nils-herrmann/clean_scirepeval huggingface dataset:
Packet-loss-dependent stabilization of NCSs with network-induced delay and packet dropout

This paper is concerned with controller design of networked control systems (NCSs) with both network-induced delay and arbitrary packet dropout. By using a packet-loss-dependent Lyapunov function, sufficient conditions for state/output feedback stabilization and corresponding control laws are derived via a switched system approach. Different from the existing results, the proposed stabilizing controllers design is dependent on the packet loss occurring in the last two transmission intervals due to the network-induced delay. The cone complementary linearation (CCL) methodology is used to solve the non-convex feasibility problem by formulating it into an optimization problem subject to linear matrix inequality (LMI) constraints. Numerical examples and simulations are worked out to demonstrate the effectiveness and validity of the proposed techniques.
Crop nutrition diagnosis expert system based on artificial neural networks

This research aims at designing an intelligent and carry-home diagnosis expert system (ES) to help inexpert farmers detect crop nutrition disorders in time. To ensure the reasoning veracity of the system, artificial neural networks (ANN) were proposed, and a single chip computer was applied for spot using possibility. Two subsystems and their corresponding ANN clusters were created according to the location where the nutrition disorders first took place. The symptoms of six crops were collected. The confidences and conclusions of symptoms diagnosis by field experts were used as input and output neurons of ANN. Study results were saved as a knowledge base in a flash memory. Using MCS-51C language, single chip computer diagnosis was realized. Field validation indicated that diagnosis errors were less than 8%. Moreover, the combination of ANN and ES can make up traditional expert system defects and improve the systems intelligence and diagnosis efficiency.
Comparison and application of signal denoising techniques based on time-frequency algorithms

Comparisons of the denoising techniques based on the wavelet shrinkage threshold (WST), general matching pursuit (GMP), and the genetic matching pursuit, so-called (GAMP), are studied in this paper. Using a Matlab program, some typical signals in engineering, such as the true and noisy Blocks, Bumps, Doppler and Quadchirp, are generated and denoised by the WST, GMP and GAMP approaches. Furthermore, denoising effectiveness and accuracy of these methods are discussed. The results suggest that the studied techniques can be used for processing different kinds of signal. The GMP- and GAMP-based methods are more powerful for keeping the high-frequency and suppressing the low-frequency components of the signals, and the WST inverse. The GAMP can save much computation time comparing with the GMP. These conclusions can also be deduced from the denoising application examples performed in the final section of this paper by using some tested stationary and nonstationary engine noise signals.
Exemplar based language recognition method for short-duration speech segments

This paper proposes a novel exemplar-based language recognition method for short duration speech segments. It is known that language identity is a kind of weak information that can be deduced from the speech content. For short duration speech segments, the limited content also leads to a large intra-language variability. To address this issue, we propose a new method. This borrows a vector quantization based representation from image classification methods, and constructs the exemplar space using the popular i-vector representation of short duration speech segments. A mapping function is then defined to build the new representation. To evaluate the effectiveness of our proposed method, we conduct extensive experiments on the NIST LRE2007 dataset. The experimental results demonstrate improved performance for short duration speech segments.
An innovative scheme for model consistency in collaborative design environment

In distributed collaborative design environments, product models and virtual studios should keep consistency to create sense of sharing the world and collaboration among physically distributed users. Model consistency is usually achieved by maintaining dynamic shared states. To make those events in physical locations virtually "seen" by the other participants, networks communications are indispensable. Because of limited network bandwidths and CPU resources in distributed and collaborative design environments, to decrease communication traffic is a critical issue. In this paper, we present a "near-sight" mode, which resorts to a method of using AOI, area of interest, to reduce efficiently communications to maintain dynamic shared states.
Electromagnetic Scattering by a Gyrotropic-Coated Conducting Sphere Illuminated From Arbitrary Spatial Angles

This communication presents the development of a Mie-based scattering theory for a gyrotropic-coated conducting sphere such that an arbitrary incident angle can be modeled analytically from an eigen-system determined by gyrotropic permittivity and permeability tensors. The incident and scattered fields can be expanded in terms of spherical vector wave functions (SVWFs). After the unknown scattering coefficients are obtained in the general gyrotropic media, the expansion coefficients associated with the eigenvectors and scattering coefficients can be determined by matching boundary conditions at the interfaces between different media. The scattering property of a gyrotropic object relies on where the illumination comes from, and hence it is different from the isotropic cases. This analytical approach enables the modeling of scattering by a gyrotropic-coated conducting sphere under arbitrary incident angles and polarizations.
Classification and mosaicking of side scan sonar image

