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Reconfigurable supercapacitor energy storage systems have been a promising choice for powering portable electronics since it omits the requirement of passive balancing elements such as resistors in the circuit, which improves the energy efficiency and alleviates the thermal heating effect of the circuit. However, the classical decentralized cell balancing method results in high voltage profiles of cells, which degrades the lifetime of cells in the repeated charging-discharging cycles. To address this challenge, we propose a cooperative cell balancing approach for reconfigurable supercapacitor energy storage systems. In the proposed method, cells communicate with their neighbours to synchronize cell voltages during the charging process. By carefully choosing the communication topology, the voltage profiles of cells can be maintained as low as possible, which alleviates the aging effects and prolongs the lifetime of cells. A laboratory hardware testbed has been built to verify the effectiveness of the proposed method. Extensive experiment results show that the proposed method can prolong the lifetime of cells considerably when compared with the decentralized control method.

In the hybrid energy storage system of electric vehicles (EVs), supercapacitors (SCs) are configured to supplement high frequency power loads to reduce battery strains. Continuous supply and quick response to power load through bidirectional power control is of great importance to attain power balance and keep the stability of bus voltage. In this paper, we propose a reference modulation based power management strategy to tackle this problem. Specifically, an adaptive SCs reference current regulation is designed to maintain sustainable power supply for SCs. A discrete lead compensator based reference smoothing control technique is integrated in to bottom layer control in order for quick transient response to stochastic power demand. With the designed SCs current regulation and reference smoothing technique, we aim to reduce the fluctuation of bus voltage. US06 driving cycles are applied in simulation and experiment to verify the effectiveness of the proposed approach.

In recent years, significant progress have been made in supercapacitor-based portable applications, where the switched-resistors circuit functions as a traditional cell balancing circuit. However, the existing charging control methods lack explicit consideration of the relatively high system temperature, leading to the decrease of the system life span and the serious thermal effect. In this paper, a temperature-suppressed charging method has been proposed to keep the system temperature at a low level during charging process. A proof is provided to demonstrate the effectiveness of this approach. Laboratory hardware setup has been completed and experimental results show that the average temperature of the system can be reduced by at least 1.2 °C compared with existing methods.

Due to the voltage limitation, large number of ultracapacitor cells need to be connected in parallel and series to provide a specific voltage level for trams. However, a large scale of series-connected ultracapacitors increase the voltage variation of the DC bus, which affects the size and efficiency of the subsequent power converter stage and undermines the energy utilization of the ultracapacitor. Moreover, ultracapacitors in series-connection inevitably suffer from voltage imbalance, which limits the capacity of energy storage system by the weakest cell. To alleviate the above problems, ultracapacitor parallel-series reconfiguration is employed to reduce the voltage variation in this paper. Particularly, a synchronous equilibrium strategy based on consensus tracking control is designed for voltage balance among series-connected ultracapacitors. First, we mathematically model the ultracapacitor-based energy storage system using the state-space averaging method. Second, the law of reconfiguration and synchronous equilibrium strategy is analyzed and designed. Finally, both simulation and experiment results verify that the proposed charging strategy effectively suppresses the voltage variation of the DC bus and evidently reduces balance time during charging.

Voltage equalization scheme is used to solve the voltage imbalance problem between serial connected supercapacitors. The inner capacitor voltage instead of terminal voltage based balancing scheme has been proven to be more accurate. The inner capacitor voltage are traditionally deduced through offline identified model parameters, which are variable due to the effect of terminal voltage, interference, and temperature, which will cause undesirable balancing deviation. To address the problem, we propose an online identified model based voltage equalization scheme to improve the equalization accuracy. The scheme is achieved by identifying model parameters through recursive-least square (RLS) technique, which has the advantage of simplicity, easy practicability. Experimental results are given to verify the effectiveness and high accuracy of the proposed cell balancing method.

Voltage equalization of series-connected supercapacitors (SCs) is of practical significance for the life and security of supercapacitors. In this paper, we proposed an efficient voltage equalization algorithm based on the consensus theory applying for switched shunting resistor circuit of supercapacitor (SC). In the proposed equilibrium strategy, a proportional-integral (PI) observer is designed to estimate the internal capacitance voltage of SCs. Then, by comparing the observation value of internal capacitance voltage with those of its neighbors, the voltages of SCs are balanced before they are fully charged and even after charging is completed. The experiment results show that the proposed method has significant superiority and wider applicability than other exiting methods for equalizing voltage.

