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    01 October 2018, Volume 23 Issue 5 Previous Issue    Next Issue

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    Economic Dispatch of Islanded Microgrid Considering a Cooperative Strategy Between Diesel Generator and Battery Energy Storage System
    MA Yiwei (马艺玮), ZHOU Yanmei (周焰梅), ZHANG Jin'ai (张锦爱), PIAO Changhao (朴昌浩)
    2018, 23 (5):  593-599.  doi: 10.1007/s12204-018-1988-8
    Abstract ( 324 )  
    For the impact of intermittent resources’ high penetration on the economic dispatch of islanded microgrid, a new economic dispatch method is presented to minimize the overall generating cost for islanded microgrid, considering a cooperative strategy between diesel generator (hereinafter referred to as DE) and battery energy storage system (BESS). The optimum economic operation range of DE and the optimal set-point between DE and BESS are presented in the cooperative dispatch strategy, in which BESS is used fully to enable DE in a lower cost and higher efficient way. The results are analyzed under various operation conditions and also prove the validity of the proposed method.
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    Improved Anchored Neighborhood Regression Enhancement for Face Recognition
    WANG Yunfei (王云飞), DING Hui (丁辉), SHANG Yuanyuan (尚媛园), SHAO Zhuhong (邵珠宏), FU Xiaoyan (付小雁)
    2018, 23 (5):  600-606.  doi: 10.1007/s12204-018-1989-7
    Abstract ( 423 )  
    Although progress in face recognition is encouraging, the accuracy rate of face recognition remains to be increased. Since the face image quality has a positive influence on face recognition accuracy, the image enhancement methods are popular in face recognition. Most current image enhancement methods aim at improving visual appearance, but cannot improve recognition accuracy remarkably. In this paper, a feature evaluation operator is designed to overcome this problem. The operator selects patches with the best quality, and then face image is reconstructed with the selected patches. The proposed algorithm is tested on two different face recognition applications. Accuracy is raised after enhancement, and the result proves that the proposed algorithm is effective.
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    Multi-Step Model Predictive Control Based on Online Support Vector Regression Optimized by Multi-Agent Particle Swarm Optimization Algorithm
    TANG Xianlun (唐贤伦), LIU Nianci (刘念慈), WAN Yali (万亚利), GUO Fei (郭飞)
    2018, 23 (5):  607-612.  doi: 10.1007/s12204-018-1990-1
    Abstract ( 413 )  
    As optimization of parameters affects prediction accuracy and generalization ability of support vector regression (SVR) greatly and the predictive model often mismatches nonlinear system model predictive control, a multi-step model predictive control based on online SVR (OSVR) optimized by multi-agent particle swarm optimization algorithm (MAPSO) is put forward. By integrating the online learning ability of OSVR, the predictive model can self-correct and adapt to the dynamic changes in nonlinear process well.
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    Research on Improved Low-Energy Adaptive Clustering Hierarchy Protocol in Wireless Sensor Networks
    ZHANG Ying (张颖), LI Peisong (李培嵩), MAO Lin (毛林)
    2018, 23 (5):  613-619.  doi: 10.1007/s12204-018-1991-0
    Abstract ( 323 )  
    In wireless sensor networks (WSNs), due to the limited battery power of the sensor nodes, the communication energy consumption is the main factor to affect the lifetime of the networks. A reasonable design of the communication protocol can effectively reduce the energy consumption of the network system. Based on low-energy adaptive clustering hierarchy (LEACH), an improved LEACH protocol in WSNs is proposed. In order to optimize the cluster head (CH) election in the cluster setup phase, the improved LEACH takes into account a number of factors, including energy consumption of communication between nodes, remaining energy of the nodes, and the distance between nodes and base station (BS). In the steady phase, one-hop routing and multiple-hop routing are combined to transmit data between CHs to improve energy efficiency. The forward CH is selected as relay node according to the values of path cost. The simulation results show that the proposed algorithm performs better in balancing network energy consumption, and it can effectively improve the data transmission efficiency and prolong the network lifetime, as compared with LEACH, LEACH-C (LEACH-centralized) and NDAPSO-C (an adaptive clustering protocol based on improved particle swarm optimization) algorithms.
