Experimental study of core MHD behavior and a novel algorithm for rational surface detection based on profile reflectometry in EAST

Microwave reflectometry is a powerful diagnostic that can measure the density profile and localized turbulence with high spatial and temporal resolution and will be used in ITER,so understanding the influence of plasma perturbations on the reflect signal is important.The characteristics of the reflect signal from profile reflectometry,the time-of-flight(TOF)signal associated with the MHD instabilities,are investigated in EAST.Using a 1D full-wave simulation code by the Finite-DifferenceTime-Domain(FDTD)method,it is well validated that the local density flattening could induce the discontinuity of the simulated TOF signal and an obvious change of reflect amplitude.Experimental TOF signals under different types of MHD instabilities(sawtooth,sawtooth precursors and tearing mode)are studied in detail and show agreement with the simulation.Two new improved algorithms for detecting and localizing the radial positions of the low-order rational surface,the cross-correlation and gradient threshold(CGT)method and the 2D convolutional neural network approach(CNN)are presented for the first time.It is concluded that TOF signal analysis from profile reflectometry can provide a straightforward and localized measurement of the plasma perturbation from the edge to the core simultaneously and may be a complement or correction to the q-profile control,which will be beneficial for the advanced tokamak operation.

Application of transient Rayleigh wave in detection of tunnel lining void area

Transient Rayleigh wave detection is a high-precision nondestructive detection method.At present,it has been widely used in shallow exploration,but rarely used in tunnel lining quality detection.Through the tunnel lining physical model experiment,the layout defects of the double-layer reinforcement lining area were detected and the Rayleigh wave velocity profile and dispersion curve were analyzed after data process-ing,which finally verified the feasibility and accuracy of Rayleigh wave method in detecting the tunnel lining void area.The results show that the method is not affected by the reinforcement inside the lining,the shallow detection is less disturbed and the accuracy is higher,and the data will fluctuate slightly with the deepening of the detection depth.At the same time,this method responds quite accurately to the thickness of the concrete,allowing for the assessment of the tunnel lining’s lack of compactness.This method has high efficiency,good reliability,and simple data processing,and is suitable for nondestructive detection of internal defects of tun-nel lining structure.

Information Theory and Data-Mining Techniques for Network Traffic Profiling for Intrusion Detection

In this paper, information theory and data mining techniques to extract knowledge of network traffic behavior for packet-level and flow-level are proposed, which can be applied for traffic profiling in intrusion detection systems. The empirical analysis of our profiles through the rate of remaining features at the packet-level, as well as the three-dimensional spaces of entropy at the flow-level, provide a fast detection of intrusions caused by port scanning and worm attacks.

Magnetic Barkhausen Noise Profile Analysis: Effect of Excitation Field Strength and Detection Coil Sensitivity in Case Carburized Steel

The sensitivity of magnetic Barkhausen noise (MBN) profile to changes in the excitation field strength and the number of turns of the detection coil was investigated in inhomogeneous material. Generally, the 0.5 mm case depth EN 36 steel specimen shows a double peak profile indicative of inhomogeneity through the detected depth the magnetized landscape. Various excitation field amplitudes were applied to the specimen and the induced MBN emission was analyzed for each magnetizing current. Excitation field at the lowest level induced an MBN emission of two peaks of equivalent heights. The first peak occurs at lower field than the second peak in the magnetization period. As the excitation field increased, the height of both peaks increased but the second peak increased in a higher rate than that of the first peak. Beyond certain excitation level, both peaks began to saturate and no increase in the MBN intensity was observed with increased excitation field strength. However, peak position and the number of Barkhausen events, indicated linearly as a function of the applied field strength. The experiment also establishes that the number of turns in the detection coil is important parameter which controls the height of the first peak. Low field peak height increases as the number of turn of the detection coil increases. The results indicate that the potential difference applied to the electromagnet and the sensitivity of the detection coil, determine the MBN profile characteristics.

