nils-herrmann/clean_scirepeval huggingface dataset:
An empirical evaluation of architectural alternatives for J2EE and Web services

Component-based technologies such as J2EE and .NET have been widely adopted to develop Web-based applications. With the emerging use of Web services and service-based architectures, such component technologies provide the necessary back-end components to execute application business logic, supporting a variety of architectural alternatives. The combination of component technologies with Web services further extends the set of architecture choices, with each providing different levels of performance. It is therefore important for an architect to understand these performance implications in the early stages of the architecture design. In this paper, we measure and analyze the performance of four alternative architectures using J2EE and Web services. The purpose is to investigate the effects on performance, identify both hardware and software bottlenecks, and analyze the architectural design trade-offs.
An Adaptive System Identification Algorithm with a General Performance Index Based on Entropy Optimization

This paper presents an entropy minimization algorithm for nonlinear system identification based on the information theory. The Parzen windowing estimator is used to approximate the entropy when the probability density functions of the variances can not be known as a priori or the variances are not realistically expressed with the traditional probability density functions. A general performance index based on the information entropy is discussed in this paper. Minimizing the performance index adopted can make the desired output of the adaptive system being tracked directly by the output of the neural network identifier. Furthermore, this performance index can be easily extended when treating other control problems. The performance of the entropy optimal algorithm is shown by several simulations with backpropagation neural networks.
A Novel Trench-Gated Power MOSFET With Reduced Gate Charge

In this letter, we propose a novel trench power MOSFET structure with a p-n junction in trench to reduce the gate charge. We utilize the 2-D device simulator, ATLAS, to investigate the characteristics of the proposed structure and compare with the conventional structure. As a result, the proposed structure exhibits 49.5% enhancement in gate-charge Qg as compared with the conventional structure, without degrading the other electrical characteristics.
Design and implementation of a multi-sensor monitoring system

Analysis of the deficiencies of the existing monitoring system, consider the needs of a complex environment, design and implement a monitoring system for a multi-sensor. Using the control unit SCM, the choice depending on the environment of various types of sensors capable of collecting a variety of sound signal, light, pressure, etc., to the exception of the collected signals are processed for transmission via a wired network, the control unit analyzes the received signal detection and judgment processing, grading discovery exception and alarm. The system overcomes the shortcomings of similar systems, a wide range of application, stability, scalability, and easy to promote. Experimental tests demonstrate the feasibility of the system.
A horizontal-winding multi-permeability distributed air-gap inductor

Distributed air-gap inductors have the advantage of reducing winding loss in high switching frequency DC/DC converters. However, they also have the disadvantage of uneven distribution of flux density, which inevitably leads to the incomplete utilization of magnetic material. This paper proposes a horizontal-winding multi-permeability distributed air-gap inductor structure to increase inductance without the increase of inductor volume. To evaluate the proposed method, a two-permeability inductor together with a single permeability inductor is fabricated. The measured results show the two-permeability inductor has higher inductance than the single-permeability one, especially at light load. Both inductors are tested in a 12V input, 1.6V output DC/DC converter to show their performance. The results show the two-permeability inductor could further improve light load efficiency of high frequency DC/DC converters.
A scalable random forest algorithm based on MapReduce

Random Forest is a popular data classification algorithm for machine learning. This paper proposes SMRF algorithm--an improved scalable Random Forest algorithm based on Map Reduce model. This new algorithm makes data classification in computer cluster or cloud computing environment for massive datasets. SMRF processes and optimizes the subsets of the data across multiple participating computing nodes by distributing. The experimental results show that the SMRF algorithm has the equally accuracy degradation but higher performance while comparing with traditional Random Forest algorithm. SMRF algorithm is more suitable to classify massive data sets in distributing computing environment than traditional Random Forest algorithm.
A study on network fault knowledge acquisition based on support vector machine

Network fault knowledge acquisition is a necessary part of intelligent network management. In the paper, knowledge acquisition of two hierarchies is designed for modern network of large scale and some performance parameters instead of management information base are used to model the network faults so that the evaluations of network fault knowledge acquisition can easily be uniformed. Our knowledge acquisition methods are based on support vector machine. Basic support vector machine learning is applied to local network fault knowledge acquisition, and incremental PSVM is improved to be adapted to global dynamic network fault knowledge acquisition. Simulations indicate the correctness and efficiency of our method and the global network fault knowledge acquisition based on proximal support vector machine is still to be improved further.
A practical minimum time control method for Buck converters based on capacitor charge balance

