The experimental outcomes indicate that the analytic solution can rapidly and precisely decide the leakage location of the pipeline. Furthermore, it presents a new approach to engineering purposes, similar to gas-liquid two-phase move advanced pipe networks, and so on. Correlators are a sort of sound measurement instrument that are used to determine the acoustic frequency induced by a leaky pipe. In Proceedings of the Conference on Learning Theory, Stockholm, Sweden, 5?9 July 2018; pp. 1455?1462. Sahidullah, M.; Saha, G. Design, analysis and experimental evaluation of block based mostly transformation in MFCCs computation for speaker recognition. Due to the nature of this analysis, members on this examine didn't agree for his or her knowledge to be shared publicly, so supporting knowledge usually are not out there. Merzlyak, M.N.; Gitelson, A.A.; Chivkunova, O.B.; Rakitin, V.Y. Non-destructive optical detection of pigment adjustments during leaf senescence and fruit ripening. Three synthetic leaks have been produced at hydrants H5.6, H10 and H11 . Virtual leaks had been essentially produced by simply opening the vane of the hydrant for several durations ranging from 10 min to 1 h through the three days of the flight marketing campaign.

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LIBZICdqfUZwMCrUm68t1t6s3/qXpPW+kpKrjJdXFTdbfOWWUFaVIUE+B8CwEAZB45r6lZ+jdN39xSdhGVJUk6bqWzhmo2lhtReVy9sNJ47ZdWpbRUIyVeaed8VM7FwX32g9d6Uuztmv1/KJcclLqGREfDagSClSmioBQIIKScjzHaq2z1k6nOsMyE3eLHLzaXENy5dvjvbVDKSWnHErSCCCMgZBBHBFRX1Rmaa6lXaFfo?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?+0e0L1Iv09NssmnnJ8lyKJyGGLTKccVHJAD20c7DkfF25FWF1M0yNWWBiBZkaTZkwX4jcRSL/AGhktxm4SG3gVIfysqfSVfFn1yMkVeDkWzF9xl/UNonqNusgZmyrvb3dkqCw4wsKaEo?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?+x+7Ox3AtW7xAuQGQ6hSEoX7jazlZJz7wokqVk5Ja41npvpLp+2QWbS4tVzddhQYkRKUlSm4r0hxaiSMJS1HWSrkk4HJNawk+5HUpxjtF5Z+dnqTPTdeomqbohCkJmXqdICVHJAW+tWD+NW5XJ512Q6t991bjjiita1qJUpROSST3JNca6RWFKUoBSlKAUpSgFbCfo0dOSWunmr9TL2GNdNQw4CBhIIXEjreVzncR/nDfBAHHBJzjXtWz36Pm2m3ezvbnHEhJuuoLncWynGSlDbUfJx55Se+DwPLFYfAMoknBySrjtnkVzAIIUQcAfCD5VwHxABHqecflXaTva?+rjHI+3PaoUCxusUn3fRshKiMuKQnOe?/xj+41gxrx5TmpJXBwkISDj/RFZode5fhabisD/ALyQnjP+io1hFqFibNu1yntxXnGGX1IcdDZKUYOMEjgdq/Vv9htuqXR51pfzTk//ABj+R43q0tfU9K/lgvq8lKTjGFICt3ngnA+419SAT9U8D55NcQkkYGMAdvyrmBjKkqHHGQk8fOvubZU5JmnaW0pAjWxNw0q3FjSlIgtOuIcDq3n460oeQ4HFNy0IcKFkt7QkbAc7q7bDpHRsll+2xdMNz5VsULXOmvB9Udb4KytzLSwY2NyUh51JawgnBwqoTcz8ASsgjsQVcfr5VyYdkNpcDclxG9OHEpcI3D5+vnXmJdEuHScPtMk?