{"id":813,"date":"2019-09-26T10:39:39","date_gmt":"2019-09-26T10:39:39","guid":{"rendered":"http:\/\/kios.ece.ucy.ac.cy\/?post_type=kbe_knowledgebase&#038;p=813"},"modified":"2019-10-03T08:35:41","modified_gmt":"2019-10-03T08:35:41","slug":"%ce%b1%ce%bd%ce%af%cf%87%ce%bd%ce%b5%cf%85%cf%83%ce%b7-%ce%bc%ce%b7-%cf%86%cf%85%cf%83%ce%b9%ce%bf%ce%bb%ce%bf%ce%b3%ce%b9%ce%ba%ce%ae%cf%82-%cf%83%cf%85%ce%bc%cf%80%ce%b5%cf%81%ce%b9%cf%86%ce%bf","status":"publish","type":"kbe_knowledgebase","link":"https:\/\/www.smartwater2020.eu\/?kbe_knowledgebase=%ce%b1%ce%bd%ce%af%cf%87%ce%bd%ce%b5%cf%85%cf%83%ce%b7-%ce%bc%ce%b7-%cf%86%cf%85%cf%83%ce%b9%ce%bf%ce%bb%ce%bf%ce%b3%ce%b9%ce%ba%ce%ae%cf%82-%cf%83%cf%85%ce%bc%cf%80%ce%b5%cf%81%ce%b9%cf%86%ce%bf","title":{"rendered":"\u0391\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7 \u039c\u03b7 \u03a6\u03c5\u03c3\u03b9\u03bf\u03bb\u03bf\u03b3\u03b9\u03ba\u03ae\u03c2 \u03a3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac\u03c2 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\u03c4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b5\u03b9\u03c2 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2 (\u03cc\u03c0\u03bf\u03c5 U \u03c3\u03c5\u03bc\u03b2\u03bf\u03bb\u03af\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b5\u03ba\u03c4\u03b9\u03bc\u03b7\u03bc\u03ad\u03bd\u03b7 \u03c4\u03b9\u03bc\u03ae \u03c4\u03b7\u03c2 \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2):<\/p>\n<p>\u03a0\u03b5\u03c1\u03af\u03c0\u03c4\u03c9\u03c3\u03b7 1: (\u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 +\/- U) \u03b5\u03ba\u03c4\u03cc\u03c2 \u03bf\u03c1\u03af\u03c9\u03bd<br \/>\n\u03a0\u03b5\u03c1\u03af\u03c0\u03c4\u03c9\u03c3\u03b7 2: \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03b5\u03ba\u03c4\u03cc\u03c2 \u03bf\u03c1\u03af\u03bf\u03c5 \u03ba\u03b1\u03b9 (\u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 &#8211; U) \u03b5\u03bd\u03c4\u03cc\u03c2 \u03bf\u03c1\u03af\u03bf\u03c5<br \/>\n\u03a0\u03b5\u03c1\u03af\u03c0\u03c4\u03c9\u03c3\u03b7 3: \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03b5\u03bd\u03c4\u03cc\u03c2 \u03bf\u03c1\u03af\u03c9\u03bd \u03ba\u03b1\u03b9 (\u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 + U) \u03b5\u03ba\u03c4\u03cc\u03c2 \u03bf\u03c1\u03af\u03bf\u03c5<br \/>\n\u03a0\u03b5\u03c1\u03af\u03c0\u03c4\u03c9\u03c3\u03b7 4: (\u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 +\/- U) \u03b5\u03bd\u03c4\u03cc\u03c2 \u03bf\u03c1\u03af\u03c9\u03bd<\/p>\n<p>The measurements recorded by a network of sensors can be considered unreliable and inaccurate. It is, therefore, necessary to quantify the uncertainty of the observed data, which can be used as an indicator of the measurements\u2019 quality. In a smart water monitoring system the potential sources of uncertainty may vary. In particular, they can be related with the sensor\u2019s manufacturing, the environmental conditions, the presence of interference, the quality of the mathematical models, etc. In the specific case of detection of abnormal behaviors based on the recorded data, accounting for the inherent uncertainty in the algorithmic design typically improves the accuracy in detecting extreme events.<\/p>\n<p>The functionality of abnormal behaviors\u2019 detection has a prominent role in the modern systems for smart monitoring of water distribution networks. The objective of this functionality is the timely and accurate production of notifications for the occurrence of abnormal behaviors, thus guaranteeing the smooth operation of the water network. Accounting for the inherent data uncertainty yields the timely generation of alerts, since a range of potential values for each individual recorded measurement is examined. In turn, the timely production of alerts enables fast decision-making to remedy the effects of extreme events, ensuring the system\u2019s autonomous operation. For instance, in the case of water distribution networks, an