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.

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.

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.

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.

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.

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.

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