Blind Quality Assessment for Image Superresolution Using Deep Two-Stream Convolutional Networks

Numerous image superresolution (SR) algorithms have been proposed for reconstructing high-resolution (HR) images from input images with lower spatial resolutions. However, effectively evaluating the perceptual quality of SR images remains a challenging research problem. In this paper, we propose a no-reference/blind deep neural network-based SR image quality assessor (DeepSRQ). To learn more discriminative feature representations of various distorted SR images, the proposed DeepSRQ is a two-stream convolutional network including two subcomponents for distorted structure and texture SR images. Different from traditional image distortions, the artifacts of SR images cause both image structure and texture quality degradation. Therefore, we choose the two-stream scheme that captures different properties of SR inputs instead of directly learning features from one image stream. Considering the human visual system (HVS) characteristics, the structure stream focuses on extracting features in structural degradations, while the texture stream focuses on the change in textural distributions. In addition, to augment the training data and ensure the category balance, we propose a stride-based adaptive cropping approach for further improvement. Experimental results on three publicly available SR image quality databases demonstrate the effectiveness and generalization ability of our proposed DeepSRQ method compared with state-of-the-art image quality assessment algorithms

A Novel Ship Collision Avoidance Awareness Approach for Cooperating Ships Using Multi-Agent Deep Reinforcement Learning

From MDPI via Jisc Publications RouterHistory: accepted 2021-09-19, pub-electronic 2021-09-25Publication status: PublishedFunder: Zhejiang Province Key Ramp;D projects,China; Grant(s): 2021C03015, R21F030005Funder: NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization; Grant(s): U1709215Ships are special machineries with large inertias and relatively weak driving forces. Simulating the manual operations of manipulating ships with artificial intelligence (AI) and machine learning techniques becomes more and more common, in which avoiding collisions in crowded waters may be the most challenging task. This research proposes a cooperative collision avoidance approach for multiple ships using a multi-agent deep reinforcement learning (MADRL) algorithm. Specifically, each ship is modeled as an individual agent, controlled by a Deep Q-Network (DQN) method and described by a dedicated ship motion model. Each agent observes the state of itself and other ships as well as the surrounding environment. Then, agents analyze the navigation situation and make motion decisions accordingly. In particular, specific reward function schemas are designed to simulate the degree of cooperation among agents. According to the International Regulations for Preventing Collisions at Sea (COLREGs), three typical scenarios of simulation, which are head-on, overtaking and crossing, are established to validate the proposed approach. With sufficient training of MADRL, the ship agents were capable of avoiding collisions through cooperation in narrow crowded waters. This method provides new insights for bionic modeling of ship operations, which is of important theoretical and practical significance

Health-related effects and improving extractability of cereal arabinoxylans

Arabinoxylans (AXs) are major dietary fibers. They are composed of backbone chains of -(1–4)- linked xylose residues to which -l-arabinose are linked in the second and/or third carbon positions. Recently, AXs have attracted a great deal of attention because of their biological activities such as their immunomodulatory potential. Extraction of AXs has some difficulties; therefore, various methods have beenusedto increase the extractability ofAXs withvaryingdegrees of success, suchas alkaline, enzymatic, mechanical extraction. However, some of these treatments have been reported to be either expensive, such as enzymatic treatments, or produce hazardous wastes and are non-environmentally friendly, such as alkaline treatments. On the other hand, mechanical assisted extraction, especially extrusion cooking, is an innovative pre-treatment that has been used to increase the solubility of AXs. The aim of the current review article is to point out the health-related effects and to discuss the current research on the extraction methods of AXs

Interval-valued Evidence Updating with Reliability and Sensitivity Analysis for Fault Diagnosis

Information fusion methods based on Dempster-Shafer evidence theory (DST) have been widely used in fault diagnosis. In DST-based methods, the monitoring information collected from sensors is modeled as multiple pieces of diagnosis evidence in the form of basic belief assignment (BBA), and Dempster’s rule is then used to combine these BBAs to obtain the fused BBA for diagnosis decision making. However, the belief structure with crisp singlevalued belief degrees in BBA may be too coarse to truthfully represent detailed fault information. Moreover, Dempster’s rule only uses a static combination process, which is unsuitable for dynamically fusing information collected at different time steps. In order to address these issues, the paper proposes a dynamic diagnosis method based on interval-valued evidential updating. First of all, the diagnosis evidence is constructed as an interval-valued belief structure (IBS), which provides a more informative scheme than BBA to model fault information. Secondly, the proposed evidential updating strategy can generate updated IBS as global diagnosis evidence by updating the previous evidence with the new incoming evidence recursively. Thirdly, the reliability and sensitivity indices are designed to evaluate and compare the performance of the proposed updating strategy with other commonly used strategies. Finally, the effectiveness of the proposed evidential updating strategy is demonstrated through some typical fault experiments of a machine rotor

Atypical chronic inflammatory demyelinating polyradiculoneuropathy with ophthalmoplegia and anti-sulfatide IgM positivity

Background Chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is a heterogeneous group of chronic immune-mediated polyradiculoneuropathies. The clinical presentation of CIDP is mainly characterized by a classic peripheral demyelinating sensory-motor type and persists for a minimum of 2 months. However, CIDP may also present with atypical symptoms. Case presentation: This report presents the case of a patient with CIDP with ophthalmoplegia and anti-sulfatide IgM antibodies. Maintenance intravenous immunoglobulin and glucocorticoid therapies were administered to the patient in accordance with the clinical, laboratory, and electrophysiological findings, which were indicative of CIDP. The treatment partially improved the symptoms, and no recurrence was detected throughout the 3-month monitoring phase. Conclusions This study combines a retrospective analysis and a literature review to explore the possible mechanism of CIDP