Passive Localization of Multiple Sources Using Joint RSS and AOA Measurements in Spectrum Sharing System

In spectrum sharing systems,locating mul-tiple radiation sources can efficiently find out the in-truders,which protects the shared spectrum from ma-licious jamming or other unauthorized usage.Com-pared to single-source localization,simultaneously lo-cating multiple sources is more challenging in prac-tice since the association between measurement pa-rameters and source nodes are not known.More-over,the number of possible measurements-source as-sociations increases exponentially with the number of sensor nodes.It is crucial to discriminate which measurements correspond to the same source before localization.In this work,we propose a central-ized localization scheme to estimate the positions of multiple sources.Firstly,we develop two computa-tionally light methods to handle the unknown RSS-AOA measurements-source association problem.One method utilizes linear coordinate conversion to com-pute the minimum spatial Euclidean distance sum-mation of measurements.Another method exploits the long-short-term memory(LSTM)network to clas-sify the measurement sequences.Then,we propose a weighted least squares(WLS)approach to obtain the closed-form estimation of the positions by linearizing the non-convex localization problem.Numerical re-sults demonstrate that the proposed scheme could gain sufficient localization accuracy under adversarial sce-narios where the sources are in close proximity and the measurement noise is strong.

A Dynamic Solution for Document Redundancies in Multiple Sources

With the development of IT,more andmore document resources are available over the Internet.Inorder to facilitate users’retrieval of the digital documents,Integrations of the multi source systems are necessary,Sincethe individual sources collect their information independently,the same papers may be stored in different source systems.The traditional solutions to the redundancy problems in thedistributed environments are usually based on the globalcatalogs which keep the redundancy information for thesyst...

Full-domain collaborative deployment method of multiple interference sources and evaluation of its deployment effect

This paper realizes the full-domain collaborative deployment of multiple interference sources of the global satellite navigation system(GNSS)and evaluates the deployment effect to enhance the ability to disturb the attacker and the capability to defend the GNSS during navigation countermeasures.Key evaluation indicators for the jamming effect of GNSS suppressive and deceptive jamming sources are first created,their evaluation models are built,and their detection procedures are sorted out,as the basis for determining the deployment principles.The principles for collaboratively deploying multi-jamming sources are developed to obtain the deployment structures(including the required number,structures in demand,and corresponding positions)of three single interference sources required by collaboratively deploying.Accordingly,simulation and hardware-in-loop testing results are presented to determine a rational configuration of the collaborative deployment of multi-jamming sources in the set situation and further realize the full-domain deployment of an interference network from ground,air to space.Varied evaluation indices for the deployment effect are finally developed to evaluate the deployment effect of the proposed configuration and further verify its reliability and rationality.

Study of energy-efficient heat resistance and cooling technology for high temperature working face with multiple heat sources in deep mine

In the present research,we proposed a scheme to address the issues of severe heat damage,high energy consumption,low cooling system efficiency,and wastage of cold capacity in mines.To elucidate the seasonal variations of environmental temperature through field measurements,we selected a high-temperature working face in a deep mine as our engineering background.To enhance the heat damage control cability of the working face and minimize unnecessary cooling capac-ity loss,we introduced the multi-dimensional heat hazard prevention and control method called"Heat source barrier and cooling equipment".First,we utilize shotcrete and liquid nitrogen injection to eliminate the heat source and implemented pressure equalization ventilation to disrupt the heat transfer path,thereby creating a heat barrier.Second,we establish divi-sional prediction models for airflow temperature based on the variation patterns obtained through numerical simulation.Third,we devise the location and dynamic control strategy for the cooling equipment based on the prediction models.The results of field application show that the heat resistance and cooling linkage method comply with the safety requirement throughout the entire mining cycle while effectively reducing energy consumption.The ambient temperature is maintained below 30℃,resulting in the energy saving of 10%during the high-temperature period and over 50%during the low-temperature period.These findings serve as a valuable reference for managing heat damage in high-temperature working faces.

