Satellite-Based Water Quality Mapping from Sequential Simulation with Parameter Outlier Removal

The satellite-based regression model provides the data model that identifies water quality for inland and coastal waters. However, the satellite regression usually depends on the selection of observation, satellite data, and model type. A resampling simulation technique, such as sequential simulation using geographically weighted regression (GWR simulation), can be applied in generating multiple realizations for water quality estimation to reduce the sampling effect and consider spatial heterogeneity. Traditional models often result in considerable underestimation in extreme observations. The GWR simulation provides the best goodness of fit and spatial varying relationship between observed water quality and remote sensing considering parameter outlier and noise removal for parameter stability. This simulation model can increase the sampling diversity from various observations and reduce the neighboring effects of observations using outlier and noise removal. The model that handles spatial uncertainty and heterogeneity is a novel tool for inferring the characteristics of water quality from a series of sample subsets.

     

基于介电泳效应的高速抛光电极分布仿真研究与实验验证

目的解决传统平面环抛过程中存在的两种问题:(1)抛光液受抛光盘和工件旋转离心力作用而抛光液在加工区域分布不均,导致加工工件高平面度差;(2)抛光液受到的离心力作用限制了抛光盘转速,导致抛光效率低。方法提出一种基于介电泳效应的平面抛光方法(DEPP),在抛光区域增加一个非均匀电场,利用中性粒子在非均匀电场中极化后受介电泳力的作用,使其具有向电极和抛光区域中心运动的现象,降低旋转离心力对抛光液的甩出作用,实现对平面工件的高速、高精度抛光。采用有限元分析软件数值模拟极化后磨粒所受介电泳力对离心力的抑制作用,优化产生非均匀电场的不同电极宽度,得到最优非均匀电场电极分布参数,实际测量优化电极后抛光液所受介电泳力的大小和方向,最后搭建试验平台验证介电泳效应高速抛光平面工件的有效性。结果提高抛光盘转速,进行抛光磨砂玻璃对比实验,加工1 h以后,采用介电泳效应抛光能完全去除玻璃磨砂层,工件平整度好,最终RMS值为0.276λ;无介电泳效应抛光后,工件中心部分磨砂层仍有存在,工件平整度相对较差,最终RMS值为0.694λ。通过测量加工去除量,介电泳效应抛光比无介电泳效应抛光的去除率提升了18%结论通过仿真模拟和实验验证,证明了调整电极布置形式以及优化电极分布参数后,介电泳效应高速平面抛光的方法能够有效提升抛光效率和抛光后工件表面平面度。     

Micro cam system driven by electrostatic comb-drive actuators based on SOI-MEMS technology

Microsystem Technologies - This paper reports a design and fabrication process of a micro cam system (MCS) with a flat-faced translating follower. The cam rim with cover diameter of 2.4 mm...     

A printed circuit board capacitive sensor for air bubble inside fluidic flow detection

This paper presents a three-electrode capacitive fluidic sensor for detecting an air bubble inside a fluidic channel such as blood vessels, oil or medical liquid channels. The capacitor is designed and fabricated based on a printed circuit board (PCB). The electrodes are fabricated by using copper via structure through top to bottom surface of the PCB. A plastic pipe is layout through the capacitive sensor and perpendicular to the PCB surface. Capacitance of sensor changes when an air bubble inside fluidic flow cross the sensor. The capacitance change can be monitored by using a differential capacitive amplifier, a lock-in amplifier, filter and an NI acquisition card. Signal is processed and calculated on a computer. Air bubble inside the liquid flow are detected by monitor the unbalance signal between the three electrode potential voltages. Output voltage depends on the volume of the air bubble due to dielectric change between capacitor’s electrodes. Output voltage is up to 53 mV when an 2.28 mm³ air bubble crosses the sensing channel. Air bubble velocity can be estimated based on the output pulse signal. This proposed fluidic sensor can be used for void fraction detection in medical devices and systems; fluidic characterization; and water–gas, oil–water and oil–water–gas multiphase flows in petroleum technology. That structure also can apply to the micro-size for detecting in microfluidic to monitor and control changes in microfluidic channels.     

