nils-herrmann/clean_scirepeval huggingface dataset:
Development of low voltage ride-through control strategy for wind power generation using real time digital simulator

With real time digital simulator (RTDS), new control strategy for low voltage ride through (LVRT) is developed for a doubly fed wind power induction generator (DFIG). Specifically, adaptive control method is proposed to limit high peak rotor current. Furthermore, current limiters are connected in three-phase rotor circuits, which are strategically controlled by thyristor switches to counter the effect of fault on DFIG operation. Simulation model is constructed in RTDS/RSCAD. Simulation results under various disturbances demonstrate the effectiveness of the new LVRT control strategies.
Performance Guaranteed Network Acceleration via High-Order Residual Quantization

Input binarization has shown to be an effective way for network acceleration. However, previous binarization scheme could be regarded as simple pixel-wise thresholding operations (i.e., order-one approximation) and suffers a big accuracy loss. In this paper, we propose a highorder binarization scheme, which achieves more accurate approximation while still possesses the advantage of binary operation. In particular, the proposed scheme recursively performs residual quantization and yields a series of binary input images with decreasing magnitude scales. Accordingly, we propose high-order binary filtering and gradient propagation operations for both forward and backward computations. Theoretical analysis shows approximation error guarantee property of proposed method. Extensive experimental results demonstrate that the proposed scheme yields great recognition accuracy while being accelerated.
A fast multiview video transcoder for bitrate reduction

Video transcoding is an efficient way to reduce the bitrate or convert the format of the original video stream to meet the requirements of different applications and various channel capacity. In this paper, we propose a fast multiview video transcoder (MVT) for bitrate reduction. Different from the H.264 transcoder, the inter-view prediction information in the input video stream is utilized to reduce the complexity of transcoding. Besides, we also utilize the mode and selected reference frame information in original stream to accelerate RD optimization calculations. Experimental results show that the proposed transcoder can achieve significant computation reduction while maintaining close RD performance compared to the fully decode and re-encode transcoder (FDET).
Design optimization of a parallel hybrid electric powertrain

The design of a hybrid electric vehicle (HEV) involves a number of variables that must be optimized for better fuel economy and vehicle performance. In this paper, global optimization algorithms-DIRECT (Divided RECTangles), simulated annealing, and genetic algorithm are used for the design optimization of a parallel hybrid electric vehicle. Powertrain system analysis toolkit (PSAT) is used as the vehicle simulator for this study. The objective of this study is to increase the overall fuel economy of a parallel HEV on a composite of city and highway driving cycle and to improve the vehicle performance. A hybrid algorithm is also developed and is applied to Rosenbrook's Banana Function for the examination of its efficiency.
An improved image compression scheme with an adaptive parameters set in encrypted domain

A growing societal awareness about privacy and security push the development of signal processing techniques in the encrypted domain. Data compression in encrypted domain attracts much attention recently years due to its avoiding the leakage of data source during compression. This paper proposes an improved block-by-block compression scheme of encrypted image with flexible compression ratio. The original image is encrypted by permuting the blocks of the image and then permuting the pixels in the blocks. In the compression, pixels chosen randomly used as reference information, and remaining pixels are compressed by coset code. At the decoder side, side information (SI) which is generated by combining correlation among blocks and image restoration from partial random samples (IRPRS) is utilized to assist the decompression. Moreover, an adaptive system parameters selection method is also given in this paper. The experimental results show that the proposed method can achieve a better reconstructed result compared with the earlier method.
From Visual Search to Video Compression: A Compact Representation Framework for Video Feature Descriptors

Visual feature descriptors have been successfully deployed in a wide range of applications, e.g. visual retrieval and analysis. To transmit these descriptors over bandwidth-limited networks, a high efficiency feature coding technique is highly desired to maximize compression capability and achieve compact feature representations. In this paper, a hybrid visual feature descriptor compression framework is presented and implemented in the encoding and decoding loops of texture videos. In particular, the multiple-hypothesis prediction is employed to effectively remove redundancies originated not only from spatial and temporal similarities, but also from reconstructed video frames. As the ultimate purpose of the transmitted descriptors is retrieval, the rate-accuracy optimization (RAO) technique is proposed to obtain the best tradeoff between the rate and retrieval performance. Such paradigm enables the conventional video stream to achieve high efficient retrieval/analysis with very low bitrate consumption. Moreover, we also demonstrate that texture video compression can also benefit from the additional information provided by the transmitted descriptors, leading to significantly improvement of coding efficiency on top of the high efficiency video coding (HEVC) standard. Extensive simulations have shown that the proposed method can offer significant bitrate reduction in representing both the descriptors and texture video frames, and meanwhile providing desirable retrieval performance.
The applications of computational intelligence in radio frequency identification research