/v88/92eG1zvy91kuq5p5zoRM+h7No3UCNI22doCzeLdHLgX5CHZi3VsxGgpJwl7BUtSXArankfVCa7XtJaGe8X9u6KXBCrxbLdDXaxLiF0OofUslE0rVglKUEgDGNwzghULR5UyO82qPLeaW1nw1JdIKcjnBHbPyrvkXO7yf3ki6S3lNqSoKcfUopI7EE+YycY9eKjn0K4ddzhcyUX21S2etyz7XliOOMLjcyrqCjhxXyXlj?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?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?/146pX/wBp/o7obpn0JcbN76n25V7nJXI2fs63ReXUPKTnZukpDAV2UpCk?+eR1x+rVw61aYf1DclLj3Hpb0mvcy9IKsLh6ifbehrS6k/VUluJJWAfJ8Gvd7G/sjdQfZj6qa01DqDVVkk6Hm296Lbwl5RkpQiSHGnHd7YDQDZd3BK8blZOcBQ9tx6ZaY6Fey97REgdSoWq9Raosl2v9xnnahSUyIrzcZonxFlYLgd2kkFRWQAK0iszRLOUIwaXb8+TSXSlK6ZQFKUoBSlKAUpSgFbcfY6saLL7N3TxhxrDq7bNmKJZ8NShJmuOJJ9fh2gK8wARgYFajq3M9CYL9t6QaAhPs?+CpnRtlC0FO0pcVH3rSpJGQRvGc?+ea1lsgSQnKwG+QTk5xX05JSFJ+EHdnt+v+ddQHxAj6oGe36+ddyB2wc4ODjzqIcEN+0NKAZt0fkAlxeD8gn/AIjUE2TqRpKyWOLZZUREhSoN4cmrDryE+8OF4MtqbT+7dJHhYUoHbu7gjiXfaJlFFyiN9giMtw8+pAz/AOGsdE6FtE/SsO8229XOXeLnMcgMW1NsRtcfbDa14dD2doS8k7tucgggDmv1j/ZtYWk/Ra2heNpSlJrGeW6nLS2SW+XhbbnjatSf8Urzp74UV9EXFZpnRZ/UL6Lgw2I89x+fHecZcQxBK9pbiuBOVKCAXASEkFWzHHI8N/e6ZM2i5yrFbrTJeduK48eMXpIebj?+FgvIOAjHip3JCuQhWCMnigxulurZElDKITRaWlJEsS46oqtyiAEvhzwlKJSobQoq?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?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?/hCkLvd?/SyLtpF7Sjt3ubYeiCL7+mSr3sEAYcDucheQDu9a80JbYwQrAyR8u9XPbnAEpA7ZzkZqhPknUmWDD9mTSNyitQNSag1Ne4jaEoUxcLopxt0bUghaUpTuyoKWc8lTiz51HPt5aY0r0k9ibqIxo2xwoPvcWBb1nYpS3G3JrDeFLJK1bUrVt3KOD+FZVwfDASkgEH19f1isTfpWLzFtvsg3+A/IDblzuVsisJwT4ihJQ6U/L4WVH/AGazSilJYNJ1JSWGzR7SlK6BCKUpQClKUApSlAc2G/GebaKtu9QTnGcZNbzbbBiWtKrRDbIYt6GITSSskhtthtKck9zjPNaVulunRq?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?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?/CcV0qyjb20kuFF8vPC83uytVptxk/ibJdNshmxwWkp4bZQnn5ADH69aqgT8RGFYSfnyK8dsCUQ2UFI2pSAMetegjOeME45z5+X9tfzsqTc5ub7s9tjGx2KVgABX1uM45/WRUYdcAo6RYbSPik3jPz4bI/3ak/dlRVgbgMYzx2x/fUQ9cJzqtGQJEVZDv7UlKaUQDhSFOJB9DyBW1L2jDNNkyXIny350pe9+Q4p1xW0DctRyTgcDk?+VdVKVkyKUpQClKUApSlAVHTV6d03qK16hYbDjlsmszEoJwFFtYVj78Yra70m1BF1FbWZ0N1TkeS0y+wo8bmnEbkKx8xzWpGtgPsA6pevOgp9qlJTvsMxEVC0A5U05vcQVE5yQS4njACUJGPMyU3yjWS7mbcaUYVpMnaQhKwkqxwBj?