nils-herrmann/clean_scirepeval huggingface dataset:
Application of data mining to production operation and control system in oil field

Introduces the basic concept of data mining technology and the commonly used methods of it, and describes the function of data mining technology and its application in general industrial production. Respectively summarize the application status of the data mining technology to production operation process in oil field, around production operation control, production operation parameter prediction, the production process fault monitoring, and production operation cost control. Explores the production application prospect of data mining technology in the oil field and finds the related operation problem of it.
GPU based sample adaptive offset parameter decision and perceptual optimization for HEVC

In this paper, a graphics processing unit (GPU) based sample adaptive offset (SAO) parameters decision scheme is proposed for High Efficiency Video Coding (HEVC). Then, in order to further improve the performance of SAO, a perceptual based optimization scheme is provided according to the adjustment of Lagrange multiplier aiming to improve the subjective performance of SAO. Experimental results demonstrate that the proposed GPU based SAO parameter decision scheme can achieve average 0.76% and 0.78% BD-rate gain in terms of PSNR (Peak Signal to Noise Ratio) and SSIM (Structure Similarity) respectively. Combined with the perceptual optimization scheme, the maximum BD-rate gain in terms of PSNR and SSIM can be up to 1.77% and 3.3% with the average as 1.23% and 1.37%. Moreover, much computation complexity of SAO can be distributed to GPU.
A cache-based bandwidth optimized motion compensation architecture for video decoder

In video decoder applications, motion compensation (MC) is bandwidth consuming because of the non-regular memory access. Especially with the popularity of UHD video and the development of new coding standard (HEVC), external memory bandwidth becomes a crucial bottleneck. In this paper, we propose an area efficiency cache-based bandwidth optimization strategy to minimize the memory bandwidth. First a four-way parallel cache architecture is described. Then partially replacement strategy is proposed to further reduce memory bandwidth and power consumption. At last a column based storage scheme is provided to reduce the precharge/active frequency. We realize this idea using high level synthesis, which allow multiple iterations with quick turnaround time for micro architecture changes, and the results show that the averagely bandwidth reduction is up to 79.9% with moderate resource utilization, which outperforms the state-of-the-art works.
Optimal microgrid energy management integrating intermittent renewable energy and stochastic load

In this paper, we focus on energy management of distributed generators (DGs) and energy storage system (ESS) in microgrids (MG) considering uncertainties in renewable energy and load demand. The MG energy management problem is formulated as a two-stage stochastic programming model based on optimization principle. Then, the optimization model is decomposed into a mixed integer quadratic programming problem by using discrete stochastic scenarios to approximate the continuous random variables. A Scenarios generation approach based on time-homogeneous Markov chain model is proposed to generate simulated time-series of renewable energy generation and load demand. Finally, the proposed stochastic programming model is tested in a typical LV network and solved by Matlab optimization toolbox. The simulation results show that the proposed stochastic programming model has a better performance to obtain robust scheduling solutions and lower the operating cost compared to the deterministic optimization modeling methods.
Image scale-smoothing, scale-differentiating and binarizing: a new frame for general-scale-edge (GSE) extraction

Traditional edge detectors are almost all based on the frame of Smoothing plus Differentiating plus Binarizing, some of which such as Canny can not provide rich description for edge information as well as can not make exact edge localization. In this paper, a new concept of General-Scale-Edge (GSE) is proposed and defined, and a novel frame for GSE extraction is constructed. Experimental results show that the proposed approach can exactly extract edges at various scales as well as provide rich forms for the description of edge information in images.
Modeling correlation noise statistics at decoder for multi-view distributed video coding

Recently, multi-view distributed video coding (MDVC) receives more and more attention, as its low-complexity encoder and high-complexity decoder coding paradigm suits for many applications such as sensor networks, in which several view sequences are required to be coded by a few power-constraint encoders. Modeling the correlation noises between original frame and side information frame is a hot research issue in distributed video coding (DVC), since it is a vital factor affecting the coding efficiency. This paper firstly proposes a novel method to model the correlation noises in MDVC. And an algorithm to online estimate the model at decoder using the knowledge of adjacent views is also presented. Experiment results show that the proposed correlation model can significantly improve coding efficiency.
Software Pipelines Design for Variable Block-Size Motion Estimation with Large Search Range

This paper presents some techniques for efficient motion estimation (ME) implementation on fixed-point digital signal processor (DSP) for high resolution video coding. First, challenges in large search-range ME are discussed. Then, based on the statistics analysis of the best matched block, correlations in variable block-size ME are removed on algorithm level while keeping almost the same performance as the original fast search strategy. Based on this modification and data reuse technique, this paper proposes an "index search" method for DSP implementation, which is a good solution to balance both high coding efficiency and high coding speed on the DSP platform. In "index search", ME is divided into three steps and highly parallel pipelines are designed for each step. The fully use of pipelines brings about 85% time reduction during ME. "Index search" can also be easily extended to achieve different search range and search patterns.
IEEE 1857: Boosting Video Applications in CPSS

