nils-herrmann/clean_scirepeval huggingface dataset:
Study and practice of 2μm program on engine advanced manufacturing

The source, purpose and significance of the engine advanced manufacturing engineering — 2μm-program is introduced. The relatively complete definition of the 2μm-program is firstly put forward. The general research goal of the 2μ-program is introduced from engine design to manufacturing process, detection, and so on. The main research contents as well as final targets of the program are presented. An application case of the 2μm-program theory in the B12 engine manufacturing quality improvement is introduced, in which the problem of machined trace on cylinder block finished surface is solved. Based on the quality statistical data analysis and the theoretical study, an optimizing process method was taken to solve the case. A machining experiment was carried out using the optimized process, and the 3D structure and flatness data of the surface is obtained using the advanced ShaPix surface detector. The results of the experiment demonstrate that the block manufacturing quality is remarkably improved, and the case settlement is very successful. The research and application of the 2μm-program will certainly play an important role in engine manufacturing technology and quality improvement.
Multi-agent consensus under delayed feedback: Fundamental constraint on graph and fundamental bound on delay

In this paper, we study the consensus problem for discrete-time multi-agent systems over an undirected, fixed network communication graph. We assume that the agents' input is subject to a constant albeit possibly unknown time delay, and employ a distributed dynamic state feedback control protocol. Drawing upon concepts and techniques from robust control, notably those concerning gain margin optimization and analytic interpolation, we derive explicit, closed-form conditions for general linear agents to achieve consensus. Our results display an explicit dependence of the consensus condition on the agent's unstable dynamics and the delay value, showing that delayed communication between agents will generally hinder consensus and impose restrictions on the network topology. We also show that a lower bound on the maximal delay allowable for consensus can be computed by a simple line search method.
Modelling the effects of walking speed on appearance-based gait recognition

Researchers in the gait community propose various features, either appearance or model based, which they believe encode certain individual traits. One of the main assumptions made in many gait recognition techniques is constant walking-speed. Even though the gait patterns are repeatable, changes in walking speed can influence the gait patterns themselves. In this work we explore how changes in walking speed affect gait parameters in terms of recognition performance. A speed-varying walking database was collected to allow us to investigate and quantify the impact of speed on gait recognition methodically. We develop a normalization procedure, which maps gait features across speeds, and demonstrate their utility in previously proposed appearance-based gait recognition methods.
A novel QAM-DCSK secure communication system

Differential Chaos Shift Keying (DCSK) employs noncoherent demodulation technology and it's unnecessary for the receiver to generate synchronous chaotic signals at the receiving end. The structure of DCSK is simple and has good anti-noise performance. However, the security performance of DCSK is not enough, and it is easier to be cracked. For the above problems, a novel QAM-DCSK system was proposed in this paper. The system combines the advantages of Quadrature Amplitude Modulation (QAM) and DCSK. The information is scattered and transmitted in two orthogonal channels, thus the security performance can be greatly enhanced. At the same time, the good performance of the old one is also remained. The theoretical analysis was deeply carried out for this system. The relationship among Bit Error Ratio (BER), the frequency spread factor M and Eb/N0 was also deeply studied. It was finally applied in an image transmission system as an example. Compared with the old one, the simulation results indicated that the performance of BER and security were greatly improved. Therefore, it has great significance in the practical application.
Principal color representation for tracking persons

This paper proposes a novel method for tracking persons based on the principal colors of human objects. First, an efficient human object representation method, principal color representation (PCR), is proposed. Asymmetric similarity measures are then proposed based on the principal color representation. These asymmetric similarity measures could be used to evaluate the matching between two individuals as well as visual evident of an individual in a group. An efficient algorithm for tracking persons as individuals or in groups is then described. The method has been tested using image sequences containing multiple moving persons frequently gathering and separating. Our test results have shown that proposed method has successfully tracked both persons as individuals or in groups, and is robust to illumination changes.
On the use of a linear data model for parameter estimation of sinusoidal FM signals

A linear data model is presented for multi-component sinusoidal frequency-modulated (FM) signals, based on the fact that a sinusoidal FM signal can be decomposed into a set of harmonic frequencies, with harmonic amplitudes given by Bessel functions of the first kind. And via several properties derived from the present data model, a novel method is proposed to estimate the parameters of the multi-component sinusoidal FM signals. The feasibility of the proposed method is demonstrated by simulation results.
Semantic retrieval of video - review of research on video retrieval in meetings, movies and broadcast news, and sports

