{"id":25283,"date":"2024-10-26T09:53:39","date_gmt":"2024-10-26T09:53:39","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=25283"},"modified":"2024-11-26T08:02:16","modified_gmt":"2024-11-26T08:02:16","slug":"%eb%94%a5-%eb%9f%ac%eb%8b%9d%ec%9d%84-%ec%9d%b4%ec%9a%a9%ed%95%9c-%ec%9e%90%ec%97%b0%ec%96%b4-%ec%b2%98%eb%a6%ac-rnn%ec%9d%84-%ec%9d%b4%ec%9a%a9%ed%95%9c-%ed%85%8d%ec%8a%a4%ed%8a%b8-%ec%83%9d","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/25283\/","title":{"rendered":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac: RNN\uc744 \uc774\uc6a9\ud55c \ud14d\uc2a4\ud2b8 \uc0dd\uc131"},"content":{"rendered":"

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\uc791\uc131\uc77c: 2023\ub144 9\uc6d4 15\uc77c<\/p>\n

\uc791\uc131\uc790: \uc870\uad11\ud615<\/p>\n<\/header>\n

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1. \uc11c\ub860<\/h2>\n

\uc778\uacf5\uc9c0\ub2a5\uc758 \ubc1c\uc804\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc801\uc778 \ubcc0\ud654\ub97c \uac00\uc838\uc624\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911\uc5d0\uc11c\ub3c4 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub294 \uae30\uacc4\uac00 \uc778\uac04\uc758 \uc5b8\uc5b4\ub97c \uc774\ud574\ud558\uace0 \uc0dd\uc131\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uae30\uc220\ub85c, \ucd5c\uadfc \uba87 \ub144\uac04 \ub9ce\uc740 \uad00\uc2ec\uc744 \ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \ub525 \ub7ec\ub2dd \uae30\uc220\uc744 \ud65c\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \ubc1c\uc804\uc740 \ub9ce\uc740 \uc5f0\uad6c\uc790\uc640 \uac1c\ubc1c\uc790\ub4e4\uc5d0\uac8c \uc0c8\ub85c\uc6b4 \uac00\ub2a5\uc131\uc744 \uc5f4\uc5b4\uc8fc\uc5c8\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uc21c\ud658 \uc2e0\uacbd\ub9dd(Recurrent Neural Network, RNN)\uc744 \uc774\uc6a9\ud55c \ud14d\uc2a4\ud2b8 \uc0dd\uc131(Text Generation)\uc5d0 \ub300\ud574 \uc2ec\uce35\uc801\uc73c\ub85c \ub2e4\ub8e8\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<\/section>\n

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2. \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub780?<\/h2>\n

\uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub294 \ucef4\ud4e8\ud130\uac00 \uc778\uac04\uc758 \uc790\uc5f0\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ud574\uc11d\ud558\ub294 \uae30\uc220\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. \uc774\ub294 \uc5b8\uc5b4\uc758 \uc758\ubbf8, \uad6c\uc870, \ud615\ud0dc\uc18c \ubd84\uc11d, \uac10\uc815\u5206\u6790 \ub4f1 \ub2e4\uc591\ud55c \uc601\uc5ed\uc73c\ub85c \ub098\ub258\uba70, \uadf8 \uc751\uc6a9 \ubd84\uc57c\ub294 \ud14d\uc2a4\ud2b8 \uc694\uc57d, \uc9c8\uc758 \uc751\ub2f5 \uc2dc\uc2a4\ud15c, \uae30\uacc4 \ubc88\uc5ed, \ud14d\uc2a4\ud2b8 \uc0dd\uc131 \ub4f1\uc73c\ub85c \ub2e4\uc591\ud569\ub2c8\ub2e4.<\/p>\n<\/section>\n

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3. \ub525 \ub7ec\ub2dd\uacfc NLP\uc758 \uad00\uacc4<\/h2>\n

