Traceability Technology for Sudden Water Pollution Accidents in Rivers

The traceability technology for sudden water pollution accidents can be used for fast, accurate identification of a pollution source in the river. A correlation optimization model with the pollution source position and release time as its parameters is established based on hydrodynamic calculation and on the coupling relationship between forward concentration probability density and backward position probability density; and the solution of the model is realized by using a differential evolution algorithm (DEA). A coupled probability density method is to convert the traceability of a sudden water pollution accident into the optimization of two minimum values. This method is simple in principle and easy in solution, realizing the decoupling of parameter of the pollution source. The concept of gradient is introduced to the differential evolution algorithm, improving the efficiency of searching process. The proposed method of traceability was applied to the emergency demonstration project of the SNWDMRP. The results indicate that the model has good efficiency of traceability and high simulation precision and that traceability results have a certain guiding significance to the emergent regulation and control of sudden water pollution events in a river

Emergency Management System for Sudden Water Pollution Accidents

The emergency management system for sudden water pollution accidents of the main canal is the integrated application of the aforesaid three key technologies and is the key to verify the effect of practical application of these technologies. The emergency management system is formed by integrating basic information, measured data, and professional models through the communication mode of network transmission. The system can provide support for emergency response in case of emergency conditions including sudden water pollution accidents and technical support for security operations of the MRP

Normalization of Pain-Evoked Neural Responses Using Spontaneous EEG Improves the Performance of EEG-Based Cross-Individual Pain Prediction

An effective physiological pain assessment method that complements the gold standard of self-report is highly desired in pain clinical research and practice. Recent studies have shown that pain-evoked electroencephalography (EEG) responses could be used as a readout of perceived pain intensity. Existing EEG-based pain assessment is normally achieved by cross-individual prediction (i.e., to train a prediction model from a group of individuals and to apply the model on a new individual), so its performance is seriously hampered by the substantial inter-individual variability in pain-evoked EEG responses. In this study, to reduce the inter-individual variability in pain-evoked EEG and to improve the accuracy of cross-individual pain prediction, we examined the relationship between pain-evoked EEG, spontaneous EEG, and pain perception on a pain EEG dataset, where a large number of laser pulses (>100) with a wide energy range were delivered. Motivated by our finding that an individual’s pain-evoked EEG responses is significantly correlated with his/her spontaneous EEG in terms of magnitude, we proposed a normalization method for pain-evoked EEG responses using one’s spontaneous EEG to reduce the inter-individual variability. In addition, a nonlinear relationship between the level of pain perception and pain-evoked EEG responses was obtained, which inspired us to further develop a new two-stage pain prediction strategy, a binary classification of low-pain and high-pain trials followed by a continuous prediction for high-pain trials only, both of which used spontaneous-EEG-normalized magnitudes of evoked EEG responses as features. Results show that the proposed normalization strategy can effectively reduce the inter-individual variability in pain-evoked responses, and the two-stage pain prediction method can lead to a higher prediction accuracy.published_or_final_versio

Rethinking the role of interference in wireless networks

This article re-examines the fundamental notion of interference in wireless networks by contrasting traditional approaches to new concepts that handle interference in a creative way. Specifically, we discuss the fundamental limits of the interference channel and present the interference alignment technique and its extension of signal alignment techniques. Contrary to this traditional view, which treats interference as a detrimental phenomenon, we introduce three concepts that handle interference as a useful resource. The first concept exploits interference at the modulation level and leads to simple multiuser downlink precoding that provides significant energy savings. The second concept uses radio frequency radiation for energy harvesting and handles interference as a source of green energy. The last concept refers to a secrecy environment and uses interference as an efficient means to jam potential eavesdroppers. These three techniques bring a new vision about interference in wireless networks and motivate a plethora of potential new applications and services

EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG With an Application to Emotion Recognition

