nils-herrmann/clean_scirepeval huggingface dataset:
High Precision Prediction of Rolling Load of Finishing Stands by Fuzzy Identification Method

Aimed at the relatively large error of traditional models of rolling load, a new Rolling Load Prediction of Finishing Stands method is set up by Fuzzy Identification. It is based on T-S fuzzy model using triangle-shaped membership functions to calculate the grade of membership for each given pattern, and using Kalman filtering to identify the consequent parameters of fuzzy model. So the prediction precision of rolling load is improved. On the basis of the measured data of the 1580 mm hot strip mill, the relation between the main hot strip mill parameters and rolling load is established using fuzzy model. Experimental results show that the prediction precision is higher, its relative error can be controlled within ±4%.
Stability and Scalability of Self-Organized Coherent Fiber Laser Array

Self-organized coherent fiber laser array using fused fiber component is analyzed. We numerically investigate the output stability, the power scalability, and the phase-locking property. The results demonstrate that if the system is driven properly, with appropriate cavity length, fiber grating reflectivity and pump gain, the fiber laser array is able to achieve stable output power and the array size limit is parameters dependent. With the increase in the fiber elements, although each laser should be pumped at a lower gain in order to achieve stable coherent combination, the total output power increases nonlinearly.
Control strategy and experimental research on girder hoister steering system based on RBF neural network PID

Order to meet the needs of construction of high-speed railway passenger dedicated line, 900t rubber-tyred girder hoister is researched and manufactured. Steering system of the hoister is controlled by electro-hydraulic proportional displacement control system, and segment-PID control strategy with dead zoon is used. However with the passage of time, steering performance of girder hoister begin to get worse and worse. Then, valve control cylinder model, equilibrium equation of the steering actuator and system model is built up. Through analyzing and referring advantage of the neural network, one kind of steering control strategy based on RBF neural network PID is presented. Based on similarity principle, this control strategy is used in the steering system of 100t ship yard transporter. The experiment has been lasted 3 months, and the result shows that the system settling time reduced by 5%, over shoot doesn't change, and performance doesn't get worse. Validity of the control strategy is validated.
The application of two-scale DSCM to measurement of shear strain field on free edge of notched thermoplastic laminate

In this paper, two-scale DSCM (digital speckle correlation method) is applied to measurement of shear strain field on free edge of a notched APC-2/AS-4 laminate specimen with configuration of [±25°]s4. The result indicates: the shear strain has evident free edge effect at laminate interfaces, especially when lay-up angles of the two adjacent layers are opposite, the free edge effect at the interface is more severe. In addition, the existence of hole will reduce free edge effect.
Event recognition with PTZ cameras

In this paper, a framework for explicit complex event recognition is proposed. The system extracts relevant features from video streams coming from PTZ cameras in order to detect, classify, and track moving objects in the scene. This information is then processed in order to recognize both simple events (instantaneous events involving a single object) and complex events. In particular, we propose a method for explicitly describing and matching complex events in terms of simpler elements (e.g. simple events, information on object's state, etc.). Experimental results prove the validity of the proposed event recognition technique. (6 pages)
Support vector machines for robust trajectory clustering

Many event analysis systems are based on the detection of uncommon feature patterns that could be associated to anomalous events; the uncommon patterns are identified by comparison with a "normality model" describing the previously acquired data. In this work we propose an anomaly detection system based on trajectory clustering with single-class support vector machines. However, SVM parameter tuning would require an a-priori estimate of the number of outlier trajectories in the training data, which is unknown. We here propose a technique for automatic estimation of the number of outliers, thus avoiding the arbitrary choice of constant tuning parameters.
Tracking sound sources by means of HMM

Video-based surveillance systems may benefit from the integration with microphone arrays for the localization of sound events. Applying the sound localization techniques to the surveillance of large areas requires addressing some open issues, such as the non uniform resolution of the microphones-based localization systems. This paper presents a new method for tracking moving sound events based on an Hidden Markov Model (HMM), which exploits a priori information derived from medium and longterm observations of the monitored area. The results obtained with simulated trajectories show that the HMM-based tracker is able to significantly reduce the localization error. Applications can be found in surveillance systems for large areas, such as square, streets, or parking lots, where it is of interest the monitoring of moving vehicles and people.
Intelligent Monitoring of Complex Environments

