nils-herrmann/clean_scirepeval huggingface dataset:
Novel UWB Bandpass Filter Using Stub-Loaded Multiple-Mode Resonator

Novel compact ultra-wideband (UWB) bandpass filter (BPF) using stub-loaded multiple-mode resonator (MMR) is presented in this letter. The MMR is constructed by loading three open stubs in a uniform-impedance resonator, i.e., one stepped-impedance stub at the center and two uniform-impedance stubs at the symmetrical side locations. Five modes, including two odd modes and three even modes, could be designed within UWB band, and two transmission zeros generated by the stepped-impedance stub improve the passband selectivity greatly. The letter shows modal resonant frequencies against the step-impedance-stub-loaded parameters, which can control the even modes flexibly, while the odd modes remain the same. A compact planar UWB BPF is simulated, fabricated and measured. The simulated and measured results are in good agreement and show good in-band filtering performance and sharp selectivity.
Exact feedback linearization of general four-level buck DC-DC converters

Four-level buck DC-DC converters (FLB), as strongly coupled, multi-input multi-output and under-actuated 4th-order nonlinear systems, require relatively complex control to meet desired demand. Traditional control methods such as proportional-integral (PI) with linear decoupling either have slow dynamic response and incomplete decoupling, which means a conflict in performance among the output voltage and several flying-capacitor (FC) voltages, or need to degrade the circuit, losing the generality. Considering all state variables of general FLB and thus building the 4th-order affine average model, this paper proposes a full-state exact feedback linearization (EFL) nonlinear control method based on differential geometry. Besides, the stability of internal dynamics caused by under-actuated characteristics is concisely analyzed when different output functions are selected. Simulation results show that proposed method has better transient and steady-state performance than PI with linear decoupling when there is a step change in input voltage or load.
A physical based underwater imaging model and camera calibration method

In order to conduct measurement or 3d surface reconstruction in underwater environments, we have to place the camera in a waterproof house and take pictures. Due to the medium of water and air having different refractive indexes, the phenomenon of refraction occurs when light propagates in different medium, which led to the light is no longer a straight line before entering the camera, the incident light will not focus on one point. Traditional calibration model in air is no longer applicable in this situation. By analyzing the physical model of light propagation in the water, and according to the imaging model in air, we propose a nonlinear underwater imaging model and camera calibration method. Through the proposed approach, the physical parameters of our model that affecting the underwater imaging are calibrated. And the measurement experiments show that we can acquire the identical accuracy as in the air, or even better.
A 66pW discontinuous switch-capacitor energy harvester for self-sustaining sensor applications

We present a discontinuous harvesting approach for switch capacitor DC-DC converters that enables ultra-low power energy harvesting. By slowly accumulating charge on an input capacitor and then transferring it to a battery in burst-mode, switching and leakage losses in the DC-DC converter can be optimally traded-off with the loss due to non-ideal MPPT operation. The harvester uses a 15pW mode controller, an automatic conversion ratio modulator, and a moving sum charge pump for low startup energy upon a mode switch. In 180nm CMOS, the harvester achieves >40% end-to-end efficiency from 113pW to 1.5μW with 66pW minimum input power, marking a >10× improvement over prior ultra-low power harvesters.
Age-dependent of electromagnetic absorption in human endocrine glands for using mobile phones

With the quick development and widespread use of mobile phones, especially mobile phones by children, has led to a rising concern about the possible adverse health effects of radio frequency electromagnetic field exposure. This study aims to present the dosimetry analysis of the electromagnetic fields induced by mobile phones on the endocrine glands for both child and adult. A finite-difference time-domain method was employed to calculate the specific absorption rate in the realistic child and adult head-neck models from exposure to a generic handset at 1750 MHz. The results show that the locally induced specific absorption rate in endocrine glands of child is larger than that of adult. The thyroid gland is the most exposed organ compared to the hypophysis and hypothalamus glands. In all of the examined cases, the specific absorption rate values in endocrine glands of child and adult are below the IEEE safety standard.
A Passive Vapor-Feed Direct Methanol Fuel Cell Based on a Composite Pervaporation Membrane

A passive micro direct methanol fuel cell (μDMFC) by using composite pervaporation membrane for vapor fuel supply without any extra power is presented. A simple and power free vaporizer, which consists of a polydimethylsiloxane (PDMS) active layer and a porous silicon support layer, is integrated with a μDMFC, converting the methanol from liquid phase to vapor phase. The novel anode vaporizer generates methanol vapor, which diffuses to and reacts with catalyst directly. The composite membranes with different parameters are prepared and tested to optimize the performance of the membrane vaporizer. The developed prototype exhibits a peak power density of 18.2 mW/cm2 and a maximum fuel efficiency of 78.4% at room temperature. It is believed that the vapor-feed μDMFC will be more applicable for microsystems.
Bridging Time Series Dynamics and Complex Network Theory with Application to Electrocardiogram Analysis

