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In reality, time series subject to the internal/external influence are usually characterized by nonlinearity, uncertainty, and incompleteness. Therefore, how to model the features of time series in nondeterministic environments is still an open problem. In this article, a novel high-order intuitionistic fuzzy cognitive map (HIFCM) is proposed, where intuitionistic fuzzy set (IFS) is introduced into fuzzy cognitive maps with temporal high-order structure. By means of IFS, the ability of model for the representation of uncertainty can be effectively improved. In order to capture the fluctuation features of series data, variational mode decomposition is utilized to decompose time series into sequences of various frequencies, based on which fine feature structures on different scales can be obtained. Each concept of HIFCM corresponds to one decomposed sequence such that casual reasoning can be achieved among the obtained features in various frequencies of time series. All parameters are learned by the particle swarm optimization algorithm. Finally, the performance of the method is verified on the public datasets, and experimental results show the feasibility and effectiveness of the proposed method.

When applying artificial intelligence technology to quantitative trading, high noise and unpredictability of market environment are the first practical problems to be considered. Therefore, how to select the learning features of the market based on rapidly changing financial data is particularly important. In this paper, the real time financial data are first processed by K-line theory, which uses candlesticks as a generalization of price movements over a period of time, so this process can play the role of de-noising. Then, the candlesticks are decomposed into different subparts by mean of a specified spatio-temporal relationship, based on which cluster analysis of the subparts to get the learning features. Further, the learning features that are clustered by the above K-lines are put into the model, and the online adaptive control of the parameters in the unknown environment is realized by the deep reinforcement learning method, so as to realize the high frequency transaction strategy. In order to verify the performance of the model, the data on different financial derivatives transactions such as stocks, financial futures and commodity futures are used. The proposal approach is compared with other methods which are based on price, fuzzified price and K-lines for features learning. In order to verify the accuracy of the proposal approach, prediction-based methods such as recurrent neural network and fuzzy neural network are used for comparison. Experimental results show that the proposed method has higher robustness and prediction accuracy.

In real applications, noises are often present in the obtained data, which would considerably affect the performance of machine learning models. Although support vector machine (SVM) is a classic and efficient learning model, however, it is sensitive to noises in the training data. In this paper, a novel support vector machine named as reconstructed training-set SVM (RTS-SVM) is proposed to implement classification for high-noise data, where the roulette cooperative coevolution algorithm (R-CC) is used to optimize the parameters of RTS-SVM. The proposed SVM model is applicable to make the classification of high-noise data by tackling with the sensitive effect of the ``soft margin'' of SVM on the original training set. By means of the hierarchical relations existing in feature sets, hierarchical grouping (HG) algorithm is applied to construct feature subsets, based on which R-CC coordinates the parameters of RTS-SVM to achieve the optimization of the whole model. The application of the proposed scheme in the classification of financial time series is mainly discussed. Besides, the proposed model is also verified by using synthetic data with high noises and daily life data sets. Examples are provided to illustrate the effectiveness and practicability of the proposed algorithm.

Since the existence of fuzziness in the real world, uncertainty could be always present in the reasoning results. Therefore, how to tackle with the hesitation existing in the process of reasoning is a meaningful issue for the construction of the inference system. In this paper, a novel interval type-2 intuitionistic fuzzy neural network based on long-short term mechanism is proposed (LSTM-IT2IFNN). By means of interval type-2 intuitionistic set, the hesitation of reasoning is described and involved to determine fuzzy rules in inference system, by which the chosen of fuzzy rules not only considers the membership value but also non-membership degree. In order to handle the regression problems with long-term time dependency, long-short term mechanism is introduced into fuzzy neural networks, which can effectively improve the memory ability of long-term knowledge. Furthermore, by means of intuitionistic sets, an extended metacognitive learning mechanism is proposed for the adaptively online learning of network structure, where it achieves when, what, and how to learn from the sequential data. The performance of the LSTM-IT2IFNN is evaluated on different regression problems, including synthetic and real-world data. By comparing to existing algorithms, the experimental results show the efficiency and accuracy of the proposed scheme.

In this paper, a method using the 5.1 surround sound format to classify the affective scenes of a movie was introduced. The affective types were decided by a web survey and several subjective listening tests, then a movie scene library with labeled affective types was obtained. The content and characteristic of the 5.1 surround sound format was analyzed, and feature parameters of the main left channel and the low-frequency effect channel were extracted as the materials for the prediction calculation. Several classic models have been used and the prediction results were compared among five frame lengths of the audio signals, and 2s used Bagged Trees model got the highest predicting accuracy.

