nils-herrmann/clean_scirepeval huggingface dataset:
Distance and Energy-Balance Based Reliable Broadcast in Mobile Ad Hoc Networks

Broadcasting is the most common operation of data transmission in mobile Ad Hoc networks. The conventional blind flooding algorithm incurs the broadcast storm problem. Moreover, energy is critical for the life and the performance of Ad Hoc networks. In order to alleviate the broadcast storm, balance the node energy in network and provide broadcast reliability, a reliable broadcasting algorithm based on the distance and the energy balance is proposed in this paper. In this algorithm, a node determines the distance to the immediate previous neighbor node by obtaining the information of neighbor node from the received data packets. Then a node will schedule the forwarding in a time-division manner according to its forwarding priority which is determined by the distance to the immediate previous node and its own residual energy. To provide reliable broadcast, the algorithm also adopts retransmission mechanism utilizing broadcast packets to acknowledge those packets correctly received instead of acknowledgement packets such as ACK. Simulation results show that this algorithm achieves high arrival rate and lower latency. Meanwhile, it can balance energy consumption of the nodes in a network and prolong the network lifetime.
Data fusion using fuzzy measures and genetic algorithms

This paper proposes an improvement on the fusion method presented previously (1994, 1998). In those methods not only the reliabilities of the sensors are not considered but also the choice of parameter k is relevant to the number of sensors and whether there is opinion close to 0.5. In our method Genetic Algorithms (GA) is used to find the optimal values for the reliabilities of sensors and fuzzy inference rules for determining the parameter k in multi-sensor fusion. Multi-step fusion and one-step fusion methods are formed based on the fusion functions. Simulation results show the effectiveness of the proposed methods.
A Congestion Control Algorithms Based on Network Measurement in Next-Generation Networks

Congestion control is the key techniques of Next-Generation-Networks. The classical TCP congestion control such as RED is difficult to configure parameters and the average queue length is related to the load level. A network-measurement based on congestion control algorithm is proposed, the essential idea is introduce macroscopic guidance according to network measurement, the measurement facilities distributed in networks are exploited to acquire the performance of network. NS simulation results show that the proposed algorithm outperforms the traditional TCP congestion control in terms of length and throughput, it can improves routers' ability, is more suitable for the Next-Generation-Networks.
An anti-radiation evaluation method for satellite power system based on quantification of margins and uncertaities

Satellite power system is the key component of the satellite system. Properly anti-radiation evaluation is an important basis for system design improvement, maintenance and health management. Traditional model-based methods or statistical methods are difficult to treat such problems due to all kinds of uncertainties and the lack of data. This paper proposed a quantitative anti-radiation evaluation method on the basis of quantification of margins and uncertainties(QMU) and combined the data and modeling. Model calibration, uncertainty analysis and other approaches are used to calculate the performance indices and assess whether it meets the expected requirements, which is crucial to the evaluation. In the application of a satellite power system evaluation, it demonstrates the efficiency of this method. Relevant issues for further research are concluded at the end, to be further studied and resolved.
An Integrated Power Module Based on the Power-System-In-Inductor Structure

This paper presents an integrated power module with the features of high power density and high efficiency. A multifunctional integrated magnetic component is designed. The component has the roles of both the filter inductor and the package of the converter. With the assist of finite-element analysis, the design and optimization of the proposed inductor are demonstrated. It has higher inductance value than the inductor used in conventional designs. The package is composed of bigger copper winding and higher thermal conductive magnetic material, leading to lower direct current resistance and better thermal performances than conventional transfer molding packaging. Benefiting from these advantages, the power module built based on the inductor can achieve higher efficiency and lower temperature rise than those based on traditional plastic packaging solution. Design of the inductor is demonstrated through the combination of analytical and simulation methods. An inductor prototype is built and used in an integrated power module to do experimental tests. Loss breakdown of the module is analyzed. The performances of the proposed power module are better than the conventional transfer molding packaging solution with the same board or other state-of-art products.
Adaptive Critic Design for Pure-Feedback Discrete-Time MIMO Systems Preceded by Unknown Backlashlike Hysteresis

This paper concentrates on the adaptive critic design (ACD) issue for a class of uncertain multi-input multioutput (MIMO) nonlinear discrete-time systems preceded by unknown backlashlike hysteresis. The considered systems are in a block-triangular pure-feedback form, in which there exist nonaffine functions and couplings between states and inputs. This makes that the ACD-based optimal control becomes very difficult and complicated. To this end, the mean value theorem is employed to transform the original systems into input–output models. Based on the reinforcement learning algorithm, the optimal control strategy is established with an actor-critic structure. Not only the stability of the systems is ensured but also the performance index is minimized. In contrast to the previous results, the main contributions are: 1) it is the first time to build an ACD framework for such MIMO systems with unknown hysteresis and 2) an adaptive auxiliary signal is developed to compensate the influence of hysteresis. In the end, a numerical study is provided to demonstrate the effectiveness of the present method.
A Self-Adjusting Size-Based Load Balance Policy for Web Server Cluster