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    Fine-Grained Opinion Extraction from Chinese Car Reviews with an Integrated Strategy
    WANG Yinglin (王英林), WANG Ming (王明)
    2018, 23 (5):  620-626.  doi: 10.1007/s12204-018-1961-6
    Abstract ( 323 )  
    With rapid development of E-commerce, a large amount of data including reviews about different types of products can be accessed within short time. On top of this, opinion mining is becoming increasingly effective to extract valuable information for product design, improvement and brand marketing, especially with fine-grained opinion mining. However, limited by the unstructured and causal expression of opinions, one cannot extract valuable information conveniently. In this paper, we propose an integrated strategy to automatically extract feature-based information, with which one can easily acquire detailed opinion about certain products. For adaptation to the reviews’ characteristics, our strategy is made up of a multi-label classification (MLC) for reviews, a binary classification (BC) for sentences and a sentence-level sequence labelling with a deep learning method. During experiment, our approach achieves 82% accuracy in the final sequence labelling task under the setting of a 20-fold cross validation. In addition, the strategy can be expediently employed in other reviews as long as there is an according amount of labelled data for startup.
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    Face Hallucination with Weighted Nuclear Norm Constraint
    TANG Songze (唐松泽), LI Heng (李恒), XIAO Liang (肖亮)
    2018, 23 (5):  627-635.  doi: 10.1007/s12204-018-1992-z
    Abstract ( 226 )  
    Face hallucination via patch-pairs leaning based methods has been wildly used in the past several years. Some position-patch based face hallucination methods have been proposed to improve the representation power of image patch and obtain the optimal regressive weighted vector. The rationale behind the position-patch based face hallucination is the fact that human face is always highly structured and consequently positioned and it plays an increasingly important role in the reconstruction. However, in the existing position-patch based methods, the probe image patch is usually represented as a linear combination of the corresponding patches of some training images, and the reconstruction residual is usually measured using the vector norm such as 1-norm and 2-norm. Since the vector norms neglect two-dimensional structures inside the residual, the final reconstruction performance is not very satisfactory. To cope with this problem, we present a weighted nuclear-norm constrained sparse coding (WNCSC) model for position-patch based face hallucination. In addition, an efficient algorithm for the WNCSC is developed using the alternating direction method of multipliers (ADMM) and the method of augmented Lagrange multipliers (ALM). The advantages of the proposed model are twofold: in order to fully make use of low-rank structure information of the reconstruction residual, the weighted nuclear norm is applied to measure the residual matrix, which is able to alleviate the bias between input patches and training data, and it is more robust than the Euclidean distance (2-norm); the more flexible selection method for rank components can determine the optimal combination weights and adaptively choose the relevant and nearest hallucinated neighbors. Finally, experimental results prove that the proposed method outperforms the related state-of-the-art methods in both quantitative and visual comparisons.
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    Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance
    WU Shaochun (吴绍春), PANG Yijie (庞毅杰), SHAO Sen (邵森), JIANG Keyuan (江科元)
    2018, 23 (5):  636-642.  doi: 10.1007/s12204-018-1993-y
    Abstract ( 355 )  
    This paper presents an advanced fuzzy C-means (FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced FCM algorithm combines the distance with density and improves the objective function so that the performance of the algorithm can be improved. The experimental results show that the proposed FCM algorithm requires fewer iterations yet provides higher accuracy than the traditional FCM algorithm. The advanced algorithm is applied to the influence of stars’ box-office data, and the classification accuracy of the first class stars achieves 92.625%.