This paper reviews the different research works on three types of video, i.e., video of meetings, movies and broadcast news, and sports video. The paper puts them into a general framework of video summarization, browsing, and retrieval. It also reviews different video representation techniques for these three types of video content within this general framework. Finally, the challenges facing the video retrieval research community are presented
Topological constraints on consensus via delayed output feedback over directed graph

This paper is devoted to the consensusability of discrete-time linear time invariant (LTI) multi-agent systems over a fixed directed network communication graph. A distributed dynamic output feedback control protocol is employed and the each agent's input is assumed to be subjected to a constant unknown time delay. Drawing upon the phase margin and gain-phase margin optimization concepts as well as analytic interpolation techniques from robust control, we develop an explicit consensusability conditions of discrete-time LTI agents which provides a limit on network topology imposed by the agent's unstable poles, non-minimum phase zeros, relative degree and communication time delay. This result also suggests that delayed communication between the LTI agents will generally hinder consensus.
DYFARS: Boosting Reliability in Fault-Tolerant Heterogeneous Distributed Systems Through Dynamic Scheduling

In this paper, we propose a dynamic and reliability- driven real-time fault-tolerant scheduling algorithm on heterogeneous distributed systems (DYFARS). Primary-backup copy scheme is leveraged by DYFARS to tolerate both hardware and software failures. Most importantly, DYFARS employs reliability costs as its main objective to dynamically schedule independent, non-preemptive aperiodic tasks, therefore system reliability is enhanced without additional hardware costs. A salient difference between our DYFARS and existing scheduling approaches is that DYFARS considers backup copies in both active and passive forms; therefore, DYFARS is more flexible than the existing scheduling schemes in the literature. Finally, simulation experiments are carried out to compare DYFARS with existing similar algorithm, experiment results show that DYFARS is superior to existing algorithm regarding both schedulability and reliability.
Canonical Image Selection by Visual Context Learning

Canonical image selection is to select a subset of photos that best summarize a photo collection. In this paper, we define the canonical image as those that contain most important and distinctive visual words. We propose to use visual context learning to discover visual word significance and develop Weighted Set Coverage algorithm to select canonical images containing distinctive visual words. Experiments with web image datasets demonstrate that the canonical images selected by our approach are not only representatives of the collected photos, but also exhibit a diverse set of views with minimal redundancy.
Cascade Category-Aware Visual Search

Incorporating image classification into image retrieval system brings many attractive advantages. For instance, the search space can be narrowed down by rejecting images in irrelevant categories of the query. The retrieved images can be more consistent in semantics by indexing and returning images in the relevant categories together. However, due to their different goals on recognition accuracy and retrieval scalability, it is hard to efficiently incorporate most image classification works into large-scale image search. To study this problem, we propose cascade category-aware visual search, which utilizes weak category clue to achieve better retrieval accuracy, efficiency, and memory consumption. To capture the category and visual clues of an image, we first learn category-visual words, which are discriminative and repeatable local features labeled with categories. By identifying category-visual words in database images, we are able to discard noisy local features and extract image visual and category clues, which are hence recorded in a hierarchical index structure. Our retrieval system narrows down the search space by: 1) filtering the noisy local features in query; 2) rejecting irrelevant categories in database; and 3) preforming discriminative visual search in relevant categories. The proposed algorithm is tested on object search, landmark search, and large-scale similar image search on the large-scale LSVRC10 data set. Although the category clue introduced is weak, our algorithm still shows substantial advantages in retrieval accuracy, efficiency, and memory consumption than the state-of-the-art.
SORT: Second-Order Response Transform for Visual Recognition

In this paper, we reveal the importance and benefits of introducing second-order operations into deep neural networks. We propose a novel approach named Second-Order Response Transform (SORT), which appends element-wise product transform to the linear sum of a two-branch network module. A direct advantage of SORT is to facilitate cross-branch response propagation, so that each branch can update its weights based on the current status of the other branch. Moreover, SORT augments the family of transform operations and increases the nonlinearity of the network, making it possible to learn flexible functions to fit the complicated distribution of feature space. SORT can be applied to a wide range of network architectures, including a branched variant of a chain-styled network and a residual network, with very light-weighted modifications. We observe consistent accuracy gain on both small (CIFAR10, CIFAR100 and SVHN) and big (ILSVRC2012) datasets. In addition, SORT is very efficient, as the extra computation overhead is less than 5%.
SBR-PO/PTD method for backward scattering of airplane model and application to ISAR image