Since the pioneering studies of small-world and scale-free networks in the late 1990s , , we have witnessed fruitful advances in our understanding of the complex connection properties and characteristics of the many diverse large-scale distributed interconnected systems, both natural and man-made, with examples ranging from the Internet, the World Wide Web, biological neural networks, protein-to-protein interaction networks, power-grids, wireless communication networks to social, economic and financial networks in human society [3][10]. Extensive efforts have been devoted to characterizing the rich connectivity patterns among the nodes (components) of such complex networks (systems), and in the course of development of research in this area, people have been prompted to address on a fundamental question: How does the fascinating yet complex topological features of a network affect or determine the collective behavior and performance of the networked system? While elegant attempts to address this core issue have been made, for example, from the viewpoints of synchronization [11][16], epidemics [17][22], evolutionary cooperation [23][26], and the control of complex networks [27][30], theoretically or empirically, this widely concerned key question still remains open in the newly emergent field of network science.
Design and Implementation of an Improved BCI System Based on Alpha Rhythm

This paper introduces an improved BCI system based on alpha rhythm, the main composition units of the system are electrodes, acquisition circuit, online detecting algorithm and outer devices. For improving the performance of BCT systems, a new algorithms for alpha wave detection is proposed, which can reduce EEG baseline wandering and energy fluctuation efficiently and make the BCI system work more stable. Based on the BCI system, experiments of media player control are carried out among different subjects, which show that the system has an excellent performance in alpha wave detection and real-time controlling.
A new approach of crowd density estimation

Crowd density estimation is important in crowd analysis, this paper proposes a new approach used for crowd density estimation. First, background is removed by using a combination of optical flow and background subtract methods. Then according to texture analysis, a set of new feature is extracted from foreground image. Finally, a self-organizing map neural network is used for classifying different crowds. Some experimental results show compared to former crowd estimation methods, the proposed approach can carry out the estimation more accurately, the rate of true classification is 85.6% on a data set of 500 images.
Automatic Real-Time Video Background Segmentation System

This paper proposes an automatic background seg- mentation system based on mixture background models and lbp contrast. An adaptive per-pixel background model is developed to set the data cost of an image graph. lbp constrast is adopted to capture the contrast information. We verify our algorithm in different video sequences including public video sequences. Experimental results of composite video demonstrate that our proposed algorithm is efficient.  Foreground segmentation is important and has a lot of ap- plications including background substitution and composition, human-computer interaction, compression, object tracking, etc. In this paper, we focus on how to segment foreground objects from monocular video streams. Recently, some automatic video matting methods for natural scenes are proposed using binocular and monocular video seg- mentation methods. Kolmogorov et al. (1) presented binocular stereo video segmentation algorithm to fuse color, contrast and stereo matching information so as to infer layers accurately and efficiently. However, this method requires synchronized stereo input so as to obtain valuable depth information. In contrast, monocular video segmentation is more popular for its convenience and low cost. The authors of (2)-(4) fused dif- ferent cues to achieve the monocular foreground segmentation based on graph cut algorithm. Although satisfactory results are obtained in their experiments, they pay less attention to the problem of shadows that foreground objects cast on the background. In fact, it is often necessary to separate the foreground object from their shadows in video segmentation (6). In this paper, we present an automatic background segmen- tation system under the circumstance of a static background and fixed web-camera. In the dynamic graph cut framework, an adaptive background model which combines the advantages of both Eigenbackground and pixel-based gaussian models is proposed to set the data cost. And the link cost based on lbp contrast is introduced to improve the segmentation spatial smooth. The automatic segmentation system we proposed can automatically generate high-quality and real-time composite video with shadow removal.
An Improved Kademlia Protocol In a VoIP System

Kademlia, which has been widely used in file sharing systems such as eMule and Overnet, concerns mainly on the resources and prefer to locate precisely the right peers despite of time consuming. We proposed an improved Kademlia protocol to adapt to VoIP systems meeting the need of real-time audio service and establishing a conversation in short delay. Beside, to provide an effective circuit switching we present timely backup channel for each peer to rescue interrupted conversation.
Diagnosis algorithm of sleep apnea syndrome

