nils-herrmann/clean_scirepeval huggingface dataset:
A post-processing method for EISI to distinguish breast lesions

To increase the accuracy of Electrical Impedance Scanning Imaging (EISI) on inspecting breast diseases, reduce the false positive and false negative, we propose a method to use Independent Component Analysis (ICA) to extract features from the original admittance data of EISI equipments. Combined with Support Vector Machine (SVM) as classifier, the proposed method could inspect breast diseases and give nonuser-dependent diagnosis results. Experimental results suggest that the features extracted by ICA are effective in classifying breast diseases. To recognize lesions within breast under inspect which is malignant or benign, the proposed method could achieve a sensitivity of 74.2% and a specificity of 82.8%. Accuracy is calculated as 80%. Combined the proposed method with regular EISI inspecting process could help to increase the reliability of EISI, avert benign biopsies.
Evaluation of heavy metals contamination in sediments of Shuangqiao River, Xiaoqinling gold belt, China

The concentration and distribution of selected heavy metals in sediments of Shuangqiao River, Xiaoqinling gold belt are studied to determine the extent of anthropogenic inputs from mining activities, The analysis shows that the mean heavy-metal concentrations (mg/kg)are various : Cu(32.3-3240), Pb(51.0-218254), Zn(32.0-9000 ), Cd(0.2-90), Cr(1.4-178), As(0.8-25) and Hg(0.04-245), Pollution Load Index (PLI), Index of Geo-accumulation (Igeo) and the Enrichment Factors (EFs) for Cu, Pb, Zn, Cd and Hg are high. In particular, Hg and Pb pollution is the heaviest than others; this reflects the intensity of anthropogenic inputs related to mining discharge into the area. The concentration of heavy metals reported in this paper will be useful as baselines for comparison in future sediment quality studies.
A deep tongue image features analysis model for medical application

With the improvement of people's living standards, there is no doubt that people are paying more and more attention to their health. However, shortage of medical resources is a critical global problem. As a result, an intelligent prognostics system has a great potential to play important roles in computer aided diagnosis. Numerous papers reported that tongue features have been closely related to a human's state. Among them, the majority of the existing tongue image analyses and classification methods are based on the low-level features, which may not provide a holistic view of the tongue. Inspired by a deep convolutional neural network (CNN), we propose a deep tongue image feature analysis system to extract unbiased features and reduce human labor for tongue diagnosis. With the unbalanced sample distribution, it is hard to form a balanced classification model based on feature representations obtained by existing low-level and high-level methods. Our proposed deep tongue image feature analysis model learns high-level features and provide more classification information during training time, which may result in higher accuracy when predicting testing samples. We tested the proposed system on a set of 267 gastritis patients, and a control group of 48 healthy volunteers (labeled according to Western medical practices). Test results show that the proposed deep tongue image feature analysis model can classify a given tongue image into healthy and diseased state with an average accuracy of 91.49%, which demonstrates the relationship between human body's state and its deep tongue image features.
Angular glint modeling and simulation for complex targets

Angular glint is an important parameter of radar targets' electromagnetic scattering characteristics. It seriously reduces guidance accuracy of illumination guidance radar and missiles' homing head antenna. Theoretical simulation of angular glint for complex targets is discussed in this paper. First, the amplitude and phase of a complex practical target's scattering field are computed by graphical electromagnetic computing algorithm. Then, the angular glint of the target is computed by the Poynting vector method. The simulation results verify the correctness and efficiency of the method.
An asymmetrical three-level dual-input bidirectional DC/AC converter with improved conversion efficiency for vehicle-to-grid application

An asymmetrical three-level dual-input (ATL-DI) bidirectional DC/AC converter is presented to interface a low voltage battery, a high voltage DC-bus and AC grid, simultaneously, for vehicle-to-grid (V2G) applications. A low voltage battery is employed as a new voltage level for the ATL-DI converter. Therefore, part of the battery power can be directly charged by or discharged into the grid, without processing by another bidirectional DC/DC converter. As a result, the power losses and power rating of the front-end DC/DC converter is reduced significantly, and the power conversion stages of the bidirectional DC/AC power conversion system are reduced to improve the overall conversion efficiency. Meanwhile, the three-level characteristic can help to reduce the switching losses and size/volume of the filter inductor of the ATL-DI DC/AC converter. Topology and operation principles of the ATL-DI bidirectional DC/AC converter are analyzed. A 2kW prototype is built and tested to verify the effectiveness and advantages of this method.
Ground testing of flight software: Dynamically configurable hardware-in-the-loop simulation of the alpha magnetic spectrometer

