X射线干涉光刻光束线关键光学元件热-结构耦合分析

为了保证上海光源X射线干涉光刻光束线的稳定性,减小热变形对实验结果的影响,对X射线干涉光刻光束线的3个关键光学元件——偏转镜、聚焦镜和精密四刀狭缝进行热-结构耦合分析。首先,计算偏转镜、聚焦镜和精密四刀狭缝所承载的功率密度;然后,建立其有限元模型;最后,获得光学元件的温度场和热变形的结果。结果表明,偏转镜和聚焦镜采用间接水冷方式可有效抑制热变形,冷却后的最大面形误差分别为7.2μrad和9.2μrad。精密四刀狭缝未冷却时,刀片组件温度介于271.56~273.27℃,刀口热变形为0.19 mm,直线导轨热变形为0.08 mm;经过铜辫子冷却后,刀片组件温度降至22.24~23.94℃,刀口热变形降至0.2μm,直线导轨热变形降至0.1μm;采用影像法和接触探头法测试后,刀口直线度、平行度和重复精度均满足技术要求。偏转镜、聚焦镜和精密四刀狭缝的热变形通过间接水冷和铜辫子的冷却方式可以得到很大程度的抑制,进而保证光斑质量。     

Rogowski coil current transducer compensation method for harmonic active power error

In the harmonic active power measurement, the highest uncertainties are generally introduced by the current and voltage transducers. In a previous paper, the authors showed that the current transformer (CT) can introduce significant errors in such measurement, especially if the phase shift between voltage and current is close to ±90°. In such condition the errors on harmonic power measurement are mainly due to the CT phase displacement. This paper shows that better results can be achieved with more linear transducers, such as the Rogowski coil current transducers (RCCTs), whose metrological performance in distorted condition can be improved, by means of a proper compensation method. The proposed method for RCCTs compensation is based on the frequency response and it allows to reduce the errors on harmonic power measurement, also for phase shift close to ±90°. The study is supported by several experimental tests.     

Experimental optimization of power-function-shaped drive pulse for stick-slip piezo actuators

Motion of a stick-slip piezo actuator is generally controlled by the parameters related to its mechanical design and characteristics of the driving pulses applied to piezoceramic shear plates. The goal of the proposed optimization method is to find the driving pulse parameters leading to the fastest and the most reliable actuator operation. In the paper the method is tested on a rotary stick-slip piezo actuating system utilized in an atomic force microscope.The optimization is based on the measurement of the actuator response to driving pulses of different shapes and repetition frequencies at various load forces. To provide it, a computer controlled testing system generating the driving pulses, and detecting and recording the corresponding angular motion response of the actuator by a position sensitive photo detector (PSPD) in real time has been developed. To better understand and interpret the experimental results, supportive methods based on a simple analytical model and numerical simulations were used as well.In this way the shapes of the single driving pulses and values of the load force providing the biggest actuator steps were determined. Generally, the maximal steps were achieved for such a combination of the pulse shapes and load forces providing high velocities at the end of the sticking mode of the actuator motion and, at the same time, lower decelerations during the slipping mode.As for the multiple driving pulses, the pulse shapes and values of repetition frequency ensuring the sticking mode of the actuator motion during the pulse rise time together with the maximum average angular rotor velocity were specified. In this way the effective and stable operation conditions of the actuator were provided.In principle, the presented method can be applied for the testing and optimization of any linear or angular stick-slip actuator.     

基于“天空地”一体化技术的安全监测系统建设与应用

西藏江达县白格村金沙江右岸于2018年10月11日和2018年11月3日先后发生2次大规模滑坡—堰塞湖堵江事件,溃堰洪水对下游拉哇库区不良地质体的稳定性造成不同程度的影响。为保障下游水电站建设安全,对拉哇库区主要不良地质体建立了基于星载InSAR技术、无人机技术和地面传感器实时监测的“天空地”一体化监测预警体系,以多维空间采集技术获取变形信息,通过智能监控平台对信息及时进行处理、分析和可视化呈现,利用平台、短信等方式向相关人员进行分级告警,取得了较好的应用效果。     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

Thermoplastic and low dielectric constants polyimides based on BPADA-BAPP

ABSTRACT

The thermoplastic and low dielectric constants polyimides were introduced. The polyimides were prepared by pyromellitic dianhydride (PMDA) or 4,4?-(4,4?-Isopropylidenediphenoxy)diphthalic anhydride (BPADA) as anhydride monomer and 4,4?-oxydianiline (ODA) or 2,2-bis(4-(4-aminephenoxy)phenyl)propane (BAPP) as amine monomer. The polyimides were well characterized by FT-IR, thermogravimetric analysis, dynamic thermomechanical analysis, dielectric measurement, and tensile test. The dielectric constants were 2.32–2.95 compared with 3.10 of ODA-PMDA polyimide, while partly polyimides were thermoplastic. The results indicated anhydride monomers, containing lateral methyl groups, made polyimides become thermoplastic. The results of molecular simulations via Materials Studio also proved this conclusion.     

智能化救援监控系统在煤矿安全生产中的应用

针对煤矿发生事故后传统救援监控系统无法实时对井下人员进行动态定位，导致矿井救援盲目性大、救援效率差、救援难度大等技术难题，为了进一步提高煤矿救援效率，通过技术研究，设计了一套以通信基站为核心的智能化救援监控系统，分析了该系统结构组成、工作原理，通过在担水沟煤矿井下实际应用效果来看，智能化救援监控系统对人员定位精准度达95%，实现人员动态位置三维成像，救援效率提高至80%以上，有效缩短了煤矿事故救援时间，取得了显著应用成效。     

Preventive replacement policies with time of operations,mission durations,minimal repairs and maintenance triggering approaches

When a mission arrives at a random time and lasts for a duration, it becomes an interesting problem to plan replacement policies according to the health condition and repair history of the operating unit, as the reliability is required at mission time and no replacement can be done preventively during the mission duration. From this viewpoint, this paper proposes that effective replacement policies should be collaborative ones gathering data from time of operations, mission durations, minimal repairs and maintenance triggering approaches. We firstly discuss replacement policies with time of operations and random arrival times of mission durations, model the policies and find optimum replacement times and mission durations to minimize the expected replacement cost rates analytically. Secondly, replacement policies with minimal repairs and mission durations are discussed in a similar analytical way. Furthermore, the maintenance triggering approaches, i.e., replacement first and last, are also considered into respective replacement policies. Numerical examples are illustrated when the arrival time of the mission has a gamma distribution and the failure time of the unit has a Weibull distribution. In addition, simple case illustrations of maintaining the production system in glass factories are given based on the assumed data.     

Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.     

Change detection in synthetic aperture radar images based on unsupervised artificial immune systems

In this paper, we propose a novel change detection method for synthetic aperture radar images based on unsupervised artificial immune systems. After generating the difference image from the multitemporal images, we take each pixel as an antigen and build an immune model to deal with the antigens. By continuously stimulating the immune model, the antigens are classified into two groups, changed and unchanged. Firstly, the proposed method incorporates the local information in order to restrain the impact of speckle noise. Secondly, the proposed method simulates the immune response process in a fuzzy way to get an accurate result by retaining more image details. We introduce a fuzzy membership of the antigen and then update the antibodies and memory cells according to the membership. Compared with the clustering algorithms we have proposed in our previous works, the new method inherits immunological properties from immune systems and is robust to speckle noise due to the use of local information as well as fuzzy strategy. Experiments on real synthetic aperture radar images show that the proposed method performs well on several kinds of difference images and engenders more robust result than the other compared methods.