Testability evaluation using prior information of multiple sources

Testability plays an important role in improving the readiness and decreasing the lifecycle cost of equipment. Testability demonstration and evaluation is of significance in measuring such testability indexes as fault detection rate（FDR） and fault isolation rate（FIR）, which is useful to the producer in mastering the testability level and improving the testability design, and helpful to the consumer in making purchase decisions. Aiming at the problems with a small sample of testability demonstration test data（TDTD） such as low evaluation confidence and inaccurate result, a testability evaluation method is proposed based on the prior information of multiple sources and Bayes theory. Firstly, the types of prior information are analyzed. The maximum entropy method is applied to the prior information with the mean and interval estimate forms on the testability index to obtain the parameters of prior probability density function（PDF）, and the empirical Bayesian method is used to get the parameters for the prior information with a success-fail form. Then, a parametrical data consistency check method is used to check the compatibility between all the sources of prior information and TDTD. For the prior information to pass the check, the prior credibility is calculated. A mixed prior distribution is formed based on the prior PDFs and the corresponding credibility. The Bayesian posterior distribution model is acquired with the mixed prior distribution and TDTD, based on which the point and interval estimates are calculated.Finally, examples of a flying control system are used to verify the proposed method. The results show that the proposed method is feasible and effective.

Fast recognition using convolutional neural network for the coal particle density range based on images captured under multiple light sources

A method based on multiple images captured under different light sources at different incident angles was developed to recognize the coal density range in this study.The innovation is that two new images were constructed based on images captured under four single light sources.Reconstruction image 1 was constructed by fusing greyscale versions of the original images into one image,and Reconstruction image2 was constructed based on the differences between the images captured under the different light sources.Subsequently,the four original images and two reconstructed images were input into the convolutional neural network AlexNet to recognize the density range in three cases:-1.5(clean coal) and+1.5 g/cm^(3)(non-clean coal);-1.8(non-gangue) and+1.8 g/cm^(3)(gangue);-1.5(clean coal),1.5-1.8(middlings),and+1.8 g/cm^(3)(gangue).The results show the following:(1) The reconstructed images,especially Reconstruction image 2,can effectively improve the recognition accuracy for the coal density range compared with images captured under single light source.(2) The recognition accuracies for gangue and non-gangue,clean coal and non-clean coal,and clean coal,middlings,and gangue reached88.44%,86.72% and 77.08%,respectively.(3) The recognition accuracy increases as the density moves further away from the boundary density.

Spatio-temporal distribution of gaseous pollutants from multiple sources in industrial buildings with different flow patterns

Energy consumption of industrial buildings has remained continuously high,and the environmental quality requirements are also constantly improving.Only by improving industrial environmental control technology based on the transport mechanism of the pollution,can the energy consumption of industrial building environmental control be further reduced,and the environmental quality of industrial buildings can be improved as well.Therefore,after verifying the numerical simulation by experiments,this study uses a self-label method to investigate the spatio-temporal distribution of gaseous pollutants from multiple time-series sources in industrial plants with different length-span ratios.The results show that,the polluted flow in plants with different aspect ratios have different flow patterns:(i)the,,Back-mixingw flow pattern occurs when the ratio of ventilation rate 6 and polluted flow rate at the exhaust height b is less than 1,i.e.,G/L_(p)<1,and(ii)the"One-way"flow pattern occurs when G/L_(p)>1.For plants with the"Back-mixing"pattern,the following source pollutants enter a density stratified environment induced by the retained pre-source pollutants.The flow of following source pollutants released at the same intensity as the precursor source can reach the roof,while those with low velocity and density difference may be blocked during the ascending process.The maximum height zm of the flow of the following source is related to both the initial Froude number Fr_(o)of the following source and the unsteady vertical density gradient of the fluid in the indoor environment dpa/dz.For plants with the,,One-way,/pattern,the flow from the following source enters into an environment with approximately uniform density.Under the condition of positive buoyancy,design parameters of ventilation corresponding to the vicinity of G/L_(p)=1 may be the optimal solution for safety and energy conservation.

