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Showing 5 results for Zareian
Volume 0, Issue 0 (in press 2024)
Abstract
Bars with standard hooks in tension are often used to anchor reinforcing bars where concrete dimensions element are not sufficient to provide the required development length for straight reinforcement. In previous researches, concrete breakout failure was the predominant failure mode of hooked bars. Closely-spaced hooks provide a lower strength per hooked bar than more widely-spaced hooked bars because the area of the breakout surface is reduced for the more closely-spaced bars. The effects of fy of bars, spacing, and confinement by ties or stirrups have been updated to reflect test results, in 25.4.3.1 of ACI 318-19. In this formula, The confining reinforcement factor ψr is based on test results reported by Ajaam et al. (2018). Base on ACI 318-19 definition, Ath is total cross-sectional area of ties or stirrups confining hooked bars. Therefore crossties are not included in the relationship. The purpose of this study is to expand the understanding of the behavior of confinement effect of crossties on development length bars with 90 degree bent hooks in tension. In this study, 3 simulated beam-column joints were tested as a continuation of previous work by Ajaam et al. (2018). The results of this experimental study show that the ACI318-19 provisions underestimate the contribution of confinement by crossties on the development length bars with 90 degree bent hooks in tension.
Volume 4, Issue 2 (4-2012)
Abstract
The relationship between the clergy as amajor social force and the Safavid state canlead our simple understandingto the exact understanding that may answer many of our questions in the social history of Iran. This paper considers the relations between the government and the clergy bystudying the fall and the rise of theclerics and the government relations, forms of their participation in policy, its development in the Safavid era and factors affecting on this evolution. This relationship can be investigated using the Tily’s theory and it can be saidthat this relationship takes many forms. Three overall types include membership in theoverall system of monarchy, influencing policy and resignation. Thismethodology is based on descriptive historical narrative approach.
Volume 12, Issue 6 (January & February 2022 2021)
Abstract
Text mining’ refers to the computational process of unstructured text analytics for extracting latent linguistic layers and themes. It is especially significant as content or thematic analysis in descriptive and interpretive studies. This process begins with structuring simple texts and proceeds with summarizing, classifiing, modelling, evaluating and interpreting the inherent textual concepts and patterns. Given that this method counts as an interdisciplinary innovation especially in discoursal studies, it is to be pursued more intensively in academic studies. Despite the multitude of English studies in this area, there has been little interest to date in text mining amongst Iranian researchers as evidenced by the critically limited number of local Persian and English studies. Thus looking into the theory and practice of text mining and its major analytic tools and methods in Persian and English, this paper aims to prepare the ground for utilizing this methodology in language studies.
The last two decades faced a major increase in the rate and accuracy of knowledge generation in language studies due to advances in interdisciplinary studies of applied linguistics and computer sciences. At the heart of methodological innovations especially in discourse studies lies ‘text mining’ whose merits have only recently been appreciated by researchers. ‘Text mining’, ‘text data mining’ or ‘Text Analysis’ is the use of different data mining algorithms and methods like natural language processing and linguistic as well as statistical techniques to derive linguistic features, significant patterns and valuable themes from the unstructured texts through collecting unstructured data, pre-processing and cleansing them to detect and remove anomalies and processing and controlling operations (Zhou et al, 2012). These processes are further broken down into feature extraction, structural analysis, text summary, text classification, text clustering, and association analysis. Text mining is actually a complicated procedure of extracting valuable, significant patterns and trends from a large number of textual data used for such functions as product suggestion analysis, social media opinion mining, and sentiment or trend analysis (He, 2013).
Dating back to Feldman and Dagan (1995), text mining is an innovative methodology with a relatively short history which is often integrated with corpus analysis to computationally analyze a large body of unstructured texts as potential inormatieofinsight. As a subfield of data mining in computer sciences and an interdisciplinary method, text mining borrows from corpus and computational linguistics, whose main purpose is to extract the meta-characters representing textual features (Pons-Porrata et al, 2007). Zhou et al (2017) believe that despite its short history, text mining has been remarkably evolved into the mainstream research methodology in many interdisciplinary areas in the wake of increasingly rapid developments in data mining.
Dating back to Feldman and Dagan (1995), text mining is an innovative methodology with a relatively short history which is often integrated with corpus analysis to computationally analyze a large body of unstructured texts as potential inormatieofinsight. As a subfield of data mining in computer sciences and an interdisciplinary method, text mining borrows from corpus and computational linguistics, whose main purpose is to extract the meta-characters representing textual features (Pons-Porrata et al, 2007). Zhou et al (2017) believe that despite its short history, text mining has been remarkably evolved into the mainstream research methodology in many interdisciplinary areas in the wake of increasingly rapid developments in data mining.
