An intelligent boundary switch is a three-phase outdoor power distribution device equipped with a controller. It is installed at the boundary point on the medium voltage overhead distribution lines. It can automatically remove the single-phase-to-ground fault and isolation phase-to-phase short-circuit fault. Firstly, the structure of an intelligent boundary switch is studied, and then the fault detection principle is also investigated. The single-phase-to-ground fault and phase-to-phase short-circuit fault are studied respectively. A method using overcurrent to judge the short-circuit fault is presented. The characteristics of the single-phase-to-ground fault on an ungrounded distribution system and compositional grounded distribution system are analyzed. Based on these characteristics, a method using zero sequence current to detect the single-phase-to-ground fault is proposed. The research results of this paper give a reference for the specification and use of intelligent boundary switches.
To avoid of manipulating search engines results by web spam, anti spam system use machine learning techniques to detect spam. However, if the learning set for the system is out of date the quality of classification falls rapidly. We present the web spam recognition system that periodically refreshes the learning set to create an adequate classifier. A new classifier is trained exclusively on data collected during the last period. We have proved that such strategy is better than an incrementation of the learning set. The system solves the starting–up issues of lacks in learning set by minimisation of learning examples and utilization of external data sets. The system was tested on real data from the spam traps and common known web services: Quora, Reddit, and Stack Overflow. The test performed among ten months shows stability of the system and improvement of the results up to 60 percent at the end of the examined period.
An application specific integrated design using Quadrature Linear Discriminant Analysis is proposed for automatic detection of normal and epilepsy seizure signals from EEG recordings in epilepsy patients. Five statistical parameters are extracted to form the feature vector for training of the classifier. The statistical parameters are Standardised Moment, Co-efficient of Variance, Range, Root Mean Square Value and Energy. The Intellectual Property Core performs the process of filtering, segmentation, extraction of statistical features and classification of epilepsy seizure and normal signals. The design is implemented in Zynq 7000 Zc706 SoC with average accuracy of 99%, Specificity of 100%, F1 score of 0.99, Sensitivity of 98% and Precision of 100 % with error rate of 0.0013/hr., which is approximately zero false detection.
We present the development of a technique for studying laser-induced magnetization dynamics, based on inductive measurement. The technique could provide a simple tool for studying laser-induced demagnetization in thin films and associated processes, such as Gilbert damping and magnetization precession. It was successfully tested using a nanosecond laser and NiZn ferrite samples and – after further development – it is expected to be useful for observation of ultra-fast demagnetization. The combination of optical excitation and inductive measurement enables to study laser-induced magnetization dynamics in both thin and several micrometre thick films and might be the key to a new principle of ultrafast broadband UV–IR pulse detection.
A modification of the descriptor in a human detector using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) is presented. The proposed modification requires inserting the values of average cell brightness resulting in the increase of the descriptor length from 3780 to 3908 values, but it is easy to compute and instantly gives ≈ 25% improvement of the miss rate at 10‒4 False Positives Per Window (FPPW). The modification has been tested on two versions of HOG-based descriptors: the classic Dalal-Triggs and the modified one, where, instead of spatial Gaussian masks for blocks, an additional central cell has been used. The proposed modification is suitable for hardware implementations of HOG-based detectors, enabling an increase of the detection accuracy or resignation from the use of some hardware-unfriendly operations, such as a spatial Gaussian mask. The results of testing its influence on the brightness changes of test images are also presented. The descriptor may be used in sensor networks equipped with hardware acceleration of image processing to detect humans in the images.
Stealth in military sonars applications may be ensured through the use of low power signals making them difficult to intercept by the enemy. In recent years, silent sonar design has been investigated by the Department of Marine Electronic Systems of the Gdansk University of Technology. This article provides an analysis of how an intercept sonar operated by the enemy can detect silent sonar signals. To that end a theoretical intercept sonar model was developed with formulas that can numerically determine the intercept ranges of silent sonar sounding signals. This was tested for a variety of applications and water salinities. Because they are also presented in charts, the results can be used to compare the intercept ranges of silent sonar and traditional pulse sonar.
The article presents the detection of gases using an infrared imaging Fourier-transform spectrometer (IFTS). The Telops company has developed the IFTS instrument HyperCam, which is offered as a short- or long-wave infrared device. The principle of HyperCam operation and methodology of gas detection has been shown in the paper, as well as theoretical evaluation of gas detection possibility. Calculations of the optical path between the IFTS device, cloud of gases and background have been also discussed. The variation of a signal reaching the IFTS caused by the presence of a gas has been calculated and compared with the reference signal obtained without the presence of a gas in IFTS's field of view. Verification of the theoretical result has been made by laboratory measurements. Some results of the detection of various types of gases has been also included in the paper.
