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Summary Functional connectivity is defined as the undirected association between two or more functional magnetic resonance imaging (fMRI) time series. Increasingly, subject-level functional connectivity data have been used to predict and classify clinical outcomes and subject attributes. We propose a single-index model wherein response variables and sparse functional connectivity network valued predictors are linked by an unspecified smooth function in order to accommodate potentially nonlinear relationships. We exploit the network structure of functional connectivity by imposing meaningful sparsity constraints, which lead not only to the identification of association of interactions between regions with the response but also the assessment of whether or not the functional connectivity associated with a brain region is related to the response variable. We demonstrate the effectiveness of the proposed model in simulation studies and in an application to a resting-state fMRI data set from the Human Connectome Project to model fluid intelligence and sex and to identify predictive links between brain regions.

Motivation The microbes that live in an environment can be identified from the combined genomic material, also referred to as the metagenome. Sequencing a metagenome can result in large volumes of sequencing reads. A promising approach to reduce the size of metagenomic datasets is by clustering reads into groups based on their overlaps. Clustering reads are valuable to facilitate downstream analyses, including computationally intensive strain-aware assembly. As current read clustering approaches cannot handle the large datasets arising from high-throughput metagenome sequencing, a novel read clustering approach is needed. In this article, we propose OGRE, an Overlap Graph-based Read clustEring procedure for high-throughput sequencing data, with a focus on shotgun metagenomes. Results We show that for small datasets OGRE outperforms other read binners in terms of the number of species included in a cluster, also referred to as cluster purity, and the fraction of all reads that is placed in one of the clusters. Furthermore, OGRE is able to process metagenomic datasets that are too large for other read binners into clusters with high cluster purity. Conclusion OGRE is the only method that can successfully cluster reads in species-specific clusters for large metagenomic datasets without running into computation time- or memory issues. Availabilityand implementation Code is made available on Github (https://github.com/Marleen1/OGRE). Supplementary information Supplementary data are available at Bioinformatics online.

In this paper, we develop a hybrid iterative learning controller (HILC) for a non-holonomic wheeled mobile platform to achieve trajectory tracking with actual complex constraints, such as physical constraints, uncertain parameters, and initial errors. Unlike the traditional iterative learning controller (ILC), the control variable selects the rotation speed of two driving wheels instead of the forward speed and the rotation speed. The hybrid controller considers the physical constraints of the robot’s motors and can effectively handle the uncertain parameters and initial errors of the system. Without the initial errors, the hybrid controller can improve the convergence speed for trajectory tracking by adding other types of error signals, otherwise, the hybrid controller achieves trajectory tracking by designing a signal compensation for the initial errors. Then, the effectiveness of the proposed hybrid controller is proven by the relationship between the input, output, and status signals. Finally, the simulations demonstrate that the proposed hybrid iterative learning controller effectively tracked various trajectories by directly controlling the two driving wheels under various constraints. Furthermore, the results show that the controller did not significantly depend on the system’s structural parameters.

In modern urban energy system, micro-grid can be an effective solution to improve the power system reliability. However, power balance and voltage stability are everlasting issues in micro-grid. In this paper, a coordinated power control strategy based on demand response (DR) technology for urban micro-grid is introduced. The key components of this novel control scheme are the smart load (SL) controller of flexible load and the grid-tie inverter of renewable energy (RE) source. Both the SL controller and the dynamic grid-tie inverter are operated as series reactive power compensator (RPC), on one hand, the SL controller offers active power variation in the flexible load for energy storage capacity reduction, on the other hand, the dynamic grid-tie inverter of RE can regulate the out-put voltage of the inverter. Detailed mathematical derivations of the proposed control paradigm are provided. Results validate the effectiveness of adopting this coordinated power control scheme for an isolated urban micro-grid with renewable energy source and energy storage system.

