nils-herrmann/clean_scirepeval huggingface dataset:
Cellar Automaton Model for Railway Transportation Safety System

The railway transportation safety system is a complex dynamic system with spatio-temporal characteristics. Taking railway line section as the research object, we propose a new cellular automation (CA) model - a safety system model for railway (SSM-R) - to simulate the railway safety. In the proposed CA model, we consider a railway line section as a cell and the neighbouring sections as the neighbors, and emphasize particularly on the transition rules including train speed limit rules, train update rules, Signal update rules, block section rules, etc. Then a simulation experiment is carried out with ArcGIS Engine 9.1 and Dot Net, the results show that the model can interpret the evolutional law of the railway transportation safety system.
Detailed Analysis of All-Optical Clock Recovery at 10 Gb/s Based on a Fiber Optical Parametric Oscillator

We demonstrate all-optical clock recovery of input return-to-zero ON-OFF-keyed signals operating at 10 Gb/s using a fiber optical parametric oscillator (FOPO). We first characterize an actively mode-locked FOPO, which serves as the basis for all-optical clock recovery. 12.5-ps pulses with a timing jitter of around 450 fs are generated. Afterward, the characteristics of the recovered clock (timing jitter, pulsewidth, and time-bandwidth product) are investigated as a function of the duty cycle, average power, extinction ratio, and optical signal to noise ratio (OSNR) of the input signal. The wavelength tunability of the recovered clock is also investigated. The results show that the FOPO can recover a good quality clock over a large range of input signal characteristics, namely, duty cycle from 33% to 60%, average input power from -23 to 6 dBm, extinction ratio above 8.7 dB, and an OSNR larger than 27 dB. The wavelength of the recovered clock signal can be tunable over 8 nm.
Measurement of cylinder diameter based on computer vision system

The forgings is widely used in mechanical manufacturing industry. The online measurement with high-precision is very important for the quality control. In order to measure the large forgings' size, a system that using linear structured light source and CCD image sensor is developed. The system puts forward to test the deformation of the light plane when the light meets the surfaces of workpieces. By analyzing the laser curve image, it calculates the spacial conic and the parameters thought coordinates transition. The measuring principle and the building process of the system are described. To detect the structured-lighted-stripe vision sensor model, a new calibration method based on the combined target is proposed. The experiments demonstrate the high repeatability and accuracy of the system. The experimental results have proved that the repeatable accuracy is within 0.04mm and the measured relative error is less than 0.13%.
ACU Optimization of pcLEDs by Combining the Pulsed Spray and Feedback Method

In this paper, the pulse-sprayed (PS) phosphor coating technique and feedback method were combined to optimize the angular color uniformity of remote phosphor-converted light-emitting diodes (pcLEDs). The geometry of the phosphor-converted element (PCE) was controlled by the PS technique and the curvature of the phosphor bearing surface. Meanwhile, the yellow and blue light irradiance distributions are as the feedback function to optimize the mass distribution of the PS PCE during the feedback iteration process. With the method proposed herein, the correlative color temperature of the optimized remote pcLED ranges from 5192 to 5263 K with a maximum deviation of only 71 K.
A Cooperative Making Multi-agent Model on Railway Daily Dispatching Plan Based on Blackboard

Establishing the new complex cooperative making information system about daily dispatching plan is the focal problem in the field of railway dispatching informationization in China now, through integrating the core function modules of each existing special business making information by using the advanced technology, Building the corresponding cooperative making model about daily dispatching plan is helpful to successfully establishing such a complex information system. In lots of modeling technology, the modeling technology formed by combining the blackboard structure and multi agent technology has extensive application prospect. And it is suitable for building the cooperative making model about daily dispatching plan. In this paper, the multi agent model based on the blackboard structure is analyzed. Then the cooperative planning multi agent model of railway daily dispatching plan is built based on the description of the internal business logics of the as-is making process, and the realization process about the model formulating is described.
Design of the mass multimedia files storage architecture based on Hadoop

With the development of social network and video sites, the amount of images and videos being uploaded to the internet is rapidly increasing, and it is more and more difficult to storage and process the mass network images and videos. How to storage, manage and classify mass network images and videos effectively, and provide an excellent experience for users has been an urgent problem. Hadoop distributed file system (HDFS) becomes a representative cloud storage platform, benefiting from its reliable, scalable and low-cost storage capability. However, HDFS is not designed for storing mass small files. This paper presents a Hadoop-based mass multimedia files storage architecture. The core idea of this architecture is that merge the small images and video files to a Bundle, and provide a unified interface to handle mass images and videos. It's a good solution to the problem of small files. The experimental results show that the approach can achieve a better performance.
Applications of Pixel-by-Pixel Calibration Method in Microscale Measurements With Pressure-Sensitive Paint