/ka9MDUqFq3AjJ8sgDNdlntqbtpm4RCskpAUD59vWooddn22WqOVrylZSKqVZ4k4k0I7Jk5Wy/NLUCXEgJzntzV2Wy+NKTwRkngk8fh+FY/Wq7zCQN3GAfnirztFzm4bAdUpOR+vl/zrnzqbk8Yk4sXoBJK9oxycKH44rWn9L71nhXZzR/RW3SkvOxHV6huQCgfDUULZjA4PCiFSCQRkAoPZVZxSrwmz2eXe7pKEeBBjOSpD687G2kJKlrOM8JSkk8Vot62dTbl1i6qak6j3Q4XeJqlsoxjwo6AEMt/7LaEA+pBPnU1pmpLU+xHVSiiyKUpXRIBSlKAUpSgFKUoCYPZAtv7W9pnp3Fwo7Ly3JO1QBwylTp7+Xwc+ZHbnFbbbe?+mTHTLTwiSFPgZ7hZ3fzrWB9H3bXZftOWK6NqWE2W3XSc5tRuG0w3WfiP8Iy8OfXA862e2uL7rbY8ZI2eE0hrHbskYFaVOEZXJ7QrJ88AZ+fau7ggc48ycH866BuUpOSQVYxnjArtT2yojbnvnt91RBnnXY7RIcUqTBaWskZygH7O4rz/4K6dUVBdlhq55BYQOe3p3qqpJzt3cH55z+s12oKs43DAODjA9PwqeFzWprEJtfBs1cY8tFF/wJ0moBStPwznsfd0dz/s1xPT/AEcpPNgh5PA/cI/uqup8P4fhCicjg/3/ADrsSsFJU5gbcHaOw?/X8qsR6nex4rT/3P9TR0aT5ivki3D020WT8Wn4YTkDhpP8AdXUeluiFEFNhjf7KcenpV2IQf4iUkc+vHzP6719AyFDbnKu3oe1TLrXU4+zc1F/85fqau2oPmEfki0FdJNDZJ/YjXHGAtYP5EV1/4n9ELTxZ0jIx8Lrn/FV6pUhYwkKJT69if0a+hIVwvOQM96mj6RdYjxd1f/sn+pp9jtv/AG4/7V+hYaujGiVA7IC0Adgl1eP7a6F9DdHLwQ1IA57Okgjz79/KpERz8KySMZx93/T8K+gkAnHB44HI/wCVTQ9Ketw4u6n++X6mHY2r/wAuPyRGi+gmjz2VLSceTozn8K6nPZ40kvafGmJB5BK0/wDB2qUTjAI27hwc18OVZyeSPvx+hUy9MevR4u5/7jX+H2mc+HH5ERv+zdoxwEl2Wcn4srbPyx/R14H/AGXtDqbBSXcnjlLR/wByptSRvO1JweeT24rju3EbQPUYzjHrViHp36R0/ZvJ/T9DD6baP/LRBDvsoaRJy2439XuqK0f5Cvtu9mGw2i4R50WUyhxpxKgUxEJJA7jOeKnTeEKSSRyM475x9n64o4d68KABPl6/nU//AKh+k7i4O8k0/dF//kyunWq/k+r/AFOtlrw2UDbgoSAVf3V270J3hSjtABIx+sf8q4lWVZSnII+z764qWBwDwTjJPlXiy7yciN4ASU88nzNQh7QCXW9B2+K04tDjrlyeQtJIIKnHCkgjt9biptworClYwB/EMcVj?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?+Zx50dLrR4KgoFpWCraknaXeAFApylQTdVhVayV3cwzgpwUled2VZzynsPSvqkqVlA7k4yVVxlxZdtuL1puUb3aWykObSvxG3WzkBba8DekkEdgoEcgcZ+xRIdcRHaYWpaiAlATuUr?/kKqyhKD0y5Jk1JZRyB7HgcAAk4J/QrkhKgN68gkAjPfFe5Wnb42AHbVMQ2lJcK1sqCEjGTlX1R29RVOABSlaClSVdlIVkKGe+fP8fKjhKO7QUk+GdrQUsgIbKiSAkcc/IAc/8AWrvYtGjLLMt2ndVartMPUF+S4bbbn5SUPPlCdy?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?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?/EpWOxOMZPHNQVri1lSwnl443N4W9SM8l+6RhxbrqKPFmDLKEqdWnOApKSMD8SMj0q+NfPNJtzlwE6XGRDhutobir2gglCs44HHhgDPABNQtcrje7Q7CvtkT4sm2ul0xycJfbI2rbJHbKVEg+RCTg4qrTdVQeoK2Zth1m/AWwz4L8BSQlaSSFErQSDu3IQMg4wFgZ3kiHp0oZw+TNzCT3PVp28uX6/SYTF8ubK/e3ZIS6nMZRbWncEI3YU0fGwkA/VUCR9U1enUyKzI0xKv7LI/aVqjGQNmEGS02CpTKvkRu2k/VUc9ioGObZZJ1ucS9J1jOVHbSEFKlELwFZyV7++xTqScZ5Qr6zaTXdqzqPC1Ew9pHTsxEp?+UnwZchlYKIzR4UN3beRkADPz7V0rlU4rL4K9OMm9j6rbtCwUlKh378V1kjkoPHmrHFfEKShsBAxjP1e3rXPHw/wCj6Y8815o6h5rkv/s+UQcYYcxkf6JrFb6QpwJ0fqbcCoMwIKMDyzKbA/AqFZUSUJchOt7froORnPcY71id9IFcojGkNcR3mXVLlR7dFZUgDCFiZHcJVk8DahQ4zyoeWSJaXsyZq?