The Automated Vehicle Detection of Highway Traffic Images by Differential Morphological Profile

Vehicle detection has been the critical part of the traffic surveillance system for many years. However, vehicle detection method is still challenging. In this paper, differential morphology closing profile is used to extract the vehicle automatically from the traffic image. Along with closing profile, some addition operation has been applied as a part of the algorithm to get the high detection and quality rate. Result demonstrated that the novel method has an excellent detection and quality percentage. We also have compared our automated detection method with other traditional image processing based methods and the results indicate that our proposed method provides better results than traditional image processing based methods.

Dim Moving Small Target Detection by Local and Global Variance Filtering on Temporal Profiles in Infrared Sequences

In this paper, the temporal different characteristics between the target and background pixels are used to detect dim moving targets in the slow-evolving complex background. A local and global variance filter on temporal profiles is presented that addresses the temporal characteristics of the target and background pixels to eliminate the large variation of background temporal profiles. Firstly, the temporal behaviors of different types of image pixels of practical infrared scenes are analyzed.Then, the new local and global variance filter is proposed. The baseline of the fluctuation level of background temporal profiles is obtained by using the local and global variance filter. The height of the target pulse signal is extracted by subtracting the baseline from the original temporal profiles. Finally, a new target detection criterion is designed. The proposed method is applied to detect dim and small targets in practical infrared sequence images. The experimental results show that the proposed algorithm has good detection performance for dim moving small targets in the complex background.

Multichannel noncoherent integration detection using high range resolution profile

A multichannel noncoherent integration detection method based on high range resolution profile was presented in this paper. According to the property of the moment generating function, the distribution characteristics of the noncoherent integrated signals with or without target presence were derived under the circumstance with noncorrelated Gaussian distribution noises. The loss of noncoherent integration was due to improper selection of integration range of cell numbers. A multi channel noncoherent integration detection scheme where the integration number in each channel va ries was proposed to solve this problem. The quality of this method for detection of various targets was evaluated. A comparison of fixed integration range cell number detection and multichannel inte gration detection for a high range resolution profile was presented. Simulation results indicated that the principle of the method was correct and performed well for unknown physical dimension targets. The method required little prior knowledge about target and was convenient for practical implementa tion.

Comparison and Adaptation of Two Strategies for Anomaly Detection in Load Profiles Based on Methods from the Fields of Machine Learning and Statistics

The Federal Office for Economic Affairs and Export Control (BAFA) of Germany promotes digital concepts for increasing energy efficiency as part of the “Pilotprogramm Einsparzähler”. Within this program, Limón GmbH is developing software solutions in cooperation with the University of Kassel to identify efficiency potentials in load profiles by means of automated anomaly detection. Therefore, in this study two strategies for anomaly detection in load profiles are evaluated. To estimate the monthly load profile, strategy 1 uses the artificial neural network LSTM (Long Short-Term Memory), with a data period of one month (1 M) or three months (3 M), and strategy 2 uses the smoothing method PEWMA (Probalistic Exponential Weighted Moving Average). By comparing with original load profile data, residuals or summed residuals of the sequence lengths of two, four, six and eight hours are identified as an anomaly by exceeding a predefined threshold. The thresholds are defined by the Z-Score test, i.e., residuals greater than 2, 2.5 or 3 standard deviations are considered anomalous. Furthermore, the ESD (Extreme Studentized Deviate) test is used to set thresholds by means of three significance level values of 0.05, 0.10 and 0.15, with a maximum of k = 40 iterations. Five load profiles are examined, which were obtained by the cluster method k-Means as a representative sample from all available data sets of the Limón GmbH. The evaluation shows that for strategy 1 a maximum F1-value of 0.4 (1 M) and for all examined companies an average F1-value of maximum 0.24 and standard deviation of 0.09 (1 M) could be achieved for the investigation on single residuals. In variant 3 M the highest F1-value could be achieved with an average F1-value of 0.21 and standard deviation of 0.06 (3 M) for summed residuals of the partial sequence length of four hours. The PEWMA-based strategy 2 did not show a higher anomaly detection efficacy compared to strategy 1 in any of the investigated companies.

