nils-herrmann/clean_scirepeval huggingface dataset:
Recent developments in digital control strategies for DC/DC switching power converters

In this paper, an overview of recent advances in digital control of low to medium power DC/DC switching converters is presented. Traditionally, such DC/DC converters have been almost exclusively controlled through analog electronics methods. However, with the steadily decreasing cost of integrated circuits, the feasibility of digitally controlled DC/DC switching converters has increased significantly. This paper outlines some of the existing design challenges related to digital control and reviews a sample of recently proposed solutions. In addition, present-day research pertaining to applications such as online efficiency optimization, controller auto-tuning and specialized non-linear control is presented. Such applications demonstrate the true advantages and potential of digital control as their complexity prevents practical implementation in the analog domain.
Blind Multiband Joint Detection in Cognitive Radio Networks Based on Model Selection

Multiband joint detection (MJD), which simultaneously detects the primary signals over multiple narrow spectrum bands and aggregate the vacant channels for secondary transmission, is taken as an essential implementation of wideband spectrum sensing for efficient cognitive radio networks. In this paper, we propose a simple but effective sensing scheme to implement blind MJD. Our scheme can be explained as an improved implementation of energy detection based on model selection. By exploiting eigenvalue analysis technique developed from random matrix theory (RMT), our method first estimates the number of occupied channels $\hat{K}$, and then judges the $\hat{K}$ channels with maximum power to be occupied by the primary users. Unlike conventional energy detection or information theoretic criterion based sensing methods, our approach doesn't require the knowledge of noise power or primary signal distribution, and thus is more suitable for blind spectrum sensing. Both theoretical analysis and simulation results validate the efficiency of the the proposed scheme.
Research on the Performances and Parameters of Interior PMSM Used for Electric Vehicles

As a kind of traction device, interior permanent-magnet synchronous machines (IPMSMs) are widely used in modern electric vehicles. This paper performs a design and comparative study of IPMSMs with different rotor topologies (spoke-type PMs, tangential-type PMs, U-shape PMs, and V-shape PMs). The research results indicate that the IPMSM with V-shape PMs is more satisfying with comprehensive consideration. Furthermore, the IPMSM with V-shape PMs is investigated in detail. The influences of geometrical parameters (magnetic bridge and angle between the two V-shape PMs under each pole, etc.) on the performances of V-shape motor are evaluated based on finite-element method (FEM). For accurate research, the effects of saturation, cross-magnetization, and the change in PM flux linkage on d- and q-axis inductances are considered. The back-electromotive force (EMF), flux leakage coefficient, average torque, torque ripple, cogging torque, power per unit volume, power factor, and flux-weakening ability are investigated, respectively. The experimental results verify the validity and accuracy of the process presented in this paper.
Inter-picture prediction based on 3D point cloud model

The explosive growth of images in the cloud calls for more efficient image compression that exploits redundancy between images. Traditional solutions to image set compression are sensitive to the diversity in the clustered image sets in the cloud. In this paper, we propose a more efficient inter-picture prediction based on 3D point cloud model. Leveraging the 3D points and camera parameters, we design a patch-wise warping algorithm to generate prediction. Preliminary experimental results show the proposed scheme significantly outperforms the JPEG and HEVC intra as long as high quality reference is provided. The average bitrate reduction is 15.1% in comparison with HEVC intra on the tested 30 images of Notre Dame. Meanwhile, the perceptual quality of the reconstructed images is improved.
Secondary Network Connectivity of Ad Hoc Cognitive Radio Networks

This letter investigates the connectivity of large-scale ad hoc cognitive radio networks (CRNs). Considering correct detection and false alarm probabilities ( Pd and Pf, we derive in closed-form the thinned density of active secondary users (SUs) in a secondary network. We show the active-probability and detectable radius of primary users (PUs) have a significant impact on the density of active SUs. Moreover, we find that increasing Pf of SUs considerably decreases the node density of active SUs. The connectivity of the secondary network is further analysed with the continuum percolation theory. Given the critical density of the secondary network without the impact of PUs, the critical density of the secondary network coexisting with PUs is estimated analytically. Simulations confirm our analysis.
Comparison of 10 MW superconducting generator topologies for direct-drive wind turbines