+TWhSlKGRSlKAUpSgFKUoBWUP0fz6B1M1JGOdytPF0HAxhMuOk?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?+eae9DJYI6W2xS0CTIfdbQcBBcUU8eeCSPyq7LJp622RhEG3x247TYI8NAxxnyxVR+od2e3qc8Z4rmE8hJSeD5jg/OsuTl7TyY44PgG1ARlISTjjsc/KvoThW9AAUAcEHnOfy?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?/R5Vj8R6V64nX/AKMy0lxL91b3YB3owPwxTwZDUXqk9t2Se2cY/nXI5IV8BIxyKteJ1p6NPrAOoJjZx?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?/JFAu0W929KkwFrQ6QdiA6Alf3GsFuvfTG86e1NPvbdqnmPdJDsh47CtLTriipXxJH1MnjPbzNZ+y9UW6/kWxi1RY0jYpTLyFK8TgcjJUR+XFWTb9TvSVuRpiGZaUKUytLyBlKwSDk985FQSq0+p0nqi4vlMl0ys5LDyu5rgtlinXyeIdmYemynyEoaaQVqVzjsOfP8AnWxH2X+kbXSrTwb1jbXhdbm225OU3McDZaAy0yUJ4UEkkknzV3+EYv8A0tItER5DzOmbct0H6/hnIPrVt9YOpOoYmqrs3GbL5i2lgBpkfG2o78KSlPOACDj5V56jSVWu6MnhJN5+B05ScaetLkiH2tPZoidVtZXTqJ0zeVGvUhLSXbVJcSlmSlppLafCWceGrahIwslKvVJ74HTIcu3S37fcIr0aVGcUy?+w8gocacScKQpJ5SoEEEHkEVsu0WI+u3kG4ouCCpjxWUxJ7CWydxylx1QBZA8RRyScqQlIGQCIc9t7owhzTsbrLarY7GlQXmbZekb?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?+nwvKrFOlNbGDMmx+0r0/ucd6XM15NsjrK9qGJjMsuPAjukx0OJA/wBZQPHavWr2qdHMfA11EnrAzgpjysfL6yAawrpWdbGDO63e110tjxHJd01VOluNp?/ohDfW85x2TuSE/ZlQHPJFYodaur146x6uVfZzQjQYqTHtsQYywxnPxEfWWTyT9w4AqwKVq5N8mRSlKwBSlKAUpSgFT97L3RMawuidf6nhIXY7W9iJHeQFInSU57jPLbZwTkYUcJ5G4CPOjPSu5dWtYs2KOpbFvjgSLlLSP6FgEAhJ7b1ZwkevPYGtgVqstq0/aYdgscFuHboDIjxmEEkNoAxjJ5UT3JJJJyTknNdzo3TvtU/GqL1V9Wc++uvCj4ceX9D48CrvnceT9/mfnVkXq7yJGrrdChwkuRbapbsiSpI+FzYobUE85AJBx6kVfTzbpT4iU5HYH51QpbCStJDDSVhOEqSnGPLt2r2c45g/ccGL9ZFwAvvraQiR4TEhvcHR9UccZ++vjGkNXW+VYnrZedSRFW5xb7bVnntJjh1TpcU54a3G9+5RJIVnjjtiqFp29uWuOiLLa8aIRlIPlnnj86kmzz7beAhyPJT4jTYSEk4KceVce?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?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?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?/Z/OjOi0R1qXNnw4p5J3uhJ578EjNWCm6Xdfe4SMdj+8PPlXNkPrVlxxZOe5OTmoqvX6sVphFG0Om028ybJJgw9IWkbFagS6pBCwlpKlZUPL6uPLHf76rC9ZOOlt2LFSvwkkNreQPh8+E85qNYTBBBJOc1eNtiqXHSFD4jXnqlxUqTc1szqwpxjFR7FTkXe5Xp4SbjLcecxgZPCRjsB5dhWK/t/zFQ+lECM26pCp18YbUkEjc0lp5WCPTclJ/2ayhShET+ILI7AdvvrCX22dIdYNfattztm0fLuVgtLCm464KhIfckOYLqlMp?