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    • 2. 发明公开
    • 유전자 알고리즘을 이용한 부하 분배 장치 및 그 방법
    • KR1020170113937A
    • 2017-10-13
    • KR1020160037906
    • 2016-03-29
    • 인하대학교 산학협력단
    • 권구인강승보
    • H04L12/803
    • H04L47/125G06N3/126H04L43/16H04L67/1002
    • 본발명은유전자알고리즘을이용한 SDN상의부하분배장치및 방법을제시하고있다. 본발명에따른유전자알고리즘을이용한 SDN상의부하분배장치는 SDN상의각 컨트롤러에부여되는부하의양을및 상기부하의양의표준편차를산출하고상기산출된값을부하불균형판단부에제공하는부하계산부; 상기부하의양 및표준편차를토대로상기각 컨트롤러에부여되는부하의양이균형한지불균형한지판단하여상기판단결과를부하조정부에제공하는부하불균형판단부; 상기부하불균형판단부에서상기각 컨트롤러에부여되는부하의양이불균형하다고판단된경우상기각 컨트롤러에부여되는부하의양을조정하는부하조정부;를포함할수 있다. 본발명에의하면, 다수의컨트롤러를사용하는 SDN 환경에서각 컨트롤러에주어지는부하의양을수치로표현할수 있다. 또한, 부하간표준편차를이용하여컨트롤러간부여되는부하량의불균형즉 부하불균형을감지할수 있으며, 부하분배알고리즘을통하여부하가과도하게주어진컨트롤러의부하의양을조정할수 있다. 최종적으로부하불균형에서기인한컨트롤러와스위치간연결실패현상을방지함으로서네트워크의신뢰성을보장할수 있다.
    • 4. 发明授权
    • 분말 성형용 조성물 개발 시스템 및 방법
    • 用于粉末成型的组合物的系统和方法
    • KR101586166B1
    • 2016-01-15
    • KR1020150027798
    • 2015-02-27
    • 경북대학교 산학협력단
    • 김지식손기선이진웅
    • B22F9/00C04B35/00
    • G06N3/126B22F3/225B22F3/227G06F19/704B22F9/00C04B35/00
    • 분말성형용조성물의점도및 탈지공정후의바인더물질들의잔류비율측면에서상기조성물의최적조성정보들을도출하는분말성형용조성물개발시스템이개시된다. 이러한시스템은복수의후보조성정보들을생성한후 이들로부터상기최적조성정보를도출하는탐색연산장치및 후보조성정보들에대응하는조성물들을합성하고분석하여후보조성정보들각각에대응하는상기점도및 탈지공정후의바인더물질들의잔류비율에대한측정정보들을탐색연산장치에제공하는합성분석모듈을구비한다. 그리고탐색연산장치는후보조성정보들및 이들에대한측정정보들을기초로최적조성정보들을도출한다.
    • 提供一种开发用于粉末成型的组合物的系统,其在用于粉末成型的组合物的粘度和脱脂过程中的粘合剂材料的残留率的方面得到最佳组成信息。 该系统包括:搜索运算单元,其产生多个候选的组成信息,然后从多个候选组成信息中抽取最佳组成信息; 以及合成分析模块,其合成和分析与组成信息的候选物相对应的组合物,以提供关于脱脂处理之后的粘合剂材料的粘度和残留率的测量信息对应于组成信息的每个候选者。 此外,搜索算术单元基于关于组成信息的候选的组成信息和测量信息的候选来绘制最佳组成信息。
    • 6. 发明公开
    • 유전 알고리즘을 이용한 전기 자동차 재배치 스케줄링 방법 및 장치
    • 使用遗传算法调度电动车辆转移的方法与装置
    • KR1020140123792A
    • 2014-10-23
    • KR1020130041087
    • 2013-04-15
    • 제주대학교 산학협력단
    • 이정훈박경린
    • G06F19/00G06N3/12
    • G06Q50/30G06N3/126
    • Disclosed are a method and an apparatus for scheduling electric vehicle relocation using a genetic algorithm in a method for relocating a vehicle provided for car sharing. The method for scheduling electric vehicle relocation using a genetic algorithm comprises the steps of: acquiring information associated with a vehicle relocation plan; generating a plurality of vectors including integer elements based on the acquired information; and generating a vehicle relocation plan, in which relocation distances are shortest, from the vectors by using a genetic algorithm. The method further includes encoding the relocation schedule into integer vectors in order to apply genetic algorithm computation.
