nils-herrmann/clean_scirepeval huggingface dataset:
An efficient statistical-based approach for road traffic congestion monitoring

In this paper, we propose an effective approach which has to detect traffic congestion. The detection strategy is based on the combinational use of piecewise switched linear traffic (PWSL) model with exponentially-weighted moving average (EWMA) chart. PWSL model describes traffic flow dynamics. Then, PWSL residuals are used as the input of EWMA chart to detect traffic congestions. The evaluation results of the developed approach using data from a portion of the I210-W highway in Califorina showed the efficiency of the PWSL-EWMA approach in in detecting traffic congestions.
Numerical Dynamic Strong Coupled Model of Linear Magnetostrictive Actuators

A numerical dynamic and magneto-mechanical strong coupled model of linear magnetostrictive actuators was found. The model includes the magneto-mechanical coupling term, which reflects the strong coupling characteristic of magnetostrictive actuators. With this model, the dynamic magnetic and the mechanical problems can be solved simultaneously. According to the proposed model and the measured magnetic characteristic of magnetostrictive material, the relation between the magnet field and the output displacement of the actuator was calculated based on the finite element method. A comparison between the calculated results and the experimental ones for a actuator was carried out and it was found that they were in agreement well. This indicates that the numerical dynamic magneto-mechanical strong coupled model can be used for the design and control of linear magnetostrictive actuators.
Comparison of gray-scale thresholding algorithms applied to coronary arteriograms

An algorithm, called the iterative trinary classification (ITC) algorithm, is presented for image segmentation. Two gray-scale thresholds are used to define three pixel classes (object, background, and undecided). On the basis of a learning process, the two thresholds are iteratively modified until the number of undecided pixels drops to a certain level, the stopping criterion. The performance of the ITC algorithm on synthetic and human coronary angiograms is compared with that of a relation algorithm and a scattering based algorithm. The ITC algorithm is computationally efficient and provides the best segmentation performance. With its strong potential for speedup by exploiting parallel processing the algorithm should be useful in various computationally intensive procedures for the analysis of coronary angiograms.<<ETX>>
Instrumentation for cell capacitance measurements: Switching sinusoidal excitations for studying cell membrane transport

This project aims at the design of new instrumentation techniques for studying the electrical properties of the cell as a means of better understanding the cellular transport processes. The approach is to integrate the concept of a lock-in amplifier on a single-electrode platform. Switching sinusoidal excitations can be used to assess small but measurable changes of cell capacitance, which can be used to make inferences about cell surface area changes during the transport processes. In this study, a cell membrane model circuit has been simulated in software. In addition, a proof-of-concept instrumentation has also been implemented with an embedded system. The results have shown the feasibility of obtaining accurate real-time measurements of the cell capacitance by time-multiplexing the sinusoidal excitation and the voltage measurement via a single patch-clamp electrode.
Adaptation in P300 Brain–Computer Interfaces: A Two-Classifier Cotraining Approach

A cotraining-based approach is introduced for constructing high-performance classifiers for P300-based brain-computer interfaces (BCIs), which were trained from very little data. It uses two classifiers: Fisher's linear discriminant analysis and Bayesian linear discriminant analysis progressively teaching each other to build a final classifier, which is robust and able to learn effectively from unlabeled data. Detailed analysis of the performance is carried out through extensive cross-validations, and it is shown that the proposed approach is able to build high-performance classifiers from just a few minutes of labeled data and by making efficient use of unlabeled data. An average bit rate of more than 37 bits/min was achieved with just one and a half minutes of training, achieving an increase of about 17 bits/min compared to the fully supervised classification in one of the configurations. This performance improvement is shown to be even more significant in cases where the training data as well as the number of trials that are averaged for detection of a character is low, both of which are desired operational characteristics of a practical BCI system. Moreover, the proposed method outperforms the self-training-based approaches where the confident predictions of a classifier is used to retrain itself.
A Real-Time Micro-sensor Upper Limb Rehabilitation System for Post-stroke Patients

There has been an urgent need for a ubiquitous and quantitative upper limb rehabilitation method for post-stroke patients. This paper presents a real-time micro-sensor-based upper limb rehabilitation system for quantitatively evaluating the patient function status and the rehabilitation training progress. The rehabilitation system mainly consists of three subsystems: a sensor subsystem, a data fusion subsystem, and a rehabilitation training subsystem. The sensor subsystem collects upper limb motion signals and transfers them to the data fusion subsystem. The data fusion subsystem fuses motion signals to obtain motion parameters. The rehabilitation training subsystem visualizes the whole rehabilitation training process in 3D virtual space, visually guiding the patient in the training, highlighting the progress and existing issues. The system can provide rehabilitation ubiquitously, reduce the cost, and bring convenience to patients and families.
Fuzzy-based Error Correction Mechanism to Improve the Precision of Intelligent Maneuvering Target Tracking

