Fuzzy-PID复合控制在温室节水灌溉中的应用

为解决温室节水灌溉的需求,综合考虑温室环境因子对灌溉量的影响,通过系统辨识的方法,建立被控对象土壤湿度的数学模型.针对湿度控制大滞后惯性环节的特点,设计了一种Fuzzy-PID复合控制的方法.该方法将模糊控制器和PID控制器并行结合,并采用梯形隶属函数的模糊切换算法进行两者的切换,实现两种控制方法的优势互补.实验仿真结果表明:Fuzzy-PID复合控制响应时间短、超调量小、稳态过程没有振荡,不仅具有较好的动态性能,而且具有比较理想的稳态品质,适用于温室节水灌溉控制.     

分子印迹技术在水质检测传感器领域的应用

分子印迹技术是制备具有特异识别能力聚合物(分子印迹聚合物)的新兴化学合成技术,分子印迹聚合物可作为传感器识别元件,用于制备水质检测传感器。总结了分子印迹技术在水质检测传感器领域的应用研究现状,探讨了其发展趋势,指出以分子印迹聚合物作为敏感识别元件的水质检测传感器具有快速、准确、稳定和可反复利用等优点,可用于检测水体中农药、除草剂、神经毒剂以及无机离子化合物等,是实现水质现场快速检测的重要技术途径。     

山西太原污灌区农田土壤汞污染状况及其生态效应

本文测定了山西太原污灌区农田土壤中汞污染状况 ,并采用盆栽模拟的方法 ,研究了汞污染对土壤酶活性、土壤有效磷含量、小麦产量以及地下水质的影响 ,结果表明污水灌区土壤中汞的累积量明显高于清水灌区 ,随着汞污染程度的加重 ,对土壤酶活性及小麦生长的抑制作用也增强 ,长期利用污水灌溉 ,土壤中汞会下移 ,从而对浅层地下水造成污染     

Mobile sensor networks for optimal leak and backflow detection and localization in municipal water networks

Leak and backflow detections are essential aspects of Water Distribution Systems (WDSs) monitoring and are commonly fulfilled using approaches that are based on static sensor networks and point measurements. Alternatively, we propose a mobile, wireless sensor network solution composed of mobile sensor nodes that travel freely inside the pipes with the water flow, collect and transmit measurements in near-realtime (called sensors) and static access points (called beacons). This study complements the tremendous progress in mobile sensor technology. We formulate the sensor and beacon optimal placement task as a Mixed Integer Nonlinear Programming (MINLP) problem to maximize localization accuracy with budget constraint. Given the high time complexity of MINLP formulation, we propose a disjoint scheme that follows the strategy of splitting the sensor and beacon placement problems and determining the respective number of sensors and beacons by exhaustive search in linear time.     

Automatic defect detection of metro tunnel surfaces using a vision-based inspection system

Due to the impact of the surrounding environment changes, train-induced vibration, and human interference, damage to metro tunnel surfaces frequently occurs. Therefore, accidents caused by the tunnel surface damage may happen at any time, since the lack of adequate and efficient maintenance. To our knowledge, effective maintenance heavily depends on the all-round and accurate defect inspection, which is a challenging task, due to the harsh environment (e.g., insufficient illumination, the limited time window for inspection, etc.). To address these problems, we design an automatic Metro Tunnel Surface Inspection System (MTSIS) for the efficient and accurate defect detection, which covers the design of hardware and software parts. For the hardware component, we devise a data collection system to capture tunnel surface images with high resolution at high speed. For the software part, we present a tunnel surface image pre-processing approach and a defect detection method to recognize defects with high accuracy. The image pre-processing approach includes image contrast enhancement and image stitching in a coarse-to-fine manner, which are employed to improve the quality of raw images and to avoid repeating detection for overlapped regions of the captured tunnel images respectively. To achieve automatic tunnel surface defect detection with high precision, we propose a multi-layer feature fusion network, based on the Faster Region-based Convolutional Neural Network (Faster RCNN). Our image pre-processing and the defect detection methods also promising performance in terms of recall and precision, which is demonstrated through a series of practical experimental results. Moreover, our MTSIS has been successfully applied on several metro lines.     