Radio frequency identification (RFID) research based upon computational intelligence (CI) is currently attracting a lot effort from the research community. Characteristics of CI, such as adaptation, fault tolerance, high computational speed and error resilience, fit the requirements of RFID research. In this article we provide an overview of the research progress in applying CI methods to various problems within RFID research area. The findings of this review should be a good source for anyone who is interested in the application of CI approaches to RFID and its corresponding fields.
Illumination compensation and feedback of illumination feature in face detection

We present an idea of multi-method integration (MMI) to compensate variety of illumination in face detection. Some traditional methods to compensate illumination based on spatial-domain and frequency-domain are introduced. An improved algorithm LogAbout for realizing more effective illumination compensation is described. Experiments and comparisons with each scheme of multi-method integration (MMI) are presented. The experiment data show that the correctly detected rate is obviously increased after illumination compensation with the scheme MMI. This idea can solve the illumination compensation problem quickly and efficiently, which fits for detecting face automatically and tracking face in real time. The idea MMI proposed in the paper can also contribute to general image processing, object detection and recognition. In the paper, a method to feedback illumination feature is also proposed, which we think is the basis to compensate illumination partially.
Hierarchical background subtraction using local pixel clustering

We propose a robust hierarchical background subtraction technique which takes the spatial relations of neighboring pixels in a local region into account to detect objects in difficult conditions. Our algorithm combines a per-pixel with a per-region background model in a hierarchical manner, which accentuates the advantages of each. This is a natural combination because the two models have complementary strengths. The per-pixel background model is achieved by mixture of Gaussians models (GMM) with RGB feature. Although precisely describing background change in high resolution, it suffers from the sensitivity to quick variations in dynamic environment. To tolerate these quick variations, we further develop a novel GMM based per-region background model, which is updated by the cluster centers obtained from a k-means clustering of the pixelspsila RGB feature in the region. Numerical and qualitative experimental results on challenging videos demonstrate the robustness of the proposed method.
Optimization of Fuzzy C-Means Clustering by Genetic Algorithms Based on Sizable Chromosome

Aiming at the predifined clustering number, strong randomness and easiness to fall into local optimum , a new self-adaptive FCM algorithm based on genetic algorithm is proposed. The number of fuzzy clustering and cluster centers are optimized by sizable-chromosome genetic algorithms (SC-GAs). Cut operator and splice operator are adopted to combination the chromosome to form new individuals. Non-uniform mutation operator is used to enhance the population diversity. The new proposed method can obtain the global optimam compared to standard FCM algorithm. The simulation experimental result s with IRIS demonstrate the feasibility and effectiveness of the new algorithm.
Auto Regressive Model and Weighted Least Squares Based Packet Video Error Concealment

In this paper, auto regressive (AR) model is applied to error concealment for block-based packet video encoding. Each pixel within the corrupted block is restored as the weighted summation of corresponding pixels within the previous frame in a linear regression manner. Two novel algorithms using weighted least squares method are proposed to derive the AR coefficients. First, we present a coefficient derivation algorithm under the spatial continuity constraint, in which the summation of the weighted square errors within the available neighboring blocks is minimized. The confident weight of each sample is inversely proportional to the distance between the sample and the corrupted block. Second, we provide a coefficient derivation algorithm under the temporal continuity constraint, where the summation of the weighted square errors around the target pixel within the previous frame is minimized. The confident weight of each sample is proportional to the similarity of geometric proximity as well as the intensity gray level. The regression results generated by the two algorithms are then merged to form the ultimate restorations. Various experimental results demonstrate that the proposed error concealment strategy is able to increase the peak signal-to-noise ratio (PSNR) compared to other methods.
Motion Aligned Spatial Scalable Video Coding

A motion aligned spatial scalable video coding scheme (MA-SSC) is proposed in this paper. Different from the traditional spatial scalable coding schemes derived from MPEG-2, in the proposed scheme only one set of intra or inter prediction modes are optimally selected by jointly considering the base and enhancement layers. Thus, it saves one set of macroblock (MB) mode and motion vectors. Moreover, the combined motion estimation can reduce the residual coding bits of the base layer. The MA-SSC and traditional spatial scalable coding schemes are both implemented based on H.264 reference software to evaluate their performance. Simulation results show that the enhancement layer coding efficiency of MA-SSC is up to 0.6dB better than that of the traditional scheme, while the base layer coding efficiency of MA-SSC decreases less than 0.3db compared with the single-layer coding
Automatic Multi-Player Detection and Tracking in Broadcast Sports Video using Support Vector Machine and Particle Filter

