Types Of Distortions Research Articles

Generalized relationships between the ionic radii of octahedral cations and the b crystallographic parameter of clays and related minerals

AbstractOver several decades, a wealth of literature has been devoted to correlations between the chemistries of phyllosilicates and their crystallographic unit-cell parameter values. The c parameter is currently used because of its relation to the layer-to-layer distance, characteristic of the various families of phyllosilicates. The b parameter is also of interest because it allows measurement of the layer lateral dimensions and inherent structural adjustments. This unit-cell distance can be extracted from X-ray diffraction traces from the (06ℓ;33ℓ) diffraction region and by attributing the main diffraction peak observed to a 060 reflection, leading to the relationship b = 6.d(060). The aim of this paper is to revisit the relationships between the b value (or equivalent) of the phyllosilicate (i.e. TO, TOT and TOTO) or hydroxide (i.e. hydroxide, oxyhydroxide and layered double hydroxide) families and the layer chemistry based on a mean ionic radius R of octahedral cations, calculated as $R = \mathop \sum \nolimits_{i = 1}^n ( {r_i.x_i} )$, where ri is the ionic radius of the octahedral cation i and xi is its molar fraction over n types of octahedral cations ($\mathop \sum \nolimits_{i = 1}^n ( {x_i} ) = 1$). The data were collected from the literature and involved both natural and synthetic samples with both dioctahedral and trioctahedral structures of the octahedral sheet. The results showed that b values can be linked strongly to R, leading to suitable linear regressions for all of the studied structures. All correlations were found to be applicable irrespective of the di- or trioctahedral nature of the octahedral sheet, and these are discussed in light of (1) the lateral dimension of the octahedral sheet and (2) the dimensional misfit between the tetrahedral and octahedral sheets. For hydroxide families, all data can be gathered on a single b vs R correlation line, and the dimensional properties of the octahedral sheet can be interpreted simply based on an oxygen–cation–oxygen mean distance. For TO structures, two general b vs R correlation trends were reported, and these were assigned to two adjustment mechanisms corresponding to distinct types of tetrahedral and octahedral distortions. For the mica TOT family, two main trends were also reported, whereas the use of the synthetic mica series allowed us to demonstrate that the obtained scattering of data was mainly driven by the presence of multiple limited solid solutions. Such chemical complexity was also noted for smectites, especially regarding the tetrahedral composition and associated variability in layer charge. This variability made it difficult to propose a general regression correlating b to R values for smectites, although the regression obtained for neutral TOT layers can apply as a first-order relation. Finally, a single general b vs R correlation was obtained for chlorites, and the observed slope of the regression was interpreted according to the role played by the isolated hydroxide sheet on the evolution of the lateral dimension of the structures.

Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.

Introduction: Despite continued technological improvements, measurement errors always reduce or distort the information that any real experiment can provide to quantify cellular dynamics. This problem is particularly serious for cell signaling studies to quantify heterogeneity in single-cell gene regulation, where important RNA and protein copy numbers are themselves subject to the inherently random fluctuations of biochemical reactions. Until now, it has not been clear how measurement noise should be managed in addition to other experiment design variables (e.g., sampling size, measurement times, or perturbation levels) to ensure that collected data will provide useful insights on signaling or gene expression mechanisms of interest. Methods: We propose a computational framework that takes explicit consideration of measurement errors to analyze single-cell observations, and we derive Fisher Information Matrix (FIM)-based criteria to quantify the information value of distorted experiments. Results and Discussion: We apply this framework to analyze multiple models in the context of simulated and experimental single-cell data for a reporter gene controlled by an HIV promoter. We show that the proposed approach quantitatively predicts how different types of measurement distortions affect the accuracy and precision of model identification, and we demonstrate that the effects of these distortions can be mitigated through explicit consideration during model inference. We conclude that this reformulation of the FIM could be used effectively to design single-cell experiments to optimally harvest fluctuation information while mitigating the effects of image distortion.

