In microgrids, paralleled converters can increase the system capacity and conversion efficiency but also generate zero-sequence circulating current, which will distort the AC-side current and increase power losses. Studies have shown that, for two paralleled three-phase voltage-source pulse width modulation (PWM) converters with common DC bus controlled by space vector PWM, the zero-sequence circulating current is mainly related to the difference of the zero-sequence duty ratio between the converters. Therefore, based on the traditional control ideal of zero-vector action time adjustment, this paper proposes a zero-sequence circulating current suppression strategy using proportional–integral quasi-resonant control and feedforward compensation control. Firstly, the dual-loop decoupled control was utilized in a single converter. Then, in order to reduce the amplitude and main harmonic components of the circulating current, a zero-vector duty ratio adjusting factor was initially generated by a proportional–integral quasi-resonant controller. Finally, to eliminate the difference of zero-sequence duty ratio between the converters, the adjusting factor was corrected by a feedforward compensation link. The simulation mode of Matlab/Simulink was constructed for the paralleled converters based on the proposed control strategy. The results verify that this strategy can effectively suppress the zero-sequence circulating current and improve power quality.

In order to improve the accuracy of fault diagnosis on wind turbines, this paper presents a method of wind turbine fault diagnosis based on ReliefF algorithm and eXtreme Gradient Boosting (XGBoost) algorithm by using the data in supervisory control and data acquisition (SCADA) system. The algorithm consists of the following two parts: The first part is the ReliefF multi-classification feature selection algorithm. According to the SCADA history data and the wind turbines fault record, the ReliefF algorithm is used to select feature parameters that are highly correlated with common faults. The second part is the XGBoost fault recognition algorithm. First of all, we use the historical data records as the input, and use the ReliefF algorithm to select the SCADA system observation features with high correlation with the fault classification, then use these feature data to build the XGBoost multi classification fault identification model, and finally we input the monitoring data generated by the actual running wind turbine into the XGBoost model to get the operation status of the wind turbine. We compared the algorithm proposed in this paper with other algorithms, such as radial basis function-Support Vector Machine (rbf-SVM) and Adaptive Boosting (AdaBoost) classification algorithms, and the results showed that the classification accuracy using “ReliefF + XGBoost” algorithm was higher than other algorithms.

The combined cooling, heating and power (CCHP) system not only has high energy efficiency but also has different load structures. Traditional separate production (SP) system and power supply system do not consider the land cost in terms of the environmental benefits, and in the aspect of the power supply reliability, the grid-connected inverter cost is also ignored. Considering the deficiency of the traditional energy supply system, this paper builds the CCHP system construction cost model. The particle swarm optimization (PSO) is adopted to find out the minimum value of the construction cost, and the optimal system construction scheme is constructed from three aspects which are system reliability, economic benefits and environmental benefits. In this paper, the typical daily data, as well as the meteorological data and the load data, in the last four years are taken as experimental dataset. The experimental results show that compared with the traditional SP system and power supply system, the CCHP system established in this paper not only achieves lower cumulative investment cost, but also has a good power supply reliability and environmental benefits.

The state of charge (SOC) estimation of the battery is one of the important functions of the battery management system of the electric vehicle, and the accurate SOC estimation is of great significance to the safe operation of the electric vehicle and the service life of the battery. Among the existing SOC estimation methods, the unscented Kalman filter (UKF) algorithm is widely used for SOC estimation due to its lossless transformation and high estimation accuracy. However, the traditional UKF algorithm is greatly affected by system noise and observation noise during SOC estimation. Therefore, we took the lithium cobalt oxide battery as the analysis object, and designed an adaptive unscented Kalman filter (AUKF) algorithm based on innovation and residuals to estimate SOC. Firstly, the second-order RC equivalent circuit model was established according to the physical characteristics of the battery, and the least square method was used to identify the parameters of the model and verify the model accuracy. Then, the AUKF algorithm was used for SOC estimation; the AUKF algorithm monitors the changes of innovation and residual in the filter and updates system noise covariance and observation noise covariance in real time using innovation and residual, so as to adjust the gain of the filter and realize the optimal estimation. Finally came the error comparison analysis of the estimation results of the UKF algorithm and AUKF algorithm; the results prove that the accuracy of the AUKF algorithm is 2.6% better than that of UKF algorithm.

