nils-herrmann/clean_scirepeval huggingface dataset:
Relative term-frequency based feature selection for text categorization

Automatic feature selection methods such as document frequency, information gain, mutual information and so on are commonly applied in the preprocess of text categorization in order to reduce the originally high feature dimension to a bearable level, meanwhile also reduce the noise to improve precision. Generally they assess a specific term by calculating its occurrences among individual categories or in the entire corpus, where "occurring in a document" is simply defined as occurring at least once. A major drawback of this measure is that, for a single document, it might count a recurrent term the same as a rare term, while the former term is obviously more informative and should less likely be removed. In this paper we propose a possible approach to overcome this problem, which adjusts the occurrences count according to the relative term frequency, thus stressing those recurrent words in each document. While it can be applied to all feature selection methods, we implemented it on several of them and see notable improvements in the performances.
Research on the interaction relationship between common rail diesel engine injection parameters based on neural network

High-altitude calibration of CA6DL2-35E3R common rail diesel engine was finished on engine high altitude simulating environment test bed. The interactions between injection parameters and its impact on engine high altitude performance were studied. Results show that the fuel delivery per cycle per cylinder increases with the adwance of injection timing and the increase of common rail pressure at the calibration sweep as a whole; fuel delivery per cycle per cylinder increased about 0.1–0.3 mg with advancing 1 ° CA injection timing and it would increase 0.07–0.1 mg with increasing 1 MPa common rail pressure averagely. The fitting error of constructed radial-based function neural network model was below 10−12 and the predictive error of which was between 1.5%, which can fulfill common rail diesel engine characteristics modeling demand. The model can help alleviate the influence of the injection timing and common rail pressure on fuel delivery per cycle per cylinder, and achieve the impact of one single injection parameter on engine performance, which can help increase the understanding of common rail diesel engine injection characteristics.
Constructing digital museum grid for science popularization exhibition

The exhibition of China Science and Technology Museum needs to exhibit the specimens of the digital museums and demonstrate the grid technology in a vivid way which meets the requirements from visualization, interaction and multimedia. This paper designs a method of constructing digital museum grid for the purpose of science popularization, based on the existing work of China Education and Research Grid. The method presented here is composed of three steps. Firstly, the human-computer interface and interaction for the science popularization exhibition is designed. Then a way of sharing digital specimen resources is given. Finally, we establish the digital museum grid system for science popularization in the grid environment. The status shows that the exhibition is working very well in China Science and Technology Museum, and has received recognition from users.
Monitoring model of Wireless sensor networks based on state

Network model is the foundation of network performance optimization and network practical applications. Some basic network monitoring requirements were presented by analyzing the issues associated with existing models and node deployment of Wireless sensor networks (WSNs). Focusing on the network monitoring performance, a monitoring model of state based WSNs was proposed. The state information of single node was denoted as a vector in the model, including work state, position state, energy state, sensing and communication relations of nodes. A WSN could be denoted as a matrix which was composed of state vectors of nodes. Several examples were illustrated to demonstrate the applicability of the proposed model in some researches of WSNs, including energy control, topology reconfiguration, and performance evaluation. The model provides a platform to evaluate capabilities, functionalities, and behaviors of a WSN from the mathematics perspective.
Contrast Study on Two Instantaneous Feedback Control Techniques of Inverter System

Instantaneous feedback control technology has obtained the widespread application in the domain of power electronics. This paper conducts the contrast study between single voltage instantaneous feedback control technology and the Dual-loop voltage Feedback Control technology based on Capacitive Current Inner-loop. Taking the PWM inverter as research object, analysis and research is made about the PWM inversion system on the basis of two different control modes. The dynamic response of single voltage instantaneous control is slow with worse stability. In contrast, not only can the dual-loop feedback control enhance the stability and the anti-disturbance capacity of the system, but also it can improve dynamic property of the system with good quality of the inverter output voltage as well as low harmonic content. This paper verifies the applicability and reliability of the dual-loop feedback control technology by Saber simulation and experimental test.
Establishment of hybrid AC/DC system model and its characteristic research

Based on the electromagnetic transient simulation software, which is called PSCAD/EMTDC, this paper establishes the detailed hybrid AC/DC system model, with UHVDC connected to the AC system through double circuit transmission lines. The converters are established by detailed models, and the generators are established with excitation and speed regulating systems. Dynamic properties of hybrid AC/DC system under different operation conditions are analyzed in this paper. The research results show that, the established hybrid AC/DC system model in this paper can reflect electromagnetic properties of different operation conditions, laying solid foundations for subsequent protection related issues in hybrid AC/DC system.
Wavelet Transform and Bagging Predictor Approaches to Cancer Identification from Mass Spectrometry-Based Proteomic Data

