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The accurate state of charge (SoC) online estimation is a significant indicator that relates to driving ranges of electric vehicles (EV). The relationship between open circuit voltage (OCV) and SoC plays an important role in SoC estimation for lithium-ion batteries. To compare with the traditional incremental OCV (IO) test and the low current OCV (LO) test, a novel OCV test which combines IO test with LO test (CIL) is proposed in this paper. Based on the reliable parameters online identification of the dual polarization (DP) battery model, two SoC estimation algorithms are compared on the accuracy, robustness and convergence speed for the entire SoC region. Meanwhile, the comparative study of the three OCV-SoC relationships fits by the corresponding OCV tests is discussed in terms of the SoC online estimation under various temperatures. The results show that the adaptive extended Kalman filter (AEKF) algorithm can better improve the accuracy and robustness of SoC estimation than that of the extended Kalman filter (EKF) algorithm. Most importantly, the OCV-SoC relationship obtains from the CIL OCV test method is applied to the AEKF algorithm, which has higher accuracy and better statistical indices of SoC estimation, especially suitable for the low temperature.

In order to meet the long-term requirements of global comprehensive three-dimensional coverage and access anytime, anywhere in the future, the space-air-ground integrated network (SAGIN) as an ideal network architecture has become an important research direction in recent years. The paper describes some typical business scenarios in future communications, analyzes the network architecture and composition of SAGIN, and studies the key technologies to realize SAGIN. Finally, it points out some of the research directions of SAGIN, such as IoT services.

In this paper, we propose a novel convolutional encoder-decoder network with skip connections, named CEDNS, to improve the performance of saliency prediction. The encoder network utilizes the DenseNet model as the stem network to extract abundant hierarchical features from input images. Subsequently, a decoder network is designed to sufficiently fuse the hierarchical features to predict saliency more accurately. Between the encoder and decoder, skip connections are employed to transfer hierarchical features produced by the former to the latter. Furthermore, the model can be trained in an end-to-end manner which is beneficial for both training and inference. The experimental results on various benchmark datasets, SALICON, MIT300, and CAT2000, show that the proposed model achieves state-of-the-art performance on several key metrics.

This paper proposes a limited discrete operation points control strategy for series hybrid electric tracked-vehicle. First, to optimize the control parameters of rule-based energy management strategy, particle swarm optimization algorithm is employed. the optimization results were applied to determine the optimal switch strategy between operation points of the engine. Second, a dynamic adaptive strategy is also proposed to achieve the coordinated control of the engine-generator set. Finally, with combination of above parts, a detailed energy management strategy and coordinated control system was proposed. The results demonstrate that the discrete operation points control strategy combined with coordinated control strategy can improve the electricity power supply performance, decrease the fluctuations of the system and stabilize the bus voltage and guarantee robust control performance.

Data fusion technology is to use data from multiple information sources (data sources) through reasonable processing methods and effective data mining methods to make correct judgments for decision makers. This paper uses data fusion technology to evaluate the health index of the multi-station system. First, the different attributes of the index are classified, and then an improved analytic hierarchy process is proposed to calculate the index weights. After judging the relevance of the index, this paper selected 12 indicators to fuse the data. Finally, this paper uses a Multi-energy station system in Beijing as an example to calculate the health index of the multi-station system, which verifies the validity of the calculation method.

At present, multi-station fusion forms a variety of modes based on various combinations of substation, data center, energy storage station and charging station. In this paper, an integrated energy system was designed, using existing substation resources, construction of data fusion center stations, energy storage station, 5 g base stations, photovoltaic power station, wind power station, gas turbine station etc., realizing the sharing of resource integration, to support smart grid, to develop into a comprehensive energy services as a typical application scenario. The simulation was created to focus on evaluating the reliability of the system and penetration of the renewable power. Results of the structural optimization model simulation show that the designed integrated control strategy of multi-energy fusion energy station has high performance and comprehensive energy efficiency.

Aiming at the shortcomings of traditional electric quantity sensor, such as poor precision and insufficient interaction, this paper uses the multivariate least squares compensation algorithm to comprehensively compensate the temperature drift and nonlinear error of the sensor; According to the design concept of functional modularization, a high-precision intelligent power sensor system with STM32 series chips as the processing core is designed.