ESG Disclosure and Sustainability Transition: A New Metric and Emerging Trends in Responsible Investments

Abstract Environmental sustainability, social engagement and robust governance gained growing attention from consumers and investors alike, leading to what we call ‘ESG finance’. ESG criteria are now shaping the behaviour and choices of enterprises, investors and consumers. Indeed laudable, the increased importance of ESG finance could raise concerns about the robustness underneath this new set of financial products. Moreover, the reliability of ESG-related data and information shared by companies may also be challenged due to the ability of those indicators to shape the public profile of companies and their attractiveness for investors. A new breed of ESG rankings and ratings is widening the metrics that consumers and investors use to make informed decisions about their consumption and investment. Yet, such rankings and ratings hinge on the individual disclosure approaches of the interested companies. This article wishes to complement available data and information about specific emissions data released by companies with the ESG disclosure levels, in particular relating to the “environment” dimension. Based on these disclosure levels, the authors build a new metric with the purpose of reducing asymmetric information and promoting more responsible investment. Starting from ESG-related data and publicly available information, a new disclosure-adjusted pollution index (namely, the GHG Scope-1 DAdj index) is developed. The second part of the article puts forward an empirical analysis on the basis of this new index, suggesting that the rush to ESG finance could be poised to generate leeway for new types of asymmetries and possible distortions in investment decision-making, also providing grounds for potentially reckless speculative attitudes, especially in the domain of product development of financial instruments that may generate new forms of risk for investors. Using the GHG Scope-1 DAdj index makes a few companies less environmentally friendly and interesting for investors who are seeking responsible and sustainable investment options. The innovative index and the empirical analysis lead the authors to suggest to “split the domains of ESG” to better gauge the relation between impact and compliance costs for companies as the individual components of environment, social engagement and governance are considered separately.

Assessing the quality of 3D point clouds using descriptors for color and geometry texture

Since the mid-20th century, the use of digital formats for visual content has revolutionized communication in society. The Internet and digital broadcasting systems, which became widely available in the 1990s, led to an incredible expansion of multimedia consumption among the public. As a result, telecommunication networks and providers were pushed to their limits to meet the growing demand for multimedia content. Traditional electronic imaging systems, such as TV broadcasting systems, were designed based on subjective quality analysis for defining parameters like the number of lines in a video. However, with the emergence of new digital visual content services that require faster and more affordable methods of evaluating human perceived quality, there has been a need for automatic quality assessment. In the past few decades, numerous visual quality models based on algorithms running on digital computers have been proposed. While these existing models are highly advanced for 2D digital imagery, a new generation of immersive media has emerged with different data structures that are not applicable to traditional 2D methods and require novel quality assessment metrics. These emerging immersive media formats provide a 3D visual representation of real objects and scenes. In this new visual format, objects can be captured, compressed, transmitted, an visualized in real-time as 3D content, allowing consumers to select free-viewpoints to view such media. One of the most popular formats for immersive media is Point Cloud (PC), which is composed of points with 3D geometry coordinates and color information, and sometimes other attributes such as reflectance and transparency. This research focuses on the quality assessment of 3D Point Clouds using novel color and geometric texture statistics. Since distortions in both color and geometry attributes of 3D visual content can affect the perceived visual quality, this work proposes using both color-based and geometry-based texture descriptors for Point Clouds to obtain visual degradation information through their statistics. Four novel Point Cloud texture descriptors are introduced in this work, three of which are color-based and one which is geometry-based. Additionally, a new voxelization method is proposed, which converts points to voxels (volume elements), and improves the performance of the color-based texture descriptors. The proposed Point Cloud quality assessment method demonstrates high performance, comparable to the state-of-the-art methods, while also being flexible and extensible to adapt to different types of distortions.