The early detection of cancer has the potential to dramatically reduce the mortality of cancer. Recently, using the mass spectrometry based proteomics to develop profiles of patient serum proteins, combined with bioinformatics algorithms has been reported as a promising method to achieve this goal. In this paper, we develop a workflow that combined wavelet transform, statistic analysis and bagging predictor to process a public ovarian cancer proteomic dataset, and ultimately obtained a discriminative proteomic pattern that can differentiate the cancer form control with high sensitivity and specificity. Compared with the previous studies, the results of our study are based on peaks of mass spectrometry and the discovered discriminative pattern is more biologically.
Application of RS-SVM in Construction Project Cost Forecasting

Evaluation of construction projects is an important task for management of construction projects.An accurate forecast is required to enable supporting the investment decision and to ensure the project's feasible at the minimal cost. So controlling and rationally determining the project cost plays the most important roles in the budget management of the construction project. Ways and means have been explored to satisfy the requirements for prediction of construction projects. Recently a novel regression technique, called Support Vector Machines (SVM), based on the statistical learning theory is exploded in this paper for the prediction of construction project cost. Nevertheless, The standard SVM still has some difficults in attribute reduction and precision of prediction. This paper introduced the theory of the Rough Set (RS) for good performance in attribute reduction, considered and extracted substances components of construction project as parameters, and seted up the Model of the Construction Project Cost Forecasting based on the RS-SVM. The research results show that the prediction accuracy of RS-SVM is better than that of standard SVM.
An application to the finite approximation of the first passage models for discrete-time Markov decision processes with varying discount factors

This paper deals with the approximation problem of the first passage models for discrete-time Markov decision processes (MDPs) with varying discount factors. For a given control model M, by using a finite-state and finite-action truncation technique, we show that the first passage optimal reward and policies of M can be approximated by those of the solvable truncated control models, and illustrate the finite approximation by a controlled queueing system in numerical results.
Texture mapping based digital rubbing method

The Chinese ancient cliff inscription is very precious historical relics. But they are scattered, lost and damaged with time passing by. The traditional way to preserve it is utilizing manual rubbing to record characters from stone surface. However, this kind of method is arduous and heavy workload, sometimes even dangerous. Furthermore, there is damage to a certain extend to the original stone due to ink and pigment. In this paper, we propose a new method to create digital rubbing by using texture map based method. First, a texture sample bank is produced from scanned manual rubbing image. Second, the images of cliff inscription are acquired, rectified, segmented. Third, the skeleton of the segmented character is extracted through distance transform. Next, the map area is determined from the skeleton and width of the strokes. Then, the texture samples are mapped to the map area. Finally, a digital rubbing image can be created after combing with the background texture that is synthesized from scanned manual rubbing. The experiment results show that our proposed method can create digital rubbing images with similar effects with the manual rubbing.
Design of an Accurate Near Infrared Optical Tracking System in Surgical Navigation

Near infrared optical tracking system (NIOTS) is widely used in biomedical application, especially in surgical navigation. It largely determines the accuracy of tracking surgical tools. So we design an NIOTS and propose the corresponding algorithms to make it work well. Two near infrared cameras (NICs) are constructed in this NIOTS and a near infrared calibration board is designed to calibrate them. The NIOTS is then calibrated using the calibration results of NICs. After the image points of markers on surgical tools are obtained, a two-step stereo matching algorithm is proposed to match the subpixel points in the left and right images. Then the three dimensional coordinates of markers are computed and the surgical tools are tracked in real time. The experimental results show the accuracy of system calibration, stereo matching, and tracking. The average tracking errors of three markers in 150 positions are 0.0090 mm, 0.0071 mm, and 0.0060 mm, which demonstrate the designed NIOTS can accurately track surgical tools.
A embedded real-time polymorphic computing platform architecture

With rising requirements on computational capability, data throughput, reconfigure-ability, reliability, scalability and adaptability to poor working conditions of complex multi-task embedded systems, traditional embedded computing platforms obviously cannot meet these requirements. How to take advantage of all kinds of advanced embedded computing technologies to build a polymorphic computing platform architecture which can dynamically reconstruct its computing resources with tasks changing becomes a research hotspot. This paper presents a new high-performance VPX-based embedded polymorphic computing platforms architecture, and implements a polymorphic prototype system. The platform is comprised of three general and specific high-speed computing resources including GPU, FPGA, PowerPC, which are interconnected by high-speed serial RapidIO (SRIO) protocol and Gigabit Ethernet. It can be configured and widely used in varieties of complex embedded computing systems.
Evaluation of air traffic management system using a hybrid model

