nils-herrmann/clean_scirepeval huggingface dataset:
A modified hybrid model of heat exchanger based on fouling effect

Hybrid model is a simple and accurate model for stable heat transfer process. Its theoretical basis shows that it is inappropriate to be applied to system with severe coefficient drifting. However, coefficient drifting is normal phenomenon in power and chemical industrial due to fouling. In this paper, a modified hybrid model of heat exchanger is proposed. The derivation of system is based on heat transfer balance and fouling principle of the heat exchanger. Considering fouling effect, a comprehensive model is derived. The catalog results with experiment data show that the proposed model can predict accurately the real-time performance of heat exchanger with error less than 3%. This model can be used in heat exchanger with high fouling risk for operational optimization, performance assessment and device maintaining.
A Bonded Type Ultrasonic Motor Using the Bending of a Crossbeam

This paper proposed a new crossbeam ultrasonic motor, which was made of a cross-shaped metal frame and eight pieces of lead zirconate titanate ceramic plates with a size of 10 mm × 3 mm × 1 mm. Two cross-cone-shaped horns were machined at two ends of the crossbeam, and the tips of the horns served as the driving feet. Ellipse trajectories of the two driving feet were generated by superimposing two orthogonal first bending vibrations with the same resonance frequency but a temporal shift of 90°. A Symmetric structure of the proposed ultrasonic motor solved the problem of mode frequencies degeneracy. A new method to excite these two bending vibrations was proposed. Finite-element method simulation was developed to obtain the vibration characteristics of the crossbeam motor. The new excitation method was compared with the traditional one, and it was found that the vibration amplitudes of the driving feet under the new excitation method were about three times that of the traditional one. At last, experiments were accomplished on a fabricated prototype. The two tested first bending resonance frequencies agreed well with each other at ~ 40.5 kHz. Mechanical output performance of the prototype was tested; the maximum speed was ~ 64 r/min, and the maximum output torque was ~ 10.4 mN · m. This paper provides a new idea for an ultrasonic motor with a miniature structure and also a new excitation method for bending vibration.
A parallel Hyper-Surface Classifier for high dimensional data

The enlarging volumes of data resources produced in real world makes classification of very large scale data a challenging task. Therefore, parallel process of very large high dimensional data is very important. Hyper-Surface Classification (HSC) is approved to be an effective and efficient classification algorithm to handle two and three dimensional data. Though HSC can be extended to deal with high dimensional data with dimension reduction or ensemble techniques, it is not trivial to tackle high dimensional data directly. Inspired by the decision tree idea, an improvement of HSC is proposed to deal with high dimensional data directly in this work. Furthermore, we parallelize the improved HSC algorithm (PHSC) to handle large scale high dimensional data based on MapReduce framework, which is a current and powerful parallel programming technique used in many fields. Experimental results show that the parallel improved HSC algorithm not only can directly deal with high dimensional data, but also can handle large scale data set. Furthermore, the evaluation criterions of scaleup, speedup and sizeup validate its efficiency.
Double planes discrete variable structure tracking algorithm

With the development of aviation computer and aviation electronics technologies, the analog tracking algorithm has been unable to meet the need in project. But discrete variable structure tracking algorithm meets the requirement. After go into the sliding mode, the traditional discrete variable structure tracking algorithm will take place of buffeting, and will have phenomenon of the exhale when the front angel is big. In light of the above factors, in order to further improve the precision guided, the no buffeting discrete variable structure double planes tracking algorithm with a compensation of the disturbance is given. The simulation results show that the discrete signal guidance law can meet the tactical UCAV indicator.
Field emitters based on the combined structures with multi-dimensional graphene-nanotube materials

Recent research on two-dimensional (2D) graphene with various functional materials has refreshed the development of field emission devices. Here, we propose field emitters based on the combined structures with multi-dimensional graphene-nanotube functional materials. Graphene is grown directly on the copper foils by using the thermal chemical vapor deposition (CVD). The carbon nanotube emitters are fabricated on the top surface of the graphene layer beyond the metallic substrate. We attempt to fabricate the carbon nanotube emitters via screen printing. The field emission performance of the designed metal-graphene-CNT structure is to be studied in a diode with a novel water cooling method. The multi-dimensional metal-graphene-CNT field emission structure is another potential candidate to the novel vacuum nano-electronic devices.
Adaptive Color-Space Transform in HEVC Screen Content Coding