Large wind turbines of 10 MW or higher power levels are desirable for reducing the cost of energy of offshore wind power conversion. Conventional wind generator systems will be costly if scaled up to 10 MW due to rather large size and weight. Direct drive superconducting generators have been proposed to address the problem with generator size, because the electrical machines with superconducting windings are capable of achieving a higher torque density of an electrical machine. However, the topology to be adopted for superconducting wind generators has not yet been settled, since the high magnetic field excitation allows for lightweight non-magnetic composite materials for machine cores instead of iron. A topology would probably not be a good option for an offshore wind turbine generator if it demands a far more expensive active material cost than others, even if it has other advantages such as light weight or small iron losses. This paper is to provide a preliminary quantitative comparison of 10 MW superconducting MgB2 generator topologies from the perspective of active material. The results show that iron-cored topologies have a cheaper active material and their sizes are relatively smaller than the others.
Research on 750kV vacuum circuit breaker composed of several vacuum interrupts in series

The paper first introduced the recent research work on super high voltage vacuum circuit breakers (VCB) in China and abroad. It proposed that the research and fabrication of supper high voltage VCB should be accelerated in China to meet the demands of developing 750kV super high voltage power systems. The advantage and significance of applying several vacuum interrupters in series as a super high voltage VCB were stated. The theoretical analysis and experimental test were also conducted to a several single-break vacuum interrupters in series as a super high voltage VCB.
Effect of EFO settings on microstructure and hardness in the FAB of copper bonding wire

Continuously increasing price of gold becomes the major driving force for substituting the gold wire with copper wire in the field of wire bonding. However, since the hardness of copper is higher than that of gold, the potential risk for copper wire bonding is that Cu FAB would cause serious splash on aluminum pad and, even worse, damage of Si die. Since free air ball (FAB) formation is controlled by an electric flame-off (EFO) process, EFO settings would have an influence on the FAB microstructure, thus fabricating FAB with different mechanical properties. However, few researches have focused on the correlation between EFO settings and mechanical properties of FAB, let alone the microstructure evolution in the FAB. In the present paper, we try to find the EFO dependence of hardness in copper FAB. Since the microstructure determines the material's properties, the effect of EFO settings on microstructure evolution is studied to try to give an explanation on the FAB hardness variation. Columnar grains in the FAB would make the material properties of FAB anisotropic. Since the FAB hardness along the wire drawing direction has a direct influence on bonding pads, FAB cross-sections are acquired perpendicular to the wire drawing direction. Electron backscatter diffraction (EBSD) technology is employed to investigate the microstructure evolution. In the orientation imaging microscopic (OIM) maps, subgrain boundaries are only found at the peripheries of FAB, mainly due to the quench effect by shield gas. Low EFO current results in larger grain size in both FAB and ball neck. Besides, low EFO current produces FAB with larger Taylor factor in the wire drawing direction. Vickers hardness tests are conducted on these cross-sections. Surprisingly, EFO settings have less influence on the FAB hardness in the wire drawing direction. This phenomenon may be attributed to the trade-off between the grain size strengthening and texture hardening.
Design of self-adapting illumination in the vision measuring system

Irradiance distribution and intensity of the test object play a key role in accuracy and stability of the vision measuring system. This paper firstly proposed a luminance transfer function in the vision measuring system, and then designed the illumination which could adjust light radiation automatically by ways of Neural Networks (NNs) and Pulse-Width Modulation (PWM) switch power. That the illumination could greatly improve the accuracy and robustness of the vision measuring system greatly has been verified by experiment. It has potential development and spreads the vision measuring system following the design of the self-adapting illumination.
CNN-based text image super-resolution tailored for OCR

Since low-resolution images may hamper the performance of optical character recognition (OCR), text image super-resolution (SR) has become an increasingly important problem in computer vision. Convolutional neural network (CNN) has been proposed for generic image SR as well as text image SR, but the previous works concern more on the objective quality (e.g. PSNR) rather than the OCR performance. In this paper, we propose a new loss function when training CNN for text image SR to facilitate OCR, and conduct model combination to further improve the performance. Also, we propose a simple yet effective image padding method to refine the image boundaries during SR. Experimental results show that we achieve an OCR accuracy of 78.10% on the ICDAR 2015 TextSR dataset, which is comparable with that of using the original high-resolution images (78.80%), and also exceeds the state-of-the-arts.
Research on monitoring technique of construction linear for Guidan Highway super large bridge

Guidan Highway super large bridge of Guangzhu railway engineering is a three-span (60m+100m+60m) continuous box girder bridge, which was constructed by using hanging basket cantilever method. On the basis of the analytic theory of 3D finite element forward/reverse, the linear monitoring of the whole bridge was done through controlling the template elevation of box girder (pre-camber). The result of monitoring shows that the linear of construction satisfies the requirements of design and specification and the bridge can be closed smoothly.
Robust control for a class of uncertain time-delay switched fuzzy systems