This paper proposes a fuzzy-based error correction mechanism (FECM) to improve the precision of an online data-driven fuzzy clustering (ODDFC) used in the maneuvering target tracking and trajectory prediction. In the ODDFC, the observed data are extracted automatically by fuzzy inference mechanism without much computation and training costs. But the improvement performance of ODDFC is slightly due to its parameters limitation and the prediction accuracy can be affected by the trajectory's curvature of moving target. So we propose ODDFC with FECM to solve the problem. In the proposed method, we use fuzzy inference system that has error correction mechanism to reduce the prediction error of ODDFC. ODDFC with FECM can predict maneuvering targets adapt quickly and have better prediction performance than ODDFC. Simulation results show that proposed method can improve the performance of ODDFC
Modulized resource management platform for wireless public network in Smart Grid communication

This paper develops a modulated platform for the resource management of wireless public network to offer monthly analysis and comparison of data communication cards. The platform is consisted of three modules, namely the management module for public communication resource, operator bill module and distribution and measurement automation module. Compared with the conventional management platform, the developed modulized platform could verify the correctness of data usage of the large number of data communication cards provided by the mobile operators and make adjustments accordingly with the data and cost usage, thus efficiently managing these public resources and accurately analyzing the data traffic carried by the wireless public network.
The impact of network competence on service innovation performance under low trust level

Network competence can affect service innovation performance. And because Chinese businesses are under low trust level, so if it is neccesary that take costs to get resources, and if the resources can affect service innovation performance. So, this paper verified that network competence is positively related to service innovation performance, and organization learning capacity is an important moderating variable between network competence and service innovation performance, that mean organization learning capacity can strengthen the positive impact of the corporate network competence on service innovation performance. It provided some clues to improve service innovation performance for enterprises' practice.
Performance evaluation of the Activity Analyzer

The purpose of this study is to evaluate the performance of the Activity Analyzer used to monitor and encourage physical activities of older adults. The Activity Analyzer is a wearable device designed to monitor the daily activity and inactivity according to a preprogrammed time schedule. The device is also capable of playing back pre-recorded messages to encourage exercises if periods of inactivity are detected under predefined conditions. At the end of the day, the daily activity data with a time resolution of 5 minutes can be retrieved from the mobile unit via a docking station for further analysis. The study provided preliminary test results from human subjects to evaluate the performance and optimize parameters of the device. The result is useful as a guideline for a larger-scale human study to assess the effectiveness of the device in terms of improving the daily activities for older adults.
A new method for assessing volumetric flows in microvascular networks based on laser Doppler and microscopic imagery

A new method was developed to produce a composite image of the microvascular network with grayscale proportional to volumetric flow. Velocities in arterioles and venules were assessed with a high resolution laser Doppler imager (LDI). The vascular structures were quantified from the micrograph with a computerized vessel detection algorithm. After registering the vascular network with the LDI scan, volumetric flows were calculated for the entire vasculature. In vivo data were obtained from the hamster cheek pouch in 6 studies.
Lattice boltzmann simulation of colloidal drop dynamics on patterned substrates for printable electronics fabrication

In this paper, the lattice Boltzmann method (LBM) is used for the direct numerical simulation of an inkjet-printed colloidal drop wetting on a patterned substrate. The single component multiphase (SCMP) model of Shan and Chen is used for the liquid-vapor multiphase flow and the particle suspension model is modified to incorporate adhesive forces between the suspended particle and the surrounding fluid phases. Several validation problems are presented to test the separate sub-models and the combined model. The combined multiphase particle suspension model is then used to study the contact angle hysteresis and the stick-slip behavior of the contact line motion as a liquid drop wetting and evaporating on a patterned substrate. Finally, the dynamics of a colloidal drop containing many suspended particles are examined with and without evaporation. Results show that colloidal jamming occurs at the liquid-vapor interface as the drop preferentially wets and evaporates on a patterned substrate. This model development is an important first step towards understanding the complex transport phenomena present in an inkjet-printed evaporating drop for printable electronics fabrication.
Research on The Management of Thermal Discharge by Coastal Power Plant

