nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive TDMA-based MAC protocol in energy harvesting wireless body area network for mobile health

This paper investigates the problem of link scheduling in a single-hop energy harvesting wireless body area network (EH-WBANs) where sensor devices's energy harvesting rates and data rates are spatially heterogeneous and temporally variant. To maximize the channel utilization with the lifetime operation, an adaptive TDMA-based protocol (AT-MAC) is proposed, which is suitable for communication in an EH-WBAN for remote monitoring of physiological signals. In this protocol, a duty cycle can be dynamically adjusted to maintain the harvested energy amount, which is always greater than power consumption. Also, a novel time-slot allocation algorithm is designed to automatically adjust duty cycle with various data traffic and harvesting rates. This algorithm is decomposed into two sub-processes: predistribution of time-slot and redistribution of time-slot. The former process takes the information of energy harvesting rates into account, regardless of the rates varying in a nondeterministic manner and among various sensor nodes. For the latter process, the number of distributed time slots will be further adjusted to cope with data traffic of spatially heterogeneousness. Simulation results demonstrate the proposed protocal is a promising candidate for realizing the lifetime operation in EH-WBANs.
Comparative Research of Portfolio Model Using Fuzzy Theory

By discussing both linear and nonlinear fuzzy situations, we give the relevant model and the solution method. First of all, regarding the target level of investment return and investment risk as the half trapezoidal fuzzy number, we give the fuzzy portfolio selection model of linear satisfaction degree. Then, to describe the investor satisfaction degree of investment return and investment risk better, a logarithmic membership function has been imported. Following, we give the fuzzy portfolio selection model of nonlinear satisfaction degree. Nonlinear optimize model can be transferred to linear programming model, so that model calculation has been simplified significantly. Finally, according as the actual data of Chinese securities market, both of models have been compared by using genetic algorithm. Result indicates that portfolio selection model based on linear and nonlinear membership function includes not only historical data, but also investors' expectation. That accords with human psychology and fact. Moreover, the nonlinear membership function adopted and parameter adjusted, it can furthest get portfolio that accords with one's expectation. Therefore, nonlinear membership function is better than linear membership function.
Controlled Auxiliary Circuit to Improve the Unloading Transient Response of Buck Converters

In this paper, a controlled auxiliary circuit is presented to improve the transient response of a Buck converter. It is well established that for converter applications with a large input/output voltage ratio, voltage overshoots (due to step-down load transients) are much larger than corresponding voltage undershoots (due to step-up load transients). Therefore, the goal of the proposed method is to reduce the overshoot. The control method only activates the auxiliary circuit during step-down load transients and operates by rapidly transferring excess load current from the output inductor of a Buck converter to the converter's input. The proposed method behaves as a controlled current source to remove a constant regulated current from the output of the Buck converter. The duration of activation of the auxiliary circuit is also regulated. The proposed circuit has the following advantages: 1) predictable behavior allowing for simplified design; 2) inherent over-current protection; and 3) low peak current to average current ratio allowing for use of smaller components. In addition, the proposed auxiliary controller estimates the magnitude of the unloading transient and sets the auxiliary current proportional to the transient magnitude. This allows for greater design flexibility and increases the auxiliary circuit efficiency for unloading transients of lesser magnitude. In this paper, it is shown through analysis, simulation, and experimental results that a large reduction of voltage overshoot and output capacitor requirements can be realized through the addition of a small MOSFET, diode, and inductor.
An accurate extrinsic camera self-calibration method in non-overlapping camera sensor networks

Accurately extrinsic camera self-calibration, namely determining the positions and orientations of the networked cameras by themselves, is essential for many applications such as surveillance, intelligent environments and traffic monitoring. This paper describes an efficient, range-free and anchor-free method for self-calibrating the extrinsic parameters of the cameras in a non-overlapping camera sensor networks. The proposed method is based on the method proposed by Ali on the 2004's CVPR. Knowledge of the locations or angles, got from the assisted sensor (accelerometer or angular-accelerometer) installed on the moving object provide additional effective constraints on the optimization problem in order to compute the cameras' poses. Simulation results show that the iteration times, the calibration error, the volume of the data needed by the improved method are far less than the original method. The advantage of the method is that it can be applied even when the target takes sharp turns out of any camera's Field of View (FoV) with little steps.
A novel two-step SVM classifier for voiced/unvoiced/silence classification of speech

In this paper, a novel method for voiced/unvoiced/silence of speech classification using the support vector machine (SVM) is proposed. This classifier can correctly classify speech frames into voiced frame, unvoiced frame and silence frame. The comparison of experimental results show that the proposed method outperforms other traditional methods. The performance of SVM for different kernel functions in the experiment was analyzed and discussed as well.
A variable switching frequency hybrid control for ZVS dual active bridge converters to achieve high efficiency in wide load range

