{"id":32156,"date":"2022-03-23T13:33:22","date_gmt":"2022-03-23T10:33:22","guid":{"rendered":"https:\/\/e-marineeducation.com\/tr\/?p=32156"},"modified":"2022-03-23T13:34:54","modified_gmt":"2022-03-23T10:34:54","slug":"seyir-emniyetini-arttirmaya-yonelik-yapay-zeka-uygulamalari","status":"publish","type":"post","link":"https:\/\/e-marineeducation.com\/tr\/seyir-emniyetini-arttirmaya-yonelik-yapay-zeka-uygulamalari\/","title":{"rendered":"Seyir Emniyetini Artt\u0131rmaya Y\u00f6nelik Yapay Zeka Uygulamalar\u0131"},"content":{"rendered":"

Bir geminin tipi veya seyir maksad\u0131; sadece en, boy gibi temel karakteristik \u00f6zellikler ile seyirdeki rota, s\u00fcrat gibi dinamik de\u011ferleri ile tespit edilebilir mi? Bu soruya g\u00fcn\u00fcm\u00fcz teknolojisinde, geli\u015fmi\u015f bilgisayarlar ile cevap bulunabilir. Yani belirli bir b\u00f6lgedeki herhangi bir geminin seyir halindeki dinamik de\u011ferleri ile temel karakteristik bilgilerini \u00e7e\u015fitli makine \u00f6\u011frenmesi algoritmalar\u0131 ile modelleyerek geminin tipi<\/em> <\/span>ve seyir maksad\u0131<\/em> <\/span>hakk\u0131nda bilimsel \u00e7\u0131kar\u0131mlar yap\u0131labilir. B\u00f6ylece seyir halindeki bir geminin niyeti \u00f6nceden tahmin edilebilir. Bu durum \u015f\u00fcphesiz ki seyir emniyetini art\u0131rmaya y\u00f6nelik \u00f6nemli bir ad\u0131md\u0131r.<\/p>\n

K\u0131saca ML Nedir?<\/strong>
\nMakine \u00f6\u011frenmesi (ML), insanlar\u0131n \u00f6\u011frenme \u015feklini taklit etmek i\u00e7in veri ve algoritmalar\u0131n kullan\u0131m\u0131na odaklanan ve do\u011frulu\u011funu kademeli olarak art\u0131ran bir yapay zeka ve bilgisayar bilimi dal\u0131d\u0131r.<\/p>\n

Makine \u00f6\u011frenmesi algoritmalar\u0131, istatistiki olarak tahminlerde bulunmak veya kararlar almak i\u00e7in e\u011fitim verileri olarak bilinen \u00f6rnek verilere dayal\u0131 modellerdir. Algoritmaya verilen \u00f6rnek veriler \u00fczerinden insan\u0131n g\u00f6remedi\u011fi ve hesaplayamad\u0131\u011f\u0131 \u00f6r\u00fcnt\u00fcler saptanarak genellenebilir modeller ortaya \u00e7\u0131kar\u0131r. \u00c7\u0131kt\u0131 olarak elde edilen bu genellenebilir modellere, model d\u0131\u015f\u0131 ba\u015fka bir veri girildi\u011finde yine y\u00fcksek do\u011fruluk oran\u0131 ile tahmin yapabilir. Makine \u00f6\u011frenimi algoritmalar\u0131, t\u0131p, e-posta filtreleme, konu\u015fma tan\u0131ma ve bilgisayarla g\u00f6rme gibi ihtiya\u00e7 duyulan g\u00f6revleri yerine getirmek i\u00e7in geleneksel algoritmalar geli\u015ftirmenin zor veya olanaks\u0131z oldu\u011fu \u00e7ok \u00e7e\u015fitli uygulamalarda kullan\u0131lmaktad\u0131r.<\/p>\n

K\u0131saca AIS Nedir?<\/strong>
\nAIS (Otomatik Tan\u0131mlama Sistemi) cihaz\u0131 otomatik bir tan\u0131mlama sistemidir. Gemileri takip etmek ve deniz trafi\u011fini d\u00fczenlemek ad\u0131na etkili bir sistemdir. VHF telsiz frekans\u0131ndan ve sinyallerinden veri g\u00f6nderme ve al\u0131\u015f\u0131 yapmaktad\u0131r. VHF al\u0131c\u0131 vericilerle donat\u0131lm\u0131\u015f gemiler ve gemi trafik hizmetleri (VTS) bu \u015fekilde g\u00fcvenilir bir \u015fekilde genel resmi g\u00f6r\u00fcnt\u00fcleyebilirler. Ancak VHF aral\u0131\u011f\u0131 yakla\u015f\u0131k azami 25 deniz mili<\/em> <\/span>ile s\u0131n\u0131rl\u0131d\u0131r.<\/p>\n