Ground testing for the flight software of the Alpha Magnetic Spectrometer (AMS) requires a virtual environment which processes commands from real controllers and provides normal or abnormal signals as needed. We introduced the hardware-in-the-loop (HIL) concept but implemented the simulation in an innovative way that skips mathematical modeling. A simulation database derived from the real-time flight data provides a virtual environment, and a dynamic configuration mechanism makes the virtual world respond to the real world. This efficient method has been successfully applied in the ground testing system for AMS and the paper presents the methodology and its applications.
Model Design of Electric System State Estimation Based on CIM

Common information model (CIM) provides a logical view of the physical aspects of energy management system information. This paper proposes a software model that extends CIM for the least square based state estimation. Three types of interfaces are included to realize input and output functions. Topology model is established according to the network elements interconnections and the states of switches. An object oriented state estimation is performed using the model in which extended classes with their relative properties are built. The CIM based state estimation model is applied to the Zaozhuang power networks and is proved effective by the operation results.
Hybrid dynamic modeling of the evaporator for the development of the advanced optimal control strategies

A hybrid model of evaporator used in air conditioning and refrigeration systems is proposed to present the dynamic behavior of the two phase flow evaporators. The model is formulated as process fundamental governing equations based on the energy and mass balance, and thermodynamic principles, while some constants and some variables which vary very little as time goes on, such as cross-sectional areas, mean void fraction, the derivative of the saturation enthalpy with respect to pressure, etc., are lumped into several unknown parameters, which are obtained by identifying the experimental data using nonlinear least squares method. The method takes advantages of both physical and empirical modeling approaches and can accurately predict the transient behaviors in real-time, which can significantly reduce the computational burden. The greatest merits of this model are the order of the model is very low and all the state variables can easily be measured, which are very suitable for model based controller design. The model validation studies on an experimental system show that the complete dynamic model developed in Matlab/Simulink can well predict the system dynamics. The developed models will have wide applications not only in controller design, but also in transient behavior analysis.
Fuzzy Tracking Control for Switched Uncertain Nonlinear Systems With Unstable Inverse Dynamics

This paper investigates the problem of adaptive fuzzy tracking control for a class of switched uncertain nonlinear systems with unstable inverse dynamics via time-dependent switching. Adaptive backstepping technique and fast average dwell time switching are exploited to set up the adaptive fuzzy control scheme for the systems under consideration. It is shown that the designed controllers and switching signals can ensure that all the signals remain bounded and the tracking error converges to a small neighborhood of the origin. Finally, simulation results are presented to demonstrate the effectiveness of the proposed design method.
Hash-Based Line-by-Line Template Matching for Lossless Screen Image Coding.

Template matching (TM) was proposed in the literature a decade ago to efficiently remove non-local redundancies within an image without transmitting any overhead of displacement vectors. However, the large computational complexity introduced at both the encoder and the decoder, especially for a large search range, limits its widespread use. This paper proposes a hash-based line-by-line template matching (hLTM) for lossless screen image coding, where the non-local redundancy commonly exists in text and graphics parts. By hash-based search, it can largely reduce the search complexity of template matching without an accuracy degradation. Besides, the line-by-line template matching increases prediction accuracy by using a fine granularity. Experimental results show that the hLTM can significantly reduce both the encoding and decoding complexities by 68 and 23 times, respectively, compared with the traditional TM with a search radius of 128. Moreover, when compared with High Efficiency Video Coding screen content coding test model SCM-1.0, it can largely improve coding efficiency by up to 12.68% bits saving on screen contents with rich texts/graphics.
Agent-based design and organization of intermodal freight transportation systems

In logistics of today's economy we have to deal with distributed systems. To support the demanding management task the multi-agent approach offers promising perspectives. Intermodal freight transportation (IFT) is a complex task involving a lot of information flowing across organizational boundaries. However, the interorganizational information flows are often slow, and sometimes even are blocked, due to lack of standardized data storage format, no rules for arranging automated transfer from one system to another, and so on. This paper presents a multi-agent system (MAS) approach to the design and organization of an integrated IFT information architecture. We propose to organize the IFT system as an open network of autonomous agents, each encapsulating one or more logistics roles, and each coordinating its activities with other agents. The architecture is first described using an abstract specification based on concepts such as agents, roles and role protocols, and then specified in details using an example. A major conclusion is that the MAS based architecture can support IFT parties to integrate their information systems in an effort to truly reach a seamless intermodal transport.
A Medical information system architecture based on workflow technology

Workflow management becomes more and more important along with the development of medical technique. Traditional medical information system cannot keep up with the ever-changing demands. The existing widely used workflow management system can not be applied to medical environment because of the complex features of medical information system. In this paper, a workflow-based medical information system architecture is proposed to resolve those problems. The architecture takes open source workflow management system- YAWL as the start point. In the architecture, the medical workflow is defined, implemented, executed and optimized in the medical workflow bus. The applications and heterogeneous legacy information systems are hanged on, as well as activated and managed by the bus. Through this way, the medical workflow problem is resolved and the process-driven data communication, heterogeneous legacy systems integration are realized.
A LBP-based Face Recognition Method with Hamming Distance Constraint

