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This work proposes a clock and data alignment scheme for the readout electronics system of the future PandaX-nT dark matter detection experiments. Since the waveform information of the photomultiplier signal is obtained by accumulating all acquisition channels of the front-end digitization module (FDM), it is required that each FDM acquisition channel should be synchronized. The readout electronics system is composed of a clock distribution module, 64 FDMs, and 4 data acquisition (DAQ) modules. The multi-board synchronous acquisition circuit can provide the same REF CLOCK and SYSREF signal for multiple FDMs boards to ensure the alignment of the sampled data collected by the analog-to-digital converter. The data aggregation circuit can ensure that the sampled data of multiple FDM boards are aligned with each other when they are aggregated to the DAQ board. The functional verification test results show that the synchronization between the prototype electronics channels is less than 80 ps, thus enabling to meet the requirements of the readout electronics prototype of PandaX-nT.

Vibratory finishing has been widely used in advanced manufacturing industry, such as aerospace, wind power equipment, and hydraulic equipment. Although the mechanism of vibratory surface finishing has been studied for decades, the fundamental issues of vibratory finishing such as the trajectories of media and workpiece during finishing are still not solved. In this study, a four-degree-of-freedom dynamic model is developed to analyze the dynamic responses of the vibratory finisher and validated by experimental tests. Coupled RecurDyn and EDEM technique is employed to simulate the trajectories of media and workpiece in a vibratory finisher. The simulation results are validated by X-ray experimental tests. Both the simulation and X-ray images show that the global trajectories of media and workpiece are a combination of three motions, circumferential motion around the center axis of the vibrating barrel, the radial elliptical motion around the center of rotation, and the self-flip movement. The trajectories of media exhibit an annular helix motion in the vibratory barrel in general. The shape, density, and weight of a workpiece and media have a significant effect on their global trajectory and local movements. The mechanism of vibratory surface finishing is better understood, which has great importance to the improvement of existing surface finishing processing technology and development of the next generation of vibratory mass finishing.

The data compression technology now is fully developed and widely used in many fields such as communication, multi-media, image information processing and so on. The large physical experiments, especially the ones with Micro-pattern Gas Detectors (MPGD), which always have many readout channels and have a lot of data to be transferred and saved, are however relatively seldom use this technology. In this paper, the real-time lossless data compression method is proposed for a general-purposed MPGD readout system. The lossless data compression can reduce the data transmission bandwidth of the system as well as keep all information of the data. The compression method discussed in the paper mainly consists of two steps. The first step is to pre-process the data according to different characteristics of different signals and the second step is to compress the pre-processed data using common lossless compression algorithm. Besides, the whole compression method is implemented in the Field-Programmable Gate Array (FPGA) and able to run real-timely. The system is then used to readout two different kinds of signals and the compression rate can reach as high as 43% and 30% respectively.

The primary issues for the popularization of electric vehicles are low energy density, short life, high cost, and long charging time of battery. In an extended-range electric vehicle, a range-extender is applied to realize the on-board electricity generation avoiding the range anxiety; and a large capacity battery and public charging facilities are not necessary. A primary issue is lack of an efficient range-extender that is light, compact and silent. The main reasons are that the efficiencies of range-extended engines now available are low; and the efficiency optimum operating points of the range-extended engines and generators are not matched. A 3-cylidner gasoline spark-ignition engine for an application in a range-extender has been investigated. Atkinson cycle, exhaust gas recirculation and gasoline direct injection are applied to suppress the knocking. At most cases, a range-extender engine has only a most frequent operating point. All design parameters and operating variables of the range-extender engine can be optimized around the single operating point to maximize the efficiency while matching to the highest efficiency point of a range-extender generator. For this purpose, an optimization and matching method by combing artificial neural network and genetic algorithm has been investigated. The optimization and matching for a range-extender engine, with no generator constraint and with three different constraints of generator efficiency maps, have been conducted. The results show that both the operating points and the optimal parameters of the range-extender engine are different under the different generator constraints. A higher maximum thermal efficiency of the range-extender engine can be achieved as the power requirement decreases. The maximum efficiency of the range-extender engine can reach 40.2%, which is because of the application of high geometrical compression ratio, exhaust gas recirculation, Atkinson cycle and single-point optimization of all parameters.

