搭载机电控制无级变速器混合动力汽车模式切换仿真分析

通过分析机电控制无级变速器(electrical-mechanical continuously variable transmission,EM-CVT)的结构特点,提出了搭载EM-CVT混合动力汽车的传动方案,建立了动力源数值模型、EM-CVT模型,分析了混合动力系统工作模式,并针对模式切换时的冲击问题,以减小冲击度为目标,通过分析典型工况下动力源输出转矩特性和各部件的动态特性,提出了基于发动机、ISG电机、自动离合器以及EM-CVT相互协调控制策略。利用MATLAB/SIMULINK仿真平台建立混合动力传动系统动力学模型,并对典型的模式切换过程进行了仿真分析。结果表明,所提出的控制策略能够有效控制混合动力传动系统在模式切换时产生的冲击,提高了混合动力汽车的驾驶舒适性。     

夏季晴天鱼塘水温垂直变化规律及原因分析

为揭示气象要素对水温垂直梯度的影响,利用洪湖国家气象站2022年夏季逐日、逐小时气象观测数据和洪湖水产基地不同深度逐分钟水温观测数据,采用数理统计和相关分析等方法,对夏季晴天池塘养殖水温垂直变化规律进行分析。结果表明:(1)夏季晴天时各层水温单峰型日变化特征明显,最小值出现在日出前,表层水温最高值出现在16:00左右,水层越深,最高值和最低值出现时间越晚,上下层的水温变化具有一定的滞后性。(2)光照对水温有显著影响,两者呈显著正相关关系,60 cm深度处相关系数达到最大;不同层次平均光照度与水温垂直梯度呈负相关,而不同层次光照梯度与水温垂直梯度呈显著正相关。(3)水温垂直梯度与风速呈显著负相关,风速越大水温垂直梯度越小,风速3～4级,水温垂直梯度小,可适时暂缓或少开增氧机。(4)水温垂直梯度与水汽压呈显著正相关,水汽压越大,水温垂直梯度越大,闷热天气要加强巡塘,及时开启增氧机。揭示了气象要素对水温垂直梯度的影响机理,为水产健康养殖提供了技术支撑。     

栽培技术优化对冬小麦根系垂直分布及活性的调控

为探索优化小麦根系构建,促进小麦根系功能发挥,以达到小麦高产高效的栽培技术,于2012—2013和2013—2014年度小麦生产季,通过大田试验,比较研究了鲁原502在旋耕-基肥撒施(RT-SF),深翻-基肥撒施(PT-SF)和苗带旋耕-间隔深松-分层深施肥(SRT-SS-DT)3种栽培技术下产量及其构成,研究麦田0~90 cm内不同土层根系形态分布及生理特性的差异。与RT-SF和PT-SF处理相比,SRT-SS-DT处理显著提高了小麦的千粒重及单位面积穗数,使最终产量提高了3.96%~13.29%。SRT-SS-DT处理促进了小麦根系生长发育,拔节后15~60 cm土层内的根长密度和根干重密度、30~75 cm土层内根系总吸收表面积和活跃吸收面积较其他处理显著提高,尤其是在施肥层(15~30 cm土层)。开花后20 d,15~30 cm土层SRT-SS-DT的根系总吸收表面积和活跃吸收面积较RT-SF提高了66.3%和56.5%,较PT-SF提高了75.9%和59.8%。SRT-SS-DT增强了15~90 cm土层的根系活力,同时减缓了生育后期根系活力的下降,开花期至花后20 d,15~30 cm土层根系活力下降值在SRT-SS-DT处理下较RT-SF和PT-SF降低了28.5%和14.9%。此外,在花后20 d,SRT-SS-DT处理小麦15~90 cm土层根系表现较低MDA含量和较高SOD活性,尤其是15~30 cm土层,根系SOD活性分别比PT-SF和RT-SF处理高20.6%和10.9%。15~90 cm土层根系活力和根干重占比与小麦产量呈显著正相关。结果表明,通过对苗带旋耕、间隔深松和分层深施肥等栽培技术的集成和优化,可以有效扩展深层土壤根系的分布,提高深层土壤根系的活性,尤其是施肥层,有助于小麦产量提高。     

应用数字图像技术估测冬小麦冠层生物量垂直分布特征的研究

用数字图像技术研究了冬小麦冠层生物量的垂直分布。表明用一行小麦图像比多行小麦图像估测小麦生物量能更好地满足线性回归关系,估测效果更佳,以此为基础进一步研究了分层像素数估测小麦冠层分层现存生物量和有效生物量的方法。利用分层绿色像素数(LGPN)指标定性分析了不同栽培模式下冬小麦群体有效生物量的垂直分布和动态变化,并确定了基于图像特征的可用于定量分析的小麦群体垂直分布指数（I）。     

