizes >300 nm, which is the largest reported grain size for C₆₀ thin films on 2D materials. A graphene/fullerene/pentacene phototransistor fabricated directly on the hybrid template has five times higher photoresponsivity than a phototransistor fabricated on a conventional graphene template supported by a SiO₂ wafer.     

Lithium Phosphonate Functionalized Polymer Coating for High-Energy Li[Ni0.8Co0.1Mn0.1]O2 with Superior Performance at Ambient and Elevated Temperatures

High-energy Ni-rich lithium transition metal oxides such as Li[Ni_0.8Co_0.1Mn_0.1]O₂ (NCM₈₁₁) are appealing positive electrode materials for next-generation lithium batteries. However, the high sensitivity toward moist air during storage and the high reactivity with common organic electrolytes, especially at elevated temperatures, are hindering their commercial use. Herein, an effective strategy is reported to overcome these issues by coating the NCM₈₁₁ particles with a lithium phosphonate functionalized poly(aryl ether sulfone). The application of this coating allows for a substantial reduction of lithium-based surface impurities (e.g., LiOH, Li₂CO₃) and, generally, the suppression of detrimental side reactions upon both storage and cycling. As a result, the coated NCM₈₁₁-based cathodes reveal superior Coulombic efficiency and cycling stability at ambient and, particularly, at elevated temperatures up to 60 ° C (a temperature at which the non-coated NCM₈₁₁ electrodes rapidly fail) owing to the formation of a stable cathode electrolyte interphase with enhanced Li⁺ transport kinetics and the well-retained layered crystal structure. These results render the herein presented coating strategy generally applicable for high-performance lithium battery cathodes.     

A secure image steganography based on modified matrix encoding using the adaptive region selection technique

This paper introduced an image steganography technique based on modified matrix encoding to enhance the perceptual quality of the stego images. Additionally, more pixel bit-planes are exploited in the data hiding process to improve the embedding capacity. The number of used image layers depends on the size of the given secret message and the texture characteristic of the cover image. The complexity of the pixel block is identified by the difference between the middle pixel and its neighbors. By performing the suitable embedding solutions of modified matrix encoding, the complexity is unchanged by the data hiding stage. Therefore, the used image regions could be determined precisely in the extraction process without using any additional information. The experimental results proved that the stego images created by the proposed approach achieved higher security against statistical and visual steganalysis techniques than the previous methods. The improvement of the security against detection by Ensemble Classifier is four times as secure as the best security performance of the existing methods.

     

TableSegNet: a fully convolutional network for table detection and segmentation in document images

International Journal on Document Analysis and Recognition (IJDAR) - Advances in image object detection lead to applying deep convolution neural networks in the document image analysis domain....     

Greedy Strategies for Convex Optimization

We investigate two greedy strategies for finding an approximation to the minimum of a convex function E defined on a Hilbert space H. We prove convergence rates for these algorithms under suitable conditions on the objective function E. These conditions involve the behavior of the modulus of smoothness and the modulus of uniform convexity of E.