Crop health is the foundation for agricultural development. In some area, due to the lack of professional botanical experts, it is difficult to correctly diagnose the plant disease in the work of planting. In this paper, we propose a novel deep neural network structure that can reliably classify plant types and plant disease using a single image of plant leaf which enables the end-to-end diagnosis of plant disease. Our proposed model consists of two sub-models, a leaf segmentation model that employs a U-Net to separate the leaves in the original image from the background which effectively eliminates interference, and a plant disease classification model based on our proposed Two-head network that classifies plant diseases with the features extracted by various popular pre-trained models. We verified our model using the plant disease dataset with 8 plant species and 19 plant diseases provided by AI Challenger 2019. Experimental results demonstrate that our final model achieves a 0.9807 accuracy of plant classification and a 0.8745 accuracy of disease recognition. We believe that the technology in our model has great potential to become the basis of fully automatic reliable plant disease classification system which can be embedded into portable devices to assist farmers in the future.

Chaotic systems is widely employed in image encryption on account of its momentous properties including nonperiodicity, ergodicity, randomness and initial state sensitivity. However, some chaos-based cryptosystems still exhibit security problems due to the weak performance of their applied chaotic maps. To address those problems, a new two-dimensional HenonChebyshev map (2D-HCM) is first proposed in this paper. It is generated by connecting Henon map and Chebyshev map where the output of one map is used to adapt the input of the others. It results in a better chaotic performance in 2D-HCM, including better chaotic behaviors, higher chaotic index, wider chaotic range and finer ergodicity than other extant chaotic maps. Using the 2D-HCM, we further design an image encryption algorithm. The proposed algorithm disorganize the pixel positions of the input plain image with random sequence generated by 2D-HCM, and then convert it into DNA-planes using DNA operation rules. Finally, the cipher image is acquired by employing the newly defined DNA-level 2D cellular automata (DNA-CA) to update the DNA-planes in each iteration where 2D-HCM produces the rule number sequences applied in the update. Our scheme has proven to be highly efficient and secure by the experimental results and security analysis.

In order to improve the acceleration response of hybrid vehicles, this paper applies Radau pseudo-spectral method to solve the trajectory optimization problem of vehicle acceleration. The mathematical model of engine-generator, battery pack and electric motor is established, and the optimal control problem of vehicle acceleration time is constructed. As for the problem is strongly nonlinear. The problem is transformed into a nonlinear programming problem by Radau pseudo-spectral method, and the numerical solution is carried out. The rotational speed and torque trajectory curves of the engine-generator set and the motor are obtained. The simulation results show that Radau pseudo-spectral method can optimize the working curve of various components of hybrid vehicles and improve response speed.

The paper considers the parameter identification of PV cells' I-V model. Based on the nonlinear equation of PV cells I-V experimental model, which cannot be used directly to analyze the parameters' value with the output current and voltage, we present a quasi-linear differential equation of dV/dI to turn the parameters to differential coefficient of the dynamic relationship of I-V. Then an improved Simplified Particle Swarm Optimization (SPSO) algorithm is proposed to identify the unknown differential coefficient in the equation, by which the optimal parameters of PV cells can be calculated. Lastly, the results of PV cells' experiment verify the validity of the improved SPSO algorithm and shows that the identified parameters, which are affected by irradiance, can be used to get a more accurate PV cells I-V model to predict PV power generation.

Visual attention is a dynamic search process of acquiring information. However, most previous studies have focused on the prediction of static attended locations. Without considering the temporal relationship of fixations, these models usually cannot explain the dynamic saccadic behavior well. In this paper, an iterative representation learning framework is proposed to predict the saccadic scanpath. Within the proposed framework, saccade can be explained as an iterative process of finding the most uncertain area and updating the representation of scenes. In implementation, a deep autoencoder is employed for representation learning. The current fixation is predicted to be the most salient pixel, with saliency estimated by the reconstruction residual of the deep network. Image patches around this fixation are then sampled to update the network for the selection of subsequent fixations. Compared with existing models, the proposed model shows the state-of-the-art performance on several public data sets.

