Table of Content

    31 May 2018, Volume 23 Issue 3 Previous Issue    Next Issue

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    After-Sale Data Based Common Rail Injector Remanufacturability Analysis
    ZHANG Jihao (张吉浩), CHEN Ming(陈铭)
    2018, 23 (3):  337.  doi: 10.1007/s12204-018-1948-3
    Abstract ( 312 )  
    The injector is the most critical and vulnerable part of a diesel engine, and injector remanufacturing seems to be a solution to change the adverse market conditions. For remanufactured injector, there are some di.erences between China and developed countries. These di.erences make a di.erent remanufacturability. New remanufacturing process should be evaluated to determine the feasibility. Fuqiang Power Company's experiment and the survey from Bosch Diesel Centers (BDCs) show the status of the used injectors that nearly one-third of the used injector can be remanufactured with low cost. It is feasible to produce quality remanufactured injector under given process. Promoting clean diesel engine is more likely to be accepted by the consumer.
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    Design and Parametric Investigation of Horizontal Axis Wind Turbine
    ABBAS Zulkarnain, ABBAS Saqlain, BUTT Zubair, PASHA Riffat Asim
    2018, 23 (3):  345.  doi: 10.1007/s12204-018-1949-2
    Abstract ( 313 )  
    This research focuses on design and calculations for the horizontal axis wind turbine to fulˉll energy demands at small scales in Pakistan. This is the design to produce about 5 kilowatts of electricity to share the load of average home appliances. Area chosen for this research is Pasni, Balochistan in Pakistan to build the wind turbine for electricity. Design values are approximated by appropriate formulas of wind energy design. In current research, turbine blade proˉle is designed by blade element momentum (BEM) theory. Warlock wind turbine calculator is used to verify the design parameters like wind speed, tip speed ratio (TSR) and e±ciency factor. E?ects of wind speed, wind power, TSR, pitch angle, blade tip angle, number of blades, blade design and tower height on power coe±cient are analyzed in this research. Maximum power coe±cient is achieved at a designed velocity of 6 m/s. Design analysis is also performed on simulation software ANSYS Fluent. It is observed that designed velocity parameter of this research is very suitable for the turbine blade, so blade designing is perfect according to wind speed range.
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    Modeling Nd3+-Yb3+-Tm3+-Er3+ Codoped Telluride Glass Fiber for 0.4 to 2.0 μm Emission Spectra
    NKONDE Sampa, JIANG Chun (姜淳)
    2018, 23 (3):  352.  doi: 10.1007/s12204-018-1950-9
    Abstract ( 309 )  
    The modeling of rare-earth-doped fiber amplifier is accomplished by utilizing the rate and propagation equations of distinct levels for a laser medium. A complex theoretical model for neodymium (Nd3+), erbium (Er3+), thulium (Tm3+) and ytterbium (Yb3+) codoped telluride glass fiber covering 0.4|2.0 μm emission spectra is presented. The emission spectra of Nd3+-Er3+-Tm3+-Yb3+ codoped telluride fiber are realized with the excitation of both 808 and 980nm lasers pumped at 500mW. Numerical methods are used to calculate the emission spectra covering 0.4|2.0 μm. With the Nd3+, Tm3+ and Yb3+ ion concentrations fixed at 2 £ 1020 ion/m3, the Er3+ ion concentration optimized to 8 £ 1020 ion/m3 and the ˉber length spanning from 0.5 to 2 m, a peak amplified spontaneous emission (ASE) power of 19.8mW is attainable, and a minimum ASE power of 7.96mW can also be achieved. The analytical techniques and results indicate that when a telluride codoped fiber with suitable ion concentrations of Nd3+, Er3+, Tm3+ and Yb3+ is excited by both 980 and 808nm pump lasers, 0.4|2.0 μm emission spectra are attainable for vast optical applications.
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    Branch-Activated Multi-Domain Convolutional Neural Network for Visual Tracking
    CHEN Yimin (陈一民), LU Rongrong (陆蓉蓉), ZOU Yibo (邹一波), ZHANG Yanhui (张燕辉)
    2018, 23 (3):  360.  doi: 10.1007/s12204-018-1951-8
    Abstract ( 409 )  
    Convolutional neural networks (CNNs) have been applied in state-of-the-art visual tracking tasks to represent the target. However, most existing algorithms treat visual tracking as an object-speciˉc task. Therefore, the model needs to be retrained for di?erent test video sequences. We propose a branch-activated multi-domain convolutional neural network (BAMDCNN). In contrast to most existing trackers based on CNNs which require frequent online training, BAMDCNN only needs o2ine training and online fine-tuning. Speciˉcally, BAMDCNN exploits category-specific features that are more robust against variations. To allow for learning category-specific information, we introduce a group algorithm and a branch activation method. Experimental results on challenging benchmark show that the proposed algorithm outperforms other state-of-the-art methods. What's more, compared with CNN based trackers, BAMDCNN increases tracking speed.
