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Multiobjective image recognition algorithm in the fully automatic die bonder


It is a very important task to automatically fix the number of die in the image recognition system of a fully automatic die bonder.


A multiobjective image recognition algorithm based on clustering Genetic Algorithm (GA), is proposed in this paper.


As a result, time consumed by one image recognition is shortened, the performance of the image recognition system is improved, and the atomization of the system is fulfilled.


Classification of plane contours (closed continuous curves) of object images is an important problem in the theory of image recognition.

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Design and analysis of smallmolecule antineoplastic agents targeting brain tumors by utilizing pattern recognition methods and


Another pattern recognition method known as nonmetric multidimensional scaling discerned finer associations that fell within categorizations by low, moderate, and higher formula weight.


1HNMR spectroscopy and pattern recognition (PR) method were used to assess the acute biochemical effects of light rare earths.


Minimum squared error (MSE) algorithm is one of the classical pattern recognition and regression analysis methods, whose objective is to minimize the squared error summation between the output of linear function and the desired output.


We use pattern recognition technology to confirm the proportionate relationship matching the database and thus achieve the goal of dynamic automatic identification of standing tree limbs.

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 Adaptive pattern recognition has been applied to predict possible location of earthquakes and their magnitude in the BeijingTianjin area and its adjacent regions. It consists of discrimination between two pattern classes on the basis of binary attributes. A trial set of coefficients are progressively modified until maximum discrimination is obtained. The basic steps in the adaptive pattern recognition algorithm are as follows:1. Set power vector W to zero;2. Set a counter K to zero;3. In the epicentres of known... Adaptive pattern recognition has been applied to predict possible location of earthquakes and their magnitude in the BeijingTianjin area and its adjacent regions. It consists of discrimination between two pattern classes on the basis of binary attributes. A trial set of coefficients are progressively modified until maximum discrimination is obtained. The basic steps in the adaptive pattern recognition algorithm are as follows:1. Set power vector W to zero;2. Set a counter K to zero;3. In the epicentres of known earthquakes order first of all, compute the discriminant score for first epicentre area, using equation D1 = WY1;4. If this epicentre area belongs to group A go to 5, otherwise go to step 8;5. If D1>0 go to step 11;6. Increase K by 1;7. Recompute the W from the formula W'=W+αY, and go to step 11;8. If D1<0 go to step 11;9. Increase K by 1;10. Recompute the W from the formula W'=W αY, and go to stcp 11;11. Repeat step 310 for second epicentre area and others;12. If K is zero then none of the epicentres areas are misclassified namely Di = WYi correctly discriminates between two groups;If K is positive, go to step 2.First, analysis is made of the geological features of historical and recent earthquake epicentres and are then grouped to 16 factors.Strong earthquake epicentres in BeijingTianjin area are classified into two pattern classes: epicentres of earthquakes with magnitude of 6.07.0 and those above 7.0.Then previously uncatagorized or unknown earthquake areas are classified into one of the two pattern classes, based on the discriminant function produced by the computer. The results then obtained from pattern recognition are noteworthy. The Tangshan earthquake occurred just between the two areas which after the calculation are shown to be the most dangerous zones.After the Tangshan earthquake another prediction was made of future earthquake location and their magnitude. No matter whether this prediction will be accurate or not, we still consider that adaptive pattern recognition may be a promising method for prediction of earthquake location and their time of occurrance and for the study of numerical prediction of earthquake or say predictive geology.  本文应用自适应图象识别预报京津及邻区可能发生强震的地点和强度。自适应图象识别方法仅是根据二进制标志作两种图象等级的判别。此方法开始用一组试验系数逐步修改直到达到最大判别。在地震数值预报研究中,可能是一条有希望的途径。  