水体氨氮原位快速检测智能传感器的研制

介绍了氨氮传感器的检测原理,给出了智能氨氮传感器的结构设计和电路设计方法,重点讲述了氨气敏和铵离子传感器的标定算法以及多传感器数据融合算法,并对其性能进行了测试和分析。该智能氨氮传感器是集成了氨气敏、铵离子和pH温度探头的复合传感器,可以用于实现水体氨氮含量的原位快速检测。     

Automatic detection of breast cancers in mammograms using structured support vector machines

Breast cancer is one of the most common cancers diagnosed in women. Large margin classifiers like the support vector machine (SVM) have been reported effective in computer-assisted diagnosis systems for breast cancers. However, since the separating hyperplane determination exclusively relies on support vectors, the SVM is essentially a local classifier and its performance can be further improved. In this work, we introduce a structured SVM model to determine if each mammographic region is normal or cancerous by considering the cluster structures in the training set. The optimization problem in this new model can be solved efficiently by being formulated as one second order cone programming problem. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM) dataset. Various types of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, are extracted from the sample images. We then select the salient features using the recursive feature elimination algorithm. The structured SVM achieves better detection performance compared with a well-tested SVM classifier in terms of the area under the ROC curve.     

Development of a physically-based model for transport of Cryptosporidium parvum in overland flow

Surface runoff from animal production facilities contains numerous microbial pathogens which pose a direct health hazard to both humans and animals. In order to preserve safe water resources and sustainable agriculture by reducing runoff-mediated contamination of agricultural watersheds, microbial transport processes need to be properly understood and quantified. Cryptosporidium parvum is a protozoan parasite responsible for the largest outbreak of waterborne disease ever recorded in US history. Infected mammals can pass as many as 10 billion Cryptosporidium oocysts per gram of faeces, hence only a few infected animals can potentially contaminate a large watershed. Little information is available on the environmental or physicochemical factors governing the transport of microbial pathogen in surface and near-surface runoff, in particular the zoonotic pathogen Cryptosporidium. The objective of this study is to develop a physically-based model for simulating transport of C. parvum oocysts in overland flow, and to compare the model results with experimental observations. Transport of oocysts in overland flow can be simulated mathematically by including terms for the concentration of the oocysts in the liquid phase (in suspension or free-floating) and the solid phase (adsorbed to the fine solid particles like clay and silt). Oocysts adsorption and decay processes are considered. These processes are solved using numerical techniques to predict spatial and temporal changes in oocyst concentrations in solid and liquid phases. The model results are also compared with experimental results to validate the model. The model output reproduced the recovery kinetics satisfactorily, but under-predicted the total recovery in a few cases when multiple peaks were observed during experiments. Similarly, the calibrated model produced a good agreement between observed and modeled total oocysts recovery. With future modification, this model may provide a promising tool for developing effective management practices for controlling microbial pathogens in surface runoff.     

Label-free Her2 detection and dissociation constant assessment in diluted human serum using a longitudinal extension mode of a piezoelectric microcantilever sensor

We have investigated real-time, label-free, in situ detection of human epidermal growth factor receptor 2 (Her2) in diluted serum using the first longitudinal extension mode of a lead zirconate-lead titanate (PZT)/glass piezoelectric microcantilever sensor (PEMS) with H3 single-chain variable fragment (scFv) immobilized on the 3-mercaptopropyltrimethoxysilane (MPS) insulation layer of the PEMS surface. We showed that with the longitudinal extension mode, the PZT/glass PEMS consisting of a 1 mm long and 127 μm thick PZT layer bonded with a 75 μm thick glass layer with a 1.8 mm long glass tip could detect Her2 at a concentration of 6–60 ng/ml (or 0.06–0.6 nM) in diluted human serum, about 100 times lower than the concentration limit obtained using the lower-frequency flexural mode of a similar PZT/glass PEMS. We further showed that with the longitudinal mode, the PZT/glass PEMS determined the equilibrium H3–Her2 dissociation constant K_d to be 3.3 ± 0.3 × 10⁻⁸ M consistent with the value, 3.2 ± 0.28 × 10⁻⁸ M deduced by the surface plasmon resonance method (BIAcore).     