One of the most important issues that power companies face when trying to reduce time and cost maintenance is condition monitoring. In electricity market worldwide, a significant amount of electrical energy is produced by synchronous machines. One type of these machines is brushless synchronous generators in which the rectifier bridge is mounted on rotating shafts. Since bridge terminals are not accessible in this type of generators, it is difficult to detect the possible faults on the rectifier bridge. Therefore, in this paper, a method is proposed to facilitate the rectifier fault detection. The proposed method is then evaluated by applying two conventional kinds of faults on rectifier bridges including one diode open-circuit and two diode open-circuit (one phase open-circuit of the armature winding in the auxiliary generator in experimental set). To extract suitable features for fault detection, the wavelet transform has been used on recorded audio signals. For classifying faulty and healthy states, K-Nearest Neighbours (KNN) supervised classification method was used. The results show a good accuracy of the proposed method.
In this paper, we present the methods to detect the channel delay profile and the Doppler spectrum of shallow underwater acoustic channels (SUAC). In our channel sounding methods, a short impulse in form of a sinusoid function is successively sent out from the transmitter to estimated the channel impulse response (CIR). A bandpass filter is applied to eliminate the interference from out-of-band (OOB). A threshould is utilized to obtain the maximum time delay of the CIR. Multipath components of the SUAC are specified by correlating the received signals with the transmitted sounding pulse with its shifted phases from 0 to 2#25;. We show the measured channel parameters, which have been carried out in some lakes in Hanoi. The measured results illustrate that the channel is frequency selective for a narrow band transmission. The Doppler spectrum can be obtained by taking the Fourier transform of the time correlation of the measured channel transfer function. We have shown that, the theoretical maximum Doppler frequency fits well to that one obtained from measurement results.
The article herein presents the method and algorithms for forming the feature space for the base of intellectualized system knowledge for the support system in the cyber threats and anomalies tasks. The system being elaborated might be used both autonomously by cyber threat services analysts and jointly with information protection complex systems. It is shown, that advised algorithms allow supplementing dynamically the knowledge base upon appearing the new threats, which permits to cut the time of their recognition and analysis, in particular, for cases of hard-to-explain features and reduce the false responses in threat recognizing systems, anomalies and attacks at informatization objects. It is stated herein, that collectively with the outcomes of previous authors investigations, the offered algorithms of forming the feature space for identifying cyber threats within decisions making support system are more effective. It is reached at the expense of the fact, that, comparing to existing decisions, the described decisions in the article, allow separate considering the task of threat recognition in the frame of the known classes, and if necessary supplementing feature space for the new threat types. It is demonstrated, that new threats features often initially are not identified within the frame of existing base of threat classes knowledge in the decision support system. As well the methods and advised algorithms allow fulfilling the time-efficient cyber threats classification for a definite informatization object.
The article reports three experiments conducted to determine whether musicians possess better ability of recognising the sources of natural sounds than non-musicians. The study was inspired by reports which indicate that musical training develops not only musical hearing, but also enhances various non-musical auditory capabilities. Recognition and detection thresholds were measured for recordings of environmental sounds presented in quiet (Experiment 1) and in the background of a noise masker (Experiment 2). The listener’s ability of sound source recognition was inferred from the recognition-detection threshold gap (RDTG) defined as the difference in signal level between the thresholds of sound recognition and sound detection. Contrary to what was expected from reports of enhanced auditory abilities of musicians, the RDTGs were not smaller for musicians than for non-musicians. In Experiment 3, detection thresholds were measured with an adaptive procedure comprising three interleaved stimulus tracks with different sounds. It was found that the threshold elevation caused by stimulus interleaving was similar for musicians and non-musicians. The lack of superiority of musicians over non-musicians in the auditory tasks explored in this study is explained in terms of a listening strategy known as casual listening mode, which is a basis for auditory orientation in the environment.
