Designment and Assessment of Far-Red Solar-Induced Chlorophyll Fluorescence Retrieval Method for the Terrestrial Ecosystem Carbon Inventory Satellite

Solar-Induced Chlorophyll Fluorescence (SIF),which is emitted by photosystem during photosynthesis under natural illumination,carries important information of actual photosynthesis of plants.Spaceborne remote sensing of SIF provides an unprecedented opportunity for monitoring global photosynthesis at regional to global scales.Up to date,in-orbit operational spaceborne sensors that are available for SIF retrieval are originally designed for atmosphere monitoring.The hyperspectral sensor onboard Chinese Terrestrial Ecosystem Carbon Inventory Satellite (CTECS) is expected to be the first operational spaceborne sensor that is specifically designed for sensing SIF from space (scheduled to be launched around 2020,2 years before the Fluorescence Explorer (FLEX) Mission).Data-driven approach has been selected as the main algorithm for far-red SIF retrieval for CTECS,but is to be refined and assessed according to sensor specifications (e.g.spectral resolution and signal-to-noise ratio).In this context,this study aims to improve the designment of far-red SIF retrieval method for CTECS.based on end-to-end simulation,we evaluate the precision and accuracy of SIF retrieval in several potential windows.We then analyze the sensitivity of SIF retrieval to number of features (nv) and fluorescence spectral shape function (hF) in the forward model in different windows.Results show that a broader fitting window increases retrieval precision,but is accompanied with lower accuracy and stronger sensitivity to nv and hF.Considering both retrieval precision and accuracy,the window of 735~758 nm with nv set to 6 and hFset as single peak Gaussian function (μ=740 nm and σ=30 nm) is selected as optimal fitting window for CTECS.SIF retrieval results based on proximal and airborne remote sensing data demonstrate the feasibility and reasonability of the designed method.Our results provide an important reference for far-red SIF retrieval for CTECS.     

Parameter study of high-temperature proton exchange membrane fuel cell using data-driven models

In this paper, a numerical model of high-temperature proton exchange membrane fuel cell (HT-PEMFC) was developed, in which the thermal and electrical properties were treated as temperature dependent. Based on the numerical simulation, the needed training data was acquired and used for the development of data-driven model via the artificial neural network (ANN) algorithm. The developed data-driven model was then used to predict the performance of HT-PEMFC. The simulation results indicated that the deviation of ANN prediction was less than 2.48% compared with numerical simulation. The effects of various influential factors, such as the geometry size of the gas flow channel, the thickness of the membrane and the operating temperature, could be predicted easily by using the ANN model. The ANN model prediction results showed that the more compact fuel cell and the higher operating temperature improved the performance of HT-PEMFC. The proposed ANN model and the parameters study will contribute to the further design and operation of HT-PEMFC.     

A data-driven ground fault detection and isolation method for main circuit in railway electrical traction system

Due to the complex and harsh operation conditions, like corrosion, aging cable and static electricity, of electrical traction drive system, ground fault will generate large short circuit current to harm the key components. Effective fault diagnosis is important, but also challenging. The conventional method used for ground fault detection only takes advantage of voltage measurements of DC-link. Other measurements onboard are also available, which are correlated with the voltage measurements. Taking the correlation into account will improve the detection performance. To this end, this paper presents a data-driven solution, which makes full use of the correlation between the voltage measurements with other measurements onboard. The proposed method consists of two components: (1) a canonical correlation analysis-based fault detection method, which takes into account the correlation within measurements; (2) a fault isolation method by means of the fault direction, which can be obtained with the available faulty data stored in the long-term operation. The developed method is applied to a traction drive system. It is shown that the proposed approach is able to improve the fault detection and isolation performance significantly with respect to three performance indicators, namely fault detection rate, detection delay and correct isolation rate, in comparison with the conventional method, which only uses the voltage measurements of DC-link.     

Data-driven intelligent monitoring system for key variables in wastewater treatment process

In wastewater treatment process (WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous (TP) and ammonia nitrogen (NH₄-N). In this intelligent monitoring system, a fuzzy neural network (FNN) is applied for designing the soft sensor model, and a principal component analysis (PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition (SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.     

