nils-herrmann/clean_scirepeval huggingface dataset:
Performance comparison of a fuel cell-battery hybrid powertrain and a fuel cell-ultracapacitor hybrid powertrain

This paper studies two hybrid power systems for vehicle applications: a fuel cell-battery hybrid powertrain and a fuel cell-ultracapacitor hybrid powertrain. First, the characteristics of fuel cell, battery, and ultracapacitor as power sources are summarized. Then the configurations of the two types of hybrid fuel cell powertrains are presented. Finally, example hybrid powertrains are designed and simulated using ADVISOR. The simulation results indicate that ultracapacitors can more effectively assist the fuel cell to meet the vehicle power demand and help achieve a better performance and a higher fuel economy.
Recognition of sequence lip images and its application

It is important in the study of Chinese lipreading to recognize Chinese language mouth shape sequences. In this paper, an approach based on parameter sequences of the mouth shape model in binary images is put forward and realized, then we discuss several possible applications, especially in the case of integration of lipreading and speech. First of all, we preprocess the captured sequence of images, then make them binary with an automatic binary threshold algorithm, extract the contour of mouth shape, fitted with two parabolas. Finally, we recognize mouth shape with HMM models using the features from the parabolas. The results of the above indicate that the system is satisfactory.
Mode dependent intra smoothing filter for HEVC

High efficiency video coding (HEVC) is the latest video coding standard, in which coding performance improvement of intra prediction comes from flexible block partitioning and advanced directional prediction. Blocks with strong directionality and regular patterns can be handled well by this finer granular directional prediction. However, irregular patterns and noises often exist in natural videos, and reference samples are not fully used in directional intra prediction. In this paper, we propose a mode dependent intra smoothing filter algorithm to improve intra prediction, which employs a Markov model to improve coding efficiency of intra prediction. Experimental results demonstrate that the proposed method can achieve average 1.1% (up to 1.8%) BD-rate reduction in all intra high efficiency test condition compared to HM 16.0.
An efficient coding method for intra prediction mode information

In H.264/AVC, the bits used for the Intra_4x4 prediction mode information usually occupy a high percentage in intra coding. Towards this issue, we present an efficient coding method for the Intra_4x4 prediction mode information. Firstly, a 3-order Markov random field is introduced to model the correlation among neighboring 4x4 blocks at picture level. Secondly, based on the conditional probabilities learned in this model, we build up a context adaptive coding scheme to code the Intra_4x4 prediction mode information. Although the probabilities and the coding scheme are initialized off-line, they can be revised by automatic adjustments. Thus the proposed algorithm is robust to a variety of video sequences. Experimental results demonstrate that the proposed method can obtain a gain up to 0.3 dB in all I-frames coding without involving any serious computational burden.
Exploiting Event-Level Parallelism for Parallel Network Simulation on Multicore Systems

This paper proposes a parallel simulation methodology to speed up network simulations on modern multicore systems. In this paper, we present the design and implementation of this approach and the performance speedups achieved under various network conditions. This methodology provides two unique and important advantages: 1) one can readily enjoy performance speedups without using an unfamiliar simulation language/library to rewrite his protocol module code for parallel simulations, and 2) one can conduct parallel simulations in the same way as when he conducts sequential simulations. We implemented this methodology and evaluated its performance speedups on the popular ns-2 network simulator. Our results show that this methodology is feasible and can provide satisfactory performance speedups under high event load conditions on wired networks.
A novel on-chip CMOS current sensor implemented by switched capacitors for a current-mode control DC-DC buck converter

A novel on-chip CMOS current sensor implemented by switched capacitors for a current — mode buck converter is presented in this paper. This proposed current sensing circuit does not need another sense MOSFET and a voltage-to-current and current-to-voltage transform circuit. We use the 0.35um DPTM CMOS process to design and simulate this circuit. Test result shows that the accuracy and the speed of the proposed current sensing circuit are high.
Pedestrian detection via logistic multiple instance boosting

Pedestrian detection in still image should handle the large appearance and pose variations arising from the articulated structure and various clothing of human bodies as well as view points. So it is difficult to design effective classifier for this problem. In this paper, we address these variations in detection via multiple instance learning, specifically logistic multiple instance boosting (LMIB). In LMIB, a example is represented as a set of instances, which implicitly encode the variations. Giving different confidence to the instances in a bag, the LMIB will automatically reduce the influence of the variations at training stage. To obtain rapid detection speed, the LMIBs are grouped into the cascaded structure. The proposed detection algorithm is tested on MIT and NRIA human datasets where promising detection results are comparable with the baseline algorithms.
Research on the Typical Partial Discharge Using the UHF Detection Method for GIS