In this paper, we present a new LBP-based face recognition method with Hamming distance constraint. The traditional LBP operator uses a uniform pattern to describe the local features and assort the other nonuniform patterns to one additional class, for images under expression and illumination condition changes, this method could cause more inaccuracy and instability. By assuming that the illumination, pose or expression changes of a face image are some kinds of "noise", we introduce the widely used Hamming distance in channel coding to LBP so as to decrease the error rate caused by these noise disturbances. Experimental results on FRGC show that our method improves the recognition performance obviously than the traditional LBP-based face recognition methods when face images are under uncontrolled circumstances.
Histogram Contextualization

Histograms have been widely used for feature representation in image and video content analysis. However, due to the orderless nature of the summarization process, histograms generally lack spatial information. This may degrade their discrimination capability in visual classification tasks. Although there have been several research attempts to encode spatial context into histograms, how to extend the encodings to higher order spatial context is still an open problem. In this paper,we propose a general histogram contextualization method to encode efficiently higher order spatial context. The method is based on the cooccurrence of local visual homogeneity patterns and hence is able to generate more discriminative histogram representations while remaining compact and robust. Moreover, we also investigate how to extend the histogram contextualization to multiple modalities of context. It is shown that the proposed method can be naturally extended to combine both temporal and spatial context and facilitate video content analysis. In addition, a method to combine cross-feature context with spatial context via the technique of random forest is also introduced in this paper. Comprehensive experiments on face image classification and human activity recognition tasks demonstrate the superiority of the proposed histogram contextualization method compared with the existing encoding methods.
Java implementation of localization and tracking application based on HDR-UWB platform

In recent years extensive research was carried out in order to develop applications for the Ultra-Wideband (UWB) technology and to resolve the practical challenges in implementing an efficient UWB communication system utilizing the UWB impulse radio for precision localization. ToA (Time of Arrival) or TDoA (Time Difference of Arrival) of ranging frame are widely used because ToA and TDoA provide high accuracy due to the high time resolution with high bandwidth of IR-UWB (Impulse Radio - UWB) signal. HDR-UWB (High Data Rate -UWB) uses MB-OFDM (Multi Band - Orthogonal Frequency Division Multiplexing) to transmit signals, which can reach a data rate of 200 Mbit/s. In this paper a Java implementation of an active LT (Localization and Tracking) method based on a HDR-UWB platform is investigated. The hardware is composed of three UWB anchors, one UWB tag and one UWB location coordinator, which is connected to a PC (Personal Computer) or a PDA (Personal Digital Assistant). The Java program, which is hosted on the PC or PDA, is used to instruct the UWB coordinator to send and receive the UWB ranging frame and localize the tag. ToA is used as a ranging parameter in this work to measure the distance between the used UWB anchors and the UWB tag. The most important aspect is to reduce the jitter of ToA caused by CPU processing and the clock drift between UWB anchors and UWB tag. In this paper a 3-way ranging mechanism to minimize the clock drift is deployed. In addition the use of the JPCAP (Java Package for CAPturing) library in Java reduces the jitter of the ToA.
A new method for disease-related gene prioritization

Prioritizing genes according to their association with a disease allows researchers to explore genes in more informed ways. Although some useful algorithms have been developed, they are based on single gene importance, gene interaction networks, or gene modules with little consideration of relative gene importance in the context of modules. In this paper, we propose to prioritize genes considering both individual genes and their affiliated modules, and utilize Gene Ontology (GO) based fuzzy measure value as well as known disease genes as heuristics. The performance of our method is comprehensively validated by using both simulated and real datasets. Results show that our method outperforms other methods in terms of disease-related gene prioritization. This work will aid researchers in the understanding of the genetic architecture of complex diseases, and improve the accuracy of diagnosis and the effectiveness of therapy.
Deep transcranial magnetic stimulation using deformed halo-circular assembly coil

Direct stimulation of deeper brain tissues by transcranial magnetic stimulation (dTMS) plays an important role in the study of reward and motivation mechanisms, which may be beneficial in the treatment of several neurological and psychiatric disorders. This paper presents numerical simulation of dTMS by considering the deformed Halo-circular assembly coil (dHCA coil). Three-dimensional distributions of the induced magnetic field and electric field in realistic head model by applying the dHCA coil were obtained by impedance method, and the results were compared with the normal Halo-circular assembly coil (HCA coil). The improved stimulation focality with reasonable stimulation depth can be achieved by employing the dHCA coil.
A simplified antenna shuffling scheme for DSTTD systems with linear receiver

