nils-herrmann/clean_scirepeval huggingface dataset:
New results on H∞ filter design for sampled-data systems with packet dropouts and transmission delays

In this study, the problem of H ∞ filtering for sampled-data systems under unreliable communication links is investigated. The phenomena of data packet dropouts and signal transmission delays are addressed in a unified framework. The objective is to design an admissible filter to guarantee the asymptotic stability of the filtering error system and minimise the H ∞ disturbance attenuation level. Thanks to the proposed novel Lyapunov–Krasovskii functional together with the improved Wirtinger's inequality and the reciprocally convex approach, novel sufficient linear-matrix-inequality-based conditions are obtained for the existence and design of admissible filters. Finally, two simulation examples are provided to illustrate the efficiency and less conservativeness of the proposed H ∞ filter design methods.
A novel teleoperation paradigm for human-robot interaction

Internet telerobotics has emerged in recent decade with direct control and supervisory control as the main teleoperation paradigms in the field of wheeled robot navigation. It is however difficult to apply these paradigms in the unknown and dynamic real-world applications, while they do not provide adequate feeling of interaction or a human-friendly control interface to the human operator. This paper proposes a novel interactive control (active supervisory control) telecommanding, which is used for Internet-based mobile robot teleoperation. Telecommanding involves two parts: basic telecommanding, using joystick commands, and advanced telecommanding, using linguistic commands. Assisted by up-to-date data visualization and media streaming technologies, telecommanding can help a novice operator to simply, easily, and remotely control a robot through the Internet navigating in an unknown and dynamic real world. Telecommanding enables the robot to autonomously handle real-world uncertainty, improves the interactivity of humans and robot, and solve the problems of arbitrary network delays and restricted bandwidth. The experiments, including an Internet-based teleoperation test over 1500 kilometers from Beijing to Hong Kong, have demonstrated the promising performance and the advantages of this paradigm over direct control and passive supervisory control.
Chaotic eye-based fault forecasting method for wind power systems

This study proposes a method for detecting possible faults in wind turbine systems in advance such that the operating state of the fan can be changed or appropriate maintenance steps taken. In the proposed method, a chaotic synchronisation detection method is used to transform the vibration signal into a chaos error distribution diagram. The centroid (chaotic eye) of this diagram is then taken as the characteristic for fault diagnosis purposes. Finally, a grey prediction model is used to predict the trajectory of the feature changes, and an extension theory pattern recognition technique is applied to diagnose the fault. Notably, the use of the chaotic eye as the fault diagnosis characteristic reduces the number of extracted features required, and therefore greatly reduces both the computation time and the hardware implementation cost. From the experimental results, it is shown that the fault diagnosis rate of the proposed method exceeds 98%. Moreover, it is shown that for oil leaks in the gear accelerator system, the proposed method achieves a detection accuracy of 90%, whereas the multilayer neural network method achieves a maximum accuracy of just 80%.
Multi-View Object Retrieval via Multi-Scale Topic Models.

The increasing number of 3D objects in various applications has increased the requirement for effective and efficient 3D object retrieval methods, which attracted extensive research efforts in recent years. Existing works mainly focus on how to extract features and conduct object matching. With the increasing applications, 3D objects come from different areas. In such circumstances, how to conduct object retrieval becomes more important. To address this issue, we propose a multi-view object retrieval method using multi-scale topic models in this paper. In our method, multiple views are first extracted from each object, and then the dense visual features are extracted to represent each view. To represent the 3D object, multi-scale topic models are employed to extract the hidden relationship among these features with respect to varied topic numbers in the topic model. In this way, each object can be represented by a set of bag of topics. To compare the objects, we first conduct topic clustering for the basic topics from two data sets, and then generate the common topic dictionary for new representation. Then, the two objects can be aligned to the same common feature space for comparison. To evaluate the performance of the proposed method, experiments are conducted on two data sets. The 3D object retrieval experimental results and comparison with existing methods demonstrate the effectiveness of the proposed method.
Balancing User Profile and Social Network Structure for Anchor Link Inferring Across Multiple Online Social Networks

Along with the popularity of online social network (OSN), more and more OSN users tend to create their accounts in different OSN platforms. Under such circumstances, identifying the same user among different OSNs offers tremendous opportunities for many applications, such as user identification, migration patterns, influence estimation, and expert finding in social media. Different from existing solutions which employ user profile or social network structure alone, in this paper, we proposed a novel joint solution named MapMe, which takes both user profile and social network structure feature into account, so that it can adapt more OSNs with more accurate results. MapMe first calculates user similarity via profile features with the Doc2vec method. Then, it evaluates user similarity by analyzing user’s ego network features. Finally, the profile features and ego network features were combined to measure the similarity of the users. Consequently, MapMe balances the two similarity factors to achieve goals in different platforms and scenarios. Finally, experiments are conducted on the synthetic and real data sets, proving that MapMe outperforms the existing methods with 10% on average.
Continuous robust control for series elastic actuator with unknown payload parameters and external disturbances