This paper considers the quadratic stability problem for a class of switched fuzzy (SF) time-delay systems. A new method based on a switching technique is presented to solve this problem. A switched fuzzy system, which differs from existing ones, is firstly employed to describe a nonlinear system, and then some new concepts about the innovated SF system are introduced. Usually, this class of systems can precisely describe continuous and discrete dynamics as well as their interactions in the complex real-world systems. Next, the switched state feedback controller for the proposed SF time-delay system is built to ensure that the relevant closed-loop system is quadratically stable. Finally, switching law of the state-dependent form achieving system quadratic stability of the SF system is given. The main conditions are given in terms of linear matrix inequalities (LMIs), hence they are easily solvable. Various comparisons of the elaborated examples are conducted to demonstrate the effectiveness of the proposed control design approach. All results illustrate good control performances as desired.
Optimization of signal coordination for closely spaced intersections

The overflow problem often happens at closely spaced intersections. In order to optimize the signal control for closely spaced intersections, geometric features and traffic flow characteristics of the intersections were analyzed and an effective synchronous control strategy was put forward for vehicles that turn left from the upstream intersection and for vehicles stopped between two intersections. Closely spaced intersections in Luoyang city was taken as an example to apply the scheme of the proposed signal coordination, then, traffic volume per unit time was chosen as the indicator to analyze the effectiveness and applicability of the control strategy. Practical operation results show that the proposed signal control scheme for closely spaced intersections could effectively solve the problem of overflow and congestion at those intersections, and improve the efficiency of traffic operation at those intersections.
An Efficient Recovery and Survival Scheme against Malware Attacks

Intricate malware can result in the failure of on-line Comprehensive Protection (CP) in distributed systems, and place the system in an unsafe state which is difficult to recover from. There lacks an effective scheme to defend against this extreme attack. In this paper, based on the Two-layer Protection and Co-operative Recovery (TPCRS) mechanism, we propose an efficient survivable scheme against malware attacks in distributed systems. The basic strategy is to deploy an Emergency Response/Recovery (ER) agent at each node to recognize the state of the system whenever the CP fails, and to carry out cooperative security among multiple nodes so that the infected nodes can be rapidly recovered. Furthermore, a Preventive Maintenance (PM) model is adopted to enhance the reliability of the distributed system. Si-mulation results demonstrate the practicality and efficiency of the proposed schemes.
An EEG-based brain-computer interface for gait training

This work presents an electroencephalography (EEG)-based Brain-computer Interface (BCI) that decodes cerebral activities to control a lower-limb gait training exoskeleton. Motor imagery (MI) of flexion and extension of both legs was distinguished from the EEG correlates. We executed experiments with 5 able-bodied individuals under a realistic rehabilitation scenario. The Power Spectral Density (PSD) of the signals was extracted with sliding windows to train a linear discriminate analysis (LDA) classifier. An average classification accuracy of 0.67±0.07 and AUC of 0.77±0.06 were obtained in online recordings, which confirmed the feasibility of decoding these signals to control the gait trainer. In addition, discriminative feature analysis was conducted to show the modulations during the mental tasks. This study can be further implemented with different feedback modalities to enhance the user performance.
Aircraft cabin thermal control strategy based on adaptive temperature and thermal sensation

An Environment Control System (ECS) in civil aircraft will control the cabin thermal environment with constant temperature control strategy. However, the passenger dress, heat loads will change with the flight process. This constant control strategy cannot satisfy these dynamic requirements and faces the challenge. An aircraft cabin thermal control strategy based on adaptive temperature and thermal sensation evaluation is presented in this paper to solve this question. The new thermal control strategy includes initial and accurate control strategies, which individually control the thermal environment. For the night flight mode, the initial control strategy with a constant supply air temperature is used to control the cabin thermal environment according to the adaptive temperature. For the day time, the accurate control strategy with a dynamic supply air temperature is used to control the cabin thermal environment according to the thermal sensation of passenger. This new control strategy can ensure passengers in the aircraft cabin to have a comfortable thermal sensation under different flight times and seasons.
Property description and comparison of BLOG community at different scales

The characteristics of complex network are studied mostly at small-scale and large-scale, while there is still much to be done at mesoscale. Firstly, by using the comment data of a blog site, an undirected network is built. From the perspective of network topology, it is divided and defined into small-scale (micro-level), mesoscale (community structure) and large-scale (macro-level). According to the characteristics of users' communication and the factors which influence network emergence, the comment network is described from the aspects of connectivity, diversity, and activity. Furthermore, some quantitative metrics are selected to analyze the network characteristics at each scale, including network structure entropy, degree distribution, rich-club, and the association between clustering coefficient and degree. It is found that the statistics are similar between mesoscale and the other two scales, indicating that the large-scale and small-scale could be understood through the mesoscale.
Image Retagging Using Collaborative Tag Propagation

