nils-herrmann/clean_scirepeval huggingface dataset:
A DMC scheme with feedforward compensation of CO2 capture process from power plant

In order to design a proper control strategy for CO2 capture process from fossil fuel power plants, a dynamic simulation model developed on general process modelling system (gPROMS) is presented first as well as its derived linear dynamic model. Then aiming at the real CO2 capture process characteristics of great inertia and frequent measurable disturbances, a linear dynamic matrix control with feedforward compensation (DMC-FF) is designed based on the linear model. The measurable disturbances such as flue gas flow rate are served as the feedforward signals in the optimization computation, rather than just the output error like CO2 capture rate error is used for the DMC feedback control. The simulation results show that the proposed scheme results in a shorter closed-loop settling time than a PID scheme with the optimal parameters, and has better performance than an ordinary DMC control scheme in the presence of flue gas flow rate disturbance.
Low Profile-High Performance Vapor Chamber Heat Sinks For Cooling High-Density Blade Servers

High-density blade servers (HDS) have been intensively demanded at IT markets in recent years since their outstanding performance, higher resource productivity and flexible manageability. Due to limited space arrangement in the chassis of the HDS, a high performance heat sink is necessary to be developed. Traditional heat sink with solid metallic base can not efficiently dissipate the heat generated by the high density chips, specially, when the area ratio which is the foot-print area of the heat sinks over the heat source area Aft/Ahs is relatively high. In the case of higher Aft/Ahs, the vapor chamber (VC) would be an idea thermal solution for the HDS applications. A VC is working based on the principle that a type of working fluid acting two-phase heat transfer within a vacuumed and sealed metallic container. The internal surface of the container is usually structured with a type of wick which can enhance evaporation and condensation heat transfer inside the device. On the other hand, the VC performance has much less effect by its total thickness comparing with solid base. Thus VC is a suitable solution when the total height of the heat sink is limited. In this paper, fundamental of thermal analysis, aspects of optimal designs as well as the factors that would impact the heat sink performance have been addressed. The performance comparisons with conventional heat sinks have also been revealed. The recommendation for implementing this kind of heat sinks to other high end applications has been also proposed.
A Fully Integrated Counter Flow Energy Reservoir for Peak Power Delivery in Small Form-Factor Sensor Systems

We present a fully integrated energy reservoir unit using a counter flow method for peak power delivery in space-constrained sensor systems. Recent advances in circuits have enabled significant reduction in the size of wireless systems such as implantable biomedical devices. As a consequence, the batteries integrated in these systems have also shrunk, resulting in high internal resistances (<inline-formula> <tex-math notation="LaTeX">$\sim 10 ~\text{k}\Omega$ </tex-math></inline-formula>). However, the peak current requirement of power-hungry components such as radios remains in the milliwatt range and hence cannot directly be supplied from the battery. Therefore, an energy reservoir with high output power but small size is required. We present an efficient energy reservoir that dynamically reconfigures a storage capacitor array using a so-called counter flow approach. By creating a voltage gradient on capacitor arrays and moving the capacitors along the slope of the gradient, the supply voltage can be maintained while the energy stored in the reservoir is delivered efficiently to the load. The counter flow energy reservoir delivers 65% of stored energy before recharging is needed which allows up to a <inline-formula> <tex-math notation="LaTeX">$12 \times$ </tex-math></inline-formula> reduction in overall capacitor size compared with our implementation of the previous method. The design supplies up to 13.6-mW output power for <inline-formula> <tex-math notation="LaTeX">$1 ~\mu \text{s}$ </tex-math></inline-formula>. We demonstrate the proposed concept with a pulsed radio, showing an <inline-formula> <tex-math notation="LaTeX">$11.5 \times$ </tex-math></inline-formula> increase in pulse length compared with the previous method.
A class of turbo-like LDPC codes and their decoding based on neural network

Turbo codes and LDPC codes are both superior to other error correcting codes. These two families of codes have some similarities. It seems a good idea to design new codes combining these two codes. Through the analysis of turbo codes, we can consider turbo codes as block codes and find their matrix H. In this paper, we give a new class of turbo-like LDPC codes combining the advantages of turbo and LDPC codes and a decoding method based on neural network. A lot of simulations have been made and the results reveal that these codes have robuster performance than turbo codes.
Preliminary research of the reconfigurable antenna based on genetic algorithms

This work presents a of genetic algorithm technique for antenna design and optimization. The genetic algorithm (GA) is a robust, stochastic search method. The reconfigurable antenna, a new antenna concept, is proposed and a method to realize the reconfigurable antenna pattern, based on genetic algorithms, is presented. Given a frequency, genetic algorithms would search one of the patterns that the antenna might produce. In this way, the pattern required could be obtained; this kind of antenna is called the pattern reconfigurable antenna, and it is a type of adaptive array. The computer simulation results indicated that the proposed method was feasible and genetic algorithm optimization was shown to be suitable for optimizing a class of problems of interest to antenna design.
Characteristics of surface acoustic waves in porous functionally graded materials

