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    31 March 2017, Volume 22 Issue 2 Previous Issue    Next Issue

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    Magnetic Field Tuning Characteristics of Bimodal Ultrasonic Motor Stator
    LI Chong (李冲), LU Cunyue* (鹿存跃), MA Yixin (马艺馨)
    2017, 22 (2):  129-132.  doi: 10.1007/s12204-017-1811-y
    Abstract ( 386 )  

    The magnetic field tuning characteristics of an ultrasonic motor (USM) stator are discussed. The stator consists of two piezoelectric ceramic transducer (PZT) plates and one sandwiched-in Terfenol-D plate. The dimensions of the stator are carefully adjusted to specifically discuss the influence of the magnetic field on the frequency difference between the longitudinal and bending modes of the stator. The frequency difference discussed in this paper is usually small and mainly caused by uneven materials, machining errors and changes in external conditions (temperature, pre-stress or load). The longitudinal and bending modes of the stator are simultaneously excited by an external electric field to generate the elliptic motion trajectories of the driving points. A direct current (DC) magnetic field is applied to decrease the difference between the two mode frequencies of the fabricated stator. In experiments, the dependences of the two mode frequencies and their difference on DC magnetic fields are all investigated. The experimental results indicate that the difference between the longitudinal and bending mode frequencies of the PZT/Terfenol-D/PZT composite stator can be tuned by changing the intensity of the external DC magnetic field.

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    Performance Evaluation of Energy Meters Under Nonlinear Loads
    GUL Mehr1,2*, TAI Nengling1 (邰能灵), MAHAR Mukhtiar Ahmed3,HUANG Wentao1 (黄文焘), LARIK Abdul Sattar3
    2017, 22 (2):  133-138.  doi: 10.1007/s12204-017-1812-x
    Abstract ( 445 )  

    Due to rapid advancements in power electronics, the utilization of electronically switched loads and nonlinear loads is increasing gradually in the electrical power system. These loads create problems of measuring instruments, when connected to the power distribution systems. In this paper, an experimental investigation has been carried out to analyze the performance of single-phase watt-hour (induction and electronic types) energy meters that are being used in Pakistan. The accuracy of the energy meters has been tested under different household nonlinear loads, at various power factors and also at different supply voltage levels. Power factor and total harmonic distortion (THD) of different household loads are also recorded in the experimental work. A hardware based experimental setup has been designed to perform the experimental work. The experimental results have been compared to Water and Power Development Authority (WAPDA) standards for energy meters.

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    Automatically Finding the Number of Clusters Based on Simulated Annealing
    YANG Zhengwu (杨政武), HUO Hong (霍宏), FANG Tao*(方涛)
    2017, 22 (2):  139-147.  doi: 10.1007/s12204-017-1813-9
    Abstract ( 415 )  

    Based on simulated annealing (SA), automatically finding the number of clusters (AFNC) is proposed in this paper to determine the number of clusters and their initial centers. It is a simple and automatic method that combines local search with two widely-accepted global analysis techniques, namely careful-seeding (CS) and distance-histogram (DH). The procedure for finding a cluster is formulated as mountain-climbing, and the mountain is defined as the convergent domain of SA.When arriving at the peak of one mountain, AFNC has found one of the clusters in the dataset, and its initial center is the peak. Then, AFNC continues to climb up another mountain from a new starting point found by CS till the termination condition is satisfied. In the procedure of climbing-up mountain, the local dense region for searching the next state of SA is found by analyzing the distance histogram. Experimental results show that AFNC can achieve consistent performance for a wide range of datasets.

