클러스터화된 선박 센서 네트워크 및 이의 효율적인 데이터 병합방법
    11.
    发明公开
    클러스터화된 선박 센서 네트워크 및 이의 효율적인 데이터 병합방법 无效
    集束式传感器网络及其有效数据聚合方法

    公开(公告)号:KR1020130022783A

    公开(公告)日:2013-03-07

    申请号:KR1020110085688

    申请日:2011-08-26

    CPC classification number: Y02D70/00 H04W84/18 H04L12/28 H04W40/02 H04W52/0212

    Abstract: PURPOSE: A ship sensor network and an effective data merging method are provided to increase the lifetime of a network by reducing the amount of data transmission. CONSTITUTION: A plurality of sensor nodes(1-3) compares a collected average value of a data value with a current data value which is periodically collected. The plurality of the sensor nodes transmits the current data to a cluster head(10) when the current data value is over the average value. The cluster head transmits an average value of a data value transmitted from a plurality of the sensor nodes to a synchronization node(20). The synchronization node transmits the average value transmitted from the cluster head to an upper analysis system. [Reference numerals] (20) Synchronization node; (AA) Legend; (BB) Sensor node; (CC) Cluster head

    Abstract translation: 目的:提供船舶传感器网络和有效的数据合并方法,通过减少数据传输量来增加网络的使用寿命。 构成:多个传感器节点(1-3)将收集的数据值的平均值与周期性收集的当前数据值进行比较。 当当前数据值超过平均值时,多个传感器节点将当前数据发送到簇头(10)。 簇头将从多个传感器节点发送的数据值的平均值发送到同步节点(20)。 同步节点将从簇头发送的平均值发送到上位分析系统。 (附图标记)(20)同步节点; (AA)图例; (BB)传感器节点; (CC)簇头

    무선 센서 네트워크에서 선택적 질의에 따른 가변적 패킷 전송 방법
    12.
    发明公开
    무선 센서 네트워크에서 선택적 질의에 따른 가변적 패킷 전송 방법 无效
    基于可变分组传输的无线传感器网络中的选择性问题

    公开(公告)号:KR1020140052096A

    公开(公告)日:2014-05-07

    申请号:KR1020120110293

    申请日:2012-10-04

    CPC classification number: H04W28/06 H04W84/18

    Abstract: According to the present invention, the packet transmission time of a sensor node is reduced as the query is small and the length of a packet is closely related to the packet transmission time of the sensor node thus reducing energy consumption by performing a selective query to increase the time of a life than a conventional method. Additionally, the packet transmission time thereof is increased depending on the selective query and approximately 1.5% of energy is decreased in terms of energy consumption and the conventional method if the selective query is two, by which the simulation of the packet transmission depending on the selective query confirms the energy consumption of the sensor node and confirms a data success rate via the simulation when an event happens using an NS-2 simulator in order to compare of energy efficiency and the conventional method.

    Abstract translation: 根据本发明,传感器节点的分组传输时间随着查询小而减小,并且分组的长度与传感器节点的分组传输时间密切相关,从而通过执行选择性查询来增加能量消耗,从而增加 生命的时间比传统的方法。 此外,根据选择性查询,其分组传输时间增加,并且在能量消耗方面,大约1.5%的能量减少,并且如果选择性查询是两个,传统方法将减少,通过该方法,根据选择性查询进行分组传输的模拟 查询确认传感器节点的能量消耗,并且通过使用NS-2模拟器发生事件时通过仿真确认数据成功率,以便比较能量效率和传统方法。

    M2M기반의 선박 항해를 위한 안전 조정 방법
    14.
    发明公开
    M2M기반의 선박 항해를 위한 안전 조정 방법 审中-实审
    基于M2M的船舶导航安全控制方法

    公开(公告)号:KR1020150033349A

    公开(公告)日:2015-04-01

    申请号:KR1020130113242

    申请日:2013-09-24

    Abstract: 본발명은선박항해를위한안전조정방법에관한것으로, 선박항해데이터를획득하고항해위험요소가있으면항해시스템을 ON 시키며획득한선박항해데이터를분석한후 항해시스템을통하여분석된선박행해데이터를기초로최적경로, 추천경로및 안전경로중 적어도하나를제공하고나서경로선택입력을획득하면안전조정을실행하는것을그 요지로한다.

    Abstract translation: 本发明涉及一种船舶导航安全控制方法。 获得船舶导航数据,当有危险的导航因素时,导航系统被打开,对所获得的船舶导航数据进行分析,在最佳路线,推荐路线和安全路线中至少提供一条 基于通过导航系统分析的船舶导航数据,此后,当输入路线选择时,执行安全控制。 邻比迪语言:AR-SA“> M2M

    주성분 분석과 원형율을 이용한 적조류 인식 방법
    15.
    发明公开
    주성분 분석과 원형율을 이용한 적조류 인식 방법 无效
    使用主成分分析和ROUNDNESS的红潮ALGAE识别方法

    公开(公告)号:KR1020120136564A

    公开(公告)日:2012-12-20

    申请号:KR1020110055569

    申请日:2011-06-09

    CPC classification number: C40B40/02 G06F17/10 G06T7/62

    Abstract: PURPOSE: A red tidal current recognition method using PCA(principal component analysis) and roundness is provided to recognize images without having a reference point of a red tidal image by using entropy and the roundness. CONSTITUTION: A vector set of a learning image is constructed by learning a red tide image through PCA(100). A candidate recognition image group is constructed by calculating the roundness of the learning image and inputted image and selecting an image which is close to the roundness(200). Entropy of an image in the candidate recognition image group is calculated(300). The entropy selects a recognition image by calculating the entropy of images in the candidate recognition image group. [Reference numerals] (100) Principal component analysis; (200) Calculating roundness; (300) Calculating entropy; (AA) Learning image; (BB) Input image

    Abstract translation: 目的:提供使用PCA(主成分分析)和圆度的红潮流识别方法,通过使用熵和圆度来识别图像而不具有红色潮汐图像的参考点。 构成:通过PCA(100)学习红潮图像构建学习图像的向量集。 通过计算学习图像和输入图像的圆度并选择接近圆度(200)的图像来构造候选识别图像组。 计算候选识别图像组中的图像的熵(300)。 熵通过计算候选识别图像组中的图像的熵来选择识别图像。 (附图标记)(100)主成分分析; (200)计算圆度; (300)计算熵; (AA)学习形象; (BB)输入图像

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