nils-herrmann/clean_scirepeval huggingface dataset:
A simplified reactive power control for single-phase grid-tied photovoltaic inverters

The objective of this paper is to propose a simplified reactive power control (SRPC) strategy for the single-phase grid-tie photovoltaic (PV) inverter. The proposed SRPC method is to sense the inverter's output current, where a simplified power calculation is integrated into the SRPC, to easily calculate the actual reactive output power. Then, the power error between the reference value and the measured one can be eliminated by using a proper compensator to improve the controlled accuracy. Besides, the proposed SRPC can reduce the computational burden, significantly, so a low-cost micro controller unit can be adopted instead of using the high-level digital signal processor. In this paper, the operational principle and mathematical derivation of the proposed SRPC strategy will be introduced. Finally, a 1 kVA prototype PV inverter with the SRPC strategy was built to verify the performance of the proposed reactive power control strategy.
Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects

Image captioning often requires a large set of training image-sentence pairs. In practice, however, acquiring sufficient training pairs is always expensive, making the recent captioning models limited in their ability to describe objects outside of training corpora (i.e., novel objects). In this paper, we present Long Short-Term Memory with Copying Mechanism (LSTM-C) &#x2014; a new architecture that incorporates copying into the Convolutional Neural Networks (CNN) plus Recurrent Neural Networks (RNN) image captioning framework, for describing novel objects in captions. Specifically, freely available object recognition datasets are leveraged to develop classifiers for novel objects. Our LSTM-C then nicely integrates the standard word-by-word sentence generation by a decoder RNN with copying mechanism which may instead select words from novel objects at proper places in the output sentence. Extensive experiments are conducted on both MSCOCO image captioning and ImageNet datasets, demonstrating the ability of our proposed LSTM-C architecture to describe novel objects. Furthermore, superior results are reported when compared to state-of-the-art deep models.
Position sensorless control of PMSM based on Extended State Observer

This paper proposes an Extended State Observer (ESO) to achieve the sensorless position control of permanent-magnet synchronous motor (PMSM). An Extended State Observer (ESO) is built according to the back electromotive force (EMF) model. It uses a nonsmooth2 structure to observe all states of the controlled object and the inside and outside disturbance of the system. Original system can be converted into linear certain system by this. Furthermore, stability and convergence analysis of the closed-loop control are given based on the Lyapunov stability theory. Simulation results verify the asymptotic convergence of ESO.
PV Power System With Multi-Mode Operation and Low-Voltage Ride-Through Capability

A multi-mode operation for the three-phase photovoltaic (PV) power system with low-voltage ride-through (LVRT) capability is proposed. With the proposed multi-mode control strategy, the active power from the PV arrays can be continuously extracted by the interleaved boost converter during LVRT, whereas the maximum power point tracking operation can be quickly achieved after the grid fault clearance. In addition, the multi-channel boost converter with interleaved operation can increase power conversion efficiency while decreasing the input current ripple. On the other hand, the current amplitude limitation control of the three-phase inverter can provide maximum reactive power under rated current amplitude during the voltage sag period and output the demanded reactive/rated current ratio to meet different LVRT codes. A three-phase 5-kVA prototype PV converter is built and tested to verify the performance of the proposed multi-mode operation strategy and the LVRT capability.
Josephson edge-triggered gates for sequential circuits

A Josephson sequential logic family with a very wide operating margin (+or-67%) and insensitivity to global parameter variations is proposed. Derived from the original idea of the edge-triggered latching comparator by C. Hamilton et al.  1985), this logic gate consists of a pair of conventional gates in series biased by a delay clock. In normal operation, switching occurs in one and only one of the gates, depending on which one has the smaller critical current. The authors have built and tested a few circuits to illustrate this logic gate design: a 32-b shift register designed by OR gates with +or-42% bias margin and +or-89% input margin, a 4-b pseudorandom sequence generator designed by exclusive-OR gates with +or-27% bias margin and +or-78% input margin, and cross section of a 6-b NOR gate decoder with +or-33% bias margin.<<ETX>>
Video Captioning with Transferred Semantic Attributes