At present, the remote meter reading problem for a large number of existing domestic diaphragm gas meters has not been reliably solved. The diaphragm gas meter uses a physical structure to measure gas consumption, so the main problem of remote meter reading is the digitization of gas consumption. Although it is possible to digitize the consumption data by modifying the internal structure of the gas meter or adding sensors, thereby realizing the function of remote meter reading, the modification process is complicated, the modification cost is high, and manual meter reading can only be used for the existing diaphragm gas meters. For this reason, this paper proposes an indirect measurement method of gas consumption based on gas pressure signal detection. The method can digitalize the metering results of the mechanical diaphragm gas meter and develop a state detection function by analyzing the pressure signal of the outlet of the meter without modifying the internal structure of a meter, thus addressing the key difficulties in the construction of a gas meter remote meter reading system. The experimental results show that this method has small calculation error and high reliability.

Based on design of the technologies of infrared photoelectric monitor spectroscopy measurement, this article gives a design of wireless infusion monitor based on Bluetooth 4.0 (BLE4.0) low energy technology. It works with smart mobile Application (APP). The wireless infusion monitor uses a Radio Frequency (RF) integrated chip CC2540 as Micro Control Unit (MCU). The signal of received infrared diode is sampled by Analog-to-Digital Converter (ADC) and processed with the threshold filtering. Then it comes to the information of infusion. The data is packaged in a custom format and send to the smart mobile APP via BLE 4.0. The infusion monitor is responsible for monitoring the infusion process in real time. A sound alarm is issued when the wireless infusion monitor detects a dangerous condition. The APP is responsible for computing the infusion information and displaying it. At the end of this paper, the feasibility of the wireless infusion monitor is verified through practical test.

Smart city is currently the main direction of development. The automatic management of instrumentation is one task of the smart city. Because there are a lot of old instrumentation in the city that cannot be replaced promptly, how to makes low-cost transformation with Internet of Thing (IoT) becomes a problem. This article gives a low-cost method that can identify code wheel instrument information. This method can effectively identify the information of image as the digital information. Because this method does not require a lot of memory or complicated calculation, it can be deployed on a cheap microcontroller unit (MCU) with low read-only memory (ROM). At the end of this article, test result is given. Using this method to modify the old instrumentation can achieve the automatic management of instrumentation and can help build a smart city.

An effective Supervisory Control and Data Acquisition (SCADA) system can improve the reliability, safety and economic benefits of a microgrid operation. In this research, the lower central controller and upper WEB (World Wide Web) monitoring system are connected by the SCADA system, which is the hub of a microgrid intelligent monitoring platform. This system contains a set of specific functions programmed by Java as a middleware and can provide communication and control functions between the central controller and the upper monitoring system. For the sake of security and stability of the microgrid, the SCADA system realizes business processing on real-time data acquisition and storage, load balancing and resource recovery, concurrent security processing, and control instruction parsing and transmission. All those functions were tested and verified in actual operation.

随着中国彻底进入移动互联网时代,移动出行以其快捷便利的优势,迅猛发展.但是其后续保障建设的滞后导致了移动出行车辆缺乏统一的监督管理平台,安全事故频发.该文基于Android和百度地图开发平台开发了一套移动出行监测系统,可同时实现1000辆车的信息管理、实时定位与跟踪、历史轨迹查询和卫星图展示等功能.首先,通过GNSS模块接收卫星信号,并利用socket技术发送给服务器,经服务器解析处理存入数据库.然后,Android客户端通过web服务器访问数据库,获取车辆、卫星信息,并在客户端实时显示.该监控系统使用线程池模型改进生产者—消费者模式,解决大容量突发性数据请求造成的严重丢包率;采用动态建表的方式,将数据分时存储在不同数据表中,解决了海量数据查询效率低的问题;使用轻量级数据格式JSON,保证了Android客户端与远程数据库之间的实时通信,并解决了手机流量消耗大的问题.经验证,该系统可高效、稳定、长时间运行.

—To improve the coding performance in JPEG image compression and reduce the computational complexity, this paper proposes a new transform called All Phase Inverse Discrete Sine Biorthogonal Transform (APIDSBT) based on the All Phase Biorthogonal Transform (APBT) and Inverse Discrete Sine Transform (IDST). Similar to Discrete Sine Transform (DST) matrix and Discrete Cosine Transform (DCT) matrix, it can be used in image compression which transforms the image from spatial domain to frequency domain. Compared with other transforms in JPEG-like image compression algorithm, the Peak Signal-to-Noise Ratio (PSNR) and subjective effects of the reconstructed images using the proposed transform and image coding scheme are better at the same bit rates, especially at low bit rates. The advantage is that the quantization process is simpler and the computational complexity is lower.

