nils-herrmann/clean_scirepeval huggingface dataset:
Improving tolerance of neural networks against multi-node open fault

Neural networks are not intrinsically fault tolerant and their fault tolerance has to be improved by employing extra mechanisms. During the last decades, some simple fault types of feedforward neural networks have been widely investigated. In this paper, a rather complicated fault type, i.e. a multi-node open fault where several hidden nodes are out of work at the same time, is formally analyzed, and an approach named T3 is proposed. The ground of T3 is the recognition that the performance of trained neural networks does not linearly decrease with the increasing of the severity of fault. T3 utilizes a validation set to build the fault curve of a trained network. It then locates the inflection point of the fault curve and repeatedly trains the network according to the corresponding fault rate so that the redundancy are appended to the network appropriately. Experiments show that T3 can improve the tolerance against multi-node open fault of some feedforward neural networks at the expense of relatively small redundancy.
Applicability analysis of CFAR algorithm for landmines detection applied to vehicle-mounted forward-looking SAR

For the vehicle-mounted forward-looking synthetic aperture radar (SAR), detecting algorithms should be effective, robust and real-time. This paper proposed a fast statistic detecting algorithm based on circulatory iteration. We also presented feasible methods to select the optimal parameters of constant false alarm rate (CFAR) detector. Experiment results showed that our algorithm had high detecting rate, low false alarm rate, good adaptability and much less computation load.
Exploiting Unlabeled Data to Enhance Ensemble Diversity

Ensemble learning aims to improve generalization ability by using multiple base learners. It is well-known that to construct a good ensemble, the base learners should be accurate as well as diverse. In this paper, unlabeled data is exploited to facilitate ensemble learning by helping augment the diversity among the base learners. Specifically, a semi-supervised ensemble method named UDEED is proposed. Unlike existing semi-supervised ensemble methods where error-prone pseudo-labels are estimated for unlabeled data to enlarge the labeled data to improve accuracy, UDEED works by maximizing accuracies of base learners on labeled data while maximizing diversity among them on unlabeled data. Experiments show that UDEED can effectively utilize unlabeled data for ensemble learning and is highly competitive to well-established semi-supervised ensemble methods.
Electrical and Mechanical Character of Smart Film for Crack Monitoring of Concrete Bridges

In the field of structural health monitoring of concrete bridges, one of the most important problems is the crack identification. In previous studies, sensitive skin for bionic crack monitoring of concrete bridges had been used to monitor the crack initiation and development in a concrete structure, and it was visualized by computer. But there are several problems that prevented this method from being widely applied. In this paper, smart film is proposed for crack monitoring of large scale concrete structures. To monitor the crack of concrete bridge by smart film, the electrical and mechanical characters of smart film were analyzed separately based on a LED (light emitting diode) circuit designed and Fracture Mechanics. The results showed that smart film can monitor cracks of concrete bridges effectively.
Construction of medical nanorobot

Ongoing development in nanotechnology and biology would enable the construction of nanorobot - nanoscale biomolecular system or NMES. Current techniques and methods in the construction of nanorobot have some limits and shortcomings, such as size, stability, robustness, biocompatibility and so on. The present work proposed the assumption that constructs nanorobot merely by biological components, discussed the possibility of fabricating nanorobot based on virus. Also demonstrated the possibility of applications in biomedical problems, paying a special attention in use bionanorobot as drug delivery system(DDS)
Differential space-time modulation with loaded eigen-beamforming for spatially correlated time-varying fading channels

Exploiting the channel correlation at transmitter, a preceded differential space-time modulation transmission scheme with loaded eigen-beamforming is present for spatially correlated time-varying fading channels. Through eigenvalue decomposition, we get the transmit eigen-beamforming matrix. Based on the analysis of the upper bound of pairwise error probability and effective signal to noise of the system, an optimal power loading scheme is derived which is dependent on total transmitted power, noise covariance, channel correlation and time-varying fading parameter. Simulation results show that that the proposed preceded DSTM scheme is obviously better than traditional DSTM without preceding and the preceded scheme in for spatially correlated fast time-varying fading channels.
Simulation research on distribution of short-circuit current of transmission lines after lightning flashover

Based on the principle of calculating asymmetric faults, this paper made accurate simulation research on distribution of short-circuit current and its factors affecting it using electromagnetic transient analysis program ATP-EMTP for 220kV transmission lines. Conculsions are gotten that most of short-circuit current is dispersed by lightning conductors and and shunt coefficient under operating frequency is very different from that under lighting current. This is of great significance theoretically for checking and selection of the lightning conductor on thermal stability[1], potential increase and positioning of flashover towers, electromagnetic influences on adjacent communication lines, economical design of grids and so on.
Signal Optimization Problem with Pedestrians Non-complying at a Single Intersection

