nils-herrmann/clean_scirepeval huggingface dataset:
Virtual array based APC for a tri-reflector CATR

Usually, the stray signals from antenna test environment and compact antenna test range (CATR) system may cause errors in the measured patterns. In order to improve the measurement accuracy of a specific antenna test system, the techniques about antenna pattern correction for mitigating the effects of these stray signals are required. In this paper, a two-unit virtual array method and its extended three-unit version have been used to correct the measured antenna pattern for an existing tri-reflector CATR system, and some simulated results are presented. It is shown that the method performs well in compensating the measurement errors caused by the quiet zone distortions, also the deviations that come from a specific interference source. Considering the sampling accuracy of the actual antenna test system, the effects of different sampling intervals also have been discussed.
Study of MEMS-based high-resistance silicon K-band planar array microstrip antenna

In order to manufacture antenna integrated with RF-MEMS switch on the same substrate, a silicon wafer was used as substrate. As the high dielectric constant of silicon (∊r=11.9) degrades an antenna efficiency and excites surface waves especially at high frequency, removing substrate under the antenna and giving birth to an air cavity is a general way to reduce the dielectric constant of silicon. In this paper, a planar array antenna of 2 elements is designed according to the research of K band antenna of air communication equipments based on MEMS technology, and its performance meets the demands, which provides an effective method for designing and manufacturing of Ku band and X band antenna. The microstrip array antenna is designed with the element and simulated in HFSS. The simulation result matches well with the demand.
Research on relationship between power grid operation modes and short-circuit capacity distribution

Different power grid operation mode corresponds with different short-circuit capacity distribution, while different distribution of short-circuit (current) capacity is contradictory to power grid operation performance and equipment tolerance influence consequence. This paper establishes functional relationship between short circuit (current) capacity of power grid and effective switch set and feasible power grid operation mode on this basis, deploys analysis on relationship between short circuit (current) capacity of power grid and feasible power grid operation mode on the basis that an equivalent topology analysis model is built, in order to take equipment tolerance into consideration in a compromised way during making of decisions concerning with power grid operation mode and extend power grid operation scheduling.
Recognition of architectural distortion in mammographic images with transfer learning

Computer-aided diagnosis (CAD) technology can improve the detection of abnormal. Such as calcifications, masses, and architectural distortion. Among the three abnormals, architectural distortion is the most difficult one to detect for both radiologists and CAD systems. In this article, we use automatic architectural distortion detection method to locate initial suspicious areas. Then, combine the transfer learning to detect architectural distortion, the reason we use transfer learning is the number of samples of architectural distortion in mini-MIAS database and Digital Database for Screening Mammography (DDSM) is small, and the number of malignant mass is much larger. The malignant mass and the architectural distortion are similar. Our objective is by transferring malignant mass information to improve the recognition rate of AD in the case of only a small amount of AD training samples.
Notice of RetractionPractice on Case-Driven Teaching Mode for the Computer Programming Course Reference CDIO Philosophy

To the shortcomings of traditional teaching mode in the computer programming course, a case-driven teaching mode was proposed. This mode used the CDIO philosophy as the reference. It guided the student to study and think the theory in the teaching process through the actual project cases. This teaching mode has achieved a good effect in the programming course of our school. The result indicates that this teaching mode can enhance the teaching quality and student's project practice ability.
On Continuous User Authentication via Typing Behavior

We hypothesize that an individual computer user has a unique and consistent habitual pattern of hand movements, independent of the text, while typing on a keyboard. As a result, this paper proposes a novel biometric modality named typing behavior (TB) for continuous user authentication. Given a webcam pointing toward a keyboard, we develop real-time computer vision algorithms to automatically extract hand movement patterns from the video stream. Unlike the typical continuous biometrics, such as keystroke dynamics (KD), TB provides a reliable authentication with a short delay, while avoiding explicit key-logging. We collect a video database where 63 unique subjects type static text and free text for multiple sessions. For one typing video, the hands are segmented in each frame and a unique descriptor is extracted based on the shape and position of hands, as well as their temporal dynamics in the video sequence. We propose a novel approach, named bag of multi-dimensional phrases, to match the cross-feature and cross-temporal pattern between a gallery sequence and probe sequence. The experimental results demonstrate a superior performance of TB when compared with KD, which, together with our ultrareal-time demo system, warrant further investigation of this novel vision application and biometric modality.
Research of Reliability for Measuring High-g Micro Array Acceleration Sensor

