public void SetProgress(int value, SENSOR sensor)
{
switch (sensor)
{
case SENSOR.EEG:
this.Invoke((MethodInvoker)delegate
{
eegProgress.Value = value;
});
break;
case SENSOR.HR:
this.Invoke((MethodInvoker)delegate
{
hrProgress.Value = value;
});
break;
case SENSOR.GSR:
this.Invoke((MethodInvoker)delegate
{
gsrProgress.Value = value;
});
break;
case SENSOR.FACE:
this.Invoke((MethodInvoker)delegate
{
faceProgress.Value = value;
});
break;
}
}
public List <SENSOR> DanhSachCamBienCuaThietBi(string IDDV)
{
AGRIBOTSYSTEMDataContext context = new AGRIBOTSYSTEMDataContext();
List <DVSS> lstDeviceSenSor = context.DVSSes
.Where(x => x.IDDV == IDDV)
.ToList();
var list = new List <DVSS>();
foreach (var e in lstDeviceSenSor)
{
list.Add(new DVSS
{
IDDV = e.IDDV,
IDSS = e.IDSS
});
}
List <SENSOR> lstSensor = new List <SENSOR>();
foreach (var item in lstDeviceSenSor)
{
SENSOR dv = context.SENSORs.FirstOrDefault(x => x.ID == item.IDSS);
dv.DVSSes = null;
dv.TYPE_SS = null;
dv.WARNINGSMAXes = null;
lstSensor.Add(dv);
}
return(lstSensor);
}
public ActionResult DeleteConfirmed(int id)
{
SENSOR sENSOR = db.SENSORS.Find(id);
db.SENSORS.Remove(sENSOR);
db.SaveChanges();
return(RedirectToAction("Index"));
}
public ActionResult Edit([Bind(Include = "Id,uid,threshold")] SENSOR sENSOR)
{
if (ModelState.IsValid)
{
db.Entry(sENSOR).State = EntityState.Modified;
db.SaveChanges();
return(RedirectToAction("Index"));
}
return(View(sENSOR));
}
public ActionResult Create([Bind(Include = "Id,uid,threshold")] SENSOR sENSOR)
{
if (ModelState.IsValid)
{
db.SENSORS.Add(sENSOR);
db.SaveChanges();
return(RedirectToAction("Index"));
}
return(View(sENSOR));
}
private static void LogSENSOR(SENSOR sensor, int i, string header)
{
string sensorHeader = header + "SENSOR ";
Log.Trace(sensorHeader + "index=" + i.ToString());
LogCOMPONENT((COMPONENT)sensor, sensorHeader);
Log.Trace(sensorHeader + "position=" + sensor.position.ToString());
LogSpecifiedDate(sensor.installTime, sensor.installTimeSpecified, sensorHeader, "installTime");
LogSpecifiedDate(sensor.birthDate, sensor.birthDateSpecified, sensorHeader, "birthDate");
LogSpecifiedProp(sensor.alarmAlert, sensor.alarmAlertSpecified, sensorHeader, "alarmAlert");
LogSpecifiedProp(sensor.alarmLow, sensor.alarmLowSpecified, sensorHeader, "alarmLow");
LogSpecifiedProp(sensor.alarmHigh, sensor.alarmHighSpecified, sensorHeader, "alarmHigh");
LogSpecifiedProp(sensor.alarmTWA, sensor.alarmTWASpecified, sensorHeader, "alarmTWA");
LogSpecifiedProp(sensor.alarmSTEL, sensor.alarmSTELSpecified, sensorHeader, "alarmSTEL");
LogSpecifiedProp(sensor.overRange, sensor.overRangeSpecified, sensorHeader, "overRange");
LogSpecifiedProp(sensor.calTimeOut, sensor.calTimeOutSpecified, sensorHeader, "calTimeOut");
LogSpecifiedProp(sensor.polarity, sensor.polaritySpecified, sensorHeader, "polarity");
LogSpecifiedProp(sensor.tempMax, sensor.tempMaxSpecified, sensorHeader, "tempMax");
LogSpecifiedProp(sensor.tempMin, sensor.tempMinSpecified, sensorHeader, "tempMin");
LogSpecifiedProp(sensor.peakReading, sensor.peakReadingSpecified, sensorHeader, "peakReading");