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    Sensorimotor Self-Learning Model Based on Operant Conditioning for Two-Wheeled Robot
    ZHANG Xiaoping1,2* (张晓平), RUAN Xiaogang1 (阮晓钢), XIAO Yao1 (肖尧), HUANG Jing1 (黄静)
    2017, 22 (2):  148-155.  doi: 10.1007/s12204-017-1814-8
    Abstract ( 401 )  

    Traditional control methods of two-wheeled robot are usually model-based and require the robot’s precise mathematic model which is hard to get. A sensorimotor self-learning model named SMM TWR is presented in this paper to handle these problems. The model consists of seven elements: the discrete learning time set, the sensory state set, the motion set, the sensorimotor mapping, the state orientation unit, the learning mechanism and the model’s entropy. The learning mechanism for SMM TWR is designed based on the theory of operant conditioning (OC), and it adjusts the sensorimotor mapping at every learning step. This helps the robot to choose motions. The leaning direction of the mechanism is decided by the state orientation unit. Simulation results show that with the sensorimotor model designed, the robot is endowed the abilities of self-learning and self-organizing, and it can learn the skills to keep itself balance through interacting with the environment.

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    Development of a Wireless Capsule Endoscope System Based on Field Programmable Gate Array
    LI Siqing*(李四青), LIU Hua (刘华)
    2017, 22 (2):  156-160.  doi: 10.1007/s12204-017-1815-7
    Abstract ( 386 )  
    A new modular and programmable wireless capsule endoscope is presented in this paper. The capsule system consumes low power and has small physical size. A new image compression algorithm is presented in this paper to reduce power consumption and silicon area. The compression algorithm includes color space transform, uniform quantization, sub-sampling, differential pulse code modulation (DPCM) and Golomb-Rice code. The algorithm is tested in a field programmable gate array (FPGA) development board, and the final result achieves 80% compression rate at 40 dB peak signal to noise ratio (PSNR). The algorithm has high image compression efficiency and low power consumption, compared to other existing works. The system is composed of the following three parts: image capsule endoscope, portable wireless receiver and host computer software. The software and hardware design of the three parts are disscussed in details.
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    Joint Optimization of Spectral and Energy Efficiency for Multi-Pair Full-Duplex Two-Way Relay Networks with Imperfect Channel State Information
    GE Jia (葛佳), QIU Mengting (邱梦婷), YU Hui* (俞晖)
    2017, 22 (2):  161-166.  doi: 10.1007/s12204-017-1816-6
    Abstract ( 328 )  
    An iterative algorithm is proposed for jointly optimizing spectral and energy efficiency in a multipair full-duplex (FD) two-way relaying (TWR) system with imperfect channel state information (CSI). Based on Dinkelbach method, a Taylor expansion based approximation method and the Generalized Lagrange Multiplier Method have been applied iteratively to obtain the near optimal relay amplified matrix and power allocation, respectively. And the simulation results illustrate the effectiveness of the proposed algorithm and the algorithm can converge quickly.
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    Optimization of CMOS Repeater Driven Interconnect RC Line Using Genetic Algorithm
    KAUR Jasmeet*, GILL Sandeep Singh, KAUR Navneet
    2017, 22 (2):  167-172.  doi: 10.1007/s12204-017-1817-5
    Abstract ( 315 )  
    In this work, optimization of complementary metal oxide semiconductor (CMOS) repeater driven interconnect resistive-capacitive (RC) line is carried out using genetic algorithm (GA). This work is aimed at powerdelay- product (PDP) minimization of RC interconnect at 180 nm technology node. The algorithm simultaneously optimizes the values of supply voltage, number of repeaters and repeater width for delay and PDP minimization. The accuracy of results obtained is verified by simulations from Cadence virtuoso tool. For delay minimization, comparison of GA results with previous results of the literature shows an improvement of 44.4% in the value of the optimal number of repeaters required. This improvement is obtained by increasing the repeater size, which also increases power dissipation, so a tradeoff has also been achieved in terms of PDP minimization. The comparison of PDP results obtained in this work, with the results at 70, 100, and 130 nm technologies from literature shows improvement in optimal number of repeaters required. The results of algorithm and simulations are in good agreement and demonstrate the validity of proposed algorithm.