With the quick development of power plant industry, the effect quantity of thermal discharge incearses gradually and so dose its on water enviormnent. But it doesn't have special criterion of the warm water drainage at present. Based on the comprehensive summary of the progress in related area and the present situation of the warm water in our country, the paper indicates our country environmental management's problems of the warm water. Take the Qingdao power plant for example. Two dimensional model of the transport and diffusion for the warm water is established. According to this numerical model, the concentration distribution of the warm water in the warm water drainage and the area of its influence are numerically predicted. Analyze the effects on ocean ecosystem brought by the warm water. Finally, propose strategy in management of warm water and utilization of waste heat.
Body activity interaction for a service robot

Body activity interaction plays an essential role in the field of intelligent robots. In this paper, we design a body activity interaction system for a service robot. In order to realize body activity interaction, the Microsoft Kinect is utilized to capture owner body movements and provide input signals of the action recognition module. Based on the RGBD and skeleton information from Microsoft Kinect, action recognition module can analyze meaning of owner body movements effectively and send orders to service robot quickly. Besides, kinematics model of the service robotic arm is built and the motion planning on the joint space is also executed. Finally, service robot receives orders and conducts robotic arm to implement handshaking or hand waving. Extensive experiments illustrate the correctness and effectiveness of our developed body activity interaction system on the platform of service robot.
A Fuzzy Neural Network Based on Particle Swarm Optimization Applied in the Speech Recognition System

Aim to the problems of fuzzy neural network depending on the initial conditions, the training time long, and being easy to fall into local minimum, this paper studied the combination of particle swarm optimization and fuzzy neural network, presented a fuzzy neural network model based on particle swarm optimization, and applied it in speech recognition system. This model improved the convergence speed of speech recognition system. The experimental results show that the system is feasible and effective.
A patch-based spatiotemporal phase unwrapping method for phase contrast MRI using graph cuts

Phase unwrapping is an important and challenging problem in phase contrast magnetic resonance imaging (PC-MRI). In this paper, we propose a new algorithm for phase unwrapping based on graph cuts. Our algorithm takes a patch-based approach which has the advantages of simplicity and robustness to phase noise. To make use of temporal information from the neighboring frames as well as spatial information from the current frame, the energy function is designed to combine both spatial and temporal constraints. The proposed method has been tested with real PC-MRI data. Experimental results demonstrate that our algorithm is capable of unwrapping images with severe phase wrapping, and that it outperforms other existing popular phase unwrapping algorithms both qualitatively and quantitatively.
Design optimization of custom engineered silver-nanoparticle thermal interface materials

Thermal interface material (TIM) is a major hurdle in heat flow for typical chip/heat sink assemblies. In many electronic devices, hot spots occur in areas of high activity during the device operation. These hot spots can lead to high thermal gradients, which in turn result in performance and reliability hindrances. The elevated, non-uniform power density confronted with conventional TIMs that contain a uniform layer of high thermal conductivity material for the entire chip can be extremely insufficient in many applications. In this paper, a custom engineered, Ag-nanoparticle (Ag-NP) TIM that targets directly to the high power density region is introduced for achieving better thermal-mechanical-electrical performance at low cost. These nanoparticles can be inkjet printed on hot spots and sintered at a relative low temperature (~120°C) to create a continuous metallic layer that is in good contact with both the chip and heat sink, whereas the conventional particle-laden TIM covers the lower power density area. A computational model is developed to examine the overall thermal performance and reliability of the hybrid Ag-NP/conventional TIM as a function of the bondline thickness, applied pressure, deposition pattern, and surface roughness. The results show great improvements compared with a high-performance indium solder.
Preliminary study on HHT-based refractory failure prediction for Kiln Shell

The Kiln brick loss is the most serious problem in Cement Plants. Usually the Kiln Shell Temperature Monitoring System (KSTMS) is monitored along the entire length of the kiln providing an essential indication of the health of the refractory material. In general, the operator through the maximum, minimum, and average kiln temperature profiles with configurable alarm zones and the Kiln Shell thermal image. The preliminary study adopted the application of Hilbert Huang Transform (HHT) to analysis the data of the KSTMS and predict the occurrence of time and location of refractory failure. The analysis result confirms the proposed method can improve the inadequate at the trend analysis for failure prediction.
Robust rank constrained kronecker covariance matrix estimation

