Table of Content

    01 April 2018, Volume 23 Issue 2 Previous Issue    Next Issue

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    Robust Fuzzy Sampled-Data Control for Dynamic Positioning Ships
    ZHENG Minjie (郑敏杰), ZHOU Yujie (周玉洁), YANG Shenhua (杨神化)
    2018, 23 (2):  209-217.  doi: 10.1007/s12204-018-1931-z
    Abstract ( 419 )  
    A robust H∞ sampled-data stabilization problem for nonlinear dynamic positioning (DP) ships with Takagi-Sugeno (T-S) fuzzy models is discussed in this paper. Input delay approach is used to convert the sampleddata DP ship system to a fuzzy system with time-varying delay. Adequate conditions are derived to determine the system’s asymptotical stability and achieve H∞ performance via Lyapunov stability theorems. Then, the fuzzy sampled-data controller is obtained by analyzing the stabilization condition. Simulation result shows that the proposed method and the designed controller for a DP ship are effective so that the DP ship can maintain the desired position, heading and velocities in the existence of varying environment disturbances.
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    Stability Analysis of a Force-Aided Lever Actuation System for Dry Clutches with Negative Stiffness Element
    LIU Fengyu (刘峰宇), CHEN Li (陈俐), FANG Chengliang (房成亮), YIN Chengliang (殷成良)
    2018, 23 (2):  218-226.  doi: 10.1007/s12204-018-1932-y
    Abstract ( 460 )  
    A force-aided lever with a preload spring is not only force-saving but also energy-saving. Therefore, it has great potential to be applied to dry clutch actuations. However, the negative stiffness of the clutch diaphragm spring introduces unstable dynamics which becomes more intensive due to the preload spring. In order to explore the intensified unstability, this paper builds dynamic models for the rotating lever coupling a negative stiffness diaphragm spring and a preload spring. The stability analysis using the Routh-Huiwitz criterion shows that the open-loop system can never be stable due to the negative stiffness. Even if the diaphragm spring stiffness is positive, the system is still unstable when the preload of the spring exceeds an upper limit. A proportionalintegral- derivative (PID) closed-loop scheme addressing this problem is designed to stabilize the system. The stability analysis for the closed-loop system shows that stable region emerges in spite of the negative stiffness; the more the negative stiffness is, the less the allowed preload is. Further, the influences of the dimensions and PID parameters on the stability condition are investigated. Finally, the transient dynamic responses of the system subjected to disturbance are compared between the unstable open-loop and stabilized closed-loop systems.
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    Intelligent Costume Recommendation System Based on Expert System
    MAO Qingqing (毛青青), DONG Aihua (董爱华), MIAO Qingying (苗清影), PAN Lu (潘璐)
    2018, 23 (2):  227-234.  doi: 10.1007/s12204-018-1933-x
    Abstract ( 370 )  
    On the basis of expert system, we design a costume recommendation system which provides customers with clothing collocation solution and more experience. We set up a costume matching knowledge base collected from experts, and represent the knowledge with production rules. By analyzing the customers’ specific physical information got through man-machine interface, the proposed system provides customers an intelligent costume recommendation strategy in accordance with blackboard model reasoning. Moreover, index adding algorithm is integrated into the traditional serial blackboard model in the system. Finally, we present experiments which show the search rate is improved significantly.
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    Decomposing and Cluster Refinement Design Method for Application-Specific Network-on-Chips
    MA Jiayi (马嘉翊), HAO Cong (郝聪), WANG Kundong (王坤东)
    2018, 23 (2):  235-243.  doi: 10.1007/s12204-018-1934-9
    Abstract ( 338 )  
    Along with higher and higher integration of intellectual properties (IPs) on a single chip, traditional bus-based system-on-chips (SoC) meets several design difficulties (such as low scalability, high power consumption, packet latency and clock tree problem). As a promising solution, network-on-chips (NoC) has been proposed and widely studied. In this work, a novel algorithm for NoC topology synthesis, which is decomposing and cluster refinement (DCR) algorithm, has been proposed to minimize the total power consumption of application-specific NoC. This algorithm is composed of two stages: decomposing with cluster generation, and cluster refinement. For partitioning and cluster generation, an initial low-power solution for NoC topology is generated. For cluster refinement, the clustering is optimized by performing floorplan to further reduce power consumption. Meanwhile, a good tradeoff between power consumption and CPU time can be achieved. Experimental results show that the proposed method outperforms the existing work.