This paper presents the applications of pixel-by-pixel (P-b-P) calibration method for pressure-sensitive paint (PSP) in microscale measurements and the comparison with conventional single-point (S-P) calibration method. PSP has been applied to microscale measurements for its advantages of acquiring detailed pressure profiles and global maps. However, conventional S-P calibration method, which is commonly applied in macroscale measurements, has its restrictions in microscale measurements, and errors up to 77% are observed under the situation of nonuniform illumination. The deviation can result in misinterpretation of physics in the flow fields during data reduction. The P-b-P calibration method which utilizes every pixel on the photodetector as an individual pressure sensor has been examined for its effectiveness for correcting the luminescence deviation in microscale measurements due to nonuniform illumination, reflection from environments and channel geometry, and self-illumination between neighboring sensors. It successfully reveals correct pressure profiles inside polydimethylsiloxane microchannel in both longitudinal and lateral directions through the pressure map, thereby increasing the effective measurement domain across the microchannel width from 50% to 80%.
Image classification by a two-dimensional hidden Markov model

For block-based classification, an image is divided into blocks, and a feature vector is formed for each block by grouping statistics extracted from the block. Conventional block-based classification algorithms decide the class of a block by examining only the feature vector of this block and ignoring context information. In order to improve classification by context, an algorithm is proposed that models images by two dimensional (2-D) hidden Markov models (HMMs). The HMM considers feature vectors statistically dependent through an underlying state process assumed to be a Markov mesh, which has transition probabilities conditioned on the states of neighboring blocks from both horizontal and vertical directions. Thus, the dependency in two dimensions is reflected simultaneously. The HMM parameters are estimated by the EM algorithm. To classify an image, the classes with maximum a posteriori probability are searched jointly for all the blocks. Applications of the HMM algorithm to document and aerial image segmentation show that the algorithm outperforms CART/sup TM/, LVQ, and Bayes VQ.
A multi-objective portfolio model considering corporate social responsibility and background risk

Most of existing portfolio models only considered the financial risk or the financial return, and ignored any other risks or any other incomes, such as the skewness or kurtosis characteristics of the portfolio. In this paper, we propose a high moment multi-objective portfolio selection model based on background risk and corporate social responsibility (CSR). Then, we deduce the analytical solution of the portfolio model with the skewness risk and the kurtosis risk, when the risk asset and the financial return of background risk are random numbers. Finally, a numerical example is given based on the Shanghai Stock Exchange data and the Shenzhen Stock Exchange data.
An On-Site Elastic Autonomous Service Network with Efficient Task Assignment

With the increasing demand of the big data processing and the personalized services in the Internet, the current service platforms such as cloud are becoming hard to meet the requirement of multiple-level quality of services (QoS) within a short execution time. In this paper, an on-site elastic autonomous service network named SEA is proposed to cope with the above problems by utilizing the available resources from edge devices. The proposed SEA is constructed by the devices at the edge of the Internet and provides multiple-level QoS service to users. The architecture of the SEA is firstly designed to maintain the edge devices efficiently and monitor the status of the available resources. Then, an efficient scheduling algorithm based on discrete particle swarm optimization (PSO) is proposed to assign the tasks to proper devices in the SEA. The evaluation shows that the average execution time based on PSO is reduced more than 20% in comparison with a state-of-the-art algorithm. The simulation also shows that the hybrid SEA-cloud service is superior to the cloud-based service.
A Novel Method Based on Self-Power Supply Control for Balancing Capacitor Static Voltage in MMC

The modular multilevel converter is one of the most attractive converter topologies for high-voltage dc transmission systems, but it needs at least 10 min during the system uncontrolled precharge stage to verify the stability and reliability of submodules (SMs), making the capacitor static voltage balancing a key issue. This paper proposes a novel method based on self-power supply control for balancing capacitor static voltage. Because of the influence of self-power supplies on capacitor voltages, the method can keep the capacitor static voltage balanced by controlling the input characteristic of SMs self-power supplies. The control signals of self-power supplies have a fixed frequency and duty ratio, and they can be determined based on capacitor voltage sorting results and self-power supply output support capacitor. Compared with previous works, this method has less computation, and it does not rely on IGBTs and additional complex circuits except for the self-power supplies. This can save on the software and hardware costs. The proposed method also leads to improved equalizing resistances and reduced active power losses of the SMs. Simulations and experimental studies were conducted, and the results confirm the effectiveness of the proposed method.
Blood Oxygen Monitoring Cloud Platform based on the Attribute Theory College of Information Engineering

