Adaptive Discretization for Computerized Tomography

Two adaptive discretization frameworks are tested for computerized tomography (CT) data reconstruction. Removal of inactive pixels is primary motivation. Efficient and user independent entropy optimized masking is employed for spatial filtering purposes. Density of nodes at high gradient of reconstructed physical property is used as adaptation criterion. An alternative option, independent from noisy projection data and nature of the physical properties, is also discussed. Sensitivity analysis between the uniform and nonuniform (evolved via adaptive route) reconstruction grid reveals the utility of nonuniform grids. Iterative and transform based reconstruction techniques are used. Outcomes are tested successfully on three real world projection data from two different compact CT setups and one commercial high-resolution micro-CT scanner.     

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.     

Convolutional neural network with adaptive inferential framework for skeleton-based action recognition

In the task of skeleton-based action recognition, CNN-based methods represent the skeleton data as a pseudo image for processing. However, it still remains as a critical issue of how to construct the pseudo image to model the spatial dependencies of the skeletal data. To address this issue, we propose a novel convolutional neural network with adaptive inferential framework (AIF-CNN) to exploit the dependencies among the skeleton joints. We particularly investigate several initialization strategies to make the AIF effective with each strategy introducing the different prior knowledge. Extensive experiments on the dataset of NTU RGB+D and Kinetics-Skeleton demonstrate that the performance is improved significantly by integrating the different prior information. The source code is available at: https://github.com/hhe-distance/AIF-CNN.     

Design of an adaptive super-twisting decoupled terminal sliding mode control scheme for a class of fourth-order systems

This paper proposes an adaptive super-twisting decoupled terminal sliding mode control technique for a class of fourth-order systems. The adaptive-tuning law eliminates the requirement of the knowledge about the upper bounds of external perturbations. Using the proposed control procedure, the state variables of cart-pole system are converged to decoupled terminal sliding surfaces and their equilibrium points in the finite time. Moreover, via the super-twisting algorithm, the chattering phenomenon is avoided without affecting the control performance. The numerical results demonstrate the high stabilization accuracy and lower performance indices values of the suggested method over the other ones. The simulation results on the cart-pole system as well as experimental validations demonstrate that the proposed control technique exhibits a reasonable performance in comparison with the other methods.     

An enhancement algorithm based on adaptive updating template with Gaussian model for Si3N4 ceramic bearing roller surface defects detection

The method based on machine vision image processing is used to detect the surface defects of Si₃N₄ bearing roller. Owing to the variety of defects, small area and low contrast, it is easy to miss or error detection. In this paper, an adaptive update template defect enhancement algorithm based on Gaussian model is proposed. First, a large number of surface images of Si₃N₄ bearing roller are collected to obtain the non-defect background statistical feature, and the background characteristic curve is fitted by Gaussian model. Further, the initial background template is gained according to the Gaussian curve. Then, combined with the gray distribute of defect images and initial background template, unique adaptive update template can be established. Finally, subtraction operation and nonlinear enhancement are used to improve the comparison of defect information and background. Through inverse sorting, adaptive threshold segmentation and Canny operation, the precise positioning of defects is realized. The enhancement algorithm can effectively enhance the contrast and eliminate the influence of noise. The average detection time is 0.84s, and the detection accuracy is 96.2%.     

Ecological Planting Imperative: Functional Systems,Not Stylized Ecologies

The ability of landscape architectural projects to mitigate the worst effects of climate change will depend upon designed ecological systems. These systems will be built with plants. Despite the recognition of ecology as an essential driver of landscapes, the professionals of landscape architecture too often lack the knowledge and practical skills to create robust vegetative systems. New approaches and tools are required. This article outlines principles and methods for designing biodiverse plant systems for urban sites. Planting methods that increase species richness, functional diversity, and spatial complexity are emphasized as a way of developing more resilient plantings. Selecting species with similar evolutionary adaptions to stress, disturbance, and competition—as well as creating multi-layered compositions of diverse plant morphologies—allows designers to create compatible, long-lived plant mixes. To balance the increased visual complexity of diverse plant mixes, the article explores design techniques to make plantings more appealing to the public. The strategies explored here are based on the projects, experience, and research of Phyto Studio, a Washington, D.C. based studio. The methods build on work described in the author’s book, Planting in a Post-Wild World, an exploration of how to create designed plant communities.     