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    Monte Carlo Based Analysis and Validation Method for Time for Single Vehicle Combat Preparation of Armored Vehicle
    CAO Junhai (曹军海), XING Biao (邢彪), DU Haidong (杜海东), SHEN Ying (申莹)
    2018, 23 (2):  244-249.  doi: 10.1007/s12204-017-1865-x
    Abstract ( 420 )  
    Time for single vehicle combat preparation (TSVCP) is an important characteristic parameter for the operational support feature of armored vehicle. During the development phase, how to validate the TSVCP of armored vehicle through analytic method is a difficult issue in analysis and validation of vehicle supportability. This paper uses Monte Carlo approach and builds a working model for single vehicle combat preparation (SVCP) of armored vehicle, thus realizes the prediction and analysis of the TSVCP of armored vehicle, and finally validates the effectiveness of the approach by example.
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    Research on Equipment Support Activity Process Simulation Based on Monte Carlo Method
    XING Biao (邢彪), SONG Tailiang (宋太亮), CAO Junhai (曹军海),DONG Yuansheng (董原生), LI Kai (李锴)
    2018, 23 (2):  250-255.  doi: 10.1007/s12204-017-1901-x
    Abstract ( 377 )  
    The influencing factors of the equipment support activity process have the characteristics of nonlinearity, high dimension, many constraints, random uncertainty and fuzzy uncertainty. Monte Carlo method can solve the above problems commendably. This paper analyzes the main equipment support activity process and establishes the sampling plan and simulation model of the medium maintenance process based on Monte Carlo method, and the simulation result verifies a fact that the medium maintenance time can be effectively reduced when parallel operation on some procedures is used. It has a practical value and can give good advice to achieve the capability of equipment supportability.
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    Angle Estimation in Monostatic MIMO Radar with Low-Complexity Beamspace Unitary ESPRIT
    XU Liqin (徐丽琴), LI Yong (李勇)
    2018, 23 (2):  256-263.  doi: 10.1007/s12204-018-1935-8
    Abstract ( 363 )  
    A low-complexity angle estimation method for multiple-input multiple-output (MIMO) radar using beamspace unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) is presented. Reduced-dimensional transformation is firstly utilized as a pre-processing to obtain the reduced-dimensional data matrix, and then a conjugate centrosymmetric discrete Fourier transform (DFT) matrix is employed to map the received data into lower-dimensional beamspace and transforms the complex covariance matrix into a realvalued one. At last, the rotational invariance structure of the real-valued signal subspace is constructed in the beamspace to obtain the estimation of direction of arrival (DOA). Compared with the other ESPRIT algorithms, the proposed method can achieve improved estimation performance with a significantly reduced computational complexity. Simulation results are presented to demonstrate the effectiveness of the proposed method.
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    Multi-Component Model of Diesel Sprays Under High Injection Pressure
    WANG Xiaorong (王筱蓉), WANG Jigang (王继刚), JIN Zhangliang (金张良),REN Guilong (任贵龙), MA Hu (马虎)
    2018, 23 (2):  264-268.  doi: 10.1007/s12204-017-1898-1
    Abstract ( 325 )  
    The combustion efficiency of a diesel engine depends not only on spray characteristics but also on fuel-air mixing characteristics. Based on the original spray model, a new spray model is established in this paper to accurately predict the diesel spray, and then a multi-component evaporation model is added into it. The model takes the influence of component concentration gradient and species on its evaporation rate in the liquid phase into account. This paper studies the spray characteristics (spray penetration, spray angle and spray morphology) and fuel-air mixing characteristics (spray area, spray volume and air entrainment mass) using the spray model, and the results are compared with the experimental results. The comparison shows that the simulated spray penetration and spray angle are close to the experimental results with the average deviations less than 3%. Moreover, this paper studies the spray area, spray volume and air entrainment using empirical formula under different conditions. And the maximum deviations of the spray volume, spray area and air entrainment mass are less than 5% as compared with the test values. Overall, this spray model can predict the diesel spray characteristics and fuel-air mixing characteristics under high injection pressure accurately.