Ultrasonic inner testing method can be used to detect almost every kind of flaws in oil and gas transmission pipeline. Due to the large number of transducers on the testing pig, a huge amount of sampling data has been produced which makes data storage and real-time processing impossible. For the purpose of reducing sampling data without loss of resolution, a compressive sampling method applied to the front end of sensor array is introduced. The paper firstly focuses on the sparsity of flaw ultrasonic echo in pipeline which is the precondition of the method. Then, compressive sampling technique based on multi-channel analog-to-information convertor (AIC) is designed to achieve low sampling rate data acquisition. The experimental results show that the flaw echoes can be accurately reconstructed with only 22% of sampling data compared with traditional sampling method. Furthermore, the flaw dual identification can be directly performed without signal reconstruction which greatly improves the time efficiency.

Minimal hepatic encephalopathy (MHE) is a common neurocognitive complication of liver cirrhosis, which have few recognizable clinical symptoms. Previous functional magnetic resonance imaging (fMRI) studies have found that widespread cortical and subcortical functional connectivity (FC) changes were significantly in patients with MHE. The goals of this study were twofold: 1) to construct predictive models for MHE, based on brain regional functional connectivity, 2) and to test feature selection method on p-value ranker based kernel principle component analysis (kPCA). Our study included thirty-two cirrhotic patients with MHE and twenty age-, gender-, and eduction-matched healthy controls. Using 1.5T MR, we obtained resting-state fMRI for each subject. Functional connectivities between 116 pairs of brain regions in patients with MHE were compared with those in control participants. Then, p-value ranker based kPCA was applied in feature selection step to reduce the dimension of input data. The best parameters of feature selection were chose based on 10-fold cross-validation of vector machines (SVMs). Finally, We found FC-based diagnostic model was accurate in differing MHE from normal controls with 86.5% accuracy, 88% specifity and 85% sensitivity.

Resting state fMRI is an emerging research area that can reveal disorders and dysfunction of human brain. Resting state functional connectivity patterns have been reported for diagnosis of different diseases. In this study, Kernel Principal Component Analysis (KPCA) method based on connectivity matrix of each functional meaningful brain regions was applied to discriminate adult with Attention-Deficit Hyperactivity Disorder (ADHD) from normal controls. Firstly, functional connectivity matrix was obtained as classify patterns. Secondly, Kendall tau rank correlation coefficient was applied to select features with high discriminative power, while KPCA was applied to find the abnormal pattern of ADHD in a much lower mainfold. Finally, the two groups of ADHD and normal participants were classified by a Support Vector Machine (SVM) classifier. Experimental results showed that SVM based on KPCA can produce a correct classification rate of 81% using a leave-one-out cross validation, which indicate that KPCA is an effective method that can greatly improve the final discriminative performance. Moreover, we examined the brain regions with statistically significant difference, like the frontal cortex, insula, cingulate cortex, postcentral gyrus, thalamus, middle temporal cortex, well confirmed with previous findings on ADHD. From the classification performance, we conclude that KPCA based on functional connectivity matrix can provide useful information for diagnosis of ADHD and even other diseases.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a wide phenotypic range, often affecting personality and communication. Previous surface-based morphometry (SBM) work only used cortical thickness to generate classification model between ASD and normal control. The goals of this study were twofold: 1) to construct predictive models for ASD, based on combination any of these five cortical measurements (cortical thicknesses (T), mean curvature (MC), Gaussian curvature (GC), folding index (FI), and curvature index(CI)) extracted from SBM, 2) and to compare these models. Our study included 22 subjects with ASD (mean age 9.2±2.1 years) and 16 volunteer controls (mean age 10.0±1.9 years). Using SBM, we obtained T, MC, GC, FI and CI for 66 brain structures for each subject. Then, we combined any of these five cortical measurements and obtained 31 combinations as classification inputs. To generate predictive models, we employed three machine-learning techniques: support vector machines (SVMs), functional trees (FTs), and logistic model trees (LMTs). We found that “thickness + mean curvature”-based classification model was modest superior to that based on thickness-based features when LMT employed, and curvature only provide limited information to thickness in ASD predictive model. Our result suggested that ASD may be cortical thickness abnormal disorder rather than cortical curvature abnormal disorder.

Solar energy has an enormous developing application capacity as one kind of green renewable energy source which can be continuously used forever. The track control system in PV adapts automatic tracking light techniques to make solar panels always perpendicular to sunlight. This way can greatly improve the generated energy of PV array, add solar energy utilization ratio, reduces the cost of photovoltaic power. This paper mainly introduces the basic structure and hardware and software design process of track control system in PV, gives the hardware circuit, parameter test methods and software control process of this system, analysis the advantages and disadvantages of sun trajectory tracking mode and electro-optical tracking mode, choose the method that electro-optical tracking as the major tracking mode while the sun trajectory tracking mode as additional tracking mode on unusual weather conditions.

There is a disagreement on profits among the related parties of BOT mode transportation infrastructure construction. By analyzing the goals, beneficial points and resource conditions of both government and chief contractors, this paper proposed a Game-Equilibrium Model on BOT-mode transportation infrastructure construction concerned investing and financing economy, social benefits analysis and price-decision policy. As the conclusion, it puts forward a best corresponding programmed between the government and the contractors under a particular condition. It also gives us some theoretical support on policy-making among the related parties on transportation infrastructure construction.