Subsynchronous Resonance (SSR) is a potential threat for long distance power transmission from doubly fed induction generator (DFIG) based wind farms with fixed series compensation (FSC). In this paper, subsynchronous resonance damping control (SSRDC) strategy based on shunt voltage source converter is proposed. Taking account of different series compensation levels and wind speeds, several potential inputs to the SSRDC are investigated. SSRDC control parameters are determined via root locus techniques to ensure the system stability of subsynchronous modes. Considering implementation feasibility in practical project and stability of supersynchronous modes, an enhanced SSRDC is proposed. Impact of transmission line and DFIG parameters and the reactive power compensation to the mitigation effect is investigated. Time domain simulation using PSCAD/EMTDC are carried out to validate the effectiveness of the SSRDC.

The volatility is one of the essential characteristics of financial time series, which is vital for the knowledge acquisition from financial data. However, since the high noise and nonsteady features, the volatility identification of financial time series is still a challenging problem. In this paper, from a perspective of granule complex network, a novel approach is proposed to study this problem. First, numeric time series is structured into fuzzy information granules (FIGs), where the segments of time series in each granule would own similar volatility features. Second, by using the transfer relations among granules, granule complex network is to be constructed, which intuitively describes the transfer processes among the different volatility patterns. Third, a novel community detection algorithm is applied to divide the granule complex networks, where granules with frequent mutual transfers would belong to the same granule community. Finally, Markov chain model is carried out to analyze the higher level of transfer processes among different granule communities, which would further describe the larger-scale transitions of volatility in overall financial time series. An empirical study of the proposed system is applied in the Shanghai stock index market, where volatility patterns of financial data can be effectively acquired and the corresponding transfer processes can be analyzed by means of the granule communities.

High noise and strong volatility are the typical characteristics of financial time series. Combined with pseudo-randomness, nonsteady and self-similarity exhibiting in different time scales, it is a challenging issue for the pattern analysis of financial time series. Different from the existing works, in this paper, financial time series are converted into granular complex networks, based on which the structure and dynamics of network models are revealed. By using variable-length division, an extended polar fuzzy information granule (FIGs) method is used to construct granular complex networks from financial time series. Considering the temporal characteristics of sequential data, static networks and temporal networks are studied, respectively. As to the static network model, some features of topological structures of granular complex networks, such as distribution, clustering and betweenness centrality are discussed. Besides, by using the Markov chain model, the transfer processes among different granules are investigated, where the fluctuation pattern of data in the coming step can be evaluated from the transfer probability of two consecutive granules. Shanghai composite index and foreign exchange data as two examples in real life are applied to carry out the related discussion.

Due to a lack of fetal imaging coils, the standard commercial abdominal coil is often used for fetal imaging, the performance of which is limited by its insufficient coverage, element number, and Signal-to-noise ratio (SNR). In this paper, a dedicated 36-channel coil array, of which size can best fit the body sizes of pregnancy gestation from 20 to 37+ weeks, was designed for fetal imaging at 3T. SNR with full phase encoding and G-factor denoted as noise amplification for parallel imaging were quantitatively evaluated by phantom studies. Compared with a commercial abdominal coil array, the proposed 36-channel fetal array provides not only SNR improvements in full phase encoding (with 10% in the region where the whole fetal body was located, and up to 40% in the edge region where the fetal brain and heart may appear) but also an augmented parallel imaging capability and remarkable SNR improvements at high acceleration factors.

With the fast development of wireless sensor networks (WSN), more attentions are paid to high data rate transmission of WSN, and hence, in IEEE 802.15.4a standard, ultra-wideband (UWB) is introduced as one of the physical layer technique to support high transmission data rate and precisie locationing applications. In order to analyze the bit error rate (BER) performance of UWB based WSN, a system model considering intra-symbol interference (IASI), inter-symbol interference (ISI), multiuser interference (MUI) and addictive white Gaussian noise (AWGN) is proposed in this paper, and then verified using simulation results. Moreover, the pulse waveforms complying with the spectrum requirement of IEEE 802.15.4a standard are given, and based on such obtained pulses, the effect of transmission data rate and user number is also shown. Results show that with the increase of SNR, the intra-symbol interference will decrease the system performance significantly, and system performance can be improve by using pulse waveforms with little intra-symbol interference., 5 pages, 6 figures, Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC), 2011

For high speed ultra-wideband (UWB) communication systems, the multipath interference exhibits a primary obstacle to improve transmission performance. In order to enhance the signal to interference plus noise ratio (SINR) in the receiver, a partial template receiver is proposed in this paper. Instead of using the conventional template, the model in this paper adopts a partial template to demodulate signals. To analyze the performance of the proposed receiver, bit error rate (BER) formulation of IR-UWB systems in the presence of multipath interference, multiuser interference and addictive white Gaussian noise (AWGN) is derived in IEEE 802.15.4a channel models. Simulation results show that, compared with the conventional correlation receiver, the proposed receiver can achieve a better BER performance for high Eb/N0, which falls in the conventional used Eb/N0 range., 4 pages, 5 figures, Communications and Networking in China (CHINACOM), 2011 6th International ICST Conference on