Log.Trace(sensorHeader + "calGasCode=" + sensor.calGasCode);
LogSpecifiedProp(sensor.calGasConcentration, sensor.calGasConcentrationSpecified, sensorHeader, "calGasConcentration");
LogSpecifiedProp(sensor.unitOfMeasurement, sensor.unitOfMeasurementSpecified, sensorHeader, "unitOfMeasurement");
Log.Trace(sensorHeader + "gasDecting=" + sensor.gasDecting);
LogSpecifiedProp(sensor.responseFactor, sensor.responseFactorSpecified, sensorHeader, "responseFactor");
Log.Trace(sensorHeader + "softwareVersion=" + sensor.softwareVersion);
Log.Trace(sensorHeader + "hardwareVersion=" + sensor.hardwareVersion);
Log.Trace(sensorHeader + "dataVersion=" + sensor.dataVersion);
LogSpecifiedProp(sensor.spanCoefMin, sensor.spanCoefMinSpecified, sensorHeader, "spanCoefMin");
LogSpecifiedProp(sensor.spanCoefMax, sensor.spanCoefMaxSpecified, sensorHeader, "spanCoefMax");
LogSpecifiedProp(sensor.zeroMin, sensor.zeroMinSpecified, sensorHeader, "zeroMin");
LogSpecifiedProp(sensor.zeroMax, sensor.zeroMaxSpecified, sensorHeader, "zeroMax");
LogSpecifiedProp(sensor.deadBand, sensor.deadBandSpecified, sensorHeader, "deadBand");
LogSpecifiedProp(sensor.filter.ToString(), sensor.filterSpecified, sensorHeader, "filter");
LogSpecifiedProp(sensor.tempCompLow, sensor.tempCompLowSpecified, sensorHeader, "tempCompLow");
LogSpecifiedProp(sensor.tempCompHigh, sensor.tempCompHighSpecified, sensorHeader, "tempCompHigh");
LogSpecifiedProp(sensor.tempCompM50, sensor.tempCompM50Specified, sensorHeader, "tempCompM50");
LogSpecifiedProp(sensor.tempCompM40, sensor.tempCompM40Specified, sensorHeader, "tempCompM40");
LogSpecifiedProp(sensor.tempCompM30, sensor.tempCompM30Specified, sensorHeader, "tempCompM30");
LogSpecifiedProp(sensor.tempCompM20, sensor.tempCompM20Specified, sensorHeader, "tempCompM20");
LogSpecifiedProp(sensor.tempCompM10, sensor.tempCompM10Specified, sensorHeader, "tempCompM10");
LogSpecifiedProp(sensor.tempCompZ0, sensor.tempCompZ0Specified, sensorHeader, "tempCompZ0");
LogSpecifiedProp(sensor.tempCompP10, sensor.tempCompP10Specified, sensorHeader, "tempCompP10");
LogSpecifiedProp(sensor.tempCompP20, sensor.tempCompP20Specified, sensorHeader, "tempCompP20");
LogSpecifiedProp(sensor.tempCompP30, sensor.tempCompP30Specified, sensorHeader, "tempCompP30");
LogSpecifiedProp(sensor.tempCompP40, sensor.tempCompP40Specified, sensorHeader, "tempCompP40");
LogSpecifiedProp(sensor.tempCompP50, sensor.tempCompP50Specified, sensorHeader, "tempCompP50");
LogSpecifiedProp(sensor.tempCompP60, sensor.tempCompP60Specified, sensorHeader, "tempCompP60");
LogSpecifiedProp(sensor.tempCompP70, sensor.tempCompP70Specified, sensorHeader, "tempCompP70");
LogSpecifiedProp(sensor.displayDecimal, sensor.displayDecimalSpecified, sensorHeader, "displayDecimal");
}
// GET: SENSORs/Delete/5
public ActionResult Delete(int?id)
{
if (id == null)
{
return(new HttpStatusCodeResult(HttpStatusCode.BadRequest));
}
SENSOR sENSOR = db.SENSORS.Find(id);
if (sENSOR == null)
{
return(HttpNotFound());
}
return(View(sENSOR));
}
public bool UpdateStatus(string ID, string value)
{
try
{