In this paper, shooting and bouncing ray - physical optics / physical theory of diffraction (SBR-PO/PTD) hybrid method is studied to investigate the backward scattering from three-dimensional (3D) electrically large target. Shadowing effects, multiple bounce, and diffraction by edges are considered in this scattering model. Time-consuming is greatly reduces compared with the numerical methods such as MoM at high frequencies. The accuracy and performance of this method are validated and evaluated by comparing with the FEKO-MLFMM for electrically small targets. What's more, this method is used to generate inverse synthetic aperture radar (ISAR) raw data for imaging applications. The process from raw RCS data to ISAR image is explained in detail. Finally, ISAR imaging of an electrically large airplane model is provided.
Improving scene classification with weakly spatial symmetry information

The bag-of-visual-words (BOW) model has been widely used in the field of scene classification. Since it ignores the spatial information, the spatial-pyramid-matching (SPM) model  was presented by partitioning the image into increasingly fine blocks and computing histograms of local features in each block. However, the spatial symmetry has never been considered explicitly in scene classification as we known. In this paper, a novel descriptor named weakly spatial symmetry (WSS) is proposed to boost the performance of image classification. After region segmentation, the spatial symmetry is represented by L1 distances of region histograms. Four kinds of spatial symmetry are extracted in blocks of increasing scales as in SPM . The WSS descriptor can be used independently or combined with BOW or SPM for scene classification. Experiments on scene-15 and caltech 101 dataset demonstrate the effectiveness of the proposed approach.
Max-SIFT: Flipping invariant descriptors for Web logo search

Logo search is widely required in many real-world applications. As a special case of near-duplicate images, logo pictures have some particular properties, for instance, suffering from flipping operations, e.g., geometry-inverted and brightness-inverted operations. Such operations completely change the spatial structure of local descriptors, such as SIFT, so that image search algorithms based on Bag-of-Visual-Words (BoVW) often fail to retrieve the flipped logos. We propose a novel descriptor named Max-SIFT, which finds the maximal SIFT value sequence for detecting flipping operations. Compared with previous algorithms, our algorithm is extremely easy to implement yet very efficient to carry out. We evaluate the improved descriptor on a large-scale Web logo search dataset, and demonstrate that our method enjoys good performance and low computational costs.
Noise-Linear Frequency Modulation Shared Waveform for Integrated Radar and Jammer System

This paper presents an integration of radar and Electronic Warfare (EW) utilizing Noise-Linear Frequency Modulation (NLFM) waveform, which is capable of simultaneous operation of radar and EW function for target detection and jamming against the Linear Frequency Modulation (LFM) Pulse Compression (PC) radar. The basic properties, detection and jamming capability of the NLFM waveform are investigated. Analysis indicates that the NLFM waveform has better range resolution and sidelobe suppression than LFM PC and random signal waveform, and that it has higher jamming efficiency against LFM PC radar than normal noise jamming waveform.
A novel energy balanced variable frequency control for input-series-output-parallel modular EV fast charging stations

At present time, the most common electrical vehicle (EV) chargers employ a two-stage design, i.e., a front-end AC/DC stage + an isolated DC/DC converter. In this paper, an isolated dual-active-bridge (DAB) based single-stage AC/DC converter was proposed, which has the power-factor-correction (PFC) and zero-voltage-switching (ZVS) functions over the full-load range. By reducing one power stage and eliminating the large DC link capacitor, a high efficiency and high power density are achieved. Such topology can be used as a modular building block to scale up to 50kW by serial connecting the input terminals and paralleling output terminals. A novel energy-balanced variable switching frequency control for such input-series-output-parallel (ISPO) modular designed is proposed. A single-phase d-q transformation is implemented to achieve zero steady-state error. Simulation analysis and experimental validation are presented.
Scene alignment by SIFT flow for video summarization

Video summarization is an efficient and flexible way to represent video data. In this paper, we use the Kernel PCA and clustering based key frame extraction to realize multilevel video representation. In order to remove the redundancy caused by large scene changes, SIFT flow scene alignment is performed on the clustering set of key frames. After alignment, one representative frame is chosen from the reconstructed cluster set on matched frame pairs. We explore the difference on data structures between frame level and scene level, and modify the FCM method on the cluster number initialization for video summarization. Experimental results are presented to verify the efficiency of our approach.
Design of controllable diode PL

A design of controllable power limiter (CPL) using parallel reverse polarities Schotty diode is introduced. The main characteristic of the proposed PL are controllable, be difference with traditional PLs, the turning point is controllable and depend on the applied bias voltages on the diodes. For verification, a CPL using symmetry structure, with two controllable bias voltages, operating range from 1.9 to 2.7 GHz is fabricated. The measured results proved the feasibility of design, with <3 dB small signal insertion loss during the whole bandwidth the PL can limit output power at the level under 12 dBm while the large input power ranges over 30 dBm.
A New Study on Distance Metrics as Similarity Measurement