In this paper, the torque tracking control problem for a class of series elastic actuators U+0028 SEAs U+0029 in the presence of unknown payload parameters and external disturbances is investigated. The uncertainties U+002F disturbances rejection problem for SEAs is addressed from the view of a continuous nonlinear robust control development. Specifically, based on the analysis of a nonlinear SEA, the generic dynamics of SEA systems is described and a novel nonlinear control framework for SEAs is constructed. Then a RISE U+0028 robust integral of the sign of the error U+0028 -based second-order filter is introduced to synthesize the control law. Moreover, the control performance is theoretically ensured by Lyapunov analysis. Finally, some experimental results are included to demonstrate the superior performance of the proposed control method, in terms of transient response and robustness.
Crowded Scene Analysis: A Survey

Automated scene analysis has been a topic of great interest in computer vision and cognitive science. Recently, with the growth of crowd phenomena in the real world, crowded scene analysis has attracted much attention. However, the visual occlusions and ambiguities in crowded scenes, as well as the complex behaviors and scene semantics, make the analysis a challenging task. In the past few years, an increasing number of works on the crowded scene analysis have been reported, which covered different aspects including crowd motion pattern learning, crowd behavior and activity analyses, and anomaly detection in crowds. This paper surveys the state-of-the-art techniques on this topic. We first provide the background knowledge and the available features related to crowded scenes. Then, existing models, popular algorithms, evaluation protocols, and system performance are provided corresponding to different aspects of the crowded scene analysis. We also outline the available datasets for performance evaluation. Finally, some research problems and promising future directions are presented with discussions.
Joint Convolutional Analysis and Synthesis Sparse Representation for Single Image Layer Separation

Analysis sparse representation (ASR) and synthesis sparse representation (SSR) are two representative approaches for sparsity-based image modeling. An image is described mainly by the non-zero coefficients in SSR, while is mainly characterized by the indices of zeros in ASR. To exploit the complementary representation mechanisms of ASR and SSR, we integrate the two models and propose a joint convolutional analysis and synthesis (JCAS) sparse representation model. The convolutional implementation is adopted to more effectively exploit the image global information. In JCAS, a single image is decomposed into two layers, one is approximated by ASR to represent image large-scale structures, and the other by SSR to represent image fine-scale textures. The synthesis dictionary is adaptively learned in JCAS to describe the texture patterns for different single image layer separation tasks. We evaluate the proposed JCAS model on a variety of applications, including rain streak removal, high dynamic range image tone mapping, etc. The results show that our JCAS method outperforms state-of-the-arts in these applications in terms of both quantitative measure and visual perception quality.
Low-Rank Multi-View Embedding Learning for Micro-Video Popularity Prediction

Recently, a prevailing trend of user generated content (UGC) on social media sites is the emerging micro-videos. Micro-videos afford many potential opportunities ranging from network content caching to online advertising, yet there are still little efforts dedicated to research on micro-video understanding. In this paper, we focus on popularity prediction of micro-videos by presenting a novel low-rank multi-view embedding learning framework. We name it as transductive low-rank multi-view regression (TLRMVR), and it is capable of boosting the performance of micro-video popularity prediction by jointly considering the intrinsic representations of the source and target samples. In particular, TLRMVR integrates low-rank multi-view embedding and regression analysis into a unified framework such that the lowest-rank representation shared by all views not only captures the global structure of all views, but also indicates the regression requirements. The framework is formulated as a regression model and it seeks a set of view-specific projection matrices with low-rank constraints to map multi-view features into a common subspace. In addition, a multi-graph regularization term is constructed to improve the generalization capability and further prevents the overfitting problem. Extensive experiments conducted on a publicly available dataset demonstrate that our proposed method achieve promising results as compared with state-of-the-art baselines.
Modeling the Evolution of Users’ Preferences and Social Links in Social Networking Services