The screen content coding (SCC) Extensions of high efficiency video coding (HEVC) employs in-loop adaptive color-space transform (ACT) technique to explore the inter-color-component redundancy, i.e., statistical redundancy among different color components. In ACT, the prediction residual signal is adaptively converted into a different color space, i.e., YCgCo. The rate-distortion criteria is employed to decide whether to code the residual signal in the original color space or YCgCo color space. Typically, the inter-color-component correlation could be reduced when ACT is enabled. The residual signal after possible color-space conversion is then coded, following the existing HEVC framework, i.e., transform if necessary, quantization and entropy coded. This paper describes the design of ACT from several points of view, from theoretical analysis to implementation details. Experimental results are also provided to demonstrate the significant coding gains of ACT in the HEVC SCC Extensions.
A hybrid water-cooled centrifugal chiller model

This paper proposed a hybrid model for water cooled centrifugal chiller. The modeling method takes advantages of both physical and empirical modeling approaches and can accurately predict performance in wide operating range and in real-time, which can significantly reduce the computational burden and increase the prediction accuracy. The model is verified with the experimental data taken from a testing system. The testing results show that the proposed model can predict accurately the performance of the real-time operating chiller. The developed models will have wide applications in operational optimization, performance assessment, fault detection and diagnosis.
Interference suppression beamforming and related robust scheme

Transmit beamforming is a powerful strategy to suppress multiple-access interference (MAI) and increase the system capacity if the downlink channel is known precisely. However, it is indeed very difficult to get the exact channel information in realistic scenarios. In this paper, we present a zero interference transmit beamforming (ZIBF) scheme which only uses the statistical information of the downlink channel. Maximin SINR strategy is exploited to satisfy the specific constraints between the total transmission power and the quality of service (QoS) requirement of individual users. The robust algorithm related to the presented scheme is also addressed. Simulation results analyze the performance of the presented scheme and demonstrate the effectiveness.
Genetic algorithm based design of power system stabilizers

This paper presents a GA based optimization scheme for simultaneous coordination of multiple machines power system damping controllers. The proposed algorithm is applied to tuning of single and multiple power system stabilizers. Controller design is tested on the small and mid-sized power systems to prove its effectiveness. PSSs are designed using MATLAB control system toolbox and optimized using GAOT toolbox. All models and simulations are carried out using MATLAB and SSAT developed by Powertech Labs Inc, Canada.
Green building designing for nursery education a case study of a nursery school green building design in Chongqing

Nursery education plays an important role in the social development, while energy-saving education is also regarded as the basic course for children. In the nursery green building project, architects, constructors and kindergarteners should pay attention to the characteristics of nursery energy-saving education through the whole progress including design, construction and management, so that the school will fit with the energy saving education for children. In this nursery school green building project in Chongqing, the architects made investigation on the local climate and site conditions, then analyzed and developed out energy-saving strategies which fit the building function and circumstance. The space in the child dimensions and the special energy-saving system will accommodate the children with convenience in nature study, energy-saving courses and outdoor activities.
Influence Spreading Path and Its Application to the Time Constrained Social Influence Maximization Problem and Beyond

Influence maximization is a fundamental research problem in social networks. Viral marketing, one of its applications, is to get a small number of users to adopt a product, which subsequently triggers a large cascade of further adoptions by utilizing “Word-of-Mouth” effect in social networks. Time plays an important role in the influence spread from one user to another and the time needed for a user to influence another varies. In this paper, we propose the time constrained influence maximization problem. We show that the problem is NP-hard, and prove the monotonicity and submodularity of the time constrained influence spread function. Based on this, we develop a greedy algorithm. To improve the algorithm scalability, we propose the concept of Influence Spreading Path in social networks and develop a set of new algorithms for the time constrained influence maximization problem. We further parallelize the algorithms for achieving more time savings. Additionally, we generalize the proposed algorithms for the conventional influence maximization problem without time constraints. All of the algorithms are evaluated over four public available datasets. The experimental results demonstrate the efficiency and effectiveness of the algorithms for both conventional influence maximization problem and its time constrained version.
Bioinformatics and Fuzzy Logic

Many biological systems and objects are intrinsically fuzzy. Fuzzy set theory and fuzzy logic are ideal frameworks for describing some biological systems/objects and providing suitable computational methods for a widely range of bioinformatics problems. In this paper, we present two examples of using fuzzy set theory in bioinformatics, one in fuzzy measurement of ontological similarity and its application in bioinformatics, and the other in the application of the fuzzy k-nearest neighbor algorithm in protein secondary structure prediction. We also review other "fuzzy" methods for bioinformatics applications.
Control System Development of the One-axis Hydraulic Road Simulator Using QFT

