nils-herrmann/clean_scirepeval huggingface dataset:
Automatic video-based analysis of animal behaviors

Vision-based animal behavior analysis is a critical and interesting problem for both biologists and computer vision scientists. In this paper, an automatic system for detecting behaviors of fruit flies is presented. Firstly, we propose an ellipse model to fit the contours of fruit flies, which efficiently detects fruit flies in a single frame. Then we associate the detection results together to form the trajectories. An AdaBoost classifier is used to analyze special behaviors of flies. The experiments show that our system can robustly track fruit flies and detect the fly behaviors with high recall rate in real time. This system has been adopted to aid biologists for research purposes.
Mining Auxiliary Objects for Tracking by Multibody Grouping

On-line discovery of some auxiliary objects to verify the tracking results is a novel approach to achieving robust tracking by balancing the need for strong verification and computational efficiency. However, the applicability and effectiveness of this approach highly depend on how to reliably validate the motion correlation between the target and the auxiliary objects so as to estimate the motion model. In this paper, we extend the algorithm of mining auxiliary objects for tracking by incorporating multibody grouping to detect the motion correlation and estimate the motion model, which imposes more general motion correlation constraints. The proposed method discovers the auxiliary objects that exhibit strong affine motion correlation and estimates the closed-form affine models. The proposed tracking algorithm shows good performance in real-world test sequences.
UML modeling based on Extendsim and its application in warship maintenance support

Three-echelon maintenance support process of warship is a dynamic and complex process. Combining with the advantages of Extendsim and UML, the UML models of warship maintenance support process were built, which including entity collaboration diagram, class diagram and activity diagrams, and a method of transforming UML models into Extendsim models was proposed. Based on these, warship maintenance support simulation software architecture named MVC (Model-View-Controller) was designed and realized. Finally, by the integration and simulation experiment of a three-echelon maintenance support simulation system of warship, the paper made an analysis of the influence factors on the capability of support system, and the simulation results proved that simulation system can simulate accurately the process of warship maintenance support.
Improved Long Radius of Curvature Measurement System for FEL Mirrors

The 53.73 meter long Duke free electron laser (FEL) cavity consists of two concave mirrors with radius of curvature longer than 27 meters. A proper radius of curvature is designed to achieve an optimal and stable operation of the FEL. This requires accurate measurements of the cavity mirror’s radius of curvature before its initial installation. Subsequent radius of curvature measurements are performed to ensure no significant deformation of the mirror occurs after a period of extensive use. A direct measurement based upon the geometric optics principles has been used at Duke FEL Lab (DFELL) for years. Recently, we have significantly upgraded this measurement apparatus by utilizing a He-Ne laser as the light source and a straight wire with a proper size as the object. In this paper we describe the details of the measurement setup and report the benefits of the recent upgrades. In addition, we report the improved data analysis technique and results of recent long radius of curvature measurements.
Decentralized multiple target tracking using netted collaborative autonomous trackers

This paper presents a decentralized approach to multiple target tracking. The novelty of this approach lies in the use of a set of autonomous while collaborative trackers to overcome the tracker coalescence problem with linear complexity. In this approach, the individual trackers are autonomous in the sense that they can select targets to track and evaluate themselves, and they are also collaborative since they need to compete for the targets against those trackers that are close to them through communication. The theoretical foundation of this new approach is based on the variational analysis of a Markov network that reveals the collaborative mechanism through fixed point iteration among these trackers and the existence of the equilibriums. In addition, a trained object detector is incorporated to help sense the potential newly appearing targets in the dynamic scene. Experimental results on challenging video sequences demonstrate the effectiveness and efficiency of the proposed method.
The Contribution of Intellectual Capital to Firms' Sustainable Growth Ability: An Empirical Investigation Based on Listed Companies in China

The principal purpose of this article is to investigate empirically the relation between intellectual capital and Chinese firmspsila sustainable growth ability. Using data drawn from a sample of 699 companies publicly traded on Chinese Stock Exchange from 2000 to 2006 and Pulicpsilas Value Added Intellectual Coefficient (VAIC) as the efficiency measure of intellectual capital, the author constructs two panel data models to examine the relationship between intellectual capital and Chinese firmspsila sustainable growth ability. Findings indicate that both physical capital and intellectual capital contribute to firmspsila sustainable growth ability positively. In addition, the empirical results show that human capital contributes to firmspsila sustainable growth ability positively, but the relation between structural capital and firmspsila sustainable growth ability is not distinct. Finally, Findings also show that most of Chinese firmspsila use of liability is reasonable and there is still much room for most of Chinese firms to grow. The results extend the understanding of the role of intellectual capital in promoting firmspsila sustainable growth ability in developing countries.
Improved Gradientface used in face recognition under varying illumination