Without persons specially assigned to manage pedestrians, pedestrians almost are reluctant to wait in developing cities, such as Xipsilaan during green phases for vehicles. Thus, some vehicles may conflict with pedestrianspsila noncompliance, and be delayed. To solve the signal optimization problem of urban traffic with pedestrianspsila noncompliance, a hybrid optimization algorithm is developed. In this algorithm, the performance index (PI), concerned on the total retained vehicles over the whole control period, includes the number of retained vehicles for waiting green signal and violating pedestrians. The number of retained vehicles for pedestrians violating is estimated by a Monte Carlo method. The simulations are conducted under the condition of heavy traffic demands. The results indicate that, under the condition of heavy traffic flow, this method is more available to protect the pedestrians than the traditional method.
Real-time motion control of a multi-degree-of-freedom variable reluctance spherical motor

Presents high-precision control strategies and bearing support mechanisms for real-time motion control of a multiple degree-of-freedom (DOF) variable-reluctance (VR) spherical motor. In particular, a resultant magnetic force model is derived which, along with the torque model, is used to develop a reaction-free control strategy to establish a non-contact support mechanism through magnetic levitation to achieve high performance motion. An equivalent angle-axis based control is also designed and implemented for real-time applications. The designed control strategy only requires limited knowledge of system dynamics, and therefore is robust and practical. A look-up table based on-line nonlinear optimization scheme is devised to facilitate the control strategy for a prototype VR spherical motor. Control experimental results verify the reaction-free control strategy and demonstrate the motion capability of the multi-DOF VR spherical motor. The concepts, methodologies, and experimental techniques developed in the paper establish an essential analytical and engineering basis for real-time motion control of VR spherical motors.
Ultrawideband Synthetic Aperture Radar Unexploded Ordnance Detection

Airborne ultrawideband (UWB) synthetic aperture radar (SAR) can perform wide-area detection of unexploded ordnance (UXO) to locate former bombing ranges efficiently. Two main issues in UWB SAR UXO detection, feature extraction, and discriminator design are considered. A space-wavenumber distribution and moment invariants-based method is proposed to extract the multi-aspect feature of UXO with both amplitude and spatial distribution information. Based on the extracted feature, a support vector machine (SVM) with hypersphere classification boundary, referred to as HS-SVM, is used as the UXO discriminator, which can be trained with a small training set of only UXO samples. Furthermore, the problem of HS-SVM kernel choice is studied, and the hidden Markov model (HMM) kernel is proved to be better than the Gaussian kernel. The efficiency of the proposed feature extraction method and the HMM kernel HS-SVM is validated using real data collected by a UWB SAR system.
Ultrawideband Synthetic Aperture Radar Landmine Detection

In this paper, we consider landmine detection using ultrawideband synthetic aperture radar, where the two main challenges are feature extraction and discriminator design. The space-wavenumber processing is proposed to retrieve the frequency-and aspect-angle-dependent scattering features of suspected objects. In order to reduce the dimensionality of the input feature vector for a discriminator, the sequential forward floating selection method is used to choose efficient features. Based on the obtained feature vector, a fuzzy hypersphere support vector machine is designed to deal with the problem of detecting landmines in an unconstrained environment. The experimental results show that the proposed method can achieve a significant improvement in detection performance for antitank mines.
Forward looking InSAR based field terrain mapping for unmanned ground vehicle

Unconstructed environments perception is a challenge problem for Unmanned Ground Vehicle (UGV) and is not well solved by traditional sensors, such as: optical camera, lidar and millimeter wave radar. This paper presents a novel forward looking InSAR (Interferometric Synthetic Aperture Radar) sensor for UGVs and focuses on reconstruction the Digital Terrain Model (DTM) of unconstructed environments. DTMs are vital to boost obstacle detection and path planning performances. The forward-looking InSAR system and its signal processing flow are briefly described. Then practical issues of applying the sensor in field environment are addressed. The impacts of height error and pitch angle error of the InSAR antenna are analyzed and solved by the split spectrum algorithm. Field experiment on a rough meadow has shown advanced perception capability of the forward-looking InSAR in complex environments.
Transductive Multilabel Learning via Label Set Propagation

