nils-herrmann/clean_scirepeval huggingface dataset:
Playfulness and innovation—A multilevel study in individuals and organizations

Quite a few studies have shown playfulness cannot be excluded from work but can be utilized to facilitate work efficiency and innovation creativity. Members' creativity can be easily inspired in a joyful atmosphere and more contribution of organizational innovation can be made as well. The empirical research is conducted with 200 companies in Taiwan as samples, and examines the relationship among personal playfulness, employee creativity, organizational playfulness climate, and organizational innovation. The findings show that personal playfulness has positive influence on employee creativity, organizational playfulness climate, and organizational innovation; employee creativity and organizational playfulness climate have positive influence on organizational innovation; employee creativity and organizational playfulness climate mediate the relationship between personal playfulness and organizational innovation.
Optimization of insulator layer with high breakdown voltage for 10 inch nanowire field emission display

In a gate-structured field emission display (FED), the insulator layer is used to keep an electrical insulation between the gate and cathode electrode. The reliability of the insulator layer is an important issue of FED fabrication, especially for large area device. In this study, the insulator layer was prepared using plasma enhanced chemical vapor deposition (PECVD) and the preparation parameters were optimized. Optimal insulator layers consisting of silicon oxide and silicon nitride stacked insulator layers were obtained and applied in a 10-inch ZnO nanowire FED device.
Integrating Spatio-Temporal Context With Multiview Representation for Object Recognition in Visual Surveillance

We present in this paper an integrated solution to rapidly recognizing dynamic objects in surveillance videos by exploring various contextual information. This solution consists of three components. The first one is a multi-view object representation. It contains a set of deformable object templates, each of which comprises an ensemble of active features for an object category in a specific view/pose. The template can be efficiently learned via a small set of roughly aligned positive samples without negative samples. The second component is a unified spatio-temporal context model, which integrates two types of contextual information in a Bayesian way. One is the spatial context, including main surface property (constraints on object type and density) and camera geometric parameters (constraints on object size at a specific location). The other is the temporal context, containing the pixel-level and instance-level consistency models, used to generate the foreground probability map and local object trajectory prediction. We also combine the above spatial and temporal contextual information to estimate the object pose in scene and use it as a strong prior for inference. The third component is a robust sampling-based inference procedure. Taking the spatio-temporal contextual knowledge as the prior model and deformable template matching as the likelihood model, we formulate the problem of object category recognition as a maximum-a-posteriori problem. The probabilistic inference can be achieved by a simple Markov chain Mento Carlo sampler, owing to the informative spatio-temporal context model which is able to greatly reduce the computation complexity and the category ambiguities. The system performance and benefit gain from the spatio-temporal contextual information are quantitatively evaluated on several challenging datasets and the comparison results clearly demonstrate that our proposed algorithm outperforms other state-of-the-art algorithms.
A Novel Approach for Design of DC HTS Cable

High temperature superconducting (HTS) cable is one of important apparatus in future smart-grid. Direct current (DC) HTS cable can provide secure and reliable transmission of electric power at the gigawatt level with several hundreds of kA and kV. Since the magnetic field of cable seriously degrades the critical current of HTS superconductor and then the current capacity, modern power converters often introduce alternating currents (AC) at other frequencies even filters reduce the magnitude of those ripple currents which resulting into ac loss since systems used to convert AC to direct current (DC) produce some current variations which are usually at frequencies of multiple power frequency. In order to reduce this loss, unlike the conventional uniform current design in AC HTS cable, a novel approach is proposed for minimizing the loss as small as possible by adjusting the inductive reactance of each layer so that the ratios of AC current amplitude to critical current are always same in layers. The magnetic field distribution and critical current are simulated by finite element method (FEM). The calculations show that the new approaches can reduce ac loss, improve efficiency, which are potential for DC current transmission HTS cable application.
Improvement of AlGaInP light emitting diode by sulfide passivation

The brightness of AlGaInP light emitting diodes (LEDs) has been raised by a factor of 1.12 at 20 mA by sulfide passivation. Meanwhile, the sulfide also can decrease leakage current of AlGaInP LEDs at -2 V to nearly one thousandth of that in the as-fabricated device. The possible causes for the brightness increase of AlGaInP LEDs after sulfide treatment including surface roughness, reduction of Fresnel loss, and effective injection of carriers were demonstrated.
Key Issues for AR-Based Digital Reconstruction of Yuanmingyuan Garden

Yuanmingyuan was called “Garden of All Gardens” before it was looted and burnt down by the Anglo-French allied forces in 1860. Nowadays there are only stone ruins that used to be parts of ancient buildings. The Digital Yuanmingyuan project is designed to provide visitors with the visualization of virtual reconstructions superimposed upon the natural field of the ruins using a personal AR (Augmented Reality) system. It can not only preserve the current appearance of the ruins, but also exhibit the original exquisite architecture of Yuanmingyuan. The technical difficulties of the system are analyzed, and the design of the system hardware and the tracking algorithm are discussed. A prototype of the proposed system is developed and the initial result of restoring one typical scene in Yuanmingyuan is presented.
Electromagnetic phenomena in HTS bulk subjected to a rotating field