In this paper, an improved Gradientface method is proposed for face recognition under varying illumination. It uses the gradient angle as the input feature. It generates the gradient vectors in difference form, and then computes the gradient angle. The gradient angle which is computed by differential equation preserves the detailed image information and it is proved to be most insensitive to the illumination. Then, the statistical relationships among angles of samples are used for classifier. The cosine distance is the cosine of angle between vectors and then it computes the distance in the gradient domain. Therefore, it achieves the best performance for our method. According to the experimental results, our method outperforms conventional methods under varying illuminations, especially in the large scale face database.
1(1/ 2) -Dimension cepstrum feature analysis of pulse signals

Tremendous calculation is a big disadvantage of bicepstrum analysis of pulse signals; to solve this problem, this paper proposes an 1(1/ 2) -dimension cepstrum feature analysis method for recognition and classification of drug users' pulse signals. This method analyzes the signal elements of human pulse signals, taking the three feature parameters: zero component Sx(0,0), value of Hs(p) and CSER as the feature components of pulse signals, which show obvious critical values between drug users and non-users. This method has been applied in the classification and recognition experiment on 15 users and 15 non-users and shows a high recognition rate of above 90%.
An Enhanced Predictive Control Structure for Networked Control System with Random Time Delays and Packet Dropouts

Due to the random and uncertain characteristic of networked induced factors, the performance of networked control systems is seriously affected, which is not merely degraded but also cause system instability. In this paper, a newly enhanced control scheme with an improved smith predictor is proposed to solve the issues caused by the complex and uncertain network conditions. A database is integrated into the network controller to store all possible outputs predicted on line from the smith predictor based on all possible transmission delays and packet dropouts to overcome the main drawback of traditional smith predictor. A selector is added to the actuator to select a suitable control signal from the predicted control sequence based on the actual network conditions. Finally the simulation results demonstrate the effectiveness of the proposed methods.
Implementation of Polyphase Channelizers for Multirate Signal Analysis

Polyphase discrete-Fourier transform (DFT) filter banks are used in a wide variety of applications involving the channelization and analysis of multi-component signals. Of particular interest are oversampled architectures that enable high-fidelity demodulation and signal processing of channelized signals. In this paper, we examine architectures of polyphase DFT filter banks that are suitable for field programmable gate array (FPGA) implementation and discuss implementation issues that arise in high speed, realtime applications.
An adaptive edge enhancing image denoising model based on fuzzy theory

PDE (Partial differential equation) is a widely used image denoising method. The anisotropic diffusion property of PDE is analyzed in this paper. For the different effects of forward and backward diffusion, some kinds of PDE smooth image well while some enhance edges. In this paper an adaptive image denoising model is proposed. The novel model diffuses differently based on gradient magnitude of image. The image parts which have large gradient magnitude are deemed as edges and textures, the adaptive model will enhance them. The selection of diffusion types changes gradually according to gradient magnitudes in different parts of image based on fuzzy theory. Both theoretical analysis and experiments have been used to verify that the novel adaptive image denoising model enhances edges while denoising.
Sparsity model for robust optical flow estimation at motion discontinuities

This paper introduces a new sparsity prior to the estimation of dense flow fields. Based on this new prior, a complex flow field with motion discontinuities can be accurately estimated by finding the sparsest representation of the flow field in certain domains. In addition, a stronger additional spar-sity constraint on the flow gradients is incorporated into the model to cope with the measurement noises. Robust estimation techniques are also employed to identify the outliers and to refine the results. This new sparsity model can accurately and reliably estimate the entire dense flow field from a small portion of measurements when other measurements are corrupted by noise. Experiments show that our method significantly outperforms traditional methods that are based on global or piecewise smoothness priors.
TDM Interrogation of Identical Weak FBGs Network Based on Delayed Laser Pulses Differential Detection

We report a time- and wavelength multiplexing sensing network based on nearly identical ultraweak fiber Bragg gratings (FBGs). In the multiplexing scheme of the network, each wavelength channel is distinguished by time delays of the two probe laser lines, and every channel works as an optical time domain reflectometry, and the response of each FBG is represented by two delay pulses corresponding to the two laser lines. The Bragg wavelengths of the FBGs are interrogated by calculating the ratio of two peak power of returned laser lines. Compared with a conventional wavelength scanning system, the proposed system can achieve relatively high interrogation speed. A proof of concept experiment with nine FBGs, at three wavelength channels was demonstrated, and fast measurements for static strain were achieved, with the sensitivity around <inline-formula><tex-math notation="LaTeX">$ - 0.0023\;{\text {dB}}/{{\mu \varepsilon }}$</tex-math></inline-formula> and dynamic range over <inline-formula><tex-math notation="LaTeX">$2500\;$</tex-math></inline-formula> for three channels.
The genetic algorithm based tuning method for symmetric membership functions of fuzzy logic control systems