Gas-insulated switchgear (GIS) plays an important role in power systems, not only because it is maintenance free and has less of an influence on environmental and industrial conditions, but also due to its low space requirements and high reliability. In order to monitor the online condition of GIS and diagnose existing defects, the ultra-high frequency (UHF) methods are widely used for detecting partial discharge (PD) owing to its sensitivity and anti-interference capability. In this paper, five types of typical PD sources are designed and fabricated to imitate the defects in GIS online. In order to distinguish different types of PDs, some feature parameters from the UHF signals are extracted and discussed. Generally, the conventional phase-resolved PD (PRPD) and pulse-sequence analysis methods are adopted to discriminate PD sources. However, sometimes it is not convenient to employ these methods because of the absence of phase information. In this research, nine feature parameters realized by processing the UHF pulse magnitude sequence q and time interval sequence Δt are introduced. The parameters can represent exactly the physical characteristics of different PDs. Based on these parameters, a new approach is developed through which pattern classification of PD can be achieved effectively without employing the phase information of the applied voltage. The clustering results obtained by performing statistical product and service solutions indicate that there is evident difference in the characteristic coefficients.
Secure watermark detection schemes

In traditional watermark detection scheme, a prover exposed a watermark to be present in a digital data to the possible dishonest verifier. However, a potential attacker is able to destroy or remove the watermark entirely once secret information like the watermark or the embedding key is known. Some of previous schemes proposed as solution haven't achieved desirable result really. Their lacking of security and validity is the most serious problem. We propose the secure watermark detection schemes for both blind watermark scheme and nonblind watermark scheme based on zero knowledge protocol and public-key encryption scheme in order to solve this problem. There no secret information that can be used for removing watermark is disclosed during the detection process. Both security and validity of watermark detection can be significantly improved using these two schemes.
Fast and effective text detection

Text in images and videos is a significant cue for visual content understanding and retrieval. In this paper, we present a fast and effective approach to locate text lines even under complex background. First, our algorithm uses the stroke filter to calculate the stroke maps in horizontal, vertical, left-diagonal, right-diagonal directions. Then a 24- dimensional feature is extracted for each sliding window and a SVM is used to obtain rough text regions. The rough text regions are further refined through a group of rules. And candidate text lines were localized more accurately through projection profile of the refined text regions. Finally another SVM classifier based on a 6-dimensional feature is used to verify the candidate text lines. The experimental results on challenging databases show that this approach can fast and effectively detect and localize text lines.
Fast intra mode selection for stereo video coding using epipolar constraint

Application of stereo video in TV industry and consumer electronics becomes very popular recently. Thus, fast algorithm for stereo video coding is highly desired because of its huge inter-view computational redundancy. In our previous work we proposed an epipolar constraint based fast inter mode selection for stereo video using motion vector of blocks as a indicator of similarity, nevertheless, intra mode selection is also highly complex in standards like H.264/AVC. In this paper, a practical fast intra mode selection is proposed to eliminate the computational redundancy by exploiting inter-view dependency based on epipolar constraint. The proposed method does not rely on disparity estimation. Instead, a sliding window is employed to generate an intra mode candidate pool from macro-blocks on the epipolar line. The candidate pool is then rectified to remove invalid modes and improve accuracy. Finally, optimal prediction mode is selected from the candidate pool. The proposed method can significantly reduce the number of mode candidates/prediction directions compared to exhaustive mode selection by 79%. Experiments on 5 HD video coded in I-frame demonstrate the overall coding time of one view is saved by 56% on average, with slightly video quality loss less than
Color image segmentation using density-based clustering

Color image segmentation is an important but still open problem in image processing. In this paper, we propose a method for this problem by integrating the spatial connectivity and color feature of the pixels. Considering that an image can be regarded as a dataset in which each pixel has a spatial location and a color value, color image segmentation can be obtained by clustering these pixels into different groups of coherent spatial connectivity and color. To discover the spatial connectivity of the pixels, density-based clustering is employed, which is an effective clustering method used in data mining for discovering spatial databases. Color similarity of the pixels is measured in Munsell (HVC) color space whose perceptual uniformity ensures the color change in the segmented regions is smooth in terms of human perception. Experimental results using proposed method demonstrate encouraging performance.
Device independence based on BSOFMS in sign language recognition