Photo sharing websites such as Flickr host a massive amount of social images with user-provided tags. However, these tags are often imprecise and incomplete, which essentially limits tag-based image indexing and related applications. To tackle this issue, we propose an image retagging scheme that aims at refining the quality of the tags. The retagging process is formulated as a multiple graph-based multi-label learning problem, which simultaneously explores the visual content of the images, semantic correlation of the tags as well as the prior information provided by users. Different from classical single graph-based multi-label learning algorithms, the proposed algorithm propagates the information of each tag along an individual tag-specific similarity graph, which reflects the particular relationship among the images with respect to the specific tag and at the same time the propagations of different tags interact with each other in a collaborative way with an extra tag similarity graph. In particular, we present a robust tag-specific visual sub-vocabulary learning algorithm for the construction of those tag-specific graphs. Experimental results on two benchmark Flickr image datasets demonstrate the effectiveness of our proposed image retagging scheme. We also show the remarkable performance improvements brought by retagging in the task of image ranking.
Prediction-Table Based Fault-Tolerant Real-Time Scheduling Algorithm

In order to predict accurately whether primary versions of real-time tasks is executable in software fault-tolerant module, a new algorithm, PTBA, prediction-table based algorithm, is presented. PTBA uses prediction-table to predict whether a host primary can meet its pre-deadline. Prediction-table contains the pre-assignment information of tasks between the current time and the alternates' notification time. If the prediction result shows that host primary has not enough time to execute, it will be aborted. Otherwise, prediction-table is referenced to schedule tasks with low overhead. The novelty of PTBA is that it schedules primaries according to their corresponding alternates' notification time and has no extra scheduling overhead in prediction-table mode. Simulation results show that PTBA allows more execution time for primaries and wastes less processor time than the well-known similar algorithms. PTBA is appropriate to the situation where the periods of tasks are short and software fault probability is low
An Adaptive Transmission Control Mechanism for Underwater Acoustic Sensor Network

An adaptive transmission control mechanism for underwater acoustic sensor networks is proposed in this paper. This mechanism controls transmission delay and releases collisions adaptively according to different topologies and the bit error rates of the wireless links periodically feedback by the receiver. Our work succeeds in releasing collisions and reducing propagation delay. Further more, the energy is saved by reducing repeated transmission. Simulations show ATC adapts the transmission time to the wireless link quality dynamically and saves about 50% energy when bit error rate is high.
Semantic Web for Content Based Video Retrieval

This paper aims to provide a semantic web based video search engine. Currently, we do not have scalable integration platforms to represent extracted features from videos, so that they could be indexed and searched. The task of indexing extracted features from videos is a difficult challenge, due to the diverse nature of the features and the temporal dimensions of videos. We present a semantic web based framework for automatic feature extraction, storage, indexing and retrieval of videos. Videos are represented as interconnected set of semantic resources. Also, we suggest a new ranking algorithm for finding related resources which could be used in a semantic web based search engine.
Learning-based objective evaluation of 3D human open meshes

Current state-of-the-art mesh quality measures evaluate closed and complete meshes obtained after mesh postprocessing applications, such as mesh simplification or watermarking, and compare them against the corresponding reference mesh. Emerging 3D immersive VR/AR applications use noisy 3D point cloud, typically from single RGB-D camera (such as Microsoft's Kinect) to generate standalone (no reference) 3D human open mesh (with boundaries) in real time, that needs evaluation. A learning-based objective measure is proposed to rate the visual quality by emulating human perception of 3D human open mesh quality. 2-pronged objective evaluation is performed: (a) Global holistic score captures the efficacy of the mesh to represent the human model as a whole, by considering mesh completeness and mesh noise. (b) Local part-based score caters to the need of varying roughness in different parts of the human body, by finding the deviation in the face normals for all the adjacent triangles in that part (segment). Learning technique aligns the objective scores with the subjective user evaluation, in turn combining the concepts of white-box and black-box evaluation for 3D meshes. Experimental results for a database, specifically generated for the purpose proves the efficacy of the proposed method.
Flexible disk scheduling for multimedia presentation servers

Objects composing a multimedia presentation need to be delivered based on the temporal relationships specified by the author or authors. Depending on the composition of multimedia presentations, disk access for retrieval may not be needed in every round. A presentation can skip a certain number of disk scheduling rounds before retrieving the next required chunk of data. In this paper, we present a min-max skip round disk access algorithm for the retrieval of multimedia presentations in a flexible manner depending on the presentation composition, client/server buffer, and server-disk/network bandwidths. The proposed min-max skip round disk scheduling algorithm can be used for push, pull, as well as hybrid approaches for delivering multimedia presentations. We consider storage of presentations on an array of disks as well as on multi-zone recording disks. A few simulation results are provided.