The Dual Active Bridge (DAB) converter with the Conventional Phase-shift Modulation (CPM) and Triangular Current Modulation (TCM) are compared in terms of the power transfer and total loss. It is noted that there exists an optimal design trade off among the switching loss, conduction loss and drive loss according to different load current and switching frequency. Therefore, the optimal switching frequency design would be beneficial to minimize the total loss and improve the performance of the DAB converter. A hybrid modulation strategy with variable switching frequency is proposed to reduce the dominant loss and improve the efficiency in wide load range. A 500 W prototype was built to verity the benefits of the proposed control method.
A broadband bandpass rectangular waveguide filter based on metamaterials

This paper presents a brand-new method and process of building a broadband bandpass filter in a flat rectangular waveguide with a passband between 6-12GHz (66.7% in percentage) using a 3-layer periodic SRR structure. Due to the unsatisfied reflection in the passband, another scale of SRRs have been utilized as the lambda/4 impedance matching layer between the air and the metamaterial filter layer in the waveguide in order to decrease the reflection coefficient which is lower than -10dB after implement.
Bandgap tunability of a liquid-filled photonic crystal fiber based on bend and temperature change

We report a highly tunable liquid-filled photonic bandgap fiber (LF-PBGF) based on both bend and temperature change. By bending the LF-PBGF and changing its temperature, the blue shift of the red edge of the bandgap resulting from bend loss and temperature increasing is speeded up, and higher bandwidth tunability is achieved. Numerical and experimental results are presented, and 177-nm bandwidth tunability is achieved by tuning the temperature of the LF-PBGF with bend radius of 5 mm from 40°C to 60°C, and the average compression rate is 8.85 nm/°C.
Control scheme for decoupling auxiliary power supply in dimmable LED drivers

Integrating an auxiliary (AUX) power supply into LED drivers is a popular trend to facilitate LED luminaire system design and reduce system cost and complexity. This AUX power supply can be used to power LED fixture cooling, lighting controller or other suitable electronic devices. In this paper, a cost effective architecture based on Flyback topology is proposed for both constant current (CC) output for LED drive and constant voltage (CV) output for AUX supply. A novel nonlinear ramp based control scheme is invented to decouple the main CC power train from the CV AUX supply and avoid LED output flickering. Small signal modeling is presented to highlight the advantages of this control scheme over conventional peak current mode control. This scheme has been implemented successfully for a 40W dimmable LED driver with a 12V 3W AUX supply.
Biomass-based rapeseed (Brassica napus L.) stem and rachis geometric parameter model

To quantify the relationships between rapeseed stem and rachis geometric parameters and the corresponding organ biomass, we presents a biomass-based model of stem and rachis geometric parameters of rapeseed (Brassica napus L.), including biomass-based total length model for ramification, maximum diameter model for ramification, length model for main stem, maximum diameter model for main stem, and length model for main rachis, designed to explain effects of cultivars and environmental conditions on rapeseed stem and rachis morphogenesis at the individual stem level. Various model variables, including organ length, maximum diameter, and corresponding organ biomass, were parameterized for rapeseed based on data derived from an outdoor experiment with rapeseed cv. Ningyou18, Ningyou16, and Ningza19. The organ dimensions of rapeseed were modelled taking corresponding organ biomass as an independent variable. Various variables in rapeseed showed marked consistency in observation and simulation, suggesting possibilities for a general rapeseed stem and rachis geometric parameter models. Our descriptive models were validated with the independent experiment data, and the results showed that these models paid a good performance and reliability for predicting length and maximum diameter for stem and rachis, which can set the stage for connection to physiological model via biomass and development of Functional Structural Rapeseed Models (FSRMs), and start with the localized production and partitioning of assimilates as affected by abiotic growth factors.
CyberGIS-BioScope: A Cyberinfrastructure-Based Spatial Decision-Making Environment for Biomass-to-Biofuel Supply Chain Optimization

BioScope, a supply-chain optimization model, is integrated in the CyberGIS Gateway as a science gateway application following a geodesign approach for spatial decision-making environment design and a service integration approach provided by the GISolve middleware services. This gateway application provides a highly interactive user environment for community users and supports optimization of biomass supply chain and the evaluation of optimization model uncertainty through a lightweight computation workflow implementation. The development of uncertainty computation workflow leveraged the job dependency feature of existing job schedulers on CI resources and significantly simplified the execution and management of cyberGIS analytics. The uncertainty analysis feature is being incorporated into CyberGIS-BioScope user environment for community access.
Analysis of residential product's value based on Structural Equation Model and Hedonic Price Theory