In HTS rotating systems, it is possible for HTS bulk to be subjected to rotating magnetic fields due to various inhomogeneities. The dynamic response of HTS will strongly affect the operating characteristics. In this paper, we rely on flux dynamics to study the magnetic behavior of HTS bulk in a rotating field. The flux evolution, the possible electromagnetic oscillation, the effect of the anisotropy and the role of transverse rotating-field component are qualitatively discussed. The associated losses are derived from numerical simulation.
Weighted Low-Rank Decomposition for Robust Grayscale-Thermal Foreground Detection

This paper investigates how to fuse grayscale and thermal video data for detecting foreground objects in challenging scenarios. To this end, we propose an intuitive yet effective method called weighted low-rank decomposition (WELD), which adaptively pursues the cross-modality low-rank representation. Specifically, we form two data matrices by accumulating sequential frames from the grayscale and the thermal videos, respectively. Within these two observing matrices, WELD detects moving foreground pixels as sparse outliers against the low-rank structure background and incorporates the weight variables to make the models of two modalities complementary to each other. The smoothness constraints of object motion are also introduced in WELD to further improve the robustness to noises. For optimization, we propose an iterative algorithm to efficiently solve the low-rank models with three subproblems. Moreover, we utilize an edge-preserving filtering-based method to substantially speed up WELD while preserving its accuracy. To provide a comprehensive evaluation benchmark of grayscale-thermal foreground detection, we create a new data set including 25 aligned grayscale-thermal video pairs with high diversity. Our extensive experiments on both the newly created data set and the public data set OSU3 suggest that WELD achieves superior performance and comparable efficiency against other state-of-the-art approaches.
Experimental Study and Optimization of HTS Bulk Levitation Unit for Launch Assist

Magnetic levitation and propulsion has been viewed as a safe, reliable, and inexpensive launch assist for sending small payloads into orbit. In order to investigate the feasibility of HTS bulk levitation system for the purpose, an experimental setup was designed and constructed to study the performance of the lifting body above permanent magnet (PM) guideway. The measurements of the levitation force and guidance force were carried out in zero-field-cooled and field-cooled modes for different topologies of HTS bulk arrangements and PM guideway configurations. The results were analyzed with computational simulations. An optimal design was discussed for the levitation system for launch assist
Failure detection and adaptive compensation for fault tolerant flight control systems

This paper presents an advanced aircraft flight control system with the capabilities of failure detection, identification and adaptive compensation in the event of the damage of control surface happening. For the proposed scheme, a fault tolerant mechanism is activated Io regain the control of the damaged aircraft. The approach is based on the detection of abrupt changes of the correlation functions between pitching, yawing and rolling rates and the failure compensation mechanism is realized by using a wavelet neural net-based PID controller.
Ultra Fine Pitch / Low Cost FCCSP Package and Chip Package Interaction (CPI) for Advanced CMOS Nodes

The advancement of Si and flip chip package technologies are driven by high performance mobile processors with high I/O counts. In Si, the back-end-of-line (BEOL) copper interconnect with extreme low-K (ELK) dielectrics has been used to lower RC latency. While for packages, fine-pitch Cu bump is introduced to meet the high I/O density. Corresponding assembly solutions such as thermal compression bonding (TCB) has been used to meet fine-pitch die bonding accuracy requirement. However, the process cost is high due to tool investment and low throughput. Mass reflow, in contrast, supporting processes in batches, enables low cost fine pitch solution. This paper presents the Chip-Package-Interaction (CPI) and assembly process characterization based on the advanced Si incorporated with (1) 60 um pitch Cu bump-on-trace (CuBOT), (2) thermal reflow, and (3) BOT substrate with one escaped trace between bumps. CPI and assembly challenges including ELK peeling, bump bridging, and Cu trace peeling will be investigated. Effects of bump structure, UBM design, polyimide (PI) opening, and substrates material on ELK stress and assembly yield will be addressed. In addition, wafer probing effects on CuBOT packaging yield and reliability will be discussed. Quick temperature cycle (QTC) test for ELK robustness check is employed to speed up Si and bump structure optimization. Full ELK stacking samples with encapsulation passed reliability qualification under JEDEC test conditions. Bias-HAST is further conducted to confirm the process integrity for fine-pitch substrates. Current carrying capability of fine pitch Cu bump is identified by electron-migration (EM) test as well.
A Portable Feather-Weight Java-Based Graphic Library for Embedded Systems on Java Processor