Many countries around the world have implemented or are in the process of implementing tighter security measures in public and private places. Such measures are becoming widespread and are applied not only at government, military, and corporate facilities, but also in civilian infrastructures. Modern surveillance system consist of different modules. A sensor layer usually consists of a network of cameras, audio arrays, physical perimeter sensors, and other types of information feeders. A surveillance layer represents the core of the intelligent system. Its role is to process all the data generated by the sensor layer.This module includes several components. A feature extraction step is responsible for reducing the enormous amount of data that the network generates. Time properties are commonly derived by keeping track of any action within the monitored environment. The module in charge of the event analysis is at the highest processing level of modern monitoring systems. Human-computer interaction is fundamental to providing relevant, timely information.
Human Action Recognition using a Hybrid NTLD Classifier

This work proposes a hybrid classifier to recognize humanactions in different contexts. In particular, the proposedhybrid classifier (a neural tree with linear discriminantnodes NTLD), is a neural tree whose nodes can be eithersimple preceptrons or recursive fisher linear discriminant(RFLD) classifiers. A novel technique to substitute badtrained perceptron with more performant linear discriminatorsis introduced. For a given frame, geometrical featuresare extracted from the skeleton of the human blob (silhouette).These geometrical features are collected for a fixednumber of consecutive frames to recognize the correspondingactivity. The resulting feature vector is adopted as inputto the NTLD classifier. The performance of the proposedclassifier has been evaluated on two available databases.
Generalized neural trees for pattern classification

In this paper, a new neural tree (NT) model, the generalized NT (GNT), is presented. The main novelty of the GNT consists in the definition of a new training rule that performs an overall optimization of the tree. Each time the tree is increased by a new level, the whole tree is reevaluated. The training rule uses a weight correction strategy that takes into account the entire tree structure, and it applies a normalization procedure to the activation values of each node such that these values can be interpreted as a probability. The weight connection updating is calculated by minimizing a cost function, which represents a measure of the overall probability of correct classification. Significant results on both synthetic and real data have been obtained by comparing the classification performances among multilayer perceptrons (MLPs), NTs, and GNTs. In particular, the GNT model displays good classification performances for training sets having complex distributions. Moreover, its particular structure provides an easily probabilistic interpretation of the pattern classification task and allows growing small neural trees with good generalization properties.
An integrated surveillance system for outdoor security

An integrated system for the detection, active tracking and recognition of people in wide outdoor environments is hereafter discussed. Specifically, a static sensor with a wide view is used to detect people inside the environment and to classify their behaviours. As outcome of anomalous activities, an active camera is selected to focus its attention on a particular person. Here, techniques for face detection are employed to determine a region of interest where to extract features used by a tracking algorithm for an autonomous gaze of a PTZ camera. Finally, a face recognition phase is considered to recognize the person of interest. Results show how the integrated system is able to detect, track and recognise people inside a tough environment such as a parking lot.
Trajectory-Based Anomalous Event Detection

During the last years, the task of automatic event analysis in video sequences has gained an increasing attention among the research community. The application domains are disparate, ranging from video surveillance to automatic video annotation for sport videos or TV shots. Whatever the application field, most of the works in event analysis are based on two main approaches: the former based on explicit event recognition, focused on finding high-level, semantic interpretations of video sequences, and the latter based on anomaly detection. This paper deals with the second approach, where the final goal is not the explicit labeling of recognized events, but the detection of anomalous events differing from typical patterns. In particular, the proposed work addresses anomaly detection by means of trajectory analysis, an approach with several application fields, most notably video surveillance and traffic monitoring. The proposed approach is based on single-class support vector machine (SVM) clustering, where the novelty detection SVM capabilities are used for the identification of anomalous trajectories. Particular attention is given to trajectory classification in absence of a priori information on the distribution of outliers. Experimental results prove the validity of the proposed approach.
Mobile ocular biometrics in visible spectrum using local image descriptors: A preliminary study

Ocular biometrics refers to personal identification using iris, conjunctival vasculature, periocular or eye movements. Contrary to most of other biometric traits, ocular biometrics does not require high user cooperation and close capture distance. Biometrics is now adopted ubiquitously as an alternative to passwords on mobile devices. Especially, ocular biometrics in the visible spectrum has attracted a lot of attention owing to the fact that it can be acquired using the regular RGB cameras already available in all mobile devices. The use of local image descriptors (i.e., analysis of microtextural features) for ocular biometrics is gaining more and more popularity because of their compactness, computationally inexpensiveness, excellent performance and flexibility. In this work, we explore the possibility of performing large scale mobile ocular biometric recognition in the visible spectrum using local image descriptors. We design a weighted fusion scheme to combine the information originating from four different local descriptors. The experimental analysis of the devised scheme, on newly collected and publicly available large scale database using three different mobile devices, shows promising results.
AVSS 2011 demo session: Smart Resource-Aware Multi-Sensor Network