In this paper we present the numerical simulation-based design of a new microfluidic device concept for electrophoretic mobility and (relative) concentration measurements of dilute mixtures. The device enables stationary focusing points for each species, where the locally applied pressure driven flow (PDF) counter balances the species electrokinetic velocity. The axial location of the focusing point, along with the PDF flowrate and applied electric field reveals the electrokinetic mobility of each species. Simultaneous measurement of the electroosmotic mobility of an electrically neutral specie can be utilized to calculate the electrophoretic mobility of charged species. The proposed device utilizes constant sample feeding, and results in time-steady measurements. Hence, the results are independent of the initial sample distribution and flow dynamics. In addition, the results are insensitive to the species diffusion for large Peclet number flows (Pe > 400), enabling relative concentration measurement of each specie in the dilute mixture.
Biometric identification systems, i.e. the systems that are able to recognize humans by analyzing their physiological or behavioral characteristics, have gained a lot of interest in recent years. They can be used to raise the security level in certain institutions or can be treated as a convenient replacement for PINs and passwords for regular users. Automatic face recognition is one of the most popular biometric technologies, widely used even by many low-end consumer devices such as netbooks. However, even the most accurate face identification algorithm would be useless if it could be cheated by presenting a photograph of a person instead of the real face. Therefore, the proper liveness measurement is extremely important. In this paper we present a method that differentiates between video sequences showing real persons and their photographs. First we calculate the optical flow of the face region using the Farnebäck algorithm. Then we convert the motion information into images and perform the initial data selection. Finally, we apply the Support Vector Machine to distinguish between real faces and photographs. The experimental results confirm that the proposed approach could be successfully applied in practice.
The contribution presents a novel approach to the detection and tracking of lanes based on lidar data. Therefore, we use the distance and reflectivity data coming from a one-dimensional sensor. After having detected the lane through a temporal fusion algorithm, we register the lidar data in a world-fixed coordinate system. To this end, we also incorporate the data coming from an inertial measurement unit and a differential global positioning system. After that stage, an original image of the road can be inferred. Based on this data view, we are able to track the lane either with a Kalman filter or by using a polynomial approximation for the underlying lane model.
The paper presents analyses of current research projects connected with explosive material sensors. Sensors are described assigned to X and γ radiation, optical radiation sensors, as well as detectors applied in gas chromatography, electrochemical and chemical sensors. Furthermore, neutron techniques and magnetic resonance devices were analyzed. Special attention was drawn to optoelectronic sensors of explosive devices.
Although the phenomenon of otoacoustic emission has been known for nearly 30 years, it has not been fully explained yet. One kind of otoacoustic emission is distortion product of the otoacoustic emission (DPOAE). New aspects of this phenomenon are constantly discovered and attempts are made to interpret correctly the obtained results. This paper discusses a new method of measuring DPOAE signals based on double phase-sensitive detection, which makes possible a real-time measurement of the DPOAE signal amplitude and phase. The method was applied for measurements of DPOAE signals in guinea pigs. Sample records are presented and the obtained results are discussed.
In this study, an artificial neural network application was performed to tell if 18 plates of the same material in different shapes and sizes were cracked or not. The cracks in the cracked plates were of different depth and sizes and were non-identical deformations. This ANN model was developed to detect whether the plates under test are cracked or not, when four plates have been selected randomly from among a total of 18 ones. The ANN model used in the study is a model uniquely tailored for this study, but it can be applied to all systems by changing the weight values and without changing the architecture of the model. The developed model was tested using experimental data conducted with 18 plates and the results obtained mainly correspond to this particular case. But the algorithm can be easily generalized for an arbitrary number of items.
This paper presents a methodology for contact detection between convex quadric surfaces using its implicit equations. With some small modifications in the equations, one can model superellipsoids, superhyperboloids of one or two sheets and supertoroids. This methodology is to be implemented on a multibody dynamics code, in order to simulate the interpenetration between mechanical systems, particularly, the simulation of collisions with motor vehicles and other road users, such as cars, motorcycles and pedestrians. The contact detection of two bodies is formulated as a convex nonlinear constrained optimization problem that is solved using two methods, an Interior Point method (IP) and a Sequential Quadratic Programming method (SQP), coded in MATLAB and FORTRAN environment, respectively. The objective function to be minimized is the distance between both surfaces. The design constraints are the implicit superquadrics surfaces equations and operations between its normal vectors and the distance itself. The contact points or the points that minimize the distance between the surfaces are the design variables. Computational efficiency can be improved by using Bounding Volumes in contact detection pre-steps. First one approximate the geometry using spheres, and then Oriented Bounding Boxes (OBB). Results show that the optimization technique suits for the accurate contact detection between objects modelled by implicit superquadric equations.