Summary Integrative analysis of multiple data sets has the potential of fully leveraging the vast amount of high throughput biological data being generated. In particular such analysis will be powerful in making inference from publicly available collections of genetic, transcriptomic and epigenetic data sets which are designed to study shared biological processes, but which vary in their target measurements, biological variation, unwanted noise, and batch variation. Thus, methods that enable the joint analysis of multiple data sets are needed to gain insights into shared biological processes that would otherwise be hidden by unwanted intra-data set variation. Here, we propose a method called two-stage linked component analysis (2s-LCA) to jointly decompose multiple biologically related experimental data sets with biological and technological relationships that can be structured into the decomposition. The consistency of the proposed method is established and its empirical performance is evaluated via simulation studies. We apply 2s-LCA to jointly analyze four data sets focused on human brain development and identify meaningful patterns of gene expression in human neurogenesis that have shared structure across these data sets. The code to conduct 2s-LCA has been complied into an R package “PJD”, which is available at https://github.com/CHuanSite/PJD.

Integrity of rails is the foundation of safe rail transportation. It is critical to detect internal rail flaws in time, and one popular solution to this issue is ultrasonic techniques. On the other hand, long short-term memory (LSTM) has been proven in text classification to which we think the ultrasonic rail flaw detection can be quite similar. In this context, this paper proposes a novel text-style sequential modeling method for ultrasonic rail flaw data and a LSTM-based deep learning model for rail flaw detection. Comparative experiments proved the feasibility and remarkable computational efficiency of the proposed modeling method and model.

This paper presents an in-depth study of the S-curve model. Under the constraints of system bandwidth, an efficient and accurate closed-form solution is proposed for the time-optimisation problem of a single actuator passing through multiple position points. In this method, the multi-point motion problem is transformed into a monotonic interval motion problem, and different motion equations are derived according to different motion conditions. Finally, the splicing and matching of acceleration between different intervals are completed through a backtracking mechanism. Since a closed-form solution formula is provided, the entire process of this method is efficient and accurate. This study not only provides a new idea and feasible solution for single-axis multi-point motion, but also contributes an important theoretical derivation to the body of research based on the S-curve model.

High penetration of new energy distributed generation brings new challenges to the power quality, therefore, the concept of electric springs (ESs) is proposed recently and arises great concern. Current research of ESs mainly focuses on the function of maintaining the stability of the AC bus voltage under the steady-state conditions, but rarely involves the frequency regulation and rapid voltage recovery during the transient period. In order to achieve these objectives, this paper proposes a flexible multi-objective coordinated control strategy for smart loads based on the ESs. The output frequency of the phase-locked loop (PLL) is controlled to track the reference value, thus the system frequency can be significantly improved during the fault. For rapid voltage recovery during the fault, the control mode switches between the reactive-power-targeting (RPT) mode and voltage-targeting (VT) mode adaptively based on the fuzzy logic, according to the working conditions of the system. The instantaneous demand for reactive power can be calculated based on the instantaneous reactive power theory (IRPT), so the RPT mode can have the effect of flexible reactive power tracking and improving the speed of voltage recovery. The simulation results of a micro-grid system based on Simulink verify the feasibility of the proposed flexible multi-objective coordinated control strategy.

Covariance estimation is essential yet underdeveloped for analyzing multivariate functional data. We propose a fast covariance estimation method for multivariate sparse functional data using bivariate penalized splines. The tensor-product B-spline formulation of the proposed method enables a simple spectral decomposition of the associated covariance operator and explicit expressions of the resulting eigenfunctions as linear combinations of B-spline bases, thereby dramatically facilitating subsequent principal component analysis. We derive a fast algorithm for selecting the smoothing parameters in covariance smoothing using leave-one-subject-out cross-validation. The method is evaluated with extensive numerical studies and applied to an Alzheimer's disease study with multiple longitudinal outcomes., Comment: 33 pages, 8 figures

Power line point cloud segmentation is one of the important tasks of airborne lidar (LiDAR) power patrol. In this paper, it is difficult to segment power line point clouds in the absence of power line point clouds, and the existing algorithm models are not comprehensive. A power line point cloud segmentation algorithm based on two models is proposed. Laser point cloud technology is widely used in transmission line operation and maintenance, and some power lines are often missing in laser point cloud data, which leads to the inaccuracy of traditional power line segmentation algorithms. In addition, at present, the academic circle mainly studies the non-coincident model of XOY plane projection of power line point cloud, but lacks the research on the coincident model of XOY plane projection. In this paper, two power line segmentation algorithms are proposed for two power line models (XOY plane projection is not coincident, XOY plane projection is coincident, hereinafter referred to as model 1 and model 2). Through practical engineering experiments and applications, the robustness and applicability of the proposed algorithm are verified, as well as the insensitivity to the lack of power line point clouds.