Though a brief account of blood oxygen monitoring service system and the Attribute Theory, combining with the working theory of blood oxygen monitoring, the author puts forward a new system of blood oxygen monitoring based on the attribute theory. The system based on it aims to standardize the process of blood oxygen monitoring service and the implementation process of crossing many platform communication modules, especially to put forward an implementation method of the monitoring equipment and administrator as well as the user and administrator management business stage. It is shown that this method can achieve the desired results which can determine the results of ontology attribute retrieval function flexibly and reasonably and more accurately obtained the data level picture and the identification flowchart which are from the attribute blood oxygen monitoring module to the attribute blood oxygen recognition module.
Design and Development of Disaster Rapid Mapping System in Case of Earthquake

Nowadays natural disasters especial earthquakes frequently occur all over the world. Thus rapid mapping of remote sensing system has practical significance. In this paper, based on the methods of disastrous expression of multi-scale geographic maps and remote sensing images, processes of fast mapping were proposed and researched. It includes quick match of base map database and disastrous thematic information, fast expression of disaster information, technologies of dynamic disaster thematic mapping, disaster-oriented rapid thematic mapping technologies. Then, according to the characteristics of different carrier, usage and resolution of point distribution, the disaster drawing templates were designed. On this basis, the disaster-oriented rapid mapping system is constructed and put into effect. It is proved that the system can provide disaster information of disaster body and disaster affected objects timely and accurately .This system can provide scientific basis for disaster prevention and disaster emergent policy to the administrator departments. It will play an active role in the information and policy-making for disaster prevention and disaster emergent of administrator departments in China.
A stagewise fast DOA estimation method based on sparse signal representation

In this paper, a stagewise approach has been adopted in the Direction of Arrival (DOA) estimation procedure to improve the speed and efficiency of the whole estimation, i.e. a coarse estimation is firstly carried out by means of Minimum Variance Distortionless Response (MVDR) to provide a smaller searching angle for the secondary high precision estimator, which is realized by a modified Orthogonal Matching Pursuit (OMP) algorithm with the access of spatial over-complete dictionary. Simulations demonstrate that compared with methods that are based on sparse signal representation approaches the proposed method could provide a drastic reduction of the calculation load and meanwhile it could keep a better resolution than that of OMP or traditional methods like Multiple Signal Classification (MUSIC) under low SNR few snapshots, which has proved its effectiveness and efficiency.
Observation of directional exitance and retrieval of soil and foliage component temperatures: case studies with bi-angular ATSR radiometric data

A mixture of foliage and soil is thermally heterogeneous, so the radiometric temperature of the mixture depends on view direction. A simple linear mixture model was applied to estimate the component surface temperatures of foliage and soil temperatures. The potential of directional observations in the thermal infrared region for land surface studies is a largely uncharted area of research. The availability of the dual-view Along Track Scanning Radiometer (ATSR) observations led to explore new opportunities in this direction. In the context of studies on heat transfer at heterogeneous land surfaces, multiangular thermal infrared (TIR) observations offer the opportunity of overcoming fundamental difficulties in modeling sparse canopies. Three case studies were performed on the estimation of the component temperatures of foliage and soil. The first one included the use of multi-angular field measurements at view angles of 0/spl deg/, 23/spl deg/ and 52/spl deg/. The second and third one were done with directional ATSR observations at view angles of 0/spl deg/ and 53/spl deg/ only. Different models have been proposed in literature to interpret observations of directional exitance: (1) simple geometric (deterministic) models of the system, (2) radiative transfer within a complete canopy, and (3) radiative transfer in an inhomogeneous thick layer of vegetation. Our approach is based on the third modeling concept. A target comprising a mixture of foliage and soil is characterized by the gap fraction, and observed radiance is described as a weighted sum of foliage radiance and soil radiance, with the weights being the gap fraction and its complement, respectively.
Asymptotic Performance of Vector Quantizers with a Perceptual Distortion Measure

Gersho's (1979) bounds on the asymptotic performance of vector quantizers are valid for vector distortions which are powers of the Euclidean norm. Yamada, Tazaki, and Gray (1980) generalized the results to distortion measures that are increasing functions of the norm of their argument. In both cases, the distortion is uniquely determined by the vector quantization error, i.e., the Euclidean difference between the original vector and the codeword into which it is quantized. We generalize these asymptotic bounds to input-weighted quadratic distortion measures and measures that are approximately output-weighted-quadratic when the distortion is small, a class of distortion measures often claimed to be perceptually meaningful. An approximation of the asymptotic distortion based on Gersho's conjecture is derived as well. We also consider the problem of source mismatch, where the quantizer is designed using a probability density different from the true source density. The resulting asymptotic performance in terms of distortion increase in decibels is shown to be linear in the relative entropy between the true and estimated probability densities.
Context based multiscale classification of images