Automatically generating natural language descriptions of videos plays a fundamental challenge for computer vision community. Most recent progress in this problem has been achieved through employing 2-D and/or 3-D Convolutional Neural Networks (CNNs) to encode video content and Recurrent Neural Networks (RNNs) to decode a sentence. In this paper, we present Long Short-Term Memory with Transferred Semantic Attributes (LSTM-TSA)&#x2014;a novel deep architecture that incorporates the transferred semantic attributes learnt from images and videos into the CNN plus RNN framework, by training them in an end-to-end manner. The design of LSTM-TSA is highly inspired by the facts that 1) semantic attributes play a significant contribution to captioning, and 2) images and videos carry complementary semantics and thus can reinforce each other for captioning. To boost video captioning, we propose a novel transfer unit to model the mutually correlated attributes learnt from images and videos. Extensive experiments are conducted on three public datasets, i.e., MSVD, M-VAD and MPII-MD. Our proposed LSTM-TSA achieves to-date the best published performance in sentence generation on MSVD: 52.8% and 74.0% in terms of BLEU@4 and CIDEr-D. Superior results are also reported on M-VAD and MPII-MD when compared to state-of-the-art methods.
A Structural Approach for Finding Real-Friend Links in Internet Social Networks

We propose a method of structural analysis for detecting true/false friend links in Internet social networks. By the balance theory in social science and observed user behaviors, we define two graph optimization problems -- One can be solved in polynomial time while the other is NP-hard. Exact and heuristic algorithms are proposed for the NP-hard problem. To show the efficiency of the algorithm, experiments on random graphs were performed. More importantly, to show the effectiveness of the new model, experiments were performed on random graphs which simulate the behavior on Internet social networks. By examining the precisions and recalls of both true and false edges, it is shown that our algorithms are efficient for moderate data and the new models are effective.
Commercial CAD tools based digital integrated development platform for laser cutting robot

A digital integrated development platform, which is based on Pro/E, ADAMS, ANSYS and Matlab, is introduced in this paper. A type of 6-DOF industrial robot for laser cutting is developed on this platform. In the virtual environment, the development efficiency is improved, as well as the reliability of the robot. The 3D parametric model of the laser cutting robot is firstly built by using Pro/E. Through the interface module between Pro/E and ADAMS, the 3D model is transferred to ADAMS. Then the kinematics model and dynamic model are set up. The model of robot control system is obtained with Matlab/Simulink. The dynamic model and the control system model are combined by ADAMS/Control. Uniting ADAMS and Matlab, the characteristic of the robot is investigated. The load file which contains the load information of the flexible part is, then, obtained. Together with this file and ANSYS, the finite element analysis of the robot could be made. Ultimately, according to the analysis result, the structure of the robot would be optimized. It can obviously save time to develop the cutting robot on this platform, which consists of the four kinds of software mentioned in this paper. The results of the simulation are close to the real. Thus, it will provide the basis for making the physical prototype.
Using cluster analysis to validate the angoff standard setting method in mixed-format assessments

Test validity is a property of the interpretation assigned to test scores. To provide an objective validating evidence for a standard-referenced assessment is especially important. In this study we utilize a statistical technique, cluster analysis, to explore the validity of one of the expert judgement technique-Yes/No Angoff standard setting method. We first segregated each examinee ability cluster using the hierarchical clustering (HC). Assume that each ability cluster is a Gaussian distribution and that the distribution of each test subject data can be modeled by mixture of Gaussians (MoG), where the mean, variance and the proportion of each cluster were initialized by the HC results. Finally, the ability clustering was implemented by the expectation maximization (EM) method. The results from the traditional standard-setting procedure and cluster analysis were compared. The study concludes that cluster analysis appears useful for helping to set standards on educational tests. In addition, it suggested that cluster analysis could be applied as a support tool to provide validating information in the process of standard setting for high-stakes achievement tests.
Simulation of the formation mechanism of coronary thrombosis based on DEM-CFD coupling

Aiming at the problem of the formation of thrombosis and coronary restenosis, the formation mechanism of thrombosis was studied in this paper. The simulation model of platelet particles was established based on the theory of discrete element. And the simulation model describing the flow of blood was built based on the principle of hemodynamic. Then by means of the DEM-CFD coupling technology, a simulation model was established to describe the circumstance that platelet flows in blood. To reveal the formation mechanism of thrombosis and solve the problem of coronary restenosis, the process of blood flowing, platelet adhering to each other and to coronary stent was simulated. The result of simulation showed that blood flow velocity has a significant influence on the formation of thrombus. The result will provide basement on the cure of thrombus and the improvement of stent in the future.
An efficient recovery scheme for mobile computing system

It is very necessary for the mobile computing system to be equipped with the checkpointing recovery facility because of the fact that the MHs are vulnerable to the failures. Previous recovery schemes based on message logging are under the condition that exchanged messages among mobile hosts are forwarded by MSSs(m-MSS-m communication). A new recovery protocol is presented for mobile host direct to mobile host(m-m) communication wireless network in this paper. The m-m communication contributes to a less contention on the wireless network and a lower latency for message transmission than m-MSS-m. Theoretical analyses and simulation results show that the proposed approach provides higher performance in terms of fail-free overhead and recovery overhead than traditional schemes.
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model