Analysis and Design of Feedback Controls Supporting Multiple Links and Heterogeneous Sources Transmission Control Protocol(TCP) Network Control System

An analysis and control approach is presented for the active queue management(AQM) problem in network control system supporting multiple links and heterogeneous sources transmission control protocol(TCP).Using additive increase multiplicative decrease(AIMD) model,some studies are carried out on multiple links and heterogeneous sources TCP network control system,and some conditions are derived to ensure the stabilization of the given feedback control system by exploiting a general LyapunovKrasovskii functional and some techniques for time-delay systems.And the controller gain is designed further.A simulation is to be provided to verify the algorithm in the paper.

A Method to Construct Plasma with Nonlinear Density Enhancement Effect in Multiple Internal Inductively Coupled Plasmas

A method is proposed to built up plasma based on a nonlinear enhancement phenomenon of plasma density with discharge by multiple internal antennas simultaneously. It turns out that the plasma density under multiple sources is higher than the linear summation of the density under each source. This effect is helpful to reduce the fast exponential decay of plasma density in single internal inductively coupled plasma source and generating a larger-area plasma with nmltiple internal inductively coupled plasma sources. After a careful study on the balance between the enhancement and the decay of plasma density in experiments, a plasma is built up by four sources, which proves the feasibility of this method. According to the method, more sources and more intensive enhancement effect can be employed to further build up a high-density, large-area plasma for different applications.

Application of Transient Electromagnetic Method with Multi-Radiation Field Sources in Deep Edge Mineral Resources Exploration

In recent years,in order to meet the practical needs of deep edge mine detection with large depth and high precision,transient electromagnetic method(TEM)near emission source detection mode has become an international advanced method(Xue et al.,2020).

Multi-Source Underwater DOA Estimation Using PSO-BP Neural Network Based on High-Order Cumulant Optimization

Due to the complex and changeable environment under water,the performance of traditional DOA estimation algorithms based on mathematical model,such as MUSIC,ESPRIT,etc.,degrades greatly or even some mistakes can be made because of the mismatch between algorithm model and actual environment model.In addition,the neural network has the ability of generalization and mapping,it can consider the noise,transmission channel inconsistency and other factors of the objective environment.Therefore,this paper utilizes Back Propagation(BP)neural network as the basic framework of underwater DOA estimation.Furthermore,in order to improve the performance of DOA estimation of BP neural network,the following three improvements are proposed.(1)Aiming at the problem that the weight and threshold of traditional BP neural network converge slowly and easily fall into the local optimal value in the iterative process,PSO-BP-NN based on optimized particle swarm optimization(PSO)algorithm is proposed.(2)The Higher-order cumulant of the received signal is utilized to establish the training model.(3)A BP neural network training method for arbitrary number of sources is proposed.Finally,the effectiveness of the proposed algorithm is proved by comparing with the state-of-the-art algorithms and MUSIC algorithm.

Multi-source genesis of continental carbonate-rich fine-grained sedimentary rocks and hydrocarbon sweet spots

This paper systematically discusses the multiple source characteristics and formation mechanisms of carbonate-rich fine-grained sedimentary rocks through the analysis of material source and rock formation.The hydrocarbon accumulation characteristics of carbonate-rich fine-grained sedimentary rocks are also summarized.The results show that the main reason for the enrichment of fine-grained carbonate materials in rift lake basins was the supply of multiple material sources,including terrestrial material input,formation of intrabasinal authigenic carbonate,volcanic-hydrothermal material feeding and mixed source.The development of carbonate bedrock in the provenance area controlled the filling scale of carbonate materials in rift lake basins.The volcanic-hydrothermal activity might provide an alkaline fluid to the lake basins to strengthen the material supply for the formation of carbonate crystals.Authigenic carbonate crystals induced by biological processes were the main source of long-term accumulation of fine-grained carbonate materials in the lake basins.Carbonate-rich fine-grained sedimentary rocks with multiple features were formed through the interaction of physical,biochemical and chemical processes during the deposition and post-deposition stages.The source and sedimentary origin of the fine-grained carbonate rock controlled the hydrocarbon accumulation in it.In the multi-source system,the types of"sweet spots"of continental shale oil and gas include endogenous type,terrigenous type,volcanic-hydrothermal type and mixed source type.