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    E-Shop Transshipment Selection Evaluation Based on Cloud Model
    LIU Sainan (柳赛男)
    2018, 23 (5):  643-649.  doi: 10.1007/s12204-018-1978-x
    Abstract ( 320 )  
    In e-commerce, a critical problem is how to transship the products between different e-shops to lessen the risk of out of stock, especially in network shopping mall of China. Most of importance is how to select the optimal one in the waiting list of e-shops to make the transshipment economical. However, many factors that influence the effect of transshipment are uncertain and fuzzy, such as customer demand, product price and inventory level. Based on the cloud model theory, a novel method for evaluation of e-shop transshipment selection in the same network shopping mall is proposed to solve the problem. The factors that influence the transshipment effect are discussed in detail. An example of selecting the optimal e-shop to transship products to the out of stock e-shop is given on the basis of the steps of our evaluation calculation. Finally, the result shows that it can effectively optimize transshipment in online shopping economically.
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    Finite-Time Attitude Tracking Control of Spacecraft with Actuator Saturation
    LIU Yueyang (刘岳洋), HU Qinglei (胡庆雷), GUO Lei (郭雷)
    2018, 23 (5):  650-656.  doi: 10.1007/s12204-018-1994-x
    Abstract ( 328 )  
    The attitude tracking control problem of a rigid spacecraft with actuator saturation is investigated in this paper. A finite-time attitude tracking control scheme is presented by incorporating sliding mode control (SMC) and adaptive technique. Specifically, a novel time-varying sliding mode manifold is first developed that aims at regulating the attitude tracking error to equilibrium point within a certain finite time. Moreover, it can be specified a priori by the designer according to the mission requirement. Subsequently, an adaptive controller is derived by using the SMC in conjunction with adaptive technique. The designed controller is capable of ensuring that the state trajectories reach to sliding regime within a finite time, and hence that attitude tracking error can converge to zero in a finite time with the aid of the developed sliding dynamics, despite the presence of exogenous disturbances, unknown inertia properties and saturation nonlinearities. Finally, the simulation experiments are carried out to demonstrate the effectiveness of the proposed control scheme.
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    GNN-CRC: Discriminative Collaborative Representation-Based Classification via Gabor Wavelet Transformation and Nearest Neighbor
    ZHANG Yanghao (张洋豪), ZENG Shaoning (曾少宁), ZENG Wei (曾威), GOU Jianping (苟建平)
    2018, 23 (5):  657-665.  doi: 10.1007/s12204-018-1960-7
    Abstract ( 298 )  
    Collaborative representation-based classification (CRC) is a distance based method, and it obtains the original contributions from all samples to solve the sparse representation coefficient. We find out that it helps to enhance the discrimination in classification by integrating other distance based features and/or adding signal preprocessing to the original samples. In this paper, we propose an improved version of the CRC method which uses the Gabor wavelet transformation to preprocess the samples and also adapts the nearest neighbor (NN) features, and hence we call it GNN-CRC. Firstly, Gabor wavelet transformation is applied to minimize the effects from the background in face images and build Gabor features into the input data. Secondly, the distances solved by NN and CRC are fused together to obtain a more discriminative classification. Extensive experiments are conducted to evaluate the proposed method for face recognition with different instantiations. The experimental results illustrate that our method outperforms the naive CRC as well as some other state-of-the-art algorithms.
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    Proxy Re-Encryption Based Multi-Factor Access Control for Ciphertext in Cloud
    SU Mang (苏铓), WANG Liangchen (汪良辰), FU Anmin (付安民), YU Yan (俞研)
    2018, 23 (5):  666-670.  doi: 10.1007/s12204-018-1962-5
    Abstract ( 335 )  
    Cloud computing provides a wide platform for information sharing. Users can access data and retrieve service easily and quickly. Generally, the data in cloud are transferred with encrypted form to protect the information. As an important technology of cloud security, access control should take account of multi-factor and ciphertext to satisfy the complex requirement for cloud data protection. We propose a proxy re-encryption (PRE) based multi-factor access control (PMAC) for cipher text in the above background. The PMAC adapts to the privacy and the protection of data confidently. We explain the motivation and some assumptions of PMAC at first. Then we define system model and algorithm. The system model and algorithm show how to create the data with corresponding accessing policy and how to grant and revoke the permission.