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    TDOA Passive Location Based on Cuckoo Search Algorithm
    JIANG Yilin (蒋伊琳), LIU Mengnan (刘梦楠), CHEN Tao (陈涛), GAO Lipeng (郜丽鹏)
    2018, 23 (3):  368.  doi: 10.1007/s12204-018-1952-7
    Abstract ( 401 )  
    Abstract: This paper formulates a new framework to estimate the target position by adopting cuckoo search (CS) positioning algorithm. Addressing the nonlinear optimization problem is a crucial spot in the location system of time di?erence of arrival (TDOA). With the application of the Levy °ight mechanism, the preferential selection mechanism and the elimination mechanism, the proposed approach prevents positioning results from falling into local optimum. These intelligent mechanisms are useful to ensure the population diversity and improve the convergence speed. Simulation results demonstrate that the cuckoo localization algorithm has higher locating precision and better performance than the conventional methods. Compared with particle swarm optimization (PSO) algorithm and Newton iteration algorithm, the proposed method can obtain the Cram?er-Rao lower bound (CRLB) and quickly achieve the global optimal solutions.
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    Adaptive Sliding-Mode Tracking Control for an Uncertain Nonlinear SISO Servo System with a Disturbance Observer
    YUE Caichenga (岳才成), CHEN Hongbina (陈红彬), QIAN Linfanga (钱林方), KONG Jianshoub (孔建寿)
    2018, 23 (3):  376.  doi: 10.1007/s12204-018-1953-6
    Abstract ( 369 )  
    An adaptive sliding mode controller with a disturbance observer (ASMC-DO) is proposed for the control of a single-input and single-output (SISO) servo system which has uncertain parameters, nonlinear friction, disturbance and input saturation. It is di±cult to choose the suitable value of the parameters. The newly designed adaptive method is used to reduce the e?ects of system time-varying parameters, such as the moment of inertia and the damp coe±cient. The robustness of object is improved. A DO is selected to approximate the compound disturbance and to render the estimate error convergent in ˉnite time. The stability and the convergence of the closed-loop system are proved by using the Lyapunov theory. Experimental results show that the proposed ASMC-DO can better satisfy the in°uence of variable parameters and external disturbance to the control precision of the SISO servo system than other two controllers. The e?ectiveness of the proposed controller is showed. The control input stability and robust performances of the input saturation system are enhanced and the chattering is reduced.
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    Gradient-Based Low Rank Method for Highly Undersampled Magnetic Resonance Imaging Reconstruction
    XU Xiaoling (徐晓玲), LIU Yiling (刘沂玲), LIU Qiegen (刘且根),LU Hongyang (卢红阳), ZHANG Minghui (张明辉)
    2018, 23 (3):  384.  doi: 10.1007/s12204-018-1927-8
    Abstract ( 340 )  
    Recently, exploiting low rank property of the data accomplished by the non-convex optimization has shown great potential to decrease measurements for compressed sensing. In this paper, the low rank regularization is adopted to gradient similarity minimization, and applied for highly undersampled magnetic resonance imaging (MRI) reconstruction, termed gradient-based low rank MRI reconstruction (GLRMRI). In the proposed method, by incorporating the spatially adaptive iterative singular-value thresholding (SAIST) to optimize our gradient scheme, the deterministic annealing iterates the procedure e±ciently and superior reconstruction performance is achieved. Extensive experimental results have consistently demonstrated that GLRMRI recovers both real- valued MR images and complex-valued MR data accurately, especially in the edge preserving perspective, and outperforms the current state-of-the-art approaches in terms of higher peak signal to noise ratio (PSNR) and lower high-frequency error norm (HFEN) values.
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    Research of Clinical Named Entity Recognition Based on Bi-LSTM-CRF
    QIN Ying (秦颖), ZENG Yingfei (曾颖菲)
    2018, 23 (3):  392.  doi: 10.1007/s12204-018-1954-5
    Abstract ( 418 )  
    Electronic Medical Records (EMR) with unstructured sentences and various conceptual expressions provide rich information for medical information extraction. However, common Named Entity Recognition (NER) in Natural Language Processing (NLP) are not well suitable for clinical NER in EMR. This study aims at applying neural networks to clinical concept extractions. We integrate Bidirectional Long Short-Term Memory Networks (Bi-LSTM) with a Conditional Random Fields (CRF) layer to detect three types of clinical named entities. Word representations fed into the neural networks are concatenated by character-based word embeddings and Contin- uous Bag of Words (CBOW) embeddings trained both on domain and non-domain corpus. We test our NER system on i2b2/VA open datasets and compare the performance with six related works, achieving the best result of NER with F1 value 0.853 7. We also point out a few speciˉc problems in clinical concept extractions which will give some hints to deeper studies.