施肥对玉米田植物线虫群落组成及垂直分布的影响

为了明确玉米田植物线虫群落组成和垂直分布,于2007年采用蔗糖梯度离心法对80份土样进行分离鉴定,共鉴定出13个植物线虫属,玉米田不同施肥方式下植物线虫大部分为共有属。化肥配施有机肥区有1个特有属为拟毛刺属(Paratri-chodorus),化肥区有1个特有属为小环属(Cri-conemella),无肥区没有特有属。玉米田植物线虫主要集中在0~40cm土层,其中,玉米田单施化肥和无肥处理植物线虫密度在20~30cm土层达到最大,然后随深度的增加逐渐下降,化肥配施有机肥处理植物线虫数量在10~20cm土层达到最大值,30~40cm土层又达到一个峰值然后下降。     

施氮量对不同株型小麦品种叶型垂直分布特征的影响

在大田生产条件下,以紧凑型高秆品种宁麦9号和矮秆品种矮抗58、中间型品种扬麦12、松散型品种淮麦17为材料,测定并分析了3个施氮水平下不同生育时期的小麦冠层叶型特征,以及开花期小麦叶长和叶宽、单叶面积、茎叶夹角、分层叶面积指数和群体透光率的垂直分布特征。结果表明,小麦叶型特征存在显著的基因型差异,而施氮量的调控作用因叶型性状和生育时期不同而各异。叶片定形后,从植株基部向上,4个小麦品种不同叶位叶片单叶面积呈“升－降－升－降”的变化趋势,而茎叶夹角呈递减趋势。其最大分层叶面积指数所在的相对冠层高度为0.60。从冠层基部向上,群体透光率逐渐增加,符合二次多项式曲线。施氮提高了单叶面积,其中扬麦12和淮麦17的增加幅度较大。施氮提高了各叶位茎叶夹角,且对宁麦9号、淮麦17和扬麦12冠层下部茎叶夹角的调控作用大于冠层上部,而对矮抗58正好相反。施氮提高了各株型小麦品种的分层叶面积指数,降低了群体透光率,但过量施氮条件下,宁麦9号和矮抗58透光率的下降幅度小于扬麦12和淮麦17。各株型小麦品种群体透光率随累积叶面积指数的增加而递减。籽粒产量为N150 (150 kg hm^-2) > N225 (225 kg hm^-2) > N75 (75 kg hm^-2)。施氮显著提高了小麦品种穗数和收获指数,但对千粒重的影响不显著。穗粒数以高氮处理最高,低氮处理其次。籽粒产量、千粒重、穗数、穗粒数和收获指数在4个株型品种间差异显著,籽粒产量为矮抗58 > 宁麦9号 > 扬麦12 > 淮麦17,穗数和穗粒数是造成籽粒产量差异的主要原因。     

垂直热皮控温技术在基建房式仓的优化应用

基建房式仓应用“垂直热皮”控温技术,采取“散热降温”和“散热均温”两种控温模式,均可有效控制“垂直热皮”粮温,但均存在控温死角点,“散热降温”模式较“散热均温”模式运行时间长、能耗高。利用“垂直热皮”控温系统,“热皮”平均粮温每天降低1℃左右,度夏期间,试验仓四周最高粮温28.4℃,平均粮温20.6℃,对照仓四周最高粮温31.3℃,平均粮温23.4℃。     

垂直深旋耕下有机物料改良酸性土壤及烤烟产质量的调控效应

为探明深旋耕配施有机物料改良烤烟根际土壤酸化特性和调控烤烟生长、产质量的效应。以烤烟品种‘NC102’为材料,在云南省石林县酸性红壤上,采用双因素裂区设计,研究耕作方式(垂直深旋耕和传统耕作)和施有机改土物料(不施改土物料、酸性有机肥、碱性有机肥和生物质炭)及其互作对烤烟根际土壤理化特性、烟株根系和植株生长形态及产质量的影响。结果表明：植烟土壤理化特性主要受耕作方式、有机改土物料及二者互作的影响,其中垂直深旋耕配施有机肥可显著降低土壤容重,提高土壤孔隙度及水解性氮、有效磷、速效钾、水溶性氯含量,尤其施碱性有机肥、生物质炭更有利于增加土壤速效钾含量,显著降低土壤交换性酸总量、土壤交换性氢(H⁺)和交换性铝(Al³⁺),提升土壤硅铝率、阳离子交换量和盐基饱和度,增强酸性土壤调酸效果。烤烟根冠形态及烟叶产量、产值性状主要受深旋耕和碱性改土物料的影响。耕作方式、有机改土物料及其互作对烟叶化学成分可用性指数影响较大,其中施碱性有机肥、生物质炭主要影响总糖、还原糖、钾及水溶性氯;垂直深旋耕显著影响烟叶6项常规化学指标。因此,在云南酸性土壤山地烟区,采用垂...     