In CPSS, video is definitely the information flow that takes the majority of traffic. Nevertheless, an enormous gap exists between the amount of video data collected and its searchability. The newly released IEEE 1857 video coding standard is an effective attempt to address this challenge. In particular, the IEEE 1857 standard surveillance groups can effectively support highly efficient surveillance video coding and objects-of-interest representations in the coding bitstream. These features make it a robust video coding standard for various video applications in CPSS.
An Adaptive De-Interlacing Algorithm Based on Texture and Motion Vector Analysis

In this paper, we propose a novel hybrid de-interlacing algorithm, which effectively combines two motion-compensated (MC) de-interlacing techniques: MC median filtering (MCMF) and adaptive recursive (AR) with one spatial approach: line averaging (LA). Despite of its drawbacks, AR is one of the best methods nowadays. MCMF helps reduce flickers and LA is very robust to erroneous motion vectors. The interpolation switches among these methods based on the proposed measurement of texture smoothness and motion vector (MV) reliability. MCMF is adopted when MV is reliable and texture is rich. LA is used when MV is unreliable and texture is smooth. AR is applied to the remaining regions. Experimental results show that the proposed algorithm is superior to the compared algorithms in terms of peak signal-to-noise ratio (PSNR) and that the de-interlaced videos have very high subjective quality
Mining Information of Attack-Defense Status from Soccer Video Based on Scene Analysis

Video content is always huge by itself with abundant information. Extracting explicit semantic information has been extensively investigated such as object detection, structure analysis and event detection. However, little work has been devoted on the problem of discovering global or inexplicit information from the huge video stream. As an implementation in this topic, this paper proposes a solution to mining the statistical global attack-defense status information from soccer video by scene analysis. Semantic scene information of play field detection, view classification, midline detection and global motion are extracted as the mid level information, and then they are fed into the finite state machine based status mining model to generate the statistical results, which will be of much usefulness for users. Experimental results reveal the feasibility of the method and more research work on the topic of discovering high-level inexplicit information from video are expected.
Macroblock-Level Adaptive Scan Scheme for Discrete Cosine Transform Coefficients

Discrete cosine transform (DCT) has been widely used in image/video coding systems, where zigzag scan is usually employed for DCT coefficient organization. However, due to local diversity of prediction errors, the fixed scan pattern, such as the traditional zigzag scan, is not efficient all the time for organizing the DCT transformed prediction errors. In this paper, the statistical distribution of prediction residuals is studied first, and two other scan patterns are introduced to complete the set of possible patterns. Then, a new adaptive scheme that adaptively chooses for each macroblock the best scan pattern to arrange the quantized DCT coefficients into arrays is presented. The best scan pattern is selected for each macroblock according to its own residual characteristics and hence leads to improved coding efficiency. Experimental results show that this macroblock-level adaptive scan scheme (MLASS) can always outperform the traditional zigzag one and the peak signal to noise ratio (PSNR) gain can be up to 0.65dB. Moreover, the extra two scan patterns do not increase memory requirement much. Additionally, a fast scan pattern decision algorithm is given for complexity reduction. This fast version of MLASS can keep almost the same coding efficiency while with much lower complexity.
T-S fuzzy coordinated control for power unit based on multi-objective optimization

Based on multi-objective optimization control, fuzzy state feedback control and Set Point tracking control strategy, A novel scheme for the coordinated control of power units is presented. Firstly, a fuzzy state space model of boiler-turbine system is built. Then, based on the model and pole placement design, a state feedback controller with asymptotic tracking performance is proposed. Because the system has nonlinear, strong coupling, variable condition, parameter time-varying characteristics, which led to the optimum parameters of pole placement are difficult to determine. In order to guarantee dynamic performance, stability and control variable constraints of the system, a multi-objective optimization method for pole placement based on particle swarm optimization is exploited. This control system has good tracking property with zero steady-state error and can reject input disturbances, the controller works well even if some parameters change. Simulation results show that the system designed by the proposed method gives satisfactory regulation quality within wide operating range.
Curse of mis-alignment in face recognition: problem and a novel mis-alignment learning solution