Hospital readmission prediction is a study to learn models from historical medical data to predict probability of a patient returning to hospital in a certain period, e.g. 30 or 90 days, after the discharge. The motivation is to help health providers deliver better treatment and post-discharge strategies, lower the hospital readmission rate, and eventually reduce the medical costs. Due to inherent complexity of diseases and healthcare ecosystems, modeling hospital readmission is facing many challenges. By now, a variety of methods have been developed, but existing literature fails to deliver a complete picture to answer some fundamental questions, such as what are the main challenges and solutions in modeling hospital readmission; what are typical features/models used for readmission prediction; how to achieve meaningful and transparent predictions for decision making; and what are possible conflicts when deploying predictive approaches for real-world usages. In this paper, we systematically review computational models for hospital readmission prediction, and propose a taxonomy of challenges featuring four main categories: (1) data variety and complexity; (2) data imbalance, locality and privacy; (3) model interpretability; and (4) model implementation. The review summarizes methods in each category, and highlights technical solutions proposed to address the challenges. In addition, a review of datasets and resources available for hospital readmission modeling also provides firsthand materials to support researchers and practitioners to design new approaches for effective and efficient hospital readmission prediction.

The switched reluctance motor (SRM) drives are emerging as an attractive solution for electric vehicle (EV) applications due to its favorable driving capacity. However, the inherent net radial force of SRM has an important effect on the vehicle ride comfort. This paper investigates the influences of net radial force induced by eccentric SRM with series/parallel circuits on vehicle ride comfort. The mechanical–electrical–magnetic coupling model of SRM is established by combing the numerical simulation and analytical method, in which the series and parallel circuits are structured, respectively. Then, quarter vibration vehicle models coupled with SRM are established. The net radial force of the two SRMs and ride comfort of the corresponding vehicles are simulated and discussed at different conditions. It is concluded that the SRM with parallel circuits generates smaller net radial forces, and the vehicle driven by SRM with parallel circuits will achieve better ride comfort due to the difference of branch currents.

This study aims to minimize the noise generated by automobile cooling fans. Fan blade structures with ridged surfaces based on bio-inspired principles are 3D printed and used to replace the conventional fan blades. The effect of the bio-inspired ridge structures on the noise reduction of the cooling fan is demonstrated by orthogonal experiments in a semi-anechoic chamber. Experimental results show that with an increase in the rotational speed, the effect of the surface textures on the acoustic performance of the cooling fan becomes more significant. For example, at a fan speed of 1750 r/min, all the bio-inspired blade designs reduce noise compared with the original fan and, in particular, the sound pressure level is reduced by 3.83 dB(A) for the design with a ridge width of 4 mm and a ridge pitch of 15 mm. Through variance analysis of the measured noise, the ridge pitch distance has the most significant impact on noise reduction under low speed conditions whilst, under high speed conditions, the ridge width has the most significant influence. In addition to the experimental studies, computational fluid dynamics (CFD) simulations of the cooling fan are carried out to explain the mechanism of noise reduction for the ridged fan blades. When the fan runs, the horseshoe vortexes generated by the ridge structures disturb the flow of the boundary layer, reduce the influence of the fluid flow on the boundary layer, and delay the transition of the fan blade laminar flow to turbulence. It is also seen that there is a reduction of the intensity of the fan blade trailing edge vortices and the scale of the secondary vortices, thereby achieving the overall aim of noise reduction. This research has significance in the noise reduction design of automobile cooling fans.

The modeling and simulation on MATLAB/Simulink of a single-phase photovoltaic inverter based on double closed-loop PI and quasi-PR control is studied by this thesis. The state space averaging method is used to construct the mathematical model of single-phase photovoltaic inverter. On the basis of the double closed-loop control strategy, the PI controller is used for the current control of the inner loop, and the quasi-PR controller is used for the outer loop control of the voltage. A simulation model was established. The output results verify that the inverter with this control strategy has perfect comprehensive performances such as a rapid dynamic response character and low total harmonic distortion of output voltage.