High-g is the acceleration value, larger than 105 g. For the requirements of detecting high g, people pay more and more attention to the reliability of an accelerometer. In this paper, the design and manufacture of high-g micro array acceleration sensor is researched to improve the reliability of the accelerometer. Owing to the superiority of MEMS manufacturing, in one processing, multiply micro-acceleration sensors with the same function can be integrated in one chip. According to the method of the array layout of multiple micro-acceleration sensors on a two-dimensional plane, the layout is ascertained by using multiple non-linear regression and orthogonal experimental design. Multiple micro-acceleration sensors lay reasonably to improve the reliability of the high-g micro array acceleration sensor. On the other hand, the linked line, which is used to transfer the signal from chip to the pin of the shell, becomes the weakness to resist the destructive impact. To the above problem, this paper focuses on thermosonic bonding and the analysis of the welding parameters such as temperature, time and pressure. With orthogonal experimental design, a number of experiments have been conducted to obtain the optimal welding parameters of pressure, temperature and time. The linked line of sensor system, which is manufactured according to these parameters, remains intact by the impact of 1.22 x 105 g. Owing to optimized design and manufacture, the reliability of the acceleration is increased to 0.9906, which meets the requirements of the application.
RRM-B: Reliable retransmission mechanism for wireless sensor networks

Wireless sensor network is a multi-hop self-organization formed by a lot of energy-limited sensor nodes. It is widely used in security surveillance, intelligent transportation, smart home, environmental monitoring and so on. The mechanism of data is the important part of the wireless sensor network. However, due to the limit sensor energy and poor channel quality, The reliability of the transmission cannot meet the requirements, so how to ensure the reliable data to the destination node is key point of the wireless sensor network. Data retransmission can improve the reliability of transmission for Wireless sensor network. For this, we introduce a reliability routing protocol based on information calculate algorithm model. The simulation results show that the proposed algorithm is energy saving, not only reducing the packet drop rate over networks, but also improving effectively the network transport fairness and the average network latency.
VIIRS ocean color research and applications

In this paper, we provide evaluations and assessments of the Visible Infrared Imaging Radiometer Suite (VIIRS) ocean color products, including normalized water-leaving radiance spectra nLw(λ) at VIIRS five spectral bands, chlorophyll-a concentration (Chl-a), water diffuse attenuation coefficients at the wavelength of 490 nm, Kd(490), and at the domain of photosynthetically available radiation (PAR), Kd(PAR). Specifically, VIIRS ocean color products derived from the NOAA Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system are evaluated and compared with in situ data from the Marine Optical Buoy (MOBY) and measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS). In general, VIIRS ocean color products are matched well with MOBY in situ measurements, and are also consistent with those from MODIS-Aqua. Ocean color products were found to be highly sensitive to some operational sensor calibration issues. We have improved sensor calibration by combining the lunar calibration into the current calibration method. Here, the ocean color products based on the new sensor calibration are evaluated. Our results show that VIIRS is capable of providing high-quality global ocean color products in support of the scientific research and operational applications.
Study on Novel Rotary-Gas Nozzle for HV SF6 Circuit Breaker Based on CFD Simulation

Blowing gas method directly affects the interrupting capability in arc-quenching chamber of puffer SF 6 circuit breaker. Aiming at the proposed novel Rotary-Gas nozzle structure of the arc-quenching chamber, finite volume method has been applied to analyze the 3D flow field. Computational fluid dynamic (CFD) simulation for the interrupting process has been performed. The simulation provides the comparison of the distribution of velocity, pressure and density for different structures in flow field. By establishing the Rotary-Gas grooves in the traditional nozzle upstream, the advantage of blowing arc rotation has been analyzed. Hence, by changing the angle and numbers of the Rotary-Gas grooves, the rational nozzle structure has been obtained.
Defect modes of one-dimensional graphene based photonic crystals

Photonic crystal is a kind of functional band gap material with periodical units and arrays. Due to its unique electromagnetic properties, such as photonic band gap and photonic localization, photonic crystal has become one of key research area across the world. Recently, it is observed that the multilayer structure of a stack of monolayer graphene sheets separated by dielectric slabs at low-terahertz frequencies possesses band-gap properties and supports a series of bandpass and band-stop regions. In this paper, considering a one-dimensional photonic crystal formed by a stack of graphene sheets and dielectric slabs, we introduce a single and dual layers defects by replacing graphene layer by another dielectric material. By using the transfer matrix method, the tunable filtering properties are discussed both for the graphene photonic crystal with a single defect or dual defects. It is found that a very narrow and two narrow transmission peaks appear for the single-layer defect or the dual-layer defect structures, respectively. Furthermore, influence of parameters, and the voltage on the defect modes are discussed. Figure 1 shows our the graphene photonic crystal with a single-layer defect and dual-layer defect. Figure 2 shows the transmission curves of the photonic crystals with no defect, single-layer defect and dual-layer defect. We can see that there are two gaps at the center frequencies of 0.6, 0.7 and 0.8 THz, respectively. For a single-layer defect, a narrow filter appears among the three gaps, and two narrow filters will appear to form dual-band narrow filters for the dual-layer defect. In the further study, we also find that all the narrow filters have a red shift with decreasing of external voltages. These properties of the defect structure would provide potential applications in tunable multi-band filters.
Evolutionary Computing Optimization for Parameter Determination and Feature Selection of Support Vector Machines

Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure, along with the feature selection, significantly influences the classification accuracy. This study simultaneously determines the parameter values while discovering a subset of features, increasing SVM classification accuracy. The study focuses two evolutionary computing approaches to optimize the parameters of SVM: particle swarm optimization (PSO) and genetic algorithm (GA). And we combine the two evolutionary methods with SVM to choose appropriate subset features and SVM parameters, experimental results demonstrate that the classification accuracy surpass traditional grid searching approach. Also the paper compares PSO with GA method based SVM classification and they have similar results. Keywords-support vector machine (SVM); genetic algorithm (GA); particle swarm optimization (PSO) ;optimization.
Investigation on insulation performance for medium voltage vacuum switch cabinet

In this paper, based on the concrete configuration of the medium voltage vacuum switch cabinet, the 3D mathematical model of electric field has been established for computing and analyzing the insulation performance. And the distributions of electric field at different locations and displacement angles have been obtained using finite element method (FEM). The locations of the maximum electric field strength and the weak insulation have been found. Moreover, by taking some improvement measurement has successfully solved the practical insulation problems.
Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition.

This paper explores multi-task learning (MTL) for face recognition. First, we propose a multi-task convolutional neural network (CNN) for face recognition, where identity classification is the main task and pose, illumination, and expression (PIE) estimations are the side tasks. Second, we develop a dynamic-weighting scheme to automatically assign the loss weights to each side task, which solves the crucial problem of balancing between different tasks in MTL. Third, we propose a pose-directed multi-task CNN by grouping different poses to learn pose-specific identity features, simultaneously across all poses in a joint framework. Last but not least, we propose an energy-based weight analysis method to explore how CNN-based MTL works. We observe that the side tasks serve as regularizations to disentangle the PIE variations from the learnt identity features. Extensive experiments on the entire multi-PIE dataset demonstrate the effectiveness of the proposed approach. To the best of our knowledge, this is the first work using all data in multi-PIE for face recognition. Our approach is also applicable to in-the-wild data sets for pose-invariant face recognition and achieves comparable or better performance than state of the art on LFW, CFP, and IJB-A datasets.
Dynamic model simulation of arc formation in vacuum circuit breaker

Based on the theories of fractal and gas discharge, microscopic morphology of vacuum arc, the process of arc formation and development of discharge channel in the vacuum circuit breaker are simulated. The effects of temperature and electromagnetic field on the arc are also considered. The opening stroke is 10mm in our work. Voltage is 75kV, and current is 1kA. The effects of temperature on the arc radius, the role of self-generated magnetic fields and thermal conduction are simulated here. The results show that electron density and the coefficient of heat conduction are increased gradually. Average temperature increases at first and decreases latter, then collision coefficient enhances. The radius of arc also increases in the stage of arc formation and development. The arc radius contract and stable arc forms after breakdown.
Novel zero-watermarking scheme based on DWT-DCT

Essential characteristics for watermark registration and detection without data modification can be used in an audio zero-watermarking scheme. In this paper, a novel audio zero-watermarking scheme based on discrete wavelet transform (DWT) and discrete cosine transform (DCT) is presented. The watermark is registered by comparing the mean absolute values of the adjacent frame coefficients after DWT and DCT. Simulation results show that the proposed scheme is strongly robust to common attacks such as AWGN, downsampling, low-pass filtering, requantization, and MP3 compression. A performance analysis of the proposed scheme shows that all bit error rates after attacks are zero.
An improved WSQ fingerprint image compression algorithm

The wavelet/scalar quantization (WSQ)  fingerprint image compression algorithm is effective and has been widely applied to fingerprint image compression. However, WSQ cannot control the compression ratio and its performance on fuzzy images is poor. We propose an improved WSQ fingerprint image compression algorithm to overcome the above shortcomings. Our algorithm uses a mixed quantizer so that the algorithm can treat fuzzy and non-fuzzy fingerprint images separately. In order to control the compression ration, an optimization parameters for a specific compression ratio. The proposed algorithm is compared with the traditional WSQ fingerprint image compression algorithm and the results are encouraging.
Monitoring Aquatic Debris Using Smartphone-Based Robots