\ub525 \ub7ec\ub2dd\uc740 \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uba38\uc2e0 \ub7ec\ub2dd\uc758 \ud55c \ud615\ud0dc\ub85c, \ud2b9\ud788 \ub9ce\uc740 \uc591\uc758 \ub370\uc774\ud130\uc5d0\uc11c \uc720\uc6a9\ud55c \ud328\ud134\uc744 \ud559\uc2b5\ud558\ub294 \ub370 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4. \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubd84\uc57c\uc5d0\uc11c\ub3c4 \uc774 \uaf43\uc744 \ud65c\uc6a9\ud558\uba74 \ub354\uc6b1 \ud5a5\uc0c1\ub41c \uc131\ub2a5\uc744 \uae30\ub300\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uacfc\uac70\uc5d0\ub294 \uc8fc\ub85c \uaddc\uce59 \uae30\ubc18 \uae30\ubc95\uc774\ub098 \ud1b5\uacc4 \uae30\ubc18 \uae30\ubc95\uc774 \uc0ac\uc6a9\ub418\uc5c8\uc9c0\ub9cc, \ub525 \ub7ec\ub2dd\uc758 \ucd9c\ud604\uc73c\ub85c \uc778\ud574 \ub354 \uc815\uad50\ud558\uace0 \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc5b8\uc5b4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud560 \uc218 \uc788\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n<\/section>\n

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4. RNN\uc758 \uae30\ubcf8 \uac1c\ub150<\/h2>\n

RNN(\uc21c\ud658 \uc2e0\uacbd\ub9dd)\uc740 \uc21c\ucc28\uc801\uc778 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uae30 \uc704\ud574 \uc124\uacc4\ub41c \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc758 \uc77c\uc885\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc778 \uc2e0\uacbd\ub9dd\uc740 \uc785\ub825 \ub370\uc774\ud130\uac00 \uace0\uc815\ub41c \ud06c\uae30\ub97c \uac00\uc84c\ub2e4\uba74, RNN\uc740 \uac00\ubcc0 \uae38\uc774 \uc2dc\ud000\uc2a4\ub97c \uc218\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc989, RNN\uc740 \uc774\uc804\uc758 \uc0c1\ud0dc \uc815\ubcf4\ub97c \uae30\uc5b5\ud558\uace0 \uc774\ub97c \uae30\uc900\uc73c\ub85c \ub2e4\uc74c \ucd9c\ub825\uc744 \uc0dd\uc131\ud558\ub294 \uad6c\uc870\ub97c \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

RNN\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uc218\uc2dd\uc73c\ub85c \ud45c\ud604\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n

\"RNN<\/p>\n

\uc5ec\uae30\uc11c, ht<\/sub><\/sub>\ub294 \ud604\uc7ac \uc228\uaca8\uc9c4 \uc0c1\ud0dc, ht-1<\/sub><\/sub>\ub294 \uc774\uc804 \uc228\uaca8\uc9c4 \uc0c1\ud0dc, xt<\/sub><\/sub>\ub294 \ud604\uc7ac \uc785\ub825 \ub370\uc774\ud130, W_hh<\/sub>\ub294 \uc228\uaca8\uc9c4 \uc0c1\ud0dc\uc758 \uac00\uc911\uce58, W_xh<\/sub>\ub294 \uc785\ub825 \ub370\uc774\ud130\uc758 \uac00\uc911\uce58, \u03c3<\/sub>\ub294 \ud65c\uc131\ud654 \ud568\uc218\uc785\ub2c8\ub2e4.<\/p>\n<\/section>\n

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5. RNN\uc758 \ud55c\uacc4<\/h2>\n

RNN\uc740 \uc21c\ucc28\uc801\uc778 \ub370\uc774\ud130 \ucc98\ub9ac\uac00 \uac00\ub2a5\ud558\uc9c0\ub9cc, \uc7a5\uae30 \uc758\uc874\uc131 \ubb38\uc81c\uc640 \uae30\uc6b8\uae30 \uc18c\uc2e4(vanishing gradient) \ubb38\uc81c \ub4f1 \uc5ec\ub7ec \ud55c\uacc4\ub97c \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ud55c\uacc4\ub97c \uadf9\ubcf5\ud558\uae30 \uc704\ud574 LSTM(\uc7a5\uae30 \ub2e8\uae30 \uae30\uc5b5 \ub124\ud2b8\uc6cc\ud06c)\uc640 GRU(\uac8c\uc774\ud2b8 \uc21c\ud658 \uc720\ub2db) \ub4f1\uc758 \ubcc0\ud615 \ubaa8\ub378\uc774 \uac1c\ubc1c\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n<\/section>\n