Multi-layer and Multi-scale feature aggregation for DIBR-Synthesized image quality assessment

Depth-Image-Based Rendering (DIBR) is one of the main fundamental techniques for generating new viewpoints in 3D video applications such as multi-viewpoint video (MVV), free viewpoint video (FVV) and virtual reality (VR). Due to the imperfections of color images, depth maps or texture restoration techniques, several types of distortions occur in synthesized views. However, most of related works evaluated the quality of DIBR-synthesized views by only detecting a specific type of distortion, such as stretching, black holes, blurring, etc., which were unable to accurately evaluate the quality of DIBR-synthesized views. In this paper, a new no-reference image quality assessment method is proposed to evaluate the quality of DIBR-synthesized images by combining multi-layer and multi-scale features of images. To be specific, the distortions introduced by different stages of virtual viewpoint synthesis are first analyzed, and then multi-layer and multi-scale features are extracted to estimate the degree of texture and structure distortions. As a result, individual quality scores associated with two types of distortions (e.g., structural distortion and texture distortion) are aggregated to an overall image quality. Experimental results on two publicly available DIBR datasets show that the method has better performance than the state-of-the-art models.Index Terms: image quality assessment, DIBR-synthesized image, distortion correction, BIQA.

Pregnancy in a Young Patient with Metastatic HER2-Positive Breast Cancer—Between Fear of Recurrence and Desire to Procreate

Breast cancer is the most frequent neoplasm among women and the second leading cause of death by cancer. It is the most frequent cancer diagnosed during pregnancy. Pregnancy-associated breast cancer is defined as breast cancer that is diagnosed during pregnancy and/or in the postpartum period. Data about young women with metastatic HER2-positive cancer who desire a pregnancy are scarce. The medical attitude in these clinical situations is difficult and nonstandardized. We present the case of a 31-year-old premenopausal woman diagnosed in December 2016 with a stage IV Luminal HER2-positive metastatic breast cancer (pT2 N0 M1 hep). The patient was initially treated by surgery in a conservative manner. Postoperatively, the presence of liver metastases was found by CT investigation. Consequently, line I treatment (docetaxel l75 mg/m² iv; trastuzumab 600 mg/5 mL sq) and ovarian drug suppression (Goserelin 3.6 mg sq at 28 days) was administered. After nine cycles of treatment, the patient's liver metastases had a partial response to the therapy. Despite having a favorable disease evolution and a strong desire to procreate, the patient vehemently refused to continue any oncological treatment. The psychiatric consult highlighted an anxious and depressive reaction for which individual and couple psychotherapy sessions were recommended. After 10 months from the interruption of the oncological treatment, the patient appeared with an evolving pregnancy of 15 weeks. An abdominal ultrasound revealed the presence of multiple liver metastases. Knowing all the possible effects, the patient consciously decided to postpone the proposed second-line treatment. In August 2018, the patient was admitted in the emergency department with malaise, diffuse abdominal pain and hepatic failure. Abdominal ultrasound found a 21-week-old pregnancy which had stopped in evolution, multiple liver metastases and ascites in large quantity. She was transferred to the ICU department where she perished just a few hours later. Conclusions/Discussion: From a psychological standpoint, the patient had an emotional hardship to make the transition from the status of a healthy person to the status of a sick person. Consequently, she entered a process of emotional protection of the positive cognitive distortion type, which favored the decision to abandon treatment and try to complete the pregnancy to the detriment of her own survival. The patient delayed the initiation of oncological treatment in pregnancy until it was too late. The consequence of this delay in treatment led to the death of the mother and fetus. A multidisciplinary team worked to provide this patient with the best medical care and psychological assistance throughout the course of the disease.

LFACon: Introducing Anglewise Attention to No-Reference Quality Assessment in Light Field Space.