基于数据驱动的信息融合及其在车辆声辨识中的应用

传统多源信息融合方法大都依赖于事先建立的理论机理模型,一般会引入一定的简化操作。然而实际中的应用往往会较为复杂,建立的理论模型一般存在一定的偏差。在某些情况下,满足性能要求的理论模型甚至无法给出。针对这样的缺陷,该文根据数据驱动的思想,提出了两种基于数据驱动的信息融合实现方法。通过联合利用基于数据的特征集与基于模型的特征集,有效弥补了模型中缺失的信息,从而提高信息融合的性能。将其运用在一个基于声音信息融合的地面车辆辨识实例中,获得了良好的识别性能,展现出将数据驱动处理思路引入信息融合的可行性和优点。     

Real-time data-driven fault diagnosis of proton exchange membrane fuel cell system based on binary encoding convolutional neural network

The performance of proton exchange Membrane fuel cell (PEMFC) fault diagnosis system plays an important role in normal operation of PEMFC. Therefore, a new fault diagnosis algorithm based on binary matrix encoding neural network called BinE-CNN is proposed. In BinE-CNN, high-dimensional features are extracted through binary encoding, and the feature maps are transferred to a convolutional neural network (CNN) to realize seven-category fault classification. For development of BinE-CNN, a PEMFC model is modeled to generate simulative datasets. Simulative test precision and Frames per second (FPS) of BinE-CNN have reached respectively 0.973 and 999.8 (better than support vector machines (SVM), long short-term memory neural network (LSTM), etc.). In experimental verification section, fault datasets are collected during bench test. After that, BinE-CNN is deployed on vehicle control unit (VCU) to verify its engineering value (real-time and precision). The result meet both requirements, with time cost of 96.15 ms and precision of 0.931.     

基于数据驱动与机理模型融合的热网水力平衡分析方法

随着环保要求的不断提高,城市集中供暖小锅炉被逐步关停,并被接入城市主干网,热网不断扩张。与此同时,热量的生产也运用地热、太阳能、工业余热、电热等多种热源,使得集中供热系统变得更加复杂。靠传统手工运算方式、或者理想机理建模方式较难对热网的结构设计及运行进行科学优化,需要通过计算机仿真建模的手段,并结合实际热网运行的数据对热网进行阻力特性辨识,才能真正起到有效的作用。本文研究了基于数据驱动与机理模型融合的集中供热网水力平衡分析模型,并利用来自热网SCADA运行数据通过多种机器学习算法对先验知识模型的参数进行学习优化,最终建立与真实热网相匹配的水力分析模型,此种方法可为热力企业的热网结构优化改造、经济运行提供技术参考。     

Context-aware sequence labeling for condition information extraction from historical bridge inspection reports

Effective upkeep of aging infrastructure systems with limited funding and resources calls for efficient bridge management systems. Although data-driven models have been extensively studied in the last decade for extracting knowledge from past experience to guide future maintenance decision making, their performance and usefulness have been limited by the level of detail and accuracy of currently available bridge condition databases. This paper leverages an untapped resource for bridge condition data and proposes a new method to extract condition information from it at a high level of detail. To that end, a natural language processing approach was developed to formalize structural condition knowledge by formulating a sequence labeling task and modeling inspection narratives as a combination of words representing defects, their severity and location, while accounting for the context of each word. The proposed framework employs a deep-learning-based approach and incorporates context-aware components including a bi-directional Long Short Term Memory (LSTM) neural network architecture and a Conditional Random Field (CRF) classifier to account for the context of words when assigning labels. A dependency-based word embedding model was also used to represent the raw text while incorporating both semantic and contextual information. The sequence labeling model was trained using bridge inspection reports collected from the Virginia Department of Transportation bridge inspection database and achieved an F1 score of 94.12% during testing. The proposed model also demonstrated improvements compared with baseline sequence labeling models, and was further used to demonstrate the capability of detecting condition changes with respect to previous inspection records. Results of this study show that the proposed method can be used to extract and create a condition information database that can further assist in developing data-driven bridge management and condition forecasting models, as well as automated bridge inspection systems.     

Data-driven order correlation pattern and storage location assignment in robotic mobile fulfillment and process automation system

With the rapid development and implementation of ICT, academics and industrial practitioners are widely applying robotic process automation (RPA) to enhance their business processes and operational efficiencies. This paper intends to address the value creation of utilizing RPA under the cloud-based Cyber-Physical Systems (CPS) in Robotic Mobile Fulfillment System (RMFS). By providing a TO-BE analysis of RPA and cloud-based CPS framework, a data-driven approach is proposed for zone clustering and storage location assignment classification in RMFS. The purpose of the paper is to gain better operational efficiency in RMFS. A modified A* algorithm is adopted for calculating the total traveling cost of each moveable rack in the case company layout. Nine common clustering algorithms are applied for the RMFS’s zone clustering. The results from the zone clustering are considered as nine scenarios for data-driven order classification to solve the storage location assignment problem. Six common classification algorithms are applied for a detailed comparison which has been conducted with thousands of orders. The results reveal that K-means, Gaussian Mixture Models, and Bayesian Gaussian Mixture Model are worked well with six supervised classification algorithms which yield an average of 95% accuracy rate and a higher customers’ expectation can be achieved under the customer-driven e-commerce economy.