According to Residential Hedonic Price Theory, house price is constituted of all the utilities that it brings to the residents. Any single house price should stand for all its characteristics. Under the impact of global financial crisis and with the economy background gets worse nowadays, the housing industry in China is facing an unprecedented challenge. For many developers, it is important and urgent to see what features are really valuable to the consumers when they choose a house. Thus, a new way of evaluating the residential product's value seems to be very useful now. Based on this, this paper categorizes residential product's characteristics into four parts: location, district, house structure and neighborhood environment, and also measures house value with two variables: price and satisfaction. The author makes full qualitative and quantitative analysis .With the use of Structural Equation Model and AMOS, 100 samples are selected to do a case study. Through this, it illustrates the regression relationship between house value and all its characteristics.
Correlation of Gate Capacitance with Drive Current and Transconductance in Negative Capacitance Ge PFETs

Correlation of gate capacitance <inline-formula> <tex-math notation="LaTeX">${C} _{G}$ </tex-math></inline-formula> with drive current <inline-formula> <tex-math notation="LaTeX">${I} _{\textsf {DS}}$ </tex-math></inline-formula> and transconductance <inline-formula> <tex-math notation="LaTeX">${G} _{m}$ </tex-math></inline-formula> in negative capacitance (NC) Ge pFETs is first investigated. Hysteresis-free NC Ge pFETs integrated with 4.5-nm HZO achieving the improved <inline-formula> <tex-math notation="LaTeX">${I} _{\textsf {DS}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${G} _{m}$ </tex-math></inline-formula> over the control devices are fabricated. A peak in the <inline-formula> <tex-math notation="LaTeX">${C} _{G}$ </tex-math></inline-formula> versus gate voltage curve is demonstrated in the NC Ge pFET, indicating the NC effect induced by HZO film. It is observed that <inline-formula> <tex-math notation="LaTeX">${I} _{\textsf {DS}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${G} _{m}$ </tex-math></inline-formula> of the NC transistors are enhanced as the <inline-formula> <tex-math notation="LaTeX">${C} _{G}$ </tex-math></inline-formula> peak gets increased. This is attributed to the fact that, as the device operates in the NC region, both <inline-formula> <tex-math notation="LaTeX">${C} _{G}$ </tex-math></inline-formula> and internal gate voltage amplification are proportional to <inline-formula> <tex-math notation="LaTeX">${C} _{\textsf {FE}}$ </tex-math></inline-formula>/(<inline-formula> <tex-math notation="LaTeX">${C} _{\textsf {FE}}+{C}_{\textsf {MOS}}$ </tex-math></inline-formula>), where <inline-formula> <tex-math notation="LaTeX">${C} _{\textsf {FE}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${C} _{\textsf {MOS}}$ </tex-math></inline-formula> are the NC of HZO and the MOS capacitance of the device, respectively.
A new knowledge reduction method based on difference-similitude set theory

A new knowledge reduction method based on difference-similitude set theory (DSST) is proposed. It describes each originating rule with the corresponding difference set DS and similitude set SS. Elements in DS are divided into 3 classes: base set, no-base set and cover-base set. Attributes only in the cover-base set can be rejected from IS immediately while computation of DS is accomplished and the remained reduction is only processed in no-base set. The reducing process is based on the implicated knowledge in DS and SS: which attribute can be rejected or not, is determined by DS (every element in DS should not be empty) and which attribute to reject is more reasonable (optimal) is determined by SS (the corresponding overlapness should be the minimum one). Being a heuristic search algorithm in reducing knowledge, it takes not only the difference but also the similitude in IS into account. Because the reduction is only processed in no-base set and in the restriction of optimal principle, the complexity of the computation is far decreased and the efficiency is far increased.
A review of nonintrusive load monitoring and its application in commercial building

Nonintrusive load monitoring (NILM) has been developed for decades in load monitoring and disaggregation. Methods of digital signal processing and statistical modeling have been applied in detecting state change events, extracting the load signatures, identifying individual device load for disaggregation, and parameterizing the transient of devices for diagnosis. In this paper, we discuss the challenges in general NILM system with methods implemented. We focus on models applied in key steps, which are “event detection” and “load recognition”. Approaches in residential and commercial buildings are evaluated, where these two typical applications have gained lot attentions recently. The challenges in commercial building are explored and studied, and potential solution and research direction are discussed. It shows that unsolved problems and modern intelligent devices require more sophisticated NILM system in the future.
Discussion and consideration on teaching reform of landscape architecture

There are new demands for the training of landscape professionals in new period. In the study, teaching situation and problems in landscape architecture are analyzed; thoughts and methods of teaching reform in landscape architecture are proposed; teaching reform methods such as optimized courses systems, improved teaching methods and consummated teaching management system etc. are discussed, Nowadays, landscape architecture teaching is faced with problems of plain and simple teaching contents and methods, insufficient teacher construction and practical teaching, insufficiency in inspiring students' innovation spirits. It is only way of landscape architecture teaching reform to optimize curriculum systems, improve teaching methods and perfect teaching management systems. So as to provide some references for the teaching reform and exploration.
A Big Data Online Cleaning Algorithm Based on Dynamic Outlier Detection