In this paper, we present the rarely concerned curse of mis-alignment problem in face recognition, and propose a novel mis-alignment learning solution. Mis-alignment problem is firstly empirically investigated through systematically evaluating Fisherface's sensitivity to mis-alignment on the FERET face database by perturbing the eye coordinates, which reveals that the imprecise localization of the facial landmarks abruptly degenerates the Fisherface system. We explicitly define this problem as curse of mis-alignment to highlight its graveness. We then analyze the sources of curse of mis-alignment and group the possible solutions into three categories: invariant features, mis-alignment modeling, and alignment retuning. And then we propose a set of measurement combining the recognition rate with the alignment error distribution to evaluate the overall performance of specific face recognition approach with its robustness against the mis-alignment considered. Finally, a novel mis-alignment learning method, named E-Fisherface, is proposed to reinforce the recognizer to model the mis-alignment variations. Experimental results have impressively indicated the effectiveness of the proposed E-Fisherface in tackling the curse of mis-alignment problem.
Analysis for the application of solar assisted heat pump in Wuhan City, China

Wuhan City located in the central of China and its climate was hot summer and cold winter. Heat pump air-conditioners were main equipments for heating or cooling in Wuhan City. Solar assisted heat pump was an energy conservation technology. However, the development and application of solar assisted heat pump at residential buildings was limited by many factors, such as sunshine duration, system construction, running model and solar energy collector, etc. Therefore, a study of analysis for the application of solar assisted heat pump system was carried out to analysis its economical efficiency and applicability in Wuhan City. The general influencing factors on the running of solar assisted heat pump were discussed. The result showed that Solar assisted heat pump could reduce the limitation of solar energy heater and heat pump, especially in heating model in winter. Solar assisted heat pump could improve the performance of heat pump system and the utilization ratio of solar energy.
Bilateral transactions determination with minimum deviations in economic dispatch and losses in hybrid electricity markets

Negotiation of secure bilateral transactions has become an important issue for better operation, planning and management of competitive electricity markets. In this paper, secure bilateral transactions have been determined with minimum deviations in economic generation schedule and real power losses for hybrid electricity markets. The generation pattern and real and reactive power loss has been obtained for both pool and bilateral demand. The secure bilateral transaction has also been determined minimizing only deviations from the scheduled transactions. The results obtained with different cases have been compared. AC Distribution Factors (ACDF) considering the impact of slack bus have been utilized for determining the line flows to consider the impact of bilateral contracts. The proposed approach has been applied on IEEE 24-bus reliability test system (RTS).
Overview of the MPEG-CDVS Standard

Compact descriptors for visual search (CDVS) is a recently completed standard from the ISO/IEC moving pictures experts group (MPEG). The primary goal of this standard is to provide a standardized bitstream syntax to enable interoperability in the context of image retrieval applications. Over the course of the standardization process, remarkable improvements were achieved in reducing the size of image feature data and in reducing the computation and memory footprint in the feature extraction process. This paper provides an overview of the technical features of the MPEG-CDVS standard and summarizes its evolution.
Joint Data Partition and Rate-Distortion Optimized Mode Selection for H.264 Error-Resilient Coding

Data partitioning (DP) is an efficient error-resilient video coding tool. Its contribution to performance improvement in the error-prone environment arises from the superior error concealment mechanisms that are available with the help of protected data partitions. Since error-concealment in terms of DP is closely related to coding mode, it is desirable to have an optimized coding mode selection scheme. However, the existing coding mode selection techniques usually assume that the same error-concealment mechanism is used for a block when it is lost, and the associated distortion also remains the same. Obviously, this assumption is not true when DP involves. In this paper, a generalized end-to-end distortion model is proposed for the rate-distortion optimized coding mode selection, which fully utilizes the superior error-concealment mechanism in terms of DP. The proposed distortion model is also advantageous in the suppression of approximation errors caused by pixel average operations such as sub-pixel interpolation and deblocking filter. Therefore, it can lead to a low-complexity solution for real-time applications such as live streaming
Linking video ADS with product or service information by web search

With the proliferation of online media services, video ads are pervasive across various platforms involving internet services and interactive TV services. Existing research efforts such as Google AdSense and MSRA VideoSense/ImageSense have been devoted to the less intrusive insertion of relevant textual or video ads in streams or web pages through text/image/video content analysis whereas the inherent semantics of video ads is much less exploited. In this paper, we propose to link video ads with relevant product/service information across E-commerce websites or portals towards ad recommendation in a cross-media manner. Firstly, we carry out semantic analysis within ad videos in which Frames Marked with Product Images (FMPI) are extracted. Secondly, we link ad videos with relevant ads on the Web by utilizing FMPI to search visually similar Product Images (e.g. appearance or logo) and to collect their accompanying text (brand name, category, description, or other tags) over popular E-commerce websites or portals such as EBay, Amazon, Taobao, etc. We search visually similar product images with Local Sensitive Hashing (LSH) in a Naïve Bayes Near Neighbor classifier. Finally, we may recommend more relevant products/services for ad videos through ranking those matched product images and categorizing useful tags of top ranked ads from the Web. Preliminary experiments have been carried out to demonstrate the idea of linking ad videos with product/service information from the Web.
Compression-Induced Rendering Distortion Analysis for Texture/Depth Rate Allocation in 3D Video Compression