Hashimi et al (2015) explained the steps involved in text mining as a semi-automated process of collecting, structuring and then analyzing textual data as follows: (a) collecting unstructured data from a variety of sources like textual documents, social media, web pages, mails, blogs, etc. using specialized corpora for organization, (b) pre-processing and cleansing the data for removing the anomalies to unveil latent valuable information using text mining tools, (c) unstructured data conversion into relevant structured formats, (d) discovering the underlying data patterns using word structures, sequences and frequency, and (e) extracting useful knowledge and storing them in a secure database for evaluation, later retrieval, trend analysis and possible decision-making. Text mining aslso makes use of lexicometrics dealing with frequency and co-occurrence analysis of vocabulary to derive structures from texts; sentiment analysis is an application of lexicometrics looking for positive or negative emotions in documents and has been used in social media analysis for evaluating public opinion (Shangzhen & Lemen, 2016).
Text mining is an area of inquiry that in itself deserves to be pursued more intensively in future studies and this paper, thus, is an attempt to review its basic principles, procedures and top analytic tools and to raise researchers’ awareness of the virtues of text mining.
Text mining is an area of inquiry that in itself deserves to be pursued more intensively in future studies and this paper, thus, is an attempt to review its basic principles, procedures and top analytic tools and to raise researchers’ awareness of the virtues of text mining.
Volume 14, Issue 2 (May & June 2023 2023)
Abstract
Since the technology of optical recognition of characters is essentially based on Latin script, almost all the algorithms and processes involved in Persian OCR systems are constructed upon the structure and scriptological features of Latin alphabet. This utilization of the means and features of Latin script to design Persian-based OCR systems however, not only has not resulted in the appropriate optical recognition of Persian characters but it also has simultaneously ended in confusion on the part of both the Persian-speaking users and the systems. This paper, therefore, begins with a short review of the significance of language and linguistics in the field of information technology in connection with OCR systems. Then, it will continue with a short history of Persian/Arabic script, while focusing on the scribal features of Persian writing system and its differences with other scripts. In the next part, for effective utilization of the formal elements of the Persian script, these elements have been categorized according to their application and significance in the process of the user’s interaction with Persian OCR systems. Furthermore, through a step by step discussion and analysis of the processes involved in optical recognition of characters based on the scriptological features of the Persian script, not only the deficiencies and faults of the current Latin-based OCR systems will be pinpointed but also a different aspect of the Persian writing system, in connection with its use in computer software, especially OCR systems, will be used so that the reader will practically notice the potentials and capabilities of this complex script in contrast to the simpler Latin writing system. In the end, in order to upgrade and improve the current algorithms employed in Persian OCR systems, the geometrical process of transferring bi-dimensional specifications into mono-dimensional ones has been utilized. The proposed algorithm, which is based on the scriptological features of Persian script, will simultaneously result in the convenient manipulation of patterns, reduction of the bulk of the database, and acceleration of the data processing rate.
1. Introduction
Since the technology of optical recognition of characters is essentially based on Latin script, almost all the algorithms and processes involved in Persian OCR systems are constructed upon the structure and scriptological features of Latin alphabet. This utilization of the means and features of Latin script to design Persian-based OCR systems however, not only has not resulted in the appropriate optical recognition of Persian characters but it also has simultaneously ended in confusion on the part of both the Persian-speaking users and the systems. Therefore, in order to present a different portrait of Persian writing system when working with computers, especially in OCR systems, this research, attempts to describe and analyze the processes involved in optical character recognition based on the scriptological features of the Persian alphabet and elaborate on its differences with the existing Latin-based systems. In line with this objective, after reviewing the history and evolution of the Persian script through different periods, this research gives a classified illustration of the scriptological features of the Persian writing system and its formal elements with a special focus on the OCR processes. Consequently, in this study, the formal elements of the Persian script are categorized according to their application and significance in the interaction of the user with the Persian OCR softwares. Furthermore, the effective utilization of these scriptological elements is expressed in the framework of the existing algorithms, as well as, in the form of a proposed algorithm. The proposed algorithm, on the one hand, results in the practical elimination of the high affectation of the existing algorithms when facing the cursiveness and elongation, of the Persian letters, which previously increased the error rate of the OCR processes; and on the other hand, it highly prevents an increase in the bulk of the database and computations, related to the stored patterns, which previously caused a decrease in the software performance.
2. Literature Review
The study of Persian/Arabic characters, representation have been studied since 1970s (Bonyani & Jahangard, 2020) and the very beginning algorithms for representing Arabic scripts have been released in 1990s. (Margner & El-Abed, 2008). Many researchers including Shafii (2014) gave up holistic segmentation of Persian characters because of difficulties resulted from some special features of Persian alphabet and only worked on sub words, representation instead. The proposed algorithm of Kiaei (2019), regardless of working on printed limited Omni-font texts did not lead to an accepted results and was inefficient facing to words sequence. Rhmati, et al. (2020) as the latest research in the field of character segmentation like many other studies considered baseline connector as a part of a character and their algorithm suggested a procedure to shorten over length baseline connectors in order to facilitate character recognition through the existing systems. The newly done studies on optical character recognition avoid the structural features in the process of recognition and primarily utilize holistic algorithm based on neural networks inorder to extract distinctive features of characters (Bonyani & Jahangard, 2020).