A Three-Dimensional Model of Transport and Diffusion of Seeding Agents within Stratus

It is essential to learn the temporal and spatial concentration distributions and variations of seeding agents in cloud seeding of precipitation enhancement. A three-dimensional puff trajectory model incorporating a mesoscale nonhydrostatic model has been formulated, and is applied to simulating the transporting and diffusive characteristics of multiple line sources of seeding agents within super-cooled stratus. Several important factors are taken into consideration that affect the diffusion of seeding materials such as effects of topography and vertical wind shear, temporal and spatial variation of seeding parameters and wet deposition. The particles of seeding agents are assumed to be almost inert, they have no interaction with the particles of the cloud or precipitation except that they are washed out by precipitation. The model validity is demonstrated by the analyses and comparisons of model results, and checked by the sensitivity experiments of diffusive coefficients and atmospheric stratification. The advantage of this model includes not only its exact reflection of heterogeneity and unsteadiness of background fields, but also its good simulation of transport and diffusion of multiple line sources. The horizontal diffusion rate and the horizontal transport distance have been proposed that they usually were difficult to obtain in other models. In this simulation the horizontal diffusion rate is 0.82 m s(-1) for average of one hour, and the horizontal average transport distance reaches 65 km after 1 4 which are closely related to the background Fields.

A head-related transfer function model for fast synthesizing multiple virtual sound sources

A head-related transfer function （HRTF） model for fast and real-time synthesizing multiple virtual sound sources is proposed. A head-related impulse response （HRIR, time- domain version of HRTF） is first decomposed by a two-level wavelet packet and then represented by a model composed of subband filters and reconstruction filters. The coefficients of the subband filters are the zero interpolation of the wavelet coefficients of the HRIR. The coefficients of the reconstruction filters can be calculated from the wavelet function. The model is simplified by applying a threshold method to reduce the wavelet coefficients. The calculated results indicate that for a model with 30 wavelet coefficients, the error of reconstructed HRIR is about 1%. And the result of a psychoacoustic test shows that a model with 35 wavelet coefficients is perceptually indistinguishable from the original HRIR. When multiple virtual sound sources are synthesized simultaneously, the computational cost of the proposed model is much less than the traditional HRTF filters.

Inverse determination of multiple heat sources’ release history in indoor environments

The heat source of an air-conditioned room has an important effect on the indoor environment. The release rates of heat sources are related to the comfort of the designed thermal environment, so they must be determined. Traditional design methods rely on iterative guess-and-correct, which consumes resources and time and cannot meet the needs of modern design. This study aims to establish an inverse model of Tikhonov regularization and least square optimization by using computational fluid dynamics (CFD), so that researchers can accurately determine the time release rate of multiple heat sources with known parameters. The temporal release rates can then be solved based on the inverse matrix operation with the temperature series at different discrete times. The study speeds up the solving process and expresses the temperature as the convolution integral between the temperature response of the thermal response factor and the arbitrary release rate. The results show that applying the above method to the quantization of the temporal release rates of three heat sources in a three-dimensional cavity can correctly determine the temporal release rates of multiple heat sources. The errors between the inversely determined release rates and the actual release rates are less than 40%.