abnormal behavior is defined as the exceedance of predetermined operational limits for the values of a physical quantity (e.g. pressure, temperature). In contrast to the na\u00efve case where a measurement is compared with the corresponding operational limits (two cases of exceeding or not the limits), accounting for the inherent uncertainty in the received measurements yields a comparison with four distinct cases, as follows (U denotes the estimated uncertainty value):<\/p>\n<p>Case 1: (measurement +\/- U) out of limits<br \/>\nCase 2: measurement outside limits and (measurement &#8211; U) within limit<br \/>\nCase 3: measurement within limits and (measurement + U) outside limits<br \/>\nCase 4: (measurement +\/- U) within limits<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<div class=\"entry-summary\">\n<div class=\"entry-summary\">\n\u039f\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03bf\u03c5 \u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1 \u03b4\u03af\u03ba\u03c4\u03c5\u03bf \u03b1\u03b9\u03c3\u03b8\u03b7\u03c4\u03ae\u03c1\u03c9\u03bd \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b8\u03b5\u03c9\u03c1\u03b7\u03b8\u03bf\u03cd\u03bd \u03b1\u03bd\u03b1\u03be\u03b9\u03cc\u03c0\u03b9\u03c3\u03c4\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b1\u03ba\u03c1\u03b9\u03b2\u03b5\u03af\u03c2. \u039a\u03c1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9, \u03bb\u03bf\u03b9\u03c0\u03cc\u03bd, \u03b1\u03bd\u03b1\u03b3\u03ba\u03b1\u03af\u03b1 \u03b7 \u03c0\u03bf\u03c3\u03bf\u03c4\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03c4\u03b7\u03c2 \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03c4\u03c9\u03bd \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd \u03b7 \u03bf\u03c0\u03bf\u03af\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03ae\u03c3\u03b5\u03b9 \u03c9\u03c2 \u03b4\u03b5\u03af\u03ba\u03c4\u03b7\u03c2 \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03c9\u03bd \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03c9\u03bd. \u039f\u03b9 \u03c0\u03b7\u03b3\u03ad\u03c2 \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03c3\u03b5 \u03ad\u03bd\u03b1&hellip;\n<\/div>\n<\/div>\n","protected":false},"author":2,"featured_media":814,"comment_status":"open","ping_status":"closed","template":"","kbe_taxonomy":[97],"kbe_tags":[],"class_list":["post-813","kbe_knowledgebase","type-kbe_knowledgebase","status-publish","has-post-thumbnail","hentry","kbe_taxonomy-97","entry"],"aioseo_notices":[],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.smartwater2020.eu\/index.php?rest_route=\/wp\/v2\/kbe_knowledgebase\/813","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.smartwater2020.eu\/index.php?rest_route=\/wp\/v2\/kbe_knowledgebase"}],"about":[{"href":"https:\/\/www.smartwater2020.eu\/index.php?rest_route=\/wp\/v2\/types\/kbe_knowledgebase"}],"author":[{"embeddable":true,"href":"https:\/\/www.smartwater2020.eu\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.smartwater2020.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=813"}],"version-history":[{"count":1,"href":"https:\/\/www.smartwater2020.eu\/index.php?rest_route=\/wp\/v2\/kbe_knowledgebase\/813\/revisions"}],"predecessor-version":[{"id":815,"href":"https:\/\/www.smartwater2020.eu\/index.php?rest_route=\/wp\/v2\/kbe_knowledgebase\/813\/revisions\/815"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.smartwater2020.eu\/index.php?rest_route=\/wp\/v2\/media\/814"}],"wp:attachment":[{"href":"https:\/\/www.smartwater2020.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=813"}],"wp:term":[{"taxonomy":"kbe_taxonomy","embeddable":true,"href":"https:\/\/www.smartwater2020.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fkbe_taxonomy&post=813"},{"taxonomy":"kbe_tags","embeddable":true,"href":"https:\/\/www.smartwater2020.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fkbe_tags&post=813"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}