nils-herrmann/clean_scirepeval huggingface dataset:
Downlink power allocation in distributed satellite system based on dynamic multi-objective optimization

Distributed satellite system (DSS) is a novel concept in satellite communication field. The system provides a flexible space scenario for multi-service network with guaranteed quality of service (QoS). To make different kinds of satellites to accomplish complex mission collaboratively, the resource must be properly allocated among different satellites with different requests. This paper investigates the downlink power allocation problem in DSS. Considering the high dynamics and the multi-constrains of the system, a dynamic multi-objective approach called constellation-level power allocation based on the immune clonal algorithm for dynamic multi-objective optimization (ICADMO) is proposed. The comparison and analysis of the performance between the proposed approach and other algorithms are carried out through the simulation results.
Design and Implement of a Novel OVAL-Compatible Vulnerability Assessment System

Due to the various kinds of information expressing and transferring standards made by different vendors, the existing security products can not interoperate with each other efficiently. In order to solve this problem, an OVAL-compatible multi- platform vulnerability assessment system (VAS) is proposed in this paper. First, it adopts OVAL as the vulnerability assessment standard. Second, it takes lightweight web server as foundational distributed computing platform. Third, it adopts the manger/agent architecture to improve assessment efficiency. Considering the fact that a computer network usually includes various kinds of operating systems, two versions of agents for Windows and Linux is implemented. So it supports multi- platform vulnerability assessment and security data sharing with other OVAL-compatible tools with high accuracy, assessing completeness and functional expansibility.
A millimeter wave dual output inter-pulse frequency agile synthesizer

A millimeter wave dual output inter-pulse frequency agile synthesizer based on the DDS chips is designed, and the overall scheme is presented. The DDS spurious origin is discussed, and the DDS selection consideration and its parameter analysis are also given. The synthesizer achieves phase noise better than -90dBc/Hz @1kHz and spurious better than -60dBc while the interpulse frequency agility is functioning and the coherence of the dual output is maintained. Finally a coherent verification scheme is described and the result is presented. The result shows that the coherence of the dual output of the synthesizer has been implemented.
Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey

Object detection, including objectness detection (OD), salient object detection (SOD), and category-specific object detection (COD), is one of the most fundamental yet challenging problems in the computer vision community. Over the last several decades, great efforts have been made by researchers to tackle this problem, due to its broad range of applications for other computer vision tasks such as activity or event recognition, content-based image retrieval and scene understanding, etc. While numerous methods have been presented in recent years, a comprehensive review for the proposed high-quality object detection techniques, especially for those based on advanced deep-learning techniques, is still lacking. To this end, this article delves into the recent progress in this research field, including 1) definitions, motivations, and tasks of each subdirection; 2) modern techniques and essential research trends; 3) benchmark data sets and evaluation metrics; and 4) comparisons and analysis of the experimental results. More importantly, we will reveal the underlying relationship among OD, SOD, and COD and discuss in detail some open questions as well as point out several unsolved challenges and promising future works.
Design and optimisation of compact hybrid quadrifilar helical-spiral antenna in GPS applications using Genetic Algorithm

A novel circularly polarized antenna, called as “hybrid quadrifilar helical-spiral antenna” (HQHSA) is proposed in this paper. A prototype of the HQHSA, operated at 1.575 GHz and intended for applications in Global Navigation Satellite Systems (GNSS), is designed using Genetic Algorithms (GA). The antenna is placed on an infinite ground plane and optimized by using the GA. Performance of the GA optimsied antenna is compared with that of the typical quadrifilar helical antenna (QHA) and quadrifilar spiral antenna (QSA). The advantages of this new design are discussed. Performance of the optimised antenna design is validated and analysed by using commercial simulators in terms of impedance match, axial ratio and gain. It is shown that the proposed HQHSA antenna can achieve good performance as a circularly polarized antenna for GNSS applications. The design also shows the capabilities of GA as an efficient optimisation tool for selecting globally optimal parameters to be used in simulations with an electromagnetic antenna design code, seeking convergence to specific design specifications.
A Cell Reconfiguration Method Based on Simulated Annealing Algorithm

Agile manufacturing cells can be considered as the logic combination of physical resources in the shop floor. Compared with fixed cells, they are more competent to adapt rapidly to changes in production environment. To fulfill the idea of agile manufacturing cell in shop floor production management, the cell reconfiguration problem should be settled firstly. According to general principles on cell reconfiguration, two indexes are presented, and the object function is also built. Simulated annealing algorithm is used to solve this optimization problem. It assures certain solution while the matrix method may not and the solution is not confined greatly by initial conditions compared with other methods.
An adaptive fast intra mode decision in HEVC

