فرآیند جستجوی اطلاعات، فرآیندی تعاملی است زیرا کاربر بر فرآیند جستجو کنترل دارد و در این مسیر، دست به انتخاب می زند. پرسش کاربر ممکن است در طی این فرآیند با توجه به بازخوردهای حاصل از نتایج بازیابی، یک سیر تکاملی را طی کند. از طرف دیگر، درون نظام بازیابی اطلاعات، فرایندهایی وجود دارند که به غیر از کاربر، توسط هیچ یک از عناصر این نظام (رایانه، رابط کاربری و...) تحقق نمی یابند. این مساله به خصوص در مورد «تعامل»، یعنی فرایند اتصال کاربر به سایر عناصر نظام و قضاوت ربط، نمود بیشتری دارد. این نوشته، نگاهی بر چیستی «تعامل» در بازیابی اطلاعات داشته است، سپس مدل سنتی بازیابی اطلاعات و نقاط قوت و ضعف آن را مرور کرده است. در ادامه، مدل های جاری بازیابی تعاملی اطلاعات (مدل اپیزودی بلکین، مدل شناختی اینگورسن، مدل چندسطحی ساراسویک، و بازخورد تعاملی اسپینک) به تفصیل، تفسیر شده اند.

Aspects of pitching and plunging motions on unsteady aerodynamic behavior of an aircraft model were studied. Extensive wind tunnel tests were performed on a standard dynamics model, SDM, oscillating in both pitch and plunge modes. Up to now, there has been little or no result on the plunging behavior of an aircraft or missile as a whole and the present experiments can be considered as one of the first attempts to study the compressible flow field over a model undergoing both pitching and plunging motions. The experiments involved measuring normal force and pitching moment of the model at Mach numbers of 0.4, 0.6 and 1.5 and oscillation frequencies of 1.25, 2.77 and 6.00 Hz. The longitudinal dynamic derivatives were then calculated from the measured data. The pitching results have been compared with the available data on the same model and good agreement has been achieved. However no plunging data on the same or a similar model was available in the literature to be compared with the present findings.

Application of the nonlinear H¥ identification method to identify a synchronous generator model is investigated in this paper. The linear H¥ identification method has been well established in the literature for robust modeling despite noise and system uncertainities. Since many practical systems such as synchronous generators are nonlinear, linear models identified for particular operating conditions do not perform well for other operating conditions. To overcome this shortcoming, the linear H¥ identification method has been modified to cover some nonlinearities of the systems such as saturation in synchronous machines. The derived proposed algorithm is then applied to a seventh order nonlinear model of a synchronous machine with saturation effect. In this study, the field voltage is considered as the input and the active output power and the terminal voltage are considered as the outputs of the synchronous machine. Simulation results show good accuracy of the identified models.

In this paper, a new effective and computationally reduced method for congestion control in high speed dynamic computer networks is introduced. The controller is designed using the well-known predictive functional control (PFC) scheme and an ARMarkov model representation that considers the system delay explicitly. Use of the multi-step-ahead predictive ARMarkov model structure within the PFC results in a simple algebraic control law that does not require recursive model output computation in the so-called prediction horizon performed in the other Model Predictive Controllers (MPC). This combination not only reduces the required computational load, but the accumulative error due to the model uncertainties decrease considerably. Packet-level simulations based on ns-2 are provided to show good performance of ARM-PFC in a large variety of topology and traffic mixtures for both queue regulation and resource utilization. Fast response, low queue fluctuations (and consequently low delay and jitter), high link utilization, good disturbance rejection, scalability, and low packet marking probability are other features of the proposed method with respect to the well-known AQM methods such as RED, PI, and REM, which are also simulated for comparison.

A modified three-dimensional dispersion model of our previous work5 was modified and used to investigate the effect of droplet size distribution in addition to droplet concentration distribution for the prediction of liquid droplet dispersion and particulate removal efficiency in a venturi type scrubber. For the sake of including droplet size distribution into the model properly, it was assumed that droplet size distribution obeys Rosin-Rammler distribution. The experimental data of Viswanathan et. al. [1] for liquid droplet dispersion and Brink and Contant [2] for particle removal efficiency were used to test the results of this new mathematical model. The results from the model show that by taking droplet size distribution into account the results of the model will be in better agreement with the experimental data.

آب پاک یکی از عوامل مهم توسعه هر منطقه است. با توجه به قرارگیری ایران در منطقه گرم و خشک و کمبود منابع آب،‏ حفاظت و تامین کیفیت آب لازم برای مصارف مختلف اهمیتی دو چندان دارد. به طور معمول ارزیابی کیفی آب های سطحی پرهزینه و زمان بر بوده و انتخاب روشی که در آن با حداقل پارامترهای هیدروشیمیایی بتوان پیش بینی به نسبت دقیقی از کیفیت آب داشت،‏ ترجیح داده می‏شود. یکی از مهم ترین پارامترهای کیفی آب در زمینه فعالیت های کشاورزی،‏ نسبت جذبی سدیم (SAR) است که تخمین و ارزیابی دقیق مقدار آن بسیار ضروری است. در این بررسی،‏ امکان سنجی تخمین شاخص کیفی SAR در رودخانه آجی چای در منطقه آذربایجان شرقی با استفاده از پارامترهای هیدروشیمیایی مختلف با مدل درختی قوانین M5 و ماشین بردار پشتیبان بررسی شد. برای بررسی دقت مدل های M5 و ماشین بردار پشتیبان از چهار آماره ضریب همبستگی (R) ،‏ نش- ساتکلیف (NSC) ،‏ جذر میانگین مربعات خطا (RMSE) و میانگین خطای مطلق مقادیر (MAE) استفاده شد. مقادیر این آماره ها برای روش ماشین بردار پشتیبان (98 /0R=،‏ 97 /0N-SC=،‏ (mg/l) 22 /6RMSE= و (mg/l) 06 /6MAE=) و برای مدل M5(98 /0R=،‏ 96 /0N-SC=،‏ (mg/l) 33 /7RMSE= و (mg/l) 9 /3MAE=) محاسبه شد. نتایج مقایسه نشان داد هر دو روش عملکرد خوبی در تخمین میزان SAR داشته اند،‏ اما مدل درختی قوانین M5 در محدوده داده های مورد استفاده روابط خطی ساده و کاربردی تر ارائه می کند.