As an underwater detection sensor, side-scan sonar plays an important role in marine survey, mineral exploration, underwater archaeology and so on. During the use of side-scan sonar, classifiication and mosaicking of collected images is essential in most cases. There are two main contributions in our work. On the one hand, we propose a supervised learning method based on kernel-based extreme learning machine (KELM) to perform image classification. As a single-hidden layer feedforward neural network, ELM has one hidden layer and one output layer. It has been proved that ELM provides better performance in classification and regression at shorter consumed time than some others, such as traditional support vector machine (SVM), without complex parameter adjustment. However, the weights of ELM hidden layer are randomly produced and the classification results of ELM are different because of this. To solve this problem, the kernel-based ELM was proposed, in which the hidden layer was processed with a kernel function to eliminate randomness. On the other hand, the side-scan sonar images and the classified image data will be geo-referenced mosaicked using positions produced by extended Kalman filter (EKF) with sensor data from an autonomous underwater vehicle (AUV). To eliminate gaps in the mosaicking images, image dilation is adopted in our work. Experimental results demonstrate that the proposed classification method works well, and the proposed image mosaicking method is applicable when concerns real side-scan sonar images.
Data Study on the Chaotic Synchronization Driven by External Chaotic Signal

The principle of the chaotic synchronization driven by external chaotic signal instead of noise signal is stated briefly. After comparing the intermittent driving method with the continuous driving method, we find that the synchronized effect of the previous is relative to the sampling period and the driving strength coefficient, and we should choose the two parameters reasonably. Then apply the chaotic synchronization driven by discrete chaotic signal to secure communication, the experiment shows that the system is robust and its keys are resourceful, so the scheme is of high security.
Two performance measures for evaluating human control strategy

In the last few years, modeling dynamic human control strategy (HCS) is becoming an increasingly popular paradigm in a number of different research areas, such as the intelligent vehicle highway system, virtual reality and robotics. Usually, these models are derived empirically, rather than analytically, from real human input-output control data. As such, there is a great need to develop adequate performance criteria for these models, as few guarantees exist about their theoretical performance. It is our goal in this paper to develop several such criteria. In this paper, we first collect driving data from different individuals through a real-time graphic driving simulator. We then model each individual's control strategy through the flexible cascade neural network learning architecture. Next, we develop two performance measures for evaluating the resulting HCS models, one dealing with obstacle avoidance, the other with tight-turning behavior. Finally, we evaluate the relative skill of different HCS models through the proposed performance criteria.
Application of pronunciation knowledge on phoneme recognition by LSTM neural network

When applied for phoneme recognition, the Connectionist Temporal Classification (CTC) objective function allows a neural network to be trained with the phoneme level transcriptions of training utterances. A limitation of the CTC is that it can not be applied directly for network training with large speech corpora, since those corpora usually only have word level transcriptions. This work extends the CTC such that a novel objective function can be evaluated even if only the word level transcriptions are available. Furthermore, various pronunciation knowledge is adopted to construct pronunciation networks which can model the pronunciations of connected speech more accurately. When combined with a bidirectional Long Short-term Memory (LSTM) network, the extended CTC achieves a phoneme error rate of 18.3% on the LibriSpeech corpus. When various pronunciation knowledge is applied, the error rate is further reduced by 18.6% relatively.
High Voltage Super-Capacitors for Energy Storage Devices Applications

Super-capacitor has the characteristics of big capacitance, high energy density, long cycle life, quick charge and discharge compared with traditional capacitors, which is regarded as a new-style energy storage element. The general demands for super-capacitors are high working voltage, big capacitance and low resistance for pulse power supply applications, especially for the pulse power supply of electromagnetic launch system applications. However the working voltage of existing super-capacitors is very low (<3.5 V), which has restricted its further applications. To meet the requirements of high voltage, it has to be connected some units of super-capacitors in series. But the total capacitance of energy storage device decreases, and the inner resistance increases. In the paper, a high voltage super-capacitor of 100 V, 5.8 mF, 0.05 Omega was introduced. The super-capacitor is composed of anode of electrolytic capacitors, cathode of electrochemical capacitors and 38 wt% H2SO4 electrolyte solution. By electric test, the super- capacitor has the properties of large capacitance and high energy density, which is an idea energy storage element. Energy storage device of 100 V/3 kJ is constructed with 100 high voltage super-capacitors in parallel; it can be set between battery and pulse load as intermediate energy storage device instead of electrolytic capacitors to enhance energy density and power density of power supply system. The test and simulation results show that the energy storage device can provide a discharge current of 2000 A, and peak power up to 200 kW.
Event-based state estimation for a class of nonlinear discrete-time complex networks with stochastic noises