While many researchers have documented methods for recognizing sign language from instrumented gloves with high accuracy, these systems suffer from notable limitations: device-dependence and lack of extensibility. An ideal recognition system should be able to switch among a variety of input devices without retraining the entire system. A bidirectional self-organizing feature maps (BSOFMs) is presented in this paper to address this problem. BSOFMs has the ability to convert the vector from different input spaces with heterogeneous representation into one in a unique feature space and enable us to get the same description. In the training process, the raw data produced by different gloves work as input and ideal output alternately. Then the device-dependent description of the hand shape is converted to the compact feature output in the device independent feature space. Based on these models, the incorporation of BSOFMs and the existing recognition framework is introduced and it is crucial to creating a useful device-independent system. Experimental results demonstrate that the proposed system has an ideal performance in device-independent application.
Signer-independent sign language recognition based on SOFM/HMM

The aim of sign language recognition is to provide an efficient and accurate mechanism to transcribe sign language into text or speech. State-of-the-art sign language recognition should be able to solve the signer-independent problem for practical application. In this paper, a hybrid SOFM/HMM system, which combines self-organizing feature maps (SOFMs) with hidden Markov models (HMMs), is presented for signer-independent Chinese sign language recognition. We implement the SOFM/HMM sign recognition system. Meanwhile, results from the HMM-based system are provided as comparison. Experimental results show the SOFM/HMM system increases the recognition accuracy by 5% than the HMM-based one. Furthermore, a self-adjusting recognition algorithm is also proposed for improving the SOFM/HMM discrimination. When it is applied to the SOFM/HMM system it can improve the recognition accuracy by 1.9%. All experiments were performed in real-time with the dictionary size 208.
Estimating the value of θ in the intra frame for ρ-domain rate control algorithms

The ρ-domain rate control algorithm is very simple and effective. There is only one parameter, θ, used in the model. θ reflects the relationship between the rate and the quantization step. Accurately estimating the θ value of the I (intra) frame is important for an efficient ρ-domain rate control algorithm because the I frame will influence the remaining frames in the GOP (group of pictures) greatly. In this paper, we propose to estimate the θ value based on the energy of the frame. The energy difference of the I frames is used to predict the new θ. A linear relationship between θ and the energy is deduced. Experimental results show that using the energy can more accurately predict the θ value of I frame than only using the θ value of the previous P (predict) frame.
Playfield detection using adaptive GMM and its application

Playfield detection is a key step in sports video content analysis, since many semantic clues could be inferred from it. In this paper we propose an adaptive GMM based algorithm for playfield detection. Its advantages are twofold. First, it can update model parameters by the incremental expectation maximization (IEM) algorithm, which enables the model to adapt to the playfield variation with time; Second, online training is performed, which saves buffer for training samples. Then, the playfield detection results are applied in recognizing the key zone of the current playfield in soccer video, in which a fast algorithm based on playfield contour and least square is proposed. Experimental results show that the proposed algorithms are encouraging.
Enhanced intra prediction and transform for video coding

Intra-coding plays an important role in video coding schemes and it has become a hot research topic of the next generation video coding standard. In this paper, we propose two techniques to improve the efficiency of the prediction and the transform, respectively, for intra-coding. First, we introduce an overlapped block intra-prediction (OBIP) method, which makes use of the intra directional prediction modes of neighboring blocks as well as the mode of the current block. A position dependent weighted sum of several possible predictors will be treated as the final prediction. Second, we propose a multiple-model Karhunen-Loève transform (MMKLT) technique to further improve the mode dependent directional transform (MDDT) method. More than one KLT will be trained offline for each intra-prediction mode based on residual variances. Experimental results indicate that the proposed two methods together can achieve an average bit-rate reduction about 11% compared with H.264 or 5% compared with MDDT in all-intra coding.
A robust descriptor based on Weber’s Law

Inspired by Weberpsilas law, this paper proposes a simple, yet very powerful and robust local descriptor, Weber local descriptor (WLD). It is based on the fact that human perception of a pattern depends on not only the change of a stimulus (such as sound, lighting, et al.) but also the original intensity of the stimulus. Specifically, WLD consists of two components: its differential excitation and orientation. A differential excitation is a function of the ratio between two terms: one is the relative intensity differences of its neighbors against a current pixel; the other is the intensity of the current pixel. An orientation is the gradient orientation of the current pixel. For a given image, we use the differential excitation and the orientation components to construct a concatenated WLD histogram feature. Experimental results on Brodatz textures show that WLD impressively outperforms the other classical descriptors (e.g., Gabor). Especially, experimental results on face detection show a promising performance. Although we train only one classifier based on WLD features, the classifier obtains a comparable performance to state-of-the-art methods on MIT+CMU frontal face test set, AR face dataset and CMU profile test set.
An efficient fractional motion estimation architecture for avs real-time full Hd video encoder