Due to the complexity of overseas anti-terrorism operations, the effectiveness of its command system is evaluated by the method of constructing evaluation system software. The evaluation system software can be divided into five levels: infrastructure layer, resource layer, support layer, application layer and standard specification layer. The functions consist of five subsystems: data collection and information, static evaluation, dynamic evaluation, expert evaluation and interaction, and comprehensive processing and visualization. The specific evaluation methods include N-ADC static evaluation method and GERT network dynamic simulation method. Finally, the main page composition and evaluation process of the evaluation system software is described.

Due to natural conditions and hardware manufacturing processes, the resolution of remote sensing images is generally low. Obtaining high-definition remote sensing images by simply improving hardware and manufacturing processes is not only costly and technically challenging but also cannot be deployed on a large scale. Aiming at the limitations of the traditional methods, this paper studies the image super-resolution reconstruction method for improving the generated anti-network. Firstly, the generator network is optimized, and an RRDB (Residual-in-Residual Dense without BN (Batch Normalization) is used. Block) module; secondly, the related idea of relativistic GAN (relativistic generative adversarial network) is introduced, the relative value of the discriminator is not the absolute value; finally, the sensation loss is improved, and the feature is used before the function is activated. The test results show that the proposed algorithm is better than SRGAN (Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network), SRCNN (Super-Resolution Convolutional Neural Network) and FSRCNN (Fast Super-Resolution Convolutional Neural Network). The clarity of the reconstructed image is improved, and the reconstructed image quality is significantly improved.

This study proposes a method for judging the existence of closed-form inverse kinematics solutions based on the Denavit&ndash, Hartenberg (DH) model. In this method, serial robots with closed-form solutions are described using three types of sub-problems from the viewpoint of solving algebraic equations. If a serial robot can be described using these three types of sub-problems, i.e., if the inverse kinematics problems can be solved by several basic problems, then there is a closed-form solution. Based on the above method, we design a set of universal closed-form inverse kinematics solving algorithms. Since there is a definite formula solution for the three types of sub-problems, the joint angles can be rapidly determined. In addition, because the DH parameters can directly reflect the linkage of the robot, the judgment of the sub-problems is also quick and accurate. More importantly, the algorithm can be applied to serial robots with low degrees of freedom. This enables the algorithm to not only quickly and accurately solve inverse kinematics problems but also to exhibit high universality. This proposed theory improves the existence conditions for closed-form reverse solutions and further promotes the development of motion control techniques for serial robots.

The Giant Steerable Science Mirror (GSSM) is the tertiary mirror system of the Thirty Meter Telescope (TMT) that relays optical beams from the secondary mirror to active instruments on Nasmyth platforms. One of the key technologies involved in GSSM functions is the error budget allocation from the system engineering of TMT. A novel approach of error analysis and allocation with strong adaptability, which is based on normalized Point Source Sensitivity (PSSn), is proposed. The relay optical function including the quality of the wavefront, the rationality of the mechanism, and the stability of the light were achieved based on the proposed method. The experiments validate the proposed method.

This article designs a novel wearable fall detection system in allusion to the problem of accidentally fall for the elderly. This article adopts the research method of combination of multi-threshold judgment, the algorithm utilizes the research method of combining accelerated speed multi-threshold judgment and attitude angle threshold value judgment, and combining multiple parameters, when meeting multiple conditions at the same time, the system judges the behavior as fall. With the verification in a series of tests for different conditions of fall, the test results indicate the system can detect the event of unintentional falls effectively and give out the alarm information. It achieves the expected effect.

