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Due to fixed window area-based stereo algorithms having given rise to relatively large matching errors around depth discontinuities, a adaptive window for stereo matching algorithm using integral image was proposed in this paper. A new similarity function and Four-direction line mask were employed in initial parallax gain aspect, not only to reduce noise effects but also to improve matching accuracy. Meanwhile, the window cost function that consists of average errors, variance of error and bias to larger windows was used to determine the size of matching and improve the matching accuracy. Simultaneously this algorithm using the integer image to enhance the efficiency conveniently, and the computation load having nothing to do without the window size, may reduce the timing operand is a constant. At last, the experiment result shows advantages of this algorithm, and it can be used in real-time system. Stereo matching is one important direction of computer vision. It is the important means gaining object depth information from many viewpoints in the image. It has vastly applying a prospect in fields such as motion target tracking, identification, medical science. Stereo matching is important and difficult step in stereo vision. Considered from the stereo matching, it may divide into area-based matching and feature-based matching [1]. Area-based matching is one matching algorithm which still is widespread at present using. Although this algorithm realize easier and obtain dense disparity map, it has some shorts such as sensitive for affine distortion, difficult to choose the size of matching window, computing largely, speed slowly and so on [2]. This paper is complied with already having the experiment instrument and the experiment environment, specifically for fixed window, the area-based matching algorithm easily produces relatively bigger matching error problem to get along in disparity discontinuous places, from the design of window cost function, initial disparity obtaining, and algorithm real time and so on. At last a fast stereo matching algorithm using adaptive window is given.

In order to reduce fuel consumption and reduce the deviation between the final battery state-of-charge (SOC) value and the target value at the same time, a novel double-layer multi-objective optimization method is proposed, which adopts an improved ant colony optimization (ACO) algorithm and the equivalent fuel consumption minimization strategy (ECMS) considering mode switching. The proposed strategy adopts a two-layer structure. In the inner layer, the ECMS considering mode switching was adopted to optimize the working mode and working point, so as to achieve the goal of reducing fuel consumption. In the outer layer, aiming at the shortcomings of traditional ACO, the heuristic factor and adaptive volatilization factor were introduced. An improved ACO method was proposed to optimize the equivalent factor, so as to achieve the goal of reducing the deviation between the final value of SOC and the target value. In order to verify the effectiveness of the proposed algorithm, it is compared with the traditional ECMS strategy and the rule-based (RB) ECMS strategy. The simulation results show that the proposed energy management strategy combining an improved ACO algorithm with ECMS considering mode switching can reduce the energy consumption of the whole ship and control the battery power.

The construction of smart grid is an important part of improving the utilization rate of electric energy. As an important way for the construction of smart grid, non-intrusive load decomposition methods have been extensively studied. In this type of method, limited by transmission cost and network bandwidth, low-frequency data has been widely used in practical applications. However, the accuracy of device identification in this case faces challenges. Due to the relatively single characteristics of low-frequency data, it is difficult to express the operating status of complex electrical appliances, resulting in low decomposition performance. In this paper, a non-intrusive load is proposed based on household electrical habits by studying the relationship between household electricity consumption habits and load status decomposition method. The Gaussian mixture model and time information are used to model the probability distribution of the electrical appliance state. This probability distribution is then used as the observation probability distribution of the factor hidden Markov model. In such a way, the BH-FHMM model is proposed. Finally, load decomposition is carried out through the load decomposition process of the FHMM model. In order to verify the performance of the proposed method, an experimental comparison is conducted based on the REDD data set. According to the results, a significant improvement in equipment recognition accuracy is obtained.

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To provide a more accurate prediction of building energy consumption, it is necessary to take into account the influence of the microclimate around a building establishing through the interaction with other buildings or the natural environment. This paper presents a method for the quantitative assessment of building performance under any given urban context by linking the urban microclimate model ENVI-met to the building energy simulation (BES) program EnergyPlus. The full microclimatic factors such as solar radiation, thermal radiation, outdoor air temperature, humidity, and wind speed have been considered in the proposed scheme. The method outlined in this paper could be useful for urban and building optimal design.

Bearing multi-fault detection from stochastic vibration signal is still a thorny task to dispose of because of the complex interplay between different fault components under severe noise interference. In such case, conventional techniques such as filter processing and envelope demodulation may cause undesired results. To overcome the limitation, this article explores a filtering-free technique combined probabilistic principal component analysis denoising with the Higuchi fractal dimension transformation to diagnose the bearing multi-faults. Fractal theory is used to optimize the model parameters and stabilize the random vibrational signal for fast Fourier transform spectrum analysis. Noise interference in the Higuchi transformation is capped using a probabilistic principal component analysis model whose parameters are optimized through embedding dimension Cao algorithm and correlation dimension Grassberger and Procaccia algorithm. The fault diagnostic scheme mainly falls into three steps. First, the original vibration signal is truncated into a series of sub-signal segments by moving window whose length is determined as twice the value of maximum time delay that is provided by examining the steady Higuchi fractal dimension value of a raw signal in a process of plotting the fractal dimension over a range of time delay. Then, the Higuchi approach is used to estimate the average fractal dimension for each segment to create a quasi-stationary Higuchi fractal dimension sequence on which, finally, the fault features are straightforwardly extracted by the fast Fourier transform algorithm. The effectiveness of the proposed method is validated using simulated and experimental compound bearing fault vibration signals. Some fault components may be clouded if applied Higuchi fractal dimension alone because of the noise interference, but using the probabilistic principal component analysis–Higuchi fractal dimension method leads to clear diagnostic results. It indicates that the proposed approach can be incorporated into bearing multi-fault extraction from raw vibration signals.