Dynamic load-altering attack detection based on adaptive fading Kalman filter in power systems

This paper presents an effective and feasible method for detecting dynamic load-altering attacks (D-LAAs) in a smart grid. First, a smart grid discrete system model is established in view of D-LAAs. Second, an adaptive fading Kalman filter (AFKF) is designed for estimating the state of the smart grid. The AFKF can completely filter out the Gaussian noise of the power system, and obtain a more accurate state change curve (including consideration of the attack). A Euclidean distance ratio detection algorithm based on the AFKF is proposed for detecting D-LAAs. Amplifying imperceptible D-LAAs through the new Euclidean distance ratio improves the D-LAA detection sensitivity, especially for very weak D-LAA attacks. Finally, the feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified based on simulations.     

Dynamic response of ferrofluidic deformable mirrors using elastomer membrane and overdrive techniques

The experimental results obtained with a ferrofluidic deformable mirror controlled by electro-magnet actuators are presented here. Using a step input through a single actuator, we obtained a steady-state settling time of 100?ms; however, different combinations of overdrive inputs can be used to decrease it to 25?ms. A new technique which consists of laying down an elastomer membrane, coated with an aluminum film, on the ferrofluid is also discussed. By adding the membrane on the ferrofluid, it further decreases the time response by a factor of 2. Furthermore, the thin aluminum layer improves the reflectivity of the mirror. Finally, using the membrane and the overdrive techniques combined, the time response is improved by a factor of 20. Numerical simulations show that ferrofluidic mirrors using membranes and improved electronics should reach settling times of the order of a millisecond. Presumably, even lower settling times could be possible.     

An analysis of adaptive planning capacity: The case of chilean water utilities

New long-term planning approaches capable of coping with uncertainties such as climate change, rapid urbanization, and changing societal values, have been put forward as a way of producing more robust and sustainable plans for the future. But is the planning practice ready for their adoption? This paper takes four key propositions from the adaptive planning literature and tests the existing capacity for adopting those propositions in the context of Chilean water utilities. We will then propose how existing capacities could be enhanced, and propose alternatives for current planning practices, highlighting the importance of implementation through experimentation.     

新一代多模型迭代算法在70 kVp成人副鼻窦CT扫描中的应用

探讨成人在70 kVp副鼻窦CT检查中降低有效辐射剂量的同时,获取最佳图像质量的新一代多模型迭代算法(Adaptive Statistical Iterative Reconstruction-V,Asir-V)的权重。本研究对20例成人患者进行副鼻窦Revolution CT检查,采用70 kVp、230 mA条件下进行螺旋CT扫描,扫描完成后对每例患者的原始数据采用不同权重(0,20%,40%,60%,80%,100%)的后置新一代多模型迭代算法(Adaptive Statistical Iterative Reconstruction-V,Asir-V)重建的方法,共获得120幅图像,并对每例6组图像进行客观评价及主观评分。客观评价包括噪声(standard deviations, SD)和对比噪声比(contrast noise ratio,CNR),主观评价由2名评价医生对图像质量评分并对两者的主观一致性进行评价。每例患者扫描完成后,记录其剂量长度乘积(dose-length product,DLP)和CT容积剂量指数(CT dose index volume,CTDI_vol),并计算其平均有效辐射剂量(effective dose,ED)。客观评价结果表明不同权重的Asir-V图像噪声随着Asir-V权重的提升而降低,CNR值随着Asir-V权重的提升而升高,且有效辐射剂量ED较常规头颈部CT检查的平均辐射剂量明显减少;主观评价结果表明不同权重的Asir-V图像评分不同,在Asir-V 60% 时评分最高,且2名医生主观评价一致性好。结合主客观评价结果,认为在70 kVp、230 mA成人副鼻窦Revolution CT检查中,Asir-V 60％的图像既可满足临床诊断的需要,也可明显降低有效辐射剂量。