In this paper, the state estimation problem is investigated for a class of discrete-time complex networks under the event-triggered framework. The event-based estimator receives the updated measurements from the sensors only when the prespecified event-triggering rule is violated. Compared with the traditional estimator with the clock driven rule, a series of event-based state estimators are developed so as to reduce unnecessary data transmissions in the communication channel. Attention is focused on the analysis and design problem of the event-based estimators for the addressed discrete-time complex networks such that the estimation error is exponentially bounded in mean square. Some sufficient conditions are obtained to ensure the existence of the desired estimators and the upper bound of the estimation error is derived. By using the convex optimization technique, the gain parameters of the desired estimators are obtained in an explicit form. Finally, a numerical example is used to show the effectiveness of the proposed estimation approach.
Novel Photoluminescence from Porous SiGe/Si Multilayer Structure

The optical properties of porous SiGe/Si multilayer films prepared through electrochemical anodization have been investigated. The visible luminescence spectra with multiple emission peaks have been detected in the temperature range from 10 K to room temperature. Photoluminescence (PL) with narrow band width is observed due to the wavelength selective effect of the microcavity. The resonance of the microcavity is confirmed by the temperature dependent PL measurement and the reflectance spectrum, which is consisted with the prediction from the thermo-optic effect.
Synthesis of novel antofine and Cryptopleurine analogues

Antofine and Cryptopleurine, natural products belong to the phenanthroindolizidine and phenanthroquinolizidine group of alkaloids, have attracted much attention because of their extremely potent inhibition of cancer cell growth. In this paper, seven analogues with different substituents on phenanthrene ring were designed and synthesized, the chemical structures of these derivatives were characterized by MS and 1H-NMR.
Analysis of response abilities of fuzzy systems based on non-singleton fuzzifier method

The response properties of fuzzy systems generalized by non-singleton fuzzifier method are discussed. Firstly, the fuzzy system with non-singleton fuzzifier, CRI method and centroid defuzzifier is discussed. A sufficient and necessary condition of the corresponding fuzzy system possessing interpolation properties is deduced. Furthermore, some sufficient conditions of fuzzy system possessing fitted properties are obtained. Then, fuzzy system with non-singleton fuzzifier, triple I method and centroid defuzzifier is studied. It is proved that fuzzy system with non-singleton fuzzifier, regular implication and triple I method is equivalent to Mamdani fuzzy system. And it also possesses interpolation properties. At last, a simulation result is provided to illustrate the validity of the conclusion.
Design Of Cleaning Vehicle Monitoring System

The cleaning vehicle is a special equipment for real-time detection and cleaning of the track. To ensure normal operation of the cleaning vehicle, we need to establish a remote monitoring system to collect all the state parameters of the entire cleaning vehicle through sensors to monitor them in real time. According to the parameters collected by sensors, we can evaluate the state of the vehicle's working condition and give corresponding treatment suggestions. The system collects various status parameters of the cleaning vehicle through STM32. After storing the data into the database, it uses VC++ to establish man-machine interface and give corresponding processing recommendations, making the vehicle more reliable and more intelligent.
Antitumor Effects of Typhonium giganteum Engl, Tuber Extracts

To study the antitumor activities of 70% ethanol extracts of Typhonium giganteum Engl. tuber in human Liver Cancer Bel-7402 and Lung Cancer A549 Cell Lines in vitro.  Bel-7402 and A549 cells proliferative activities were measured by MTT method in vitro. Cells apoptosis were analyzed by morphology analysis.  Bel-7402 and A549 cells were treated with of Typhonium giganteum Engl. tuber extracts (TGE) at concentrations from 1.0, 10.0 to 100.0 mg·L-1 for 24 to 72 h, cellular proliferation were inhibited with the increase of concentration and time. Compared with the control group, the significant apoptotic morphology changes were observed.  70% ethanol extracts of Typhonium giganteum Engl. tuber can decrease the proliferation rate and induce apoptosis of Bel-7402 and A549 cells. Taken together, these results suggested that Typhonium giganteum Engl. was a promising herbal drug in the therapy of human Liver Cancer and Lung Cancer.
Three electricity market principles and electricity value equivalent (EVE) pricing methodology

This paper presents the three market principles and the electricity value equivalent (EVE) pricing method. The developed electricity market pricing methodology have solved the three great difficult market problems in the world. We have also researched the market character of EVE pricing methods using analysis method and the intelligent agent-based method, taking into account the bidding action (Bertrand game) and the withholding capacity action (Cournot game) of participants and comparing with the uniform cleaning pricing (UCP) and pay as bid (PAB) pricing. Besides the market efficiency and the capacity adequate return, the results show that the EVE pricing method can induce the power suppliers bidding according to their true operation cost and without capacity withholding. This is an invaluable, market character. We call it market 'inductivity' which is different from reliability, stability and robustness etc. As we know that there are no such properties for any other pricing methods.
Redundant design of A CAN BUS Testing and Communication System for space robot arm