Sleep apnea syndrome (SAS) is monitored and examined clinically with polysomnography. However, it is expensive and complex to operate, which significantly affects the natural sleep of human. To evaluate the value of heart rate variability (HRV) in diagnosing SAS, we propose a new method for SAS classification based on fuzzy support vector machine (FSVM). Detrended Fluctuation Analysis (DFA) and Autoregressive (AR) model spectrum estimation are used to analyze R-R interval sequence of 38 healthy subjects and 28 SAS subjects during various sleep stages. Scaling exponents of age, gender and HRV at each sleep stage, as well as low/high frequency are selected as SAS characteristic parameters and FSVM is used to classify SAS. Results indicate that the proposed method can diagnose SAS effectively and the classification accuracy rate of SAS is 93.94%. Compared with current SAS diagnosis methods, this method is more simple and efficiently.
A review of process mining algorithms

Process mining has been widely applied in lots of fields, which can discover workflow models from event logs, helps to design or re-design process models, and brings convenience to workflow management system. The main function of process mining algorithms is to provide us with valuable objective information hidden in event logs, which plays a crucial important role on the implementation of new operation business process, and audit, analysis and improvement of the existing ones. In this paper, we firstly describe the application background of process mining technology, and then list the frame structure of the technology at home and abroad, followed by summarizing and analyzing the algorithm of important domestic and abroad research, and forecast the future direction for research at last.
Real-time foreground segmentation based on a fused background model

A fused background model that combines the eigenbackground with Gaussian models is proposed. We adopt the eigenspace model to build the intensity information for each pixel. Unimodal Gaussian density methods with less computational cost are used to describe color information for each pixel. An adaptive strategy is used to integrate the two models. Using the fused background model, we subtract the background from the current video frame to obtain the foreground object. Shadow removal based on chroma color method and post-processing are discussed in the end. Experimental results prove that our model is robust to noise and illumination change due to inheriting eigenbackground and Gaussian's model advantages to improve the segmentation results.
Numerical Analysis of Solid Fuel Scramjet Combustors

Based on the combustor model and experimental data of Ref.2, numerical simulations of three different combustor geometries presenting three situations with solid fuel regression were conducted using FLUENT software. The combustor inlet airflow had a Mach number of 1.5, total temperature of 1270 K and total pressure of 30 atm. The HTPB fuel and a global one- step reaction mechanism were used. The results of non reacting computation reveal that the airflow velocity deceases in the majority zone of combustor with the solid fuel boundary regression. The results of reacting computation reveal that the supersonic zone in the divergent section of three cases gets larger than non reaction case. Combustion takes place in the vicinity of solid fuel wall. Combustion efficiency is in the range of 35%~45%. Specific thrust and specific impulse both decrease with fuel regression and are both lower than experimental results.
Reliability demonstration test model for binomial system with reliability growth

Many complex engineering systems have reliability growth in successive test stages. In most existing reliability demonstration test models, reliability growth is not incorporated as modeling assumption. In this paper, for binomial system with 2 test stages, a reliability demonstration test model is presented that naturally takes possible reliability growth into consideration. Likelihood ratios are defined for various possible situations, and statistical decision rules are derived. With a given example, the relative lower decision risk of the proposed model is illustrated by comparison with that of classical demonstration test model.
Improving throughout of continuous k-nearest neighbor queries with multi-threaded techniques

Traditional moving objects database has faced the rapid evolution of modern CMP processor. To evaluate massive concurrent continuous queries towards moving objects, parallel processing techniques and cache-conscious algorithms adapting to memory hierarchy and multi-core architecture should be developed to maximize the processor computation abilities. This paper introduces a multi-staged engine (MSE) for high performance and adaptable execution of massive concurrent continuous queries processing, which exploits pipeline strategy and departs the continuous query processing into three simultaneous stages: preprocessing, executing and dispatching modules to improve the parallelism with multi-threaded technology. Based on MSE framework and grid index for moving objects, we present a multi-threaded algorithm (MT-CNN) for massive continuous k nearest neighbor queries processing. MT-CNN algorithm uses threaded workload parallelism and cache-conscious execution reorganization strategies to improve the spatial and temporal locality. Experimental evaluation on a dual-core platform and analysis show that MT-CNN algorithm achieves a performance improvement over the existing traditional optimization counterparts.
A hybrid dialogue management approach for a flight spoken dialogue system

Finite-state based dialogue management approaches are powerful for simple tasks with strict and reliable dialogue control, while the plan-based dialogue management approaches are feasible for tasks with several topics or one complex topic. However, neither one is good enough for dialogue control throughout in a dialogue task providing a flight information enquiry and ticket reservation service. To solve this problem, a hybrid dialogue management approach is proposed, in which the plan-based topic forest structure is combined with some finite-state control, so that a proper management element can function in different dialogue situations.
State-Driven Priority Scheduling Mechanisms for Driverless Vehicles Approaching Intersections

