• 한국토양비료학회지
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• 제45권2호
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• pp.280-286
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• 2012

본 연구는 바이오가스 생산시설과 연계하는 시설채소 오이의 청정에너지 농업 시스템 구축을 위하여 물질 및 에너지 수지 분석하였으며, 물질 및 에너지 수지 분석을 통해 시설채소 청정에너지 시스템의 도입 방안을 검토하였다. 시설 채소 오이 재배지의 연간 가온용 순에너지요구량 ($E_{YHED}$)을 충족시키는 바이오가스양은 촉성과 반촉성 재배에서 각각 9,482, $2,636Nm^3\;10a^{-1}$ (60% 메탄함량을 기준)이었으며, 바이오가스 생산을 위해서 각각 양돈슬러리 511, $142m^3\;10a^{-1}$가 요구되었다. 해당 양돈슬러리에서 발생하는 질소(N)와 인산 ($P_2O_5$)은 촉성재배에서 1,788, $511kg\;10a^{-1}$, 반촉성 재배에서 497, $142kg\;10a^{-1}$이었으며, 비료성분의 농지환원을 위해서는 촉성 재배의 경우 질소시비 기준 7.5 ha, 반촉성 재배의 경우 질소시비 기준 2.1 ha의 오이재배 면적이 요구되었다. 가온기간 중 촉성 재배에서 일일 가온에너지 요구량 ($E_{i,DHED}$)은 최소 7.7, 최대 515.1, 평균 $310.2Mcal\;10a^{-1}\;day^{-1}$을 나타냈으며, 반촉성 재배에서 일일 가온에너지 요구량 ($E_{i,DHED}$)은 최소 5.3, 최대 258.0, 평균 $165.1Mcal\;10a^{-1}\;day^{-1}$을 나타났다. 촉성 및 반촉성 재배에서 일일 가온에너지 요구량 ($E_{i,DHED}$)의 평균치를 기준으로 산출한 바이오가스 생산 시설의 양돈슬러리 유입용량은 각각 3.3, $1.7m^3\;day^{-1}$이었으며, 일일 가온에너지 요구량 ($E_{i,DHED}$)의 최대값을 기준으로 한 유입용량은 각각 5.4, $2.7m^3\;day^{-1}$로 나타났다. 또한 소화액의 처리측면에서 지역특성에 따라 액비이용을 고려한 바이오가스 생산시설 용량설계와 하절기의 잉여 바이오가스 활용 방안의 모색이 필요하였다.

• 수산해양기술연구
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• 제29권4호
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• pp.247-259
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• 1993

The fishing experiment was carried out by the training ship Saebada in order to analyse the mesh selectivity for trawl cod-end, in the Southern Korea Sea and the East China Sea from June. 1991 through August, 1992. The trawl cod-end used in this experiment has the trouser type of cod-end with cover net. and the mesh selectivity was examined for the five kinds of the opening of mesh in its cod-end part. A total of 163 hauls, of which having mesh size 51.2mm ; A 89, 70.2mm ; B 54, 77.6mm ; C 55, 88.0mm ; D 52 and 111.3mm ; E 20 were used respectively. Selection curves and selection parameters were calculated by using a logistic function, S=1/(1+exp super(-(aL+b)) ). The mesh election master curves were estimated by S=1/(1+exp super(-[a(L/M)+$\beta$]) ). and the optimum mesh size were calculated with (L/M) sub(50) of master curve. In these cases 'a' and '$\alpha$' are slope, 'b' and '$\beta$' are intercept. 'L' is body length of the target species of fishes, 'M' is the mesh size, and 'S' denotes mesh selectivity. In this report, the four species of compressed form fishes were taken analized according to fish shape, and 'S' denotes mesh selectivity. In this report, the four species of compressed form fishes were taken analized according to fish shape, and the results obtained are summarized as follows: 1. Red seabream Pagrus major(Temminct et Schlegel) and yellow porgy Dentex tumifrons(Temminct et Schlegel) ; Selection rate in each mesh size of A, B, C, D and E were 99.7%, 97.5%, 91.4%, 76.7% and 57.8% respectively. Selection parameters 'a' and 'b' of mesh sizes C, D and E were 2.65 and -28.62, 4.40 and -77.73, 2.31 and -46.99, and their selection factors were 1.39, 2.10, 1.83 respectively. Selection parameters of master curve '$\alpha$' and '$\beta$' were 3.05 and -5.65 respectively, and (L/M) sub(50) was 1.85. The optimum mesh size of Red seabream was 141mm. 2. Filefish Thamnaconus modestus (Gunther) ; Selection rate in each mesh size of A, B, C, D and E were 99.6%, 98.3%, 91.2%, 80.0% and 48.6% respectively. Selection parameters 'a' and 'b' of mesh sizes C, D and E were 5.82 and -55.10, 2.92 and -36.90, 3.91 and -63.09, and their selection factors were 1.35, 1.44, 1.45 respectively. Selection parameters of master curve '$\alpha$' and '$\beta$' were 3.02 and -4.32 respectively, and (L/M) sub(50) was 1.43. The optimum mesh size was 129mm. 3. Target dory Zeus faber Valenciennes ; Selection rate in each mesh size of A, B, C, D and E were 99.7%, 100%, 83.2%, 91.6% and 65.0% respectively. Selection parameters 'a' and 'b' of mesh sizes C, D and E were 3.85 and -32.46, 4.19 and -57.38, 2.45 and -40.03, and their selection factors were 1.09, 1.56, 1.47 respectively. Selection parameters of master curve '$\alpha$' and '$\beta$' were 2.64 and -3.53 respectively, and (L/M) sub(50) was 1.34. The optimum mesh size was 127mm. 4. Butterfish Psenopsis anomala (Temminct et Schlegel) ; Selection rate in each mesh size of A, B, C, D and E were 99.2%, 34.1%, 46.5%, 14.3% and 2.4% respectively. Selection parameters 'a' and 'b' of mesh sizes B, C and D were 5.35 and -71.70, 5.07 and -69.25, 3.31 and -62.06 and their selection factors were 1.91, 1.75, 2.13 respectively. Selection parameters of master curve '$\alpha$' and '$\beta$' were 3.16 and -6.24 respectively, and (L/M) sub(50) was 1.98. The optimum mesh size was 71mm.

