nils-herrmann/clean_scirepeval huggingface dataset:
Development of an S-band 50 kW average power klystron

This paper reports the design considerations, simulation results and test specifications of an S-band, 70 MHz bandwidth, 50 kW average power klystron, which was developed by the Institute of Electronics, Chinese Academy of Sciences (IECAS) before August, 2007. The representative result is 56 kW average output power under the conditions of 89 mus RF pulse width, 300 Hz repetition rate, and continuous eight hours reliability testing.
Design method of wide-area damping controller based on FOA algorithm

With regard to the interval low-frequency oscillation problems in large-scale interconnected power system, a design method based on Fruit Optimization Algorithm (FOA) for the wide-area time-delay damping controller is proposed in this paper. Systems such as the New England 10-generator 39-bus system is employed to validate the effectiveness of the method presented. The system is linearized at first, and modal analysis is conducted to find out the interval oscillation modes and corresponding damping ratios, under which the controllability and observability are obtained. Through the maximum observability index the Wide-area feedback signal is selected appropriately. Afterwards, structure of the wide-area controller is analyzed and the FOA algorithm is adopted to optimize the parameters. Finally, the simulation results show that the proposed design method of wide-area controller can better improve the interval oscillation damping ratios, and puts forward a new thought for the optimal control of low-frequency oscillation.
Application of Boosting to Chinese word sense disambiguation

AdaBoost.M1 is a well known boosting-based method for improving the accuracy of a given machine-learning algorithm. In this paper, we modify AdaBoost.M1 for Chinese word sense disambiguation. Unlike AdaBoost.M1 that adapts weights of training sets, in our modified algorithm, we provide a new method to adapt the classifiers' weights. The base classifiers are trained on a small set of labeled examples, and then augmented by a large number of unlabeled examples. We report the results of systematic experimentation performed on a standard Chinese People Daily corpus (co-developed by Institute of Computational Linguistics (ICL) of Peking University, People's Daily and Fujitsu Limited) of 12 thousand articles. These experiments demonstrate the advantage of our new weight adaptation method and its ability to overcome the noise factor within the unlabeled examples. We also demonstrate that the overall accuracy is improved as the number of classifiers increases.
Classification of tongue images based on doublet and color space dictionary

Recently, pathological diagnosis plays a crucial role in many areas of medicine, and some researchers have proposed many models and algorithms for improving classification accuracy by extracting excellent feature or modifying the classifier. They have also achieved excellent results on pathological diagnosis using tongue images. However, pixel values can't express intuitive features of tongue images and different classifiers for training samples have different adaptability. Accordingly, this paper presents a robust approach to infer the pathological characteristics by observing tongue images. Our proposed method makes full use of the local information and similarity of tongue images. Firstly, tongue images in RGB color space are converted to Lab. Then, we compute tongue statistics information. In the calculation process, Lab space dictionary is created at first, through it, we compute statistic value for each dictionary value. After that, a method based on Doublets is taken for feature optimization. At last, we use XGBOOST classifier to predict the categories of tongue images. We achieve classification accuracy of 95.39% using statistics feature and the improved classifier, which is helpful for TCM (Traditional Chinese Medicine) diagnosis.
A mini multi-joint wall climbing robot based on the vibrating suction method

Vibration suction is a kind of suction method which is newly introduced by researchers. It is a key technique of wall-climbing robots and will determine the movement gait and mechanical structures of robots. According to the theory of vibration suction, a new type of vibration suction module is designed. Experiments are carried out to test the suction performance of the module, and the result shows that the module has enough reliability. A mini multi-joint wall climbing robot is designed based on vibration suction. Experiments proved that this robot can finish three kinds of movements: in straight line, turning and waling form one face to another successfully.
A Hybrid Consensus and Clustering Method for Protein Structure Selection

In protein tertiary structure prediction, a crucial step is to select near-native structures from a large number of predicted structural models. Over the years, many methods have been proposed for the protein structure selection problem. Despite significant advances, the discerning power of current approaches is still unsatisfactory. In this paper, we propose a new algorithm, CC-Select, that combines consensus with clustering techniques. Given a set of predicted models, CC-Select first calculates a consensus score for each structure based on its average pair wise structural similarity to other models. Then, similar structures are grouped into clusters using multidimensional scaling and clustering algorithms. In each cluster, the one with the highest consensus score is selected as a candidate model. Using extensive benchmark sets of a large collection of predicted models, we compare CC-Select with existing state-of-the-art quality assessment methods and show significant improvement.
A multi-camera approach to tracking and localization of people with coexisting robots