A fuzzy logic control system which was tuned by different genetic coding schemes were examined. The first coding scheme is to encode the allowed shifting range between the adjacent centroids of membership functions. The second coding method is to encode the three parameters of a set of symmetric membership functions (SMPs). And the third one is to encode the rule table. The effectiveness of using these approaches were compared one another by controlling a linear time invariant second order plant. By using a general quadratic cost function, we analyzed step responses of varied coding strategies. These coding methods were evaluated by both the averaged dropping rate of the cost and the averaged elapse time per generation. Simulations showed the proposed encoding scheme which uses genetic algorithm searching on the best parameter set of symmetric membership functions outperforms the other two schemes in the delineated systems.
Ultrasonic TOFD image registration algorithm of sub-pixel accuracy based on DFT

This article is based on the correlation of FFT(Fast Fourier Transform) to study the ultrasonic TOFD(Time Of Flight Diffraction) image registration algorithm and according to the principle of template matching algorithm, the process is simple and the registration locations are accurate, so that the detected images are clear and defected features are obvious. The subsequent sub-pixel image registration algorithm of TOFD will improve registration accuracy to sub-pixel level, to further perfect the TOFD image registration algorithms, more conducive to subsequent image processing.
Synthesizing Dynamic Patterns by Spatial-Temporal Generative ConvNet

Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that a spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The model defines a probability distribution on the video sequence, and the log probability is defined by a spatial-temporal ConvNet that consists of multiple layers of spatial-temporal filters to capture spatial-temporal patterns of different scales. The model can be learned from the training video sequences by an analysis by synthesis learning algorithm that iterates the following two steps. Step 1 synthesizes video sequences from the currently learned model. Step 2 then updates the model parameters based on the difference between the synthesized video sequences and the observed training sequences. We show that the learning algorithm can synthesize realistic dynamic patterns.
An iterative based feedforward-feedback control approach to high-speed AFM imaging

AFM imaging requires precision positioning of the AFM probe relative to the sample in all x-y-z axes, especially the vertical z-axis direction. Recently, the current-cycle-feedback iterative-learning-control (CCF-ILC) approach is proposed for high-speed AFM imaging. The CCF-ILC feedforward-feedback 2 degree-of-freedom (DOF) controller design has been successfully implemented for iteratively imaging on one scanline. In this article, we extend this CCF-ILC approach to the entire imaging of samples. The main contribution of this article is the analysis and the use of the CCF-ILC approach for tracking sample profiles with variations between scanlines (called line-to-line sample variations). The convergence (stability) of the CCF-ILC system is analyzed for the general case where the line-to-line sample variation occurs at each iteration. The allowable line-to-line sample profile variation is quantified. The performance improvement of the CCF-ILC is discussed by comparing the tracking error of the CCF-ILC technique to that of using feedback control alone. The proposed CCF-ILC control approach is illustrated by implementing it to the z-axis direction control in AFM imaging. Experimental results show that the imaging speed can be significantly increased by using the proposed approach.
Bibliometric and social network analysis of the SoS field

This exploratory work combines bibliometric methods and social network analysis to provide a knowledge map on the development of system of systems research field. For this purpose, the study mined the published articles related to system of systems until 2012 indexed by Web of Knowledge platform including SCI-EXPANDED, SSCI, CPCI-S, and other scientific literature databases. The auctorial, institutional, national patterns in system of systems research are revealed using bibliometric methods. Social network analysis is used to discover relationships between authors and key terms in the research field. Based on this quantitative analysis, the study found the influential journals, key researchers, leading institutions and countries, and major research areas in system of systems research field. Last, suggestions are made on the future development of system of systems field.
Duke storage ring tune measurements system using razor blade and photomultiplier

We present in this paper the description of the Duke storage ring tune measurements system. An unusual feature of this system is the use of synchrotron radiation to sense oscillation of the electron beam. This system includes a lens, a razor blade placed at the focus and a photomultiplier. Our experience shows that this system can operate on -70 dBm level and detect electron beam oscillations as small as one micron.
Spatial Random Partition for Common Visual Pattern Discovery