Prioritization of candidate genes for attention deficit hyperactivity disorder by computational analysis of multiple data sources

Attention deficit hyperactivity disorder(ADHD)is a common,highly heritable psychiatric disorder charac-terized by hyperactivity,inattention and increased im-pulsivity.In recent years,a large number of genetic studies for ADHD have been published and related ge-netic data has been accumulated dramatically.To pro-vide researchers a comprehensive ADHD genetic re-source,we previously developed the first genetic data-base for ADHD(ADHDgene).The abundant genetic data provides novel candidates for further study.Meanwhile,it also brings new challenge for selecting promising candidate genes for replication and verification research.In this study,we surveyed the computational tools for candidate gene prioritization and selected five tools,which integrate multiple data sources for gene prioritiza-tion,to prioritize ADHD candidate genes in ADHDgene.The prioritization analysis resulted in 16 prioritized can-didate genes,which are mainly involved in several major neurotransmitter systems or in nervous system development pathways.Among these genes,nervous system development related genes,especially SNAP25,STX1A and the gene-gene interactions related with each of them deserve further investigations.Our results may provide new insight for further verification study and facilitate the exploration of pathogenesis mechanism of ADHD.

Predicting Stock Price Movement with Multiple Data Sources and Machine Learning Models

Stock price trend prediction is a challenging issue in the financial field.To get improvements in predictive performance,both data and technique are essential.The purpose of this paper is to compare deep learning model(LSTM)with two ensemble models(RF and XGboost)using multiple data.Data is gathered from four stocks of financial sector in China A-share market,and the accuracy and F1-measure are used as performance measure.The data of the past three days is applied to classify the rise and fall trend of price on the next day.The models’performance are tested under different market styles(bull or bear market)and different market activities.The results indicate that under the same conditions,LSTM is the top algorithm followed by RF and XGBoost.For all models applied in this study,prediction performance in bull markets is much better than in bear markets,and the result in active period is better than inactive period by average.It is also found that adding data sources is not always effective in improving forecasting performance,and valuable data sources and proper processing may be more essential than providing a large quantity of data source.

Optimal Rate Allocation Algorithm for Multiple Source Video Streaming

Video streaming is one of the most important applications used in the best-effort Internet. This paper presents a new scheme for multiple source video streaming in which the traditional fine granular scal-able coding was rebuilt into a multiple sub-streams based transmission model. A peak signal to noise ratio based stream rate allocation algorithm was then developed based on the transmission model. In tests, the algorithm performance is about 1 dB higher than that of a uniform rate allocation algorithm. Therefore, this scheme can overcome bottlenecks along a single link and smooth jitter to achieve high quality and stable video.

Towards combining multiple geophysical datasets to determine earthquake source parameters in China

1.Difficulties of conventional seismic studies on earthquake source parameters Earthquake source parameters,including magnitude,location,focal mechanism,rupture process are key factors for understanding seismogenic environment,mitigating seismic hazards,estimating earthquake triggering,and tectonic analysis.Traditionally,source parameters are determined by seismological methods.For example,Fang L H et al.(2014)relocated the 2012 Ms6.6 Xinjiang Xinyuan earthquake sequence using local seismograms based on the double difference method,and obtained the distribution of

Design and validation on a multiple sound source fast-measurement system of near-field head-related transfer functions

Near-field head-related transfer functions （HRTFs） are essential to scientific re- searches of binaural hearing and practical applications of virtual auditory display. High ef- ficiency, accuracy and repeatability are required in a near-field HRTF measurement. Hence, there is no reference which intents on solving the measuring difficulties of near-field HRTF for human subjects. In present work, an efficient near-field HRTF measurement system based on computer control is designed and implemented, and a fast calibration method for the system is proposed to first solve the measurement of near-field HRTF for human subjects. The efficiency of measurement is enhanced by a comprehensive design on the acoustic, electronic and mechanical parts of the system. And the accuracy and repeatability of the measurement are greatly im- proved by carefully calibrating the positions of sound source, subject and binaural microphones. This system is suitable for near-field HRTF measurement at various source distances within 1.0 m, for both human subject and artificial head. The time costs of HRTF measurement at a single sound source distance and full directions has been reduced to less than 20 minutes. The measurement results indicate that the accuracy of the system satisfies the actual requirements. The system is applicable to scientific research and can be used to establish an individualized near-field HRTF database for human subjects.