An eigenfunction expansion method is carried out to calculate surface acoustic waves (SAWs) propagation in porous functionally graded materials (FGMs) in this article. The properties of the material are assumed to vary continuously across the thickness according to a known variation law. Laguerre polynomial and the relationship between porosity and materials' elastic constants and density are then employed to obtain the SAWs' dispersion curves in iron- based alumina FGMs with different porosity, sample's thickness and material properties' distribution. The effect of porosity, sample's thickness and material properties' distribution on phase velocities of SAWs in porous FGMs are respectively discussed in detail. The conclusion forms theoretical basis for reversing parameters of porous FGMs.
Hausdorff dimension of continuous functions with at most finite UV points on closed intervals

In this paper, we present a new method to investigate continuous functions with at most finite unbounded variation points on closed intervals. We need to point out that, although the issue is interesting in the prior investigations, the new method should be emphasized. Using this method, Hausdorff dimension of any continuous functions with one unbounded variation point on closed intervals is 1 has been given. Furthermore, definition of unbounded variation point has been given. In the end, if unbounded variation point is at most finite, corresponding conclusion still holds.
Unified modeling and design of reservation-based cooperation mechanisms for intelligent vehicles

With Intelligent Transportation Systems (ITS) evolving to novel cooperative ITS (C-ITS), adaptive behavior cooperations of intelligent vehicles have been becoming one more and more prominent problem. Considering the cyber-physical cooperative characteristics of C-ITS, uniform cooperative mechanisms are modeled and designed for automated vehicles in this paper. After analyzing procedures of typical V2X-communication-based behaviors, such as passing through intersections, lane-changing, overtaking and so on, related traffic objects are modeled firstly, and then, all vehicular behaviors are uniformly abstracted into a “Reserve Advance, Act Later” (RAAL) procedure. Further, standard vehicular actions and states are extracted and also modeled uniformly, and any cooperative behavior can be presented as a sequence of states within an event-triggered state automata. On this basis, V2X-based centralized and decentralized cooperation mechanisms are put forward with the RAAL procedure, spatial-temporal distance transformation and cooperation policies. Finally, all these models and mechanisms are implemented within QoS-CITS simulator, with which experiments are carried out and the feasibility and validity of proposed methods are demonstrated.
Direct prototyping of 3D micro supercapacitors based on in-situ fabricated nanoporous carbon electrodes

We present the direct prototyping of three-dimensional (3D) micro supercapacitors using nanoporous carbon electrodes that are in-situ fabricated on chips. The approach successfully incorporates nano templating method into photolithography technology for the first time, so that functional electrodes are prepared from directly photo patterned precursor. Two preliminary prototypes with different electrode configurations are tested and show specific capacitances of more than 5mFcm-2, which is comparable to existing micro supercapacitors. The novel method allows for scalable design and batch fabrication of energy storage devices and full integration of them into micro systems.
Cascaded Collaborative Regression for Robust Facial Landmark Detection Trained Using a Mixture of Synthetic and Real Images With Dynamic Weighting

A large amount of training data is usually crucial for successful supervised learning. However, the task of providing training samples is often time-consuming, involving a considerable amount of tedious manual work. In addition, the amount of training data available is often limited. As an alternative, in this paper, we discuss how best to augment the available data for the application of automatic facial landmark detection. We propose the use of a 3D morphable face model to generate synthesized faces for a regression-based detector training. Benefiting from the large synthetic training data, the learned detector is shown to exhibit a better capability to detect the landmarks of a face with pose variations. Furthermore, the synthesized training data set provides accurate and consistent landmarks automatically as compared to the landmarks annotated manually, especially for occluded facial parts. The synthetic data and real data are from different domains; hence the detector trained using only synthesized faces does not generalize well to real faces. To deal with this problem, we propose a cascaded collaborative regression algorithm, which generates a cascaded shape updater that has the ability to overcome the difficulties caused by pose variations, as well as achieving better accuracy when applied to real faces. The training is based on a mix of synthetic and real image data with the mixing controlled by a dynamic mixture weighting schedule. Initially, the training uses heavily the synthetic data, as this can model the gross variations between the various poses. As the training proceeds, progressively more of the natural images are incorporated, as these can model finer detail. To improve the performance of the proposed algorithm further, we designed a dynamic multi-scale local feature extraction method, which captures more informative local features for detector training. An extensive evaluation on both controlled and uncontrolled face data sets demonstrates the merit of the proposed algorithm.
An interactive video segmentation approach based on GrabCut algorithm