Light field imaging can capture both the intensity information and the direction information of light rays. It naturally enables a six-degrees-of-freedom viewing experience and deep user engagement in virtual reality. Compared to 2D image assessment, light field image quality assessment (LFIQA) needs to consider not only the image quality in the spatial domain but also the quality consistency in the angular domain. However, there is a lack of metrics to effectively reflect the angular consistency and thus the angular quality of a light field image (LFI). Furthermore, the existing LFIQA metrics suffer from high computational costs due to the excessive data volume of LFIs. In this paper, we propose a novel concept of "anglewise attention" by introducing a multihead self-attention mechanism to the angular domain of an LFI. This mechanism better reflects the LFI quality. In particular, we propose three new attention kernels, including anglewise self-attention, anglewise grid attention, and anglewise central attention. These attention kernels can realize angular self-attention, extract multiangled features globally or selectively, and reduce the computational cost of feature extraction. By effectively incorporating the proposed kernels, we further propose our light field attentional convolutional neural network (LFACon) as an LFIQA metric. Our experimental results show that the proposed LFACon metric significantly outperforms the state-of-the-art LFIQA metrics. For the majority of distortion types, LFACon attains the best performance with lower complexity and less computational time.

Reference free auscultation quality metric and its trends

Stethoscopes are used ubiquitously in clinical settings to ‘listen’ to lung sounds. The use of these systems in a variety of healthcare environments (hospitals, urgent care rooms, private offices, community sites, mobile clinics, etc.) presents a range of challenges in terms of ambient noise and distortions that mask lung signals from being heard clearly or processed accurately using auscultation devices. With advances in technology, computerized techniques have been developed to automate analysis or access a digital rendering of lung sounds. However, most approaches are developed and tested in controlled environments and do not reflect real-world conditions where auscultation signals are typically acquired. Without a priori access to a recording of the ambient noise (for signal-to-noise estimation) or a reference signal that reflects the true undistorted lung sound, it is difficult to evaluate the quality of the lung signal and its potential clinical interpretability. The current study proposes an objective reference-free Auscultation Quality Metric (AQM) which incorporates low-level signal attributes with high-level representational embeddings mapped to a nonlinear quality space to provide an independent evaluation of the auscultation quality. This metric is carefully designed to solely judge the signal based on its integrity relative to external distortions and masking effects and not confuse an adventitious breathing pattern as low-quality auscultation. The current study explores the robustness of the proposed AQM method across multiple clinical categorizations and different distortion types. It also evaluates the temporal sensitivity of this approach and its translational impact for deployment in digital auscultation devices.

Protecting the mental health of the future workforce: exploring the prevalence of cognitive distortions among nursing students.

Evidence suggests that stress and anxiety are commonly experienced by nursing students. Cognitive distortions, or negative thinking styles, are linked to stress and anxiety and have been shown to have a detrimental effect on mental health. Therefore, identification of cognitive distortions among nursing students may serve to prevent the development of mental health issues among this population. To explore the prevalence of cognitive distortions among a sample of nursing students, identify which types were more common and determine how these types varied by sociodemographic characteristics. A cross-sectional online questionnaire survey was conducted with undergraduate nursing students at a university in Palestine. All students enrolled during the 2020-21 academic year ( n =305) were invited to participate, of whom 176 responded. Of the 176 students who responded, 9 (5%) had severe levels of cognitive distortion, 58 (33%) had moderate levels, 83 (47%) had mild levels and 26 (15%) had healthy levels. Of the nine types cognitive distortions described in the questionnaire, respondents engaged most in emotional reasoning, followed by perfectionist thinking and 'What if?' The cognitive distortions that respondents were least likely to engage in were polarised thinking and overgeneralising. Single, first-year and younger respondents had a significantly higher level of cognitive distortions. The results emphasise the importance of identifying and managing cognitive distortions in nursing students, not only in the mental health clinics at the university where the study took place, but also in preventive well-being services. Universities should prioritise nursing students' mental health.