Diagnostics of composite castings, due to their complex structure, requires that their characteristics are tested by an appropriate description method. Any deviation from the specific characteristic will be regarded as a material defect. The detection of defects in composite castings sometimes is not sufficient and the defects have to be identified. This study classifies defects found in the structures of saturated metallic composite castings and indicates those stages of the process where such defects are likely to be formed. Not only does the author determine the causes of structural defects, describe methods of their detection and identification, but also proposes a schematic procedure to be followed during detection and identification of structural defects of castings made from saturated reinforcement metallic composites. Alloys examination was conducted after technological process, while using destructive (macroscopic tests, light and scanning electron microscopy) and non-destructive (ultrasonic and X-ray defectoscopy, tomography, gravimetric method) methods. Research presented in this article are part of author’s work on castings quality.
In this work the construction of experimental setup for MEMS/NEMS deflection measurements is presented. The system is based on intensity fibre optic detector for linear displacement sensing. Furthermore the electronic devices: current source for driving the light source and photodetector with wide-band preamplifier are presented.
This research was conducted to investigate the natural, quantitative composition of the most common Fusarium species directly in fields of northeastern Poland. The concentration of Fusarium spp. and grain quality traits (yield, 1,000 kernel weight, test weight, grain moisture, ergosterol content, protein content, gluten content and starch content) were compared in four wheat varieties (Mandaryna, Struna, Kandela and Arabella). Obtained results indicated a relation between grain moisture, test weight, ergosterol content, yield and fungi concentration. Protein, starch and gluten content was similar in all wheat varieties. Fusarium culmorum was the most common pathogen in Mandaryna and Struna and F. graminearum in Kandela and Arabella. Fusarium avenaceum and F. poae occurred in low amounts in all wheat varieties except Mandaryna. Fusarium oxysporum was found in comparable concentrations in Struna, Kandela and Arabella. Struna despite medium Fusarium spp. colonization possessed the most desirable grain quality compared to other varieties. We carried out real-time PCR detection of Fusarium spp. which is an efficient, cost effective and time saving method in evaluating the development of fungal diseases which are not visible in standard observations.
Perception takes into account the costs and benefits of possible interpretations of incoming sensory data. This should be especially pertinent for threat recognition, where minimising the costs associated with missing a real threat is of primary importance. We tested whether recognition of threats has special characteristics that adapt this process to the task it fulfils. Participants were presented with images of threats and visually matched neutral stimuli, distorted by varying levels of noise. We found threat superiority effect and liberal response bias. Moreover, increasing the level of noise degraded the recognition of the neutral images to higher extent than the threatening images. To summarise, recognising threats is special, in that it is more resistant to noise and decline in stimulus quality, suggesting that threat recognition is a fast ‘all or nothing’ process, in which threat presence is either confirmed or negated.
Automatic gender detection is a process of determining the gender of a human according to the characteristic properties that represent the masculine and feminine attributes of a subject. Automatic gender detection is used in many areas such as customer behaviour analysis, robust security system construction, resource management, human-computer interaction, video games, mobile applications, neuro-marketing etc., in which manual gender detection may be not feasible. In this study, we have developed a fully automatic system that uses the 3D anthropometric measurements of human subjects for gender detection. A Kinect 3D camera was used to recognize the human posture, and body metrics are used as features for classification. To classify the gender, KNN, SVM classifiers and Neural Network were used with the parameters. A unique dataset gathered from 29 female and 31 male (a total of 60 people) participants was used in the experiment and the Leave One Out method was used as the cross-validation approach. The maximum accuracy achieved is 96.77% for SVM with an MLP kernel function.
Keypoint detection is a basic step in many computer vision algorithms aimed at recognition of objects, automatic navigation and analysis of biomedical images. Successful implementation of higher level image analysis tasks, however, is conditioned by reliable detection of characteristic image local regions termed keypoints. A large number of keypoint detection algorithms has been proposed and verified. In this paper we discuss the most important keypoint detection algorithms. The main part of this work is devoted to description of a keypoint detection algorithm we propose that incorporates depth information computed from stereovision cameras or other depth sensing devices. It is shown that filtering out keypoints that are context dependent, e.g. located at boundaries of objects can improve the matching performance of the keypoints which is the basis for object recognition tasks. This improvement is shown quantitatively by comparing the proposed algorithm to the widely accepted SIFT keypoint detector algorithm. Our study is motivated by a development of a system aimed at aiding the visually impaired in space perception and object identification.