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</div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div><div class="verticalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div></div></div></div></div></div></div><div class = 'S5'><span>Frist, we normalize the data</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>mean_state = mean(training_state);</span></span></div></div><div class="inlineWrapper"><div class = 'S2'><span style="white-space: pre;"><span>std_state = std(training_state);</span></span></div></div><div class="inlineWrapper"><div class = 'S2'><span style="white-space: pre;"><span>traning_state_nor = (training_state-mean_state)./std_state;</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre;"><span>obs_state_nor = (obs_state-mean_state)./std_state;</span></span></div></div></div><div class = 'S5'><span>Then, we then calculate the distance and the weight.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>h = 3;</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S3'><span style="white-space: pre;"><span class="variableHighlight">d</span><span> = max(traning_state_nor-obs_state_nor,[],2)</span></span></div><div class = 'S4'><div class="inlineElement eoOutputWrapper embeddedOutputsVariableMatrixElement" uid="3062449D" data-scroll-top="null" data-scroll-left="null" data-width="802" data-testid="output_1" style="width: 832px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="matrixElement veSpecifier" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 802px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;">d = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: normal; color: rgb(179, 179, 179); font-size: 12px;">4×1</span></div></div><div class="valueContainer focusedInteractiveOutput" data-layout="{"columnWidth":66,"totalColumns":1,"totalRows":4,"charsPerColumn":10}" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="variableValue" style="width: 68px; white-space: pre; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"> 2.3337
3.5441
2.6825
2.8556
</div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div><div class="verticalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div></div></div></div></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre;"><span>w = 1./sqrt(2*pi)./h.*exp(-</span><span class="variableHighlight">d</span><span>.^2./2./h.^2);</span></span></div></div></div><div class = 'S5'><span>After that, we canculate the AAKR value</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div class = 'S8'><span style="white-space: pre;"><span>nc_state = (w.')*training_state/sum(w)</span></span></div><div class = 'S4'><div class="inlineElement eoOutputWrapper embeddedOutputsVariableMatrixElement" uid="6EFE06FD" data-scroll-top="null" data-scroll-left="null" data-width="802" data-testid="output_2" style="width: 832px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="matrixElement veSpecifier" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 802px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;">nc_state = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: normal; color: rgb(179, 179, 179); font-size: 12px;">1×3</span></div></div><div class="valueContainer" data-layout="{"columnWidth":66,"totalColumns":3,"totalRows":1,"charsPerColumn":10}" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="variableValue" style="width: 200px; white-space: pre; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"> 7.0488 17.8350 32.4677
</div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div><div class="verticalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div></div></div></div></div></div><div class="inlineWrapper outputs"><div class = 'S9'><span style="white-space: pre;"><span>RUL_pre = (w.')*traning_RUL/sum(w)</span></span></div><div class = 'S4'><div class='variableElement' style='font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 12px; '>RUL_pre = 6.1579</div></div></div></div><div class = 'S5'><span>Finally we plot it.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>plot([0;1;2],training_state,</span><span style="color: rgb(170, 4, 249);">'k--'</span><span>)</span></span></div></div><div class="inlineWrapper"><div class = 'S2'><span style="white-space: pre;"><span>hold </span><span style="color: rgb(170, 4, 249);">on</span><span>;</span></span></div></div><div class="inlineWrapper"><div class = 'S2'><span style="white-space: pre;"><span>plot([0;1;2],nc_state,</span><span style="color: rgb(170, 4, 249);">'r-.*'</span><span>)</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S3'><span style="white-space: pre;"><span>plot([0;1;2],obs_state,</span><span style="color: rgb(170, 4, 249);">'r-+'</span><span>)</span></span></div><div class = 'S4'><div class="inlineElement eoOutputWrapper embeddedOutputsFigure" uid="C430BF4D" data-scroll-top="null" data-scroll-left="null" data-testid="output_4" style="width: 832px;"><div class="figureElement" style="cursor: default;"><div class="figureContainingNode" style="width: 560px; max-width: 100%; display: inline-block;"><div class="GraphicsView" data-dojo-attach-point="graphicsViewNode,backgroundColorNode" id="uniqName_164_39" widgetid="uniqName_164_39" style="width: 100%; height: auto;"><img 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##### SOURCE BEGIN #####
%% Usage of AAKR for RUL prediction
training_state=[8.99,17.75,32.17;
4.34,19.42,33.12;
8.00,16.15,35.04;
5.91,18.47,29.59];
traning_RUL=[5.76;6.25;6.82;5.85];
obs_state = [4.15,14.53,28.96]
%%
% Frist, we normalize the data
mean_state = mean(training_state);
std_state = std(training_state);
traning_state_nor = (training_state-mean_state)./std_state;
obs_state_nor = (obs_state-mean_state)./std_state;
%%
% Then, we then calculate the distance and the weight.
h = 3;
d = max(traning_state_nor-obs_state_nor,[],2)
w = 1./sqrt(2*pi)./h.*exp(-d.^2./2./h.^2);
%%
% After that, we canculate the AAKR value
nc_state = (w.')*training_state/sum(w)
RUL_pre = (w.')*traning_RUL/sum(w)
%%
% Finally we plot it.
plot([0;1;2],training_state,'kREPLACE_WITH_DASH_DASH')
hold on;
plot([0;1;2],nc_state,'r-.*')
plot([0;1;2],obs_state,'r-+')
##### SOURCE END #####
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