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    Block Principle Component Analysis with Lp-norm for Robust and Sparse Modelling
    TANG Ganyi (唐肝翌), LU Guifu (卢桂馥)
    2018, 23 (3):  398.  doi: 10.1007/s12204-018-1955-4
    Abstract ( 360 )  
    Block principle component analysis (BPCA) is a recently developed technique in computer vision and pattern classiˉcation. In this paper, we propose a robust and sparse BPCA with Lp-norm, referred to as BPCALp-S, which inherits the robustness of BPCA-L1 due to the employment of adjustable Lp-norm. In order to perform a sparse modelling, the elastic net is integrated into the objective function. An iterative algorithm which extracts feature vectors one by one greedily is elaborately designed. The monotonicity of the proposed iterative procedure is theoretically guaranteed. Experiments of image classiˉcation and reconstruction on several benchmark sets show the e?ectiveness of the proposed approach.
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    Research on Web Page Classification Method Based on Query Log
    YE Feiyue (叶飞跃), MA Yixing (马祎星)
    2018, 23 (3):  404.  doi: 10.1007/s12204-017-1899-0
    Abstract ( 282 )  
    Web page classification is an important application in many fields of Internet information retrieval, such as providing directory classiˉcation and vertical search. Methods based on query log which is a light weight version of Web page classiˉcation can avoid Web content crawling, making it relatively high in e±ciency, but the sparsity of user click data makes it di±cult to be used directly for constructing a classifier. To solve this problem, we explore the semantic relations among di?erent queries through word embedding, and propose three improved graph structure classification algorithms. To re°ect the semantic relevance between queries, we map the user query into the low-dimensional space according to its query vector in the ˉrst step. Then, we calculate the uniform resource locator (URL) vector according to the relationship between the query and URL. Finally, we use the improved label propagation algorithm (LPA) and the bipartite graph expansion algorithm to classify the unlabeled Web pages. Experiments show that our methods make about 20% more increase in F1-value than other Web page classification methods based on query log.
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    Theoretical, Simulation and Experimental Analysis of Microfluidic Droplet Generation and Recovering Process with Applications in Frying Oil Assessments
    SHI Xinqun (史心群), DENG Ning (邓宁), WANG Zhiheng (王志恒), CAO Ning (曹宁), CHEN Jinbo (陈金波), GE Ji (葛继), WU Zhizheng (吴智政), LIU Mei (刘梅)
    2018, 23 (3):  411.  doi: 10.1007/s12204-018-1930-0
    Abstract ( 344 )  
    he research on micro°uidic droplet size prediction has been extensive and fruitful, while the droplet deforming process has been seldom studied. In this paper, a frying-oil-assessing micro°uidic device was designed to study the droplet deforming and recovering processes, which were dominated by channel geometry, °ow rates, sheath °ow viscosity and interfacial tension of the two phases. Theoretical expressions of the deforming process and its extreme value were obtained for the ˉrst time, supported by simulation and experiments. Theoretical, simulation and experimental results indicated that the steady-state droplet length could be a useful parameter for frying oil assessment.
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    Simplified Markov Model for Reliability Analysis of Phased-Mission System Using States Merging Method
    YAN Hua (闫华), GAO Li (高黎), QI Lei (漆磊), WAN Ping (万平)
    2018, 23 (3):  418.  doi: 10.1007/s12204-018-1944-7
    Abstract ( 349 )  
    This paper presents a simpliˉed Markov model to evaluate the reliability of phased-mission system (PMS). The time cost and storage requirement are very huge for traditional Markov model to analyze the PMS reliability as the number of components increases to a large scale. The states merging method proposed in this paper can account for the PMS with subsystems consisting of identical components, and similar PMSs are common in real-world systems. The simpliˉed Markov model by states merging has smaller number of system states, compared with the traditional one. Furthermore, for the above subsystems, the size of our model increases only linearly as the number of components increases, while the size of the traditional model exponentially increases. Finally, the e?ectiveness and correctness of our approach are analyzed by comparing with the traditional Markov method.