This paper proposes a novel day-ahead dynamic economic dispatch model for the wind integrated power system, aiming to minimize the total cost of generation and reserve capacity of all units. The proposed model is formulated as a chance-constrained stochastic nonlinear programming (CSNLP) problem, in which the constraints of regulating characteristics of units are taken into account. A two-stage iterative algorithm, based on sequential quadratic programming (SQP) method and chance-constraint checking, is developed to solve the CSNLP efficiently. Finally, the proposed dispatch strategy is illustrated in an IEEE30 power system with six generators and a wind farm.

How to develop students' capacity for computational thinking during the teaching process of computer programming, is one of the basic goals of computer fundamental education. Using C program language as example, this paper discusses how to train thinking capability of students during the programming language teaching. Computer programming languages have the features of mass content and practical techniques, and it is difficult for students to master one in short period of time. We provide some advices about how to organize the teaching content and experiments are proposed for improving the teaching results, and human thinking characters are exploited for increasing creative capability. The teaching methods proposed have proved effective for training thinking capability of students.

The 3DTown project is focused on the development of a distributed system for sensing, interpreting and visualizing the real-time dynamics of urban life within the 3D context of a city. At the heart of this technology lies a core of algorithms that automatically integrate 3D urban models with data from pan/tilt video cameras, environmental sensors and other real-time information sources. A key challenge is the "three-dimensionalization" of pedestrians and vehicles tracked in 2D camera video, which requires automatic real-time computation of camera pose relative to the 3D urban environment. In this paper we report preliminary results from a prototype system we call 3DTown, which is composed of discrete modules connected through pre-determined communication protocols. Currently, these modules consist of: 1) A 3D modeling module that allows for the efficient reconstruction of building models and integration with indoor architectural plans, 2) A GeoWeb server that indexes a 3D urban database to render perspective views of both outdoor and indoor environments from any requested vantage, 3) Sensor modules that receive and distribute real-time data, 4) Tracking modules that detect and track pedestrians and vehicles in urban spaces and access highways, 5) Camera pose modules that automatically estimate camera pose relative to the urban environment, 6) Three-dimensionalization modules that receive information from the GeoWeb server, tracking and camera pose modules in order to back-project image tracks to geolocate pedestrians and vehicles within the 3D model, 7) An animation module that represents geo-located dynamic agents as sprites, and 8) A web-based visualization module that allows a user to explore the resulting dynamic 3D visualization in a number of interesting ways. To demonstrate our system we have used a blend of automatic and semi-automatic methods to construct a rich and accurate 3D model of a university campus, including both outdoor and indoor detail. The demonstration allows web-based 3D visualization of recorded patterns of pedestrian and vehicle traffic on streets and highways, estimations of vehicle speed, and real-time (live) visualization of pedestrian traffic and temperature data at a particular test site. Having demonstrated the system for hundreds of people, we report our informal observations on the user reaction, potential application areas and on the main challenges that must be addressed to bring the system closer to deployment.

In this work the goal is to develop a distributed system for sensing, interpreting and visualizing the real-time dynamics of urban life within the 3D context of a city focusing on typical, useful dynamic information such as walking pedestrians and moving vehicles captured by pan-tilt-zoom (PTZ) video cameras. Three-dimensionalization of the data extracted from video cameras is achieved by an algorithm that uses the Manhattan structure of the urban scene to automatically estimate the camera pose. Thus, if the pose of the video camera changes, our system will automatically update the corresponding projection matrix to maintain accurate geo-location of the scene dynamics.

Bit error rate (BER) performance of impulse radio Ultra-Wideband (UWB) systems in the presence of intrasymbol interference, inter-symbol interference, multiuser interference and addictive white Gaussian noise (AWGN) is presented in this paper. By analyzing the indoor office LOS channel model defined by IEEE 802.15.4a Task Group and deducing the variance for intra-symbol interference (IASI), inter-symbol interference (ISI) and multiuser interference (MUI), the system BER expression is obtained and verified by MATLAB simulations. Through comparing the simulation results with and without intra-symbol interference, the conclusion that intra-symbol interference cannot be neglected is drawn-moreover, such interference will significantly decrease performance of UWB based wireless sensor networks (WSN). Then, the BER performance of UWB systems in multiuser environment is also analyzed and analysis results show that multiuser interference will further worsen the transmission performance of UWB systems., Comment: 5 pages, 2 figures, Communications and Networking in China (CHINACOM), 2011 6th International ICST Conference on