An efficient load balance policy should adjust its parameters as the arrival and service characteristics of the incoming workload change. In this paper, we propose a self-adjusting size-based load balance policy called SASB, and implement a prototype of SASB. In SASB, the dispatcher distributes requests according to the request content size and tries to balance the load, measured in "occupying resource time", among real servers. The dispatcher periodically predicts the statistical characteristics of future workload based on recent workload history, and then adjusts system parameters according to the prediction. By its locality-aware nature, SASB achieves high cache hit ratio in addition to balancing the load. For the sizes of realistic Web documents follow heavy-tailed distribution, distributing "long" and "short" jobs to different real servers helps SASB reduce the average job slowdown. Experiments show that SASB, as compared with some existing classic load balance policies, achieves superior performance
A Task Scheduling Algorithm Based on Genetic Algorithm and Ant Colony Optimization in Cloud Computing

An efficient approach to task scheduling algorithm remains a long-standing challenge in cloud computing. In spite of the various scheduling algorithms proposed for cloud environment, those are mostly improvements based on one algorithm. And it's easy to overlook limitations of the algorithm itself. Aiming at characteristics of task scheduling in cloud environment, this paper proposes a task scheduling algorithm based on genetic-ant colony algorithm. We take the advantage of strong positive feedback of ant colony optimization (ACO) on convergence rate of the algorithm into account. But the choice of the initial pheromone has a crucial impact on the convergence rate. The algorithm makes use of the global search ability of genetic algorithm to solve the optimal solution quickly, and then converts it into the initial pheromone of ACO. The simulation experiments show that under the same conditions, this algorithm overweighs genetic algorithm and ACO, even has efficiency advantage in large-scale environments. It is an efficient task scheduling algorithm in the cloud computing environment.
Chinese Character Learning by Synchronization in Wilson-Cowan Oscillatory Neural Networks

The cognition and learning of Chinese characters is a good example of human visual perception and learning. The synchronization behavior between neuronal groups in cortical areas is one of the core mechanisms in visual image perception and recognition. We built a neural network with locally coupled Wilson-Cowan oscillators to learn Chinese characters. Neurons in each group representing the same feature of a character will be synchronized so that the common features of Chinese characters will be extracted by the network,such as a common character in two-character words or a common radical component of characters. Further more, each feature is represented by a stable fixed point attractor in the dynamic neural network.
An Identity Authentication System Based on Face Biometrics and Eye Localization

this paper presents a proposal for a person authentication system, which localizes facial landmarks and extracts biometrical features for face authentication. An efficient algorithm for eye localization and biometrical feature extraction and person identification is developed by using Gabor filters. We build artificial average eye models for eye location. Databases of biometrical features around the eye area of clients are constructed. For authentication, Schwartz inequality and the sum square error (SSE) are used. And experimental results on the proposed system are presented.
A Practical Transformer Core Loss Measurement Scheme for High-Frequency Power Converter

A practical method to measure transformer core loss in a high-frequency switching mode power converter is proposed. The method is also applicable when the transformer has dc bias in the magnetizing current. Practical issues to minimize the measurement error are discussed. The measurement results are very close to simulation results obtained from a time-domain finite element analysis solver. Detailed error analysis for the proposed method provides useful guidelines on transformer core loss measurement for switching mode power supplies.
Cooperative control of VSC-HVDC connected offshore wind farm with Low-Voltage Ride-Through capability

The Low-Voltage Ride-Through (LVRT) has become an important grid requirement for offshore wind farms connecting with Voltage Source Converter based High Voltage Direct Current (VSC-HVDC) links. In this paper, a cooperative control strategy with LVRT ability is proposed for a VSC-HVDC connected variable speed Squirrel-Cage Induction Generator (SCIG) wind farm. The approach employs a DC-link voltage versus offshore AC-bus frequency droop control on the offshore converter of VSC-HVDC link. Thus, the back-to-back converters of SCIG wind turbines can adjust the generated active power based on the AC-bus frequency deviations, so that a fast power reduction on the wind farm side can be achieved.  Simulation results confirm the effectiveness of the proposed control method.
Adaptive Continuous Current Source Drivers for 1-MHz Boost PFC Converters