To effectively clean the large-scale, mixed and inaccurate monitoring or collective data, reduce the cost of data cache and ensure the consistent deviation detection on timing data of each cycle, a big data online cleaning algorithm based on dynamic outlier detection has been proposed. The data cleaning method is improved by local outliner detection upon density, sampling cluster uniformly dilution Euclidean distance matrix retaining some corrections into next cycle of cleaning, which avoids a sampling causing overall cleaning deviation and reduces amount of calculation within data cleaning stable time, enhancing the speed greatly. Finally, the distributed solutions on online cleaning algorithm based on Hadoop platform.
A novel and facile route for the synthesis of water-soluble cadmium sulfide quantum dots

Herein, a novel and facile method for preparing highly water-soluble CdS nan oparticles has been developed. Cadmium acetate dehydrate was mixed in thiourea solution, using ethylenediaminetetraacetic acid (EDTA) as complexing agent. Electronic accelerator was used as radiation source. CdS nanoparticles were prepared rapidly at room temperature under atmospheric pressure without any kind of toxic reagents. Unlike those traditional routes, this method is facile and green, free of high temperature and toxic reagent. The prepared CdS nanoparticles had an average diameter about 3nm. The product showed quantum confinement fluorescing in the blue region and dispersed in water well, which make it potentially using as fluorescent biomarkers. Meantime, the prepared CdS nanoparticles showed the photocatalytic activity to degrade organic pollutant.
An Examination of Multivariate Time Series Hashing with Applications to Health Care

As large-scale multivariate time series data become increasingly common in application domains, such as health care and traffic analysis, researchers are challenged to build efficient tools to analyze it and provide useful insights. Similarity search, as a basic operator for many machine learning and data mining algorithms, has been extensively studied before, leading to several efficient solutions. However, similarity search for multivariate time series data is intrinsically challenging because (1) there is no conclusive agreement on what is a good similarity metric for multivariate time series data and (2) calculating similarity scores between two time series is often computationally expensive. In this paper, we address this problem by applying a generalized hashing framework, namely kernelized locality sensitive hashing, to accelerate time series similarity search with a series of representative similarity metrics. Experiment results on three large-scale clinical data sets demonstrate the effectiveness of the proposed approach.
A semi-infinite programming approach to continuously constrained LQ optimal control problems

Consider the class of LQ optimal control problems with continuous linear state constraints, that is, constraints imposed on every instant of the time horizon. This class of problems are known to be difficult to solve numerically. In this paper, a computational method based on a semi-infinite programming approach is given. The LQ optimal control problem is formulated as a positive quadratic infinite programming problem. This can be done by considering the control as the decision variable while taking the state as a function of the control. After parameterizing the decision variable, an approximate quadratic semi-infinite programming problem is obtained. It is shown that as we refine the parameterization, the solution sequence of the approximate problems converge to the solution of the infinite programming problem (hence to the solution of the original optimal control problem). Numerically, the semi-infinite programming problems obtained above can be efficiently solved using an algorithm based on a dual parameterization method.
A Glimpse of Energy-Efficient Location-Triggered Mobile Application Design and Implementation in Data Collection Scenarios

With the prevalence of localization techniques in smart phones, we see the blossom of user-trigger location-based applications in the last few years. However, only minorities of applications can be triggered by the predefined locations without human interaction to perform the associated actions in certain scenarios. One reason is that the inevitable power-intensive operation of location detection by GPS sensor causes battery draining issue. While there exists some work on energy efficiency in continuous location tracking, energy-efficient location detection for matching predefined location of interest has not been fully addressed by previous work. This paper proposes a unified framework for designing energy-efficient location-triggered applications. Our system triggers data collection only when the user reaches pre-defined location of interests. We propose an energy-efficient location detection scheme that support duty-cycles for GPS in cooperation with other on-device sensors intelligently. The results of our real-world experiment demonstrate that mobile devices can reduce the usage of GPS and lower energy consumption significantly in location-triggered applications.
B-Scan Image That Processed by CNN and Wavelets

In this paper, the edge detection cellular neural network (CNN) has been applied to preprocess ultrasound B-Scan images of Chronic Hepatitis B(CHB) patients' livers. The preprocessed images are further converted via the wavelet transform. The ratio of the standard deviation of the coefficients of diagonal detail to the standard deviation of the preprocessed image seems to correlate with the degrees of the damage of patient's livers. It is expected that the method provided in this paper will be helpful for the diagnosis of biomedical images.