Intelligent prosthetics aim to provide a communication channel between disabled people and external world. Among them, walking-assistive device to help people with lower-limb disability attracts attention but still limited by unnatural gait or lack accurate real-time control. Here we studied the coupling between the upper limbs and the lower limbs since the whole body actually involved during walking and there exists coordination among limbs. We collected motion information of different joints from healthy people during bipedal walking by attitudes sensors. After gait cycle identified and normalized, the relationship between different limbs was built. Based on the polynomial approximation, the knee joint angle can be estimated by the shoulder. Thus in the future it can be potentially applied to control walking-assistive prosthetics by the upper limbs movement instead of the motion information of lower limbs.

Widening the draft resources is the key of academic journal of university. To improve the quality and efficiency of university journals' soliciting contributions, a judgment model of highly cited papers in journal of universities was proposed. First, the core elements of high-cited papers were obtained by using the analysis of highly cited papers in China National Knowledge Infrastructure (CNKI). Secondly, the Analytic Hierarchy Process (AHP) in operational research was applied to the research on the high-cited papers of college journals, the evaluation model was constructed, and the judgment matrix was established by paired comparison method. The weights of each influencing factor were calculated and finally sorted according to the importance. Finally, through the practice verification of the “Journal of Shenyang Aerospace University” for the past two years, the proposed model has good adaptability and practical effect.

This work focuses on the analysis for multicast services in fifth-generation (5G) wireless communication system. We investigate the physical- layer wireless multicast technology in massive multi-input multi-output (MIMO) mutual coupling channel model, and proposed the hybrid unicast/multicast transmission system. The mutual coupling channel model is adopted to describe the channel characteristics under the linear antenna array scenario and the rectangular antenna array scenario. The proposed hybrid transmission scheme adopts multicast beamforming in the multicast groups as well as multi-user MIMO (MU-MIMO) linear precoding in the unicast group to increase system throughput. The null-space method based interference cancellation is further performed between each group to eliminate signal leakage generated from each group. Comparisons between two types of antenna array configurations, different channel models, linear precoding as well as multicast beamforming, and user grouping strategies for multicast services are presented and analyzed by simulation.

In concept of contemporary space design education, 'artistic concept' and its education thought are gradually paid attention and produce a certain degree of influence. Using means of digital media, the eastern and western designers reveal their different interpretations of 'artistic concept space' in the work. With today's rapid development of new media technology, new teaching means also emerge in endlessly, which provides a bridge for the combination of 'artistic concept' and contemporary design education. In today's mode of design education, how to make the ideas of artistic space develop further based on mature western design education concept, and how to combine the new media technology with space design education better to inspire students' creativity and imagination, has become a problem that contemporary education workers facing with.

In this paper, we study the robust H ∞ exponential filtering problem for uncertain neutral stochastic distributed delay systems with interval time-varying delay. By Lyapunov-Krasovskii functional theory and free-weight matrix method, a linear stochastic real lemma has been established, with which the H ∞ mean-square exponentially stable filter is designed. A numerical example is provided to demonstrate the effectiveness of the proposed approach.

For autonomous stochastic differential equations (SDEs), it has been shown that the Euler-Maruyama (EM) method produces almost sure exponential stability and moment exponential stability of SDEs under some conditions which include the linear growth condition. In this work, we extend these results to non-autonomous SDEs, and prove that the exact solution is almost sure exponential stability and moment exponential stability under the local linear growth condition which is weaker than the linear growth condition. It is shown that the EM method maintains corresponding stability. A numerical example is presented to illustrate the effectiveness of this result.

This paper presents a novel scheme of adaptive self-organization network (SON) by integrating cognitive radio (CR) with inter-cell interference coordination (ICIC) for LTE-A heterogeneous networks (HetNets). Particularly, we take advantage of the spectrum sensing function from CR and the radio resource layering function from ICIC. Our work addresses the issues of smart low-power node (SLPN) development, which associates appropriate sectorization with radio resource allocation during the self-organization process. We further develop a Hungary algorithm based self-organization strategy to improve the SLPN adaptive optimization. Simulation results show that our proposed scheme can achieve considerable gain in terms of throughput and coverage, with extra rewards of high flexibility and low complexity in HetNet SON.