Compared with H.264/AVC, the emerging video coding standard, High Efficiency Video Coding (HEVC), introduces a lots of intra prediction modes in intra coding, which brings a real challenge in practice. Although the Rough Mode Decision (RMD) scheme in HEVC provides a fast mode decision, it is a fixed strategy for different blocks with the same size and does not consider their different characteristics. In this paper, we analyze the relation between a block's texture characteristics and its best coding mode. Based on the analysis, we further develop an adaptive strategy for fast mode decision in intra coding for HEVC. Experimental results show significant computational saving in All-Intra High Efficiency and All-Intra Low Complexity test conditions compared to the default HM-4.0 with a marginal BD-rate increase.
Automatic Allophone Deriving for Korean Speech Recognition

In Korean, the pronunciations of phonemes are severely affected by their contexts. Thus, using phonemes directly translated from their written forms as basic units for acoustic modeling is problematic, as these units lack the ability to capture the complex pronunciation variations occurred in continuous speech. Allophone, a sub-phone unit in phonetics but served as independent phoneme in speech recognition, is considered to have the ability to describe complex pronunciation variations. This paper presents a novel approach called Automatic Allophone Deriving (AAD). In this approach, statistics from Gaussian Mixture Models are used to create measurements for allophone candidates, and decision trees are used to derive allophones. Question set used by the decision tree is also generated automatically, since we assumed no linguistic knowledge would be used in this approach. This paper also adopts long-time features over conventional cepstral features to capture acoustic information over several hundred milliseconds for AAD, as co-articulation effects are unlikely to be limited to a single phoneme. Experiment shows that AAD outperforms previous approaches which derive allophones from linguistic knowledge. Additional experiments use long-time features directly in acoustic modeling. The results show that performance improvement achieved by using the same allophones can be significantly improved by using long-time features, compared with corresponding baselines.
A novel restoration algorithm of the turbulence degraded images based on maximum likelihood estimation

The point spread function (PSF) of the turbulence degrading system, as well as the variance of the observation noise and the model of the original image, are unknown a priori in practical imaging processes when the aero-optic effect exists. Both of the PSF and the variance of the observation noise have to be identified from the turbulence degraded images before restoring them. An approach of identification and restoration for the turbulence degraded images based on the parameter estimation of the autoregressive moving average (ARMA) model are proposed. The turbulence degraded image is expressed as an autoregressive moving average process. In the algorithm proposed in this paper, the maximum likelihood (ML) approach is used to the identification of the ARMA parameters. The expectation maximization (EM) algorithm is employed to optimize the nonlinear likelihood function in an efficient way. This identification and restoration algorithm is used to the wind tunnel experimenting images, and the experimental results show that the restoration effect is improved obviously. The estimated version of the original image, and the PSF of the degrade process are simultaneously obtained from the degraded image obtained via the wind tunnel experiment in the process of the ARMA parameters being identified.
Fisher+Kernel criterion for discriminant analysis

We simultaneously approach two tasks of nonlinear discriminant analysis and kernel selection problem by proposing a unified criterion, Fisher+Kernel criterion. In addition, an efficient procedure is derived to optimize this new criterion in an iterative manner. More specifically, original input vector is first transformed into a higher dimensional feature matrix through a battery of nonlinear mappings involved in different kernels. Then, based on the feature matrices, FKC is presented within two coupled projection spaces: one projection space is used to search for the optimal combinations of kernels; while the other encodes the optimal nonlinear discriminating projection directions. Our proposed method is a unified framework for both kernel selection and nonlinear discriminant analysis. Besides, the algorithm potentially alleviates overfitting problem existing in traditional KDA and has no singularity problems in most cases. The effectiveness of our proposed algorithm is validated by extensive face recognition experiments on several datasets.
Impact of Trench Dimensions on the Device Performance of GaN Vertical Trench MOSFETs

In this letter, we have examined the impact of trench dimensions on the breakdown voltage and ON-resistance of trench MOSFETs fabricated on sapphire and bulk GaN substrates. Contrary to simulation studies, the breakdown voltage decreased with an increase in trench dimensions in devices fabricated on sapphire substrates. However, such breakdown voltage dependence with trench dimensions was not observed in devices fabricated on bulk GaN substrates of the same area. The observed trend on GaN on sapphire devices was associated with the equivalently reduced number of dislocations per device area. These results give an insight into how dislocations could affect breakdown voltage in power MOSFETs.
A survey of multi-domain unified modeling of complex product and optimization design techniques