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    A New Fusion Chemical Reaction Optimization Algorithm Based on Random Molecules for Multi-Rotor UAV Path Planning in Transmission Line Inspection
    YANG Qing (杨轻), YANG Zhong (杨忠), HU Guoxiong (胡国雄), DU Wei (杜威)
    2018, 23 (5):  671-677.  doi: 10.1007/s12204-018-1981-2
    Abstract ( 294 )  
    A fusion chemical reaction optimization algorithm based on random molecules (RMCRO) is proposed to meet the special demand of power transmission line inspection. This new algorithm improves the shortcomings of chemical reaction algorithm by merging the idea of repellent-attractant rule and accelerates convergence by using difference algorithm. The molecules in this algorithm avoid obstacles and search optimal path of transmission line inspection by using sensors on multi-rotor unmanned aerial vehicle (UAV). The option of optimal path is based on potential energy of molecules and cost function without repeated parameter adjustment and complicated computation. By compared with an improved particle swarm optimization (IMPSO) in different circumstances of simulation, it can be concluded that the new algorithm presented not only can obtain more optimal path and avoid to trap in local minimum, but also can keep related sensors in a more stable status.
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    A Chinese Question Answering System in Medical Domain
    FENG Guofei (冯郭飞), DU Zhikang (杜智康), WU Xing (武星)
    2018, 23 (5):  678-683.  doi: 10.1007/s12204-018-1982-1
    Abstract ( 321 )  
    Question answering systems offer a friendly interface for human beings to interact with massive online information. It is time consuming for users to retrieve useful medical information with search engines among massive online websites. An effort is made to build a Chinese Question Answering System in Medical Domain (CQASMD) to provide useful medical information for users. A large medical knowledge base with more than 300 thousand medical terms and their descriptions is firstly constructed to store the structured medical knowledge data, and classified with the FastText model. Furthermore, a Word2Vec model is adopted to capture the semantic meanings of words, and the questions and answers are processed with sentence embedding to capture semantic context information. Users’ questions are firstly classified and processed into a sentence vector and a matching algorithm is adopted to match the most similar question. After querying the constructed medical knowledge base, the corresponding answers to previous questions are responded to users. The architecture and flowchart of CQASMD is proposed, which will play an important role in self disease diagnosis and treatment.
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    Two-Factor Fuzzy Time Series for Equipment Data Prediction
    HE Shanhong (何善红), RONG Baizhong (荣百中), QU Meng (瞿勐), WANG Shuangfei (王双飞), LI Huanhuan (李欢欢), WANG Fengyang (王冯阳), WU Jin (吴进)
    2018, 23 (5):  684-690.  doi: 10.1007/s12204-018-1983-0
    Abstract ( 332 )  
    The data forecasting of plant equipment plays an important role in assurance of the safe and reliable operation of the plant equipment. Thus, it is necessary to improve the accuracy of data forecasting of the equipment. A new two-factor fuzzy time series algorithm is proposed to forecast the data of the plant equipment. This method not only overcomes the limitations of one factor fuzzy time series algorithm, but also overcomes the drawbacks of traditional two-factor fuzzy time series algorithm. The collected data is used in the power plant to conduct experiments, where the metrics is Mean Absolute Percentage Error (MAPE). The results show that this method is superior to the existing two-factor fuzzy time series algorithms, and yields good results in the equipment prediction.
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    Discretization Algorithm Based on Particle Swarm Optimization and Its Application in Attributes Reduction for Fault Data
    ZHENG Bo (郑波), LI Yanfeng (李彦锋), FU Guozhong (付国忠)
    2018, 23 (5):  691-695.  doi: 10.1007/s12204-018-1964-3
    Abstract ( 294 )  
    In order to increase the fault diagnosis efficiency and make the fault data mining be realized, the decision table containing numerical attributes must be discretized for further calculations. The discernibility matrix-based reduction method depends on whether the numerical attributes can be properly discretized or not. So a discretization algorithm based on particle swarm optimization (PSO) is proposed. Moreover, hybrid weights are adopted in the process of particles evolution. Comparative calculations for certain equipment are completed to demonstrate the effectiveness of the proposed algorithm. The results indicate that the proposed algorithm has better performance than other popular algorithms such as class-attribute interdependence maximization (CAIM) discretization method and entropy-based discretization method.