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    Research of Adaptive Neighborhood Incremental Principal Component Analysis and Locality Preserving Projection Manifold Learning Algorithm
    DENG Shijie (邓士杰), TANG Liwei (唐力伟), ZHANG Xiaotao (张晓涛)
    2018, 23 (2):  269-275.  doi: 10.1007/s12204-018-1936-7
    Abstract ( 378 )  
    In view of the incremental learning problem of manifold learning algorithm, an adaptive neighborhood incremental principal component analysis (PCA) and locality preserving projection (LPP) manifold learning algorithm is presented, and the incremental learning principle of algorithm is introduced. For incremental sample data, the adjacency and covariance matrices are incrementally updated by the existing samples; then the dimensionality reduction results of the incremental samples are estimated by the dimensionality reduction results of the existing samples; finally, the dimensionality reduction results of the incremental and existing samples are updated by subspace iteration method. The adaptive neighborhood incremental PCA-LPP manifold learning algorithm is applied to processing of gearbox fault signals. The dimensionality reduction results by incremental learning have very small error, compared with those by batch learning. Spatial aggregation of the incremental samples is basically stable, and fault identification rate is increased.
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    A Discrete Bat Algorithm for Disassembly Sequence Planning
    JIAO Qinglong (焦庆龙), XU Da (徐达)
    2018, 23 (2):  276-285.  doi: 10.1007/s12204-018-1937-6
    Abstract ( 361 )  
    Based on the bat algorithm (BA), this paper proposes a discrete BA (DBA) approach to optimize the disassembly sequence planning (DSP) problem, for the purpose of obtaining an optimum disassembly sequence (ODS) of a product with a high degree of automation and guiding maintenance operation. The BA for solving continuous problems is introduced, and combining with mathematical formulations, the BA is reformed to be the DBA for DSP problems. The fitness function model (FFM) is built to evaluate the quality of disassembly sequences. The optimization performance of the DBA is tested and verified by an application case, and the DBA is compared with the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and differential mutation BA (DMBA). Numerical experiments show that the proposed DBA has a better optimization capability and provides more accurate solutions than the other three algorithms.
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    Method for Predicting Crack Initiation Life of Notched Specimen Based on Damage Mechanics
    LIU Jianhui (刘俭辉), WEI Yaobing (韦尧兵), YAN Changfeng (剡昌锋), LANG Shanshan (郎珊珊)
    2018, 23 (2):  286-290.  doi: 10.1007/s12204-017-1900-y
    Abstract ( 358 )  
    Based on the theory of damage mechanics, a method for fatigue crack initiation life prediction of notched components is proposed in this paper. The damage evolution equation of notched specimen under tensioncompression loading is obtained in term of closed-form solution. The crack initiation life of notched specimen is estimated by the proposed method even when material and stress concentration factor are different. It has been verified that the result calculated by the proposed method agrees with the experimental result. The proposed method is concise, effective and feasible to practical application.
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    Reliability Evaluation of Compressor Systems Based on Universal Generating Function Method
    WEN Kai (温凯), LI Yichen (李熠辰), YANG Yang (杨洋), GONG Jing (宫敬)
    2018, 23 (2):  291-296.  doi: 10.1007/s12204-018-1929-6
    Abstract ( 346 )  
    At present, universal generating function (UGF) is a reliability evaluation technique which holds the bare-looking and easily program-realized merits in multi-state system. Thus, it is meaningful to apply this method to an actual industry system. Compressor systems in natural gas pipelines are series-parallel multi-state systems, where the compressor units in each compressor station work in a parallel way and these pressure-boosting stations in the pipeline are series connected. Considering the characteristic of gas pipelines, this paper develops two different UGFs to evaluate the system reliability. One (Model 1) establishes a system model from every compressor unit while the other (Model 2) considers the whole system as a combination of multi-state components. Besides, all the parameters of “weight” in UGFs are obtained from thermal-hydraulic models based on the actual engineering and “probability” from Monte Carlo simulation. The results show that the system reliabilities calculated by different UGFs are approximately equal. In addition, the demand of gas and the gas pipeline transportation system show a reverse trend. Because the number of parameters needed in Model 2 is far less than that needed in Model 1, Model 2 is simpler programming and faster solved.
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    Feature Selection, Deep Neural Network and Trend Prediction
    FANG Yan (方艳)
    2018, 23 (2):  297-307.  doi: 10.1007/s12204-018-1938-5
    Abstract ( 371 )  
    The literature generally agrees that longer-horizon (over a month) predictions make more sense than short-horizon ones. However, it’s an especially challenging task due to the lack of data (in unit of long horizon) and economic data have a low S/N ratio. We hypothesize that the stock trend is largely dictated by driving factors which are filtered by psychological factors and work on behavioral factors: representative indicators from these three aspects would be adequate in trend prediction. We then extend the Stepwise Regression Analysis (SRA) algorithm to constrained SRA (cSRA) to carry out a further feature selection and lag optimization. During modeling stage, we introduce the Deep Neural Network (DNN) model in stock prediction under the suspicion that economic interactions are too complex for shallow networks to capture. Our experiments indeed show that deep structures generally perform better than shallow ones. Instead of comparing to a kitchen sink model, where over-fitting can easily happen with a shortage of data, we turn around and use a model ensemble approach which indirectly demonstrates our proposed method is adequate.