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6. \ud14d\uc2a4\ud2b8 \uc0dd\uc131(Text Generation)<\/h2>\n

\ud14d\uc2a4\ud2b8 \uc0dd\uc131\uc740 \uc8fc\uc5b4\uc9c4 \uc785\ub825\uc5d0 \ub300\ud574 \uc0c8\ub85c\uc6b4 \ud14d\uc2a4\ud2b8\ub97c \uc0dd\uc131\ud558\ub294 \uacfc\uc815\uc744 \ub9d0\ud569\ub2c8\ub2e4. \ud2b9\ud788 RNN\uc740 \uc774\uc804 \ub2e8\uc5b4\uc758 \uc815\ubcf4\ub97c \uae30\uc5b5\ud558\uace0 \uc774\ub97c \uae30\ubc18\uc73c\ub85c \ub2e4\uc74c \ub2e8\uc5b4\ub97c \uc608\uce21\ud558\ub294 \ub370 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4. \uc774\ub294 \uc18c\uc124, \uc2dc, \ub274\uc2a4 \uae30\uc0ac, \ub300\ud654\ub85d \ub4f1 \ub2e4\uc591\ud55c \ud14d\uc2a4\ud2b8\ub97c \uc0dd\uc131\ud558\ub294 \ub370 \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/section>\n

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7. RNN\uc744 \uc774\uc6a9\ud55c \ud14d\uc2a4\ud2b8 \uc0dd\uc131 \ub2e8\uacc4<\/h2>\n

7.1 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n

\ud14d\uc2a4\ud2b8 \uc0dd\uc131\uc744 \uc704\ud55c \uccab \ubc88\uc9f8 \ub2e8\uacc4\ub294 \ub370\uc774\ud130 \uc900\ube44\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \ub300\ub7c9\uc758 \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uc5ec \uc774\ub97c \uc815\uc81c\ud558\uace0, \ub2e8\uc5b4 \uc2dc\ud000\uc2a4 \ud615\ud0dc\ub85c \ubcc0\ud658\ud558\uc5ec \ubaa8\ub378 \ud559\uc2b5\uc5d0 \uc801\ud569\ud55c \ud615\ud0dc\ub85c \ub9cc\ub4e4\uc5b4\uc57c \ud569\ub2c8\ub2e4.<\/p>\n

7.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n

\ub370\uc774\ud130\uac00 \uc900\ube44\ub418\uba74, \ub2e8\uc5b4 \uc778\ucf54\ub529, \ud328\ub529 \uc801\uc6a9, \ud6c8\ub828 \ubc0f \uac80\uc99d \ub370\uc774\ud130\uc14b\uc73c\ub85c \ub098\ub204\ub294 \ub4f1\uc758 \uc804\ucc98\ub9ac \uacfc\uc815\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 RNN\uc744 \uc704\ud55c \uc785\ub825 \ub370\uc774\ud130\uc640 \ucd9c\ub825 \ub370\uc774\ud130\ub97c \uc27d\uac8c \uad6c\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

7.3 \ubaa8\ub378 \uc124\uacc4<\/h3>\n

\ub2e4\uc74c \ub2e8\uacc4\ub294 RNN \ubaa8\ub378\uc744 \uc124\uacc4\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. Keras\ub098 TensorFlow\uc640 \uac19\uc740 \ud504\ub808\uc784\uc6cc\ud06c\ub97c \uc0ac\uc6a9\ud558\uc5ec RNN \ub808\uc774\uc5b4\ub97c \uad6c\ucd95\ud558\uace0, \ucd9c\ub825 \ub808\uc774\uc5b4\ub97c \uc124\uc815\ud558\ub294 \ub4f1\uc758 \uacfc\uc815\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4.<\/p>\n