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    Hybrid Reliability Parameter Selection Method Based on Text Mining, Frequent Pattern Growth algorithm and Fuzzy Bayesian Network
    SHUAI Yong1;2 (帅勇), SONG Tailiang3 (宋太亮), WANG Jianping1 (王建平), ZHAN Wenbin4 (詹文斌)
    2018, 23 (3):  423.  doi: 10.1007/s12204-018-1945-6
    Abstract ( 274 )  
    Reliability parameter selection is very important in the period of equipment project design and demon- stration. In this paper, the problem in selecting the reliability parameters and their number is proposed. In order to solve this problem, the thought of text mining is used to extract the feature and curtail feature sets from text data ˉrstly, and frequent pattern tree (FPT) of the text data is constructed to reason frequent item-set between the key factors by frequent patter growth (FPG) algorithm. Then on the basis of fuzzy Bayesian network (FBN) and sample distribution, this paper fuzziˉes the key attributes, which forms associated relationship in frequent item-sets and their main parameters, eliminates the subjective in°uence factors and obtains condition mutual information and maximum weight directed tree among all the attribute variables. Furthermore, the hybrid model is established by reason fuzzy prior probability and contingent probability and concluding parameter learning method. Finally, the example indicates the model is believable and e?ective.
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    Research of Springing and Whipping Influence on Ultra-Large Containerships' Fatigue Analysis
    REN Huilong (任慧龙), ZHANG Kaihong (张楷弘), LI Hui (李辉)
    2018, 23 (3):  429.  doi: 10.1007/s12204-018-1956-3
    Abstract ( 335 )  
    Rules of Classiˉcation Societies all around the world have made changes on design wave loads' value and fatigue in°uence factor modiˉcation due to the in°uence of springing and whipping on ultra-large containerships. The paper ˉrstly introduced 3-D linear hydroelastic theory in frequency domain and 3-D nonlinear hydroelastic theory in time domain, considering large amplitude motion nonlinearity and slamming force due to the severe relative motion between ship hull and wave. Then the spectrum analysis method and time domain statistical analysis method were introduced, which can make fatigue analysis under a series of standard steps in frequency and time domain, respectively. Finally, discussions on the in°uence factor of springing and whipping on fatigue damages of 8500TEU and 10000TEU containerships with di?erent loading states were made. The fatigue assessment of di?erent position on the midship section was done on the basis of nominal stress. The fatigue damage due to whipping can be the same as the fatigue damage due to springing and even sometimes can be larger than the springing damage. Besides, some suggestions on calculating load case selection were made to minimize the quantity of work in frequency and time domain. Thus, tools for fatigue in°uence factor modiˉcation were provided to meet the demand of IACS-UR.
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    Product Quality Evaluation Method Based on Product Gene Theory
    LI He (李贺), HUANG Hongzhong (黄洪钟), YIN Yichao (殷毅超), ZHANG Kaiyan (张凯延), HUANG Peng (黄鹏)
    2018, 23 (3):  438.  doi: 10.1007/s12204-018-1946-5
    Abstract ( 386 )  
    Traditional quality inspection based product quality evaluation method with complex process has high operating cost and requires more professional knowledge. To remove the above limitation, this paper leads product gene theory into product quality evaluation. Methods of quality in°uencing factors based modeling and encoding are established. Combined with similarity theory and product gene theory, a product gene similarity analysis based quality evaluation method is proposed. The proposed method is low cost, operates easily and requires less specialized knowledge. A case study is conducted to prove the correctness and feasibility of this method.
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    Dynamic Behavior of Two Part Towing Cable System During Turning
    GAO Hui1 (高慧), WANG Zhibo2 (王志博)
    2018, 23 (3):  444.  doi: 10.1007/s12204-018-1928-7
    Abstract ( 317 )  
    An improved numerical method is used to simulate the dynamic behavior of a two part towing cable systems during turnings. In U turns and full turns, periodical heave motions are found both for the towed vehicle and for the depressor. Periodic motions of the subsea units and of the cable surface tension are closely related to the turning parameters, such as turning velocity and turning radius. System parameters, such as length of the second cable and the vehicle hydrodynamics, also damp turning instability.
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    Reliability Analysis of Filtering Reducers Considering Temperature Correction and Shock Load of Space
    ZHANG Xiaoqiang1 (张小强), GAO Huiying2 (高会英),FU Guozhong1 (付国忠), HUANG Hongzhong1 (黄洪钟)
    2018, 23 (3):  456.  doi: 10.1007/s12204-018-1947-4
    Abstract ( 263 )  
    Owing to some excellent properties (such as large transmission ratio, high reliability, high precision, large sti?ness, small volume and long service life), ˉltering reducers are suitable for occasions with high require- ments (e.g. space equipment). However, the tough working condition in space (including frequent changes of temperature, and large temperature di?erence combined with shock loads) may signiˉcantly a?ect the reliability. In this paper, the expressions of the minimal and maximal instantaneous transmission ratios (ITRs) considering temperature correction are developed. The minimal and maximal ITRs from simulation are used to verify the accuracy. Moreover, the reliability of a ˉltering reducer under di?erent temperatures and the shock load are calculated, respectively. The research is beneˉcial to the design of spacecraft mechanism.
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