In this paper, we consider the problem of robustly estimating a structured covariance matrix (CM). Specifically, we focus on CM structures that involve Kronecker products of low rank matrices, which often arise in the context of array processing (e.g. in MIMO-Radar, COLD array, and STAP). To tackle this problem, we derive a new Constrained Tyler's Estimators (CTE), which is defined as the minimizer of the cost function associated to Tyler's estimator under Kronecker structural constraint. Algorithms to compute these new CTEs are derived based on the Majorization-Minimization algorithmic framework.
Waveforms in the arterial tree

System identification techniques were employed to characterize the dynamics in the arterial system. Arterial pressure and flow waveforms were obtained from computer simulations using a complex model and from experiments in domestic swine. Transfer functions were identified by using an iterative least-squares approach. It was shown that the aortic input impedance can be sufficiently characterized by a 1st-order system and the transfer function between aorta and femoral artery is dominated by a 2nd-order system. Within a scale factor the estimated aortic flow waveform matched well with the measured (less than 1% error). Thus, the proposed method provides for the cycle-by-cycle determination of flow waveform from aortic root pressure and should be useful for studying circulatory dynamics and regulatory control mechanisms.<<ETX>>
A novel noise suppression solution for low cost microcontroller-based touch panel: Using inverse charge transfer capacitive sensing method

The proposed method uses a simultaneous pre-charging schema to sensing capacitance change on the sensing pad. In order to compensating the unstable sensing value due to the changing in power supply, an extra reference capacitor pair is adding to the schema. With simultaneous pre-charging schema, all of the sensing capacitors (Cs) including reference capacitors are charged to same voltage level before transferring charge to sensing pads. Therefore, the charge transfer times of all sensing channels do not interference by unstable power supply because transfer times can be compensated by reference channel. As for the noise injected during touching, the sensing pad discharge and manual leakage control allow using high volume Cs to construct a lower bandwidth passive analog filter front end without being stalling the measuring time significantly, so the stability of input level sensing is guaranteed. By combining the two noise-resisted features, touch sensing design implemented in a single microcontroller does not rely on high quality LDO and even can be feed from bulk converter directly.
The design of signal acquisition and processing of Rotational Raman temperature measuring lidar system

In order to realize the real-time monitoring of the atmospheric temperature, Rotational Raman temperature measuring principle is selected in a variety of common temperature measurement principle and a Rotational Raman temperature measuring lidar system is designed. This system is based on the lidar as the experimental basis and LabVIEW as the theoretical basis which can get the lower atmospheric temperature profile curve after collecting signal data, so that people can get the lower atmosphere temperature with more convenience in real-time. Some experiments were carried out and the results show that the system can be accurate to get the lower atmospheric temperature profile curve in real time.
Enhanced monitoring of abnormal emergency department demands

This paper presents a statistical technique for detecting signs of abnormal situation generated by the influx of patients at emergency department (ED). The monitoring strategy developed was able to provide early alert mechanisms in the event of abnormal situations caused by abnormal patient arrivals to the ED. More specifically, This work proposed the application of autoregressive moving average (ARMA) models combined with the generalized likelihood ratio (GLR) test for anomaly-detection. ARMA was used as the modelling framework of the ARMA-based GLR anomaly-detection methodology. The GLR test was applied to the uncorrelated residuals obtained from the ARMA model to detect anomalies when the data did not fit the reference ARMA model. The ARMA-based GLR hypothesis testing scheme was successfully applied to the practical data collected from the database of the pediatric emergency department (PED) at Lille regional hospital center, France.
Adaptive Backstepping Sliding Mode  $H_{\infty} $ Control of Static Var Compensator

The robust H∞ control problem is addressed for a single machine infinite bus system with static var compensator (SVC), the damping coefficient uncertainty and external disturbances in this paper. Storage functions of the control system are constructed based on modified adaptive backstepping sliding mode control and Lyapunov methods. A nonlinear robust H∞ controller and a parameter updating law are obtained simultaneously. The controller cannot only attenuate the influences of external disturbances on the system output, but also has strong robustness for system parameters variations. Since the controller design is based completely on the nonlinear dynamic system without any linearization, the nonlinear property of the dynamic system is well preserved. The simulation results show that more rapid speed response and stronger robustness can be obtained by the proposed method than the conventional adaptive backstepping and adaptive backstepping sliding mode control methods.
Multi-color modulation in solid-state display based on phase changing materials

A multi-color solid-state display structure has been fabricated and characterized, which is based on phase changing material (PCM) and indium tin oxide. By modulating the crystalline phase of PCM, three or more colors can be realized dynamically on a single structure. Experimental results agree well with simulations. This work demonstrates that PCM-based solid-state display structure has potential to achieve more colors than previously reported works.