Self-Supervised Representation Learning for Video Quality Assessment

No-reference (NR) video quality assessment (VQA) is a challenging problem due to the difficulty in model training caused by insufficient annotation samples. Previous work commonly utilizes transfer learning to directly migrate pre-trained models on the image database, which suffers from domain inadaptation. Recently, self-supervised representation learning has become a hot spot for the independence of large-scale labeled data. However, existing self-supervised representation learning method only considers the distortion types and contents of the video, there needs to investigate the intrinsic properties of videos for the VQA task. To amend this, here we propose a novel multi-task self-supervised representation learning framework to pre-train a video quality assessment model. Specifically, we consider the effects of distortion degrees, distortion types, and frame rates on the perceived quality of videos, and utilize them as guidance to generate self-supervised samples and labels. Then, we optimize the ability of the VQA model in capturing spatio-temporal differences between the original video and the distorted version using three pretext tasks. The resulting framework not only eases the requirements for the quality of the original video but also benefits from the self-supervised labels as well as the Siamese network. In addition, we propose a Transformer-based VQA model, where short-term spatio-temporal dependencies of videos are modeled by 3D-CNN and 2D-CNN, and then the long-term spatio-temporal dependencies are modeled by Transformer because of its excellent long-term modeling capability. We evaluated the proposed method on four public video quality assessment databases and found that it is competitive with all compared VQA algorithms.

The Distortion of Public–Private Partnerships in China: An Institutional Perspective of Central–Local Government Relations

How to explain the distortion of public–private partnerships (PPPs) is underexplored. Drawing on principal–agent theory, this article proposes an institutional incentive-driven framework. Based on a case study of PPPs in China, this article finds that central–local government relationships play a crucial role in shaping PPP performance. Goal incongruence and information asymmetry lead to two types of distortion. First, PPPs become a political task for local governments to respond to higher-level governments’ needs. Second, PPPs serve as financing tools to create political achievements. These opportunistic behaviors violate the goals of the central government’s PPP policy and increase government debt risks.

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A thorough experimental investigation on airfoil turbulence interaction noise

This paper on airfoil turbulence interaction noise reveals the nature of the relation between the distortion type of turbulent structures and radiated far-field noise. The turbulence interaction phenomenon is explored through comprehensive simultaneous hot-wire, surface pressure, and far-field noise measurements. Two grid turbulence cases are utilized to examine the effect of the coherent structure's length scale compared to the airfoil's leading-edge radius. The results show that the turbulent structures with a size comparable to the leading-edge radius disperse into smaller three-dimensional structures, losing their spatial coherence in the vicinity of the stagnation point. In contrast, the structures with larger integral length scales distort into highly coherent two-dimensional structures, yielding an increase in the surface pressure fluctuation energy spectra and the chordwise extent of the affected area by the interaction phenomenon, which is found to be responsible for the increased levels of far-field noise. The turbulence characteristics of the flow far upstream of the stagnation point determine the unsteady loading behavior at the stagnation point yet have little influence on the unsteady loading of the full airfoil chord. The stagnation point velocity fluctuations manifest a strong link to the remainder of the airfoil chord, as well as the near-field hydrodynamic to far-field acoustic signal coherence, while demonstrating no communication with the surface pressure fluctuations at the stagnation point.

COMPARISON OF SIFT AND ORB METHODS IN IDENTIFYING THE FACE OF BUDDHA STATUE

The statue is part of the heritage facial recognition process which is immobile and artistically stylized. Identifying the similarities between the statues can help provide an important reference for tourism in recognizing the faces of the statues which are different and have almost the same characteristics in every country, especially in Indonesia, among the facial recognition of the statues based on the condition, color, and shape of the face. The purpose of this study is to apply the original images that have characteristics, partially done manually to various types of transformations and calculate matching evaluation parameters such as the number of key points in the image, the level of matching, and the required execution time for each algorithm. To confirm the efficiency of the proposed method, experiments were carried out on private data sets obtained from statues under low light conditions and in different poses. The data was taken based on the image of the Buddha's face and matched with the facial image of the Buddha statue available in the database using comparisons resulting from data processing using the Sift and ORB methods with various types of transformations. The result will be seen in the image that is matched with the best algorithm for each type of distortion. The faces tested are images of the faces of the Buddha statues that are recognized, and photos of some of the original statues that were not saved due to unclear lighting and camera distance factors. The results show that the number of key points generated is the number of key points, the ORB method gives fewer results compared to the SIFT method and the average SIFT recognition and processing time shows better performance for an average of 100% at a SIFT matching rate of 2% with time 0.400285 and the ORB method is 1% for the time 0.400961

