Detection performance and inversion processing of logging-while-drilling extra-deep azimuthal resistivity measurements

We present systematic investigations on the physics,detection performance and inversion of logging-while-drilling extradeep azimuthal resistivity measurements(EDARM).First,the definitions of EDRAM measurements are discussed,followed by the derivation of the attenuation and phase-shift geometrical factors to illustrate the relative contributions of formation units to the observed signals.Then,a new definition of detection depth,which considers the uncertainty of inversion results caused by the data noise,is proposed to quantify the detection capability of ED ARM.Finally,the B ayesian theory associated with Markov chain Monte Carlo sampling is introduced for fast processing of EDARM data.Numerical results show that ED ARM is capable of detecting the azimuth and distance of remote bed boundaries,and the detection capability increases with increasing spacing and resistivity contrast.The EDARM tool can accommodate a large range of formation resistivity and is able to provide the resistivity anisotropy at arbitrary relative dipping angles.In addition,multiple bed boundaries and reservoir images near the borehole are readily obtained by using the Bayesian inversion.

Cued search algorithm with uncertain detection performance for phased array radars

A cued search algorithm with uncertain detection performance is proposed for phased array radars. Firstly, a target search model based on the information gain criterion is presented with known detection performance, and the statistical characteristic of the detection probability is calculated by using the fluctuant model of the target radar cross section （RCS）. Secondly, when the detection probability is completely unknown, its probability density function is modeled with a beta distribution, and its posterior probability distribution with the radar observation is derived based on the Bayesian theory. Finally simulation results show that the cued search algorithm with a known RCS fluctuant model can achieve the best performance, and the algorithm with the detection probability modeled as a beta distribution is better than that with a random selected detection probability because the model parameters can be updated by the radar observation to approach to the real value of the detection probability.

Anomalous Cell Detection with Kernel Density-Based Local Outlier Factor

Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.

A high performance waveform and a new ranging method for the proximity detector

Signal modulation is an essential design factor for proximity detectors and directly affects the system's potential performance.In order to achieve the advantages of chaotic codes bi-phase modulation(CCBPM)and linear frequency modulation(LFM) simultaneously,this paper designed a waveform which combined chaotic codes bi-phase modulation and linear frequency modulation(CCBPM-LFM) for proximity detectors.The CCBPM-LFM waveform was analyzed in the aspect of time delay resolution(TDR) and Doppler tolerance(DT) based on ambiguity function(AF).Then,a ranging method,which we called instant correlation harmonic demodulation(ICHD),was presented for the detector using the CCBPM-LFM waveform.By combining time domain instant correlation with harmonic demodulation,the ICHD solved the problem caused by combination modulation and made the most of the linear frequency modulation(LFM) harmonics and the correlation of chaotic codes.Finally,a prototype was implemented and ranging experiments were carried out.From the theoretical analysis and experimental results,the proximity detector used the CCBPM-LFM waveform has an outstanding detection performance.

Application of modified multiple subarrays detection method to multibeam bathymetry system

Either amplitude based methods or phase based methods are usually used in multibeam bathymetry system and chosen in practical measurement by ad hoc criteria. The errors of the detection are often caused by choosing wrong detection method. In this paper a modified multiple subarrays amplitude-phase united detection method is proposed , which uses both the amplitude and phase of echo from bottom.This new method not only realizes the super-wide swath coverage, but also improves the detection performance by using the phase differences among subarrays in place of phases of subarrays to estimate the phase slope image. Experimental results are also analyzed and discussed to demonstrate the effectivity of the proposed approach.