Detection of butt weld of laser-MIG hybrid welding of thin-walled profile for high-speed train

Purpose–This study aims to solve the problem of weld quality inspection,for the aluminum alloy profile welding structure of high-speed train body has complex internal shape and thin plate thickness(2–4 mm),the conventional nondestructive testing method of weld quality is difficult to implement.Design/methodology/approach–In order to solve this problem,the ultrasonic creeping wave detection technology was proposed.The impact of the profile structure on the creeping wave detection was studied by designing profile structural test blocks and artificial simulation defect test blocks.The detection technology was used to test the actual welded test blocks,and compared with the results of X-ray test and destructive test(tensile test)to verify the accuracy of the ultrasonic creeping wave test results.Findings–It is indicated that that X-ray has better effect on the inspection of porosities and incomplete penetration defects.However,due to special detection method and protection,the detection speed is slow,which cannot meet the requirements of field inspection of the welding structure of aluminum alloy thin-walled profile for high-speed train body.It can be used as an auxiliary detection method for a small number of sampling inspection.The ultrasonic creeping wave can be used to detect the incomplete penetration welds with the equivalent of 0.25 mm or more,the results of creeping wave detection correspond well with the actual incomplete penetration defects.Originality/value–The results show that creeping wave detection results correspond well with the actual non-penetration defects and can be used for welding quality inspection of aluminum alloy thin-wall profile composite welding joints.It is recommended to use the echo amplitude of the 10 mm 30.2 mm 30.5 mm notch as the criterion for weld qualification.

Study on Rhizoma Chuanxiong based on capillary electrophoresis with amperometric detection

A high-performance capillary electrophoresis with amperometric detection(CE-AD) method has been developed for the analysis of seven bioactive ingredients,namely ferulic acid(FA),vanillin,vanillic acid,p-hydroxybenzoic acid,caffeic acid,gallic acid and protocatechuic acid,in Rhizoma Chuanxiong.The effects of several factors such as the acidity and concentration of running buffer,the separation voltage,the applied potential to working electrode and the injection time were investigated.Under the optimum condit...

A Novel Immune System Model and Its Application to Network Intrusion Detection

Based on analyzing the techniques and architecture of existing network Intrusion Detection System (IDS), and probing into the fundament of Immune System (IS), a novel immune model is presented and applied to network IDS, which is helpful to design an effective IDS. Besides, this paper suggests a scheme to represent the self profile of network. And an automated self profile extraction algorithm is provided to extract self profile from packets. The experimental results prove validity of the scheme and algorithm, which is the foundation of the immune model.

A DETECTION METHOD OF HIGH RESOLUTION RADAR TARGETS BASED ON POSITION CORRELATION

This paper studies a detection method of targets of high resolution radar operating at the band of millimeter-wave(32-38GHz) under the background of the clutters, and proposes a new nonparametric detection method, which not only does less computation, but also is able to detect multiple extended targets radially distributed along distance "corridor", based on the position (range) correlation information of one-dimensional range images(or called range profiles) of high resolution radar targets. The experimental results, on the real echo data of tank illuminated by the millimeter-wave stepped frequency high resolution radar, have certified that such a method presented in this paper is a very effective detection method for multiple extended targets.

Automatic Change Detection for Road Networks from Images Based on GIS

Up to now, detailedstrategies and algorithms of automaticchange detection for road networksbased on GIS have not been discussed.This paper discusses two differentstrategies of automatic change detec-tion for images with low resolution andhigh resolution using old GIS data,and presents a buffer detection andtracing algorithm for detecting roadfrom low-resolution images and a newprofile tracing algorithm for detectingroad from high-resolution images. Forfeature-level change detection (FL-CD), a so-called buffer detection algo-rithm is proposed to detect changes offeatures. Some ideas and algorithms ofusing GIS prior information and somecontext information such as substructures of road in high-resolution imagesto assist road detection and extractionare described in detail.