+NIwhCQNpHw5z8W0R28HKeTarJJGG1K5PMux3VsPtLbdbUULQtJCkqBwQQexBrjV0rilKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAZ4dEZsU9JtKlIQsCBtC21c7kuKBB8iQQQR6j5VfDb7LbvvKHkqUOyT8Khn/p5ZqH/AGdb9ZZ3S6x2OLc4q7hATKRJih5PjIzJdcCijO7btcT8WMd/Q1f97nJt8QSFr2JSsJJJ7Z9a9layj9nhL3I4NZPxZR97L4t2qrjbF74cxbQJwoBXwkfMHj0qLOtMly7WO5SnQgLcc3qCU4GfkKqES/pUrb4gPljOQKpOvHEv6UkqCt3ByT+dSXGJUpNeRpSyprJ1dO0l3SFtirICmGePUgnNXW0t9nG4BaPLB5FWXod9CtPxC0vCkJx38xV6RJjMo?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?/1iVJK8+ZXuPAwRUb0reNScMqLxk1cYy5RKNi643CNLP7UtyG4xxtTCUv8Ad4AH1XFq3Zwf4hyo84wKnNm?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?+no1l0hKn292VGkNOteGtqS5nJWAR9bkYJrlVJKT4LcNi3223FfUaUoAZ7Zqr2BUKW44DLjgtY35cGU/cOfyqFnTPnNp96uEh70DjqlD8zVPMWRBX7zEdcZd7hbZKSD9oq1S6S6zWt7e4jleqnwZP+PAY/dxA9KdJwMJ2Nn/eP4CvHqeDMiuRPf3mw88gkRUf9wnjGRngnk81Z/SnW9zhqQifsmOqjq8N17lTau4Vnz4z3quR5zt1ucmW+suKC9uTyfnXVs7Cjb3SpQW+MtsrV7qVSg6knt5HugxwkDCSST+FXdpnTr1+nsWeKz4smWsNoBHAz3J+QGSfkDVAjlKAMgAnjFX50u1lb9IaoZudza3R3WjGWsDcpncR8YHc4xg45wTj0r0rpunBygsy7HDdTW0nwTDd9VMdKINh6c6I0s7f71IaWqPAbfSx?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?/CjDO2c/JHS6avXcvcUjQ9oYj2e86tjNBLNxLdgsyFJ5ENn4VLB8wpQ/FHzq4+uV3c6I9KVSnkJRdFtCPBbU7sJkr7LTx3Qoqdx5+ERkZyL00pbbejUlsiQYu+y6WS22w3nhak9ifXJTk+u0+tYp+3j1d/xhdVzpm2vf9macaSypKQAlcpSdyzkfW2ghIz2O/jnnxs4zdVLG3L/JfLc70dOhvO/C/MxnJKiVKJJPJJpSlWCEUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFVO0alvVjym3zVJaUQVNLAW2fntPAPfkYPJ5qmUrMZOLzF7mGk9mTZ0n169qLVtus0hhxh9wuOfu1bmlbEKWeFHcn4UnzVk?+meMhDyMj8qwWgz5tsltT7bMeiyWTubeZWULQfUEcipc0h7R9+t22Lq+Ai7MZV/nLIDUgZ7ZA+BYHphJ55VXoOn9WjCPh3DfxOZdWLlLXS+RkgwtbagptW1We4NS3pJiRJs6I9jt6nb7dMQvHSOUM9wgHsnlSjxjvk9hiANH9QNI63U2jTt5belL2/5k6PClAlOSA2eV4xyUbkj1qVIXtA9NOmsH3fUerYDSku7ktxnA++lQABCm2ty0jgYJHrTrFanVdPS01v8OxmwhOGvOz2JV6wP2D2cOi10nXVDK77GZU80pRJS9JUAlKOCDjeUp?/8AEPOtR0+dKuc6RcpzynpMt1b7ziu61qJKiftJNZAe1f7T6uu82NAtTsw26M94rrjyQgPKSna0Ep+sAAVkk43FQ4G0E48V57Tpb3y3uzqyecJLgUpShqKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUB//2Q==" width="306px" alt="monitoring"> A detector with a built-in alarm will sound a siren, so that you just can hear where the leak is coming from, without having your smartphone. Like the Phyn Plus, the Phyn senses the water stress in your home, can detect when there's a drop in stress and utilizing algorithms, can let you know if it's being attributable to a burst pipe or just a leaky toilet. Because it would not embrace a shutoff valve, the Phyn may be easily put in by a home-owner, underneath any sink. The one caveat is that you'll additionally need a power source close by.