Scheduling driverless vehicles with different priorities to pass through intersections efficiently and safely has been becoming an important passing-through intersection (PTI) problem in the field of novel intelligent traffic systems (ITS), which is increasingly becoming cyber-physical-fused and social-service-oriented. Considering new emerging features with possible priorities, a novel centralized priority scheduling mechanism is mainly explored in this paper. First, related pivotal aspects of environment and driverless vehicles are modeled by fusing their physical and kinematic characters. Based on these models, PTI-related motions are further abstracted as several reservation-oriented standard states and actions. Then, an event-triggered and state-driven autonomous control procedure is designed. By mapping vehicular relations in spatiotemporal domain into time-distance windows, a universal passing-through principle, rules, and priority-based scheduling mechanisms are proposed and described in detail. Finally, a priority scheduling algorithm sPriorFIFO is proposed and designed. These models and mechanisms are then implemented within an algorithm simulator, through which scheduling performances are verified and evaluated.
Distributed Model Based Sampling Technique for Privacy Preserving Clustering

The sharing of data has been proven beneficial in data mining applications. However, privacy regulations and other privacy concerns may prevent data owners from sharing information for data analysis. To resolve this challenging problem, data owners must design a solution that meets privacy requirements and guarantees valid data clustering results. To achieve this dual goal, we introduce a new method for privacy-preserving clustering based on the probability distributed model for clustering. This paper proposes square wave-based clustering model, gauss distribution-based clustering model and the multivariate normal distribution-based clustering model based on the information provided by the K-means clustering result separately. And we mainly show the correctness and feasibility of the sampled data for clustering by experiment. This new preserving technique can be used not only for distributed clustering, but also for simultaneous clustering rule and data hiding.
A Review of Data Mining-Based Financial Fraud Detection Research

Nationwide, financial losses due to financial statement frauds (FSF) are mounting. The industry recognizes the problem and is just now starting to act. Although prevention is the best way to reduce frauds, fraudsters are adaptive and will usually find ways to circumvent such measures. Detecting fraud is essential once prevention mechanism has failed. Several data mining algorithms have been developed that allow one to extract relevant knowledge from a large amount of data like fraudulent financial statements to detect FSF. Detecting FSF is a new attempt; thus, several research questions have often being asked: (1) Can FSF be detected? How likely and how to do it? (2) What data features can be used to predict FSF? (3) What kinds of algorithm can be used to detect FSF? (4) How to measure the performance of the detection? And (5) How effective of these algorithms in terms of fraud detection? To help answer these questions, we conduct an extensive review on literatures. We present a generic framework to guide our analysis. Critical issues for FSF detection are identified and discussed. Finally, we share directions for future research.
A constant-on-time based buck controller with active PFC for universal input LED system

A constant-on-time (COT) based buck controller for driving LED lamp is presented in this paper. The chip integrates active power factor correction (PFC) and is suitable for universal input application. The buck converter is working in critical conduction mode (CRM). With COT control strategy, the input current of the buck converter could well follow the input voltage, thus high power factor (PF) can be attained. Also, the implementation of floating ground architecture helps to simplify the design of the control circuits. Furthermore, since the buck converter operates under CRM, the dynamic loss is also reduced. The chip is designed in CSMC 1.0 μm 40V process. Simulation results show that a power factor of 0.98 is achieved; the deviation of output average driving current is within ±3%, and the power efficiency at full load with 220Vrms input is 96%.
Finite Element Analysis of Thermal Problems in Gas-Insulated Power Apparatus With Multiple Species Transport Technique

Thermal analysis is paramount in the design process of gas-insulated power apparatus. In this paper, a multiple species transport technique is proposed to deal with the heat transfer problem of gas-insulated power apparatus, in which the ambient air as well as SF6 gas is included in the solution region. The FEM is employed to investigate the coupled eddy current, fluid, and thermal fields. Temperature-dependent electric and thermal properties of the materials are considered. The power losses calculated from electromagnetic analysis are used as heat input for fluid and thermal analysis, in which the need for convective boundary condition is eliminated. The proposed method is applied to the thermal analysis of both single- and three-phase bus bars and is validated by comparison with the analytical method and experimental results reported in the literature.
Simplified discrete-time modeling and dynamic characteristics analysis of PI-controlled voltage source inverter

The characteristic of voltage source inverter under Pi-control is much more complex than under P-control. When system parameters are not properly designed, this system may appear unstable oscillation at different frequency. The stability boundary of control parameters have both upper and lower limit. in previous works on discrete-time modeling and analysis, P-controller is always taken as object of study for simplicity, which is different from practical applications where Pi-controller is often adopted. Therefore, previous works have limited research significance. in this paper, a simplified discrete-time model of voltage source inverter under Pi-control is derived. Based on analysis of the proposed model, different unstable phenomenon and mechanism corresponding to upper and lower stability boundary have been revealed. Moreover, high accuracy stability boundary has been derived and verified by experiment.