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    Stock Market Forecasting with Financial Micro-Blog Based on Sentiment and Time Series Analysis
    WANG Yinglin (王英林)
    2017, 22 (2):  173-179.  doi: 10.1007/s12204-017-1818-4
    Abstract ( 430 )  
    During the past few decades, time series analysis has become one popular method for solving stock forecasting problem. However, depending only on stock index series makes the performance of the forecast not good enough, because many external factors which may be involved are not taken into consideration. As a way to deal with it, sentiment analysis on online textual data of stock market can generate a lot of valuable information as a complement which can be named as external indicators. In this paper, a new method which combines the time series of external indicators and the time series of stock index is provided. A special text processing algorithm is proposed to obtain a weighted sentiment time series. In the experiment, we obtain financial micro-blogs from some famous portal websites in China. After that, each micro-blog is segmented and preprocessed, and then the sentiment value is calculated for each day. Finally, an NARX time series model combined with the weighted sentiment series is created to forecast the future value of Shanghai Stock Exchange Composite Index (SSECI). The experiment shows that the new model makes an improvement in terms of the accuracy.
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    Robust Segmentation, Shape Fitting and Morphology Computation of High-Throughput Cell Nuclei
    SONG Jie (宋杰), XIAO Liang* (肖亮), LIAN Zhichao (练智超)
    2017, 22 (2):  180-187.  doi: 10.1007/s12204-017-1819-3
    Abstract ( 431 )  
    Accurate nuclear classification (e.g., grading of renal cell carcinoma (RCC) biopsy images) is important to better understand fundamental phenomena such as tumor growth. In this paper, an automated pipeline is proposed to quantitatively analyze RCC data. A novel segmentation methodology is firstly used to delineate cell nuclei based on minimum description length (MDL) constrained B-spline curve fitting. From the obtained segmentations, thirteen features are then extracted based on five types of characteristics. These features are used to classify cell nuclei in biopsy images. Associations among nuclei are computed and represented by graphical networks to enable further analysis. Finally, a support vector machine (SVM) based decision-graph classifier is introduced to classify the biopsy images with the purpose of grading. Experimental results on real RCC data show that our SVM-based decision-graph classifier achieves 95.20% of classification accuracy while the SVM classifiers achieve 93.33% of classification accuracy.
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    A Comprehensive Method to Reject Detection Outliers by Combining Template Descriptor with Sparse 3D Point Clouds
    GUO Li (郭立)
    2017, 22 (2):  188-192.  doi: 10.1007/s12204-017-1820-x
    Abstract ( 418 )  
    We are using a template descriptor on the image in order to try and find the object. However, we have a sparse 3D point clouds of the world that is not used at all when looking for the object in the images. Considering there are many false alarms during the detection, we are interested in exploring how to combine the detections on the image with the 3D point clouds in order to reject some detection outliers. In this experiment we use semi-direct-monocular visual odometry (SVO) to provide 3D points coordinates and camera poses to project 3D points to 2D image coordinates. By un-projecting points in the tracking on the selection tree (TST) detection box back to 3D space, we can use 3D Gaussian ellipsoid fitting to determine object scales. By ruling out different scales of detected objects, we can reject most of the detection outliers of the object.
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    Big Data Framework for Quantitative Trading System
    DAI Shuji1 (戴书吉), WU Xing1,2* (武星), PEI Mengqi1 (裴孟齐), DU Zhikang1 (杜智康)
    2017, 22 (2):  193-197.  doi: 10.1007/s12204-017-1821-9
    Abstract ( 557 )  
    Massive trading data are produced in securities market every day. Besides, the amount of relevant social media data is also growing fast. It is a vital problem of making use of these data. Facing on the growing amount of data, using big data framework is a necessary and reasonable method. Then, a big data framework for quantitative trading system is proposed in this paper. In the framework, Apache Spark is chosen as the distributed computing framework to process trading data, and Apache HBase as the distributed database is used to store data. After introducing the whole framework, we discussed data sources and the structure of quantitative trading layer in detail.