A control method is presented in this paper which utilizes the concept of capacitor charge balance to achieve optimal dynamic response for Buck converters undergoing a rapid load change. The proposed charge balance method is implemented with analog components and is cheaper and more effective than its digital counterparts since complex arithmetic and sampling delay is eliminated. The proposed controller will consistently cause the Buck converter to recover from an arbitrary load transient with the smallest possible voltage deviation in the shortest possible settling time. Since the controller is non-linear during transient conditions, it is not limited by bandwidth/switching frequency. Unlike conventional linear controllers, the dynamic response (voltage deviation, settling time) of the proposed controller can be accurately predicted using a set of equations. This greatly simplifies the design process of the output filter. Simulation and experimental results show the functionality of the controller and demonstrate the superior dynamic response over that of a conventional linear controller.
A novel design of heatsink-less LED base fluorescent lamp retrofit

A new “heatsink-less” LED based T8 retrofit design is proposed in this study. The lamp consisted of light emitting elements directly attached on a flexible substrate. The substrate is mount on to the inner wall of a full cylindrical glass tube. The design is intended to simplify the lamp construction. However, since the metal-based heat sink is eliminated, the thermal design is a concern. Finite element analysis was performed to optimize the thermal design by varying metal line routing on the flexible substrate, and the glass tube structure. The optimal result shows that the junction temperatures of the LED inside the lamp is about 52°C driven under nominal current. The simulation result is comparable with the experimental measurement. In addition, Darveaux's fatigue prediction model was implemented to calculate the thermal cycles for crack initiation and propagation, based on the inelastic strain energy density accumulated per cycle during thermal cycling. The details of the design and the optimization strategy is discussed.
An architecting method for distributed process-intensive systems

This paper introduces an architecting method for distributed process-intensive systems. Traditional methods (e.g. object-orientation, structured analysis or component/service-based designs) decompose a process-intensive system into entities with attached domain-specific operations (process constituents). This results in fine-grained Remote Procedure Calls in distributed systems which are often detrimental to quality attributes such as performance, loose-coupling, adaptability and interoperability. Our method tailors the REpresentational State Transfer (REST) principles used for hypermedia data transfer to process-intensive systems by making process constituents into resources, and attaching a set of standard operations. Distributed processes interoperate by adhering to these operations and exchanging process information. In our method, process information exchange contains not only typical meta-information about a process, but also process fragments that indicate possible next-steps and interconnectedness for process coordination purposes. We have implemented our method in a Web environment and conducted a case study providing initial validation of its benefits.
A Ranking Approach on Large-Scale Graph With Multidimensional Heterogeneous Information

Graph-based ranking has been extensively studied and frequently applied in many applications, such as webpage ranking. It aims at mining potentially valuable information from the raw graph-structured data. Recently, with the proliferation of rich heterogeneous information (e.g., node/edge features and prior knowledge) available in many real-world graphs, how to effectively and efficiently leverage all information to improve the ranking performance becomes a new challenging problem. Previous methods only utilize part of such information and attempt to rank graph nodes according to link-based methods, of which the ranking performances are severely affected by several well-known issues, e.g., over-fitting or high computational complexity, especially when the scale of graph is very large. In this paper, we address the large-scale graph-based ranking problem and focus on how to effectively exploit rich heterogeneous information of the graph to improve the ranking performance. Specifically, we propose an innovative and effective semi-supervised PageRank (SSP) approach to parameterize the derived information within a unified semi-supervised learning framework (SSLF-GR), then simultaneously optimize the parameters and the ranking scores of graph nodes. Experiments on the real-world large-scale graphs demonstrate that our method significantly outperforms the algorithms that consider such graph information only partially.
Distributed range-free localisation algorithm for wireless sensor networks

A novel distributed range-free localisation algorithm to accurately locate regular nodes in wireless sensor networks is presented. Existing methods reduce the possible area where a regular node would be located by using anchors (i.e. nodes with known locations) only. These methods are effective only when anchor density is high. In contrast, the work presented here reduces the possible area of a regular node iteratively, using both anchor node locations and the possible areas of its neighbouring and non-neighbouring ‘regular’ nodes from early iteration. This allows a significant improvement in the localisation accuracy. Simulations verify the advantage of the proposed algorithm and show that the localisation error can be controlled within 15% with off-the-shelf devices.
Dynamic Neural Network for Recognizing Interspike Interval Sequences