In this paper, we propose to use imbalanced learning for hospital readmission prediction. The goal is to predict whether a patient, based on his/her current hospital visit records, is likely going to be re-admitted or not within 30-days after being discharged from the current hospital visit. The main challenge of hospital readmission prediction is twofold: (1) the readmission visits (i.e., the positive class) are a small portion of the total hospital visits, representing a severe class imbalance problem for learning; (2) due to privacy and health regulation, the information available for patient characterization is limited; and is often only limited to the payment level information. However, there are over 80,000 procedures code, representing a high dimensionality and high sparsity problem for learning. Motivated by the above challenges, in this paper, we design an imbalanced learning strategy to create features from patient hospital visit, by combining patient demographic information, ICD-10 clinical modification (CM) and procedure codes (PCS), and Clinical Classification Software Refined (CCSR) conversion. Instead of directly using ICD-10-CM/PCS code to characterize patients, we convert each patient’s visit to CCSR code space with a smaller feature space. By using random sampling approach to balance the sample distributions in the training set, our method achieves good performance to predict patient readmission.

In order to identify the threat types of small targets in complex sea and sky background according to operational rules. Using GA to modeling operational rules of the targets which include the action trajectory and the action speed. On the basis of crossover operator, mutation operator of GA to generate the original population. Calculating the optimal individual adaptive value, which take as the discriminating reference value. Comparison of adaptive function values of target actual motion trajectory and adaptive function of optimal motion trajectory value results to verify the validity of the modeling. Due to particularity of the battlefield ,less the priori knowledge of the attack target, can not achieve feature comparison in the cases to recognition. Decompose the operational rules into multi-function optimization problem which is GA skilled.

In graph theory, Hamiltonian path refers to the path that visits each vertex exactly once. In this paper, we designed a method to generate random Hamiltonian path within digital images, which is equivalent to permutation in image encryption. By these means, building a Hamiltonian path across bit planes can shuffle the distribution of the pixel’s bits. Furthermore, a similar thought can be applied for the substitution of pixel’s grey levels. To ensure the randomness of the generated Hamiltonian path, an adjusted Bernoulli map is proposed. By adopting these novel techniques, a bit-level image encryption scheme was devised. Evaluation of simulation results proves that the proposed scheme reached fair performance. In addition, a common flaw in calculating correlation coefficients of adjacent pixels was pinpointed by us. After enhancement, correlation coefficient becomes a stricter criterion for image encryption algorithms.

A parameter optimization algorithm for the PI controller of permanent magnet synchronous motor (PMSM) based on improved adaptive firefly algorithm (IAFA) is proposed. The PI parameters of current and speed are set by improved adaptive firefly algorithm. This paper introduces 7 dimensions firefly population and adaptive step adjustment mechanism in the algorithm, which is fast and convenient to obtain the optimal solution accurately. The traditional parameter optimization method usually uses an operator to get the target parameter after repeated debugging in the actual system, which is inefficient and relies heavily on production experience. In order to optimize this problem, multidimensional population and adaptive step adjustment strategy are proposed to speed up the iteration and get the global optimal. The results demonstrate that PMSM-IAFA has fast convergence rate and high computational accuracy. It significantly outperforms the other state-of-the-art FA variants in majority of the tested instances.

Constrained layer damping (CLD) is an effective method for suppressing the vibration and sound radiation of lightweight structures. In this article, a two-level optimization approach is presented as a systematic methodology to design position layouts and thickness configurations of CLD materials for suppressing the sound power of vibrating structures. A two-level optimization model for the CLD structure is developed, considering sound radiation power as the objective function and different additional mass fractions as constraints. The proposed approach applies a modified bi-directional evolutionary structural optimization (BESO) method to obtain several optimal position layouts of CLD materials pasted on the base structure, and sound power sensitivity analysis is formulated based on sound radiation modes for the position optimization of CLD materials. Two strategies based on the distributions of average normalized elemental kinetic energy and strain energy of the base plate are proposed to divide optimal position layouts of CLD materials into several subareas, and a genetic algorithm (GA) is employed to optimally reconfigure the thicknesses of CLD materials in the subareas. Numerical examples are provided to illustrate the validity and efficiency of this approach. The sound radiation power radiated from the vibrating plate, which is treated with multiple position layouts and thickness reconfigurations of CLD materials, is emphatically discussed.