Constructing online switching barriers: examining the effects of switching costs and alternative attractiveness on e-store loyalty in online pure-play retailers

Developing switching barriers to retain customers has become a critical marketing strategy for online retailers. However, research on the role of switching barriers in e-retailing is still limited. Recent trends show that when competitors are just one click away, it is questionable if customer loyalty can be achieved at all in online environments. This leads to the research question on whether switching barriers have any impact on e-loyalty in pure-play retailers. The paper examines the influence of switching barriers on customer retention (i.e., e-store loyalty) and further investigates the moderating effects of switching costs and alternative attractiveness. Data were gathered via a survey of 590 shoppers of online pure-play retailers in the UK. Findings show that customer satisfaction and the two dimensions of switching barriers (perceived switching costs and perceived attractiveness of alternatives) significantly influence customer loyalty. Contrary to findings in earlier studies, it was found that switching costs did not moderate the relationships between satisfaction and loyalty nor between perceived attractiveness of alternatives and loyalty. The paper makes imperative recommendations to develop switching barriers and to foster loyalty along with suggestions for future research.     

High quality voice conversion using prosodic and high-resolution spectral features

Voice conversion methods have advanced rapidly over the last decade. Studies have shown that speaker characteristics are captured by spectral feature as well as various prosodic features. Most existing conversion methods focus on the spectral feature as it directly represents the timbre characteristics, while some conversion methods have focused only on the prosodic feature represented by the fundamental frequency. In this paper, a comprehensive framework using deep neural networks to convert both timbre and prosodic features is proposed. The timbre feature is represented by a high-resolution spectral feature. The prosodic features include F0, intensity and duration. It is well known that DNN is useful as a tool to model high-dimensional features. In this work, we show that DNN initialized by our proposed autoencoder pretraining yields good quality DNN conversion models. This pretraining is tailor-made for voice conversion and leverages on autoencoder to capture the generic spectral shape of source speech. Additionally, our framework uses segmental DNN models to capture the evolution of the prosodic features over time. To reconstruct the converted speech, the spectral feature produced by the DNN model is combined with the three prosodic features produced by the DNN segmental models. Our experimental results show that the application of both prosodic and high-resolution spectral features leads to quality converted speech as measured by objective evaluation and subjective listening tests.     

Factors influencing the decision to crowdsource: A systematic literature review

Crowdsourcing is currently attracting much attention from organisations for its competitive advantages over traditional work structures regarding how to utilise skills and labour and especially to harvest expertise and innovation. Prior research suggests that the decision to crowdsource cannot simply be based on perceived advantages; rather multiple factors should be considered. However, a structured account and integration of the most important decision factors is still lacking. This research fills the gap by providing a systematic literature review of the decision to crowdsource. Our results identify nine factors and sixteen sub-factors influencing this decision. These factors are structured into a decision framework concerning task, people, management, and environmental factors. Based on this framework, we give several recommendations for managers making the crowdsourcing decision.     

Simultaneous Design of Parallel Distributed Output Feedback and Anti‐windup Compensators for Constrained Takagi‐Sugeno Fuzzy Systems

This paper is devoted to developing a novel approach to deal with constrained continuous‐time nonlinear systems in the form of Takagi‐Sugeno fuzzy models. Here, the disturbed systems are subject to both input and state constraints. The one‐step design method is used to simultaneously synthesize the dynamic output feedback controller and its anti‐windup strategy. A parameter‐dependent version of the generalized sector condition is used together with Lyapunov stability theory to derive linear matrix inequality design conditions. Based on this result and for different design specifications, the synthesis of an anti‐windup based dynamic output feedback controller is expressed on the form of convex optimization problems. A physically motivated example is given to illustrate the effectiveness of the proposed method.     

A Nom historical document recognition system for digital archiving

A Nom historical document recognition system is being developed for digital archiving that uses image binarization, character segmentation, and character recognition. It incorporates two versions of off-line character recognition: one for automatic recognition of scanned and segmented character patterns (7660 categories) and the other for user handwritten input (32,695 categories). This separation is used since including less frequently appearing categories in automatic recognition increases the misrecognition rate without reliable statistics on the Nom language. Moreover, a user must be able to check the results and identify the correct categories from an extended set of categories, and a user can input characters by hand. Both versions use the same recognition method, but they are trained using different sets of training patterns. Recursive X–Y cut and Voronoi diagrams are used for segmentation; k–d tree and generalized learning vector quantization are used for coarse classification; and the modified quadratic discriminant function is used for fine classification. The system provides an interface through which a user can check the results, change binarization methods, rectify segmentation, and input correct character categories by hand. Evaluation done using a limited number of Nom historical documents after providing ground truths for them showed that the two stages of recognition along with user checking and correction improved the recognition results significantly.