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    Numerical Study on Water Depth Effects on Hydrodynamic Forces Acting on Berthing Ships
    WANG Huaming1,3 (王化明), SHENG Xue1 (盛学), WANG Shilai2* (王世来), CHEN Lin2 (陈林),YUAN Zhiming4 (元志明), WU Qiaorui1 (吴巧瑞)
    2017, 22 (2):  198-205.  doi: 10.1007/s12204-017-1822-8
    Abstract ( 392 )  
    Due to the restrictions of ports, maneuverability of berthing ships will be affected significantly by water depth. In the present study, numerical simulation of the berthing maneuver of a ship with prescribed translational motion is performed by solving the Reynolds-averaged Navier-Stokes (RANS) equations based on overset grid, and the effects of the quaywall and freesurface are taken into consideration. To validate the present numerical method, comparison is performed between our results and the other results or measurements. It is found that the agreement is significantly better than that resulting from previous CFD-based approach. Subsequently, the effects of various water depths are investigated to evaluate their influences on hydrodynamic forces. The present results can provide helpful guidance on safe maneuvering for vessels’ berthing and fender system design in quays.
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    Hybrid Control of Delay Induced Hopf Bifurcation of Dynamical Small-World Network
    DING Daweia,b* (丁大为), ZHANG Xiaoyunb (张肖芸), WANG Niana,b* (王年), LIANG Donga,b (梁栋)
    2017, 22 (2):  206-215.  doi: 10.1007/s12204-017-1823-7
    Abstract ( 339 )  
    In this paper, we focus on the Hopf bifurcation control of a small-world network model with time-delay. With emphasis on the relationship between the Hopf bifurcation and the time-delay, we investigate the effect of time-delay by choosing it as the bifurcation parameter. By using tools from control and bifurcation theory, it is proved that there exists a critical value of time-delay for the stability of the model. When the time-delay passes through the critical value, the model loses its stability and a Hopf bifurcation occurs. To enhance the stability of the model, we propose an improved hybrid control strategy in which state feedback and parameter perturbation are used. Through linear stability analysis, we show that by adjusting the control parameter properly, the onset of Hopf bifurcation of the controlled model can be delayed or eliminated without changing the equilibrium point of the model. Finally, numerical simulations are given to verify the theoretical analysis.
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    Compensation of Pressure Enthalpy Effects on Temperature Fields for Throttling of High-Pressure Real Gas
    LUO Yuxi1* (罗语溪), LIANG Jiuxing1 (梁九兴), WANG Xuanyin2 (王宣银), XU Zhipeng3 (徐志鹏)
    2017, 22 (2):  216-223.  doi: 10.1007/s12204-017-1824-6
    Abstract ( 400 )  
    For the pressure enthalpy of high pressure pneumatics, the computational fluid dynamics (CFD) simulation based on ideal gas assumption fails to obtain the real temperature information. Therefore, we propose a method to compensate the pressure enthalpy of throttling for CFD simulation based on ideal gas assumption. Firstly, the pressure enthalpy is calculated for the pressure range of 0.101 to 30 MPa and the temperature range of 190 to 298 K based on Soave-Redlich-Kwong (S-R-K) equation. Then, a polynomial fitting equation is applied to practical application in the above mentioned range. The basic idea of the compensation method is to convert the pressure enthalpy difference between inlet air and nodes into the compensation temperature. In the above temperature and pressure range, the compensated temperature is close to the real one, and the relative temperature drop error is below 10%. This error is mainly caused by the velocity difference of the orifice between the real and ideal gas models. Finally, this compensation method performs an icing analysis for practical high pressure slide pilot valve.
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    Hierarchical Control Strategy of Trajectory Tracking for Intelligent Vehicle
    ZHANG Qian* (张茜), LIU Zhiyuan (刘志远)
    2017, 22 (2):  224-232.  doi: 10.1007/s12204-017-1825-5
    Abstract ( 361 )  
    In order to track the desired trajectory for intelligent vehicle, a new hierarchical control strategy is presented. The control structure consists of two layers. The high-level controller adopts the model predictive control (MPC) to calculate the steering angle tracking the desired yaw angle and the lateral position. The low-level controller is designed as a gain-scheduling controller based on linear matrix inequalities. The desired longitudinal velocity and the yaw rate are tracked by the adjustment of each wheel torque. The simulation results via the high-fidelity vehicle dynamics simulation software veDYNA show that the proposed strategy has a good tracking performance and can guarantee the yaw stability of intelligent vehicle.