This paper presents an algorithm to segment images into four classes: background, photograph, text and graph. There are two important aspects about the algorithm. The first is that the algorithm takes a multiscale approach, which adaptively classifies an image at different resolutions. The multiscale structure enables accurate classification at class boundaries as well as fast classification overall. The second is that the context information, which is accumulated in the process of classification, is used to improve the classification accuracy.
CAT: A Hierarchical Image Browser Using a Rectangle Packing Technique

The recent revolution of digital camera technology has resulted in much larger collections of images. Image browsing techniques thus become increasingly important for overview and retrieval of images in sizable collections. This paper proposes CAT (clustered album thumbnail), a technique for browsing large image collections, and its interface for controlling the level of details (LOD). As a preprocessing, this new system applies tree-structured clustering to images based on their keywords and pixel values, and selects representative images for each cluster. When a user specifies one or multiple keywords, CAT extracts a branch of the tree structure that contains clusters described by the user-specified keywords. A hierarchical data visualization technique is developed to display the tree structured organization of images using nested rectangular regions. Interlocked to the zooming operation, CAT selectively shows representative images while zooming out, or individual images while zooming in.
Levenberg-Marquardt neural network for gear fault diagnosis

In this study we are trying with the Levenberg-Marquardt neural network model to the problem of gear fault diagnosis. By using second derivative information, the network convergence speed is promoted and the generalization performance is enhanced. Taking a certain gearbox fault signal acquisition experimental system for instance, Matlab software and its neural network toolbox are used to model and simulate. The simulation result shows that Levenberg-Marquardt neural network has a good performance for the common gear fault diagnosis and it can identify various types of faults stably and accurately. Furthermore, compared with conventional BP neural network, the Levenberg-Marquardt neural network reduces training epochs and promotes diagnosis accuracy.
Context-aware event-driven stereo matching

Similarity measuring plays as an import role in stereo matching, whether for visual data from standard cameras or for those from novel sensors such as Dynamic Vision Sensors (DVS). Generally speaking, robust feature descriptors contribute to designing a powerful similarity measurement, as demonstrated by classic stereo matching methods. However, the kind and representative ability of feature descriptors for DVS data are so limited that achieving accurate stereo matching on DVS data becomes very challenging. In this paper, a novel feature descriptor is proposed to improve the accuracy for DVS stereo matching. Our feature descriptor can describe the local context or distribution of the DVS data, contributing to constructing an effective similarity measurement for DVS data matching, yielding an accurate stereo matching result. Our method is evaluated by testing our method on groundtruth data and comparing with various standard stereo methods. Experiments demonstrate the efficiency and effectiveness of our method.
Dense Captioning with Joint Inference and Visual Context

Dense captioning is a newly emerging computer vision topic for understanding images with dense language descriptions. The goal is to densely detect visual concepts (e.g., objects, object parts, and interactions between them) from images, labeling each with a short descriptive phrase. We identify two key challenges of dense captioning that need to be properly addressed when tackling the problem. First, dense visual concept annotations in each image are associated with highly overlapping target regions, making accurate localization of each visual concept challenging. Second, the large amount of visual concepts makes it hard to recognize each of them by appearance alone. We propose a new model pipeline based on two novel ideas, joint inference and context fusion, to alleviate these two challenges. We design our model architecture in a methodical manner and thoroughly evaluate the variations in architecture. Our final model, compact and efficient, achieves state-of-the-art accuracy on Visual Genome [23] for dense captioning with a relative gain of 73% compared to the previous best algorithm. Qualitative experiments also reveal the semantic capabilities of our model in dense captioning.
Design of Miniaturized Microstrip Dual-Mode Filter With Source-Load Coupling

A miniaturized microstrip dual-mode filter with different responses is developed in this letter. A coupling and routing scheme is presented to model the operations of this filter. The new filter reduces the occupied area up to 33% compared with the conventional one at 1.89 GHz. Meanwhile, the proposed circuit enables the second harmonic response to be effectively suppressed. A demonstrator filter is designed and validated with good agreement between the full-wave electromagnetic simulations and measurements.
A fast quasi-dense matching algorithm with an adaptive window

This paper presents a fast quasi-dense matching algorithm using an adaptive window. The algorithm starts from a set of sparse seed matches, then propagates to the neighboring pixels, finally the most points in the images are matched. During matching, we apply the convolution to the normalized cross correlation (NCC).The confidence coefficient is introduced and the search window is varied in inverse proportion to it. The algorithm has been tested with stereo images and the results demonstrate its accuracy and efficiency. The algorithm also can be applied to a wide range of image pairs including those with large disparity or without rectification even if part of the images are less textured. In particular, with big images and a large disparity range our algorithm turns out to be significantly faster.