People tracking and localization in situations with coexisting service robots is a challenging task due to the non-rigid nature of human body, complex background and frequent occlusions among people and robots. A multi-view approach is proposed to people-tracking and localization whilst the robotpsilas capability of perceiving environment is utilized. Initiated by person detection, tracking within single camera is performed by particle filtering based on the weighed color histogram. A multi-camera collaboration method is proposed to track multiple people and obtain integrated people location based on ground plane homographics. This approach enables people-tracking using multiple cameras with overlapped field-of-views and outperforms the single-view approaches in tracking accuracy especially in situations with partial occlusions. The effectiveness of proposed approach is validated by an implementation in a home-care surveillance system, in which people are supported by robot assistants for an easy living.
Adaptive Power Control for Asymmetric Two-Way Amplify-and-Forward Relaying With Individual Power Constraints

This paper addresses the problem of adaptive power control of a two-way amplify-and-forward (AF) relaying network based on outage probability minimization. Unlike most of the existing works, our network scenario has asymmetric traffic requirements, and each terminal in the network is subject to an individual power constraint in addition to a total power consumption constraint of the network. To fulfill power control, instantaneous or statistical channel state information (CSI) is assumed to be available at the transmitter of each terminal. By dividing the overall power control problem into a few subproblems corresponding to different power constraints, and using the traffic knowledge along with the CSI, two power control strategies, called short- and long-term strategies, are proposed, leading to closed-form solutions for individual transmit powers at the relay and the sources that are adapted to certain traffic requirements and channel conditions. Simulation results are provided to demonstrate the effectiveness of the proposed power control strategies. It is shown that the short-term strategy can achieve a significant performance gain in terms of the outage probability, regardless of symmetric or asymmetric traffics and channels, and meanwhile, the long-term strategy is more appropriate to asymmetric traffic and channel conditions.
Compression of compound images by combining several strategies

Compound images are combinations of text, graphics and natural images. They possess characteristics different from those of natural images, such as a strong anisotropy, sparse color histograms and repeated patterns. Former research on compressing them has mainly focused on developing certain strategies based on some of these characteristics but has failed so far to fully exploit them simultaneously. In this paper, we investigate the combination of four up-to-date strategies to construct a comprehensive scheme for compound image compression. We have implemented these strategies as four types of modes with variable block sizes. Experimental results show that the proposed scheme achieves significant coding gains for compound image compression at all bitrates.
Modulation-doped field-effect transistors with an 8-nm InGaAs/InAs/InGaAs quantum well

We show that the channel thickness of modulation-doped field-effect transistors (MODFETs) based on an InAlAs/InGaAs heterojunction can be reduced from 15 to 8 nm without any degradation of the main DC and RF figures of merits. Furthermore, short-channel effects, which are pronounced in sub-0.1-/spl mu/m devices, can be effectively suppressed by this thin-channel design. The retainment of high performance and alleviation of short-channel effects are attributed to the excellent two-dimensional electron gas (2-DEG) confinement by the inserted InAs layer. The successful channel thinning opens up the possibility of employing high-quality thin-channel structures for MODFETs with gate lengths below 0.05 /spl mu/m.
A Multi-Volume Visualization Framework for Spatial Aligned Volumes after 3D/3D Image Registration

A new method for the visualization of spatial aligned volumes after 3D/3D image registration is presented in this paper. This work aims at displaying the full information of the multi-volume in the same scene. First, we form a vectorial volume from the spatial aligned multi-volume. Then, a statistical vectorial volume classification method based on neighborhood weighted Gaussian mixture model is applied to analyze the vectorial volume and get material distribution information. Finally, several rendering techniques are adapted to visualize the parameters. We imply an application case: the visualization of preoperative kidney planning system to express our visualization method, but this method is not limited to the specific application case. According to the levels where the data intermixing occurs, our method mix the data at the earliest level so that it is called the acquisition level intermixing method.
A Novel Masses Detection Algorithm Using Model-based Location and ANFIS-based Segmentation

Breast cancer is one of the most dangerous tumors for middle-aged and older women, and mammography is its most reliable early detection method. In this paper, a novel algorithm was proposed to assist the radiologists to detect the masses hidden in the mammograms. At first, two models were established to represent all kinds of masses, and according to the different type, different methods were used to detect the masses. For those masses inside the fatty tissue, iterative thresholding could be applied to locate them, while for those masses inside the denser tissue, black hole registration based on discrete wavelet transform (DWT) were utilized for their labeling. And then, filling dilation technique was used to extract the whole masses from the backgrounds, according to their labels. ANFIS, Canny edge detector and energy field method were applied to adjust the segmentation process, and the final result validated the advantages of this algorithm.
A new method for loudspeaker small noise vibration detection based on wavelet package