This paper presents the one-axis hydraulic road simulator control technology for reproducing the random input signal to implement the real road data. The simulator consists of the hydraulic pump, servo valve, hydraulic actuator and its control equipments. The force control system using QFT is utilized to control the simulator effectively and illustrates a tracking performance of the closed-loop controller with low order transfer function Gi(s) and pre-filter F(s) for a parametric uncertain plant. A force controller is designed to communicate the control signal between simulator and digital controller. Tracking specification is satisfied with upper and lower bound tolerances on the steep response of the system to the reference signal. The efficacy of the QFT force controller is verified through the numerical simulation, in which combined dynamics and actuation of the hydraulic servo system are tested. The simulation results show that the proposed control technique works well under uncertain hydraulic plant system. The Labview software is used to make up for the real controller in the real-time basis, and the experimental works show that the proposed algorithm works well for the one-axis hydraulic road simulator.
CUBIC: identification of regulatory binding sites through data clustering

Transcription factor binding sites are short fragments in the upstream regions of genes, to which transcription factors bind to regulate the transcription of genes into mRNA. Computational identification of transcription factor binding sites remains an unsolved challenging problem though a great amount of effort has been put into the study of this problem. We have recently developed a novel technique for identification of binding sites from a set of upstream regions of genes, that could possibly be transcriptionally coregulated and hence might share similar transcription factor binding sites. By utilizing two key features of such binding sites (i.e., their high sequence similarities and their relatively high frequencies compared to other sequence fragments), we have formulated this problem as a cluster identification problem. That is to identify and extract data clusters from a noisy background. While the classical data clustering problem (partitioning a data set into clusters sharing common or similar features) has been extensively studied, there is no general algorithm for solving the problem of identifying data clusters from a noisy background. In this paper, we present a novel algorithm for solving such a problem. We have proved that a cluster identification problem, under our definition, can be rigorously and efficiently solved through searching for substrings with special properties in a linear sequence. We have also developed a method for assessing the statistical significance of each identified cluster, which can be used to rule out accidental data clusters. We have implemented the cluster identification algorithm and the statistical significance analysis method as a computer software CUBIC. Extensive testing on CUBIC has been carried out. We present here a few applications of CUBIC on challenging cases of binding site identification.
Bigraphical Model of Context-Aware in Ubiquitous Computing Environments

In this paper, we give a bigraphical model for context-aware mobile systems. We describe how to represent context-aware behaviors by means of Bigraphical Reactive System (BRS), which is proposed to provide a uniform way to model ubiquitous applications. A bigraph consists of two graphs, namely place graph and link graph respectively. We can depict mobility and context-awareness using bigraphs in mobile applications. Besides, bigraphs provide an algebra language by which we can model for applications formally. Finally, the formalisms are explicitly illustrated through a simple but non-trivial smart phone example.
Design of ultra-low noise power system for high-precision detectors

In this paper, we introduce an ultra-low noise power system designed for high precision detectors, like CCD detector. We build low noise power circuit for the front-end electronics, whose noise generally needs to be under 1 mVrms. Because the system may be used in low temperature environment as in Antarctica, the influence of electronic equipment working at 213K-193K is fully considered in the beginning of the design. As a result, the power system passed noise and long-time low temperature test.
Distance Metric Learning Using Privileged Information for Face Verification and Person Re-Identification

In this paper, we propose a new approach to improve face verification and person re-identification in the RGB images by leveraging a set of RGB-D data, in which we have additional depth images in the training data captured using depth cameras such as Kinect. In particular, we extract visual features and depth features from the RGB images and depth images, respectively. As the depth features are available only in the training data, we treat the depth features as privileged information, and we formulate this task as a distance metric learning with privileged information problem. Unlike the traditional face verification and person re-identification tasks that only use visual features, we further employ the extra depth features in the training data to improve the learning of distance metric in the training process. Based on the information-theoretic metric learning (ITML) method, we propose a new formulation called ITML with privileged information (ITML+) for this task. We also present an efficient algorithm based on the cyclic projection method for solving the proposed ITML+ formulation. Extensive experiments on the challenging faces data sets EUROCOM and CurtinFaces for face verification as well as the BIWI RGBD-ID data set for person re-identification demonstrate the effectiveness of our proposed approach.
A Task Operation Model for Resource Allocation Optimization in Business Process Management