Priority analysis of critical factors and comprehensive evaluation of different development alternatives are very important for construction and improvement of national air traffic management system (ATMS). This paper presents a hybrid model that decomposes the evaluation problem into two levels. In the upper-level, an analytic network process (ANP) is given to evaluate the importance values of critical factors in ATMS. In the low level, the utility values of alternatives under each factor are estimated by the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In order to derive global scores of ATMS alternatives, a simplified synthesis process is provided. Finally, a numerical example is given for illustration.
A capacitively-coupled biomedical instrumentation amplifier employing chopping and auto-zeroing

This paper presents a low power high precision instrumentation amplifier (IA) for biomedical electronics such as EEG, ECG and neural recording. By using capacitively-coupled topology, wide input common-mode range and low power consumption is achieved. Chopping and auto-zeroing techniques are employed simultaneously to alleviate their own side-effects. A DC cancellation loop is used to suppress electrode offset. In addition, adverse effect of parasitic capacitances of input transistors is analyzed and alleviated. Improved switched-capacitor (SC) CMFB circuits are proposed to mitigate capacitance load effect. This IA is implemented in SMIC 0.18μm CMOS process. -3dB bandwidth of the IA can be set as 1 kHz or 10 kHz to fit different applications like EEG or neural recording. Simulation results show that it achieves equivalent input noise power spectrum density (PSD) of 42nV/√Hz, a noise efficiency factor (NEF) of 3.1 and CMRR of 128dB. It consumes 3.7μA from a 1V supply.
Fuzzy Kernel Fisher Discriminant Algorithm with Application to Face Recognition

In this paper, a new kernel Fisher discriminant (KFD) algorithm with fuzzy set theory is studied. KFD algorithm is effective to extract nonlinear discriminative features of input samples with kernel trick. While conventional KFD algorithm assumes the same level of relevance of each sample to the corresponding class. In this paper, a novel KFD algorithm named fuzzy kernel Fisher discriminant (FKFD) is proposed. Distribution information of samples is represented with fuzzy membership degree in this paper. Furthermore, this information is utilized to redefine the corresponding scatter matrices, which are different to the conventional KFD algorithm and effective to extract discriminative features from overlapping (outlier) samples. Experimental results on ORL face database demonstrate the effectiveness of the proposed method
Scattering by two parallel plasma cylinders

Multiple scattering by two plasma cylinders is investigated rigorously applying boundary value method verified validity with simulation method simultaneously, based upon the property of incident wave traveling in the argon plasma by cathode discharging. Approximate expressions for the far field, taking into account the interaction between the plasma cylinders, are derived briefly after some mathematical manipulations. Numerical results are presented for the case of E-polarized incidence with varied plasma collision frequencies, plasma densities and different separation distances between the cylinders, comparisons of the scattering distributions are given in detail as well. It is found that the plasma density, plasma collision and separation between the plasma cylinders play innegligible roles in scattered energy redistributing, which is useful in the future research of intelligent plasma antenna.
False-Positive Reduction on Lung Nodules Detection in Chest Radiographs by Ensemble of Convolutional Neural Networks

Aiming at the problem that traditional lung nodules detection method can only get low sensitivities with a lot of false positives, we propose a new framework of ensemble of convolutional neural networks (E-CNNs) and use it to significantly reduce the number of false positive on lung nodules detection in chest radiographs (CXRs). First, unsharp mask technique is used to enhance the nodules in the CXRs. Then, we cut patches in the <inline-formula> <tex-math notation="LaTeX">$229\times229$ </tex-math></inline-formula> image containing or not containing nodule from the enhanced CXRs, which correspond to the positive and negative samples. Third, three optimized CNNs of different input sizes and different depths, namely, CNN1, CNN2 and CNN3, are constructed to detect lung nodule separately, and their input sizes are <inline-formula> <tex-math notation="LaTeX">$12\times12$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$32\times32$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$60\times60$ </tex-math></inline-formula>, and the number of layers are 5, 7, and 9, separately. Finally, a logical AND operator is used to fuse the results of CNN1, CNN2, and CNN3, and E-CNNs are constructed for detecting lung nodules. Our experimental results on the Japanese Society of Radiological Technology database show our proposed E-CNNs attain a sensitivity of 94% and 84% with an average of 5.0 false-positives (FPs) per image and 2.0 FPs per image, respectively, in a five-fold cross-validation test, which far outperforms the state of the art.
Fast visual object tracking via distortion-suppressed correlation filtering

Visual object tracking is a basic research unit in the construction of smart cities, it focuses on establishing a dynamic appearance model to represent the target in complex scenarios. In this paper, a distortion-suppressed correlation filtering based tracking method (DSCFT) is proposed. Our approach tackles distortions caused by spatial similarity comparison and temporal appearance updating. We establish our method under a Bayesian framework, where spatial and temporal appearance are embedded in likelihood and prior respectively. Firstly, The spatial distortion is handled by modifying weight windows and utilizing a proposal selection strategy to better track targets under fast motion and background clutters. Secondly, temporal information is retained in updating stage as a prior to represent dynamic variations of the target. Moreover, a multi-scale filtering scheme is integrated when updating the temporal appearance to boost the scale sensitivity. Experimental results dedicate the effectiveness and robustness of our DSCFT on benchmark videos.
Time Series Forecasting Model with Error Correction by Structure Adaptive Support Vector Machine