The problem of multilabel classification has attracted great interest in the last decade, where each instance can be assigned with a set of multiple class labels simultaneously. It has a wide variety of real-world applications, e.g., automatic image annotations and gene function analysis. Current research on multilabel classification focuses on supervised settings which assume existence of large amounts of labeled training data. However, in many applications, the labeling of multilabeled data is extremely expensive and time consuming, while there are often abundant unlabeled data available. In this paper, we study the problem of transductive multilabel learning and propose a novel solution, called Trasductive Multilabel Classification (TraM), to effectively assign a set of multiple labels to each instance. Different from supervised multilabel learning methods, we estimate the label sets of the unlabeled instances effectively by utilizing the information from both labeled and unlabeled data. We first formulate the transductive multilabel learning as an optimization problem of estimating label concept compositions. Then, we derive a closed-form solution to this optimization problem and propose an effective algorithm to assign label sets to the unlabeled instances. Empirical studies on several real-world multilabel learning tasks demonstrate that our TraM method can effectively boost the performance of multilabel classification by using both labeled and unlabeled data.
A state space model for modern feedback control of optical fiber drawing process

Fiber diameter nonuniformities in optical fiber drawing processes can have adverse effects on system performance. In this paper we develop a method for obtaining models of the drawing process that lend themselves to the application of optimal control of fiber diameter nonuniformities. The method presented here consists of using the basic conservation laws of mass, momentum, and energy to construct state space models of the drawing process that incorporate all the relevant variables of the system in a multiple-input/multiple-output framework. An LQG optimal controller is synthesized to illustrate the applicability of the state space model.
A coarray based MIMO array design method for UWB imaging

In MIMO radar imaging applications, the configuration of antenna array is one of the key techniques for system design. It has great effects on image quality, parameters estimation and targets detection. The performance of the array is related not only to the topology of the array and the number of array elements, but also the spectrum of the radiated signal. The proper array design is helpful to simplify system structure and signal processing. In this paper, a novel method is proposed to design a sparse linear MIMO array that transmits UWB stepped frequency waveform for imaging. The simulation results validate the proposed method.
M3MIML: A Maximum Margin Method for Multi-instance Multi-label Learning

Multi-instance multi-label learning (MIML) deals with the problem where each training example is associated with not only multiple instances but also multiple class labels. Previous MIML algorithms work by identifying its equivalence in degenerated versions of multi-instance multi-label learning. However, useful information encoded in training examples may get lost during the identification process. In this paper, a maximum margin method is proposed for MIML which directly exploits the connections between instances and labels. The learning task is formulated as a quadratic programming (QP) problem and implemented in its dual form. Applications to scene classification and text categorization show that the proposed approach achieves superior performance over existing MIML methods.
A new optimization method for microwave broad band amplifier based on orthogonal design

A new optimization method for microwave broadband amplifier based on orthogonal design is presented. This approach is more effective for the optimization of the complicated objective function and the indistinct expression than conventional optimization methods. It has fast convergence rate and can overcome the shortcoming of conventional optimization methods that rely on starting values strongly. The example performed with orthogonal design shows that it is effective and useful for the optimization of microwave broadband amplifier.
Face Recognition Under Occlusions and Variant Expressions With Partial Similarity

Recognition in uncontrolled situations is one of the most important bottlenecks for practical face recognition systems. In particular, few researchers have addressed the challenge to recognize noncooperative or even uncooperative subjects who try to cheat the recognition system by deliberately changing their facial appearance through such tricks as variant expressions or disguise (e.g., by partial occlusions). This paper addresses these problems within the framework of similarity matching. A novel perception-inspired nonmetric partial similarity measure is introduced, which is potentially useful in dealing with the concerned problems because it can help capture the prominent partial similarities that are dominant in human perception. Two methods, based on the general golden section rule and the maximum margin criterion, respectively, are proposed to automatically set the similarity threshold. The effectiveness of the proposed method in handling large expressions, partial occlusions, and other distortions is demonstrated on several well-known face databases.
Notice of RetractionApplication of Single Linear Blade Diamond Tools to Cutting of Unitary Wave-Front Corrector

Interference will possibly happen between the tools and the surfaces to be machined while cutting the unitary wave-front correctors with single linear blade diamond tools. According to the mathematic model of the work piece surface and the tool path, the mathematical model is established to show the minimum setting angle while there is no interference between the tools and the surfaces to be machined. Also, the maximum setting angle meeting the ordered surface roughness is deduced based on the relationship between the microcosmic unevenness height of optical surfaces and the parameters of cutting edges and the setting angle. Practical cutting experiments are performed on an ultra-precision lathe. The results of experiments indicate that it is feasible to machine unitary wave-front correctors by linear blade nature diamond tool. The mathematics model of the minimum setting angle between the linear blade diamond tool and work piece's surface is practical when machining unitary wave-front correctors.
Research on Spatial Resolution of One-Stationary Bistatic Ultrahigh Frequency Ultrawidebeam–Ultrawideband SAR Based on Scattering Target Wavenumber Domain Support