Electric vehicle charging station is the infrastructure to provide electric energy for electric vehicles (EV). Reasonable location of charging station is of great significance to promote the popularization of EVs. Firstly, the ownership of EVs in the selected area is forecasted. According to the ownership and driving characteristics of various types of EVs, the total charging demand of EVs and the charging demand of each network node are obtained. Secondly, the location model of charging station is established to minimize the sum of operation and maintenance costs of charging station and user loss costs. Finally, weighted Voronoi diagram and adaptive particle swarm optimization are used. The results show that this method can optimize the annual average comprehensive cost of charging station. Graphic user interface (GUI) is designed to display the result of location, which makes the location scheme more intuitive.

In this column, we describe a new design method for a precise finite impulse response (FIR) compensation filter as a component in a delta-sigma modulator (DSM), also referred to as a Δ-∑ modulator. Several cost functions are first proposed based on their compensation purpose. Then the steepest descent method is adopted to find out the optimum filter coefficients. By applying this method, it is possible to achieve a relatively flat frequency response magnitude in the passband even if the order of the filter is low.

Limited by the operation mode of ‘heat-set’ is one of the main reasons for the large amount of wind abandonment in Northeast China. Reasonable allocation of electric heat storage system is an effective means to improve the wind power absorption capacity of regional power grid. In this paper, an optimal capacity allocation model of electric heat storage system considering peak regulation auxiliary services is proposed, and the optimal capacity of electric heat storage system is solved based on game theory. Firstly, the economic benefits of each stakeholder participating in peak regulation ancillary service market are analyzed by allocating electric heat storage equipment in thermal power plants. Secondly, a bi-level programming model for optimal capacity allocation of electric thermal storage system is established. Finally, the model is optimized by particle swarm optimization and Cplex hybrid optimization method. The optimal configuration scheme of the electric heat storage system is obtained by solving the optimization problem. The example shows that the model is reasonable and effective.

Recently, geosynchronous synthetic aperture radar (GEO-SAR), which can achieve continuous Earth observation missions with large coverage and short revisit period, has drawn extensive attention. The wide-swath imaging mode of GEO-SAR presents great potentials for bistatic observation applications. Geosynchronous bistatic SAR (GEO-BiSAR) can not only complete large scene imaging task, but also provide the conditions for ground moving-target indication (GMTI). In this paper, the 3D velocity estimation for moving target via GEO-BiSAR are investigated. And the velocity estimation problem is extended to three-dimensional (3D) domain, considering that targets always move in a 3D space. To solve the 3D velocity of moving targets via GEO-BiSAR, the 3D velocity raw signal model is established, and a method based on fractional Fourier transform (FrFT) is proposed. First, the Doppler centroids and Doppler rates in the GEO-BiSAR are estimated by the FrFT. Second, one of the Doppler rates is used to estimate the azimuth velocity. Then, the range and height velocity are obtained by solving a Doppler centroid equation set. Finally, simulation experiments are presented to demonstrate the effectiveness of the method.

In this paper, the field measured wheel wear data of high-speed trains are studied by variance analysis, and prediction models are developed using NAR and BP neural networks. The results show that the wheel position has a significant effect on the wheel wear, and the position of the carriage has little influence on the wheel wear. The NAR neural network can be used to predict the dynamic change of wheel diameter and therefore to predict the wheel wear of high-speed trains. The wheel diameter data are classified and the range of wheel wear can be predicted by means of training the BP neural network.

This paper provides an online virtual 7home decoration system, which integrates web interaction technique into virtual home decoration. The main functions of this system include house design, house decoration, and data management. For the house design, an automatic joint method of the walls is proposed, in order to design various house. For the house decoration, we introduce the wall decoration principle corresponding to the algorithm of discretization on the walls, which contains doors or windows, and triangularization on the irregular floors. To variety of different textures and collage regulations, we provide a new texture collage mapping method. Technology of rendering to texture is used to generate a small geometry texture, which will be mapped into a large target object on the basis of the texture's structural properties. As a result, the whole texture continuity is also maintained. In data management aspect, we embed a browser into this system so that users can conveniently obtain decoration models on the basis of web interaction. After these models have been downloaded, they can be shown in virtual scene according to guide information. In addition, some quotation lists can be generated automatically. Experimental results show that an authentic house can be easily presented in our system. Copyright © 2013 John Wiley & Sons, Ltd.