Recently, current source drivers (CSDs) have been proposed to reduce the switching loss and gate drive loss in megahertz (MHz) dc-dc converters, in which the duty cycle normally has a steady-state value. However, different from dc-dc converters, the duty cycle of the power factor correction (PFC) converters is modulated fast and has a wide operation range during a half-line period in ac-dc applications. In this paper, an adaptive full-bridge CSD is proposed for the boost PFC converters. The proposed CSD can build adaptive drive current inherently depending on the drain current of the main power MOSFET. Compared to the CSDs with the constant drive current, the advantage of the adaptive drive current is able to reduce the switching loss further when the MOSFET is with a higher switching current, while minimize the drive circuit loss when the MOSFET is with a lower switching current. Therefore, the adaptive CSD is able to realize better design tradeoff between the switching loss and drive circuit loss so that the efficiency can be optimized in a wide operation range. Furthermore, no additional auxiliary circuit and control are needed to realize the adaptive current by the proposed CSD. The experimental results verified the functionality and advantages. For a 1-MHz/300-W boost PFC converter, the proposed CSD improves the efficiency from 89% using a conventional voltage driver to 92.2% (an improvement of 3.2%) with 110 Vac input, 380 V output, and full-load condition.
A modified asymmetrical pulse-width-modulated resonant DC/DC converter topology

A modified asymmetrical pulse-width-modulated resonant DC/DC converter employing an auxiliary circuit is proposed in this paper. The auxiliary circuit consists of a network of two capacitors and an inductor. The aim of this network is to produce zero-voltage-switching (ZVS) over a wide input voltage range, while reducing the voltage and current stress across the resonant inductor. A detailed analysis is presented and performance curves are given. Experimental results for a 35 W, 5 V converter show an efficiency of about 81% at a constant operating frequency of 500 kHz.
Analysis of Cointegration between Macroeconomic Variables and Stock Index

Stock exchange market, that has function of accommodating capital and optimizing resource allocation, is a part of capital markets. And it play important role of economy development. Research of relationship between stock exchange market and macroeconomic variables has realism significance. This paper, according to Shanghai stock exchange market index representing stock market, chooses 8 macroeconomic variables to research both long time balance and short time fluctuation relation between Shanghai stock exchange market index and macroeconomic variables using unit root testing, cointegration analysis and vector error correction model. The result of practical example indicates that there exist longtime stabilization relation between Shanghai stock exchange market index and macroeconomic variables. The development of stock exchange market has some promotional effect on economy. That shows that Chinese stock exchange market reflects the development level of macroeconomic.
Complex GIS Query Based on Rule Engine

In previous geographic information inquiry, query condition is either fixed in program or providing an SQL inquiry mode for users. The former condition is unalterable while the latter demands users to be equipped with certain SQL query language knowledge. The article introduces how to use a rule engine to make inquiries through simple combination between natural semantic modules with the support of rule base. First, users formulate query plans through simple combination between natural language modules according to their own demands. Then, users deliver the query scheme to the rule engine for reasoning & matching, find the correct matching rule, and execute this rule. Finally, execution results are returned to users.
A solution of anonymous email identification based on writing structural pattern

This paper presents a method to analyze the anonymous email in digital forensics. The FP-growth algorithm is used to analyze the user's email in the method to get the user's email writing structural pattern. The impact of the user's writing structural pattern on analysis of anonymous email is different from each other. The AHP method is used to calculate the weight of a user's different writing structural pattern. For a given anonymous email, writing structural pattern matching and weight calculation can help investigators to make a better decision and find out the author of anonymous email in forensic work.
A Better Range-Free Localization Algorithm in Wireless Sensor Networks

Localization is a fundamental issue for many applications in wireless sensor networks. This paper proposes a better range-free algorithm based on DV-Hop, which increases localization accuracy without increasing hardware or communication costs. First, this algorithm utilizes average hop-size correction to reduce ranging errors. Second, the algorithm uses an improved equation solving method to reduce the impact of errors inherent in distance measurement, and the weighted least square method to improve localization accuracy. Finally, the algorithm uses additional information in the equation solving process to correct node coordinates. Simulation results show that the performance of the proposed algorithm is significantly better than the basic DV-Hop algorithm and other similar algorithms.
An automatic method for identifying different variety of rice seeds using machine vision technology