3. Discussion
Using the concept of baseline connector (BC) in the design of the proposed algorithm, the connected characters will all have an identical BC component. This means that each instance of the BC, regardless of its length, will be identified as one identical component. This way, the BC component of each character and its variable extra stretches are removed by means of algorithms and mathematical processes and replaced by an identical special code. This is different from the common known methods of character segmentation in which the whole character including the BC component goes through an image processing stage. Here, in the pattern comparison stage, the system at first recognizes the BC component and removes its extra stretches and then compares the remaining letter image with the stored patterns. By removing the BC component from the text image and replacing it with a simple code, contrary to what is customary: 1) the letter segmentation process occurs naturally and successfully; 2) instead of comparing a letter image with all existing patterns, due to the presence of a BC component code, the comparison and recognition process occurs only between the letter image (raw letter) and the patterns belonging to the same set since based on the position of the BC component, the letters can be divided into four sets: a) letter + BC (= initial letters); b) BC + letter (= final letters); c) BC+letter+BC (= medial letters); d) isolated form without a BC on either side (= isolated letters). As a result, instead of comparing the raw letter and trying to match it with all the existing patterns, this comparison is made only between the raw letter (letter image) and the patterns in one of the above four main groups; 3) by removing the BC component, which occurs in various lengths and in practice has no effect on the reading of the word, this component is removed from the letter image and thereby a great number of patterns whose difference lies in their length of the BC component will be eliminated and thus the process of pattern recognition is sped up; 4) on the other hand, the BC component and its extension, i.e. the baseline, divides the letter components into two groups: a) above the baseline; b) below the baseline. The classification of components into upper and lower sets based on the BC component results in further simplification of the pattern comparison process since: a) this way, upper elements are compared only with upper patterns and lower elements are compared only with lower patterns; b) instead of the overall comparison of the raw image frame with the patterns, first the baseline of the raw element is matched with the baseline of the pattern and then the comparison is made in the two upper and lower sections.
4. Conclusion
The functioning status of present Persian OCR soft wares indicates that there are two main challenges in doing research in this field, one related to solving Persian script problems and another concerned with algorithm design and programming. In this study it was determined that the origin of the current challenges is that the programmers ignored the original function and existing philosophy of baseline connector and consider it as a part of the Persian words. This study tried to improve Persian OCR sub word segmentation throughout utilizing an outstanding feature of baseline connector and at the same time its formal elimination. Base line connector forms a large part of Persian texts and its formal deletion from the raw patterns has caused an impressive reduction in the volume of errors in the processing level. Furthermore, considering baseline connector as a criterion can lead to the possibility of Persian scripts and patterns classification. Consequently, instead of comparing one raw element with all patterns, the comparing procedure has limited to homogenous groups and the processing speed has increased. Finally, in order to upgrade and improve the current algorithms employed in Persian OCR systems, the geometrical process of transferring bi-dimensional specifications into mono-dimensional ones has been utilized. The proposed algorithm, which is based on the script logical features of Persian script, will simultaneously result in the convenient manipulation of patterns, reduction of the bulk of the database, and acceleration of the data processing rate.
Rahim Dallali Isfahani, Mohammad Vaez Barzani, Saeid Zareian,
Volume 16, Issue 3 (Autumn 2016 2016)
Volume 16, Issue 3 (Autumn 2016 2016)
Abstract
Economic development, social welfare and improvement of living standards are main issues in economic planning, which requires higher economic growth as precondition. Time preference plays an important role in the health of the economy, given its contribution to capital formation, economic growth, and interest rate. The existence of the time preference reflects social impatience with the present value of consumption relative to future consumption. Therefore, if present generation attributes high weigh to itself in allocating resources between current and future generations, the available resources will decrease, and economic growth will stabilize in a lower rate. According to the microeconomic theoretical foundations, and using mathematical logic, this study provides a logical reasoning in analyzing macroeconomic phenomena. This research aims to illustrate how to time preference impacts economic growth. Using MATLAB software and calibrating the model for Iran’s economy, the optimal paths of consumption, savings, investment and economic growth are extracted with and without commitment. The results show that the optimal paths of capital stock, consumption and economic growth are in higher levels for full commitment than no- commitment, and these converge at the end-points. The final section will examine how to change in time preference and its impact on the optimal paths of variables. Running various scenarios show that an increase in the rate of time preference reduces economic welfare, the effective rate of time preference and economic growth.
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