Performance Analysis of Techniques Used for Determining Land Mines

Today, remote sensing is used for different methods and different purposes. In all of the detection methods, some considerations such as low energy consumption, low cost, insensitivity to environmental changes, high accuracy, high reliability and robustness become important. Taking into account these facts, remote sensing methods are used in applications such as geological and archeological research, engineering areas, health services, preserving and controlling natural life, determination of underground sources, controlling air, sea and road traffic, military applications, etc. The method to be used is based on the object type to be detected, material to be made, and location to be found. The remote sensing methods from the past up to today can be listed as acoustic and seismic, ground penetration radar (GPR) detection, electromagnetic induction, infrared (IR) imaging, neutron quadrupole resonance (NQR), thermal neutron activation (TNA), neutron back scattering, X-ray back scattering, and magnetic anomaly detection. In these methods, detected raw images have to be processed, filtered and enhanced. In order to achieve these operations, some algorithms are needed to be developed. In this study, the methods used in detecting land mines remotely and their performance analysis have been given. In this way, the last situation on the advantages and disadvantages of methods used, application areas and detection accuracies are determined. Furthermore, the algorithms such as transmission line matrix (TLM), finite difference time-domain (FDTD), the method of moment (MoM), split step parabolic equation (SSPE) and image processing and intelligent algorithms are presented in detail.

A General Linguistic Steganalysis Framework Using Multi-Task Learning

Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts,by performing binary classification.While it remains an unsolved problem and poses a significant threat to the security of cyberspace when various categories of non-steganographic or steganographic texts coexist.In this paper,we propose a general linguistic steganalysis framework named LS-MTL,which introduces the idea of multi-task learning to deal with the classification of various categories of steganographic and non-steganographic texts.LS-MTL captures sensitive linguistic features from multiple related linguistic steganalysis tasks and can concurrently handle diverse tasks with a constructed model.In the proposed framework,convolutional neural networks(CNNs)are utilized as private base models to extract sensitive features for each steganalysis task.Besides,a shared CNN is built to capture potential interaction information and share linguistic features among all tasks.Finally,LS-MTL incorporates the private and shared sensitive features to identify the detected text as steganographic or non-steganographic.Experimental results demonstrate that the proposed framework LS-MTL outperforms the baseline in the multi-category linguistic steganalysis task,while average Acc,Pre,and Rec are increased by 0.5%,1.4%,and 0.4%,respectively.More ablation experimental results show that LS-MTL with the shared module has robust generalization capability and achieves good detection performance even in the case of spare data.

Wavefront detection performance analysis of plenoptic sensor

A numerical simulation model of plenoptic sensor aberration wavefront detection is established to simulate and analyze the detection performance of plenoptic sensor aberration wavefront for different turbulence intensities.The results show that the plenoptic sensor can achieve better distortion wavefront detection,and its wavefront detection accuracy improves with turbulence intensity.The unique optical structure design of the plenoptic sensor makes it more suitable for aberration wavefront detection in strong turbulent conditions.The wavefront detection performance of the plenoptic sensor is not only related to its wavefront reconstruction algorithm but also closely related to its structural parameter settings.The influence of structural parameters on the wavefront detection accuracy of plenoptic sensors under different turbulence intensities is simulated and analyzed.The variation law of wavefront detection accuracy and structural parameters under different turbulence intensities is summarized to provide a reference for the structural design and parameter optimization of plenoptic sensors.

On detection performance of MIMO radar for Rician target

By using spatial diversity, multiple-input-multiple-output （MIMO） radar can improve detection performance for fluctuating targets. In this paper, we propose a spatial fluctuation target model for MIMO radar, where targets are classified as non-fluctuating target, Rayleigh target and Rician target. Based on Stein＇s lemma, we use relative entropy to study detection performance of optimum detector for Rician target. It is found that in low signal noise ratio （SNR） region, the performance improvement of MIMO radar for detecting Rician target depends on array gain, which is related to the number of receivers. In high SNR region, the improvement of performance depends on diversity gain, which is related to the product of the number of receivers and the number of transmitters. The conclusions of this paper are important for designing MIMO radar system.