Soil particle size distribution and its relationship with soil water and salt under mulched drip irrigation in Xinjiang of China

Soil particle size distribution(PSD),one of the most important soil physical attributes,is of great importance to soil water movement,soil erosion and soil solute migration.In this study,the soil PSD of 563 soil samples from the mulched drip irrigated cotton fields in Xinjiang of China were measured by laser diffraction particle size analyzer.The soil PSD characteristics and its relations with soil water and salt were studied by using the combined methods of textural triangle,fractal and multifractal analys...     

A novel feature-based tracking approach to the detection, localization, and 3-D reconstruction of internal defects in hardwood logs using computer tomography

A novel feature-based tracking approach based on the Kalman filter is proposed for the detection, localization, and 3-D reconstruction of internal defects in hardwood logs from cross-sectional computer tomography (CT) images. The defects are simultaneously detected, classified, localized, and reconstructed in 3-D space, making the proposed scheme computationally much more efficient than existing methods where the defects are detected and localized independently in individual CT image slices and the 3-D reconstruction of the defects accomplished via correspondence analysis across the various CT image slices. Robust techniques for defect detection and classification are proposed. Defect class-specific tracking schemes based on the Kalman filter, B-spline contour approximation, and Snakes contour fitting are designed which use the geometric parameters of the defect contours as the tracking variables. Experimental results on cross-sectional CT images of hardwood logs from select species such as white ash, hard maple, and red oak are presented.     

Development of a water clear of sea ice detection algorithm from enhanced SeaWinds/QuikSCAT and AMSR-E measurements

We develop and evaluate water clear of sea ice (open water following ice cover) detection algorithms that make use of Scatterometer Image Reconstruction (SIR) SeaWinds/QuikSCAT (QuikSCAT) backscatter (σ°) and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) brightness temperature (T_B) measurements. Algorithm validation was performed within Canadian Arctic waters using the Canadian Ice Service Digital Archive (CISDA) ice charts, NASATeam ice concentration estimates, extended AVHRR Polar Pathfinder (APP-x) albedo data, RADARSAT-1 imagery, and MODIS imagery. Results indicate that the temporal evolution of QuikSCAT σ°, AMSR-E polarization ratio (PR18), and AMSR-E vertical spectral gradient ratio (GR3618) can detect water clear of sea ice events, however mean differences due to frequency dependent characteristics of the data (spatial resolution; sensitivity to open water) were apparent. All water clear of sea ice algorithms are in good agreement with the timing and clearing patterns given by the CISDA. The QuikSCAT algorithm provided a more representative ice edge and more details on the ice clearing process due to higher spatial resolution, however, transient clearing events were better represented by the AMSR-E PR(18) or (GR3618) algorithm. By exploiting the strengths of each sensor, we found that a QuikSCAT and AMSR-E fused algorithm provide improved open water area estimates by as much as 11%. The fusion of QuikSCAT and AMSR-E PR(18) yielded in the most spatially representative open water detection. The residual surface of the water clear of sea ice algorithms was found to provide another measure of the average September minimum pan-Arctic sea ice extent within 6% of the NASATeam algorithm estimates.     

Radar detection of high-energy cosmic rays in non-Gaussian background using a time-frequency technique

Cosmic rays are the highest-energy observable particles in the universe. Their study opens a new frontier for scientists to better understand the nature of the universe. This paper reports our study of a bistatic radar approach that is being developed for remote sensing of cosmic-ray induced air showers. In this context, we propose a robust detection technique based on time-frequency domain for the received radar echoes. These echoes are modeled as linear-downward chirp signals, characterized by very short sweep periods, low energies, and corrupted by non-stationary and non-Gaussian background noise. In addition, the related parameters of the received echoes are variable within some expected ranges, determined by the physical parameters of the air showers. In this paper, we explore the performance of the proposed detection method through an extensive theoretical analysis. We derive formulae for probability of the correct detection, as well as false-alarm rate. Numerical simulations and experimental results that corroborate our analysis are also presented.     