1 Abstract—A time delay estimation method based on wavelet transform and speech envelope is proposed for distributed microphone arrays. This method first extracts the speech envelopes of the signals processed with multi-level discrete wavelet transform, and then makes use of the speech envelopes to estimate a coarse time delay. Finally it searches for the accurate time delay near the coarse time delay by the crosscorrelation function calculated in time domain. The simulation results illustrate that the proposed method can accurately estimate the time delay between two distributed microphone array signals.

This paper studies the influence of Writing RoadmapTM 2.0 (WRM) and teacher grading on student A and Student B respectively. By comparing the grades of pre-tests of both students, we found that, in the post-test essays, student A demonstrated much achievement in word choice, fluency and conventions while student B showed little improvement in these three dimensions. Compared with student B, student A's essay was superior to that of student B in the above three dimensions as well. The comparative research indicates that the feedback given by the WRM helped student A improve his writing proficiency effectively, especially in word choice, syntax, spelling and grammar. But the system couldn’t provide student A with enough feedback and revision suggestions on ideas and contents as well as organization. On the contrary, the global scoring adopted by the teacher could only provide student B with limited feedback, thus was of little help to improve his writing quality.

CORRIGENDUM: Separable Bilayer Microfiltration Device for Viable Label-free Enrichment of Circulating Tumour Cells

For multipath effects compensation in complicated hedge-hop circumstances, methodologies based on orthogonal frequency-division multiplexing (OFDM) radar signal are proposed to get target tracking information extracting equation set in this paper, after hedge-hop multipath model presentation and hedge-hop circumstances influence analysis on seeker guidance radar error output. By comparison with traditional multipath analysis and compensation method, the proposed approach can get target direction accurately at one sensing period without the limitation of relative velocity and position between target and radar(seeker). Sample Simulation result is also presented to verify the effectiveness of the proposed approach.

For the problems that encountered in present breast cancer detection and imaging systems, such as terrible and unpopular diagnosis experience for time-consuming system processing procedure, image blurring caused by patients' inevitable motion also due to long diagnosis time, signal echo from normal tissue which is regarded as clutter for imaging and reduce system sensitivity and penetration depth, and etc., a multi-carrier complementary phasecoded (MCPC) radar signal, based on orthogonal frequency division multiplexing (OFDM) technology is applied in this paper. Analysis and simulation result shows that MCPC is very suitable for breast cancer detection and imaging; MCPC signal can be used for multi-antenna parallel work with nearly interference-free properties, which makes fast breast imaging feasible; MCPC signal has self clutter suppression ability for designated area during breast cancer detection, which can be used to improve system sensitivity and imaging quality without resolution loss or additional power requirement.

Today, thanks to a variety of electronic media, our cognitive world has been expanded to a practically unlimited degree. Since 1960s, with the rapid growth of the automated evaluation system (AES), many benefits, such as frequent writing practice, immediate scoring, objective reporting, instructional tools, form-focused feedback, context-focused feedback, formative feedback, and diagnostic feedback, etc. have been provided for English writing teaching. Thus it is important to have a good understanding of how to integrate AES into English writing teaching. This paper, based on a classroom report, first introduces how to use Writing Roadmap TM (WRM) 2.0, published by CTB/McGraw-Hill, to generate feedback on college English writings in EFL classroom and develop the learner's cognitive process of writing, and then discusses its advantages and issues that need attention.

In the grid environment, making use of resources is more important than sharing them safety. In order to achieve the sharing and management of grid resources, the grid resources providers and visitors need to negotiate resources sharing agreements, and solve the grid resources access control problem. Based on the safety certificate framework, an improved access control system was proposed in this paper, which was the double certificate authentication model (DCAM). The experimental results showed that the improved system strengthened the security of the grid access control, and enhanced the stability and reduce the time delay of the user identity authentication. Compared with the traditional grid access control system, the improved system has relatively obvious improvement in performance, so it has certain practical value in the grid application.

Speech recognition is to study so that the machine can accurately hear the voice of the contents of people's problems, that is, accurate identification of words. Since the 90's it has been the focus of speech recognition has shifted to large vocabulary, continuous speech, speaker-up, man-machine voice communication has become a research focus. Use one kinds of arithmetic to study speech recognition: Dynamic Time Warping (DTW). Build a basic test platform of speech recognition using DTW.