ANALYSIS OF THE IMPACT OF DISTORTION ON SOUND RECORDINGS AS ANTI FORENSIC ACTIVITIES

Anti-forensics on audio is aimed at complicating investigations on audio forensics, on sound recordings. Sound recordings can be altered or manipulated in various ways as well as the provision of distortion effects on sound recordings. Effect such distortions will make it difficult for investigators to find out the owner of the original voice. Analysis of distortion effects on sound recordings for anti-forensic activities, has not been widely carried out. Distortion can be an effective anti-forensic technique because the sound produced will be noisy, making it difficult for investigators to conduct investigations. In this study, testing was carried out using 3 types of distortion, namely Hard Clipping, Hard Overdrive and Odd Harmonics. To find out the extent to which the three types of distortions make it difficult to identify the owner of the original sound, the variables that affect each type of distortion are set at low, medium, and high levels. Formant values from the original and distorted sound samples were compared for later analysis using the Anova One-Way approach to show whether the original sound was identical and the other three voices were distorted. The test was carried out using 10 sound samples. From the results of the anova analysis, it is known that the types of Distortion of Hard Clipping and Odd Harmonics with variables at high levels can manipulate sound recordings, making it difficult to recognize the authenticity of a sound recording. Unlike the case with the type of Distortion of Hard Overdrive with variable level high – low and Hard Clipping and Odd Harmonics with variable level low – medium, it proves that sound recordings can still be identified.

Enhance Mobile Phone Digital Camera Images Using Band Reject Filter and Discrete Fourier Transform

These days, We know that mobile phones with camera are very common to used by most of the people because of their ease of use and carry . So I figured I need for mobile phone images processing. These images are often subjected to a variety of types of distortion, such as noise, poor illumination, and blur due to transmission , image acquisition and mobile movement during capturing scene. This Degradation leads to produce distorted images are not suitable for viewing or application. To overcome this degradation, this paper presents image improvement in mobile phone by distortion removal using Band Reject Filter and DFT to obtain on the best clear image. Periodic noise is sinusoidal of several of certain frequencies and periodic in nature. It looks identical to the bars that the image is covered. The proposed method is subjected to eliminate this noise from a set of images using Band Reject Filter using discrete Fourier transform that attenuate and replace a number of degradation bands. Then, computes the peak signal to noise ratio metric for each band. After that, select the largest value of each band for the purpose the making one clear image. Finally, the bands that carry value large are merged to produce a one image. The final image is converted to the spatial domain using an inverse Fourier transform. The experimental outcomes indicate that the proposed method improves the degraded image by a greater percentage without using the better value for metric and makes image more clear and informative compare with the input image.

Geometric frustration of Jahn-Teller order in the infinite-layer lattice.

The Jahn-Teller effect, in which electronic configurations with energetically degenerate orbitals induce lattice distortions to lift this degeneracy, has a key role in many symmetry-lowering crystal deformations1. Lattices of Jahn-Teller ions can induce a cooperative distortion, as exemplified by LaMnO3 (refs. 2,3). Although many examples occur in octahedrally4 or tetrahedrally5 coordinated transition metal oxides due to their high orbital degeneracy, this effect has yet to be manifested for square-planar anion coordination, as found in infinite-layer copper6,7, nickel8,9, iron10,11 and manganese oxides12. Here we synthesize single-crystal CaCoO2 thin films by topotactic reduction of the brownmillerite CaCoO2.5 phase. We observe a markedly distorted infinite-layer structure, with ångström-scale displacements of the cations from their high-symmetry positions. This can be understood to originate from the Jahn-Teller degeneracy of the dxz and dyz orbitals in the d7 electronic configuration along with substantial ligand-transition metal mixing. A complex pattern of distortions arises in a [Formula: see text] tetragonal supercell, reflecting the competition between an ordered Jahn-Teller effect on the CoO2 sublattice and the geometric frustration of the associated displacements of the Ca sublattice, which are strongly coupled in the absence of apical oxygen. As a result of this competition, the CaCoO2 structure forms an extended two-in-two-out type of Co distortion following 'ice rules'13.