A dynamic detection method to improve SLAM performance

Simultaneous localization and mapping(SLAM) technology is a research hotspot in the field of intelligent mobile robot, and many researchers have developed many classic systems in the past few decades. However, most of the existing SLAM methods assume that the environment of the robot is static, which results in the performance of the system being greatly reduced in the dynamic environment. To solve this problem, a new dynamic object detection method based on point cloud motion analysis is proposed and incorporated into ORB-SLAM2. First, the method is regarded as a preprocessing stage, detecting moving objects in the scene, and then removing the moving objects to enhance the performance of the SLAM system. Experiments performed on a public RGB-D dataset show that the motion cancellation method proposed in this paper can effectively improve the performance of ORB-SLAM2 in a highly dynamic environment.

Analysis of Sucrose Esters with Long Acyl Chain by Coupling of HPLC-ELSD with ESI-MS System

The analysis of sucrose esters with long acyl chain by improved high performance liquid chromatographic method with evaporative light scattering detection （HPLC-ELSD） and electrospray ionization mass spectrum （ESI-MS） is investigated. The improved HPLC-ELSD method for the separation and quantitation of commercial and synthesized sucrose esters is described. Samples are analyzed by means of a reversed-phase （RP） HPLC using a Hypersil C8 column （250 mm× 4.6 mm, 5 μm particle size） with methanol-tetrahydrofuran （vo）ume ratio of 90 ： 10） and water under gradientcondition as the mobile phase, in which the flow rate is 1.0 ml·min^-1 and the column temperature is set at 40℃. This procedure provides a complete separation and determination ot monoester, diester, triester and higher esters with different acyl chain lengths in each fraction by a single run, in combination with the ESI-MS technology. With this method, it is possible to determine the approximate compositions of monoto polyesters in one analysis and quantitate pure positional isomers precisely using an external standard method. It is found that the method of ESI-MS coupling with HPLC system for the analysis of sucrose esters is straight forward, rapid and inexpensive, and can be readily applied in synthesis, purification and structure studies.

Imidazolium ionic liquid as the background ultraviolet absorption reagent for determination of morpholinium cations by high performance liquid chromatography-indirect ultraviolet detection

A novel analytical method was developed for determining morpholinium cations lacking ultraviolet absorption groups.This determination was carried out by high performance liquid chromatographyindirect ultraviolet（HPLC-1UV） detection using imidazolium ionic liquid as background absorption reagents,and imidazolium ionic liquid aq.soln.-organic solvent as mobile phase by a reversed-phase C18 column.The background ultraviolet absorption reagents,imidazolium ionic liquids and organic solvents were investigated.The imidazolium ionic liquid in the mobile phase is not only the background ultraviolet absorption reagent for IUV,but also an active component to improve the separation of morpholinium cations.It was found that morpholinium cations could be adequately determined when0.5 mmol/L 1-ethyl-3-methylimidazolium tetrafluoroborate aq.soln./methanol（80：20,v/v） was used as mobile phase with an IUV detection wavelength of 210 nm.In this study,the baseline separation of Nmethyl,ethylmorpholinium cations（MEMo） and N-methyl.propylmorpholinium cations（MPMo） was successfully achieved in 8.5 min.The detection limits（S/N = 3） for MEMo and MPMo were 0.15 and0.29 mg/L,respectively.This simple and practical method has been successfully applied to the determination of two morpholinium ionic liquids synthesized by the chemistry laboratory.

Durbin test with enhanced detection performance to mismatched signal

This paper deals with the problem of detecting a signal whose amplitude is a scaling factor in the presence of homogeneous Gaussian noise with unknown covariance matrix.Since no uniformly most powerful test exists for the problem at hand,we devise and assess a detection strategy based on the well-known Durbin test design criteria.The closed-form expressions for the probabilities of false alarm and detection of the Durbin test are derived,which show that it bears a constant false alarm rate property against the noise covariance matrix.At the analysis stage,the performance of the new receiver is assessed,also in comparison with some classical adaptive detectors,both in matched and in mismatched signal cases.The results show that the proposed detector achieves a visible performance improvement in the presence of severe steering vector mismatch,while maintaining an acceptable detection loss for matched signal.