Exactly power load forecasting especially the short-term load forecasting is of important significance in the case of energy shortage today. In power system, due to the complexity of the historical load data and the randomness of a lot of uncertain factors influence, the observed historical data showed linear and nonlinear characteristics. A hybrid methodology is proposed to take advantage of the unique strength of autoregressive integrated moving average (ARIMA) and SVM (support vector machine) networks in linear and nonlinear modeling, which is an error correction method to create synergies in the overall forecasting process. ARIMA model is used to generate a linear forecast in the first stage, and then SVM is developed as the nonlinear pattern recognition to correct the estimation error in ARIMA forecast. The effectiveness of the hybird-model has been tested by one example. The experimental results show that the hybrid model can more effectively improve the forecasting accuracy than ARIMA-BP.
Power system short-term load forecasting based on cooperative co-evolutionary immune network model

The main objective of short-term load forecasting (STLF) is to provide load predictions for generation scheduling, economic load dispatch and security assessment at any time. A new forecasting approach is designed in this paper and the novel method based on the cooperative co-evolutionary and the immune algorithm is proposed. The cooperative co-evolutionary immune network is used to evolve the structure and parameters of neural network. The proposed cooperative co-evolutionary immune network model has been implemented based on the actual data and compared with the traditional Radial-Basis Function (RBF) network method. The test results reveal that the cooperative co-evolutionary immune network method possesses far superior forecast precision than the Radial-Basis Function neural network method.
Constructed Landscaping Combination Constructed Wetlands System Used for Sewage Treatment

We constructed a combinant landscape constructed wetland for sewage treatment based on landscape plants in this study. The results are summerized as follows: the system uses model as: A(biological pretreatment pond)-B(biochemistry pond)- C(subsurface flow wetland)-D(surface flow wetland)-E(the third level vertical-flow wetland)-F(second-level vertical-flow wetland) -G(landscape surface flow wetland)-H(biology pond), this combination craft sewage treatment is performed effectively, and the system outcome water quality achieves the first-level A standard. In this system, through plant's screening, we found out the nearly 30 plants fitted the system with high compatibility, strong purification, high landscape value and high economic value, which were Mainly: Cannaindica, Iris ensata, Acoruscalamus, Iris tectorum, Iris germanica, Thalia dealbata, Cyperus alternifolius, Juncus effusus, Pontederia cordata, Phragmites austral is, Pennisetum purpureum, Paulownia tomentosa, Ipomoea aquatica, Zephyranthes candida, Liriope spicata, Lolium perenn, Oenanthe clecumbens, Trifolium, Reineckiacarnea, Cuphea ignea, Salix babylonica, Fatsia japonica, Lucidum Ait, Cuphea ignea, Lythrum salicaria, Metasequoia glyptosto boides, Eichhornia crassipes, Salvinia natans, Nelumbo nucifera, Nymphaea tetragona, Nymphoides peltatm.
Temporal Information Services in Large-Scale Vehicular Networks Through Evolutionary Multi-Objective Optimization

Temporal information services are critical in implementing emerging intelligent transportation systems. Nevertheless, it is challenging to realize timely temporal data update and dissemination due to an intermittent wireless connection and a limited communication bandwidth in dynamic vehicular networks. Some previous studies have considered the temporal data dissemination in vehicular networks, but they are limited to the service region, which is inside the coverage of roadside units. To enhance system scalability, it is imperative to exploit the synergic effect of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications for providing efficient temporal information services in such an environment. With the above motivations, we propose a novel system architecture to enable efficient data scheduling in hybrid V2I/V2V communications by having the global knowledge of network resources of the system. On this basis, we formulate a temporal data upload and dissemination (TDUD) problem, aiming at optimizing two conflict objectives simultaneously, which are enhancing the data quality and improving the delivery ratio. Furthermore, we propose an evolutionary multi-objective algorithm called MO-TDUD, which consists of a decomposition scheme for handling multiple objectives, a scalable chromosome representation for TDUD solution encoding, and an evolutionary operator designed for TDUD solution reproduction. The proposed MO-TDUD can be adaptive to different requirements on data quality and delivery ratio by selecting the best solution from the derived Pareto solutions. Last but not least, we build the simulation model and implement MO-TDUD for performance evaluation. The comprehensive simulation results demonstrate the superiority of the proposed solution.