The invited talks are: How to Compare Alternative Architectures by Radia Perlman of Intel; Portals 4: Enabling Application/Architecture Co-Design for High-Performance Interconnects by Ron Brightwell of Sandia National Laboratories; and Electronic-Photonic Integration within Switches and Routers by Mike Watts of MIT. Brief author biographies are also included.
Automatic reconfiguration of video sensor networks for optimal 3D coverage

During the last years, the research in the field of video analytics has focused more and more on video sensor networks. Although single-sensor processing is still an open research field, practical applications nowadays require video analysis systems to explicitly consider multiple sensors at once, since the use of multiple sensors can lead to better algorithms for tracking, object recognition, etc. However, given a network of video sensors, it is not always clear how the network should be configured (in terms of sensor orientations) in order to optimize the system performance. In this work we propose a method to compute a (locally) optimal network configuration maximizing the coverage of a 3D environment, given that a relevance map of the environment exists, expressing the coverage priorities for each zone. The proposed method relies on a transformation projecting the observed environment into a new space where the problem can be solved by means of standard techniques such as the Expectation-Maximization algorithm applied to Gaussian Mixture Models.
Person Orientation and Feature Distances Boost Re-identification

Most of the open challenges in person re-identification arise from the large variations of human appearance and from the different camera views that may be involved, making pure feature matching an unreliable solution. To tackle these challenges state-of-the-art methods assume that a unique inter-camera transformation of features undergoes between two cameras. However, the combination of view points, scene illumination and photometric settings, etc., together with the appearance, pose and orientation of a person make the inter-camera transformation of features multi-modal. To address these challenges we introduce three main contributions. We propose a method to extract multiple frames of the same person with different orientation. We learn the pair wise feature dissimilarities space (PFDS) formed by the subspace of pair wise feature dissimilarities computed between images of persons with similar orientation and the subspace of pair wise feature dissimilarities computed between images of persons non-similar orientations. Finally, a classifier is trained to capture the multi-modal inter-camera transformation of pair wise images for each subspace. To validate the proposed approach we show the superior performance of our approach to state-of-the-art methods using two publicly available benchmark datasets.
Adaptive high order neural trees for pattern recognition

In this paper, a new classifier, called adaptive high order neural tree (AHNT), is proposed for pattern recognition applications. It is a hierarchical multi-level neural network, in which the nodes are organized into a tree topology. It successively partitions the training set into subsets, assigning each subset to a different child node. Each node can be a first-order or a high order perceptron (HOP) according to the complexity of the local training set. First order perceptrons split the training set by hyperplanes, while n-order perceptrons use n-dimensional surfaces. An adaptive procedure decides the best order of the HOP to be applied at a given node of the tree. The AHNT is grown automatically during the learning phase: its hybrid structure guarantees a reduction of the number of internal nodes with respect to classical neural trees and reaches a greater generalization capability. Moreover, it overcomes the classical problems of feed-forward neural networks (e.g., multilayer perceptrons) since both types of perceptrons does not require any a-priori information about the number of neurons, hidden layers, or neuron connections. Tests on patterns with different distributions and comparisons with classical neural tree-based classifiers have been performed to demonstrate the validity of the proposed method.
A new feature clustering method for object detection with an active camera

Feature based methods for ego-motion estimation are widely used in computer vision but they must deal with errors in feature tracking. In this paper, we propose a robust real-time method for ego-motion estimation by assuming an affine motion of the background from the previous to the current frame. A new clustering technique is applied on image's subareas to select in a fast and reliable way three features for the affine transform computation. The previous frame after being warped according to the computed affine transform is processed with the current frame by a change detection method in order to detect mobile objects. Results are presented in the context of a visual-based surveillance system for monitoring outdoor environments.
Video Analysis in Pan-Tilt-Zoom Camera Networks