AGRIBOTSYSTEMDataContext context = new AGRIBOTSYSTEMDataContext();
SENSOR dv = context.SENSORs.FirstOrDefault(x => x.ID == ID);
if (dv != null)
{
dv.NUMBER = value;
context.SubmitChanges();
return(true);
}
}
catch { }
return(false);
}
public static SENSOR ToSensor(string kks_code)
{
SENSOR sensorRes = new SENSOR()
{
id_object = -1,num_item = -1
};
int value = -1;
if ((kks_code.Contains("OBJECT") == true) &&
(kks_code.Contains("ITEM") == true) &&
(kks_code.Contains("_") == true))
{
if (int.TryParse(kks_code.Split('_')[0].Substring("OBJECT".Length),out value) == true)
{
sensorRes.id_object = value;
if (int.TryParse(kks_code.Split('_')[1].Substring("ITEM".Length),out value) == true)
{
sensorRes.num_item = value;
}
else
{
;
}
}
else
{
;
}
}
else
{
;
}
return(sensorRes);
}
private IEnumerable<Tuple<List<double>, int>> GetTrainingData(SENSOR machine, int start, int trainingEnd)
{
//Split into training & prediction set
List<List<double>> featureVectors = new List<List<double>>();
List<int> timeStamps = new List<int>();
if (machine == SENSOR.GSR)
{
int stepSize = 100;
for (int i = 0; i < _fdAnomaly.gsrData.Last().timestamp - _fdAnomaly.gsrData.First().timestamp - GSR_DURATION + GSR_DELAY; i += stepSize)
{
List<double> featureVector = new List<double>();
List<double> data = _fdAnomaly.gsrData.SkipWhile(x => (x.timestamp - _fdAnomaly.gsrData.First().timestamp) < i + GSR_DELAY).TakeWhile(x => i + GSR_DURATION > (x.timestamp - _fdAnomaly.gsrData.First().timestamp)).Select(x => (double)x.resistance).ToList();
if (data.Count == 0) continue;
featureVector.Add(data.Average());
featureVector.Add(data.Max());
featureVector.Add(data.Min());
double avg = data.Average();
double sd = Math.Sqrt(data.Average(x => Math.Pow(x - avg, 2)));
featureVector.Add(sd);
featureVectors.Add(featureVector);
timeStamps.Add(i);
}
}
featureVectors = featureVectors.NormalizeFeatureList<double>(Normalize.OneMinusOne).ToList();
var dataSet = featureVectors.Zip(timeStamps, (first, second) => { return Tuple.Create(first, second); });
var trainingSet = dataSet.SkipWhile(x => x.Item2 < start).TakeWhile(x => x.Item2 < trainingEnd);
return trainingSet;
}
public static List <DataReading> GetDataFromInterval(List <DataReading> original, int start, SENSOR machine)
{
int offset = 0;
int duration = 0;
if (machine == SENSOR.GSR)
{
offset = 2000;
duration = 5000;
}
List <DataReading> splicedData = new List <DataReading>();
splicedData.AddRange(
original.Where(
x => x.timestamp >= (start + original.First().timestamp + offset) &&
x.timestamp <= (start + original.First().timestamp + duration
)
)
);
return(splicedData);
}
private async Task<NoveltyResult> DoVotingNoveltyDetection(SENSOR sensor, int start, int end, double nu, string type)
{
string sensorPath = path + "/" + sensor.ToString();
var data = featureVectors[sensor].Select(x => x.Features).ToList();
ConcurrentStack<SVMParameter> svmParams = new ConcurrentStack<SVMParameter>();
svmParams.PushRange(GenerateVotingSVMParameters(nu).ToArray());
SetProgressMax(svmParams.Count + 1);
NoveltyResult bestResult = null;
Mutex bestResultMu = new Mutex(false, sensor.ToString());