In order to realize the accuracy of weighted network community division, based on the analysis of existing weighted network community dividing algorithm, a community dividing algorithm based on co-neighbor nodes and similarity is proposed. Firstly, the similarity between nodes is defined, and the weight of the edge is added to the local modularity function. Secondly, the Burt structural hole principle is used to select the important nodes. Then, the local clustering method is used to continuously expand the community. Finally, the judgment is made. The change of the Local modularity determines whether to continue the clustering, so as to obtain a more accurate result. Experiments on the simulation dataset and Zachary’s Karate club dataset show that compared with the CRMA algorithm, the algorithm is effectively improved in weighted network division time and accuracy.

Summary Motivated by a functional magnetic resonance imaging (fMRI) study, we propose a new functional mixed model for scalar on function regression. The model extends the standard scalar on function regression for repeated outcomes by incorporating subject-specific random functional effects. Using functional principal component analysis, the new model can be reformulated as a mixed effects model and thus easily fit. A test is also proposed to assess the existence of the subject-specific random functional effects. We evaluate the performance of the model and test via a simulation study, as well as on data from the motivating fMRI study of thermal pain. The data application indicates significant subject-specific effects of the human brain hemodynamics related to pain and provides insights on how the effects might differ across subjects.

The visualization of flow field is based on the continuity of the fluid motion. But under normal circumstances, the methods of obtaining flow field data are different, resulting in uneven data distribution and errors, cause discontinuity phenomenon in the flow field. For discrete flow field data, this paper uses the method of eigenvector direction to detect the abnormal data of discrete flow field. According to the criterion of “similarity in the space field vector”, the data are repaired to solve the non-uniformity existing in the flow field. By The numerical simulation, the experiment shows the continuous visualization of the eigenvectors of each data point is conducive to the objective representation of the discrete flow field visualization.

Since the advent of virtual reality technology, great changes have taken place in modern education and training. It conducts an in-depth study of its fundamental cause, that is, the virtual training method has distinct advantages over the traditional training method. The electric power skill training platform based on the virtual reality technology was established in this paper. Three-dimensional digital modeling of high-risk, invisible, and inaccessible scenarios in electric power industry had been structured by the computer system, in order to construct an immersive virtual environment and the simulation of real-life space would be realized. The electric power skill training platform could provide the somatosensory interaction system to enhance the trainees' simulation experience so that they can feel the authenticity of the training to the greatest extent. Trainees are enabled to have an accurate perception of various objects in the virtual environment through the senses of sight, hearing and touch by applying a variety of high-performance interactive devices, which is beneficial to improve the actual operation level of trainees, improve training quality and training efficiency, and reduce training cost input.

A set of detecting system for transformer noise was developed, which could achieve identify far-field and near-site sound source by using the acoustic localization technology and algorithm microphone array technology. According to its characteristics of sound field distribution, the mode of developed damping and noise reduction patches installed on the surface of transformer was designed and optimized and the distributed vibration absorption technique based on acoustic topology optimization was created. Thereby the reduction of transformer noise radiation would be achieved.

The general concept of smart load (SL) was originally introduced to provide practical implementation in grid power─frequency support applications. In this paper, a specific feature of SL which is composed of a SL controller and a linear load is introduced, after that, a comprehensive discussion on the application of SL for voltage stabilization and energy storage capacity reduction in an isolated microgrid is presented. The key component of this novel SL is the controller which is operated as a series reactive power compensator (RPC), unlike the traditional reactive power compensation technologies, it offers not only reactive power compensation but also active power variation in the linear load. Results validate the effectiveness of adopting this SL for a isolated microgrid with renewable energy source and energy storage system.

In vehicular ad-hoc networks (VANET), node density is constantly changing in both time and space. Well communication quality depends on the nodes density. Knowing the density of the vehicle communication system is very important for achieving better wireless communication performance. In recent years, many researchers have proposed a resource allocation scheme based on vehicle density. Therefore, in this paper, we introduced an acceleration aware density estimation (VAADE) algorithm to map the relationship between vehicle density and resource requirements. In VAADE, a car estimates the density of the driving road according to its own velocity and acceleration in real time. VAADE is accurate because it employs both velocity and acceleration, which are sufficient for density estimation. The simulation results indicate that the proposed algorithm can map the relationship between the communication resource demands and the acceleration.