Nowadays, the doorbell style is varied and many modern bells are electronic products but most of their functions are single. In this paper, it will show a design method of the digital doorbell using Protel99 SE software. Its mainly controller is AT89C52 SCM and its input device is 4×4 keyboard, DS1302 clock circuit and AT24C02 storage circuit and ISD1420 voice circuit are its intermediate response equipments, and playback speech circuit as well as HD44780 LCD display circuit as its intermediate response equipments. The working status of this digital doorbell can be set by users themselves, besides a common electronic bell function; it also has some more new functions. Through the time of keying and different music has been set up, the doorbell user can easily distinguish different types of visitors. In addition, the doorbell can also provide some information to the visitors, in the form of voice or text. What's more, the user can query the information every time, to make sure if there are visitors or not. In a word, the doorbell is a humanized design.

The key technology of greenhouse facilities is the monitoring of environmental parameters. Now, monitoring system of greenhouse is based on wire transmission. It is complicated to route wire and difficult to maintain. Also its reliability and anti-interference performance will degrade because of heat, light and acid. This paper leads a low-power and short range ZigBee technique into greenhouse monitoring system. In order to compose intelligent network sensor system, the paper analyses the composition of network nodes power consumption, proposes low-power design method both in hardware and software. The paper selects CC2430 module which composed of transceiver and microprocessor, and use SHT15 temperature humidity sensor. After hardware and software debugging, this wireless network can acquire and transmit data of greenhouse temperature and humidity accurately and rapidly, and this system resistant to stable work, tight structure, large loads and low power consumption.

Support vector machine is a new method developed recently. It has shown good performance in many field and mostly outperformed neural networks. So SVM is introduced to the field of intelligent modeling of helicopter, and an online modeling method for helicopter based on SVM is proposed. It is shown by simulation that the online SVM modeling method leads to a significant speed up in the training process, whilst maintain the generalization precision.

Neural networks with good nonlinear mapping abilities can be applied to build simulation model of helicopter. But they have some difficulties such as hardness of selecting network structure, slow convergence speed, local minimum, and over-fitting. To avoid above problems, a method for building simulation model of helicopter based on Wavelet Support Vector Machine (WSVM) is proposed. Marr wavelet is used to construct one-dimension wavelet kernel, and the rationality of the wavelet kernel is proved. Subsequently the wavelet kernel is extended to multi-dimensional case. Based on pretreatment of practical flight data, rotational speed model for landing process of helicopter with rotor self-rotating is built with WSVM. Compared with neural network model, WSVM model possess some advantages such as simple structure, fast convergence speed and high generalization ability. It is shown by theoretic analysis and simulation results that WSVM method to build simulation model of helicopter is feasible.

We synthetically applied computer vision, genetic algorithm and artificial neural network technology to automatically identify the tomatoes that had physiological diseases. Firstly, the tomatoes’ images were captured through a computer vision system. Then to identify cavernous tomatoes, we analyzed the roundness and detected deformed tomatoes by applying the variation of fruit’s diameter. Secondly, we used a Genetic Algorithm (GA) trained artificial neural network. Experiments show that the above methods can accurately identify tomatoes’ shapes and meet requests of classification; the accuracy rate for the identification for tomatoes with physiological diseases was up to 100%. tomato with physiological disease; computer vision; artificial neural network; genetic algorithms

Support vector machine (SVM) is a new modeling method. It has shown good performance in many field and mostly outperformed neural networks. An online modeling method based on SVM is proposed. It has a budget parameter to explicitly control the number of support vectors that used to define the predictor. The online modeling method was applied to construct intelligent model of helicopter. It is shown by simulation that the online SVM modeling method is feasible.

During the time of manual image repair, people tend to follow the principle of the first repair after the overall structure repair of texture details. In this paper, taking into account that the partial differential equation model has strong capacity of keeping linear structure, and texture synthesis model has good effect in repairing the texture information, the defect image is decomposed into structural image and texture image by wavelet transformation, and then the structural image is repaired by partial differential equation model; the texture image is repaired by texture synthesis technique. The experimental results show that the algorithm can effectively improve the quality of image inpainting.