تحليل فضايي-مکاني مراکز آتش¬نشاني با استفاده از سيستم اطلاعات جغرافيايي

مقدمه: تعيين مکان مناسب جهت تأسيس ايستگاه‌هاي آتش¬نشاني يکي از مهمترين وظايف و اهداف مديران شهري است که بايد قبل از اجرا، در يک چارچوب سيستماتيک آماده¬سازي گردد. درواقع، هدف اصلي مکان¬يابي، جلوگيري از هدر رفتن هزينه¬ها و تضمين کارآيي بهينه ايستگاه‌ها در تقابل با سيستم شهري است. دسترسي سريع، به موقع و ارزان به مراکز آتش‌نشاني در هر جامعه بخصوص در جوامع شهري امري مهم و ضروري مي¬باشد. روش¬ها: روش تحقيق توصيفي-تحليلي بوده است. اطلاعات مورد نياز با استفاده از نقشه 2000/1 کاربري اراضي، مشاهده ميداني و مطالعه طرح‎هاي مرتبط با شهر جهرم جمع¬آوري شده است که با استفاده از نرم¬افزارGIS، فرايند تحليل سلسله مراتبي AHP به عنوان مدل مورد استفاده در وزن¬دهي معيارها، در قالب مقايسات زوجي و بر اساس نظرات کارشناسان اعمال گرديده است. کار پردازش و تجزيه و تحليل داده¬ها مطابق معيارها و استانداردهاي برنامه¬ريزي شهري انجام گرفته است و در پايان مناسب¬ترين مکان¬ها براي ايجاد مراکز مورد نظر تعيين شده است. يافته¬ها: براي بررسي وضع موجود مراکز آتش‎نشاني از روش حريم¬يابي استفاده شده است که با شعاع عملکردي2000 و 1500متري، بخش-هاي شرقي و غربي شهر جهرم تحت پوشش ايستگاه¬هاي آتش¬نشاني قرار نمي¬گيرند. براي مکان-يابي مراکز آتش¬نشاني با استفاده از GIS مراحل مختلفي بايد طي شود: 1- شناسايي داده¬هاي مورد استفاده، 2- شناسايي عوامل تأثير¬گذار در مکان¬يابي مراکز آتش‌نشاني جديد، 3- ورود عوامل تأثيرگذار به سيستم اطلاعات جغرافيايي، 4- ارزش¬گذاري لايه‌هاي اطلاعاتي، 5- هم‌پوشاني لايه¬ها با در نظر گرفتن ضريب اهميت معيار¬ها، 6- ورود وزن نهايي به GIS و ترکيب لايه¬هاي اطلاعاتي، 7- تطبيق نتايج الگوي مکان¬يابي با واقعيات زميني. نتيجه¬گيري: با توجه به افزايش جمعيت در آينده، توسعة شهر و افزايش مهاجرت از روستاهاي اطراف به شهر و کمبود امکانات و مراکز آتش-نشاني موجود در پاسخگويي به نياز¬ها، ايجاد مرکز آتش¬نشاني جديد براي شهر جهرم ضروري به نظر مي¬رسد. نتايج اين تحقيق، به‌کارگيري لايه¬هاي اطلاعاتي مختلف و کارآمدي سامانه اطلاعات جغرافيايي GIS را به خصوص در مکان¬يابي مراکز آتش¬نشاني و همچنين ارزيابي وضعيت موجود در شهر جهرم را نشان مي¬دهد. بنابراين پس از تطبيق نتايج الگوي مکان¬يابي با واقعيت موجود در منطقه مورد مطالعه و با درنظرگرفتن کليه پارامترهاي مؤثر در فرايند مکان¬يابي، درنهايت 2 مکان براي ايجاد ايستگاه آتش‌نشاني جديد مناسب تشخيص داده ش

Persian Text Classification Enhancement by Latent Semantic Space

Heterogeneous data in all groups are growing on the web nowadays. Because of the variety of data types in the web search results, it is common to classify the results in order to find the preferred data. Many machine learning methods are used to classify textual data. The main challenges in data classification are the cost of classifier and performance of classification. A traditional model in IR and text data representation is the vector space model. In this representation cost of computations are dependent upon the dimension of the vector. Another problem is to select effective features and prune unwanted terms. Latent semantic indexing is used to transform VSM to orthogonal semantic space with term relation consideration. Experimental results showed that LSI semantic space can achieve better performance in computation time and classification accuracy. This result showed that semantic topic space has less noise so the accuracy will increase. Less vector dimension also reduces the computational complexity

Simplified inverse filter tracked affective acoustic signals classification incorporating deep convolutional neural networks

Facial expressions, verbal, behavioral, such as limb movements, and physiological features are vital ways for affective human interactions. Researchers have given machines the ability to recognize affective communication through the above modalities in the past decades. In addition to facial expressions, changes in the level of sound, strength, weakness, and turbulence will also convey affective. Extracting affective feature parameters from the acoustic signals have been widely applied in customer service, education, and the medical field. In this research, an improved AlexNet-based deep convolutional neural network (A-DCNN) is presented for acoustic signal recognition. Firstly, preprocessed on signals using simplified inverse filter tracking (SIFT) and short-time Fourier transform (STFT), Mel frequency Cepstrum (MFCC) and waveform-based segmentation were deployed to create the input for the deep neural network (DNN), which was applied widely in signals preprocess for most neural networks. Secondly, acoustic signals were acquired from the public Ryerson Audio-Visual Database of Affective Speech and Song (RAVDESS) affective speech audio system. Through the acoustic signal preprocessing tools, the basic features of the kind of sound signals were calculated and extracted. The proposed DNN based on improved AlexNet has a 95.88% accuracy on classifying eight affective of acoustic signals. By comparing with some linear classifications, such as decision table (DT) and Bayesian inference (BI) and other deep neural networks, such as AlexNet+SVM, recurrent convolutional neural network (R-CNN), etc., the proposed method achieves high effectiveness on the accuracy (A), sensitivity (S1), positive predictive (PP), and f1-score (F1). Acoustic signals affective recognition and classification can be potentially applied in industrial product design through measuring consumers’ affective responses to products; by collecting relevant affective sound data to understand the popularity of the product, and furthermore, to improve the product design and increase the market responsiveness