An automatic method for identifying different variety of rice seeds using machine vision technology was investigated, and a detection system which was consisted of an automatic inspection machine and an image-processing unit, was also developed. The system could continually present matrix-positioned rice seed to CCD cameras, and singularize each rice seed image from the background. The inspection machine comprised scattering and positioning devices, a photographing station, a parallel discharging device, and a continuous conveyer belt with carrying holes for the rice seed. The rice seeds' image was achieved continuously by single chip controlled device. The line was suspended per second by the device, and the images of seeds were collected by the camera during the intervals. Image analysis was carried out by Visual C++ 6.0. Color features in RGB (red, green, blue) and color spaces were computed. A back-forward neural network was trained to identify rice seeds. Almost all 86.65% rice seeds were correctly identified. The correct classification rates for five rice varieties were: No.5 ‘Xiannong’ of 99.99%, ‘Jinyougui’ of 99.93%,‘You166’ of 98.89%, No. 3 ‘Xiannong’ of 82.82% and ‘Medium you’ 463 of 86.65%, respectively. Based on the results, it was concluded that the system was enough to use for inspection of varieties of different rice seeds based on their appearance characters of seeds.
Control of nonlinear systems with full state constraints using integral Barrier Lyapunov Functionals

In this paper, we present controller design for strict feedback nonlinear systems with full state constrains. An Integral Barrier Lyapunov Functionals (iBLF) is employed to obtain the adaptation law and the controllers. Compared with existing methods, the unknown parameters are considered in the system. Under the proposed iBLF-based control, we show that tracking errors are achieved without violation of any constraint and the closed loop signals remain bounded. The stability of the closed-loop system is proven by using the Lyapunov theorem.
A New Way to Process B-Scan Images

Image processing is an important phase in order to improve the accuracy both for diagnosis procedure and for surgical operation. Medical diagnosis is one of the most important areas in which image processing procedures are usefully applied. In this paper, we use the Cellular Neural Networks to research diagnostic images. We present a robustness design theorem for the optimal edge detector cellular neural network (OEDGE CNN). As applications, the CNN can detect successfully the embossment of B-scan image. Our findings show that CNN may provide a useful tool for medical image processing. To this point, a novel approach to image processing has been developed in the work.
Deep and discriminative feature learning for fingerprint classification

Fingerprint classification is still a challenging problem because of its large intra-class variance, small inter-class variance and strong noise in the fingerprint patterns. Traditional methods usually utilize some human-defined features to classify fingerprints, however, these features are relatively shallow and local, and they cannot solve this problem well. In this paper, we propose a deep convolutional neural network called Res-FingerNet to solve this problem. The network can extract more abstract and global features from fingerprint images. Moreover, in order to reduce intra-class variance and enlarge inter-class variance of the fingerprints, we utilize center loss in the network training stage so that the learned deep features are more discriminative for fingerprint classification task, and our experimental results show that the classification accuracy increases about 1.5% by center loss. The classification method has been measured on the public fingerprint database NIST-DB4 and it achieves a classification accuracy of 97.9% which surpasses most of existing fingerprint classification methods.
Double-station-based information gathering in wireless sensor networks

DSBIG, a double-station-based information gathering in wireless sensor networks, is proposed in this paper. The algorithm defines transmitting coordinate of cluster using double BS. And then, collecting row and paralleling column are defined by transmitting coordinate. The sensor nodes in the same cluster formed a chain using a greedy approach. In operation, the nodes in every cluster communicate with each other concurrently and LEADER transmits information received from the collecting rows to the closest BS. Simulation results show that DSBIG not only achieve reduction in energy consumption and delay time, but also improve energy efficiency and lengthen network lifetime compared with other algorithms.
A Line Cycle Skipping method to improve the light load efficiency and THD of PFC converters

Light load efficiency is becoming a very important performance specification for power supplies. To address the increasing requirements for higher efficiency and lower total harmonic distortion (THD) without increasing the cost or complexity of the design, a Line Cycle Skipping (LCS) method is proposed. This method can be applied to all PFC topologies. A 100W universal line input voltage PFC boost converter prototype is built to demonstrate the effectiveness of the proposed method.
A High Efficiency Synchronous Buck VRM with Current Source Gate Driver

In this paper, a new current source gate drive circuit is proposed for high efficiency synchronous buck VRMs. The proposed circuit achieves quick turn-on and turn-off transition times to reduce switching loss and conduction loss in power MOSFETS. The driver circuit consists of two sets of four control switches and two very small inductors (typically 50 nH-300 nH each at 1 MHz). It drives both the control MOSFET and synchronous MOSFET in synchronous buck VRMs. An analysis, design procedure, optimization procedure and experimental results are presented for the proposed circuit. Experimental results demonstrate an efficiency of 86.6% at 15 A load and 81.9% at 30 A load for 12 V input and 1.3 V output at 1 MHz.