In this paper, a novel multiple objects detection and tracking approach based on support vector machine and particle filter is proposed to track players in broadcast sports video. Compared with previous work, the contributions of this paper are focused on three aspects. First, an improved particle filter called SVR particle filter is proposed as the player tracker by integrating support vector regression (SVR) into sequential Monte Carlo framework. SVR particle filter enhances the performance of classical particle filter with small sample set and improves the efficiency of tracking system. Second, support vector classification combined with playfield segmentation is employed to automatically detect the players in sports video as the initialization of tracker. Third, a unified framework for automatic object detection and tracking is proposed based on support vector machine and particle filter. The experimental results are encouraging and demonstrate that our approach is effective
HEVC Lossless Coding and Improvements

The lossless coding mode of the High Efficiency Video Coding (HEVC) main profile that bypasses transform, quantization, and in-loop filters is described. Compared to the HEVC nonlossless coding mode with the smallest quantization parameter value (i.e., 0 for 8-b video and -12 for 10-b video), the HEVC lossless coding mode provides perfect fidelity and an average bit-rate reduction of 3.2%-13.2%. It also significantly outperforms the existing lossless compression solutions, such as JPEG2000 and JPEG-LS for images as well as 7-Zip and WinRAR for data archiving. To further improve the coding efficiency of the HEVC lossless mode, a sample-based angular intra prediction (SAP) method is presented. The SAP employs the same prediction mode signaling method and the sample interpolation method as the HEVC block-based angular prediction, but uses adjacent neighbors for better intra prediction accuracy and performs prediction sample by sample. The experimental results reveal that the SAP provides an additional bit-rate reduction of 1.8%-11.8% on top of the HEVC lossless coding mode.
Application of Case-based Reasoning Theory in Fault Diagnosis of Power Transformer

Transformer deficiencies usually take many years to gestate before developing into a problem or failure. Thus the loss induced by problems or failures could be decreased if they are diagnosed earlier and a maintenance action is adopted duly. Many methods had been developed to finish this purpose, but the diagnosis results were still unsatisfied. In this paper, a case-based reasoning method is utilized to diagnose the incipient fault of power transformer. At first, the principle of case-based reasoning is introduced. And then, the re-division of transformer fault is carried out in order to give more specific and useful diagnosis conclusion. After that, a case-based diagnosis method is constructed, and detail diagnosis course is presented. This diagnosis result could give more definite fault position, thus it will give better instruction for the transformer maintenance. At last, the application results are illustrated, and it shows that this purposed method improves the reliability and practicality
Improving chroma intra prediction for HEVC

YCbCr color space is widely used in previous video coding standards such as H.264 and the latest video coding standard HEVC. Although the conversion from RGB to YCbCr reduces the inter-channel redundancy significantly, there are still some correlations among Y, Cb and Cr. In order to reduce the inter-channel redundancy, LM mode was proposed based on HEVC in which the reconstructed luma component is used to predict the chroma components through a linear model. The model parameters are derived by the reconstructed causal pixels. In this paper, in order to get more accurate parameters, an adaptive template selection method is proposed in which we extend the neighboring pixels and remove unavailable pixels before the calculation of parameters. Besides, adaptive Cr prediction is proposed. Specifically, in the prediction of Cr component, predictions from Y, Cb and both Y and Cb are exploited simultaneously. By combining these two methods, the BD-rate reduction is 0.3%, 2.0% and 3.0% (0.3%, 1.2% and 1.8%) under all-intra high QP (normal QP) setting based on HM12.0 compared to original LM mode for Y, Cb and Cr components respectively.
Research on the discrimination of pornographic and bikini images

Our research extends the general technologies detecting pornographic images to prevent the benign images whose content is approximate with the pornographic ones from being screened. This paper presents a multiple-step method to distinguish the benign bikini photos from pornographic photos. The proposed approach utilizes the information about the body shape and face to determine the feature region where the specific patterns of the skin distribution of bikini model's body are represented, and uses a neural network to learn these, patterns to recognize the bikini photos. The experimental results show that more than 80% normal bikini photos are recognized from the pornographic images by the method, and at the same time few pornographic images are mistaken as bikini photos. The results indicate it is possible to distinguish the bikini photos from pornographic ones.
Improved disparity vector derivation in 3D-HEVC