One of the most important direction of bioinformatics is the genome structural variation discovery. In this paper, firstly we explain four sequencing-based methods, i.e., Read-pair, Read-depth, Split-read and Assembly. Secondly, the advantage and disadvantage of the approaches are discussed. Thirdly, the combination of the methods is proved to be a better one. Finally, a novel combined strategy was analyzed that will be the direction of the future.

A substantial number of sequencing-based methods for discovering structural variation have recently sprung up, among which technologies utilizing read pairs have made significant progress in providing high accuracy. Based on in-depth analysis of the state-of-the-art computational methods for identifying structural variation with read pairs, strategies for improving accuracy of structural variation prediction are summarized and classified into several categories, such as conservative mapping, clustering, distribution-based strategies and so on. Specific elaboration and concrete comparison of the strategies are carried on. It may become a future trend to develop an approach that is an organic combination of multi-class strategies for structural variation detection.

Depth acquisition for moving objects becomes increasingly critical for some applications such as human facial expression recognition. This paper presents a method for capturing the depth maps of moving objects that uses a one-shot black-and-white stripe pattern with the features of simplicity and easily generation. Considering the accuracy of a matching is crucial for a precise depth map but the matching of variant-width stripes is sparse and rough, the phase differences extracted by Gabor filter to achieve a pixel-wise matching with sub-pixel accuracy are used. The details of the derivation are presented to prove that this method based on the phase difference calculated by Gabor filter is valid. In addition, the periodic ambiguity of the encoded stripe is eliminated by the epipolar segment covering a given depth range at a camera-projector calibrating stage to decrease the calculation complexity. Experimental results show that our method can get a dense and accurate depth map of a moving object.

The center-surround comparison principle is widely used in existing bottom-up saliency estimation models. However, most of them are based on local image processing techniques which are hard to handle texture regions well as a relatively large neighborhood is required to represent textures. In this paper, we propose a nonlocal patch-based reconstruction approach to reformulate the center-surround comparison. In the proposed approach, the saliency is measured by the reconstruction residual of representing the central patch with a linear combination of its surrounding patches. As a generalization of Itti et al.'s classical center-surround comparison scheme, the proposed approach performs well on images with symmetric structures where Itti et al.'s method fails, as well as on general natural images. Numerical experiments show the proposed approach produces better results compared to the state-of-the-art algorithms on several public databases.

In this paper, we propose a pilot-aided channel estimation scheme for Orthogonal Frequency-Division Multiplexing (OFDM) systems where channels are assumed to be both time-varying and sparse. Basis Expansion Models (BEM) are often used to model and reconstruct time-varying channel taps. In this paper, the framework of BEM is applied to OFDM systems with time-varying sparse channels. A new method to detect the positions of significant taps is proposed based on the use of Constant Amplitude Zero Auto Correlation (CAZAC) sequence. Based on the results of detection, BEM based estimation is implemented to estimate the detected taps. The numerical simulations illustrate that the proposed two-step estimation scheme for significant channel taps outperforms direct estimation methods for all the channel taps and also this method can reduce the required pilots and thus reduce the computational load and improve the spectral efficiency.

In this paper, an improved method for reconstruction of doubly selective wireless channels in piloted-aided OFDM systems based an existing estimation method is proposed. In this re-expansion channel estimation process, the first few Fourier coefficients of each channel tap are estimated from the pilot information and the received signal firstly. Then the channel taps are estimated in the framework of Basis Expansion Model (BEM) from their respective Fourier coefficients. In the process of recovering BEM coefficients, instead of using the inverse method which is a Least Square (LS) problem, this paper proposes an improved method of recovering BEM coefficients from the estimated Fourier coefficients based on the Minimum Mean Square Error (MMSE) criterion. The proposed method is validated by simulating a system conforming to the IEEE 802.16e standard. Numerical results illustrate the performance gains achieved by the improved method.