Detection of text in images using SUSAN edge detector

Text embedded in images is one of many important cues for indexing and retrieval of images and videos. In the paper, we present a novel method of detecting text aligned either horizontally or vertically, in which a pyramid structure is used to represent an image and the features of the text are extracted using SUSAN edge detector. Text regions at each level of the pyramid are identified according to the autocorrelation analysis. New techniques are introduced to split the text regions into basic ones and merge them into text lines. By evaluating the method on a set of images, we obtain a very good performance of text detection.

DEPTH PROFILING OF ~1H AND/OR ~4He IN SOLIDS BY ERD WITH ^(19)F IONS

1H or 4He depth profiling in 1H or 4He implanted silicon samples was performed by elastic recoil detection (ERD) with multicharged 19F ions at a small accelerator. Optimization of the experimental parameters such as incident ions energy and scattering geometry were calculated by computer simulation. Depth resolution of about 20-30nm at depth of 400nm for 1H and at depth of 300nm for 4He can be obtained, respectively.

ELASTIC RECOIL DETECTION ANALYSIS OF LIGHT ELEMENTS IN THIN FILMS USING 35 MeV^(35) Cl^(6+) BEAM

In this paper, an elastic recoil detection analysis method is described using 35 MeV 35Cl as incident ions. This method can determine and profile simultaneously H, D, He, C and O or in the other case, H, C, N and O. The depth resolution for the elements heavier than He is better than 20 nm. It has been applied to study the Co/Si and TiN thin films, and the depth profiles of He implanted in monocrystal silicon.

Elimination of Wind Wave Effects on Sub-Bottom Profiler Record

With the presence of wind waves, the swaying of survey vessel may effect the quality of sub-bottom profiler records and, therefore, it is necessary to correct the distortions induced by wave action. A major issue is to distinguish wind wave effect and real bedforms such as sand waves. In this paper, a bandstop filter is designed according to the frequency features of wind wave effect to treat the distortion of seabed topography by wind waves. The technique is used to correct the sub-bottom profile in order to eliminate the wave-induced distortions for the sub-bottom profile records from the Yangtze Estuary. This study shows that the undulate seabed record is resulted from wave action, rather than the presence of sand waves, and the filtration technique helps to eliminate the wave effect and recover the real morphology of seabed and the sediment sequence underneath. In addition, a method for data processing is proposed for the case that the record indeed represents a combination of wave effects and real bedforms.

Masquerade Detection Using Support Vector Machine

A new method using support vector data description (SVDD) to distinguishlegitimate users from mas-queradcrs based on UNIX user command sequences is proposed Sliding windowsare used to get low detection delay. Experiments demonstrate that the detection effect usingenriched sequences is better than that of using truncated sequences. As a SVDD profile is composedof a small amount of support vectors, our SVDD-based method can achieve computation and storageadvantage when the detection performance issimilar to existing method.

An Adaptive Anomaly Detection Algorithm Based on CFSFDP

CFSFDP(Clustering by fast search and find of density peak)is a simple and crisp density clustering algorithm.It does not only have the advantages of density clustering algorithm,but also can find the peak of cluster automatically.However,the lack of adaptability makes it difficult to apply in intrusion detection.The new input cannot be updated in time to the existing profiles,and rebuilding profiles would waste a lot of time and computation.Therefore,an adaptive anomaly detection algorithm based on CFSFDP is proposed in this paper.By analyzing the influence of new input on center,edge and discrete points,the adaptive problem mainly focuses on processing with the generation of new cluster by new input.The improved algorithm can integrate new input into the existing clustering without changing the original profiles.Meanwhile,the improved algorithm takes the advantage of multi-core parallel computing to deal with redundant computing.A large number of experiments on intrusion detection on Android platform and KDDCUP 1999 show that the improved algorithm can update the profiles adaptively without affecting the original detection performance.Compared with the other classical algorithms,the improved algorithm based on CFSFDP has the good basic performance and more room of improvement.