Monitoring aquatic debris is of great interest to the ecosystems, marine life, human health, and water transport. This paper presents the design and implementation of SOAR-a vision-based surveillance robot system that integrates an off-the-shelf Android smartphone and a gliding robotic fish for debris monitoring in relatively calm waters. SOAR features real-time debris detection and coverage-based rotation scheduling algorithms. The image processing algorithms for debris detection are specifically designed to address the unique challenges in aquatic environments. The rotation scheduling algorithm provides effective coverage for sporadic debris arrivals despite camera's limited angular view. Moreover, SOAR is able to dynamically offload compute-intensive processing tasks to the cloud for battery power conservation. We have implemented a SOAR prototype and conducted extensive experimental evaluation. The results show that SOAR can accurately detect debris in the presence of various environment and system dynamics, and the rotation scheduling algorithm enables SOAR to capture debris arrivals with reduced energy consumption.
Survey on the external insulation of 10 kV outdoor vacuum circuit breaker

Taking the combined effects of porcelain insulation casing, the silicon rubber external insulation and floating shield on the electric field into account, the electric field has been calculated for three kinds of vacuum circuit breakers with different external insulation structures using the finite element method (FEM). The potential of floating shield has been well considered and calculated. Based on the computing results, the influences of floating shield and the silicon rubber insulation are analyzed. The theoretical basis for further optimization design of external silicon rubber and the development of outdoor vacuum circuit breaker is provided.
Group context learning for event recognition

We address the problem of group-level event recognition from videos. The events of interest are defined based on the motion and interaction of members in a group over time. Example events include group formation, dispersion, following, chasing, flanking, and fighting. To recognize these complex group events, we propose a novel approach that learns the group-level scenario context from automatically extracted individual trajectories. We first perform a group structure analysis to produce a weighted graph that represents the probabilistic group membership of the individuals. We then extract features from this graph to capture the motion and action contexts among the groups. The features are represented using the “bag-of-words” scheme. Finally, our method uses the learned Support Vector Machine (SVM) to classify a video segment into the six event categories. Our implementation builds upon a mature multi-camera multi-target tracking system that recognizes the group-level events involving up to 20 individuals in real-time.
The improvement of transmittance in capacitive grid polarizer with dielectric substrates by antireflection coating

This paper presents an approach for the improvement of transmittance in capacitive grid polarizer with dielectric substrates by introducing antireflection(AR) coating. The capacitive grid polarizer using Cu material is printed in the dielectric substrate of PTFE. According to different position relations between grid and substrate, the four structures of the AR coating are verified by the reflection coefficient of TE wave and transmission coefficient of TM wave working at the frequency range from 1GHz to 500GHz. Comparing different AR structures, the periodic strips with back AR have much better performance, where the coefficients of reflection and transmission are more than 0.993 and 0.93 respectively.
Wavelet Multiscale Products Based Genetic Fuzzy Clustering for Image Edge Detection Analysis

A new edge detection algorithm by combining multiscale wavelet technique and genetic fuzzy clustering algoithm is proposed in this paper (called WGFCA), which can realize edge detection of input image automatically. Based on the theory that signals and noise have different characters along wavelet decomposition scales, WGFCA firstly multiply the wavelet coefficient of input image at adjacent scales to enhance edge structure and suppress noise, then, in order to restrain noise further, WGFCA apply fuzzy median filter to the result obtained above. Finally, edge map of input image is obtained by the great unsupervised classifying technique-genetic fuzzy clustering based on an effective feature extraction algorithm. Experiment results demonstrated promising performance of the proposed edge detection algorithm
Person-specific expression recognition with transfer learning

A key assumption of traditional machine learning is that both the training and test data share the same distribution. However, this assumption does not hold in many real-world scenarios. For example, in facial expression recognition, the appearance of an expression may vary significantly for different people. Previous work has shown that learning from adequate person-specific data can improve facial expression recognition results. However, because of the difficulties of data collection and labeling, person-specific data is usually very sparse in real-world applications. Learning from the sparse data may suffer from serious over-fitting. In this paper, we propose to learn a person-specific facial expression model through transfer learning. By transferring the informative knowledge from other people, it allows us to learn an accurate person-specific model for a new subject with only a small amount of his/her specific data.