    • 本发明公开了一种在汽车共享车辆搬迁方法中使用遗传算法调度电动车辆搬迁的方法和装置。 使用遗传算法调度电动车辆搬迁的方法包括以下步骤:获取与车辆搬迁计划相关联的信息; 基于所获取的信息生成包括整数元素的多个向量; 以及通过使用遗传算法从所述向量生成重定位距离最短的车辆搬迁计划。 该方法还包括将重定位调度编码为整数向量以便应用遗传算法计算。
    • 8. 发明公开
    • 일사량 예측 방법 및 장치
    • 用于预测每日太阳辐射水平的方法和装置
    • KR1020140021179A
    • 2014-02-20
    • KR1020120087104
    • 2012-08-09
    • 연세대학교 산학협력단
    • 홍태훈구충완이민현지창윤김지민김현중
    • G06F19/00G06F17/30
    • Y02A90/15G01W1/10G06F17/18G06F17/30601G06N3/126Y02E10/50
    • A method for predicting a daily solar radiation level according to the embodiments of the present invention includes the steps of: providing a case database to express each case by a dependent variable including the daily solar radiation level according to the subdivisions of the seasons and a plurality of independent variables including geographical attributes and meteorological attributes measured according to the subdivisions of the seasons and a test case to which independent variable test values are given; dividing the meteorological attributes and the daily solar radiation level according to the subdivisions of the seasons in the cases of the case database into groups according to the subdivisions of the seasons; and estimating a daily solar radiation level prediction value based on a search result according to the subdivisions of the seasons searched from the group according to the subdivisions of the seasons in the case database with regard to the given independent variable test values. [Reference numerals] (AA) Grasp insolation properties; (BB) Meteorological observation document DB; (CC) Grouping by season; (DD) Prediction model; (EE) Insolation by season
    • 根据本发明的实施例的用于预测日常太阳辐射水平的方法包括以下步骤:提供病例数据库,以通过依赖于每个季节的细分的日变化量的因变量来表示每个病例, 的自变量,包括根据季节细分测量的地理属性和气象属性以及给出独立变量测试值的测试用例; 根据季节细分,根据案例数据库中季节的分类将气象属性和日照量分为几组; 以及根据所述组中搜索到的所述季节的细分,根据所述病例数据库中关于给定的独立变量测试值的季节的细分,来估计每日太阳辐射水平预测值。 (附图标记)(AA)抓握日照特性; (BB)气象观测资料DB; (CC)按季分组; (DD)预测模型; (EE)按季度推迟
    • 10. 发明授权
    • 풍력터빈 설비에 있어서 유지보수를 위한 유전 알고리즘을 이용한 스케줄링
    • 风力涡轮机设备维护基于遗传算法的调度
    • KR101299391B1
    • 2013-08-22
    • KR1020120062992
    • 2012-06-13
    • 손범수
    • 손범수
    • G06F19/00G06N3/12
    • F03D80/50G06N3/126
    • PURPOSE: A wind turbine maintenance scheduling method establishes an optimal scheduling for securing sufficient reserve power, reduces human errors, and decreases maintenance time and cost. CONSTITUTION: In terms of a gene's maintenance time, the genetic shape of initial parent groups is created within a normal distribution not to concentrate on a certain period of time. A share of the demand for reserve power for a wind turbine is calculated. A parent gene is selected according to the demand share. A breeding process or a mutation process is implemented regarding the parent gene. A maintenance schedule is developed as a result of the breeding process or the mutation process. [Reference numerals] (AA) Start; (BB) Define a period that requires maintenance for a whole wind turbine using a gene; (CC) Calculate the share (Y) of the annual maintenance period in a certain period of time (k); (DD) Is k/Y within a normal distribution ?; (EE) Add the result to a value group (initial parent group N); (FF) Define a reserve power value and a reserve power rate; (GG) Select a pair of chromosomes for the reserve power rate; (HH) Generate a first children chromosome by mating the parent chromosomes; (II) Is the reserve power of the first children larger than that of the parent ?; (JJ) Generate a second children chromosome by mutating the parent chromosomes; (KK) Is the first children or the second children fit for the maintenance period requirement ?; (LL) Add the first children or the second children to a children group; (MM) Is the number of the children group identical to the number (N) of the value group ?; (NN) Change the generation; (OO) Is a repetition number ending condition (M) satisfied ?; (PP) Evaluate an optimization rate; (QQ) End
    • 目的:风力发电机维护调度方法为确保足够的备用电力,减少人为错误,降低维护时间和成本建立了最佳调度。 构成:就基因的维持时间而言,初始亲本基因的遗传形态是在正常分布内产生的,而不是集中在一段时间内。 计算风力发电机组备用电力需求的一部分。 根据需求份额选择亲本基因。 对亲本基因实施育种过程或突变过程。 作为育种过程或突变过程的结果,开发维护计划。 (附图标记)(AA)开始; (BB)定义需要使用基因维护整个风力发电机的时期; (CC)计算在一定时间内(k)的年维护期的份额(Y); (DD)是正态分布中的k / Y吗? (EE)将结果添加到值组(初始父组N); (FF)定义储备功率值和储备功率; (GG)选择一对染色体作为备用功率; (HH)通过交配母体染色体产生第一个儿童染色体; (二)第一个孩子的储备权是否比父母的储备权大吗? (JJ)通过突变母体染色体产生第二个儿童染色体; (KK)第一个孩子还是第二个孩子适合维修期限? (LL)将第一名儿童或第二名儿童加入儿童团体; (MM)子组的编号与值组的数目(N)相同吗? (NN)改变一代; (OO)重复数结束条件(M)是否满足? (PP)评估优化率; (QQ)结束