Multi-domain unified modeling of complex product and optimization design technology is one integration description methodology, which can describe how to construct the unified modeling of complex product and carry out the synthetical optimization based on various fields. To achieve the seamless integration of unified modeling of complex product and optimization design and realize the system-level optimization of complex product , the research status at home and abroad about multi-domain unified modeling of complex product and optimization design techniques were summarized, and the main technical issues of multi-domain unified modeling of complex product and optimization design were raised. Then the overall architecture of multi-domain unified modeling and optimization design platform on complex product was presented, and the system architecture of multi-domain optimization design platform was provided. Finally, the key technologies and research prospects of multi-domain unified modeling of complex product and optimization design techniques were also discussed.
FPGA On-Board Computer design based on hierarchical fault tolerance

Safety is a crucial requirement of On-Board Computer (OBC) design of a satellite, especially for the new type OBC--takes FPGA as central processor. Upon that this paper proposes a plan of FPGA OBC design and adds hierarchical fault tolerant concept to enhance the reliability of the OBC system. The fault tolerant architecture can be divided into three hierarchic ranks, containing single-CPU reconfiguration, component unit transfer and dual-CPU subrogation. One of the above fault manage mode will be chosen to deal with problems according to error situation in-orbit. In the worst cases, all three modes may be used. The last part of the paper gives the functional verification approach under development for the hierarchical fault tolerant OBC design.
Broadband circularly polarized patch antenna for small satellites applications

A novel broadband circularly polarized annular patch antenna is presented in this paper. The antenna consists of two annular ring patches which serve as an excitation patch and a parasitic patch, respectively. To produce circular polarization, small perturbations in the shape of triangles are loaded on the patches. To validate the concept, a prototype antenna at S band is designed and fabricated. The measured results confirm the antenna is able to achieve good impedance match and low axial ratio values over a wide range of frequencies. The simulated results agree well with measured results, which show an impedance bandwidth (S11<;-10dB) of 19.5%, and an axial ratio bandwidth (AR<;3dB) of 10.9%. The antenna has a compact size and low cost, thus useful for small satellite data downlink and Telemetry Tracking and Command (TTC) applications.
50-nm Metamorphic High-Electron-Mobility Transistors With High Gain and High Breakdown Voltages

We report the design, fabrication, and characterization of ultrahigh-gain metamorphic high-electron-mobility transistors (MHEMTs) with significantly enhanced breakdown performance. In this letter, an asymmetrically recessed 50-nm Gamma-gate process has been successfully applied to epitaxial designs with double-sided-doped InAs-layer-inserted channels grown on GaAs substrates. The critical gate recess width has been optimized for device performance, including transconductance, breakdown voltage, and gain. The employment of a device passivation process greatly minimizes the adverse impacts that the aggressive vertical and lateral scaling would have introduced for pursuing enhanced performance. As a result, we have achieved 1.9-S/mm transconductance and 800-mA/mm maximum drain current at a drain bias of 1 V, 9-V off-state breakdown voltage, approximately 3.5-V on-state breakdown voltage, and 14.2-dB maximum stable gain at 110 GHz. To our knowledge, this is a record combination of gain and breakdown performance reported for microwave and millimeter-wave HEMTs, making these devices excellent candidates for ultrahigh-frequency power applications.
Identification of Masses and Microcalcifications in the Mammograms Based on Three Neural Networks: Comparison and Discussion

The incidence of breast cancer has been quickly raised in China recently. Mammography is regarded as the most reliable detection method of breast tumor and with the increase of cases, computer-aided diagnosis/detection (CAD) has been widely studied and applied to assist the radiologists. In the previous research, we had studied the detection algorithm of the microcalcifications (MCs) and the masses. And the identification on whether a focus is positive or negative is presented in this paper. In order to choose an effective classifier, three neural networks are used to identify the focuses respectively, and their performance is compared and then discussed. The experiments show that CMAC has very different performance for the training set and the testing set., ANFIS only performs well when the dimensionality of the features of the samples is comparatively low, while MLP is the most stable classifier, especially when the dimensionality of the features increases and the number of the samples is limited. And the final classification precision of the focuses is: for 120 mammograms of 60 cases, the true positive rate of the masses and the MCs are respectively 93.6% and 96.9%, and the false positive per image of them are 0.63 and 0.2.
Finite element analysis and vibration control of the Substation Equipment Water Washing Robot with Hot-line