The problem of blocking artifacts is very common in block-based image and video compression, especially at very low bit rates. In this paper, we propose a post-processing method for JPEG-coded image deblocking via sparse representation and adaptive residual threshold. This method includes three steps. First, we obtain the dictionary by online dictionary learning and the compressed images. The dictionary is then modified by the histogram of oriented gradient (HOG) feature descriptor and K-means cluster. Second, an adaptive residual threshold for orthogonal matching pursuit (OMP) is proposed and used for sparse coding by combining blind image blocking assessment. At last, to take advantage of human visual system (HVS), the edge regions of the obtained deblocked image can be further modified by the edge regions of the compressed image. The experimental results show that our proposed method can keep the image more texture and edge information while reducing the image blocking artifacts.

Web-scale image understanding is drawing more and more attention from the computer vision and multimedia domain. To solve the key problem of visual polysemia and concept polymorphism in the image understanding, this paper proposes a semantic dictionary to describe the images on the level of semantic. The semantic dictionary characterizes the probability distribution between visual appearances and semantic concepts, and the learning procedure of semantic dictionary is formulated into a minimization optimization problem. Mixed-norm regularization is adopted to solve the above optimization for learning the concept membership distribution of visual appearance. Furthermore, to improve the generalization ability of the semantic description, we propose the semantic expansion technology, where a concept transferring matrix is learnt to quantize the implicit relevancy among the concepts. Finally, the distributed framework on the basis of the semantic dictionary is constructed to speed up the large scale image understanding. The semantic dictionary is validated in the tasks of large scale semantic image search and image annotation.

The vertical or horizontal edges don’t dominate in some frames of video sequence, so the conventional discrete cosine transform (DCT) may not be the best choice for those frames. Directional DCT framework behaves better than conventional DCT in coding performance for images where directional edges dominate. In addition, the all phase biorthogonal transform (APBT), which is used in image compression instead of DCT, can also help to improve the performance of compression. In the light of directional DCT and APBT, directional APBT (D-APBT) is proposed and applied to H.263 video coding. Experimental results show that this framework can indeed improve the coding performance remarkably.

Discrete cosine transform (DCT) based JPEG standard significantly improves the coding efficiency of image compression, but it is unacceptable event in serious blocking artifacts at low bit rate and low efficiency of high-definition image. In the light of all phase digital filtering theory, this paper proposes a novel transform based on discrete sine transform (DST), which is called all phase discrete sine biorthogonal transform (APDSBT). Applying APDSBT to JPEG scheme, the blocking artifacts are reduced significantly. The reconstructed image of APDSBT-JPEG is better than that of DCT-JPEG in terms of objective quality and subjective effect. For improving the efficiency of JPEG coding, the structure of JPEG is analyzed. We analyze key factors in design and evaluation of JPEG compression on the massive parallel graphics processing units (GPUs) using the compute unified device architecture (CUDA) programming model. Experimental results show that the maximum speedup ratio of parallel algorithm of APDSBT-JPEG can reach more than 100 times with a very low version GPU. Some new parallel strategies are illustrated in this paper for improving the performance of parallel algorithm. With the optimal strategy, the efficiency can be improved over 10%.

 —A novel linear transform, called All Phase Biorthogonal Transform (APBT), was generated from All Phase Digital Filter (APDF) theory. APBT can be used in image compression instead of the conventional Discrete Cosine Transform (DCT), and the corresponding image compression scheme is called APBT-based JPEG (APBT-JPEG) which can achieve better coding performance, especially at low bit rates. With in-depth mathematical analysis on the emerging APBT-JPEG and conventional DCT-based JPEG (DCT-JPEG), we bring forward a unique insight into the relation between them. The relation is that APBT-JPEG can be implemented by DCT-JPEG, using a new quantization table deduced from mathematical analysis. To the best of our knowledge, it is the first time to reveal it, which can be considered to the main contribution of this paper. Finally, experiment results obtained with the test images have verified our proposed conclusion both in terms of objective quality and subjective effect.