7.4 \ubaa8\ub378 \ud559\uc2b5<\/h3>\n

\ubaa8\ub378\uc774 \uc644\uc131\ub418\uba74, \uc900\ube44\ub41c \ub370\uc774\ud130\ub85c \ud559\uc2b5\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \uc190\uc2e4 \ud568\uc218(loss function)\ub97c \uc815\uc758\ud558\uace0, \ucd5c\uc801\uc758 \uac00\uc911\uce58\ub97c \ucc3e\uae30 \uc704\ud55c \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998(\uc608: Adam, SGD)\ub97c \uc120\ud0dd\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774 \ub2e8\uacc4\ub294 \uc8fc\uc5b4\uc9c4 \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud328\ud134\uacfc \uaddc\uce59\uc744 \ud559\uc2b5\ud558\ub294 \ub370 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4.<\/p>\n

7.5 \ud14d\uc2a4\ud2b8 \uc0dd\uc131<\/h3>\n

\ubaa8\ub378\uc774 \ud559\uc2b5\ub41c \ud6c4, \uc8fc\uc5b4\uc9c4 \ucd08\uae30 \ub2e8\uc5b4\ub098 \ubb38\uc7a5(seeds)\uc73c\ub85c\ubd80\ud130 \uc0c8\ub85c\uc6b4 \ud14d\uc2a4\ud2b8\ub97c \uc0dd\uc131\ud558\ub294 \uacfc\uc815\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4. \uc774 \ub2e8\uacc4\uc5d0\uc11c\ub294 \ub09c\uc218\uc131\uc744 \ubd80\uc5ec\ud558\uc5ec \ub2e4\uc591\uc131\uc744 \ud655\ubcf4\ud558\uac70\ub098, \ubb38\uc790 \uae30\ubc18 \ub610\ub294 \ub2e8\uc5b4 \uae30\ubc18\uc73c\ub85c \ud14d\uc2a4\ud2b8\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/section>\n

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8. Python\uc744 \uc774\uc6a9\ud55c RNN \ud14d\uc2a4\ud2b8 \uc0dd\uc131 \uc608\uc81c<\/h2>\n

\uc544\ub798\ub294 Python\uacfc Keras\ub97c \uc0ac\uc6a9\ud558\uc5ec RNN \ubaa8\ub378\uc744 \uad6c\uc131\ud558\uace0 \ud14d\uc2a4\ud2b8\ub97c \uc0dd\uc131\ud558\ub294 \uae30\ubcf8\uc801\uc778 \uc608\uc81c\uc785\ub2c8\ub2e4.<\/p>\n