This paper analyzes and designs a Testing and Communication System (TCS), which is used for the simulation of controlling and communicating for the space robot arm. Arming at enhancing the reliability of the operation of the space robot arm, a new Hot Double redundant strategy is adopted during the designing of TCS. The new Hot Double Redundant way is utilized to back up CAN BUS, which is the medium of data transmission and reception. The new network structure based on CAN BUS is validated on a nine nodes experiment system. The simulation results demonstrate the effectiveness of the redundancy strategy, and also the reliability and fault-tolerant ability of the TCS is testified by experiments.
Modelling and analysis for self-organization of urban traffic flow

Urban traffic system is a typically complex system. In this paper, 2-dimensional cellular automaton model is introduced to study the property of self-organization for urban traffic flow. Firstly, this paper formulates the BML model in Boolean algebra. Furthermore, the paper extends and improves the BML model to find out the self-organization characteristics in different circumstances. A common self-organization phenomenon, jamming transition is simulated based on stochastic jamming transition CA model. The evolution rules of this model are formulated and simulated as well. Some insights obtained from the simulation studies have been put forward and analyzed in detail. Besides, Some issues in practise including anisotropy, open boundary, car following and stochastic traffic signal control and so forth, are also proposed and studied in this paper.
The analysis for the reason of limit cycle generated by friction in servo system

In this paper, limit cycle which results from friction during low-velocity servo system has been discussed. First, the piecewise linearization method has been applied to change nonlinear stribeck model to linear model. Second, with analysis of equilibrium property, it is proved the existence of limit cycle and its certain region. The analysis reveals that it is because of stability of equilibrium repeatedly changing results in the limit cycle during low-velocity state. In addition, it also points that the key point to avoid this phenomenon is to keep robust stability of the close-loop system.
Enhanced Semi-Supervised Learning for Automatic Video Annotation

For automatic semantic annotation of large-scale video database, the insufficiency of labeled training samples is a major obstacle. General semi-supervised learning algorithms can help solve the problem but the improvement is limited. In this paper, two semi-supervised learning algorithms, self-training and co-training, are enhanced by exploring the temporal consistency of semantic concepts in video sequences. In the enhanced algorithms, instead of individual shots, time-constraint shot clusters are taken as the basic sample units, in which most mis-classifications can be corrected before they are applied for re-training, thus more accurate statistical models can be obtained. Experiments show that enhanced self-training/co-training significantly improves the performance of video annotation
Non-destructive Estimation of Rape Nitrogen Status Using SPAD Chlorophyll Meter

Chlorophyll meter, soil plant analysis development (SPAD) is a simple, portable diagnostic tool that measures the greenness or relative content of leaves. The objective of this study was to analyze the SPAD change rule with the different growth stage and different fertilizers to the rape. The SPAD distribution map in a representative rape was tracked and analyzed, and the spatial SPAD distribution map in a given leaves was obtained by using the interpolate grid functions of geographic information system (GIS) software. The experiment was conducted in the farm of Zhejiang University. Three nitrogen fertilizers and two repeated experiments were carried on in this research. Tracking experiment was started from 21st March 2005 to 28 April 2005. In order to delay tracking time, the expanded leaves in the third branches from the top was chosen and tracked. The result shows that different growth stages and different fertilizers have different SPAD values, but the change rule is regular, which will be beneficial for the further nitrogen management. Spatial SPAD distribution maps of the leaves will be useful for the development of digital farming and further analyzing the internal SPAD distribution
Optimization of human control strategy with simultaneously perturbed stochastic approximation

Modeling the dynamic human control strategy (HCS) is becoming an increasingly popular paradigm in a number of different research areas, ranging from robotics to intelligent vehicle highway systems. Usually, HCS models are derived empirically, rather than analytically, from real human input-output data. While these empirical models offer an effective means of transferring intelligent behaviors from humans to robots and other machines, the models are not explicitly optimized with respect to potentially important performance criteria. We therefore propose an iterative algorithm for optimizing an initially stable HCS model with respect to an independent, user-specified performance criterion. We first collect driving data from different individuals through a real-time graphic driving simulator. Next, we describe how we model each individual's control strategy through flexible cascade neural networks. Once we have initially stable HCS models, we propose simultaneously perturbed stochastic approximation (SPSA) to optimize these models with respect to a chosen performance criterion. Finally, we describe and discuss some experimental results with the proposed algorithm.