In the High Efficiency Video Coding (HEVC) based 3D video coding, 3D-HEVC, the disparity vector (DV) derivation is critical for inter-view motion prediction, inter-view residual prediction, disparity-compensated prediction (DCP) or any other tools exploiting inter-view correlation. In HTM-5.0.1, the DV is derived from some spatial and temporal neighbors to locate a corresponding block in another view. This paper advocates modifications of the DV derivation to reduce the complexity and to achieve slightly better coding efficiency. Firstly, we propose to remove the additional temporal block, unify the DV searching order for all views, and to impose restrictions on the temporal blocks for memory access bandwidth and complexity reduction in DV derivation. We also propose an improved DV searching order for slightly better coding performance. These proposed methods were adopted into the 3D-HEVC standard in the 3rd JCT-3V meeting in Jan. 2013.
Quality-progressive coding for high bit-rate background frames on surveillance videos

A remarkable compress performance was achieved in the surveillance video coding when high-quality reconstructed background frames were referenced in long-term. However, the high-quality reconstructed background frames lead to bursting bit-rate peaks in the video transmission, which may cause obvious communication delay or buffer overflow. In order to address this problem, the paper proposes a quality-progressive coding algorithm for smoothing the bursting peaks caused by high-quality reconstructed background frames. Instead of a single high-quality reconstructed background frame, we divide the background frame into a set of frames, which include the basic reconstructed background frame of normal-quality (basic part) and a series of reconstructed residual frames (residual part), for transmission. Moreover, the modeled background frame and the residual frames should be encoded into the bit-stream and transmitted every several frames, and the coding bits of two frame types above should be limited to the target range of coding bits, which is based on the channel capacity, so that avoids the bursting bit-rate peak and the transmission delay. Background frames are reconstructed by summing up the basic part and reconstructed residual frames one by one, and the last reconstructed background frame becomes a high-quality reconstructed background frame. Of course, each reconstructed background frame act as a prediction reference for its subsequent frames. Experimental results on an opening dataset, PKU-SVD-A, show that the proposed approach can smooth the bit-rate of high-quality reconstructed background frame in surveillance video coding, and achieve 0.57% bit-rate saving on average compared with HEVC-S.
Influence of the structure dimension of GIS on the partial discharge

Partial discharge (PD) monitoring plays an important role in the online maintenance of the gas insulated switchgear (GIS). In recent years, the ultra high frequency (UHF) method for PD detection arouses great interest of many researchers because of the high sensitivity and anti-interference capability. The coupling of UHF signal is the key to the detection of partial discharge in GIS. The structure size of GIS has great impacts on the PD signal, because the cutoff frequency of the high order mode of the electromagnetic (EM) wave depends on the diameters of the inner conductor and enclosure. In this paper, the influence of the inner conductor diameter is investigated by varying the center conductor size. The inner coupler is adopted to receive the partial discharge signal, and two types of PD sources are tested in the experiment. Firstly, the amplitude and the duration of the time domain signal and the frequency spectrum are focused, and their variations with the size of inner conductor are analyzed. Besides that, In order to research the changes of the TEM mode component and the high order mode component, low pass (LP), band pass (BP) and high pass (HP) filters are applied to process the PD signals, and the cumulative energy rates are calculated to indicate the energy distribution. In addition, a simulation using the FDTD method is conducted for comparison with the experimental results, and it indicates that the results of experiment and simulation are almost consistent.
Predicting Molecular Formulas of Fragment Ions with Isotope Patterns in Tandem Mass Spectra

A number of different approaches have been proposed to predict elemental component formulas (or molecular formulas) of molecular ions in low and medium resolution mass spectra. Most of them rely on isotope patterns, enumerate all possible formulas for an ion, and exclude certain formulas violating chemical constraints. However, these methods cannot be well generalized to the component prediction of fragment ions in tandem mass spectra. In this paper, a new method, FFP (Fragment ion Formula Prediction), is presented to predict elemental component formulas of fragment ions. In the FFP method, the prediction of the best formulas is converted into the minimization of the distance between theoretical and observed isotope patterns. And, then, a novel local search model is proposed to generate a set of candidate formulas efficiently. After the search, FFP applies a new multiconstraint filtering to exclude as many invalid and improbable formulas as possible. FFP is experimentally compared with the previous enumeration methods, and shown to outperform them significantly. The results of this paper can help to improve the reliability of de novo in the identification of peptide sequences.
Combining template matching and block motion compensation for video coding

A new motion compensation mode of TM_BMC is proposed by this paper to improve video coding efficiency. In TM_BMC, the inter-prediction efficiency is improved by combining the predictor of template matching and predictor of traditional block motion compensation, the coding cost for motion vector of traditional block motion compensation is reduced by utilizing motion vector derived by template matching as motion vector predictor. Experiment results on H.264 KTA platform show that average 7.07% and up to 11.26% bit-rate can be saved by locally adaptive motion compensation mode between the traditional motion compensation mode of BMC and the proposed mode of TM_BMC.