Currently, many digital analysis methods are widely used for loudspeaker fault detection. These methods are mainly focused on characteristics like frequency response, impedance and Thiele-Small parameters etc. However, for small noise vibration detection, especially for online detecting, no suitable method is available. Loudspeaker small noise vibration detection is still relied on human hearing, which may harm the tester. By researching characteristics of loudspeaker small noise vibration, we propose a new detecting method using wavelet package. The key algorithm for this new method is how to separate the response signal of the loudspeaker and choose suitable wavelet package for each subsection of response signal to quickly generate the noise vibration curve for each loudspeaker online. By comparing the noise vibration curve of loudspeaker with those of normal ones, we can easily pick out loudspeakers with small noise vibration faults. The simulation result shows that the new method is effective and stable.
An Approach to Model Cross-organizational Processes using Object Petri net

Business process modeling is an important topic of enterprise modeling. Modeling process can help us to define new process, evaluate and improve existing ones. As the development of Internet and B2B commerce, business partnerships are being fostered in order to broaden the scope of their market. Business process thus has been scatted over multiple organizations. At the same time, each participant of the cooperation has their own private process. So how to define and model cross-organizational process while keep each participant's autonomy poses new challenge to us. In this paper, we will focus on two problems:!) How can we guarantee the private process of each participant is consistent to the coordination protocol specified by message sequence charts? 2) How can we formalize the execution of the interaction protocol, which means the execution of private processes conforms to the interaction protocol? An approach based on object Petri net and behavior inheritance will be proposed in this paper to solve the above two problems.
How Can Intra Correlation Be Exploited Better?

This paper studies how to better exploit intra correlation in image/intra-frame coding. Unlike the previous assumption, where the correlation between samples is determined only by their distance, image is considered to present local orientation property. The correlation between samples is determined by not only the distance but also the link orientation. Thus, a directional Filtering Transform (dFT) is proposed in this paper to exploit local orientation correlation among samples. It consists of directional filtering and optional transform. Furthermore, this paper analyzes the theoretical coding gain of the proposed directional filtering in terms of signal power spectral density (PSD). Both numerical results and actual compression results of the intra-frame coding in H.264 demonstrate the advantages of the proposed dFT with decoding complexity increased slightly.
A Novel Chinese Multi-Document Summarization Using Clustering Based Sentence Extraction

This paper proposes a strategy for Chinese multi-document summarization based on clustering and sentence extraction. It adopts the term vector to represent the linguistic unit in Chinese document, which obtains higher representation quality than traditional word-based vector space model in a certain extent. As for clustering, we propose two heuristics to automatically detect the proper number of clusters: the first one makes full use of the summary length fixed by the user; the second is a stability method, which has been applied to other unsupervised learning problems. We also discuss a global searching method for sentence selection from the clusters. To evaluate our summarization strategy, an extrinsic evaluation method based on classification task is adopted. Experimental results on news document set show that the new strategy can significantly enhance the performance of Chinese multi-document summarization
High electron mobility InGaAs-GaAs field effect transistor with thermally oxidised AlAs gate insulator

A GaAs-based high electron mobility metal-oxide-semiconductor field effect transistor (MOSFET) is fabricated using wet thermal oxidation of AlAs. Enhanced transport properties and interface smoothness result from use of a pseudomorphic In/sub 0.2/Ga/sub 0.8/As channel layer, yielding a high transconductance of 50 mS/mm (5 /spl mu/m gate length).
Identifying Causal Effects from Data for the Clinical Ventilation Process Modelling

Proper modeling of the ventilation process is crucial to the effective operation of computerized ventilator management systems. We aim to develop a ventilation modeling technique, which depends less on lung dynamics assumptions, is able to describe the ventilation process quantitatively, and includes only clinically available parameters. We propose a Granger-causality based technique to identify causal relationships among ventilation variables, as the structural constraints typically provided by the subjective theory, controlled experiments or directed acyclic graphs (DAGs) are not available. We examine the performance of the proposed modeling methodology from different perspectives with real data. Domain knowledge confirmed and experiments show that the model outperforms the Vector Autoregression (VAR) and Neural Network methods. The proposed method provides initial insights into the data based ventilation process modeling.
Action and Event Recognition in Videos by Learning From Heterogeneous Web Sources