Deep convolutional neural networks (CNNs) have proven highly effective for visual recognition, where learning a universal representation from activations of convolutional layer plays a fundamental problem. In this paper, we present Fisher Vector encoding with Variational Auto-Encoder (FV-VAE), a novel deep architecture that quantizes the local activations of convolutional layer in a deep generative model, by training them in an end-to-end manner. To incorporate FV encoding strategy into deep generative models, we introduce Variational Auto-Encoder model, which steers a variational inference and learning in a neural network which can be straightforwardly optimized using standard stochastic gradient method. Different from the FV characterized by conventional generative models (e.g., Gaussian Mixture Model) which parsimoniously fit a discrete mixture model to data distribution, the proposed FV-VAE is more flexible to represent the natural property of data for better generalization. Extensive experiments are conducted on three public datasets, i.e., UCF101, ActivityNet, and CUB-200-2011 in the context of video action recognition and fine-grained image classification, respectively. Superior results are reported when compared to state-of-the-art representations. Most remarkably, our proposed FV-VAE achieves to-date the best published accuracy of 94.2% on UCF101.
Development of a Robot System for Pipe Welding

Large amount of stainless steel tubes would be applied in the desalination system. If the tubes are welded manually, there would be a lot of the repeated work accompanied with the extremely low efficiency and the welding quality could not guaranteed either. So the best choice to finish the work is using a robotic welding system. In this paper, a robotic system to be employed in welding the pipes for desalination of sea water is introduced. The robotic system contains four revolute axes and could be mounted on a branch pipe, so it can both maneuver between the pipes and adjust to a wide range of pipes’ diameter. Moreover, it is very convenient to take with for it is compact and lightweight. The mathematical method for calculating of the geometry of welding path of the intersecting cylinders is also explained. The welding robot, a digital welding power and the gas shielded system are included in the welding system. The robot movement is controlled by a micro-controller and it could communicate with the host IPC (industrial personal computer) through RS232. Other devices are directly controlled by the host IPC. Finally, a simulation is taken and the result shows that the welding system is able to meet the requirements.
Automatic Transcription for Music with Two Timbres from Monaural Sound Source

A new approach to automatic music transcription for music with two timbres from a monaural sound source is proposed in this paper. The system is mainly composed of two parts, a fundamental frequency detector and a timbre discriminator. In the fundamental frequency detector, the short time Fourier transform (STFT) and a peak detection algorithm are used. By combining the relative magnitude of the fundamental frequency and its harmonics, a characteristic timbre vector is computed. The timbre discrimination is done by the classification of the timbre vectors with the support vector machine (SVM). Particularly, by designing the training and classification procedure in SVM, the mixed-timbre signal can also be classified in this system. Using a small database of polyphonic music of two timbres as the testing input, a 73% hit rate is achieved by this system.
A low-cost recovery protocol for mobile computation

Mobile computing brings new challenges and requirements for checkpointing and recovery protocol. Existing recovery schemes for mobile environments typically either create global consistent checkpoints that don't guarantee independent recovery or can only support mobile-MSS-mobile (m-MSS-m) communication that exchanged messages among mobile hosts are forwarded by MSSs. We present a recovery protocol based on message logging for mobile host direct to mobile host(m-m) communication wireless network. The m-m communication benefits a less contention on the wireless network and a lower latency for message transmission than m-MSS-m. Finally simulation results indicate that the performance of our approach is higher than that of traditional approaches in terms of fail-free overhead and recovery overhead.
Skew Cyclic Codes over a Non-chain Ring

We study skew cyclic codes over a nonchain ring, which generalizes our previous results in IEICE Trans. on Fundamentals of Electronic Communications and Computer Sciences, 2015. We describe generator polynomials of skew cyclic codes over this ring and investigate the structural properties of skew cyclic codes over the ring by a decomposition theorem. The generator polynomial of the dual code of a skew cyclic code are obtained. Moreover, the idempotent generators of skew cyclic codes are considered. Some examples are also presented to illustrate the discussed results.
Web_Soc: a Socratic-dialectic-based collaborative tutoring system on the World Wide Web

A Web/spl I.bar/Soc tutoring system for recursion concept learning is proposed. The system integrates different modes of learning, synchronous and asynchronous, collaborative and individualized, into a World Wide Web (WWW) environment. The paper proposes a collaborative learning protocol. Based on the protocol, the principles of the Socratic dialogue are applied to the collaborative learning of recursion concepts. In order to evaluate the effects on learning of the Web/spl I.bar/Soc system, an educational experiment was conducted. The results show that the effects from the Web/spl I.bar/Soc system were more obvious than merely studying articles.
DVS: Dynamic Variable-Width Striping RAID for Shingled Write Disks