We introduce statistical methods for predicting the types of human activity at sub-second resolution using triaxial accelerometry data. The major innovation is that we use labeled activity data from some subjects to predict the activity labels of other subjects. To achieve this, we normalize the data across subjects by matching the standing up and lying down portions of triaxial accelerometry data. This is necessary to account for differences between the variability in the position of the device relative to gravity, which are induced by body shape and size as well as by the ambiguous definition of device placement. We also normalize the data at the device level to ensure that the magnitude of the signal at rest is similar across devices. After normalization we use overlapping movelets (segments of triaxial accelerometry time series) extracted from some of the subjects to predict the movement type of the other subjects. The problem was motivated by and is applied to a laboratory study of 20 older participants who performed different activities while wearing accelerometers at the hip. Prediction results based on other people's labeled dictionaries of activity performed almost as well as those obtained using their own labeled dictionaries. These findings indicate that prediction of activity types for data collected during natural activities of daily living may actually be possible.

For the 12-pulse rectifier system, a testing method of larger-power rectifier real-time loss (LRRL) is proposed in this paper. Firstly, the characteristics of valve side current and thyristor terminal voltage are researched and analyzed on basis of the measurement results. Then, a calculation method for LRRL can be presented according to the characteristics mentioned above. Finally, the practical testing results of an industry DC power system demonstrate the correctness of the current and voltage characteristics analyzed above, and the feasibility of the testing and calculating method of the LRRL. This method can be used to solve a lot of problems, such as LRRL measurement, power supply equipment and system's efficiency accounting. Furthermore, it can provide date for the assessment of energy saving.

With the rapid development of multimedia technology, large amounts of digital information turn into easy storage and transmission, at the same time, copyright protection and authentication become a problem to be solved. Digital Video is composed of a series of continuous static images, because of its own characteristic, general image watermarking technology is difficult to direct extension to the video signal. In this paper, by using DCT transform and DWT transform, combined with the mean quantization algorithm, using three dimensional frequency domain transformation, introduces two kinds of transformation to the 3D video watermarking technology, utilize the quantitative test method to prove the existence of the watermark, and through the attacked experiments to verify the low complexity and well imperceptibility and robustness about the video watermarking algorithm.

This monograph details the implementation and use of the CollocInfer package in R for smoothing-based estimation of continuous-time nonlinear dynamic systems. These routines represent an extension of the generalized profiling methods in Ramsay, Hooker, Campbell, and Cao (2007) for estimating parameters in nonlinear ordinary differential equations. An interface to the fda package is included. The package also supports discretetime systems. We describe the methodological and computational framework and the necessary steps to use the software. Equivalent functionality is available in MATLAB.

Accelerometers have been widely deployed in public health studies in recent years. While they collect high-resolution acceleration signals (e.g., 10-100 Hz), research has mainly focused on summarized metrics provided by accelerometers manufactures, such as the activity count (AC) by ActiGraph or Actical. Such measures do not have a publicly available formula, lack a straightforward interpretation, and can vary by software implementation or hardware type. To address these problems, we propose the physical activity index (AI), a new metric for summarizing raw tri-axial accelerometry data. We compared this metric with the AC and another recently proposed metric for raw data, Euclidean Norm Minus One (ENMO), against energy expenditure. The comparison was conducted using data from the Objective Physical Activity and Cardiovascular Health Study, in which 194 women 60-91 years performed 9 lifestyle activities in the laboratory, wearing a tri-axial accelerometer (ActiGraph GT3X+) on the hip set to 30 Hz and an Oxycon portable calorimeter, to record both tri-axial acceleration time series (converted into AI, AC, and ENMO) and oxygen uptake during each activity (converted into metabolic equivalents (METs)) at the same time. Receiver operating characteristic analyses indicated that both AI and ENMO were more sensitive to moderate and vigorous physical activities than AC, while AI was more sensitive to sedentary and light activities than ENMO. AI had the highest coefficients of determination for METs (0.72) and was a better classifier of physical activity intensity than both AC (for all intensity levels) and ENMO (for sedentary and light intensity). The proposed AI provides a novel and transparent way to summarize densely sampled raw accelerometry data, and may serve as an alternative to AC. The AI's largely improved sensitivity on sedentary and light activities over AC and ENMO further demonstrate its advantage in studies with older adults.