This research tries to improve the portability of Java AWT(Abstract Window Toolkit) for embedded systems. We want to have a new graphic library which is easier to port than current AWT and to shorten the porting time. The proposed graphic library consists of two layers. The bottom (native) layer defines a minimum set of graphic primitives that needs porting, and all the other AWT required primitives are group into the upper (Java) layer. We implemented the graphic library to support AWT on two configurations. One is a purely software Implementation and the other is equipped with a Java processor. The performance of the former is not practical, and the latter has good improvements as compared with the former. Further enhancement to the proposed library is undertaking.
Adaptive cross coupling control for CNC machines

Currently, most multi-axis in the modern CNC machines manipulate individual axial control in each axis of motion which is separately driven and the control loop only one axis. It is known that modeling uncertainty and nonlinear disturbance significantly affect performance in the tracking control on each axis and the contour cutting in CNC machines. Control design based on the traditional approaches in each axis may be insufficient to handle that kind of task in multi-axis motion platforms. An approach is presented here to improve the CNC cutting performance in contour tracking by incorporating adaptive proportional-integral-derivative (APID) and cross-coupling control (CCC) for the purpose. We propose an improved method for precision multiple motion control by combining individual axis control and CCC in the controller design. It consists of two parts: APID and sliding mode control which is with adjustable PID control gains influenced from a sliding hyper plane to reduce tracking error of each axis.; adaptive CCC works to deal with the tracking error of the individual axis together with the coupling error and applies the combined error to a decoupling controller for contour error minimization. Extensive simulation results based on the three-axis motion is compared to traditional PID control showing the effectiveness of our proposed design.
A saddle superconducting magnet for experimental MHD generator

A saddle superconducting magnet for experimental MHD generator has been designed, constructed and tested. The magnet has /spl phi/ 44 cm warm bore, 103 cm effective magnetic length and 4 T central field. It consists of 17 layers, each layer has two circular saddle coils with 54.5/spl deg/ segment cross section. The conductor is 2 mm /spl times/ 20 mm NbTi monolithic provided by Supercoil Inc. The coils are assembled on the bore tube by spiral bandings. The performances and the test results of the saddle superconducting magnet is presented.
A Direct AC–DC and DC–DC Cross-Source Energy Harvesting Circuit with Analog Iterating-Based MPPT Technique with 72.5% Conversion Efficiency and 94.6% Tracking Efficiency

The proposed cross-source energy (CSE) harvesting system accepts universal energy sources, including the ac and dc sources. The buck-boost conversion of CSE automatically converts ac or dc input into dc output without being limited by the type of universal energy source. The CSE technique simultaneously supplies the regulated output for the system loading and the battery charging output. A test chip fabricated in the VIS Bipolar, CMOS, and DMOS (BCD) process can optimally derive harvest energy with 72.5% power efficiency when one solar input source is used. A backup converter is designed to complement the CSE technique to guarantee the regulation of output voltage. The proposed analog iterating-based (AIB) maximum power point tracking (MPPT) technique achieves 94.6% tracking efficiency without complex data calculation and storage, which are better results compared with those of previous research.
IPARBC: An Improved Parallel Association Rule Based on MapReduce Framework

Recently, data mining in big data has become an important concern for researchers. Data mining, which refers to mining the relationship between items in a dataset, has been applied by businesses to seek profitable outcomes. Association rule mining algorithms such as Apriori and the FP-growth are efficient methods for discovering relations between items in large databases. To enhance the performance, many researches tend to enhance the traditional method using the MapReduce framework. In this paper, we proposed an improved association rule algorithm (IPARBC) based on MapReduce framework. The concept of combinatorial mathematics is used as the theoretical basis of the algorithm, and in order to improve mining performance by MapReduce framework, we address the high volume problem of big data. Experimental results show that the proposed algorithm outperform other algorithms substantially in terms of runtime.
Research on application of fuzzy fault tree analysis in the electronic equipment fault diagnosis

The complexity of electronic equipment structure makes the fault has characteristics of complexity, ambiguity and uncertainty. Based on the description of basic theory and aim at the problem of ambiguity and uncertainty of the electronic equipment failure probability, the paper introduced fuzzy set theory into electronic fault tree analysis and scientifically analyzed the various kinds of fuzzy information that be confronted by the failure of the electronic equipment. A material example of the application of fuzzy fault tree analysis method in the electronic fault diagnosis system was also provided. It proves that the method can analyze and process random uncertainty and fuzzy uncertainty failure event at the same time, which can effectively solve the above problems of electronic equipment faults.
Floating Steiner Trees