In this paper, a new antenna shuffling scheme which can improve both diversity and multiplexing gains for double space time transmit diversity (DSTTD) system is proposed. This scheme needs low computational complexity and limited feedback information. Based on the theoretical analysis, we can also know that achieving more diversity gain dose not opposite to achieve more multiplexing gain. The performance of the DSTTD system using the proposed scheme is analyzed, and it shows better bit error rate and channel capacity than the conventional method.
Directional filtering transform

This paper proposes the directional filtering transform (dFT, in order to distinguish from the common usage on DFT) to better exploit intra-frame correlation in H.264 intra-frame coding. It consists of a directional filtering and an optional DCT transform. In the proposed directional filtering, there are two different approaches. One is the uni-directional filtering (UDF) that is similar to H.264 directional intra prediction. In this approach, only samples from neighboring blocks can be used in prediction. Another is bidirectional filtering (BDF) that exploits the correlations among samples from not only neighboring blocks but also the current block. The prediction structure in this approach is hierarchical multi-layer. In this paper, we present mathematical analyses on UDF and BDF and show the advantage to combine them together. The proposed dFT is integrated into H.264 intra-frame coding too. The preliminary experimental results in H.264 demonstrate its superiority.
A Flow Rationing Strategy over Multiple Flow Classes on Restricted Paths

With the trend of globalization and increasing transportation volume, integrated automated shipment handling systems have appeared in major cargo terminals. In such ter- minals, a critical decision is the route selection linking various origins (such as system gate points) and destinations (such as storage racks). We study such a routing optimization problem and propose a flow rationing routing strategy, which reserves various sets of paths for corresponding cargos and only admits the cargos with less importance on more important paths only when their saturability is low. A Markov Decision Processes model is developed to make the decision on the admission policy. Extensive numerical experiment is conducted to prove the effectiveness of the routing strategy our proposed.
Integer Programming realization of the Transmit Antenna Shuffling and Receive Antenna Selection in DSTTD systems

The double space time transmit diversity (DSTTD) system was proposed to obtain both transmit diversity gain and spatial multiplexing gain simultaneously. The DSTTD scheme outperforms general multiple input multiple output (MIMO) scheme, however the performance can be enhanced by using receive antenna selection (RAS) scheme and transmit antenna shuffling (TAS) scheme. In this paper, we realized the RAS algorithm for DSTTD system using integer programming (IP), which is aims to minimize the system bit-error rate (BER). Combined with TAS scheme, the transmit antennas of the DSTTD systems are grouped according to the shuffling pattern information fed back by the receiver. Also, the integer programming algorithm can be used in choosing the shuffling pattern. By this way, the performance of the DSTTD systems can be strengthened further. As the results show, the system capability achieves further improvement with limited information redundancy.
A MEMS-Based Ionization Gas Sensor Using Carbon Nanotubes

We demonstrate here the successful operation of an ionization gas sensor with a gap spacing S of 2 or 7plusmn0.4 mum and using carbon nanotubes as the electrode material. The device is chip based and fabricated using a microelectromechanical system process. Application of a bias of 0.6-1.8 V (Sap2 mum) or 12-15 V (Sap7 mum) to the electrodes generates an electric field that is sufficient to field ionize He, CO2, and their mixtures in air with high sensitivity and selectivity. The approach is considered as effective for lowering the operation voltage of ionization gas sensors below 36 V (safety voltage criterion) and is significant for the development of the smart device in this field
Guest Editorial Special Section on Visual Saliency Computing and Learning

Vision and multimedia communities have long attempted to enable computers to understand image or video content in a manner analogous to humans. Humans’ comprehension to an image or a video clip often depends on the objects that draw their attention. As a result, one fundamental and open problem is to automatically infer the attention attracting or interesting areas in an image or a video sequence. Recently, a large number of researchers explore visual saliency models to address this problem. The study on visual saliency models is originally motivated by simulating humans’ bottom-up visual attention and it is mainly based on the biological evidence that humans’ visual attention is automatically attracted by highly salient features in the visual scene, which are discriminative with respect to the surrounding environment.
Integrated Approach of Quality Function Deployment to Quality Assurance of Service Products Design

In this paper, the fuzzy cluster analysis, fuzzy analytic hierarchy process and fuzzy house of quality are integrated, which are introduced in the quality assurance of service products design. Firstly, the customer quality requirements are made a hierarchical division by fuzzy clustering. Based on this, used the fuzzy analytic hierarchy process to determine weights of the customer quality requirements. Then, the fuzzy sets method is leaded in the relationship matrix of house of quality for evaluation. So the result can provide accurate and reliable information on quality requirements for quality assurance of service products design. At last, there is an example to illustrate in detail.