A ve B tipi olmak \u00fczere iki tip AIS cihaz\u0131 vard\u0131r. A tipi daha g\u00fc\u00e7l\u00fcd\u00fcr. Dolay\u0131s\u0131yla 25 deniz miline kadar veri g\u00f6nderebilir. B tipi AIS cihaz\u0131 ise 5-10 deniz mili mesafede etkili olur. A tipi AIS 12.5 kW<\/em><\/span> g\u00fcce sahipken B tipi AIS 2 kW<\/em><\/span> g\u00fcce sahiptir. AIS, seyir halinde her 2-10 saniyelik zaman<\/em><\/span> diliminde sinyal g\u00f6nderir. Sabit demirde bulunan teknelerde ve gemilerde ise 3-4 dakikada<\/em> <\/span>bir sinyal g\u00fcncellemesi yapar. AIS verileri; IMO numaras\u0131, geminin en boy bilgileri gibi statik bilgilerden ve geminin h\u0131z\u0131, rotas\u0131 gibi dinamik bilgilerden olu\u015fmaktad\u0131r.<\/p>\n

Uluslararas\u0131 Denizcilik \u00d6rg\u00fct\u00fc (IMO)’n\u00fcn belirledi\u011fi SOLAS kurallar\u0131 gere\u011fi 300 GRT<\/em><\/span> ve daha b\u00fcy\u00fck gemilerde ve t\u00fcm yolcu ta\u015f\u0131yan<\/em><\/span> ticari gemilerde bulundurulmas\u0131 zorunludur. AIS verileri bir\u00e7ok bilgiyi bar\u0131nd\u0131rmakla beraber her geminin AIS verisi olmayabilir. Buna \u00f6rnek olarak:<\/p>\n

* Sava\u015f gemileri
\n* 300grt\u2019dan daha k\u00fc\u00e7\u00fck gemiler
\n* Bal\u0131k\u00e7\u0131lar
\n* Teknik ar\u0131zalardan dolay\u0131 AIS al\u0131c\u0131-vericilerini kapatabilir.<\/p>\n

S\u0131n\u0131fland\u0131rma Probleminin Ele Al\u0131nmas\u0131<\/strong>
\nYukar\u0131da belirtilen ara\u015ft\u0131rma sorusunu \u00e7\u00f6z\u00fcmlemek i\u00e7in Danimarka ve \u0130sve\u00e7 aras\u0131nda bulunan Kattegat Bo\u011faz\u0131<\/em><\/span> ele al\u0131nm\u0131\u015ft\u0131r. Her b\u00f6lgede a\u011f\u0131rl\u0131kl\u0131 olarak farkl\u0131 rota ve s\u00fcrat kullan\u0131labilmektedir. \u00d6rne\u011fin s\u0131\u011fl\u0131k, ada, adac\u0131k, kayal\u0131k ve k\u0131y\u0131ya yak\u0131n yerlerde neta ge\u00e7mek ad\u0131na geminin karakteristik \u00f6zelliklerine uygun bir \u015fekilde belirgin rota\/s\u00fcratler kullan\u0131lmaktad\u0131r. Se\u00e7ilen b\u00f6lge olan Kattegat Bo\u011faz\u0131\u2019da bu \u015fekilde dar su yollar\u0131n\u0131n oldu\u011fu yo\u011fun bir trafik b\u00f6lgesidir.<\/p>\n

Temel ama\u00e7, Kattegat Bo\u011faz\u0131\u2019nda AIS bilgilerini kullanarak bir makine \u00f6\u011frenmesi modeli kurmak ve daha sonra bu b\u00f6lgeden ge\u00e7en herhangi bir geminin en, boy, draft, rota ve s\u00fcrat bilgilerini kullanarak gemi tipini tahmin etmektedir. Bu maksatla \u2018MMSI\u2019, \u2018En\u2019, \u2018Boy\u2019, \u2018Draft\u2019, \u2018Seyir Durumu\u2019, \u2018Rota\u2019, \u2018S\u00fcrat\u2019 ve \u2018Pruva\u2019 bilgilerini modelimizde ba\u011f\u0131ms\u0131z de\u011fi\u015fken olarak kullan\u0131lm\u0131\u015ft\u0131r. Tahmin edilecek hedef, yani ba\u011f\u0131ml\u0131 de\u011fi\u015fken olarak da gemi tipi kullan\u0131lm\u0131\u015ft\u0131r.<\/p>\n