The expressions and parameters of moving image art, based on experimental film, video art and media art

This thesis is dedicated to the development of the aesthetic expressions of three main genres of “Moving Image Art” from the 20's until today, so as to refine their different forms and directions. Furthermore, it takes the moving image as one of the clues to track the evolution of media in contemporary art, which is a course from sole-media to inter-media. In this research, “Moving Image Art” refers to “experimental” moving images created by contemporary artists, excluding commercial moving images such as industrial films and TV programs. The history of “Moving Image Art”is divided into three periods according to my study, which are “The Film Times”, “The Cassette Times”, and “The Digital Times”. In correspondence, three genres of moving image art that were born in the three eras are “Experimental Film”,”Video Art” and “Media Art”. “The Film Times” is from the 20s to the 60s of the 20th century. A new division of “artistic”, “avant-garde” films that compared to standardized fiction or documentary films was called “Experimental Film”. Through studies of some leading experimental filmmakers like Viking Eggeling, Oskar Fischinger, Fernand Leger, Stan Brakhage, the first aesthetic expression of this genre is defined as “moving painting", due to the fact that their works are often animations composed of abstract paintings without coherent movement. From the works of Maya Deren and Andy Warhol, the second aesthetic expression is summarized as "extreme narrative", the extensions of which are “metaphoric narrative” and “minimalist narrative”. “The Cassette Times” is between the 60s and the 90s, therein the new category of moving image art is “Video Art”. From the representative works of Nam June Paik, Bruce Nauman, William Wegman and Bill Viola, three chief aesthetic expressions of Video Art are analyzed. First,"nonlinear time", which means that to be watched in a fixed sequence is often not vital for video art works, nevertheless experimental films basically follow linear timeline. The second is "conceptual", which indicates that many video art works focus more on concept than on film language, while the latter is crucial in experimental films. The last,"space based", because a video art work is often shown as an installation interacting with the exhibiting space, yet a typical experimental film is just shown on a screen. “The Digital Times” started since the 90s, until today, in our time “Media Art” with moving images has become a tendency of “Moving image Art”. After some analysis on the works of Stelarc, Mariana Rondón and Cao Fei, as well as some multi-media performances, two dominant aesthetic expressions of media art are deduced. One is "Interaction", obviously manifested in Internet art works and interactive performances. The other one is"Intermedia", which encompasses media art works that are made of various medias overlapping each other. Among media art works, the role of the moving image is distinct in every work, increasingly it has become a component or a method, but not the main media of the creation, because today’s art has a hybrid face

DATA-DRIVEN BAYESIAN METHOD-BASED TRAFFIC CRASH DRIVER INJURY SEVERITY FORMULATION, ANALYSIS, AND INFERENCE

Traffic crashes have resulted in significant cost to society in terms of life and economic losses, and comprehensive examination of crash injury outcome patterns is of practical importance. By inferring the parameters of interest from prior information and studied datasets, Bayesian models are efficient methods in data analysis with more accurate results, but their applications in traffic safety studies are still limited. By examining the driver injury severity patterns, this research is proposed to systematically examine the applicability of Bayesian methods in traffic crash driver injury severity prediction in traffic crashes. In this study, three types of Bayesian models are defined: hierarchical Bayesian regression model, Bayesian non-regression model and knowledge-based Bayesian non-parametric model, and a conceptual framework is developed for selecting the appropriate Bayesian model based on discrete research purposes. Five Bayesian models are applied accordingly to test their effectiveness in traffic crash driver injury severity prediction and variable impact estimation: hierarchical Bayesian binary logit model, hierarchical Bayesian ordered logit model, hierarchical Bayesian random intercept model with cross-level interactions, multinomial logit (MNL)-Bayesian Network (BN) model, and decision table/na\xefve Bayes (DTNB) model. A complete dataset containing all crashes occurring on New Mexico roadways in 2010 and 2011 is used for model analyses. The studied dataset is composed of three major sub-datasets: crash dataset, vehicle dataset and driver dataset, and all included variables are therefore divided into two hierarchical levels accordingly: crash-level variables and vehicle/driver variables. From all these five models, the model performance and analysis results have shown promising performance on injury severity prediction and variable influence analysis, and these results underscore the heterogeneous impacts of these significant variables on driver injury severity outcomes. The performances of these models are also compared among these methods or with traditional traffic safety models. With the analyzed results, tentative suggestions regarding countermeasures and further research efforts to reduce crash injury severity are proposed. The research results enhance the understandings of the applicability of Bayesian methods in traffic safety analysis and the mechanisms of crash injury severity outcomes, and provide beneficial inference to improve safety performance of the transportation system