Compared with the traditional one-stationary bistatic narrowbeam-narrowband (NB) synthetic aperture radar (SAR), the coupling between the azimuth and range wavenumbers for the one-stationary bistatic ultrahigh frequency (UHF) ultrawidebeam-ultrawideband (UWB) SAR is much larger due to the large fractional signal bandwidth and wide antenna beamwidth, which may affect the behavior of spatial resolutions. Considering the wavenumber coupling, the more accurate spatial resolutions for the one-stationary bistatic UHF UWB SAR are proposed based on the wavenumber domain support of the scattering target in this paper. First, the one-stationary bistatic SAR imaging geometry is provided, and then the spatial wavenumber of the scattering target for the one-stationary bistatic UHF UWB SAR is analyzed. Second, based on the spatial wavenumber spectrum, spatial resolutions of the one-stationary bistatic UHF UWB SAR are derived in detail. In addition, the squint angles of radars are considered in the derivation of the spatial resolutions. Besides, the narrowing/broadening factors defined as the ratio of the proposed spatial resolutions to traditional spatial resolutions are presented, which describe the effects of the fractional bandwidth and associated integration angle on the spatial resolutions. Finally, simulation results are given to prove the correctness and validity of the proposed resolutions.
Study on fuel economy operating modes of tractor equipped with CVT

A simple fuel economy operating mode and a comprehensive fuel economy operating mode of tractor equipped with CVT are proposed in this paper. The former takes the minimum fuel consumption rate point in the whole engine operating range as the target, the throttle opening keeps constant, the gear ratio of CVT is adjusted by the controller which is a single parameter control system. The later takes the optimum fuel economy curve of engine operating as the target, which enables tractor not only to obtain the optimum fuel economy but also to keep a stable speed set by driver under variable resistance. Both throttle opening and gear ratio of CVT are as the control parameters, the controller for this mode is a double parameter control system. In this paper, a single close loop and double close loop control system are established for the two tractor operating modes respectively. Simulation results show that the two kinds of control system can meet the requirements of the simple and the comprehensive fuel economy operating modes. The single closed-loop and the double closed-loop control system can provide an important basis for the development of tractor equipped with CVT.
A novel InSAR based off-road positive and negative obstacle detection technique for unmanned ground vehicle

Off-road positive and negative obstacle detection is a challenge problem to be solved by unmanned ground vehicle. Traditional sensors, such as: optical camera, lidar and millimeter wave radar, have limited performance in off-road environments, especially when obstacles are far away or covered by sparse grasses. We have proposed a forward-looking InSAR sensor to tackle the problem and have built a rail-based InSAR prototype. The forward-looking InSAR can provide more information of harsh off-road environments than existing unmanned ground vehicle (UGV) based radars. The forward-looking InSAR can provide a scattering image, a coherence image and a digital terrain model (DTM) of the same scene ahead the radar during each scan. Each type of image can highlight some unique features of an obstacle. In this paper, an obstacle detection method is proposed by combining the shadow feature and the edge scattering feature. The principle method is close related to the scattering property difference between positive obstacles, negative obstacles and other objects. Positive obstacle feature large amplitude followed by low coherence area; while at the same time, negative obstacles feature low coherence area followed by large amplitude. Other objects don't have the unique feature. To mitigate false alarms, shadows are segmented in coherence images, and edge scattering features are extracted in scattering image. Firstly, the coherence image is converted into a binary image by applying a threshold. Shadow areas are roughly segmented as their coherences are low. Then the binary image is filtered by morphologic opening operation to eliminate small patches. Subsequently, an edge detection operation is applied to the filtered image. The edges of positive and negative obstacle are among the detected edge image. For each position of the detected edge, a cut is performed on the same position in the scattering image to extract a slice along the range direction. The judgment is formed by calculating the energy ratio between the near half slice of the farther half slice. Finally, positive and negative obstacles can be discriminated by comparing the judgment with two thresholds in an unsupervised fashion. We have conducted a field experiment on a ground covered by sparse grasses. A pit and a mound are deliberately built in the experiment scene. Experimental results have validated the proposed method.
Fast time domain approach for bistatic forward-looking sar imaging based on subaperture processing and local beamforming

A fast time-domain approach called fast factorized backprojection (FFBP) algorithm based on the subaperture processing and local beamforming is presented for the bistatic forward-looking SAR (BFSAR) imaging. It not only precisely handles spatial-variance, serious range-azimuth coupling and complicated motion errors, but also greatly improves imaging efficiency compared with the backprojection (BP) algorithm. Besides, it requires the local beamforming from radar echoes as an intermediate stage in the slant-range plane instead of ground plane, which is accurately referenced to transmitter and receiver tracks. Finally, it analyzes the bistatic range error considering the motion errors to derive the requirements of selecting the size of subapertures and subimages. Experimental results show the validity of the proposed FFBP algorithm.