抛秧水稻生长发育与产量形成的生态生理机制Ⅱ. 秧苗田间垂直分布格局及其生态生理效应

抛秧稻秧苗在田间垂直方向上浅栽而变异大的特点(-0.5～2.5cm)使分蘖节入土状况不同,秧姿各异.同时,浅栽后,分蘖节及土中茎鞘周际环境发生深刻变化,总体上温、肥、气、压(受土壤压力)等生态因素改善,但因分蘖节处于土-水-气的交界层面上,这些生态因素在这个层面上变化大,使水稻生长受这些因素胁迫机率大.分蘖节入土状况在活棵     

A fuzzy logic multicasting algorithm for ad hoc networks

To increase the multicasting efficiency of Ad hoc networks, a fuzzy logic multicasting algorithm (FLMA) is proposed. FLMA adopts the fuzzy logic to tolerate the imprecise information caused by dynamic network topology. The two input variables of the fuzzy logic system are the relative degree of the additional coverage node number and the relative degree of the residual energy. The deferring time of rebroadcast is the output variable of the fuzzy logic system, which is used to optimize the priority of the nodes to rebroadcast. FLMA reduces the redundant retransmission and the chance of the contention and collision, while balances the energy consumption of the nodes. Simulation results reveal that the FLMA achieves better performance than BCAST in terms of the network lifetime, average end-to-end delay, the average number of drops per node and the throughput.     

Research and implementation on handwritten numeralrecognition technique based on fuzzy mode

Based on simulation and numeral recognition of the 24-bit bmp images of postal codes obtained by CCD transducer, a binarization method based on coloring matter is proposed, which is employed to extract the binary image of the frame line of postal code and the binary image of the mail character separately. The procedure of image processing is analyzed, which includes detecting and separating borderlines of postal code frames, image binarization, smoothing noise-removal, slant rectification, extraction of postal code numerals, position normalization processing, subdivision of digital image, feature extraction of character recognition, and so on. The pretreatment process of image recognition is described. Then, character feature extraction is conducted for pretreated images, while the angle correctness for postal code frames and characters is performed. Finally, the recognition result is determined. In this method, the difficulty of separating frame line from postal code character line is eliminated. In order to improve recognition rate, the combination of many methods of handwritten numeral is used, resulting in improved recognition rate of postal codes. The experiment shows that this method can obtain higher recognition rate, providing reference for the separation technique in postal system.     

Drift error compensation algorithm for fiber optic gyro based on T-S fuzzy modeling

A drift error nonlinear compensation algorithm for Fiber Optic Gyro (FOG) is presented based on T-S fuzzy model with the antecedent parameters identified by G-K clustering algorithm and the error model of T-S fuzzy model with the consequent parameters identified by least square algorithm. The computed results show that this model can compensate the original data effectively, while the error principles of FOG do not need to be understood well. Comparing with the original data, compensation with linear fitting and compensation with neural network, the absolute error of the proposed model reduces by 99%, 96% and 10%, respectively. The error variance reduces by 99%, 98% and 20%, respectively. The results indicate that this proposed algorithm can be simply operated with high precision and easy to realize in engineering.     

Growing and pruning dynamic recurrent fuzzy neural network optimal control on firing rate to feed water ratio for supercritical unit

By analyzing a certain once through boiler’s FR/FW control system,a new control scheme based on growing and pruning dynamic recurrent fuzzy neural network (GAP-DRFNN)is proposed. This GAP-DRFNN can synthetically study main relative state parameters about FR/FW control,so as to calculate the optimal FR/FW by using least temperature deviation value of outlet of moisture separator as its training signal. As the data of current main relative state parameters input,GAP-DRFNN through structure learning can automatically increase and pruning neurons,and adjust the parameters and the recurrent weight of neural network dynamically based on stochastic gradient descent algorithm. The experimental results show the good performance for the system in variable conditions and this scheme’s celerity and precise on FR/FW control,it has better quality than traditional PID control method.     