Disk data density improvement will eventually be limited by the super-paramagnetic effect for perpendicular magnetic recording. Of the various new technologies being explored, Shingled Magnetic Recording (SMR) exposes as the most promising one to achieve high areal density and only make little changes to the manufacturing process. At present, high-capacity SMR drives are available from Seagate and HGST. Since SMR is leading next generation disk technology and increasing SMR drives will be used in storage systems, there is a great need to look over the current RAID storage techniques based on HDDs again. In this paper, we proposed a dynamic variable-width striping RAID (DVS-RAID) for SMR drives to reduce the parity updating cost. DVS-RAID never overwrites the old data, but always constructs a new full or partial stripe (variable-width stripe), and writes to the SMR drives through appending. In addition, taking the access characteristics of SMR drives into consideration, we present a new write cache management that exploits both spatial and temporal localities. The experiment with six real-world traces demonstrates that DVS- RAID exhibits a slightly lower performance than HDD- based RAID on update intensive workloads. However the performance of DVS-RAID is better than HDD-based RAID with sequential access, read-dominated workloads or workloads with rarely update.
Learning context-aware sparse representation for single image super-resolution

This paper presents a novel learning-based method for single image super-resolution (SR). Given an input low-resolution image and its image pyramid, we propose to perform context-constrained image segmentation and construct an image segment dataset with different context categories. By learning context-specific image sparse representation, our method aims to model the relationship between the interpolated image patches and their ground truth pixel values from different context categories via support vector regression (SVR). To synthesize the final SR output, we upsample the input image by bicubic interpolation, followed by the refinement of each image patch using the SVR model learned from the associated context category. Unlike prior learning-based SR methods, our approach does not require the reoccurrence of similar image patches (within or across image scales), and we do not need to collect training low and high-resolution image data in advance either. Empirical results show that our proposed method is quantitatively and qualitatively more effective than existing interpolation or learning-based SR approaches.
Conversion of Mate-Pair Reads into Long Sequences for Improving Assembly Scaffolding

Mate-pair sequencing is a technology for sequencing two ends of long DNA fragments, which has been widely used in genome scaffolding. Although the cost of mate-pair sequencing is now affordable, its accuracy has been limited by the lower quality and contamination. The 3rd generation sequencing is able to generate long reads for genome scaffolding. However, the error rates and cost are still too high. This paper aims to convert low-cost mate-pair reads into long reads using computational approaches, which has the benefits of both mate-pair reads and long reads for scaffolding. We test our methods by using several real datasets and validate the accuracy of converted long reads. In addition, the scaffolding results are compared using mate-pair reads, long reads, and mixture of both material.
A new structural knowledge assessment based on weighted concept maps

Concept maps have been widely put to educational uses. Concept maps in the past were limited in their assessment standards. Some only take concept nodes into consideration; other evaluation criteria use concept nodes as the basis, with relation links playing a minor role. To address this problem, this study proposes a new style of concept map, called weighted concept map, which assigns a weight to each proposition in a concept map to represent its importance. This study proposes a new assessment with qualitative comparison founded on a weighted concept map.
Using Augmented Reality to Promote Homogeneity in Learning Achievement

The issue of individual differences among learners has thus far received the most attention from education researchers. Many teachers strive to develop a learning strategy with significant effect on the majority of learners. Through literature analysis, this study found that not only could Augmented Reality (AR) improve learning effectiveness, it could also reduce the impact of individual differences on learning outcomes. Therefore, this study designed a set of AR-aided teaching systems to help teachers supplement curriculum content using AR. Sixty-six participants from elementary schools were involved in this study. The results confirmed that AR can help learners to achieve better learning outcomes, and can effectively improve learning achievement in non-high-scoring groups, enabling them to perform closer to those in high-scoring groups and reducing the gap in overall learning level. This study also discovered that technological barriers could reduce the benefits of AR in teaching contexts. Authoring tools with low operating thresholds are valuable for AR-aided teaching systems.
A novel low-light-level optical switch by using nano-opto-mechanics technology

An all-optical switch that controls a strong light with a weak one based on nonlinear cavity enhanced optomechanical effect is demonstrated. It consists of a micro-ring resonator with suspension arcs, and a bus waveguide. “On” and “off” states of the probe light depend on the position of the suspension arcs that is under the control by a weak signal light. By use of nano-opto-mechanics system (NOMS) technology, the device is fabricated on silicon photonics platform with a foot-print size of only 50 μm × 50 μm. The switch is studied through both modeling and experiment. The observed control light is 380 times smaller in intensity than the probe light. Potential applications of this switch include quantum information networks, optical memories, on-chip optical signal processing, etc.