GrabCut is a classical efficient algorithm used in image segmentation. In this paper we apply the approach to video matting in respects as follow: First, user scribbles interactive information in the key frame, which is similar to the interactive segmentation method GrabCut algorithm. Secondly, estimate the probable foreground region by block-matching and then get the segmentation result by GrabCut operator. In addition, morphological approaches are used to make the extracted foreground smooth. Experiments result is demonstrated at the end of this paper, proving the method is efficient, especially to the video in which foreground object moves.
Electromagnetic environment of 1000kV UHV AC substation

Three UHV transformer substations have been on commercial run. UHV substation integrates a series of high-voltage and secondary electrical equipments which brings out electromagnetic environment deserving more attention. If the electromagnetic environment of substation is severe, it will affect the neighbouring radio stations' normal work and also the residents around and worker at the UHV substation. This paper discusses the electromagnetic environment of Chinese UHV AC substation, and put forward some measurements to electromagnetic environmental amendatory
Logistic Regression for Detecting Fraudulent Financial Statement of Listed Companies in China

This paper examines published data to develop a model of Logistic Regression for detecting factors associated with Fraudulent Financial Statement (FFS). After an exhaustive exploitation of prior work used financial ratios, 21 ratios are selected as potential predictors of FFS and a series of experiments have been conducted to determine the optimal parameters for Logistic model. Then, we propose an appropriate model for detecting FFS of listed companies in China and compare its predictive ability with other detecting models using a data set of 174 listed companies in China including 87 with FFS and 87 with non-FFS during the period 1993-2007. The results demonstrate that the predictive ability of the model proposed in this paper is higher than other models at about 10% by using the optimal parameters determined and indicate the importance of financial ratios, which could benefit both internal and external auditors, taxation and other state authorities.
Measuring the Spread of Fuzzy Variable by L-S Integral

In credibility theory, variance is usually used as a measure of the variation of a possibility distribution about the expected value. Since the variance is defined via nonlinear fuzzy integral, its computation is difficult for general fuzzy variables. To avoid this difficulty, this paper defines the spread of a fuzzy variable based on Lebesgue-Stieltjes (L-S) integral. Our approach is to find an "average distance" from the expected value to the possible values of the fuzzy variable. For discrete and several common continuous fuzzy variables such as triangular and normal ones, we present their computation formulas about spread. The relationships between spread and variance are also discussed for common fuzzy variables. The established formulas are useful that can be employed to model practical fuzzy optimization problems.
High-Precision Sigma-Delta Modulator Design for Electrocardiogram Signal Acquisition

As a result of population aging, remote electrocardiograph (ECG) monitoring systems have increasingly gained attention. The traditional ECG signal acquisition system has many limitations. This paper presents a high-precision sigma-delta modulator for ECG signal acquisition. The circuit is implemented in a SMIC-EE 0.18 μm process. Simulation results indicate that the signal-to-noise ratios with and without the SMIC-EE 0.18 μm model are 105 and 81.2 dB, respectively, and the corresponding effect numbers of bits are 17.15 and 13.2 bits, respectively, which meet the requirements of a remote ECG monitoring system.
A Decision Model for Dynamic Capabilities Based on Learning Effects

The innovation-based competition becomes the mainstream in time of the new economy. Under this form of competition, conducts of rivals, changes of environments, or emergences of new technologies all can erode the heterogeneous competencies of the target company very quickly. There are two ways for companies to adapt to the changing environment, the ad hoc problem solving mode and the dynamic routine mode. This paper discussed how to choose the proper change mode by comparing their cost structures. We found that the slope of the cost curve of the dynamic routines would decline with the increase of the frequency of changes because of the learning effects. Moreover, every company has its special turning point based on the cost structure and learning efficiency. Then we deduced a equation to find such turning point, and discussed the methods to calculate the key parameters in the decision model and its typical uses under two special scenarios
Co-Clustering of Time-Evolving News Story with Transcript and Keyframe

This paper presents techniques in clustering the same-topic news stories according to event themes. We model the relationship of stories with textual and visual concepts under the representation of bipartite graph. The textual and visual concepts are extracted respectively from speech transcripts and keyframes. Co-clustering algorithm is employed to exploit the duality of stories and textual-visual concepts based on spectral graph partitioning. Experimental results on TRECVID-2004 corpus show that the co-clustering of news stories with textual-visual concepts is significantly better than the co-clustering with either textual or visual concept alone
A Fast Image Segmentation Approach based on Level Set Method

