国产和进口阴离子交换树脂膜埋置法测定土壤中的有效磷

对采自全国各地20个土壤样品进行测定.结果表明,树脂膜法测定的土壤磷值与碳酸氢钠法测定的土壤有效磷和植物实际吸磷量间具有极显著的相关性,国产树脂膜可代替进口树脂膜用于大田土壤有效磷测定.     

测定土壤有效钾的新方法——阳离子交换树脂膜法

对采自全国各地20个土壤样品进行测定.结果表明,阳离子交换树脂膜埋置法测定土壤有效钾的最佳埋置时间为20min.树脂膜法测定的土壤钾值与中性醋酸铵法测定的土壤有效钾和植物实际吸钾量之间具有极显著的相关性.国产树脂膜可代替进口树脂膜用于土壤有效钾测定.     

黑龙江北部土壤中主要重金属和微量元素状况及其评价

以黑龙江省北部地区的嫩江市、五大连池市和克山县土壤中重金属和微量元素为研究对象,评价了该地区土壤中全量锌、铜、铅、镉和有效态锌、铜、锰、铁的分布状况、污染程度和丰缺程度。研究结果表明:土壤重金属含量在克山、五大连池和嫩江地区存在一定的差异,表现出一定的地域变化特点。除了全锌含量低于黑龙江省土壤背景值外,其它3种元素(铜、铅和镉)都高于背景值,按当地标准已达轻度污染程度;根据国家标准,这3个地区土壤中有效锌和和有效铜含量均达适中和丰富水平,有效铁和有效锰达到丰富和很丰富水平。但尚需根据该地区的气候、作物和土壤的具体情况制订适合当地的土壤微量元素评价标准。     

泥炭和牛粪对土壤中微量元素活性的影响

盆栽试验的结果表明牛粪、泥炭对外源微量元素形态分配的影响总趋势是一致的;四种微量元素化学有效态的主要给源不完全相同,但主要是以交换态和松结合有机态为主;土壤中生物有效性Cu、Zn、Fe、Mn的影响因素比较复杂,可能与有机质对不同元素具有强弱不同的络合能力以及Fe、Mn元素自身的变价有关.     

Optimization of a proton exchange membrane fuel cell membrane electrode assembly

A computational framework for fuel cell analysis and optimization is presented as an innovative alternative to the time consuming trial-and-error process currently used for fuel cell design. The framework is based on a two-dimensional through-the-channel isothermal, isobaric and single phase membrane electrode assembly (MEA) model. The model input parameters are the manufacturing parameters used to build the MEA: platinum loading, platinum to carbon ratio, electrolyte content and gas diffusion layer porosity. The governing equations of the fuel cell model are solved using Netwon’s algorithm and an adaptive finite element method in order to achieve near quadratic convergence and a mesh independent solution respectively. The analysis module is used to solve the optimization problem of finding the optimal MEA composition for maximizing performance. To solve the optimization problem a gradient-based optimization algorithm is used in conjunction with analytical sensitivities. By using a gradient-based method and analytical sensitivities, the framework presented is capable of solving a complete MEA optimization problem with state-of-the-art electrode models in approximately 30 min, making it a viable alternative for solving large-scale fuel cell problems.     

Stable and optimal controls of a proton exchange membrane fuel cell

In this paper, the trajectory tracking problem of a proton exchange membrane (PEM) fuel cell is considered. To solve this problem, stable and optimal controllers are proposed. The stable and optimal techniques have the objective that the system states should reach the desired trajectories while in the first, the tracking error is minimised, and in the second, the tracking error and inputs are minimised. The effectiveness of the proposed techniques is verified by simulations.     

New technique for detection and height estimation of buried metallic objects

A new technique for the detection and height estimation of buried metallic objects is developed. This technique is bused on the phase characteristics of both transmitted and received signals. A discrete Fourier transform via a fast Fourier transform algorithm is used to compute the phase characteristic. The height of the buried metallic object is estimated by detecting the transition frequency and measuring the phase difference between the transition frequency and the next frequency component in the spectrum of the received signal. Computer simulation studies show that this technique is efficient, reliable, and provides sufficient accuracy.     