The balance theory of 3D garment simulation tries to integrally study the complex system of human body-fabric-environment. The theory includes the geometrical balance, dynamic balance, pressure balance, and heat and moisture transfer balance. In this paper, the ICAD-balance equations are established based on the framework of balance theory and the heat and moisture transfer model of garment. A finite volume method is employed to solve the equations. Experiment results show that ICAD-balance equations can excellently simulate real situation. Therefore, our balance theory provides a good tool for customized clothing design, electronic mirror for clothing-test-platform, and comfortable clothing evaluation research.

In order to achieve full light utilization and satisfy the demand for far-field illuminance of LED,automated optimization method in sequential mode of ZEMAX was proposed in the design of collimator with far-field illuminance for LED source.The system was established in ZEMAX′s multiple configurations,and the user defined merit function was written out using ZEMAX Programming Language(ZPL) macros language.In the simulation,the material of lens is PMMA and the source is Lambertian-type LED light source,of which divergence angle is 180°.Optimum parameters of collimator were obtained via running an optimization.The optical simulation results show that an efficiency of 87% is achieved under a view angle of ±0.1° and for a point LED source.

Based on the three-dimensional Liu system with a nonlinear term of square, this paper appends a state variable to the system, and further adds a driving signal of the sine signal to construct a novel 4-demensional nonautonomous hyperchaotic Liu system. The appended variable is formed by the product of the nonlinear quadratic term of the original state variables and the driving signal. Through adjusting the frequency of the driving signal, the system can be controlled to show some different dynamic behaviors. By numerical simulations, the Lyapunov exponent spectrums, bifurcation diagrams and phase diagrams of the novel systems are analyzed. Furthermore, the corresponding hardware circuits are implemented. Both the experimental results and the simulation results confirm that the method is feasible. The method, which adjusts the frequency of the input sine signal to control the system to show different dynamic behaviors, can make the dynamic property of the system become more complex, but easier to be controlled accurately as well.

The performance of synchronous reluctance motor (SynRM) degrades due to chaos when its systemic parameters fall into a certain area. To control the undesirable chaos in SynRM, a passive control law is presented in this paper, which transforms the chaotic SynRM into an equivalent passive system. It is proved that the equivalent system can be asymptotically stabilized at the set equilibrium point, namely, chaos in SynRM can be controlled. Moreover, in order to eliminate the influence of undeterministic parameters, an adaptive law is introduced into the designed controller. Computer simulation results show that the proposed controller is very effective and robust against the uncertainties in systemic parameters. The present study may help to maintain the secure operation of industrial servo drive system.

Interaction between transmission control protocol (TCP) and random early detection (RED) gateway in the Internet congestion control system has been modelled as a discrete-time dynamic system which exhibits complex bifurcating and chaotic behaviours. In this paper, a hybrid control strategy using both state feedback and parameter perturbation is employed to control the bifurcation and stabilize the chaotic orbits embedded in this discrete-time dynamic system of TCP/RED. Theoretical analysis and numerical simulations show that the bifurcation is delayed and the chaotic orbits are stabilized to a fixed point, which reliably achieves a stable average queue size in an extended range of parameters and even completely eliminates the chaotic behaviour in a particular range of parameters. Therefore it is possible to decrease the sensitivity of RED to parameters. By using the hybrid strategy, we may improve the stability and performance of TCP/RED congestion control system significantly.

In this paper, we propose a new model of weighted small-world biological neural networks based on biophysical Hodgkin–Huxley neurons with side-restrain mechanism. Then we study excitement properties of the model under alternating current (AC) stimulation. The study shows that the excitement properties in the networks are preferably consistent with the behavior properties of a brain nervous system under different AC stimuli, such as refractory period and the brain neural excitement response induced by different intensities of noise and coupling. The results of the study have reference worthiness for the brain nerve electrophysiology and epistemological science.