During the process of establishing the capacitivmicro-accelerometer PSPICE Simulation Model, consider in the precise electrical model of the capacitance, According tthe characteristics of MEMS capacitance, established micraccelerometer PSPICE simulation model. In the model, thelectrical and mechanical coupling process of Capacitiv MEMS sensor was analyzed. By using this simulation mode the closed-loop detection circuit was simulated by PSPICE The simulation results show that the closed-loop capacitanc detection circuit can effectively improve the linearity and thmeasure range of the capacitive micro-accelerometer, it caeliminate mechanical resonance. But the closed-loop system' stability and response speed must be ensured. Therefore, the stablishment of capacitive micro-accelerometer PSPIC simulation model is very valuable to the design and simulatio of the closed-loop capacitive detection circuit.

Two new schemes of video MDC is proposed in this paper. One is a video MDC scheme based on Scalar Quantification (MDSQ) and MPEG-2 framework. The other one is a video MDC scheme based on both Scalar Quantification and Wyner-Ziv coding (MDWZ). Experiment results show that, the reconstruction quality of these two scheme are obviously superior to traditional MPEG2 codec. In addition, MDWZ scheme performs better than MDSQ scheme at any point at the cost of coding efficiency.

Traditionally, for a cotton spinning corporation, its quality data acquisition, analysis, and monitoring were separated. In order to improve the quality management of the enterprise, this paper completes and depicts the framework construction of integrative system of quality data acquisition, analysis, and monitoring oriented to the cotton spinning corporations. It also elaborates the technology of quality data acquisition, analysis, and monitoring in the process of production management of this enterprise. The executive results shows that this system has enhanced the quality awareness, significantly increased the products quality, and reinforced the core competitiveness of the enterprise.

AODV is an on-demand routing protocol for mobile ad hoc network, with advantages of low load of control, low power consumption as well as low-bandwidth overhead. The routing table in AODV, however, maintains only one route to the specified node. Therefore, the source node needs to re-initiate route discovery process as a route fails. To solve this problem, an improved method is presented in this paper. The proposed scheme suggests that each source node maintains an alternative route to the specified destination node. When the primary route fails, the source node will use the backup route to send packets. Simulation results show that this modification can not only improve the packet delivery fraction but also reduce the average end to end delay, the routing overhead and the route discovery frequency.

RFID technology is considered as one of the most influential information technologies, since the transmission between reader and other equipments is completely opened through wireless channel, this has potential risks. This paper analyzes RFID system's hidden danger, designs an RFID authentication protocol based on shared key and proves its advantages compared with the other protocols. In order to reducing the calculation burden of tag and reader, this protocol gives full play to the role of RFID middleware and database ,and makes effort to achieve system's security ultimately.

Dendritic cell(DC) is a kind of natural immunocyte, and also is a kind of typical antigen presenting cell, which can stimulate and inhibit immune system. The function of the dendritic cell in the human body can be abstractly extracted as an dendritic cell algorithm(DCA). In this paper we try to extract a new immune algorithm based on the dendritic cell. Through the simulation experiment, an new anomaly detection based on DCA shows more advantages, compared with the conventional anomaly detections.

E-business is an advanced commerce pattern, which is centered on electronic trade, and takes actualizing buddy benefit and information share together such as customers, suppliers, sellers, and logistic distribution enterprises as the target. The integrated e-business is an advanced e-business, which is based upon collaboration and effective approaches to solve the problems in the enterprise such as ldquoinformation islandrdquo and ldquoresource islandrdquo. CPC is a kind of advanced business management idea and a kind of advanced commerce solution based on the Web, which conforms to the requirement of the mass customization. It locates customers and services at the center, and realizes cooperation and intercommunication between the related people in the whole lifecycle. According to the actual development of the CPC theory and the domestic e-business, the integrated e-business and some relative problems based on CPC are researched in this paper. The workflow and the system framework of the integrated e-business system based on CPC are proposed. Finally, the characteristics of the integrated e-business system based on CPC are summarized briefly.

Dynamic analysis of the coupled logistic map redounds to know and predict the characteristics of high-dimension complex nonlinear system. Using the method combining calculation and experiment, the following conclusions are shown: (1) The boundary equation of the first bifurcation of the coupled logistic map in the parameter space is given out. (2) Chaotic patterns of the coupled logistic map may emerge out of double-periodic bifurcation and Hopf bifurcation, respectively. (3) The boundary between periodic and non-periodic regions in the attraction basin of the coupled logistic map is fractal, which indicates the impossibility to predict the moving result of the points in phase plane. (4) The structures of the Mandelbrot-Julia sets are determined by the control parameters, and their boundaries have the fractal characteristic.