\"Kattegat
Kattegat Bo\u011faz\u0131<\/figcaption><\/figure>\n

Veri seti, 358351 g\u00f6zlemden yani AIS mesaj\u0131ndan olu\u015fmaktad\u0131r. Fakat bir geminin birden fazla AIS mesaj\u0131 olabilece\u011finden veri setindeki tekil gemi say\u0131s\u0131 3894\u2019t\u00fcr. Bu gemilerin tiplerine g\u00f6re da\u011f\u0131l\u0131m\u0131 a\u015fa\u011f\u0131dad\u0131r.<\/p>\n\n\n\n\n\t\n\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t
S.No<\/th>Gemi Tipi<\/th>Gemi Say\u0131s\u0131<\/th>Oran\u0131 (%)<\/th>\n<\/tr>\n<\/thead>\n
1<\/td>Kargo<\/td>1716<\/td>44.06<\/td>\n<\/tr>\n
2<\/td>Tarama Gemisi<\/td>53<\/td>1.36<\/td>\n<\/tr>\n
3<\/td>Bal\u0131k\u00e7\u0131<\/td>325<\/td>8.35<\/td>\n<\/tr>\n
4<\/td>Y\u00fcksek H\u0131zl\u0131 Tekne (HSC)<\/td>27<\/td>0.69<\/td>\n<\/tr>\n
5<\/td>Devlet Gemisi<\/td>20<\/td>0.51<\/td>\n<\/tr>\n
6<\/td>Askeri Gemi<\/td>79<\/td>2.03<\/td>\n<\/tr>\n
7<\/td>Feribot<\/td>123<\/td>3.16<\/td>\n<\/tr>\n
8<\/td>Pilotaj Tekne<\/td>41<\/td>1.05<\/td>\n<\/tr>\n
9<\/td>Turistik Gemi<\/td>414<\/td>10.63<\/td>\n<\/tr>\n
10<\/td>Liman Botu<\/td>12<\/td>0.31<\/td>\n<\/tr>\n
11<\/td>Arama Kurtarma Gemisi<\/td>135<\/td>3.47<\/td>\n<\/tr>\n
12<\/td>Yelkenli<\/td>215<\/td>5.52<\/td>\n<\/tr>\n
13<\/td>Tanker<\/td>642<\/td>16.49<\/td>\n<\/tr>\n
14<\/td>Yedekleme Yapan\/Yap\u0131lan<\/td>14<\/td>0.36<\/td>\n<\/tr>\n
15<\/td>Kat\u0131r<\/td>78<\/td>2.00<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

Tabloda yer alan gemi s\u0131n\u0131flar\u0131ndan %2<\/em><\/span>\u2019nin alt\u0131nda orana sahip olanlar\u0131 toplula\u015ft\u0131r\u0131lm\u0131\u015ft\u0131r. \u00c7\u00fcnk\u00fc kullan\u0131lan makine \u00f6\u011frenmesi algoritmas\u0131 bu az oranl\u0131 s\u0131n\u0131flarda \u00f6r\u00fcnt\u00fcy\u00fc yakalayamaz. Ba\u015fka bir deyi\u015fle bu s\u0131n\u0131flar\u0131 \u00f6\u011frenmek i\u00e7in yeterli veri yoktur. Dolay\u0131s\u0131yla %2\u2019nin alt\u0131nda orana sahip y\u00fcksek h\u0131zl\u0131 tekneleri, devlet gemilerini, pilotaj tekneleri, liman botlar\u0131n\u0131 ve yedekleme yapan\/yap\u0131lan gemileri bir s\u0131n\u0131f olarak birle\u015ftirilmi\u015ftir. Bu gemi tipleri modele seyrek s\u0131n\u0131f olarak girilmi\u015ftir.<\/p>\n

Makine \u00d6\u011frenmesi Modelinin Kurulmas\u0131
\n<\/strong>B\u00fct\u00fcn de\u011fi\u015fkenleri haz\u0131rlad\u0131ktan sonra makine \u00f6\u011frenmesi modeli kurulmu\u015ftur. \u00d6rne\u011fimizde h\u0131zl\u0131 ve ba\u015far\u0131l\u0131 sonu\u00e7lar verdi\u011fi bilinen Light GBM Makine \u00d6\u011frenmesi Algoritmas\u0131 kullan\u0131lm\u0131\u015ft\u0131r. Light GBM, karar a\u011fac\u0131 tabanl\u0131 \u00e7al\u0131\u015fan bir algoritmad\u0131r.<\/p>\n