h2>How Does Leak Detection Work?</h2> The PCNN is predicated on neural networks composed of two or extra impartial convolutional streams which course of different inputs concurrently. The perception design of the PCNN is flexible, and the recognized info can be fully utilized . The concept of PCNN has been utilized in quite a few fields, together with spatiotemporal satellite picture fusion , patient test matching , object tracking , and mechanical fault detection . In this work, we propose a novel software of PCNN for water leak detection by combining handcrafted features with deep representations. Moreover, we reveal the applicability of the Mel frequency cepstral coefficients as further handcrafted options for leak detection. Figure 4b reveals a web site picture of the leak signal assortment, whereas Figure 4c reveals the leak level after excavation. In the detected pipelines, the diameters vary from DN50 to DN300 mm, buried depths range from 0.6 to 2.4 m, and the pressure ranges from zero.2 to 0.four MPa. The plastic pipes are primarily polyvinyl chloride or polyethylene pipes, while metal pipes or forged iron pipes comprise the majority of the steel pipes.

div style="text-align:center"> <iframe width="565" height="319" src="https://www.youtube.com/embed/EPwCK6sNjOo" frameborder="0" alt="water leak detection" allowfullscreen></iframe></div> According to the Nyquist?Shannon sampling theorem, the pattern rate is sufficient to seize the effective bandwidth of leak signals for each plastic and metallic pipes. Figure three reveals the architecture of PCNN used on this paper, and the hyperparameters of the community have been optimized. The PCNN employs two impartial convolutional constructions to process the enter handcrafted options and uncooked indicators in parallel. Human ears respond differently to different frequencies of sound waves. The Mel frequency is extra similar to human auditory responses. For this purpose, MFCCs’ options show improved robustness compared to traditional auditory features . These convolutional neural networks are the variants of the reference model presented in Figure 5b. Table four.The performance of convolutional neural networks with handcrafted features as input. These convolutional neural networks are variants of the reference model introduced in Figure 5a. In addition, CNN-MFCCs performs higher than CNN-TFD and CNN-Raw, which means that MFCCs’ options are more effective for leak detection than TFD options or deep representations. CNN-MFCCs&TFD presents a better outcome than CNN-TFD and CNN-MFCCs, indicating that MFCCs’ and TFD options complement one another.

h3>Information Availability Assertion</h3> However, once we cut power to a Sensor Pod, our outcomes didn’t are available as quick . D-Link’s plug-in hub has an optionally available water-sensing cable and might assist as a lot as 16 remote Water Sensor Pods. We set up each system according to its instructions, downloaded the mandatory apps, and linked them to a Wi-Fi community or a smart-home hub when wanted. For each of our tests, we used apps on an iPhone SE, an iPad, and a Samsung Galaxy J7 working Android Oreo, when attainable. Be certain to place them in areas where leaking water would pool. The Eve Water Guard will let you understand if you've sprung a leak and has the power to set off other HomeKit? gadgets, however in any other case offers limited third-party help. The hub can join with up to 16 add-on sensors, responds shortly when it senses water or if it loses energy, and includes a loud siren. The system supports Google Assistant for asserting activity on sensible audio system, in addition to the web service and app IFTTT for triggering other sensible gadgets when it finds a leak. Like each other leak sensor, it has a few quirks, but it’s still the finest choice for monitoring a couple of spot in a house. The more affordable models are just leak detectors that typically lack a Wi-Fi radio and talk via Bluetooth or work as part of a home automation system. To be particular, acoustic sensors, corresponding to pressure sensors, accelerometers, and hydrophones, are deployed on or inside the pipeline in acoustic technology. As a end result, the purpose of those procedures is to discover unusual sounds or noises attributable to the water leaks in pipes. An alarm system is activated if there are any variations outside of a certain vary. It will be feasible to seek out the leak since the acoustic sign would be the highest when close to the leak.