How to effectively and efficiently extract valid and reliable features from high-dimensional electroencephalography (EEG), particularly how to fuse the spatial and temporal dynamic brain information into a better feature representation, is a critical issue in brain data analysis. Most current EEG studies work in a task driven manner and explore the valid EEG features with a supervised model, which would be limited by the given labels to a great extent. In this paper, we propose a practical hybrid unsupervised deep convolutional recurrent generative adversarial network based EEG feature characterization and fusion model, which is termed as EEGFuseNet. EEGFuseNet is trained in an unsupervised manner, and deep EEG features covering both spatial and temporal dynamics are automatically characterized. Comparing to the existing features, the characterized deep EEG features could be considered to be more generic and independent of any specific EEG task. The performance of the extracted deep and low-dimensional features by EEGFuseNet is carefully evaluated in an unsupervised emotion recognition application based on three public emotion databases. The results demonstrate the proposed EEGFuseNet is a robust and reliable model, which is easy to train and performs efficiently in the representation and fusion of dynamic EEG features. In particular, EEGFuseNet is established as an optimal unsupervised fusion model with promising cross-subject emotion recognition performance. It proves EEGFuseNet is capable of characterizing and fusing deep features that imply comparative cortical dynamic significance corresponding to the changing of different emotion states, and also demonstrates the possibility of realizing EEG based cross-subject emotion recognition in a pure unsupervised manner

The Research And Application Of Remote Sensing Monitoring Method Of Actual Irrigated Area

The value of actual irrigated area is an important indicator of irrigation water management, but due to wide space range of irrigated district, ground manual monitoring is very difficult to achieve. Remote sensing methods have a wide rapid coverage, high efficiency, real-time, objective and other advantages, which can be used to solve the difficulties in monitoring irrigated area. In this paper, a remote sensing monitoring method of irrigated area based on modified perpendicular drought index (MPDI) is researched and the differential thresholds for distinguishing irrigation are analyzed and proposed. The method was applied to 5 rounds of actual irrigated area monitoring in Hetao irrigated district, inner Monglia, China., using the satellite images of HJ1A/1B CCD, China, and verified by ground tests. The results show that the method is of high precision, and can provide help for enhancing the management level of irrigated districts

Physical layer security jamming : Theoretical limits and practical designs in wireless networks

Physical layer security has been recently recognized as a promising new design paradigm to provide security in wireless networks. In addition to the existing conventional cryptographic methods, physical layer security exploits the dynamics of fading channels to enhance secured wireless links. In this approach, jamming plays a key role by generating noise signals to confuse the potential eavesdroppers, and significantly improves quality and reliability of secure communications between legitimate terminals. This article presents theoretical limits and practical designs of jamming approaches for physical layer security. In particular, the theoretical limits explore the achievable secrecy rates of user cooperation based jamming whilst the centralized, and game theoretic based precoding techniques are reviewed for practical implementations. In addition, the emerging wireless energy harvesting techniques are exploited to harvest the required energy to transmit jamming signals. Future directions of these approaches, and the associated research challenges are also briefly outlined

Influence of Individual Differences in fMRI-Based Pain Prediction Models on Between-Individual Prediction Performance

Decoding subjective pain perception from functional magnetic resonance imaging (fMRI) data using machine learning technique is gaining a growing interest. Despite the well-documented individual differences in pain experience and brain responses, it still remains unclear how and to what extent these individual differences affect the performance of between-individual fMRI-based pain prediction. The present study is aimed to examine the relationship between individual differences in pain prediction models and between-individual prediction error, and, further, to identify brain regions that contribute to between-individual prediction error. To this end, we collected and analyzed fMRI data and pain ratings in a laser-evoked pain experiment. By correlating different types of individual difference metrics with between-individual prediction error, we are able to quantify the influence of these individual differences on prediction performance and reveal a set of brain regions whose activities are related to prediction error. Interestingly, we found that the precuneus, which does not have predictive capability to pain, could also affect the prediction error. This study elucidates the influence of interindividual variability in pain on the between-individual prediction performance, and the results will be useful for the design of more accurate and robust fMRI-based pain prediction models