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    Real-Time Pressure Based Diagnosis Method for Oil Pipeline Leakage
    LIU Jinhai* (刘金海), MA Yanjuan (马艳娟), WU Zhenning (吴振宁), WANG Gang (汪刚)
    2017, 22 (2):  233-239.  doi: 10.1007/s12204-017-1826-4
    Abstract ( 362 )  
    As detecting the pressure signal is the main method in the real-time leak diagnosis of long pipeline, an abnormal pressure diagnosis method is proposed to make the leak diagnosis rapidly and accurately. Firstly, a combination filter algorithm is designed to realize noise reduction. Then, an anomaly detection algorithm is designed to detect abnormal pressure on the head and tail of the pipeline. Finally, the relevancy of the detected novelties is computed by Pearson correlation coefficient to identify the leakages. The experimental results show that the proposed method can rapidly detect the leakage with few false alarms and accurately locate the position of the leakage.
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    Sliding Mode Robust Fault-Tolerant Control for Uncertain Systems with Time Delay
    YANG Pu* (杨蒲), NI Jiangfan (倪江帆), PAN Xu (潘旭), GUO Ruicheng (郭瑞诚)
    2017, 22 (2):  240-246.  doi: 10.1007/s12204-017-1827-3
    Abstract ( 390 )  
    Considering the modeling uncertainties and external disturbance, a kind of sliding mode robust H∞ fault-tolerant control method for time delay system with actuator fault is proposed. The upper-bound of the uncertainties is considered as a known constant, while the upper-bound of the actuator fault is unknown. A sufficient condition for the existence of an integral sliding mode dynamics is given in terms of linear matrix inequality (LMI). A novel adaptive law is given to estimate the unknown upper-bound of faults. On this basis, a type of sliding mode robust H∞ fault-tolerant control law is designed to guarantee the asymptotic stability and the H∞ performance index of the system. Finally, the simulation on quad-rotor semi-physical platform demonstrates the reliability and validity of the method.
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    Approximate Method for Reliability Assessment of Complex Phased Mission Systems
    ZHOU Hang1,2 (周行), LI Xiangyu1 (李翔宇), HUANG Hongzhong1* (黄洪钟)
    2017, 22 (2):  247-251.  doi: 10.1007/s12204-017-1828-2
    Abstract ( 370 )  
    Phased-mission systems (PMSs) have wide applications in engineering practices, such as manmade satellites. Certain critical parts in the system, such as cold standby, hot standby and functional standby, are designed in redundancy architecture to achieve high reliability performance. State-space models such as Markov process have been used extensively in previous studies for reliability evaluation of PMSs with dynamic behaviors. The most popular way to deal with the dynamic behaviors is Markov process, but it is well known that Markov process is limited to exponential distribution. In practice, however, the lifetime of most machinery products can follow non-exponential distributions like the Weibull distribution which cannot be handled by the Markov process. In order to solve this kind of problem, we present a semi-Markov model combined with an approximation algorithm to analyze PMS reliability subjected to non-exponential failures. Furthermore, the accuracy of the approximation algorithm is investigated by comparing to an accurate solution, and a typical PMS (attitude and orbit control system) is analyzed to demonstrate the implementation of the method.
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    Research on Visual Autonomous Navigation Indoor for Unmanned Aerial Vehicle
    ZHANG Yang* (张洋), L ¨U Qiang (吕强), LIN Huican (林辉灿), MA Jianye (马建业)
    2017, 22 (2):  252-256.  doi: 10.1007/s12204-017-1829-1
    Abstract ( 456 )  
    The aim of this paper is to study visual autonomous navigation of unmanned aerial vehicle (UAV) in indoor global positioning system (GPS) denied environment. The UAV platform of the autonomous navigation flight control system is designed and built. The principle of visual localization and mapping algorithm is studied. According to the characteristics of UAV platform, the visual localization is designed and improved. Experimental results demonstrate that the UAV platform can realize the tasks of autonomous localization, navigation and mapping based on visual in unknown environments.
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