• 한국농림기상학회:학술대회논문집
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• 한국농림기상학회 2014년도 추계 학술발표논문집
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• pp.1-24
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• 2014

2012년 상반기 미국의 옥수수 생산량은 재배면적의 증가 등으로 20% 이상 증가할 것으로 예측되었다. 하지만 2012년 봄 미국의 폭염과 가뭄이 발생하였고, 그 현상이 지속될 것으로 예측되면서 많은 경제학자들, 국제곡물수급 관련 전문가들은 미국의 옥수수 생산량이 감소할 것으로 예측했다. 실제로, 2013년 미국 농무부 (USDA)의 작물생산 총 보고서에서 2012년 미국 폭염과 가뭄으로 미국의 2012년 옥수수 생산량은 2011년에 비해 20% 감소했다고 발표했다. 많은 연구에서 곡물 생산량을 예측하지만 기상이변과 함께 작물의 생물학적 반응뿐 아니라 경제모형이 결합된 연구는 많지 않다. 본 연구에서는 기상이변과 작물모형, 경제모형을 결기상합하여 미국의 최대 옥수수 생산지역의 옥수수 생산량을 예측하고 생산량의 변화가 개발도상국의 식량안보에 어떤 영향을 줄 것인가를 예측하였다. 기상이변 시나리오를 재현하기 위해 미국 NOAA의 NCDC에서 미국의 폭염 발생 연도의 정보를 획득하고 해당 연도에 대하여 미국 전역의 기상관측소에서 6월부터 8월까지의 월별 일 기상자료 (최고 및 최저기온, 강수량)를 수집하였으며 기준연도 (1950-2000)에 산술평균 방법으로 폭염/가뭄 정보를 적용했다. 미래 시나리오 (2050)는 CGIAR의 CCAFS에서 $CO_2$ emission scenario에 따라 A1B와 B1, 전지구 모형에 따라 CSIRO-MK 3와 MIROC 3.2를 다운로드하였으며, 해상도는 5 arc-minutes (적도에서 10km)이다. 작물모형 (CERES-Maize)으로부터 출력된 옥수수의 생물리학적 결과는 경제모형의 단위 (FPU)로 다시 정리되어 사회경제, 정책과 농업생산을 예측하기 위해 글로벌 경제모형 (IMPACT2)에 입력되었다. 작물모형에서 기준연도에 비해 미국 폭염과 가뭄에 의한 옥수수 생산량은 29% 감소할 것으로 예측되었다. 미래 시나리오 B1의 CSIRO-MK 3과 MIROC 3.2에서는 36% 감소할 것으로 예측되었으며, A1B의 CSIRO-MK 3에서는 38%, MIROC 3.2에서는 58% 감소할 것으로 나타났다. 미국의 기상이변으로 인한 옥수수 생산량의 감소는 전세계 옥수수 시장에 부정적 영향을 끼칠 것으로 예상되면서 세계 옥수수 소비의 감소로 이어질 것으로 예측되었다. 사하라 사막 이남 아프리카 (SSA)의 나라들에서 가장 많은 기아인구가 발생하고 그 외 남아시아와 라틴 아메리카, Caribbean 지역의 나라들에서 기아와 함께 식량 불안이 증가할 것으로 나타났다. 옥수수를 매일 섭취하는 사람들은 옥수수 생산량 감소에서 비롯된 옥수수 소비 감소에 즉각적으로 반응하지 못함으로써 영양불균형에 처하는 등 식량수급의 불안정은 이러한 개발도상국 지역에서 계속 악화될 것으로 나타났다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1041-1043
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• 1993

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].