Distance metric is widely used in similarity estimation. In this paper we find that the most popular Euclidean and Manhattan distance may not be suitable for all data distributions. A general guideline to establish the relation between a distribution model and its corresponding similarity estimation is proposed. Based on maximum likelihood theory, we propose new distance metrics, such as harmonic distance and geometric distance. Because the feature elements may be from heterogeneous sources and usually have different influence on similarity estimation, it is inappropriate to model the distribution as isotropic. We propose a novel boosted distance metric that not only finds the best distance metric that fits the distribution of the underlying elements but also selects the most important feature elements with respect to similarity. The boosted distance metric is tested on fifteen benchmark data sets from the UCI repository and two image retrieval applications. In all the experiments, robust results are obtained based on the proposed methods
Design of Broadband Power Amplifiers Based on Resistive-Reactive Series of Continuous Modes

In this letter, the bandwidth of the series of continuous modes (SCMs) power amplifiers (PAs) is extended. The SCMs are composed of a series of continuous working modes between Class-B/J and Class-F continuums. The second harmonic load of the series of continuums is purely reactive. By introducing resistive part to the purely reactive second harmonic load of the series of continuous modes, the operation bandwidth of power amplifiers is expanded. A broadband PA working across 0.8-3.05 GHz (117% bandwidth) is simulated and fabricated. Experimental results show that the drain efficiencies in interesting band are 57.4%-79%. The fabricated PA can deliver output power of 10.1-20.7 W.
Control Under Stochastic Multiplicative Uncertainties: Part I, Fundamental Conditions of Stabilizability

In this two-part paper we study stabilization and optimal control of linear time-invariant systems with stochastic multiplicative uncertainties. We consider structured multiplicative perturbations, which, unlike in robust control theory, consist of static, zero-mean stochastic processes, and we assess the stability and performance of such systems using mean-square measures. While Part 2 of this paper tackles and solves optimal control problems under the mean-square criterion, Part 1 is devoted to the stabilizability problem. We develop fundamental conditions of mean-square stabilizability which ensure that an open-loop unstable system can be stabilized by output feedback in the mean-square sense. For single-input single-output systems, a general, explicit stabilizability condition is obtained. This condition, both necessary and sufficient, provides a fundamental limit imposed by the system's unstable poles, nonminimum phase zeros and time delay. For multi-input multi-output systems, we provide a complete, computationally efficient solution for minimum phase systems possibly containing time delays, in the form of a generalized eigenvalue problem readily solvable by means of linear matrix inequality optimization. Limiting cases and nonminimum phase plants are analyzed in depth for conceptual insights, revealing, among other things, how the directions of unstable poles and nonminimum phase zeros may affect mean-square stabilizability in MIMO systems. Other than their independent interest, stochastic multiplicative uncertainties have found utilities in modeling networked control systems pertaining to, e.g., packet drops, network delays, and fading. Our results herein lend solutions applicable to networked control problems addressing these issues.
A novel ball detection framework for real soccer video

Despite a lot of research efforts in sports video analysis, soccer video indexing remains a challenging task due to the lack of structure in a soccer game that could help in structure analysis. In particular, little work was done in detecting and tracking the ball whose trajectory could play a crucial role for detecting key events. We propose a novel framework for accurately detecting the ball for broadcast soccer video. Our framework combines both direct and indirect insights to identify the ball rather than conventional simple template matching methods. It has three key components. First we infer the ball size range from the player size. Next non-ball objects are removed to reduce the possible ball candidates. Last but not least, a Kalman filer-based procedure mines candidate trajectories in candidate feature images. Then, a procedure selects the reliable ball trajectories from them. The experimental results on two 1000-frame sequences confirm that the proposed framework is very effective and obtain a better result than existing methods.
Fast traumatic brain injury CT slice indexing via anatomical feature classification

Computed tomography (CT) is used widely in traumatic brain injury diagnosis. One axial brain CT scan consists of multiple slices with different heights along the brain axial direction. Indexing of brain CT slices is to order the slices and align each individual slice onto the corresponding brain axial height, which is an important step in content-based image retrieval and computer-assisted diagnosis. Current existing methods for this indexing task are through the image registration techniques by registering 2D image slices onto a 3D brain atlas. In this paper, instead of using the registration methods, we propose a fast indexing method using anatomical feature classification. In our method, the brain CT scan is divided into 6 height levels along the axial direction so that slices in each level share similar anatomical structure. In this way, the indexing problem becomes a classification problem that one series of scan slices are to be classified into 6 classes. Experimental results show that the proposed method is effective and efficient.