{"id":5,"date":"2021-09-11T03:31:32","date_gmt":"2021-09-11T03:31:32","guid":{"rendered":"https:\/\/jungquantlab.phenocapture.com\/?page_id=5"},"modified":"2022-09-13T09:52:38","modified_gmt":"2022-09-13T09:52:38","slug":"research","status":"publish","type":"page","link":"https:\/\/quantlabnote.hongik.ac.kr\/lab\/research\/","title":{"rendered":"RESEARCH"},"content":{"rendered":"

(Under construction)<\/em><\/span><\/strong><\/p>\n

Development of tree disease diagnosis technology using wireless sensing and chemical analysis<\/strong>
\nPine wilt disease (PWD) caused by Bursaphelenchus xylophilus<\/em> is a major tree disease that threatens pine forests worldwide. Current RGB color or hyper-spectral analysis of images captured with drones to diagnose pine wilt disease has limited usefulness.We have developed a new technology for PWD using remote wireless sensing technology through joint research and cooperation with industry-academia and quasi-governmental institute (Korea Forestry Promotion Institute under the Korea Forest Service). To diagnose this disease, we have developed battery-powered remote sensing devices capable of long-range (LoRa) communication and installed them in pine trees (Pinus densiflora<\/em>). Upon analyzing the collected tree sensing signals, we found that the mean absolute deviation (MAD) of the sensing signals was useful for distinguishing between uninfected and infected trees. The MAD of infected trees was greater than that of uninfected trees from August of the year, and in the two-dimensional plane, consisting of the MAD value in July and that in October, the infected and uninfected trees were separated by the first-order boundary line generated using linear discriminant analysis.<\/p>\n

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High-throughput efficacy and toxicity analysis using image analysis and small animals<\/strong>
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– Visual Studio C\/VB.NET, MATLAB, JAVA \ub4f1 \uc5b8\uc5b4\ub85c \uc54c\uace0\ub9ac\uc998 \ubc0f \ud504\ub85c\uadf8\ub7a8 \uac1c\ubc1c<\/p>\n

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(Under construction) Development of tree disease diagnosis technology using wireless sensing and chemical analysis Pine wilt disease (PWD) caused by Bursaphelenchus xylophilus is a major tree disease that threatens pine forests worldwide. Current RGB color or hyper-spectral analysis of images captured with drones to diagnose pine wilt disease has limited usefulness.We have developed a new […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"_links":{"self":[{"href":"https:\/\/quantlabnote.hongik.ac.kr\/lab\/wp-json\/wp\/v2\/pages\/5"}],"collection":[{"href":"https:\/\/quantlabnote.hongik.ac.kr\/lab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/quantlabnote.hongik.ac.kr\/lab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/quantlabnote.hongik.ac.kr\/lab\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/quantlabnote.hongik.ac.kr\/lab\/wp-json\/wp\/v2\/comments?post=5"}],"version-history":[{"count":8,"href":"https:\/\/quantlabnote.hongik.ac.kr\/lab\/wp-json\/wp\/v2\/pages\/5\/revisions"}],"predecessor-version":[{"id":130,"href":"https:\/\/quantlabnote.hongik.ac.kr\/lab\/wp-json\/wp\/v2\/pages\/5\/revisions\/130"}],"wp:attachment":[{"href":"https:\/\/quantlabnote.hongik.ac.kr\/lab\/wp-json\/wp\/v2\/media?parent=5"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}