Model kurulduktan sonra modeli test etmek maksad\u0131yla yine Kattegat Bo\u011faz\u0131\u2019nda 10 Mart 2022\u2019den sonra seyir yapan 10 farkl\u0131 gemi bilgisini modele girilmi\u015ftir. Dikkat edilece\u011fi \u00fczere model kurulurken 1 Ocak \u2013 10 Mart 2022 tarihleri aras\u0131ndaki verileri kullan\u0131lm\u0131\u015ft\u0131. Yani tahmin i\u00e7in belirlenen 10 \u00f6rnek veri seti d\u0131\u015f\u0131ndan bir \u00f6rnektir. Modelin do\u011fruluk skoru 0,67 \u00e7\u0131km\u0131\u015ft\u0131r. A\u015fa\u011f\u0131daki tabloda da yap\u0131lan 10 tahminin 7\u2019sinin do\u011fru sonu\u00e7 verdi\u011fi g\u00f6zlenmi\u015ftir.<\/p>\n

Bir geminin tipini bildikten sonra amac\u0131n\u0131 da az \u00e7ok bilebiliriz. Bu durum denizlerdeki durumsal fark\u0131ndal\u0131\u011f\u0131 art\u0131rmaya, dolay\u0131s\u0131yla seyir emniyetini art\u0131rmaya y\u00f6nelik bir ad\u0131m olarak de\u011ferlendirilebilir.<\/p>\n\n\n\n\n\t\n\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t
S.Nu<\/th>Draft<\/th>S\u00fcrat<\/th>En x Boy<\/th>Rota<\/th>Seyir Durumu<\/th>Gemi Tipi<\/th>Tahmin<\/th>\n<\/tr>\n<\/thead>\n
1<\/td>5,5<\/td>12,1<\/td>1305<\/td>NNE<\/td>Yol \u00fcst\u00fcnde<\/td>Kargo<\/td>Kargo<\/td>\n<\/tr>\n
2<\/td>De\u011fer Yok<\/td>0,1<\/td>48<\/td>WNW<\/td>Di\u011fer<\/td>Turistik<\/td>Turistik<\/td>\n<\/tr>\n
3<\/td>3<\/td>11,9<\/td>960<\/td>WSW<\/td>Yol \u00fcst\u00fcnde<\/td>Kargo<\/td>Kargo<\/td>\n<\/tr>\n
4<\/td>De\u011fer Yok<\/td>0<\/td>48<\/td>ENE<\/td>Yol \u00fcst\u00fcnde<\/td>Arama Kurtarma<\/td>Arama Kurtarma<\/td>\n<\/tr>\n
5<\/td>11,7<\/td>15,4<\/td>8136<\/td>SSE<\/td>Yol \u00fcst\u00fcnde<\/td>Kargo<\/td>Tanker<\/td>\n<\/tr>\n
6<\/td>11,6<\/td>5<\/td>13570<\/td>ESE<\/td>Yol \u00fcst\u00fcnde<\/td>Tanker<\/td>Tanker<\/td>\n<\/tr>\n
7<\/td>De\u011fer Yok<\/td>0,3<\/td>De\u011fer Yok<\/td>ENE<\/td>S\u00fcr\u00fcklenen<\/td>Turistik<\/td>Turistik<\/td>\n<\/tr>\n
8<\/td>8,3<\/td>12,5<\/td>3624<\/td>NNW<\/td>Di\u011fer<\/td>Tanker<\/td>Kargo<\/td>\n<\/tr>\n
9<\/td>10,8<\/td>12,6<\/td>4800<\/td>NNW<\/td>Di\u011fer<\/td>Kargo<\/td>Kargo<\/td>\n<\/tr>\n
10<\/td>De\u011fer Yok<\/td>1,6<\/td>10<\/td>SSW<\/td>Yol \u00fcst\u00fcnde<\/td>Arama Kurtarma<\/td>Turistik<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n","protected":false},"excerpt":{"rendered":"

Bir geminin tipi veya seyir maksad\u0131; sadece en, boy gibi temel karakteristik \u00f6zellikler ile seyirdeki rota, s\u00fcrat gibi dinamik de\u011ferleri ile tespit edilebilir mi? Bu soruya g\u00fcn\u00fcm\u00fcz teknolojisinde, geli\u015fmi\u015f bilgisayarlar ile cevap bulunabilir. Yani belirli bir b\u00f6lgedeki herhangi bir geminin seyir halindeki dinamik de\u011ferleri ile temel karakteristik bilgilerini \u00e7e\u015fitli makine \u00f6\u011frenmesi algoritmalar\u0131 ile modelleyerek geminin …<\/p>\n","protected":false},"author":22602,"featured_media":32176,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[522],"tags":[5036,5035,5037],"class_list":["post-32156","post","type-post","status-publish","format-standard","has-post-thumbnail","","category-ozel-yazilar","tag-machine-learning","tag-makine-ogrenmesi","tag-serkan-erdonmez-yazilari"],"acf":[],"yoast_head":"\nSeyir Emniyetini Artt\u0131rmaya Y\u00f6nelik Yapay Zeka Uygulamalar\u0131 - 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