Pan-tilt-zoom (PTZ) cameras are able to dynamically modify their field of view (FOV). This functionality introduces new capabilities to camera networks such as increasing the resolution of moving targets and adapting the sensor coverage. On the other hand, PTZ functionality requires solutions to new challenges such as controlling the PTZ parameters, estimating the ego motion of the cameras, and calibrating the moving cameras.This tutorial provides an overview of the main video processing techniques and the currents trends in this active field of research. Autonomous PTZ cameras mainly aim to detect and track targets with the largest possible resolution. Autonomous PTZ operation is activated once the network detects and identifies an object as sensible target and requires accurate control of the PTZ parameters and coordination among the cameras in the network. Therefore, we present cooperative localization and tracking methods, i.e., multiagentand consensus-based approaches to jointly compute the target's properties such as ground-plane position and velocity. Stereo vision exploiting wide baselines can be used to derive three-dimensional (3-D) target localization. This tutorial further presents different techniques for controlling PTZ camera handoff, configuring the network to dynamically track targets, and optimizing the network configuration to increase coverage probability. It also discusses implementation aspects for these video processing techniques on embedded smart cameras, with a special focus on data access properties.
Anomalous trajectory patterns detection

In the field of event analysis, the detection of anomalous events has often been based on the creation of a model representing the most common patterns of activity detected within a monitored scene. This way, anomalous events can be identified by comparison with the model as patterns differing from typical events. In particular, trajectories of moving objects have often been used as a feature for anomalous event detection. In this paper we propose a combination of clustering and SVM techniques in order to automatically detect anomalous trajectories.
Classification of Local Eigen-Dissimilarities for Person Re-Identification

The task of re-identifying a person that moves across cameras fields-of-view is a challenge to the community known as the person re-identification problem. State-of-the art approaches are either based on direct modeling and matching of the human appearance or on machine learning-based techniques. In this work we introduce a novel approach that studies densely localized image dissimilarities in a low dimensional space and uses those to re-identify between persons in a supervised classification framework. To achieve the goal: i) we compute the localized image dissimilarity between a pair of images; ii) we learn the lower dimensional space of such localized image dissimilarities, known as the “local eigen-dissimilarities” (LEDs) space; iii) we train a binary classifier to discriminate between LEDs computed for a positive pair (images are for a same person) from the ones computed for a negative pair (images are for different persons). We show the competitive performance of our approach on two publicly available benchmark datasets.
Discriminant Context Information Analysis for Post-Ranking Person Re-Identification

Existing approaches for person re-identification are mainly based on creating distinctive representations or on learning optimal metrics. The achieved results are then provided in the form of a list of ranked matching persons. It often happens that the true match is not ranked first but it is in the first positions. This is mostly due to the visual ambiguities shared between the true match and other “similar” persons. At the current state, there is a lack of a study of such visual ambiguities which limit the re-identification performance within the first ranks. We believe that an analysis of the similar appearances of the first ranks can be helpful in detecting, hence removing, such visual ambiguities. We propose to achieve such a goal by introducing an unsupervised post-ranking framework. Once the initial ranking is available, content and context sets are extracted. Then, these are exploited to remove the visual ambiguities and to obtain the discriminant feature space which is finally exploited to compute the new ranking. An in-depth analysis of the performance achieved on three public benchmark data sets support our believes. For every data set, the proposed method remarkably improves the first ranks results and outperforms the state-of-the-art approaches.
Event classification for automatic visual-based surveillance of parking lots

In this paper, a visual-based surveillance system for real-time event detection and classification in parking lots is presented. The focus is on the high-level part of the system, i.e., the event recognition (ER) module, which is able to analyze two kinds of events (i.e., simple and composite events) that occur in the observed scene. Simple events are represented by single moving objects, e.g., vehicles, pedestrians, etc., while a composite event is represented by a set of temporally consecutive simple events, e.g., people exiting a car just entered in the parking area. An adaptive high order neural tree (AHNT) is applied for recognizing both objects and complex events.
Fast good features selection for wide area monitoring

Recently the surveillance of wide areas has pointed the interest of the research community. The use of active vision seems to be the most effective solutions for these needs. Against the better acquiring resolution there is the problem of the apparent motion inducted by the camera motion known as ego-motion. Feature based methods for ego-motion estimation are widely used in computer vision but they deal with feature recovery and with errors in feature tracking. In this paper, we propose a fast method to extract and select new features during camera motion. This is achieved by adopting a reference map containing well trackable features that is updated at each frame by introducing new good features related to regions appearing in the current image. A new procedure is applied to reject badly tracked features. The current frame and the background after compensation are processed by a change detection method in order to locate mobile objects. Results are presented in the context of a visual-based surveillance system for monitoring outdoor environments.