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    Damage Detection for Simply Supported Bridge with Bending Fuzzy Stiffness Consideration
    ZHOU Yu (周宇), DI Shengkui (狄生奎), XIANG Changsheng (项长生), WANG Lixian (王立宪)
    2018, 23 (2):  308-319.  doi: 10.1007/s12204-018-1939-4
    Abstract ( 319 )  
    The benchmark of a simply supported beam with damage and bending fuzzy stiffness consideration is established to be utilized for damage detection. The explicit expression describing the Rotational Angle Influence Lines (RAIL) of the arbitrary section in the benchmark is presented as the nonlinear relation between the moving load and the RAIL appeared, when the moving load is located on the damage area. The damage detection method is derived based on the Difference of the RAIL Curvature (DRAIL-C) prior to and following arbitrarily section damage in a simply supported beam with bending fuzzy stiffness consideration. The results demonstrate that the damage position can be located by the DRAIL-C graph and the damage extent can be calculated by the DRAIL-C curve peak. The simply supported box girder as a one-dimensional model and the simply supported truss bridge as a three-dimensional model with the bending fuzzy stiffness are simulated for the validity of the proposed method to be verified. The measuring point position and noise intensity effects are discussed in the simply supported box girder example. This paper provides a new consideration and technique for the damage detection of a simply supported bridge with bending fuzzy stiffness consideration.
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    Impacts of CaO Solid Particles in Carbon Dioxide Absorption Process from Ship Emission with NaOH Solution
    WANG Zhongcheng (王忠诚), LIU Xiaoyu (刘晓宇), ZHOU Peilin (周培林), XU Leping (许乐平)
    2018, 23 (2):  320-326.  doi: 10.1007/s12204-018-1940-y
    Abstract ( 413 )  
    CO2 emitted from ship exhaust is one of the major sources of atmospheric pollution. In order to reduce ship CO2 emissions, this paper comes up with the idea of recovering CO2 from ship exhaust by NaOH solution and improves the absorption rate by adding CaO solid particles. The effect mechanism of CaO solid particles on CO2 absorption efficiency is analyzed in detail, and the mathematical model is deduced and the CaO enhancement factor is calculated through experiments. Experiment result demonstrates that the effect of CaO solid particles on the absorption of CO2 in alkali solution is significant. The absorption rate of pure CO2 gas, the simulated ship exhaust gas and 6135AZG marine diesel engine emission can be increased by 10%, 15.85% and 10.30%, respectively. So it can be seen that CaO solid particles play an important role in improving the absorption efficiency of ship CO2 emission.
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    Research on Multi-Objective Real-Time Optimization of Automatic Train Operation (ATO) in Urban Rail Transit
    HE Tong (何彤), XIONG Ruiqi (熊瑞琦)
    2018, 23 (2):  327-335.  doi: 10.1007/s12204-018-1941-x
    Abstract ( 405 )  
    The determination and optimization of Automatic Train Operation (ATO) control strategy is one of the most critical technologies for urban rail train operation. The practical ATO optimal control strategy must consider many goals of the train operation, such as safety, accuracy, comfort, energy saving and so on. This paper designs a set of efficient and universal multi-objective control strategy. Firstly, based on the analysis of urban rail transit and its operating environment, the multi-objective optimization model considering all the indexes of train operation is established by using multi-objective optimization theory. Secondly, Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve the model, and the optimal speed curve of train running is generated. Finally, the intelligent controller is designed by the combination of fuzzy controller algorithm and the predictive control algorithm, which can control and optimize the train operation in real time. Then the robustness of the control system can ensure and the requirements for multi-objective in train operation can be satisfied.
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    Erratum to: Novel Electrically Stimulated Catalytic Converter Prototype for Replacement of Conventional Auto Exhaust Emission Converters
    HAMADE Thomas A.
    2018, 23 (2):  336-336.  doi: 10.1007/s12204-018-1942-9
    Abstract ( 328 )  
    The original version of this article unfortunately contained errors in captions of Fig. 13. The corrections are given below. Figure 13 should be...
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