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Nowadays, smart monitoring devices such as digital fault indicator (DFI) have been installed in distribution systems to provide sufficient information for fault location. However, it is still a challenge to extract effective features from massive data for single-line-to-ground (SLG) fault-section location. This work proposes a novel method of fault-section location using a 1-D convolutional neural network (1-D CNN) and waveform concatenation. After SLG fault occurs, DFI measures the transient zero-sequence currents at double-ends of the line section, which could be concatenated to construct characteristic waveform. The features of characteristic waveforms would be extracted adaptively by 1-D CNN to locate the fault section. Furthermore, the problem where the on-site recorded data are hard to collect would be solved because 1-D CNN only needs a small number of samples for training in practical applications. The experimental results verified that the proposed method could work effectively under various fault conditions, even if a few DFIs are out of order. [ABSTRACT FROM AUTHOR]

Feature extraction for fault signals is critical and difficult in all kinds of fault detection schemes. A novel simple and effective method of faulty feeder detection in resonant grounding distribution systems based on the continuous wavelet transform (CWT) and convolutional neural network (CNN) is presented in this paper. The time-frequency gray scale images are acquired by applying the CWT to the collected transient zero-sequence current signals of the faulty feeder and sound feeders. The features of the gray scale image will be extracted adaptively by the CNN, which is trained by a large number of gray scale images under various kinds of fault conditions and factors. The features extraction and the faulty feeder detection can be implemented by the trained CNN simultaneously. As a comparison, two faulty feeder detection methods based on artificial feature extraction and traditional machine learning are introduced. A practical resonant grounding distribution system is simulated in power systems computer aided design/electromagnetic transients including DC, the effectiveness and performance of the proposed faulty feeder detection method is compared and verified under different fault circumstances. [ABSTRACT FROM PUBLISHER]

Contact measurements of the respiratory rate using conventional electrocardiogram equipment requires patients to wear chest straps that can cause skin irritation and discomfort. Therefore, a real-time robust non-contact technique is developed for the measurement of respiratory rate variation. The changes of a simple harmonic motion between inhalation and exhalation from the human’s upper body can be observed from visual appearance. Therefore, in this paper, to characterize the motion, a salient region is automatically selected in the energy map resulted from Haar-like features through consecutive frames. Furthermore, a median optical flow signal is used to acquire the primary respiratory rate signal. The effective respiratory frequencies resulted from vertical motion variation are decomposed by median motion signal and then characterized by zero-crossing method before the frequencies were rectified by an estimation using the noise elimination methods. In the experiment, the performance is evaluated using an extensive data set obtained under distinct four respiratory types, regular respiration, respiration with body motion, respiration with distinct poses, and respiration with distinct capturing distances. Our approach develops a convenient non-contact method to evaluate the respiratory rate with the achievement of measurement under farther distance and also outperforms the current state-of-the-art approach in terms of high correlation coefficient. Therefore, the experiment results show its efficacy for real-world environment. [ABSTRACT FROM PUBLISHER]

Speeded-Up Robust Features (SURF) is a robust and useful feature detector for various vision-based applications but it is unable to detect symmetrical objects. This paper proposes a new symmetrical SURF descriptor to enrich the power of SURF to detect all possible symmetrical matching pairs through a mirroring transformation. A vehicle make and model recognition (MMR) application is then adopted to prove the practicability and feasibility of the method. To detect vehicles from the road, the proposed symmetrical descriptor is first applied to determine the region of interest of each vehicle from the road without using any motion features. This scheme provides two advantages: there is no need for background subtraction and it is extremely efficient for real-time applications. Two MMR challenges, namely multiplicity and ambiguity problems, are then addressed. The multiplicity problem stems from one vehicle model often having different model shapes on the road. The ambiguity problem results from vehicles from different companies often sharing similar shapes. To address these two problems, a grid division scheme is proposed to separate a vehicle into several grids; different weak classifiers that are trained on these grids are then integrated to build a strong ensemble classifier. The histogram of gradient and SURF descriptors are adopted to train the weak classifiers through a support vector machine learning algorithm. Because of the rich representation power of the grid-based method and the high accuracy of vehicle detection, the ensemble classifier can accurately recognize each vehicle. [ABSTRACT FROM PUBLISHER]

Modeling human crowds is an important issue for video surveillance and is a challenging task due to their unpredictable behavior. In this paper, the position of an isolated region that comprises an individual person or a set of occluded persons is detected by background subtraction. Each isolated region is considered a vertex and a human crowd is thus modeled by a graph. To construct a graph, Delaunay triangulation is used to systematically connect vertices and therefore the problem of event detection of human crowds is formulated as measuring the topology variation of consecutive graphs in temporal order. To effectively model the topology variations, local characteristics, such as triangle deformations and eigenvalue-based subgraph analysis, and global features, such as moments, are used and are finally combined as an indicator to detect if any anomalies of human crowd(s) present in the scene. Experimental results obtained by using extensive dataset show that our system is effective in detecting anomalous events for uncontrolled environment of surveillance videos. [ABSTRACT FROM PUBLISHER]

Given the rapid expansion of car ownership worldwide, vehicle safety is an increasingly critical issue in the automobile industry. The reduced cost of cameras and optical devices has made it economically feasible to deploy front-mounted intelligent systems for visual-based event detection for forward collision avoidance and mitigation. While driving at night, vehicles in front are generally visible by their taillights and brake lights. The brake lights are particularly important because they signal deceleration and potential collision. Therefore, in this paper, we propose a novel visual-based approach, based on the Nakagami-m distribution, for detecting brake lights at night by analyzing the taillights. Rather than using the knowledge of the heuristic features, such as the symmetry, position, and size of the rear-facing vehicle, we focus on finding the invariant features to model brake light scattering by Nakagami imaging and therefore conduct the detection process in a part-based manner. Experiments on an extensive data set show that our proposed system can effectively detect vehicle braking under different lighting and traffic conditions and thus demonstrates its feasibility in real-world environments. [ABSTRACT FROM PUBLISHER]

Advanced vehicle safety is a critical issue in recent years for automobiles, especially when the number of vehicles is growing rapidly worldwide. The decreasing cost of cameras makes it feasible to have an intelligent system of visually based event detection in front for forward collision avoidance and mitigation. When driving at night, vehicles in front are generally visible by their taillights. The brake lights are particularly important because drivers need to focus on them. Therefore, in this paper, we propose a novel approach that can detect brake lights at night using a camera by analyzing the signal in both spatial and frequency domains. Unlike the traditional approaches that employ the knowledge of the heuristic features, such as symmetry and position of rear facing vehicle, size, and so on, we focus on finding the invariant features from the regions of brake lights in the frequency domain and therefore can conduct the detection process in a part-based manner. Experiments with an extensive dataset show that our proposed system can efficiently and effectively detect brake lights under different lighting and traffic conditions, and thus prove its feasibility in real-world environments. [ABSTRACT FROM PUBLISHER]

Gender recognition is a challenging task in real life images and surveillance videos due to their relatively lowresolution, under uncontrolled environment and variant viewing angles of human subject. Therefore, in this paper, a system of real-time gender recognition for real life images is proposed. The contribution of this work is three-fold. In order to make the system robust, a mechanism of decision making based on the combination of surrounding face detection, context-regions enhancement and confidence-based weighting assignment is designed. Experiment results obtained by using extensive dataset show that our system is effective and efficient in recognizing genders for uncontrolled environment of real life images. [ABSTRACT FROM PUBLISHER]