This paper describes E-Health Monitoring (EHM) ecosystem and current EHM market segments. Based on IoT reference model, three EHM technical models are proposed. Model 1 focuses on device to device communication; model 2 focuses on network providing connection only; model 3 focuses on network combined with platform. Model 3 is equipped with a service support platform which is a new entity comparing with model 2. And then, several key technologies are proposed for service support platform of model 3.

More and more congestions are emerging because of the increasing of air traffic flow in china. in order to facilitate civil-aviation traffic, a flight conflict resolution method based on ACO (Ant Colony Optimization) is surveyed in this paper. the optimization objective function which not only includes trajectory length factor but also passengers comfort factor is constructed. an ACO model for flight conflict resolution is established. the experimental results show that the optimized trajectory achieved by ACO is better than those obtained by GA and PSO.

Nodes layout is a critical issue affecting the quality of service of camera networks. The layout aims at adopting the least number of cameras to cover the whole scene with expected observation quality. This is generally formulated as a non-convex optimization problem which is hard to be solved in polynomial time. In this paper, we propose an efficient near-optimal convex solution for layout guaranteeing satisfactory observation quality based on an anisotropic sensing model of camera. We construct the sensing model based on the physical imaging process. This model provides an accurate measurement of observation quality for the directional nonuniform sensing field of cameras. The layout is treated as selecting the best subset of cameras from a redundant initial layout with numerous cameras. Based on this characteristic and the sensing model, we formulate the layout as a sparse l 0 problem which is further relaxed to a convex l 1 minimization. Therefore, the near-optimal layout is efficiently obtained via convex optimization. Simulation results confirm the effectiveness of the proposed approaches.

This paper introduces a new sparse model for robust and reliable optical flow estimation. We show that the sparsity is directly related to gradient fields of optical flow. According to theory on sparse signal recovery, we rigorously formulate the optical flow estimation as an l 0 optimization problem. Considering the piecewise continuous nature of motion, the basic optical flow constraint is regularized by an l 1 norm. Then, with convex relaxation, the solution is obtain via the ordinary or reweighted l 1 optimization approach. Experimental results show the proposed method performs better than traditional methods and deals well with the discontinuities on motion boundaries.

The arguments of many scholars in the drafting process of new bankruptcy law were mainly concentrated upon the liquidation order of labor creditor's right and security creditor's right. Provisions in the first and second drafts of bankruptcy law have tremendous divergence on this issue. Therefore, which one should enjoy the priority? This paper analyzes the social values of labor creditor's right taking priority over security interest which is suggested by some scholars, and hence makes clear the position of labor creditor's right.

The concentrations of BTEX (Benzene, Toluene, m,p -xylenes and o-xylene) were monitored at roadsides of three kinds of urban road (open road, cross road and street canyon) in Beijing during 2008-2009. Each sampling was taken with the change of traffic volume at 7:30, 11:30, 14:30 and 17:30. Air samples were pre-concentrated on Tenax-TA tubes, which were analyzed by a GC system coupled with FID in laboratory after thermal desorption. This study presents the contamination status, temporal and spatial variations of BTEX in ambient air. During the monitoring period, diurnal average mass concentration of BTEX was 8.8-80.0 μg/m3, and exhibited two peaks. The highest mass concentration of BTEX reached 229.69 μg/m3 in summer while the lowest appeared in autumn. The air of street canyon had the highest concentration compared to the other two kinds of road. We also chose Guangzhou as the control city, which had a higher concentration of BTEX.

A complex logistics (CL) is a netlike supply chain (SC) logistics system which is always in an uncertain environment, consisting of manufacturers, wholesalers and retailers. This paper describes a simulation optimization tool based on the nested partitions (NP) method, CLSim-NP, developed to configure the optimal order-up-to level of each entity in a CL where customer demand is uncertain. Three types of models are combined in CLSim-NP: (1) CL fuzzy models to get the optimal order-up-to level of each entity in a CL where customer demand is described by fuzzy sets and two inventory control policies, decentralized control and retailer based control, are considered, (2) a nested partitions search model to partition CL order-up-to level configuration space on those recommended by the fuzzy models, and (3) a CL simulation model to evaluate CL performance achieved over time for each CL order-up-to level configuration. The application of CLSim-NP is proved by an illustrative example.

