Identifiability and Identification of Trace Continuous Pollutant Source

Accidental pollution events often threaten people’s health and lives, and a pollutant source is very necessary so that prompt remedial actions can be taken. In this paper, a trace continuous pollutant source identification method is developed to identify a sudden continuous emission pollutant source in an enclosed space. The location probability model is set up firstly, and then the identification method is realized by searching a global optimal objective value of the location probability. In order to discuss the identifiability performance of the presented method, a conception of a synergy degree of velocity fields is presented in order to quantitatively analyze the impact of velocity field on the identification performance. Based on this conception, some simulation cases were conducted. The application conditions of this method are obtained according to the simulation studies. In order to verify the presented method, we designed an experiment and identified an unknown source appearing in the experimental space. The result showed that the method can identify a sudden trace continuous source when the studied situation satisfies the application conditions

Approach to Identifying Raindrop Vibration Signal Detected by Optical Fiber

Optical Fiber Vibration pre-Warning System (OFVWS) is widely applied to pipeline transportation, defense boundary and military base. One of its key technologies is signal feature extraction and vibration source identification. However, some harmless vibration signals often affect the reliability of this identification process due to the false alarms. Therefore, it is very important to identify various harmless vibration signals effectively. In this paper, we analyze the energy distribution feature of nature raindrop vibration signal detected by optical fiber. Based on this analysis, we develop an energy information entropy model and an approach to identify the harmless raindrop vibration signal. Study shows that the nature raindrop vibration signal can be detected and identified automatically by extracting the energy information entropy value and combining with the statistical detection method. The field tests result also showed that this approach based on energy information entropy model is able to effectively identify harmless raindrop vibration signal. Its identification probability is high and its false alarm and false recognition probability is low, hence the working performance of the OFVWS can be improved by using the presented approach

Linking drought indices to impacts in the Liaoning province of China

Drought is an inherent meteorological characteristic of any given region, but is particularly important in China due to its monsoon climate and the “three ladder” landform system. The Chinese government has constructed large-scale water conservation projects since 1949, and developed drought and water scarcity relief frameworks. However, drought still causes huge impacts on water supply, environment and agriculture. China has, therefore, created specialized agencies for drought management called Flood Control and Drought Relief Headquarters, which include four different levels: state, provincial, municipal and county. The impact datasets they collect provide an effective resource for drought vulnerability assessment, and provide validation options for hydro-meteorological indices used in risk assessment and drought monitoring. In this study, we use the statistical drought impact data collected by the Liaoning province Drought Relief Headquarter and meteorological drought indices (Standardized Precipitation Index, SPI and Standard Precipitation Evaporation Index, SPEI) to explore a potential relationship between drought impacts and these indices. The results show that SPI-24 and SPEI-24 are highly correlated to the populations that have difficulties in obtaining drinking water in four out of the six cities studied. Three impacts related to reservoirs and the availability of drinking water for humans and livestock exhibit strong correlations with SPI and SPEI of different accumulated periods. Results reveal that meteorological indices used for drought monitoring and early warning in China can be effectively linked to drought impacts. Further work is exploring how this information can be used to optimize drought monitoring and risk assessment in the whole Liaoning province and elsewhere in China

Drought risk assessment of spring maize based on APSIM crop model in Liaoning province, China

Drought risk assessment is a vital part of drought risk management, which plays an important role in drought mitigation. Due to its complexity, drought risk is difficult to define and challenging to quantitatively assess, as the drought impacts associate with many social sectors. This contribution method the issue by quantitatively evaluating the yield loss due to drought as a function of the drought severity indicator in Liaoning province, China for spring maize using logarithmic regression. As crop water deficit is essence to identify agricultural drought, it developed a drought severity indicator using the crop water stress coefficient and duration. The Agricultural Production Systems sIMulator (APSIM) crop model was employed to simulate the spring maize growth to obtain daily water deficit during the growth period (May to September) and yield. The relationship between drought severity frequency and yield loss rate due to drought was established to assess the drought risk of spring maize when drought severity frequency is equal to 20%, 10%, 5% and 2%. The results show that Chaoyang and Fuxin have the highest drought risk in four levels of drought severity frequency whilst the lowest drought risk was identified in Tieling. The central Liaoning province has a moderate drought risk. For a specific drought severity frequency, drought risk increases from east to west in Liaoning province whilst it varies in each city at different drought severities. This method can predict yield loss due to drought for drought early warning. Drought risk maps presents spatial characteristics that can help to agricultural drought mitigation and the development of drought preparedness plan in Liaoning province

Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition

Optical fiber pre-warning systems (OFPS) based on Φ-OTDR are applied to many different scenarios such as oil and gas pipeline protection. The recognition of fiber vibration signals is one of the most important parts of this system. According to the characteristics of small sample set, we choose stochastic configuration network (SCN) for recognition. However, due to the interference of environmental and mechanical noise, the recognition effect of vibration signals will be affected. In order to study the effect of noise on signal recognition performance, we recognize noisy optical fiber vibration signals, which superimposed analog white Gaussian noise, white uniform noise, Rayleigh distributed noise, and exponentially distributed noise. Meanwhile, bootstrap sampling (bagging) and AdaBoost ensemble learning methods are combined with original SCN, and Bootstrap-SCN, AdaBoost-SCN, and AdaBoost-Bootstrap-SCN are proposed and compared for noisy signals recognition. Results show that: (1) the recognition rates of two classifiers combined with AdaBoost are higher than the other two methods over the entire noise range; (2) the recognition for noisy signals of AdaBoost-Bootstrap-SCN is better than other methods in recognition of noisy signals

Method for Identifying Mechanical Vibration Source Based on Detected Signals by Optical Fiber 1

Abstract: A Optical Fiber Vibration pre-Warning System (OFVWS) has an ability to detect and identify vibration signals by using a optical fiber cable lain along with pipes, hence it has been widely applied in some safety fields, such as the pipeline transportation, national defense, military base and so on. In the application of the OFVWS, one of the key issues is to identify harmful mechanical vibration sources quickly and efficiently. In this paper, we analyzed vibration signal produced by mechanical vibration sources and extracted the signal features transmitted by the OFVWS. A conclusion was drawn that most of mechanical vibration sources have an obvious feature of fundamental frequency. Based on this conclusion, we developed a method to detecting and recognizing a mechanical vibration source based on a variation coefficient of fundamental frequency periods. This method is able to accurately judge if the detected vibration signals having a feature of fundamental frequency or not by calculating and analyzing its variation coefficient of fundamental frequency periods. Field test results showed that this method can identify various harmful mechanical vibration sources, and have a high probability of detection and recognition, and a low probability of false alarm. Copyright © 2013 IFSA

Mental Workload Classification Method Based on EEG Cross-Session Subspace Alignment

Electroencephalogram (EEG) signals are sensitive to the level of Mental Workload (MW). However, the random non-stationarity of EEG signals will lead to low accuracy and a poor generalization ability for cross-session MW classification. To solve this problem of the different marginal distribution of EEG signals in different time periods, an MW classification method based on EEG Cross-Session Subspace Alignment (CSSA) is presented to identify the level of MW induced in visual manipulation tasks. The Independent Component Analysis (ICA) method is used to obtain the Independent Components (ICs) of labeled and unlabeled EEG signals. The energy features of ICs are extracted as source domains and target domains, respectively. The marginal distributions of source subspace base vectors are aligned with the target subspace base vectors based on the linear mapping. The Kullback–Leibler (KL) divergences between the two domains are calculated to select approximately similar transformed base vectors of source subspace. The energy features in all selected vectors are trained to build a new classifier using the Support Vector Machine (SVM). Then it can realize MW classification using the cross-session EEG signals, and has good classification accuracy

Classification of mental workload based on multiple features of ECG signals

Mental Workload (MW) has become an important problem in the design of man-machine systems, so its related detection and analysis technology has aroused extensive interest and concern. Researches show electrocardiogram (ECG) physiological signals can be an appropriate indicator to reflect the level of human MW. When the operator is in the state of high MW, the cardiac load will increase, and the period and shape of ECG signals accordingly change. Heart Rate Variability (HRV) is the most widely used ECG feature to assess the MW. However, its classification precision is not high enough. In order to improve this, multiple features of ECG signals are extracted to classify the MW in this paper. Besides the RR interval feature of HRV, the other three features, T and P wave power, QRS complex power and Sample Entropy (SampEn) of ECG waveform, are further extracted and applied to assess MW. The Support Vector Machine (SVM) is used to establish the classification model, and the grid search and cross-validation algorithm are used to optimize its parameters. The results show the MW classifier based on multiple features of ECG signals can evidently improve the classification accuracy. This will be helpful to realize a real time and online MW classification using ECG data