چکیده (انگلیسی):

Clean water is one of the important factors in any region''s development. Since Iran is located in an arid and semi-arid area with scarce water resources، preservation of water required for various uses and maintenance of its quality takes redoubles this importance. Evaluation of surface water is normally a costly and time-consuming process. Therefore، a method is preferred which has the minimum number of hydrochemical parameters and can yield a relatively accurate prediction of water quality. One of the most significant qualitative parameters of water for agricultural uses is the sodium absorption ratio (SAR)، the factor which should be estimated and evaluated accurately. This research employed various hydrochemical parameters، a model tree using the M5-Rules، and a Support Vector Machine to study the feasibility of estimating the qualitative index SAR in the Ajichai River located in East Azerbaijan Province. The four statistics of correlation coefficient (R)، Nash-Sutcliffe coefficient (NSC)، Root Mean Square Error (RMSE)، and Mean Absolute Error (MAE) were used to determine the accuracy of both M5 model and the Support Vector Machine.
The study region was the Ajichai River on the northern hillsides of the Sahand Mountain. Hydrochemical data from the Hydrometric Station in Vanyar was used to evaluate and predict the SAR in the river. The Vanyar Station has the longitude of 46 ̊24 east، the latitude of 38 7́ north، and the altitude of 1460 meters. Effects of Total Dissolved Solids (TDS)، Electrical Conductivity (EC)، PH، chlorine (Cl-)، sulfate (SO42+)، calcium (Ca2+)، magnesium ( Mg2+) and sodium ( Na+) parameters on SAR were determined in SAR estimation. The model tree M5-Rules is a new data mining method. The main goal of this model is derived from regression trees. The difference is that this model has regression functions in its leaves instead of constant values and classification tags. The major advantage of the model tree M5-Rules over regression trees is that the model tree M5-Rules is much smaller than regression trees. Furthermore، regression functions normally do not include many parameters. A decision tree usually consists of four parts of root، branches، nodes، and leaves. Each node corresponds to a certain characteristic، and the branches represent values of the intervals. These intervals consider known values for each of the characters. The branching operation takes place with one of the predictor variables. The branching intervals are selected in a way that the sum of squared deviations from the mean of the data in each node is minimized. The branching criterion indicates the amount of the error in the related node، and the model calculates the minimum expected error as a result of each characteristic testing in the related node. The model error is generally assessed by measuring the predicted unobserved target values accuracy. In this research، the WEKA software which is developed at Waikato University in New Zealand was used to model the M5 method. Modeling was performed with this software using the option of M5-Rules which present simple and linear rules. Support Vector Machines are data mining algorithms similar to the model tree M5 and the artificial neural network. There are two groups of Support Vector Machines: Support Vector Classification (SVC) and Support Vector Regression. Furthermore، Support Vector Machines are based on the concept of decision planes that define decision boundaries، i.e. a decision plane separates data with different tags from each other. The goal in a linearization algorithm with the help of a Support Vector Machine، the assumptions of the input value of xi، and the output value of yi is to find a function with the minimum deviation (ε) from the yis (ε is the amount of deviation). In this research، the Statistica software is used for modeling the SAR values employing Support Vector Regression.
In the modeling of the SAR values by using the tree model M5-Rules، the best answer was obtained when 66 percent of the data was allocated to training and the rest to testing. To model the SAR values using the Support Vector Machine، various functions were tested as kernel functions، and it was found that the RBF function exhibited the best performance in the modeling of the SAR values. Among the 10 scenarios studied in this research، the best one was selected. The four statistics of correlation coefficient (R)، Nash-Sutcliffe coefficient (NSC)، Root Mean Square Error (RMSE)، and Mean Absolute Error (MAE) were used to determine the accuracy of both M5 model and the Support Vector Machine. The obtained values of these calculations were: R =0.98، N-SC=0.97، RMSE=6.22 (mg/l)، MAE=6.06 (mg/l) for the Support Vector Machine method; and R=0.98، N-SC=0.96، RMSE=7.33 (mg/l)، and MAE=3.9 (mg/l) for the M5 model. Results of the comparison indicated that both methods studies in this work، i.e. Support Vector Regression and the M5 model، were highly capable of predicting the SAR values in the Ajichai River، using the available data. However، the M5 model is recommended to be used due to the fact that the formulas employed in this method are so simple and linear.