Fractional motion estimation (FME) as a complement to integer motion estimation (IME) conducts higher compression rate in video coding. Based on multiple reference frames and all modes of variable-block-size (VBS) motion search, the common FME algorithm and architecture generally causes high computational complexity. It consumes plenty of time and hardware resources to process the real-time (30fps) high definition (HD) video compression. In this paper, we propose an efficient FME algorithm and architecture that significantly reduce the number of the candidate sub-pixels by predicting quarter motion vector and cost less hardware by saltatory interpolation. Compared with the conventional FME architecture, the proposed method reduces resources significantly and accelerates the processing speed. 75% circuit area and 68% computational complexity are saved with only 0.1dB drop in performance and similar bit rate saving on average. Experiments of various sequences show that HD video (1920×1080) can be processed in real-time at frequency of 100MHz and it is verified in an AVS HD encoder on a Xilinx Virtex-6 FPGA prototype system.
Application of multi-agent technique in petroleum production

Multi-agent system (MAS) is a set of intelligent agents which can perform common tasks by communicating, cooperating and collaborating with each other. It has the abilities of self-organization, self-learning and self-reasoning. In recent years, MAS is one of the hot researches in the field of distributed artificial intelligence. It is widely used in various aspects of the social field and provides a new idea for complex system. This paper firstly has a review to introduce multi-agent technique implemented in many social fields to show its extensive usability in different social fields and well ability to solve different complex problems. Petroleum production processes possess the characteristics of complexity, multi-variables and uncertainty etc. Traditional production techniques in petroleum field have faced lots of limitations in modern environment. It is necessary to adopt advanced and appropriate methods to improve production techniques. As a well theory for solving complex problems, MAS is utilized by more and more researchers to deal with different difficult problems in petroleum production processes.
Globally Variance-Constrained Sparse Representation for Image Set Compression

Sparse representation presents an efficient approach to approximately recover a signal by the linear composition of a few bases from a learnt dictionary, based on which various successful applications have been observed. However, in the scenario of data compression, its efficiency and popularity are hindered due to the extra overhead for encoding the sparse coefficients. Therefore, how to establish an accurate rate model in sparse coding and dictionary learning becomes meaningful, which has been not fully exploited in the context of sparse representation. According to the Shannon entropy inequality, the variance of data source bounds its entropy, which can reflect the actual coding bits. Hence, in this work a Globally Variance-Constrained Sparse Representation (GVCSR) model is proposed, where a variance-constrained rate model is introduced in the optimization process. Specifically, we employ the Alternating Direction Method of Multipliers (ADMM) to solve the non-convex optimization problem for sparse coding and dictionary learning, both of which have shown state-of-the-art performance in image representation. Furthermore, we investigate the potential of GVCSR in practical image set compression, where a common dictionary is trained by several key images to represent the whole image set. Experimental results have demonstrated significant performance improvements against the most popular image codecs including JPEG and JPEG2000.
A Fast and Lossless IDCT Design for AVS2 Codec

The emerging ultra-HD video content and the latest generation video coding standards such as HEVC and AVS2 involve significant computational complexity increase. Aiming to reduce the complexity of IDCT process, a fast IDCT design for AVS2 is presented in this paper, which bases on skipping the calculations of zero coefficients. After statistical analysis, we design several patterns for transform blocks with different sizes to detect the distribution of non-zero coefficients. If a transform block conforms to one of our designed patterns, the corresponding simplified IDCT function will be executed. Experimental results showed that our strategy could reduce the computation time by 19.3% on average under various test conditions. Moreover, our method will not result in any coding performance loss.
IISM: an image internal semantic model for image database based on relevance feedback

A semantic model - IISM (image internal semantic model) is introduced. Unlike other semantic extracting methods, IISM extracts the semantic information not by image segmentation and image understanding, but by analyzing relevance feedback image retrieval results. For relevance feedback image retrieval system, the images relevant to query are pointed as positive example, otherwise the images irrelevant to query are pointed as negative examples. It is assumed that these positive examples are related in semantic content. IISM computes comprehensive pair-wise mutual information for all images through analyzing the results of relevance feedback image retrieval. An association with a high mutual information means that one image is semantically associated with another. Semantic retrieval and clustering is carried out based on these association relationships.