Multi-objective control strategy for parallel HEV using fuzzy logic

A fuzzy multi-objective control strategy is proposed for parallel hybrid electric vehicles (PHEV). By the concept of motor equivalent fuel consumption, the overall vehicle fuel economy and the corresponding emissions can be treated as the optimization objectives. Then a minimum average weighted deviation method is designed to find the Pareto optimal solution with considering the variations of objective preference in different district. The simulation on ADVISOR reveals that, compared with the rule based control strategy (RBCS), with the premise that there is no loss to power performance, the proposed fuzzy multi-objective control strategy (FMCS) not only improves fuel economy and emission level but also maintains battery SOC within its operation range more effectively.     

Fault diagnosis of rotating machinery based on self-learning fuzzy spiking neural network

For fuzziness classific boundry of fault diagnosis of rotating machinery and traditional neural network algorithms difficulted to solve contradiction between application problems example scale and netwok scale,a methord of self-learning fuzzy spiking neural network is put forward. The methord overcomes unavailability of cluster analysis on classific boundry of fault diagnosis of rotating machinery by species encoding of pulse sequence and unsupervised learning. The method shows that it effectively solves boundary fuzziness problem on fault diagnosis of rotating machinery,and greatly improves efficiency of fault diagnosis.     

A fast quantitative identification algorithm of colorimetric visual-sensor-array based on basic units matching

There are many difficulties to process a colorimetric visual-sensor-array by traditional processing methods, such as complicated manual analysis due to many data and hard to finish varieties and concentrations identification simultaneously, etc. In view of colorimetric-sensor-array’s same location response to the same gas, a fast quantitative identification algorithm of colorimetric visual-sensor-array based on basic units matching which can solve these problems is proposed. First, denoising and feature extraction are processed by setting experienced threshold to reduce redundancies and lessen manual analysis. Second, a creative qualitative analysis method based on basic units is put forward, which not only reduces computation, but also increases efficiency and precision. Finally, a ANFIS of NH3 concentration recognition utilizing advantages of fuzzy logic and neural network is used to distinguish low concentration NH3. The advantage of this algorithm is that varieties and concentrations of different gases could be detected successively, solving the problem of recognition errors caused by characteristic data infection when varieties and concentrations of different gases are detected simultaneously.The results of template matching based on basic units show that the classification accuracy of NH3, Cl2 and SO2 are 100%. The low concentration NH3 classification accuracy is also very high after species identification with measurement errors below 5%.     

Approximation of the necessary and sufficient conditions for stability of Takagi Sugeno fuzzy control systems

An extensive analysis on the existing results for the stability of Takagi Sugeno fuzzy control is given. For the Lyapunov functions, the common quadratic Lyapunov functions, piecewise quadratic Lyapunov functions, fuzzy Lyapunov functions, nonquadratic Lyapunov functions and homogenously polynomially parameterized Lyapunov functions are considered. For the control laws, the parallel distributed compensation laws, non parallel distributed compensation laws and homogenously polynomially parameterized control laws are considered. Based on the analysis, by applying the Polya's theorem and the techniques for homogenous polynomials, the approaches for approximating the necessary and sufficient conditions for stability of T S fuzzy control are given.     

Virtual plant based on fuzzy neuron Inference of physiology process

A fuzzy neuron inference of physiological process (FNI-PP) based virtual plant growth model is proposed. Using machine learning theory, the model can automatically learn and fit the plant growth function according to measured data and extract the plant growth rules. During plant growth, the source and sink organs respond the surrounding virtual environment according to its inbuilt growth function, and produce, allocate and consume assimilates as well as update the L-grammar representing the plant structure. The model can automatically adjust parameters of the growth function and the L-grammar to respond the environmental heterogeneity. Cayenne-based simulations show that the model can accurately extract the growth function and the structural pattern of the plant, and vividly demonstrate the response to environment.     

The predictive control of an incremental model based on tracking error

An algorithm of incremental model predictive control is proposed to solve a class of industrial production process real-time tracking problems. The basic idea is to establish a predictive model by adopting speed response on the basis of tracking error, and realize the optimization of the second time performance by using the soft constraint of the increment of control quantity and the online rolling optimization method. The theoretical analysis and Matlab simulation results show that the control algorithm can obtain good tracking performance, smooth dynamic response of the controlled object and strong robustness of the closed-loop system.