Automatically discovering common visual patterns from a collection of images is an interesting but yet challenging task, in part because it is computationally prohibiting. Although representing images as visual documents based on discrete visual words offers advantages in computation, the performance of these word-based methods largely depends on the quality of the visual word dictionary. This paper presents a novel approach base on spatial random partition and fast word-free image matching. Represented as a set of continuous visual primitives, each image is randomly partitioned many times to form a pool of subimages. Each subimage is queried and matched against the pool, and then common patterns can be localized by aggregating the set of matched subimages. The asymptotic property and the complexity of the proposed method are given in this paper, along with many real experiments. Both theoretical studies and experiment results show its advantages.
High-Conversion-Ratio Bidirectional DC–DC Converter With Coupled Inductor

In this paper, a high-conversion-ratio bidirectional dc-dc converter with coupled inductor is proposed. In the boost mode, two capacitors are parallel charged and series discharged by the coupled inductor. Thus, high step-up voltage gain can be achieved with an appropriate duty ratio. The voltage stress on the main switch is reduced by a passive clamp circuit. Therefore, the low resistance RDS(ON) of the main switch can be adopted to reduce conduction loss. In the buck mode, two capacitors are series charged and parallel discharged by the coupled inductor. The bidirectional converter can have high step-down gain. Aside from that, all of the switches achieve zero voltage-switching turn-on, and the switching loss can be improved. Due to two active clamp circuits, the energy of the leakage inductor of the coupled inductor is recycled. The efficiency can be further improved. The operating principle and the steady-state analyses of the voltage gain are discussed. Finally, a 24-V-input-voltage, 400-V-output-voltage, and 200-W-output-power prototype circuit is implemented in the laboratory to verify the performance.
An Improved AdaBoost face detection algorithm based on the weighting parameters of weak classifier

Weighting parameters are introduced to ensure the weak classifier that comes with the False Rejection Rate (FRR) to significantly reduce the False Acceptance Rate (FAR). Knowing that the Haar-Like features redundancy, the most effective combination of features is chosen from all the features upon the completion of the classifier training, aiming to improve the speed and rate of face recognition. The results show that the improved AdaBoost algorithm saw an improved recognition rate of 15% compared to the traditional algorithm, where the video image sequence presented an average face recognition rate of 21.5ms/frame, being able to meet the requirements of real-time face detection.
A framework approach to accommodation of multimedia communications for training systems

In a virtual training classroom that supports classroom interactions with real-time audio and video (A/V), we adopted the peer-to-peer model for A/V streaming. Using the Jini network technology, we integrated the peer-to-peer subsystem into the client-server centric Web-based training systems. Not only does the lookup service of the Jini network provide a mechanism for objects to discover remote services, Jini also provides the proxy and remote events, two patterns to accomplish remote control. When an object needs to control many activities associated with a remote object, the proxy pattern is more appropriate. By holding a proxy of the remote object, the controlling object can freely invoke any remote function. On the other hand, if an object needs to react to one event associated with each of the related objects, the remote event mechanism is a handier means. We also implemented an application-level simplified multipoint A/V streaming, which is useful for the companies whose networks do not support multicast over the Internet.
Symplectic models for general insertion devices

A variety of insertion devices (IDs), wigglers and undulators, linearly or elliptically polarized, are widely used as high brightness radiation sources at the modern light source rings. Long and high-field wigglers have also been proposed as the main source of radiation damping at next generation damping rings. As a result, it becomes increasingly important to understand the impact of IDs on the charged particle dynamics in the storage ring. In this paper, we report our recent development of a general explicit symplectic model for IDs with the paraxial ray approximation. High-order explicit symplectic integrators are developed to study real-world insertion devices with a number of wiggler harmonics and arbitrary polarizations.
A segmented resistor-string DAC based stimulus generator for ADC linearity testing

This paper presents a segmented resistor-string digital-to-analog converter (DAC) based stimulus generator for analog-to-digital converter (ADC) linearity testing. By using the folded main resistor string, the capacitive loading is reduced to enhance the operating speed of the DAC. The complexity, the number of unit resistors, and the area cost of the DAC are reduced by using the parallel-connected sub-resistor strings. The theoretical analyses of the area cost, settling behavior, and the integral/differential non-linearities (INL/DNL) are presented to evaluate the trade-offs among the performance of the proposed DAC, the design parameter, and the mismatch of the resistor. Due to the features of simple structure and low capacitance loading, the proposed DAC is combined with a field-programmable gate array (FPGA) device and other circuitry to practically generate a ramp stimulus for ADC linearity testing. The testing result by using the proposed segmented DAC based stimulus generator shows a certain agreement to that by using the expensive instrument.