Sociologists have long converged that the evolution of a Social Networking  Service(SNS) is driven by the interplay between users’ preferences (reflected in user-item interaction behavior) and the social network structure (reflected in user-user interaction behavior). Nevertheless, traditional approaches either modeled these two kinds of behaviors in isolation or relied on a static assumption of a SNS. Thus, it is still unclear how do the roles of the dynamic social network structure and users’ historical preferences affect the evolution of SNSs. Furthermore, can transforming the underlying social theories in the platform evolution modeling process benefit both behavior prediction tasks? In this paper, we incorporate the underlying social theories to explain and model the evolution of users’ two kinds of behaviors in SNSs. Specifically, we present two kinds of representations for users’ behaviors: a direct (latent) representation that presumes users’ behaviors are represented directly (latently) by their historical behaviors. Under each representation, we associate each user's two kinds of behaviors with two vectors at each time. Then, for each representation, we propose the corresponding learning model to fuse the interplay between users’ two kinds of behaviors. Finally, extensive experimental results demonstrate the effectiveness of our proposed models for both user preference prediction and social link suggestion.
A 10 GW Tesla-Driven Blumlein Pulsed Power Generator

A repetitive 0.6 MV, 10 GW Tesla-driven Blumlein pulsed power generator, with an overall energy efficiency in excess of 90%, has been designed, manufactured and demonstrated by the Pulsed Power Group at Loughborough University. This paper describes the application of various numerical techniques used to design a successful generator, such as filamentary modeling and electrostatic and transient circuit analysis. All the major parameters of both the Tesla transformer and the Blumlein pulse-forming line were determined, enabling accurate modeling of the overall unit to be performed. The wide-bandwidth embedded sensors used to monitor the dynamic characteristics of the overall system are also presented. Experimental results obtained during this major experimental program are compared with theoretical predictions and the way ahead toward generating faster output voltage impulses is considered.
Baseline performance of LTE positioning in 3GPP 3D MIMO indoor user scenarios

Positioning in currently deployed LTE networks is made via a combination of enhanced cell identity (E-CID), observed time difference of arrival (OTDOA) and global navigation satellite system (A-GNSS) information. Both simulations and field trials indicate acceptable performance for outdoor users. However, today the majority of the connections to cellular networks are established from terminals located indoors. This paper provides baseline positioning performance results based on the 3GPP 3D MIMO deployment and propagation model that has been adopted in the 3GPP Release (Rel.) 13 study item on indoor positioning enhancements. Horizontal and vertical accuracies are investigated for both outdoor-only and outdoor-indoor network deployments.
Different performance of UV laser induced surface flashover

The flashover phenomenon for the unilluminated case along the interface of solid dielectric in vacuum is the main factor which restricts the performance of high pulsed power devices, and there are plenty of investigations devoted into determining which parameters are important in predicting flashover behavior and the theoretical understanding involved in the process in the past several decades. In this paper, we studied the flashover performance in the UV laser illuminated case. A test bed comprised of a pulsed (2 ¿s) high voltage power supply, a vacuum chamber, and an excimer laser was assembled to test the flashover behavior of PMMA insulators under pulsed voltage and UV laser irradiation. Both flat electrode and finger electrode were used in our experiments. A commercial energy meter was used to measure the UV fluence for each laser pulse. The flashover voltage and flashover current were measured for different illuminated positions to determine the flashover time delay. The experiment results show that the UV laser irradiation induced surface flashover has different phenomena compared to the unilluminated case. It was inferred that the cathode triple junction is the most vulnerable area under UV illumination and it is more important to shield this area against UV irradiation in the design of vacuum insulators. These results will provide new guidelines to improve the surface flashover voltage when designing pulsed power systems.
Robust Online Matrix Factorization for Dynamic Background Subtraction

We propose an effective online background subtraction method, which can be robustly applied to practical videos that have variations in both foreground and background. Different from previous methods which often model the foreground as Gaussian or Laplacian distributions, we model the foreground for each frame with a specific mixture of Gaussians (MoG) distribution, which is updated online frame by frame. Particularly, our MoG model in each frame is regularized by the learned foreground/background knowledge in previous frames. This makes our online MoG model highly robust, stable and adaptive to practical foreground and background variations. The proposed model can be formulated as a concise probabilistic MAP model, which can be readily solved by EM algorithm. We further embed an affine transformation operator into the proposed model, which can be automatically adjusted to fit a wide range of video background transformations and make the method more robust to camera movements. With using the sub-sampling technique, the proposed method can be accelerated to execute more than 250 frames per second on average, meeting the requirement of real-time background subtraction for practical video processing tasks. The superiority of the proposed method is substantiated by extensive experiments implemented on synthetic and real videos, as compared with state-of-the-art online and offline background subtraction methods.
Compact Circularly Polarized Patch Antenna With Wide Axial-Ratio Beamwidth

This letter presents a compact circularly polarized patch antenna with wide axial-ratio (AR) beamwidth. The simulation reveals that by etching two pairs of asymmetric slots on the patch and loading coupled strips, the phase differences between the radiated orthogonal fields become flatter and maintain approximately at 90° over a wide angular range; hence, a wide AR beamwidth is achieved. Experimental results agree well with the simulation results. Particularly, this antenna achieves a wide AR beamwidth of 188° in both the principal and diagonal planes.
On railway flood control emergency materials reserve network location optimization