In 3D video applications, the virtual view is generally rendered by the compressed texture and depth. The texture and depth compression with different bit-rate overheads can lead to different virtual view rendering qualities. In this paper, we analyze the compression-induced rendering distortion for the virtual view. Based on the 3D warping principle, we first address how the texture and depth compression affects the virtual view quality, and then derive an upper bound for the compression-induced rendering distortion. The derived distortion bound depends on the compression-induced depth error and texture intensity error. Simulation results demonstrate that the theoretical upper bound is an approximate indication of the rendering quality and can be used to guide sequence-level texture/depth rate allocation for 3D video compression.
Rate-GOP Based Rate Control for High Efficiency Video Coding

In this paper, a Rate-GOP based frame level rate control scheme is proposed for High Efficiency Video Coding (HEVC). The proposed scheme is developed with the consideration of the new coding tools adopted into HEVC, including the quad-tree coding structure and the new reference frame selection mechanism, called reference picture set (RPS). The contributions of this paper mainly include the following three aspects. Firstly, a RPS based hierarchical rate control structure is designed to maintain the high video quality of the key frames. Secondly, the inter-frame dependency based distortion model and bit rate model are proposed, considering the dependency between a coding frame and its reference frame. Thus the distortion and bit rate of the coding frame can be represented by the distortion and bit rate of its reference frame. Accordingly, the Rate-GOP based distortion model and rate model can be achieved via the inter-frame dependency based distortion model and bit rate model. Thirdly, based on these models and a mixed Laplacian distribution of residual information, a new ρ-domain Rate-GOP based rate control is proposed. Experimental results demonstrate the proposed Rate-GOP based rate control has much better R-D performance. Compared with the two state-of-the-art rate control schemes for HEVC, the coding gain with BD-PSNR can be up to 0.87 dB and 0.13 dB on average respectively for all testing configurations. Especially for random access low complexity testing configuration, the BD-PSNR gain can be up to 1.30 dB and 0.23 dB respectively.
Reduced Reference Stereoscopic Image Quality Assessment Based on Binocular Perceptual Information

In this paper, we propose a novel reduced reference stereoscopic image quality assessment (RR-SIQA) metric by using binocular perceptual information (BPI). BPI is represented by the distribution statistics of visual primitives in left and right views' images, which are extracted by sparse coding and representation . Specifically, entropy of the left view's image and entropy of the right view's image are used to represent monocular cue. Their mutual information is used to represent binocular cue. Constructively, we represent BPI as three numerical indicators . The difference of the original and distorted images' BPIs is taken as perceptual loss vector. The perceptual loss vector is used to compute the quality score for a stereoscopic image by a prediction function which is trained using support vector regression (SVR). Experimental results show that the proposed metric achieves significantly higher prediction accuracy than the state-of-the-art reduced reference SIQA methods and better than several state-of-the-art full reference SIQA methods on the LIVE phase II asymmetric databases.
PQ-WGLOH: A bit-rate scalable local feature descriptor

In this paper, we propose a compact yet discriminative local descriptor which tackles the wireless query transmission latency in mobile visual search. The descriptor captures gradient statistics of canonical patches over a log-polar location grid whose parameters are optimized using training samples. We quantize the resulting descriptor using product quantization. The descriptor achieves about 95% bits reduction compared with 128-Byte SIFT and allows adaptation of descriptor lengths to support user required performance. Moreover, accurate matching of descriptors with low complexity is allowed within several table lookup operations. We perform a comprehensive comparison with SIFT, GLOH and CHoG in the context of image retrieval, image matching and object localization. We achieve competing matching and retrieval performance with SIFT, GLOH with much fewer bits. In particular, the descriptor outperforms CHoG at the same bits on eight data sets contributed to MPEG Compact Descriptor for Visual Search(CDVS) Standardization.
Can ant algorithms make automated guided vehicle system more intelligent?

In manufacturing environment, an automated guided vehicle (AGV) system is composed of a set of driver-less vehicles that transport goods and materials between distinct workstations and storage locations of shops. In soft computing area, ant algorithms are a series of population-based approaches inspired by various behaviors of real ant colonies. During the last two decades, ant algorithms have achieved a great success in solving many combinatorial optimization problems. In this article we make an attempt to study the feasibility of applying ant algorithms to different problems encountered in AGV system design and control. By making use of ant algorithms' strengths, we hope to provide the readers with alternative options for solving conventional AGV system design and control problems, as well as to point out some new directions for AGV system research.