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    Fault Diagnosis of Rolling Element Bearing Using Multi-Scale Lempel-Ziv Complexity and Mahalanobis Distance Criterion
    YU Kun (俞昆), TAN Jiwen (谭继文), LIN Tianran (林天然)
    2018, 23 (5):  696-701.  doi: 10.1007/s12204-018-1965-2
    Abstract ( 343 )  
    A new fault diagnosis technique for rolling element bearing using multi-scale Lempel-Ziv complexity (LZC) and Mahalanobis distance (MD) criterion is proposed in this study. A multi-scale coarse-graining process is used to extract fault features for various bearing fault conditions to overcome the limitation of the single stage coarse-graining process in the LZC algorithm. This is followed by the application of MD criterion to calculate the accuracy rate of LZC at different scales, and the best scale corresponding to the maximum accuracy rate is identified for fault pattern recognition. A comparison analysis with Euclidean distance (ED) criterion is also presented to verify the superiority of the proposed method. The result confirms that the fault diagnosis technique using a multi-scale LZC and MD criterion is more effective in distinguishing various fault conditions of rolling element bearings.
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    Inspection-Based Policy Considering Human Errors for Three-Stage Delay Time Degradation Systems
    ZHAO Fei (赵斐), LIU Xuejuan (刘学娟), PENG Rui (彭锐)
    2018, 23 (5):  702-706.  doi: 10.1007/s12204-018-1966-1
    Abstract ( 298 )  
    A periodic inspection policy for a single component system based on a three-stage failure process is proposed, and two different kinds of failures covering “hard” and “human” are considered in the proposed policy. The system is periodically inspected and inspections are perfect so that they can identify the intended defect. If the severe defect is detected by an inspection, an immediate repair is needed. However, once the system is identified to be in the minor defective state, there are two options. The first is to do nothing till the arrival of identifying the severe defect or hard failure, and the second is to repair immediately. Repair for any defect can renew the system with a limited probability such that the system may fail after repair due to human errors, which is common in many industrial applications. Two models are constructed by minimizing the expected cost per unit time and compared. We provide a numerical example to demonstrate the proposed model.
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    Reliability Analysis of Systems with Common Cause Failure Based on Stress-Strength Interference Model
    ZHANG Shuai (张帅), Lü Ruoning (吕若宁), SI Shubin (司书宾), REN Fangyu (任方宇)
    2018, 23 (5):  707-710.  doi: 10.1007/s12204-018-1968-z
    Abstract ( 326 )  
    Many mechanical systems have the characteristics of multiple failure modes and complex failure mechanisms. On the basis of stress-strength interference (SSI) model, this paper takes the mechanical system with common cause failure (CCF) as the research object. The relationship between the stress distribution and the strength distribution is studied, and the failures of components are independent of each other under the deterministic stress. Then, the concept of conditional reliability is introduced to build the system reliability models under the action of one-stress and multi-stress for both series and parallel systems. Finally, the corresponding properties of the proposed methods are discussed to show their advantages.
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    Research on Multi-Parameter Evaluation of Electric Vehicle Power Battery Consistency Based on Principal Component Analysis
    WANG Liye (王立业), WANG Lifang (王丽芳), LIAO Chenglin (廖承林), ZHANG Wenjie (张文杰)
    2018, 23 (5):  711-720.  doi: 10.1007/s12204-018-1987-9
    Abstract ( 280 )  
    Electric vehicle power battery consistency is the key factor affecting the performance of power batteries. it is not scientific to evaluate the consistency of the battery depending on voltage or capacity. In this paper, multiparameter evaluation method for battery consistency based on principal component analysis is proposed. Firstly, the characteristic parameters of battery consistency are analyzed, the principal component score can be used as the basis for evaluating the consistency of the battery. Then, the function that multi-parameter evaluation of battery consistency is established. Finally, battery balancing strategy based on fuzzy control is developed. The basic principle of fuzzy control is to fuzzy the input quantity based on expert knowledge, and the fuzzy control quantity is obtained by fuzzy control rule. The results are verified by test.
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