In view of multivariable strong coupling system with multiple time delays, a new multivariable decoupling internal model control method is proposed in this paper. Based on the model of the plant, a pre-compensator is designed to decouple the system into open-loop diagonal dominant system. The compensated diagonal elements are used as internal model to design a multi-loop internal model controller, and the diagonal filter is designed with improved Luus-Jaakola optimization algorithm. Then the internal model controller is converted into PID controller which can be used in DCS applications. Finally, this new method is applied in a reheat gas temperature control system of steam-steam heat exchanger in a 200 MW unit, and the control results show that the control structure can be tuned easily and achieved good trade-off between performance and robustness.

A pixel-wise adaptive model for estimating the just-noticeable difference (JND) in spatial domain is proposed in this paper. As the human visual system (HVS) can be considered as a multichannel system, we assume that there exist two channels in the HVS, which deliver luminance adaption factor and texture masking factor, respectively. Both channels affect the JND threshold in a cooperative manner. The texture regions are with abundant redundancy and can tolerate much noise. The disorder degree and spatial masking of the texture are considered to estimate the texture masking effect, for deducing such JND threshold that coincides with the HVS. Finally, the luminance adaptation factor and texture masking factor are combined nonlinearly. Various experiments confirm the improved model has a better visual effect than models proposed before.

In this paper, an approach for object tracking is proposed based on the online color information fusion scheme. The fusion scheme, is performed by projecting multi-channel color images to a one-dimensional pseudo gray scale space. This dimensionality reduction simplifies the designation of the tracking algorithm. The fusing coefficients are determined by taking the Fisher linear discriminant analysis to maximize the discriminative capability after taking the projection. The robustness of the approach lies in exploiting the appearance discrimination of the object in cluttered scenarios with varying illumination conditions. This scheme is embedded into a mean-shift tracking system and the experimental results show our scheme can enhance the discriminant characters in the changing environment and hence producing robust tracking results.

In this paper, we consider a nonlinear neutral stochastic system with variable delays and give conditions to ensure that the zero solution is asymptotically mean square stable by m eans of fixed point theory. These conditions do not require the roundedness of delays, nor do they ask for a fixed sign on the coefficient functions. An asymptotically mean square stable theorem with necessary and sufficient condition is proved. Some results are improved and generalized

On the basis of the analysis of canonical genetic algorithm's shortage, a novel genetic algorithm based on dynastic changes mechanism of nation (DCGA) is proposed. In DCGA, populations one by one evolve according to the relay race mode, by which the DCGA holds a mechanism of transferring from a local optimal solution to another better one and finally finding the global optimal solution under the guidance of knowledge, which is continuously updated along with the alternation of population evolution. To evaluate DCGA, TSP is applied. The results of simulation show that DCGA can avoid premature convergence and evolutionary stagnation efficiently and has the strong ability of realizing the global optimization.

The nonlinear transmission control protocol (TCP) dynamic model in the domain of Internet congestion control is considered in this paper. An observer is designed to estimate the unmeasured state. By applying the backstepping recursive technique and Lyapunov direct method, a kind of active queue management (AQM) algorithm is designed to asymptotically stabilize the closed-loop system through output feedback. Simulation results show that the system has desired steady state performance including less tuning time, lower packet loss rate, higher link utilization than the traditional random early detection (RED) or proportional integral (PI) algorithms, and robust quality under varying network environment.

In the course of capturing vein images from image capturing devices, displacement deviation, angle deviation and scale changes are inherent. In order to overcome these problems arosed by above factors, an approach to vein image recognition using the angle matrixes of feature points is presented in this paper. In the phase of feature extraction, all feature points and multisection points between them are obtained first, then the angle matrix of the feature points are calculated and arranged to form an angle matrix sequence. In the matching phase, the type and number of the feature points were first compared, if they mach, the angle matrix sequence was compared. Experimental results show the method is feasible.