\n            \n            import numpy as np\n            from keras.models import Sequential\n            from keras.layers import Dense, LSTM, Embedding\n            from keras.preprocessing.sequence import pad_sequences\n            from keras.preprocessing.text import Tokenizer\n\n            # \ub370\uc774\ud130 \ub85c\ub4dc\n            text = \"\uc5ec\uae30\uc5d0 \uc0ac\uc6a9\ud560 \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc785\ub825\ud558\uc138\uc694.\"\n            corpus = text.lower().split(\"\\n\")\n\n            # \ub370\uc774\ud130 \uc804\ucc98\ub9ac\n            tokenizer = Tokenizer()\n            tokenizer.fit_on_texts(corpus)\n            total_words = len(tokenizer.word_index) + 1\n            input_sequences = []\n            for line in corpus:\n                token_list = tokenizer.texts_to_sequences([line])[0]\n                for i in range(1, len(token_list)):\n                    n_gram_sequence = token_list[:i + 1]\n                    input_sequences.append(n_gram_sequence)\n\n            # \ud328\ub529\n            max_sequence_length = max([len(x) for x in input_sequences])\n            input_sequences = pad_sequences(input_sequences, maxlen=max_sequence_length, padding='pre')\n            input_sequences = np.array(input_sequences)\n\n            # X\uc640 y \uc815\uc758\n            X, y = input_sequences[:, :-1], input_sequences[:, -1]\n            y = np.eye(total_words)[y]  # \uc6d0-\ud56b \uc778\ucf54\ub529\n\n            # \ubaa8\ub378 \uc815\uc758\n            model = Sequential()\n            model.add(Embedding(total_words, 100, input_length=max_sequence_length-1))\n            model.add(LSTM(150))\n            model.add(Dense(total_words, activation='softmax'))\n            model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n            # \ubaa8\ub378 \ud559\uc2b5\n            model.fit(X, y, epochs=100, verbose=1)\n\n            # \ud14d\uc2a4\ud2b8 \uc0dd\uc131\n            input_text = \"\uc8fc\uc5b4\uc9c4 \ud14d\uc2a4\ud2b8\ub97c \uae30\ubc18\uc73c\ub85c\"\n            for _ in range(10): # 10\uac1c\uc758 \ub2e8\uc5b4 \uc0dd\uc131\n                token_list = tokenizer.texts_to_sequences([input_text])[0]\n                token_list = pad_sequences([token_list], maxlen=max_sequence_length-1, padding='pre')\n                predicted = model.predict(token_list, verbose=0)\n                output_word = tokenizer.index_word[np.argmax(predicted)]\n                input_text += \" \" + output_word\n\n            print(input_text)\n            <\/code>\n            <\/pre>\n
\uc774 \ucf54\ub4dc\ub294 \uae30\ubcf8\uc801\uc778 RNN \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud14d\uc2a4\ud2b8\ub97c \uc0dd\uc131\ud558\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc740 \ubcf4\ub2e4 \ub2e4\uc591\ud55c \ubc29\uc2dd\uc73c\ub85c \ubaa8\ub378\uc744 \ud29c\ub2dd\ud558\uac70\ub098, \uc5ec\ub7ec \uce35\uc758 RNN\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc131\ub2a5\uc744 \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/section>\n
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9. \uacb0\ub860<\/h2>\n
\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525 \ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc640 RNN\uc744 \uc774\uc6a9\ud55c \ud14d\uc2a4\ud2b8 \uc0dd\uc131 \uae30\ubc95\uc5d0 \ub300\ud574 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. RNN\uc740 \ubb38\ub9e5\uc744 \uc774\ud574\ud558\uace0 \uc608\uce21\ud558\ub294 \ub370 \ub9e4\uc6b0 \uc720\uc6a9\ud55c \ubaa8\ub378\uc774\uc9c0\ub9cc, \uae30\uc6b8\uae30 \uc18c\uc2e4 \ubb38\uc81c \ub4f1 \uba87 \uac00\uc9c0 \ud55c\uacc4\ub3c4 \uc874\uc7ac\ud569\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uc774\ub7ec\ud55c \ubb38\uc81c\ub97c \uadf9\ubcf5\ud558\uae30 \uc704\ud55c \ub2e4\uc591\ud55c \uae30\ubc95\ub4e4\uc774 \uc5f0\uad6c\ub418\uace0 \uc788\uc73c\uba70, \uc55e\uc73c\ub85c \ub354 \ubc1c\uc804\ub41c \ud615\ud0dc\uc758 \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uae30\uc220\uc774 \uae30\ub300\ub429\ub2c8\ub2e4.<\/p>\n
\ub354 \ub098\uc544\uac00, RNN \uc678\uc5d0\ub3c4 Transformer \ubaa8\ub378\uacfc \uac19\uc740 \ucd5c\uc2e0 \uae30\uc220\ub4e4\uc774 NLP \ubd84\uc57c\uc5d0\uc11c \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc73c\uba70, \uc774\uc5d0 \ub300\ud55c \uc5f0\uad6c\uac00 \ud65c\ubc1c\ud788 \uc774\ub8e8\uc5b4\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubaa8\ub378\uc744 \ud1b5\ud574 \uc6b0\ub9ac\ub294 \ub354\uc6b1 \uc790\uc5f0\uc2a4\ub7fd\uace0 \ucc3d\uc758\uc801\uc778 \ud14d\uc2a4\ud2b8 \uc0dd\uc131\uc774 \uac00\ub2a5\ud574\uc9c8 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<\/section>\n