Resource allocation, as an integral part of business process management (BPM), is more widely acknowledged by its importance for process-aware information systems. Despite the industrial need for efficient and effective resource allocation in BPM, few scientifically-grounded approaches exist to support these initiatives. In this paper, a new approach of resource allocation optimization is proposed, built on the concepts that is part of an operation-oriented view on process optimization. Essentially, the proposed approach automatically generates a specific task operation model (TOM) for a particular business process. In addition, in order to support end users in making sensible resource allocations, an ant colony optimization-based algorithm is presented, which makes it possible to search an optimal task operation path on the generated TOM. This allows one to suggest how a business user should efficiently allocate resources to perform the tasks of a particular process case. The feasibility of the presented approach is demonstrated by a simulation experiment. The experimental results show that the proposed approach outperforms reasonable heuristic approaches to satisfy process performance goals, and it is possible to improve the current state of BPM.
Fast Algorithms for Linear and Kernel SVM+

The SVM+ approach has shown excellent performance in visual recognition tasks for exploiting privileged information in the training data. In this paper, we propose two efficient algorithms for solving the linear and kernel SVM+, respectively. For linear SVM+, we absorb the bias term into the weight vector, and formulate a new optimization problem with simpler constraints in the dual form. Then, we develop an efficient dual coordinate descent algorithm to solve the new optimization problem. For kernel SVM+, we further apply the l2-loss, which leads to a simpler optimization problem in the dual form with only half of dual variables when compared with the dual form of the original SVM+ method. More interestingly, we show that our new dual problem can be efficiently solved by using the SMO algorithm of the one-class SVM problem. Comprehensive experiments on three datasets clearly demonstrate that our proposed algorithms achieve significant speed-up than the state-of-the-art solvers for linear and kernel SVM+.
A non-isolated three-port converter for stand-alone renewable power system

A novel non-isolated three-port converter (NI-TPC) is proposed interfacing one PV port, one bidirectional battery port and one load port. Single stage power conversion between any two of the three ports is achieved. The topology is derived by decoupling the bidirectional power flow path of the conventional structure into two unidirectional ones. Two of the three ports can be tightly regulated to achieve maximum power harvesting for PV or charge control for battery, and maintain the load voltage constant at the same time, while the third port is left flexible to compensate the power imbalance of the converter. Operation states are analyzed. The multi-regulator competition control strategy is presented to achieve autonomous and smooth state switching when the PV input power fluctuates. The analysis is verified by the experimental results.
A Reduced-Order Method for Coherent Transport Using Green’s Functions

A reduced-order method is presented to efficiently calculate Green's functions connecting contacts or leads to all the points in a nanostructure in the coherent transport limit. The proposed approach samples a small subset of spatial grid points on the lead and a small subset of energy grid points to build a reduced-order model. The efficacy of the algorithm is demonstrated by applying it to calculate both the electron density and transmission in a resonant tunneling structure, a MOSFET, and a bilayer graphene device. The match in features of both the electron density and transmission versus energy with conventional methods to model devices is excellent while a large reduction in computational time is demonstrated.
Co-labeling: A New Multi-view Learning Approach for Ambiguous Problems

We propose a multi-view learning approach called co-labeling which is applicable for several machine learning problems where the labels of training samples are uncertain, including semi-supervised learning (SSL), multi-instance learning (MIL) and max-margin clustering (MMC). Particularly, we first unify those problems into a general ambiguous problem in which we simultaneously learn a robust classifier as well as find the optimal training labels from a finite label candidate set. To effectively utilize multiple views of data, we then develop our co-labeling approach for the general multi-view ambiguous problem. In our work, classifiers trained on different views can teach each other by iteratively passing the predictions of training samples from one classifier to the others. The predictions from one classifier are considered as label candidates for the other classifiers. To train a classifier with a label candidate set for each view, we adopt the Multiple Kernel Learning (MKL) technique by constructing the base kernel through associating the input kernel calculated from input features with one label candidate. Compared with the traditional co-training method which was specifically designed for SSL, the advantages of our co-labeling are two-fold: 1) it can be applied to other ambiguous problems such as MIL and MMC, 2) it is more robust by using the MKL method to integrate multiple labeling candidates obtained from different iterations and biases. Promising results on several real-world multi-view data sets clearly demonstrate the effectiveness of our proposed co-labeling for both MIL and SSL.
A Bus Protocol Application in the Network Based Ac Servo System

The rapid development of the network technology, especially that of the fieldbus, is changing the traditional control system. In order to solve the problem of the linkage synchronization and the network data transmission of the Multi-Ac Servo system, we recently designed a CAN bus protocol named CANServo. It is a high-level protocol of CAN bus , it is used to replace traditional parallel data transmission between NC and servos. In this study, we described the method to solve the synchronization problem, the setting up of the communications, the strategy of dynamic reorganization of priorities, the structure of the buffer track and the operation of the track. In the end, we illustrated the results of the experiment which the correctness of our design can be proved by.