The functions of neural system, such as learning, recognition and memory, are emerging from the dynamic mechanisms of neurons and synapses. A dynamic neural network is designed to discuss how the dynamic mechanisms in the neurons and synapses work in recognizing interspike intervals (ISIs). The dynamic synaptic transmission mechanisms and the properties of integrate-and-fire neurons are the key mechanisms, based on which the input interspike interval sequences are decomposed into isolated spikes. The synaptic delay times modulated by STDP learning rule is another key mechanism in the ISIs recognition, based on which the ISIs are learned and saved in the delay times. After learning, the neural network could recognize whether different input sequences include the same consecutive ISIs. This model shows that the dynamic mechanisms of neurons and synapses in brain are powerful, even under a simple network structure.
A New Compact High-Power Microwave Phase Shifter

A new compact low-loss fast phase-shift high-power microwave (HPM) phase shifter (PS) is proposed, designed, and cold and high-power tested. Firstly, based on solving the scattering matrix and eigenvectors, we design a novel HPM dual circular polarizer, and then a dumbbell-like metal plug driven by a high-speed servomotor is used to slide a short circuit along the dual circular polarized port to adjust the output RF phase, which varies 180° by moving the plug with a quarter of the guided wavelength. The X-band PS has a total length of 9.5 cm and a power capacity achieved 300 MW at 30-ns HPM pulse. A fast phase shift of 310° was achieved within 0.1 s in test, a high precision of phase shift 1° can be realized, and the tested insertion loss was <; 0.15 dB.
A novel design approach for developing chemical sensing platforms using inexpensive technologies

In this paper we describe a novel design methodology for fabricating chemical sensing platforms by utilising well-established inexpensive technologies. In our approach, the sensing site and the active part of the transducer are designated as two separate entities, facilitating the use of a multitude of sensing membranes and/or supporting substrates as well as the employment of discrete off-the-shelf devices. The versatility of this method is demonstrated through prototype chemical sensing platforms based on Pt/SixNy and Au/TiO2 sensing sites, fabricated on glass-microscope slides and printed circuit boards respectively, with discrete MOSFETs integrated on the same platforms. Finally, this concept was further exploited by designing and fabricating a universal instrumentation board that incorporates both the active transducers as well as appropriate read-out circuitry, demonstrating near to Nerstian chemical sensitivities.
A scheduling algorithm in the randomly heterogeneous multi-core processor

The increasing scale of multi-core processors are likely to be randomly heterogeneous by design or because of diversity and flaws. The latter type of heterogeneity introduced by some unforeseen variable factors such as the manufacturing process variation is especially challenging because of its unpredictability. In this environment, thread scheduler and global power manager must handle such randomly heterogeneous. Furthermore, these algorithms must supply high efficiency, scalability and low overhead because future multi-core processors may have a number of cores on a single die. This paper presents a variation-aware scheduling algorithm for application scheduling and power management. Thread switching and sampling among different cores in the multi-core processor introduce obvious overhead than previous many-core scheduling algorithms. Proposed scheme records the information of swapped thread of preferential core and uses tabu search-based randomly heterogeneous scheduling algorithm(TSR) to avoid the occurrence of repeated sampling and reduce the migration frequency and sampling frequency of a thread. The experimental results show that TSR algorithm has decreased 45.7% of thread migration and 42.2% of the sampling time as compared with local search algorithm. This paper regards the transcendental Hungarian offline scheduling algorithm as the baseline. ED2 of TSR only decrease by 8.58% as compared with that of Hungarian offline scheduling algorithm, but compared with the random search scheduling algorithm, ED2 of TSR decreased by 39.4%.
Command Filter-Based Adaptive Neural Tracking Controller Design for Uncertain Switched Nonlinear Output-Constrained Systems

In this paper, a new adaptive approximation-based tracking controller design approach is developed for a class of uncertain nonlinear switched lower-triangular systems with an output constraint using neural networks (NNs). By introducing a novel barrier Lyapunov function (BLF), the constrained switched system is first transformed into a new system without any constraint, which means the control objectives of the both systems are equivalent. Then command filter technique is applied to solve the so-called “explosion of complexity” problem in traditional backstepping procedure, and radial basis function NNs are directly employed to model the unknown nonlinear functions. The designed controller ensures that all the closed-loop variables are ultimately boundedness, while the output limit is not transgressed and the output tracking error can be reduced arbitrarily small. Furthermore, the use of an asymmetric BLF is also explored to handle the case of asymmetric output constraint as a generalization result. Finally, the control performance of the presented control schemes is illustrated via two examples.
Effect of digital control on phase-shifted full-bridge system stability and dynamic performance