NITS-IQA Database: A New Image Quality Assessment Database.

This paper describes a newly-created image database termed as the NITS-IQA database for image quality assessment (IQA). In spite of recently developed IQA databases, which contain a collection of a huge number of images and type of distortions, there is still a lack of new distortion and use of real natural images taken by the camera. The NITS-IQA database contains total 414 images, including 405 distorted images (nine types of distortion with five levels in each of the distortion type) and nine original images. In this paper, a detailed step by step description of the database development along with the procedure of the subjective test experiment is explained. The subjective test experiment is carried out in order to obtain the individual opinion score of the quality of the images presented before them. The mean opinion score (MOS) is obtained from the individual opinion score. In this paper, the Pearson, Spearman and Kendall rank correlation between a state-of-the-art IQA technique and the MOS are analyzed and presented.

Image Quality Assessment–driven Reinforcement Learning for Mixed Distorted Image Restoration

Due to the diversity of the degradation process that is difficult to model, the recovery of mixed distorted images is still a challenging problem. The deep learning model trained under certain degradation declines significantly in other degradation situations. In this article, we explore ways to use a combination of tools to deal with the mixed distortion. First, we illustrate the limitations of a single deep network in dealing with multiple distortion types and then introduce a hierarchical toolkit with distinguished powerful tools. Second, we investigate how an efficient representation of images combined with a reinforcement learning (RL) paradigm helps to deal with tool noise in continuous restoration. The proposed method can accurately capture the distortion preferences for selecting the optimal recovery tools by RL agent. Finally, to fully utilize random tools for unknown distortion combinations, we adopt the exploration scheme with various quality evaluation methods to achieve more quality improvements. Experimental results demonstrate that the peak signal-to-noise ratio of the proposed method is 3.30 dB higher than other state-of-the-art RL-based methods on the CSIQ single distortion dataset and 0.95 dB higher on the DIV2K mixed distortion dataset.

Effects of Inlet Swirl Distortion on a Multi-Stage Compressor with Inlet Guide Vanes and Stall Margin Enhancement Method

Inlet swirl distortion is generally considered as a type of velocity distortion, and inlet guide vanes (IGVs) are widely used in the multi-stage compressor of aero-engines to eliminate the tangential velocity of the swirl flow. However, few studies have explored whether there still exists some negative influence of inlet swirl distortion on the compressor, even after the installation of IGVs. Therefore, in this study, the influence of various types of inlet swirl distortions on a multi-stage compressor with the installation of IGVs is investigated. A swirl distortion generator installed in the inlet duct was designed to produce various types of swirl flow patterns. When the distortion intensity increased to some degree, there still existed a decrease in the compressive capability and an obvious additional efficiency loss. The inlet twin swirl distortion was accompanied by total pressure distortion, so even with the installation of IGVs, there was still a significantly negative influence on the performance of the multi-stage compressor, especially the stall margin. Subsequently, to improve the stall margin under inlet swirl distortion, the stall precursor-suppressed (SPS) casing treatment was installed in the first stage of the multi-stage compressor. It could enhance the stall margin of the compressor with no obvious change in the characteristic curves and no additional efficiency loss under various types of inlet swirl distortions, and its mechanism was verified by capturing the dynamic pressure characteristics.