Pattern recognition is a subject that has been developed in the last twenty years and it has already been widely applied in many fields of science. I. M. Gelfand, P. Press and others have applied it to seismic zoning. In this paper, we use the method in earthquake prediction to predict the time of occurrence of an impending strong earthquake.The entire period of time to be investigated is divided into many intervals, each of which is assigned a rating, D for dangerous intervals and N for nondangerous intervals,... Pattern recognition is a subject that has been developed in the last twenty years and it has already been widely applied in many fields of science. I. M. Gelfand, P. Press and others have applied it to seismic zoning. In this paper, we use the method in earthquake prediction to predict the time of occurrence of an impending strong earthquake.The entire period of time to be investigated is divided into many intervals, each of which is assigned a rating, D for dangerous intervals and N for nondangerous intervals, according to a definite criterion. The characters of seismicity of mediumsize earthquakes before a strong earthquake are put forward by a problem table, then pattern recognition is made in two steps:(1). "learning" Answers to m questions of P intervals form a m×P matrix, binary coded "yes" or "no". By "learning". New features to one or combinations of two or three questions, called the "characteristics" of D and N canbe recognized.(2). "Voting" The difference of the numbers of D and N characteristics is A. The interval is recognized as being cangerous (D) when △ is equal to or larger than a threshold value, otherwise it is recognized as being nondangerous (N).The results show that during a definite period of time before a strong earthquake, when the mediumsize earthquake activity rises to a certain level and the ratio between the number of earthquakes of a definite magnitude to the number of earthquakes with magnitude a half unit smaller is above normal and also when the number of mediumsize earthquakes increases with time, then taking all such characterisitics together into consideration, a strong earthquake would be expected within the next interval of time.In addition, controlled experiments have also been performed, showing that the results of pattern recognition is rather stable.  图象识别是近二十年来发展起来的一门学科,它已广泛应用于许多领域中。盖尔芬德(I.M.Gelfand)、普雷斯(F.Press)等人将它用于地震危险区的划分。本文将图象识别方法用于地震预测中,以识别强震发生的时间。 按一定标准将所研究的全部时间划分为危险时间段D和不危险时间段N。以问题表的形式提出大地震前中等地震活动的特性,然后分两步进行图象识别: 1.“学习”。对P个时间段m个问题的回答是m×p的矩阵,回答以二进制(是或非)表示。通过“学习”,识别出一个、两个或三个问题组合的新“特征”,称之为D和N的“性质”。 2.“投票”。D和N“性质”数目的差是△,当△大于或等于某阈值时,则识别为危险段D,否则为N。 结果表明,大地震发生前的一定时期内,中等地震活动增至一定水平、相差半级的中等地震活动水平的比值较正常情况增高以及大震前中等地震活动随时间增强等“性质”的综合,表明未来时间段內可能发生大地震。 此外还作了控制试验,说明图象识别结果是稳定的。  In 1965, an article titled "Fuzzy Sets" by L.A.Zadeh, an American scholar, was published. In this paper, the author put forward the notation of "fuzzy sets" and striked out the method of solving the problems of fuzzy mathematics. In recent years, the fuzzy sets theory has been developing rapidly and is followed with great interest by more and more scientists in different specific fields such as the cybernetics, the pattern recognition as well as the computer science. They have been trying to solve problems with... In 1965, an article titled "Fuzzy Sets" by L.A.Zadeh, an American scholar, was published. In this paper, the author put forward the notation of "fuzzy sets" and striked out the method of solving the problems of fuzzy mathematics. In recent years, the fuzzy sets theory has been developing rapidly and is followed with great interest by more and more scientists in different specific fields such as the cybernetics, the pattern recognition as well as the computer science. They have been trying to solve problems with fuzzy mathematics and some positive results have already been achieved.In this paper, the author firstly examined the fuzzy phenomenon appeared in the evaluation of the reservoirs and set up the relations of dependency between the evaluation factors and the evaluation objects by means of the fuzzy sets theory. By using the fuzzy comprehensive evaluation, the differences of various operators were analyzed and the mathematic models of the generalized operators were determined. With these as the fundamental work, the evaluation with some accuracy for oil and water formation has thus been made. In order to improve the flexibility of the computer and the correctness as well as the objectiveness of the evaluation, the ways of renewing one's knowledge and correcting the rerors were introduced.  1965年、美国学者查德(L.A.Zadeh)发表了一篇“模糊集会”的论文,提出了模糊集合的概念和解决模糊数学问题的方法.近年来,模糊集合理论有了很大的发展,引起了控制论、图象识别、计算机等学科的科学工作者的极大关注,他们试用该方法处理问题并取得了一些可喜的进展.本文分析了储集层油气评价工作中存在的模糊现象,运用模糊集合理论建立了评价因素对评价对象的隶属度;采用模糊综合评价方法,分析了各种算子的差异,确定了广义算子的数学模型;在储集层油气评价这样一个复杂的系统中,对油水层作出了有一定精度的评价.文章还讨论了不断补充新知识和修正错误的再学习方法,使模糊数学方法在认识和总结问题方面能吸收人脑对于复杂系统判别的特点,有利于提高电子计算机的灵活性,评价的正确性及客观性.   << 更多相关文摘 
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