Digital control has become the mainstream of DC/DC converter control technique. However, conventional analog control is less effective compared with analog control. In this paper, taking phase-shifted full-bridge ZVZCS (Zero-Voltage-Zero-Current-Switching) converter as an example,we analyze in detail the effect of the digital control on system stability and dynamics in the sampling process, zero-order-hold, and the control of one-step-delay. It shows that the introduction of all aspects of digital control makes the open-loop zero, pole points with the sampling frequency change a lot. It not only affects the closed-loop characteristic equation coefficients and order, reduces the ranges of critical and stable gain greatly, but also changes the dynamic performance of a system. Furthermore, through experiments, we validate that the theoretical analysis is correct.
Computational Challenges for Power System Operation

As the power grid technology evolution and information technology revolution converge, power grids are witnessing a revolutionary transition, represented by emerging grid technologies and large scale deployment of new sensors and meters in networks. This transition brings opportunities, as well as computational challenges in the field of power grid analysis and operation. This paper presents some research outcomes in the areas of parallel state estimation using the preconditioned conjugated gradient method, parallel contingency analysis with a dynamic load balancing scheme and distributed system architecture. Based on this research, three types of computational challenges are identified: highly coupled applications, loosely coupled applications, and centralized and distributed applications. Recommendations for future work for power grid applications are also presented.
A trust propagation scheme in VANETs

In vehicular ad hoc networks (VANETs), a source node must rely on other nodes to forward its packets on multi-hop routes to the destination. Trust propagation is the principle by which new trust relationships can be derived from pre-existing trust relationship. Modeling for trust propagation is a fundamental block in an ad hoc environment. The main contribution of this work is a novel scheme to enhancing trust propagation in VANETs. We achieve trust routing by introducing the concept of attribute similarity to find some potential friendly nodes among strangers. Based on similarity degree we put forward a new forwarding behavior. Also we give the recommendatory methods to calculate the similarity degree of attribute. We present numerical results which demonstrate the effectiveness of the proposed trust propagation scheme. Our work appears to be the first to incorporate attribute similarity into trust routing in VANETs.
An Interleaved LLC Resonant Converter Operating at Constant Switching Frequency

The interleaving technique is necessary for LLC resonant converters to achieve high power level. The advantages include expanded power capacity, lower output ripple current, and higher light-load efficiency by using phase shedding. However, conventional frequency-controlled LLC converters will lose regulation in individual phases if all the phases are operating at the same switching frequency, causing load sharing problem. Existing load sharing solutions for interleaved LLC converters all have limitations. In this paper, a switch-controlled capacitor (SCC) modulated LLC converter (SCC-LLC) is presented to solve the load-sharing problem. With constant switching frequency, interleaving and phase shedding can be achieved. A 600-W, two-phase interleaved constant frequency SCC-LLC prototype is built to verify the feasibility and demonstrate the advantages.
Disseminating real-time messages in opportunistic mobile social networks: A ranking perspective

There has been a significant body of work on evaluating node criticality in information networks. However, most of the existing works are developed for static networks and are not applicable to dynamic settings where connectivities among nodes change frequently over time. In this paper, we treat an opportunistic mobile social network as a time-evolving, dynamic graph, and propose a scheme to ascertain the information dissemination capability for each node based on its contact history. In particular, we analyze the node importance in spreading or forwarding real-time messages which are assumed to become less important or even stale over time. To this end, we take a dynamic walk counting approach to calculate all possible temporal-spatial routes associated with each node, by using the down-weighting method. Since the age of a message increases with time, the old walks are discounted to represent the fading influence on the target node. Extensive experiments are conducted based on 4 real-world trace datasets, and the results show that, our analytical result is effective at ranking the node criticality in disseminating or acquiring real-time messages in opportunistic mobile social networks.
A hue-saturation histogram difference method to vehicle detection

To prevent moving shadows being false detected as moving vehicles, this paper presents a Hue-Saturation Histogram Difference (HSHD) method for vehicles detection. In the method, H-S histogram of each frame in the detection area is counted firstly. Then, comparing the differences between histograms of two adjacent frames, the peak of differences may stand for a car. Experiments on videos shot in daytime and nighttime show HSHD algorithm is feasible and robust, and the detection accuracy rates are over 97% in daytime, and over 90% even in nighttime.