The insulator strings in substation which are long time exposed in the outdoor environment are easily accumulation industrial and natural filth. So they need to be cleaned regularly to prevent the power outages and other incidents because of flashover phenomenon which occurs in the weather of rain and fog and other inclement weather. The substation equipment water washing robot with hot-line is employed in the substation to clean the insulator strings with the same method of artificial flushing. The theory of finite element analysis and modal analysis are based in this paper. The reliability and precision of robot's structural in the process of washing operation are ensured by stress distribution and deformation from the static analysis; The low order natural frequencies and mode shapes are carried out through modal analysis and a vibration control and optimization scheme is proposed to reduce the effects of vibration in the condition of the flushing operation, and the robot's stability can be improved.
A novel complementary and modular tubular permanent magnet flux-switching motor

A complementary and modular tubular flux-switching permanent magnet (CMTFSPM) machine is proposed and investigated, which is constituted of several modular units. Finite element analysis (FEA) is used to investigate and compared the flux linkage, back-EMFs, detent force, self and mutual inductances and thrust performance with the existing tubular permanent magnet motor. The results reveal that the proposed CMTFSPM machine can offer symmetrical back-EMF waveforms, smaller cogging force, lower force ripple, and higher magnet utilization factor than the existing one.
Energy efficient power allocation and relay location for asymmetric bi-directional relaying

This paper is concerned with energy efficient bidirectional relaying subject to a target quality of service constraint with a special focus on asymmetric system traffics. First, by exploiting the long-term channel knowledge, the optimal power allocation problem with a probabilistic constraint on the system outage probability is solved, resulting in a novel power allocation technique. Then, the combination of the proposed power allocation technique with relay location is investigated to further reduce the system power consumption, leading to a joint power allocation and relay location algorithm. Simulation results show that our proposed algorithm can significantly reduce the system power consumption, while ensuring the required quality of service of the system.
A novel on-line auto-tuning PI controller for the superheat of evaporator with electronic expansion valve

A novel on-line auto-tuning PI control algorithm for the superheat of the evaporator is proposed in this paper. A first-order plus dead-time model is firstly employed to describe the system process, and the model parameters are obtained from the robust identification algorithm using the on-line experimental data. Then, a PI controller, whose parameters are tuned by the Ziegler-Nichols method, is designed to control the superheat at the outlet of evaporator by adjusting the opening of the electronic expansion valve. The MATLAB simulation results show the superheat can well track the change of its setting point through the effective control of the electronic expansion valve. The wide application of this controller will greatly increase the heat transfer efficiency of the evaporator.
Mechanism analysis and modeling research of novel hybrid excitation guiding system

The structure of a novel hybrid excitation non-contact and low power consumption guiding system put forward and its operating principle is analyzed. The theory of equivalent magnetic circuit is used for deducing horizontal force. The relation of force, current, and displacement is got. The effect of the magnetic length of PM(permanent magnet) on the magnetic force is analyzed. The design result is carried out by using the finite element methods. A summary of the relationship between the length of PM, electromagnetic potential, displacement and horizontal force is given. Simulation results show that the hybrid excitation guiding system is reasonable.
Adaptive model following speed control method of permanent magnet synchronous motor

Permanent magnet synchronous motor (PMSM) servo system requires a high dynamic performance of speed. The control object of speed loop, which can be described using a two-order transfer function, is the PMSM regulated by current loop. PI controller is generally used in speed control, in which the performance improvement is limited by the strong coupling and nonlinearity of the system. In this paper, an adaptive model following speed controller (AMFC) with state feedback control and model following methods is designed based on the Popov hyperstability theory and modeled in the MATLAB. The simulation results show that, compared with PI control, AMFC raises the speed loop dynamic performance of PMSM by compensating the influence of coupling and nonlinearity, so it is more suitable for servo system.
Fractal scale descriptors based on 3d wavelet moments for 3d objects

In this paper, based on 3d wavelet moments we present a new method called fractal scale descriptors for 3d objects. Just like wavelet moments, they are still robust to translation, rotation and scale, and have the multi-resolution features in the radial direction, which can handle noise to some extent and provide multi-level features to satisfy various requirements. Furthermore the new method is prior to the original 3d wavelet moments in computational complexity by using the fast algorithm of the spherical harmonics together with the Mallat algorithm of the wavelets.
Aero-engine performance optimization based on whale optimization algorithm

Under the goal of pursuing safety, aero-engine is optimized by using an optimization algorithm to improve its performance and the comprehensive performance of the airplane. A nonlinear programming model is established to optimize the acceleration progress of a certain turbofan engine, where the rotor speed is involved in the objective function. Then, a novel nature-inspired optimization algorithm, called Whale Optimization Algorithm (WOA), which is used to solve the optimization problem of the aero-engine. Simulation results show that the WOA has a strong border search capacity and the engine's acceleration capability has been improved.