본 논문은 연결정보가 없는 3차원 점으로부터 차이분할메쉬를 직접 생성하는 새로운 메쉬 복원 알고리즘을 제안한다. 차이분할메쉬는 표현해야 하는 각 정점을 1차원 상수 차이 값으로 표현하는 메쉬로써 저장해야하는 데이타양을 획기적으로 줄이면서 분할메쉬의 장점인 분할연결성을 가지므로 다단계 표현이 가능한 새로운 메쉬 표현법이지만 차이분할메쉬를 생성하는 기존의 방법은 레인지 데이타로부터 복원된 비정형 메쉬로부터 차이분할메쉬로의 변환을 거쳐야 하는 문제점이 있다. 제안 알고리즘은 비정형 메쉬로부터의 메쉬 변환과정이 필요 없이 연결정보가 없는 레인지 데이타로부터 차이값에 기반한 분할메쉬를 매쉬 복원과정에서 직접 생성해 낼 수 있다. 기본 아이디어는 연결정보가 없는 점 데이타만을 이용하여 이에 근사한 파라메트릭 도메인을 생성한 후 도메인 상의 각 정점의 법선 벡터 방향으로 세부 표면 정보를 샘플링 하는 것이다. 이를 위하여 우리는 분할곡면 근사기법을 적용한 파라매트릭 도메인 생성기법을 적용하여 입력 점 데이타에 최대한 근사하는 기본메쉬를 생성하며, 연결정보가 없는 점 데이타로부터 세부 표면정보를 올바르게 샘플링 하기 위한 법선벡터와 교차하는 유효한 삼각형 판정기준을 제시한다. 또한 기존 메쉬 복원 기법에서 사용되던 전역 에너지 최적화 방법 대신 입력 데이타를 고려한 지역적 라플라시안 평활화를 이용하여 고품질의 메쉬를 빠르게 복원할 수 있다. 이렇게 복원된 차이분할메쉬는 적은 데이터로 자세한 표현이 가능해져 메쉬 간략화나 압축 등의 후처리 과정이 필요 없으며. 분할연결성을 이용한 다단계 애니메이션등의 다양한 응용분야에 활용 가능하다.6으로 남녀학생 모두 총 열량에 대한 지질의 섭취비율이 높았다. 인, 비타민 B
Petrochemical, K-Ar dating, Sand Rb/Sr isotopes, metallogenic zoning, paleomagnetic and geotectonic studies of the Gyongsang basin were carried out to examine applicability of plate tectonics to the post-late Cretaceous igneous activity and metallogeny in the southeastern part of Korean Peninsula. The results obtained are as follows: 1. Bulgugsa granitic rocks range from granite to adamellite, whose Q-Ab-Or triangular diagram indicates that the depth and pressure at which the magma consolidated increase from coast to inland varying from 6 km, 0.5-3.3 kb in the coastal area to 17 km, 0.5-10 kb in the inland area. 2. The volcanic rocks in Gyongsang basin range from andesitic to basaltic rocks, and the basaltic rocks are generally tholeiitic in the coastal area and alkali basalt in the inland area. 3. The volcanic rocks of the area have the initial ratio of
Fuzzy logic based Control Theory has gained much interest in the industrial world, thanks to its ability to formalize and solve in a very natural way many problems that are very difficult to quantify at an analytical level. This paper shows a solution for treating membership function inside hardware circuits. The proposed hardware structure optimizes the memoried size by using particular form of the vectorial representation. The process of memorizing fuzzy sets, i.e. their membership function, has always been one of the more problematic issues for the hardware implementation, due to the quite large memory space that is needed. To simplify such an implementation, it is commonly [1,2,8,9,10,11] used to limit the membership functions either to those having triangular or trapezoidal shape, or pre-definite shape. These kinds of functions are able to cover a large spectrum of applications with a limited usage of memory, since they can be memorized by specifying very few parameters ( ight, base, critical points, etc.). This however results in a loss of computational power due to computation on the medium points. A solution to this problem is obtained by discretizing the universe of discourse U, i.e. by fixing a finite number of points and memorizing the value of the membership functions on such points [3,10,14,15]. Such a solution provides a satisfying computational speed, a very high precision of definitions and gives the users the opportunity to choose membership functions of any shape. However, a significant memory waste can as well be registered. It is indeed possible that for each of the given fuzzy sets many elements of the universe of discourse have a membership value equal to zero. It has also been noticed that almost in all cases common points among fuzzy sets, i.e. points with non null membership values are very few. More specifically, in many applications, for each element u of U, there exists at most three fuzzy sets for which the membership value is ot null [3,5,6,7,12,13]. Our proposal is based on such hypotheses. Moreover, we use a technique that even though it does not restrict the shapes of membership functions, it reduces strongly the computational time for the membership values and optimizes the function memorization. In figure 1 it is represented a term set whose characteristics are common for fuzzy controllers and to which we will refer in the following. The above term set has a universe of discourse with 128 elements (so to have a good resolution), 8 fuzzy sets that describe the term set, 32 levels of discretization for the membership values. Clearly, the number of bits necessary for the given specifications are 5 for 32 truth levels, 3 for 8 membership functions and 7 for 128 levels of resolution. The memory depth is given by the dimension of the universe of the discourse (128 in our case) and it will be represented by the memory rows. The length of a world of memory is defined by: Length = nem (dm(m)＋dm(fm) Where: fm is the maximum number of non null values in every element of the universe of the discourse, dm(m) is the dimension of the values of the membership function m, dm(fm) is the dimension of the word to represent the index of the highest membership function. In our case then Length=24. The memory dimension is therefore 128*24 bits. If we had chosen to memorize all values of the membership functions we would have needed to memorize on each memory row the membership value of each element. Fuzzy sets word dimension is 8*5 bits. Therefore, the dimension of the memory would have been 128*40 bits. Coherently with our hypothesis, in fig. 1 each element of universe of the discourse has a non null membership value on at most three fuzzy sets. Focusing on the elements 32,64,96 of the universe of discourse, they will be memorized as follows: The computation of the rule weights is done by comparing those bits that represent the index of the membership function, with the word of the program memor . The output bus of the Program Memory (μCOD), is given as input a comparator (Combinatory Net). If the index is equal to the bus value then one of the non null weight derives from the rule and it is produced as output, otherwise the output is zero (fig. 2). It is clear, that the memory dimension of the antecedent is in this way reduced since only non null values are memorized. Moreover, the time performance of the system is equivalent to the performance of a system using vectorial memorization of all weights. The dimensioning of the word is influenced by some parameters of the input variable. The most important parameter is the maximum number membership functions (nfm) having a non null value in each element of the universe of discourse. From our study in the field of fuzzy system, we see that typically nfm 3 and there are at most 16 membership function. At any rate, such a value can be increased up to the physical dimensional limit of the antecedent memory. A less important role n the optimization process of the word dimension is played by the number of membership functions defined for each linguistic term. The table below shows the request word dimension as a function of such parameters and compares our proposed method with the method of vectorial memorization[10]. Summing up, the characteristics of our method are: Users are not restricted to membership functions with specific shapes. The number of the fuzzy sets and the resolution of the vertical axis have a very small influence in increasing memory space. Weight computations are done by combinatorial network and therefore the time performance of the system is equivalent to the one of the vectorial method. The number of non null membership values on any element of the universe of discourse is limited. Such a constraint is usually non very restrictive since many controllers obtain a good precision with only three non null weights. The method here briefly described has been adopted by our group in the design of an optimized version of the coprocessor described in [10].