All phase digital filter (APDF) is a new type of linear phase FIR digital filter which was proposed in recent years, and a general method to design 2-D FIR digital filters called 2-D APDF is presented in this paper.Firstly, the theory of biorthogonal wavelet transform and 2-D APDF is expounded.Secondly, a novel algorithm is proposed to implement biorthogonal wavelet transform by using 2-D APDF based on DFT and IDCT.The relations between two kinds of filters are discussed.As an important application of biorthogonal wavelet transform, multi-resolution analysis of 2-D image signal could be used to test the feasibility and applicability of the proposed algorithm.Finally, the test image can be reconstructed perfectly in the multi-resolution analysis of 2-D image experiment using MATLAB tool in this paper, and the analysis indicates that the proposed method performs well.

As the international video coding standard, MPEG-2 has been widely used in today’s digital video applications. However, its quantization operation is complex. To simplify this operation, we propose a new algorithm for the intra frame transform-coding in this paper, instead of the conventional algorithm that using discrete cosine transform (DCT). This new algorithm is based on the all phase biorthogonal transform (APBT) theory, which has three kinds of forms in accordance with different transform matrices, referred to as the all phase Walsh biorthogonal transform (APWBT), the all phase discrete cosine biorthogonal transform (APDCBT), and the all phase inverse discrete cosine biorthogonal transform (APIDCBT). Compared with the conventional DCT, APBT reduces the inter-pixel redundancy and the computational complexity using the uniform quantization for the intra frames transform-coding. Experimental results show that the peak signal to noise ratio (PSNR) of the proposed algorithm performs close to the DCT for the tested frames, and there is no difference in visual quality.

Human skin detection in images is desirable in many practical applications, e.g., human-computer interaction and adult-content filtering. However, existing methods are mainly suffer from confusing backgrounds in real-world images. In this paper, we try to address this issue by exploring and combining several human skin properties, i.e. color property, texture property and region property. First, images are divided into superpixels, and robust skin seeds and background seeds are acquired through color property and texture property of skin. Then we combining color, region and texture properties of skin by proposing a novel skin color and texture based graph cuts (SCTGC) to acquire the final skin detection results. Comprehensive and comparative experiments show that the proposed method achieves promising performance and outperforms many state-of-the-art methods over publicly available challenging datasets with a great part of hard images. (C) 2015 Elsevier Inc. All rights reserved.

This paper proposes new concepts of the windowed all phase biorthogonal transform (WAPBT), which is inspired by the all phase biorthogonal transform (APBT). In the light of windowed all phase digital filter theory, windowed all phase biorthogonal transforms is proposed. The matrices of WAPBT based on DFT, WHT, DCT and IDCT are deduced, which can be used in image compression instead of the conventional DCT, and the image compression scheme proposed in this paper is called WAPBT-based JPEG (WAPBT-JPEG). With optimal window sequence of WAPBT for image compression obtained by using generalized pattern search algorithm (GPSA), the peak signal to noise ratio (PSNR) and visual quality of the reconstructed images using the WAPBT-JPEG is outgoing DCT-based JPEG (DCT-JPEG) and APBT-based JPEG (APBT- JPEG) approximately at all bit rates. What is more, by comparison with DCT-JPEG, the advantage of proposed scheme is that the quantization table is simplified and the transform coefficients can be quantized uniformly. Therefore, the computing time becomes shorter and the hardware implementation is easier. Index Terms—Windowed all phase biorthogonal transform (WAPBT), image compression, discrete cosine transform (DCT), JPEG algorithm, generalized pattern search algorithm (GPSA), windowed all phase digital filter (APDF)

 —All Phase Biorthogonal Transform (APBT), a DCT-like transform generated from All Phase Digital Filter (APDF), can be used in baseline JPEG by replacing conventional Discrete Cosine Transform (DCT), and the image compression scheme is called APBT-based JPEG (APBT-JPEG). APBT-JPEG can achieve better coding performance but at the expense of an increased computational complexity, because there is no fast algorithm for computing APBT. With our in-depth study on APBT-JPEG, we have found the relation between APBT-JPEG and DCT-JPEG (baseline JPEG) in this paper. In order to avoid extra computational complexity in APBT-JPEG, we propose a novel quantization table used in DCT-JPEG and an almost identical coding performance is achieved compared with APBT-JPEG. Tested by natural images, experimental results show that compared with other quantization tables, at low bit rates in DCT-JPEG, the performance both in terms of PSNR and visual quality can be improved by using our proposed quantization table, and the blocking artifacts in reconstructed image have been reduced significantly. For these reasons, we can foresee that the proposed quantization table will be widely used in the future.