ul> <li>Lai, W.W.; Chang, R.K.; Sham, J.F.; Pang, K. Perturbation mapping of water leak in buried water pipes by way of laboratory validation experiments with high-frequency Ground Penetrating Radar .</li> <li>The cross correlation methodology is broadly utilized in leak detection and location for pipes.</li> <li>Editors choose a small number of articles lately printed in the journal that they imagine might be particularly interesting to readers, or important in the respective analysis area.</li> <li>For example, if you stay in a cold local weather, you presumably can set the sensor to alert you if the temperature drops to 32 degrees Fahrenheit, a degree that can result in burst pipes.</li></ul> Water-related emergencies and leaks typically happen when the power goes off, so it’s a good idea to spend money on a smart water leak detector with extended battery life. Any good water leak detector helps catch small leaks earlier than they trigger vital injury. Automatic shut-off features are finest if you wish to immediately reduce your water supply at the first signal of a leak, and temperature sensors are a good suggestion if you live in an space the place pipes are prone to freezing. Left unchecked, small leaks underneath your water heater, washer or sink could cause significant issues all through your personal home or business. The SLD system has undergone extensive testing at Siemens Energy’s devoted leak check loop in Houston. The first field software took place in 2021 on an offshore manufacturing platform within the Gulf of Mexico, where it was used to observe four steel crude oil flowline risers. Live simulation of a Spontaneous leak from our Liquid Demo Unit in Houston. Siemens Energy is prepared to remotely simulate leaks during demos to indicate the accuracy and quickness of our product line. More advanced methods, corresponding to fiber-optic sensing, can resolve this downside. Additionally, mannequin 6 contains a easier construction which facilitates subsequent characteristic fusion. Based on the results, the hyperparameters and architecture of mannequin 6 have been chosen for the characteristic engineering stage. In the project, leaks were positioned utilizing a mix of several methods such as leak noise correlators, listening rods, and digital amplified listening devices.

h3>Preliminary Analysis: Choice Of Optical Spectral Bands Utilizing Hyperspectral Cameras And A Thermal Infrared Digital Camera</h3> During the subsequent flight on 11 September 2019, the primary sample shrunk whereas the second one appeared extra elongated . The second anomaly was dominated out as a leak as a outcome of its location was primarily north, i.e., uphill, of the pipeline . A ground-truth measurement on 10 September 2019, confirmed that the imply moisture stage was about 13% along the first WI-anomaly while reaching up to 23% locally , whereas it was solely about 7% a quantity of meters away. Sparse green vegetation could possibly be seen alongside the WI anomaly, as opposed to dry vegetation away from it . More refined approaches use an SVAT mannequin to define the water content from the place of the consultant level contained in the triangular scatter . Shah, J.; Mishra, B. Customized IoT enabled wi-fi sensing and monitoring platform for sensible buildings. https://ctxt.io/2/AABQLShRFQ ” refers to the use of numerical evaluation and information buildings for the study and backbone of issues involving fluid flows . It is beneficial to make use of a high-speed supercomputer, which is incessantly wanted to handle the most difficult points. Software that enhances the precision and velocity of sophisticated modeling conditions, such as transonic or turbulent flows, is the outcome of the continuing analysis. Torres, L., Besançon, G., Navarro, A., Begovich, O., & Georges, D. Examples of pipeline monitoring with nonlinear observers and real-data validation. The saliency map is the heat map that visualizes the relevance between the characteristic or knowledge point in the enter instance and the prediction of the model. The Vanilla gradient algorithm is the original saliency map algorithm for supervised deep studying. Quantitative analysis of the Vanilla gradient has demonstrated that it is comparatively sturdy compared with other algorithms.


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Last-modified: 2023-10-05 (木) 02:04:49 (217d)