Social acceptance by senior citizens and caregivers of a fall detection system using range sensors in a nursing home

We developed a fall detection system with a status view function using range sensors in nursing homes and investigated how it was evaluated by seniors and caregivers about their intention to use and feelings of security. Our system calculates the positions and heights of seniors using range sensors for falling detection and sends an alert to the terminals of caregivers. Moreover, the system sends silhouette images from the range sensors that display the largest appearance of a person to the terminal to provide detailed information of seniors for the caregivers. In user evaluation, seniors and caregivers watched the three videos: simulated out-of-bed sensor, fall detection under constant observation and fall detection without constant observation. Participants answered questionnaires and were interviewed after watching each video. As a result, the seniors indicated significantly higher intention to use and feelings of security in the second and third videos than in the first video. Most seniors could accept being constantly monitored by the caregivers because they deemed safety to be more important than privacy. A few seniors (often healthy individuals) felt nervous under constant observation. Caregivers commented on the importance of flexibly switching the functions of the fall detection system to reflect individual status.     

A unified framework for fault detection and isolation of sensor and actuator biases in linear time invariant systems using marginalized likelihood ratio test with uniform priors

This study aims to present a fault detection and isolation (FDI) framework based on the marginalized likelihood ratio (MLR) approach using uniform priors for fault magnitudes in sensors and actuators. The existing methods in the literature use either flat priors with infinite support or the Gamma distribution as priors for the fault magnitudes. In the current study, it is assumed that the fault magnitude is a realization of a uniform prior with known upper and lower limits. The method presented in this study performs detection of time of occurrence of the fault and isolation of the fault type simultaneously while the estimation of the fault magnitude is achieved using a least squares based approach. The newly proposed method is evaluated by application to a benchmark CSTR problem using Monte Carlo simulations and the results reveal that this method can estimate the time of occurrence of the fault and the fault magnitude more accurately compared to a generalized likelihood ratio (GLR) based approach applied to the same benchmark problem. Simulation results on a benchmark problem also show significantly lower misclassification rates.     

Development of a winter snow water equivalent algorithm using in situ passive microwave radiometry over snow-covered first-year sea ice

A snow water equivalent (SWE) algorithm has been developed for thin and thick snow using both in situ microwave measurements and snow thermophysical properties, collected over landfast snow covered first-year sea ice during the Canadian Arctic Shelf Exchange Study (CASES) overwintering mission from December 2003 to May 2004. Results showed that the behavior of brightness temperatures (T_bs) in thin snow covers was very different from those in a thick snowpack. Microwave SWE retrievals using the combination of T_b 19 GHz and air temperature (multiple regression) over thick snow are quite accurate, and showed very good agreement with the physical data (R² = 0.94) especially during the cooling period (i.e., from freeze up to the minimum air temperature recorded) where the snow is dry and cold. Thin snow SWE predictions also showed fairly good agreement with field data (R² = 0.70) during the cold season. The differences between retrieved and in situ SWE for both thin and thick snow cover are mainly attributable to the variations in air temperature, snow wetness and spatial heterogeneity in snow thickness.     

Measuring biomass changes due to woody encroachment and deforestation/degradation in a forest-savanna boundary region of central Africa using multi-temporal L-band radar backscatter

Satellite L-band synthetic aperture radar backscatter data from 1996 and 2007 (from JERS-1 and ALOS PALSAR respectively), were used with field data collected in 2007 and a back-calibration method to produce biomass maps of a 15 000 km² forest-savanna ecotone region of central Cameroon. The relationship between the radar backscatter and aboveground biomass (AGB) was strong (r² = 0.86 for ALOS HV to biomass plots, r² = 0.95 relating ALOS-derived biomass for 40 suspected unchanged regions to JERS-1 HH). The root mean square error (RMSE) associated with AGB estimation varied from ~ 25% for AGB < 100 Mg ha^− 1 to ~ 40% for AGB > 100 Mg ha^− 1 for the ALOS HV data. Change detection showed a significant loss of AGB over high biomass forests, due to suspected deforestation and degradation, and significant biomass gains along the forest-savanna boundary, particularly in areas of low population density. Analysis of the errors involved showed that radar data can detect changes in broad AGB class in forest-savanna transition areas with an accuracy > 95%. However, quantitative assessment of changes in AGB in Mg ha^− 1 at a pixel level will require radar images from sensors with similar characteristics collecting data from the same season over multiple years.