A note on diagnosis and performance degradation detection in automatic control systems towards functional safety and cyber security

This note addresses diagnosis and performance degradation detection issues from an integrated viewpoint of functionality maintenance and cyber security of automatic control systems.It calls for more research attention on three aspects:(i)application of control and detection uni ed framework to enhancing the diagnosis capability of feedback control systems,(ii)projection-based fault detection,and complementary and explainable applications of projection-and machine learning-based techniques,and(iii)system performance degradation detection that is of elemental importance for today's automatic control systems.Some ideas and conceptual schemes are presented and illustrated by means of examples,serving as convincing arguments for research e orts in these aspects.They would contribute to the future development of capable diagnosis systems for functionality safe and cyber secure automatic control systems.

Study on the performance of data fusion system for sonar signal detection

The mathematical model and fusion algorithm for multisensor data fusion are presented, and applied to integrate the decisions obtained by multiple sonars in a distributed detection system. Assuming that all the sonars and the fusion system operate at the same false alarm probability, the expression for the detection probability of the fusion system is obtained. Computer simulations reveals that the detection probability and detection range of the fusion system are significantly improved compared to the original distributed detection system.

The Diagnostic performance of galactomannan detection for invasive pulmonary aspergillosis in non-nentropenic hosts

Objective To evaluate the diagnostic performance of galactomannan(GM)detection in serum and BALF for invasive pulmonary aspergillosis(IPA)in non-neutropenic hosts.Methods A prospective study was performed for 1 356 non-neutropenic hosts admitted to the Department of Pulmonary and Critical Care Medicine of

Performance of the low pressure MWPCs for f ission fragments under a high background

Two couples of low pressure multi-wire proportional chambers （MWPC） were located in the target chamber to detect fission fragments in a hypernuclei producing experiment at Thomas Jefferson National Laboratory （Jlab）. In the experiment, a continuous wave （CW） electron beam was applied to form hypernuclei by electromagnetic interaction. In the target chamber, the high energy （1.853 GeV） and high intensity （500 nA） primary electron beam caused a high particle background, which influenced the detection of the fission fragments. This report described the design of the MWPCs and studied the fission-fragment detecting performance of them under such a high background. The efficiency of the MWPCs was given with the help of a high resolution kaon spectrometer. At the same time, the background particles were discussed with a Monte Carlo code based on GEANT4.

Simultaneous and interference-free determination of eleven nonsteroidal anti-inflammatory drugs illegally added into Chinese patent drugs using chemometrics-assisted HPLC-DAD strategy

In this work, a smart strategy that combines three-way high performance liquid chromatography-diode array detection(HPLCDAD) data with second-order calibration method based on alternating trilinear decomposition(ATLD) algorithm was proposed for simultaneous determination of eleven non-steroidal anti-inflammatory drugs(NSAIDs) illegally added into Chinese patent drugs and health products. All target analytes were rapidly eluted out within 14.5 min under a simple gradient elution. With the aid of the prominent "second-order advantage" of the ATLD algorithm, three HPLC problems, i.e. peak overlaps, unknown interferences and baseline drift, could be mathematically calibrated, and pure signals of target analytes could be extracted out from heavy-interference but information-rich HPLC-DAD data. The average spiked recoveries for all target analytes were in the range of 95.9%–106.4% with standard deviations lower than 7.5%. Validation parameters including sensitivity(SEN), selectivity(SEL), limit of detection(LOD), limit of quantitation(LOQ) and precisions of intra-day and inter-day were calculated to validate the accuracy of the proposed method, quantitative results were further confirmed by the classic HPLC method, which proved that chemometrics-assisted HPLC-DAD analytical strategy was highly efficient, accurate and green for drug-abuse monitoring of NSAIDs in Chinese patent drugs and health products.