In this paper, we propose new approaches for action and event recognition by leveraging a large number of freely available Web videos (e.g., from Flickr video search engine) and Web images (e.g., from Bing and Google image search engines). We address this problem by formulating it as a new multi-domain adaptation problem, in which heterogeneous Web sources are provided. Specifically, we are given different types of visual features (e.g., the DeCAF features from Bing/Google images and the trajectory-based features from Flickr videos) from heterogeneous source domains and all types of visual features from the target domain. Considering the target domain is more relevant to some source domains, we propose a new approach named multi-domain adaptation with heterogeneous sources (MDA-HS) to effectively make use of the heterogeneous sources. In MDA-HS, we simultaneously seek for the optimal weights of multiple source domains, infer the labels of target domain samples, and learn an optimal target classifier. Moreover, as textual descriptions are often available for both Web videos and images, we propose a novel approach called MDA-HS using privileged information (MDA-HS+) to effectively incorporate the valuable textual information into our MDA-HS method, based on the recent learning using privileged information paradigm. MDA-HS+ can be further extended by using a new elastic-net-like regularization. We solve our MDA-HS and MDA-HS+ methods by using the cutting-plane algorithm, in which a multiple kernel learning problem is derived and solved. Extensive experiments on three benchmark data sets demonstrate that our proposed approaches are effective for action and event recognition without requiring any labeled samples from the target domain.
ADON: Application-Driven Overlay Network-as-a-Service for Data-Intensive Science

Campuses are increasingly adopting hybrid cloud architectures for supporting data-intensive science applications that require “on-demand” resources, which are not always available locally on-site. Policies at the campus edge for handling multiple such applications competing for remote resources can cause bottlenecks across applications. These bottlenecks can be proactively avoided with pertinent profiling, monitoring and control of application flows using software-defined networking and pertinent selection of local or remote compute resources. In this paper, we present an “application-driven overlay network-as-a-service” (ADON) that manages the hybrid cloud requirements of multiple applications in a scalable and extensible manner by allowing users to specify requirements of the application that are translated into the underlying network and compute provisioning requirements. Our solution involves scheduling transit selection, a cost optimized selection of site(s) for computation and traffic engineering at the campus-edge based upon real-time policy control that ensures prioritized application performance delivery for multi-tenant traffic profiles. We validate our ADON approach through an emulation study and through a wide-area overlay network testbed implementation across two campuses. Our workflow orchestration results show the ADON effectiveness in handling temporal behavior of multi-tenant traffic burst arrivals using profiles from a diverse set of actual data-intensive applications.
A Novel PV Microinverter With Coupled Inductors and Double-Boost Topology

A coupled-inductor double-boost inverter (CIDBI) is proposed for microinverter photovoltaic (PV) module system, and the control strategy applied to it is analyzed. Also, the operation principle of the proposed inverter is discussed and the gain from dc to ac is deduced in detail. The main attribute of the CIDBI topology is the fact that it generates an ac output voltage larger than the dc input one, depending on the instantaneous duty cycle and turns ratio of the coupled inductor as well. This paper points out that the gain is proportional to the duty cycle approximately when the duty cycle is around 0.5 and the synchronized pulsewidth modulation can be applicable to this novel inverter. Finally, the proposed inverter servers as a grid inverter in the grid-connected PV system and the experimental results show that the CIDBI can implement the single-stage PV-grid-connected power generation competently and be of small volume and high efficiency by leaving out the transformer or the additional dc-dc converter.
An online self-constructing wavelet fuzzy neural network for machine condition monitoring

The subject of machine condition monitoring is charged with developing new technologies to diagnose the machinery problems. A problem with diagnostic techniques is that they require constant human interpretation of the results. Fuzzy neural networks show good ability of self-adaption and self-learning, wavelet transformation or analysis shows the time frequency location characteristic and multi-scale ability. Inspired by these advantages, a wavelet fuzzy neural network (WFNN) is proposed for fault diagnosis in this paper. This fuzzy neural network uses wavelet basis function as membership function whose shape can be adjusted on line so that the networks have better learning and adaptive ability. An on-line learning algorithm is applied to automatically construct the wavelet fuzzy neural network. There are no rules initially in the wavelet fuzzy neural network. They are created and adapted as on-line learning proceeds via simultaneous structure and parameter learning. The advantages of this learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. The results of simulation show that this SWFNN network method has the advantage of faster learning rate and higher diagnosing precision.
Analysis and synthesis of a multi-arm conical spiral antenna to form shaped-conical radiation patterns

A multi-arm conical spiral wire antenna used in low orbit satellite communications is proposed, which can form cardio or conical patterns by adjusting its geometrical parameters. It has better polarization characteristic than the two-arm spiral antenna proposed in the reference. With the increase of its height and spiral constant its beam deviation from the axis increase while its power gain decrease to form conical radiation patterns. Several graphs about the influence of the parameters on radiation patterns are presented in this paper for the optimization of the antenna.