Starting from the influencing factor analysis of process innovation strategy in manufacturing enterprises, this paper discussed process innovation strategy in manufacturing enterprises by comprehensive use of matching analysis of internal and external elements and strategic analysis of lifecycle matrix. From the perspective of enterprise scale, this paper combined with cases, discussed the choice of process innovation strategy in large-scale, small and medium-sized manufacturing enterprises.

gamma-aminobutyric acid (GABA) is the major inhibitory neurotransmitter for the normal function of human brain. It is difficult to detect GABA signal with the conventional single quantum filter technique due to its relatively low concentration and overlapping with other signals from creatine (Cr), glutathione (GSH), as well as macromolecules. A point-resolved spectroscopy (PRESS)-localized double quantum filter (DQF) with improvements was applied to the detection of GABA on a 1.5 T clinical scanner. The sequence efficiency and the degree of suppression of uncoupled resonances was examined in phantom experiments after numerical analysis of the editing performance. Excellent GABA signal retention at 3.01 ppm and robust suppression of overlapping metabolites was achieved due to high selectivity of the read pulse and the optimization of the order of the gradients. The spectral editing efficiency was measured to be 40% and the suppressed Cr ratio was 1250:1. That was approximately the same as the result of theoretical analysis.

An analysis approach that based on the Granger causality method is proposed to study the directional effective influence between brain regions in the fMRI data. Granger causality was employed to extracted BOLD signals to investigate effective connectivity in the brain network. Data from an emotion task were used to validate this method, and the emotion pathway was partly clarified illustrated focus on the right amygdala. The presented results, exploring directional influences between activated brain regions, indicated that the Granger causality mapping combined with spectral matrix factorization and principal component analysis in the frequency domain could improve upon existing applications of traditional pairwise Granger causality mapping method in human brain effective connectivity studies.

Functional near-infrared spectroscopy (fNIRS) is a non-invasive method for studying functional activation via monitoring changes of the hemodynamic properties in brain and event-related experimental in examining cognitive processes is very useful but much more flexible in data analysis. To validate the usefulness of independent component analysis (ICA) of event-related fNRIS data, a "high-motivation" GKT (guilty knowledge test) task was applied. A very efficient method of maximization suited called FastICA was applied to maximize the contrast function in practice. Compared with the fMRI studies of ICA, the oxy-HB (oxygenated hemoglobin) signals can be decomposed and the active areas can be found in emotional behavior. The time courses and active regions of the event-related ICA components were consistent across trials. ICA approach delivers valid fNIRS amplitude estimations and enables the analysis of event-related fNIRS data series which are highly relevant in particular for cognitive fNIRS studies.

Modern spectral estimation techniques have been applied to ISAR imaging in order to enhance the resolution for many years. The application of APES (Amplitude and Phase EStimation) filtering approach in ISAR imaging is presented. APES is an adaptive filtering approach which is referred to as the amplitude and phase estimation of a sinusoid, for complex spectral estimation. The APES method can yield more accurate spectral estimates with much lower sidelobes and narrower spectral peaks than FFT method and ESPRIT method. Therefore, the higher resolution can be obtained by using APES method instead of FFT method and ESPRIT method when the target flies smoothly. The simulation results show the effectiveness of APES method.

Superresolution array processing methods have been applied to inverse synthetic aperture radar (ISAR) imaging in order to enhance the resolution for many years. Nevertheless, because the excellent performances of the methods are based on the accurate knowledge of the data model, the performance of superresolution methods is degraded extremely, when the signal-to-noise-ratio is not very high, and the number of snapshots is not very large or the system errors exist in the array. This drawback prevents greatly their applications in practical systems. So, the study of robust processing methods is main subject at present. The relaxation (RELAX) algorithm can relax the hypothesis of the additive noise and the system errors. The application of the RELAX algorithm in ISAR imaging is presented in this paper. The RELAX algorithm can yield more accurate spectral estimates than the fast Fourier transform (FFT) or estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Therefore, the higher resolution can be obtained by using the RELAX algorithm instead of the FFT or ESPRIT algorithm when the target flies smoothly. The simulation results show the effectiveness of RELAX algorithm.

This paper proposes a reuse-oriented process component representation framework for the description and classification of process components (PCs). The key features of our approach are the division of process component descriptions into a goal-oriented PC's general information description (PGD), an implementation-oriented PC's specification description (PSD) and an improvement-oriented PC's data description (PDD). A facet-based PC classification scheme and an XML-based process description method is presented. A retrieval model based on tree-containment matching is given, which can improve the recall while maintaining a high level precision. Finally, a reuse based process and project management system (P2MS) to support process improvement and control is also introduced in this paper.