In order to solve the railway flood prevention emergency materials reserve network location optimization problem which satisfies emergency time restraint, the least cost of reserve and risk protection maximization, facility location models of emergency service including location set cover model and p-median model are used to build a multi-objective model. A solving procedure based on enumeration method is given. And then the paper puts the flood control emergency materials reserve network location problem of some railway bureau as an empirical analysis. Analytic results indicate that using the multi-objective programming model and algorithm established in this article can ensure the condition of covering all emergency points, the least reserve cost and risk protection maximization. So the model is more applicable to the reserve network location optimization problem of railway flood prevention emergency materials.
Image Restoration: From Sparse and Low-Rank Priors to Deep Priors [Lecture Notes]

The use of digital imaging devices, ranging from professional digital cinema cameras to consumer grade smartphone cameras, has become ubiquitous. The acquired image is a degraded observation of the unknown latent image, while the degradation comes from various factors such as noise corruption, camera shake, object motion, resolution limit, hazing, rain streaks, or a combination of them. Image restoration (IR), as a fundamental problem in image processing and low-level vision, aims to reconstruct the latent high-quality image from its degraded observation. Image degradation is, in general, irreversible, and IR is a typical ill-posed inverse problem. Due to the large space of natural image contents, prior information on image structures is crucial to regularize the solution space and produce a good estimation of the latent image. Image prior modeling and learning then are key issues in IR research. This lecture note describes the development of image prior modeling and learning techniques, including sparse representation models, low-rank models, and deep learning models.
Using Feedback to Improve Learning: Differentiating between Correct and Erroneous Examples

Research indicates that the combination of correct and erroneous examples has a positive effect on knowledge acquisition and transfer if learners have adequate prior knowledge. If only erroneous examples were provided, students with low prior knowledge may need additional supports so that their procedural skills can be effectively promoted. For this reason, this study, taking subtraction as the learning domain, conducted a 2 × 2 factorial design of quasi-experiment varying in example types (correct vs. erroneous examples) and feedback settings (with vs. without feedback). Results indicated that feedback may promote transfer and retention in learning from worked-out examples. However, there was no significant difference between correct and erroneous examples in helping students learn subtraction procedures. According to the results of our questionnaire on cognitive load, it was likely caused by the complexity of the organization and presentation of erroneous examples. We suggest that clear and intelligible presentation can be supported by computer technologies.
A novel substrate-assisted RESURF technology for small curvature radius junction

A novel substrate-assisted (SA) RESURF technology aiming at improving off-state breakdown voltage (BV) of PN junction with small curvature radius is proposed and experimentally demonstrated in this paper. The SA RESURF technology not only realizes small curvature radius in the fingertip region, but also reduces electric field concentration in the curved metallurgical junction. Low-doped P-substrate, which increases depletion of the small curvature radius junction and reduces electric field concentration in the curved metallurgical junction, is adopted in the source fingertip region. Owing to the existence of low-doped P-substrate, the abrupt PN junction with small curvature radius is adjusted to low-doped PN junction with large curvature radius. The SA RESURF technology can be widely applied to lateral high voltage devices with small curved junction, especially to lateral super junction devices. A CBSLOP-LDMOS with the proposed SA RESURF technology has been developed. The experimental results show that the CBSLOP-LDMOS exhibits off-state BV of 700 V and specific on-resistance (Ron, sp) of 142 mΩ·cm2.
The Innovative Study of the Theory and Application of a Kind of Management Based on Contract

According to the need and development of the practice of project management the article proposes a new concept of management based on contract at first time, and sets up a conceptual model for it. What's more, it expresses a detailed exposition of the connotation of the management based on contract and analyzes the tremendous role of it in promoting the level of China's project management theory and practice. Through the practice of the management based on contract in the project of HuangPu Bridge Of Pearl River in GuangZhou, the article shows the process of innovation and application of the management based on contract in highway project.
Troposcatter OTH passive sensing with Cohen's class distribution based on an equal-slope Hough transform

The tropospheric scatter mechanism is potentially valuable for over-the-horizon (OTH) passive sensing, two limitations of this mechanism as large propagation losses and scattering fading diffusion are analyzed. Based on the diffusion properties, the received signals are analyzed with a bilinear Cohen's class distribution, of which the parameterization function (i.e. the kernel) is built from the diffusion coefficient to enhance the SNR. After that, target signals are focused in a parameter space by an equal-slope Hough transform (HT) with a priori information of the intra-pulse frequency interval, the focus-points can be effectively accumulated to detect the OTH targets.