Adaptive peak seeking control of a proton exchange membrane fuel cell

The primary aim of operating any fuel cell (PEMFC) system is to produce the power/electricity at maximum efficiency. The cell voltage/current manipulation appear to be the most suitable choice for controlling the power density. However, the power density exhibits a highly nonlinear and complex dynamic relationship with respect to the cell voltage. Since the process output variable (i.e. power density) itself is the objective function for the optimization, there exists a singularity at the optimum operating condition. In addition, the location of the optimum operating point changes with time due to the occurrence of variety of disturbances and/or changes in the operating conditions. Thus, the need to operate the PEMFC at its peak power density and track the shifting optimum turns out to be a challenging control problem. The task of on-line optimizing control of PEMFC poses difficulties in real time control due to its fast dynamics and it is impractical to employ a mechanistic model for locating the changing optimum on-line. In this context the adaptive optimizing control scheme developed by Bamberger and Isermann (1978) [1] appears interesting. Their scheme is based on on-line adaptation of a nonlinear black box time series models and facilitates analytical computation of changing optimum. Recently, Bedi et al. (2007) [2] have developed a closed form multi-step predictive control law under nonlinear internal model control framework using a black-box nonlinear model and employed it for peak power control in PEMFC. From the viewpoint of PEMFC operation, this nonlinear IMC controller meets the demand on the fast computations as a closed form solution is obtained for the nonlinear control problem at each time step. In this work, we propose to develop an adaptive optimizing control scheme, which combines the attractive features of the on-line optimization approach proposed by Bamberger and Isermann (1978) [1] and closed form control law developed by Bedi et al. (2007) [2]. We demonstrate the effectiveness of the proposed adaptive optimizing scheme by conducting simulation studies on the distributed an along-the-channel model of PEMFC. Analysis of the simulation results indicate that the proposed adaptive optimizing control scheme satisfactorily tracks the shifting optimum operating point in the face of changing unmeasured disturbances     

The impacts of exchange rates on Australia's domestic and outbound travel markets

In Australia, domestic tourism generated AUD 71 billion in 2010–11, representing approximately 75.2% of national tourism revenue. While the number of domestic overnight visitors increased by 2.34% in that year, the number of Australians traveling overseas grew by 7.45%. In fact, the dramatic appreciation of the Australian dollar against major currencies has motivated more Australians to travel overseas rather than within their own country. The purpose of this study is to examine the economic factors that influence the demand for Australian domestic and outbound tourism. In particular, the research explores the extent to which the appreciation of Australian dollar has affected the Australian domestic tourism industry. Using panel generalized least squares models, the empirical findings show that exchange rates influence the Australians’ decisions to travel.     

Determination of soil attribute contents by means of reflected electromagnetic energy

Remote sensing involves huge data bases of spectral reflectance in developing a new soil analysis method. The objective of this work was to use spectral reflectance data to determine the contents of physical, chemical, and mineralogical attributes of 9 soil classes with a sensor in the laboratory. To that end, a database was organized containing information on 3,300 soil samples and their respective analyses and radiometry, from the Brazilian states of São Paulo and Paraná. With the use of spectral data, multiple regression equations were obtained for 20 soil attributes. The physical and mineralogical attributes presented had, for the most part, coefficients of determination higher than 0.70, while for chemical attributes this value was, in general, smaller than 0.50. Attributes whose models obtained R² > 0.50 were tested on unknown samples that did not take part in the generation of the model. The model‐estimated soil content values of those samples were then compared with values determined in the laboratory through traditional chemical analysis. Sand, silt, clay, Fe₂O₃, SiO₂, TiO₂, Al₂O₃, Ki, and Kr presented results that were statistically similar between the estimated and determined data. The results indicated that it is possible to determine the content of these soils attributes with spectral analysis, which minimizes cost and time.     

Parameter derivation of a proton exchange membrane fuel cell based on coevolutionary ribonucleic acid genetic algorithm

Precise modeling of a polymer electrolyte membrane fuel cell (PEMFC) is a crucial issue in analyzing and controlling electrical energy production. In this paper, a novel semiexperimental model is proposed for forecasting of PEMFC output voltage. As well, the coevolution ribonucleic acid genetic algorithm (coRNA‐GA) is presented as a novel estimation approach for determination of proposed model coefficients. This optimization method is motivated by the biological RNA, encodes the chromosomes by RNA nucleotide basics, and accepts a few RNA operations. This paper proposed several genetic operators to preserve the diversity of particles, and two sets from particles are chosen using various validation functions. In these two subpopulations, different evolutionary methods have been employed for balancing of seeking and extraction. Input pressure of cathode is chosen in this paper as a further parameter for modifying the depiction of concentration overvoltage (V_con ) in the case of conventional Amphlett's PEMFC system. Finally, the performance of the coRNA‐GA algorithm, as well as the precision of the obtained model, is authenticated via empirical results. Also, the obtained results are compared with some other methods, and the superiority of the proposed model is demonstrated in voltage prediction accuracy.     

Robust comprehension of natural language instructions by a domestic service robot

We present a method through which domestic service robots can comprehend natural language instructions. For each action type, a variety of natural language expressions can be used, for example, the instruction, ‘Go to the kitchen’ can also be expressed as ‘Move to the kitchen.’ We are of the view that natural language instructions are intuitive and, therefore, constitute one of the most user-friendly robot instruction methods. In this paper, we propose a method that enables robots to comprehend instructions spoken by a human user in his/her natural language. The proposed method combines action-type classification, which is based on a support vector machine, and slot extraction, which is based on conditional random fields, both of which are required in order for a robot to execute an action. Further, by considering the co-occurrence relationship between the action type and the slots along with the speech recognition score, the proposed method can avoid degradation of the robot’s comprehension accuracy in noisy environments, where inaccurate speech recognition can be problematic. We conducted experiments using a Japanese instruction data-set collected using a questionnaire-based survey. Experimental results show that the robot’s comprehension accuracy is higher in a noisy environment using our method than when using a baseline method with only a 1-best speech recognition result.