All level set based image segmentation methods are based on an assumption that the level set function is or close to a signed distance function (SDF). Small time step and costly reinitialization procedure must be applied to guarantee this assumption, and this will do slow down the segmentation process. In this article, we propose a fast image segmentation approach based level set method, we use an external energy term based on Mumford-Shah functional model (1989) and Chan-Vese model (2001) that drives the motion of the zero level set toward the desired image features, such as object boundaries, while an internal energy term to keep the level set function to a SDF. Experiments results show that our approach can use a large time step to speed up the segmentation process and achieve good results both on synthetic and real images and medical images
Differential Stepped-Slot UWB Antenna With Common-Mode Suppression and Dual Sharp-Selectivity Notched Bands

A differential stepped-slot UWB antenna with common-mode suppression and dual sharp-selectivity notched bands is proposed in this letter. The antenna adopts stepped slot as radiator to achieve ultrawideband. Under differential-mode operation, the stepped slot can be effectively excited by feeding microstrip, and differential signals can be radiated. However, under common-mode operation, the stepped slot would not be excited so that the common-mode suppression can be achieved. In order to achieve dual sharp-selectivity notch-band characteristic, quarter-wavelength slits are etched on the ground, and half-wavelength stubs are introduced beside the microstrip, respectively. As a result, dual second-order notched bands with sharp selectivity are obtained. The measured results show that the proposed antenna obtains good common-mode suppression characteristic and dual sharp-selectivity notched bands from 5.1 to 6.0 GHz and 7.83 to 8.47 GHz. Good agreement is achieved between the measured results and the simulated results.
Fault Diagnosis Method Based on D-S Theory of Evidence and AHP

A new approach to fault diagnosis is presented, with a combination of D-S theory of evidence and AHP. The method for constructing assignment function of basic probability based on AHP with 5-unit preference scale is proposed in detail and its comparison is different from the pairwise comparison in the standard AHP. In addition, the weight of each sensor is taken into account in the process of combination. An illustrative example is given to expatriate on its application to fault diagnosis. After theses pieces of evidence from multisensors are combined, the reliability of the diagnostic result is improved significantly and its uncertainty is reduced remarkably, which shows the proposed approach to fault diagnosis is reasonable and effective
Study on predicting method for acute hypotensive episodes based on wavelet transform and support vector machine

The occurrence of acute hypotensive episodes (AHE) in intensive care units (ICU) seriously endanger the lives of patients, and are depended mainly on the expert experience of doctors to treat currently. How to detect and predict AHE in advance has become a clinical problem which is highly paid attention to by the medical world. In this paper, the theory of medical Informatics has been applied to achieve the prediction of the occurrence of AHE. In our study, the mean arterial blood pressure (MAP) signals of those who experienced AHE and those who not are both described on different scales by using wavelet transform. Through the extraction of the median and maximum from the wavelet coefficients for learning and training based on support vector machine (SVM), a predicting model with a predictive accuracy of 90% has developed. The experiment demonstrates that the proposed approach has better prediction accuracy comparing with the one which extracts statistical parameters from the MAP signals directly, and is beneficial to early prediction of AHE and intervention.
A digital front-end of 16-bit audio delta-sigma DAC with improved CSE method and novel DWA

To achieve area-efficiency and high SNR, a novel digital front-end of a 16-bit audio DAC including a 4-stage interpolator and a 3rd-order delta-sigma (ΣΔ) modulator is proposed. An improved common subexpression elimination (CSE) method is used for implementing the interpolator to save the hardware overhead. And a novel data weighted averaging (DWA) technique named as dual cycle shifted DWA is applied to the 4-bit ΣΔ modulator to reduce the mismatch errors without introducing signal-dependent tones. Implemented in a standard 0.18-μm 1P6M LOGIC salicide process, the proposed design achieves a peak SNR of 103.9-dB and a DR of 104.3-dB, which proves that the proposed work achieves the design goal well.
Chinese firms' internationalization paths by strategic asset-seeking outward foreign direct investment

FDI (Foreign Direct Investment) outflows from People's Republic of China increased dramatically since increasing firms' accelerated internationalization. But the conditions of Chinese firms' outward FDI are quite different from that of most firms from developed countries. FDI from developed countries to China is usually exploitation of advantages the investors already possessed like cutting-edge technology and global brand reputation. But FDI from China to developed countries is exploration of such advantages. In other word, outward FDI becomes an effective way for Chinese firms to gain strategic assets for internationalization. This paper adopts strategic asset-seeking as the motivation of Chinese firms' outward FDI and identified different internationalization paths through case studies. Based on interviews and complementary secondary materials we found out, 1) the most important two strategic assets for Chinese firms' internationalization are technology and brand, 2) the three different internationalization paths are path by technology sourcing outward FDI, path by brand acquiring outward FDI, and path by both technology and brand seeking outward FDI.