We study the reproducing placement problem, which finds application in layout-driven logic synthesis. In each phase, a module (or gate) is decomposed into two (or more) simpler modules. The goal is to find a "good" placement in each phase. The problem, being iterative in nature, requires an iterative algorithm. In solving the RPP, we introduce the notion of minimum floating Steiner trees (MFST). We employ an MFST algorithm as a central step in solving the RPP. A Hanan-like theorem is established for the MFST problem, and two approximation algorithms are proposed. Experiments on commonly employed benchmarks verify the effectiveness of the proposed technique.
Study on ESD Effects of GaAs HBT Power Amplifiers for DCS/GSM Dual Band Handsets

Study on electrostatic discharge (ESD) impact on the GaAs HBT power amplifier (PA) in DCS/GSM dual band handsets is performed in this paper, which has been regarded as its main reliability issue accounting for most failures of radio frequency integrated circuits. In order to suppress the positive ESD impact on the PA in an effective but very economic way, a set of forward ESD diodes at its input port is replaced by the PN junction of GaAs HBT transistor in its first-stage. Alternatively, to introduce a set of thin film resistors (TFRs) at the front of a HBT transistor is necessary for improving the S11-parameter and stability of PA; however, their electrothermal breakdown possibilities do exist. Therefore, both performance degradation and breakdown events of several PA dies are measured for different ESD voltages described by the human body model, with different TFRs chosen for both DCS and GSM bands, respectively. Their transient temperature responses are also captured and compared for different ESD waveforms, which are predicted using the in-house developed algorithm based on the hybrid time-domain finite-element method.
Human Parsing with Contextualized Convolutional Neural Network

In this work, we address the human parsing task with a novel Contextualized Convolutional Neural Network (Co-CNN) architecture, which well integrates the cross-layer context, global image-level context, semantic edge context, within-super-pixel context and cross-super-pixel neighborhood context into a unified network. Given an input human image, Co-CNN produces the pixel-wise categorization in an end-to-end way. First, the cross-layer context is captured by our basic local-to-global-to-local structure, which hierarchically combines the global semantic information and the local fine details across different convolutional layers. Second, the global image-level label prediction is used as an auxiliary objective in the intermediate layer of the Co-CNN, and its outputs are further used for guiding the feature learning in subsequent convolutional layers to leverage the global image-level context. Third, semantic edge context is further incorporated into Co-CNN, where the high-level semantic boundaries are leveraged to guide pixel-wise labeling. Finally, to further utilize the local super-pixel contexts, the within-super-pixel smoothing and cross-super-pixel neighbourhood voting are formulated as natural sub-components of the Co-CNN to achieve the local label consistency in both training and testing process. Comprehensive evaluations on two public datasets well demonstrate the significant superiority of our Co-CNN over other state-of-the-arts for human parsing. In particular, the F-1 score on the large dataset   reaches $81.72\,\text{percent}$ by Co-CNN, significantly higher than $62.81\,\text{percent}$ and $64.38\,\text{percent}$ by the state-of-the-art algorithms, M-CNN   and ATR   , respectively. By utilizing our newly collected large dataset for training, our Co-CNN can achieve $85.36\,\text{percent}$ in F-1 score.
Power Flow and Generator-Converter Schemes Studies in Ship MVDC Distribution Systems

The medium-voltage dc (MVDC) distribution system is a new shipboard power distribution technology that has been extensively discussed and studied by many shipbuilding corporations and ship-owners in recent years. As different types of power converters and ac and dc power distributions simultaneously involved in the system, the system performance for different connections of generators to MVDC converters under varying steady-state operating conditions is essential and important in the design stage of the electrical power distribution system. This type of problem can be analyzed by performing power flow studies. This paper aims presenting a methodology for power flow studies in ship MVDC distribution systems for system planning purpose. A power flow model of the power converters in steady-state is derived to consider medium and low voltage characteristics of electric power system on this type of ship. An ac/dc power flow solution approach then is developed to incorporate ac-system and dc-system models and the ac/dc interface buses in the analysis for determining an appropriate connection of generators and MVDC converters and power converter setting values. Test results of two alternatives for the direct connection of generators to MVDC converters under varying steady-state operating conditions are presented and compared to determine the proper generator-power converter scheme.
GA-Based Multiobjective PID Control for a Linear

This paper presents a robust output tracking control design method for a linear brushless DC motor with modeling uncertainties. Two frequency-domain specifications directly related to the mixed sensitivity function and control energy consumption are imposed to ensure stability and performance robustness. With regard to time-domain specifications, the rise time, maximum overshoot and steady-state error of the step response are considered. A generalized two-parameters proportional, integral, and derivative (PID) control framework is developed via a genetic searching approach ensuring the specifications imposed. The proposed design method is intuitive and practical that offers an effective way to implement simple but robust solutions covering a wide range of plant perturbation and, in addition, provides excellent tracking performance without resorting to excessive control. Extensive experimental and numerical results for a linear brushless motor confirm the proposed control design approach.