A methodology for investigating learning is developed using activation distributions, as opposed to standard voxel-level interaction tests. The approach uses tests of dimensionality to consider the ensemble of paired changes in voxel activation. The developed method allows for the investigation of non-focal and non-localized changes due to learning. In exchange for increased power to detect learning-based changes, this procedure sacrifices the localization information gained via voxel-level interaction testing. The test is demonstrated on an arc-pointing motor task for the study of motor learning, which served as the motivation for this methodological development. The proposed framework considers activation distribution, while the specific proposed test investigates linear tests of dimensionality. This paper includes: the development of the framework, a large scale simulation study, and the subsequent application to a study of motor learning in healthy adults. While the performance of the method was excellent when model assumptions held, complications arose in instances of massive numbers of null voxels or varying angles of principal dimension across subjects. Further analysis found that careful masking addressed the former concern, while an angle correction successfully resolved the latter. The simulation results demonstrated that the study of linear dimensionality is able to capture learning effects. The motivating data set used to illustrate the method evaluates two similar arc-pointing tasks, each over two sessions, with training on only one of the tasks in between sessions. The results suggests different activation distribution dimensionality when considering the trained and untrained tasks separately. Specifically, the untrained task evidences greater activation distribution dimensionality than the trained task. However, the direct comparison between the two tasks did not yield a significant result. The nature of the indication for greater dimensionality in the untrained task is explored and found to be non-linear variation in the data.

Subject of the halo-chaos control in beam transport networks (channels) has become a key concerned issue for many important applications of high-current proton beam since 1990'. In this paper, the magnetic field adaptive control based on the neural network with time-delayed feedback is proposed for suppressing beam halo-chaos in the beam transport network with periodic focusing channels. The envelope radius of high-current proton beam is controlled to reach the matched beam radius by suitably selecting the control structure and parameter of the neural network, adjusting the delayed-time and control coefficient of the neural network.

In real-world network evolution, the aging effect is universal. We propose a microscopic model for aging networks, which suggests that the activity of a vertex is the result of the competition of two factors: pullulation and decrepitude. By incorporating the pullulation factor into previous models of aging networks, both the global and individual aging effect curves in our model are single peaked, which agrees with the empirical data well. This model can generate networks with scale-free degree distribution, large clustering coefficient and small average distance when the decrepitude intensity is small and the network size not very large. The results of our model show that pullulation may be one of the most important factors affecting the structure and function of aging networks and should not be neglected at all.

In this paper, the proportional synchronization between drive system and response system is achieved by using the concept of generalized synchronization. The phase space of all variables in response system can be expanded and compressed flexibly. Meanwhile, the 6-D hyperchaotic chua's circuit is considered as an illustrative example to demonstrate the effectiveness of the proposed approach. Furthermore, focusing on the shortcoming of the long transient behavior during the process of synchronization, a feedback method is adopted to shorten the transitional time of synchronization, which will provide an effective way for speeding up the transmitting velocity of code in chaotic multiple access communication.

Information Extraction (IE) systems that can exploit the vast source of textual information that is the internet would provide a revolutionary step forward in terms of delivering large volumes of content cheaply and precisely, thus enabling a wide range of new knowledge driven applications and services. However, despite this enormous potential, few IE systems have successfully made the transition from laboratory to commercial application. The reason may be a purely practical one—to build useable, scaleable IE systems requires bringing together a range of different technologies as well as providing clear and reproducible guidelines as to how to collectively configure and deploy those technologies. This paper is an attempt to address these issues. The paper focuses on two primary goals. Firstly, we show that an information extraction system which is used for real world applications and different domains can be built using some autonomous, corporate components (agents). Such a system has some advanced properties: clear separation to different extraction tasks and steps, portability to multiple application domain, trainability, extensibility, etc. Secondly, we show that machine learning and, in particular, learning in different ways and at different levels, can be used to build practical IE systems. We show that carefully selecting the right machine learning technique for the right task and selective sampling can be used to reduce the human effort required to annotate examples for building such systems.