int count = 1;
List<Task> tasks = new List<Task>();
for (int i = 0; i < threadMAX.Value; i++)
{
Task task = Task.Run(() => PredictionVoteThread(ref count, sensor, start, end, ref svmParams, data, svmParams.Count, ref bestResult, bestResultMu, type));
tasks.Add(task);
}
await Task.WhenAll(tasks);
bestResultMu.Dispose();
return bestResult;
}
private void PredictionNuThread(ref int count, SENSOR sensor, int start, int end, ref ConcurrentStack<SVMParameter> svmParams, List<SVMNode[]> data, int svmCount, ref NoveltyResult bestCoveredResult, ref Mutex mutex, ref ConcurrentBag<string> nuResults)
{
OneClassClassifier occ = new OneClassClassifier(data);
List<OneClassFV> anomali = new List<OneClassFV>();
List<Events> eventResult = new List<Events>();
int maxCount = svmCount;
string sensorPath = path + "/" + sensor.ToString();
foreach (Events p in events)
{
var evt = p.Copy();
eventResult.Add(evt);
}
while (!svmParams.IsEmpty)
{
SVMParameter svmParam = null;
svmParams.TryPop(out svmParam);
if (svmParam == null)
{
break;
}
anomali = new List<OneClassFV>();
occ.CreateModel(svmParam);
anomali.AddRange(occ.PredictOutliers(featureVectors[sensor].Where(x => start < x.TimeStamp && x.TimeStamp < end).ToList()));
PointsOfInterest dPointsOfInterest = new PointsOfInterest(anomali);
foreach (Events evt in eventResult)
{
evt.SetPointOfInterest(dPointsOfInterest);
}
NoveltyResult tempResult = new NoveltyResult(dPointsOfInterest, eventResult, start, end, svmParam, anomali);
/*NoveltyResult.ConfusionMatrix cfm = tempResult.CalculateConfusionMatrix();
decimal tpr = ((decimal)cfm.TruePostive) / ((decimal)cfm.TruePostive + cfm.FalseNegative);
decimal fpr = 1 - ((decimal)cfm.TrueNegative / ((decimal)cfm.TrueNegative + cfm.FalsePostive));
*/
double temp = tempResult.FlaggedAreaSize();
double temp2 = tempResult.CalculateTotalNormalArea();
double areaCovered = ((double)tempResult.FlaggedAreaSize() / tempResult.CalculateTotalNormalArea() > 1) ? 1 : tempResult.FlaggedAreaSize() / (double)tempResult.CalculateTotalNormalArea();
nuResults.Add($"{tempResult.parameter.Nu.ToString()}:"
+ $"{tempResult.CalculateHitResult().eventHits/ (double)tempResult.CalculateHitResult().eventsTotal};"
+ $"{tempResult.CalculateHitResult().hits / ((double)tempResult.CalculateHitResult().misses+ tempResult.CalculateHitResult().hits)};"
+ $"{areaCovered}");
mutex.WaitOne();
count++;
SetProgress(count, sensor);
mutex.ReleaseMutex();
}
Log.LogMessage(sensor + " done!");
}
private void PredictionThread(ref int count, SENSOR sensor, int start, int end, ref ConcurrentStack<SVMParameter> svmParams, List<SVMNode[]> data, int svmCount, ref NoveltyResult bestCoveredResult, ref Mutex mutex)
{
OneClassClassifier occ = new OneClassClassifier(data);
List<OneClassFV> anomali = new List<OneClassFV>();
List<Events> eventResult = new List<Events>();
List<OneClassFV> outliersFromSam = new List<OneClassFV>();
int maxCount = svmCount;
string sensorPath = path + "/" + sensor.ToString();
foreach (Events p in events)
{
var evt = p.Copy();
eventResult.Add(evt);
}
while (!svmParams.IsEmpty)
{
SVMParameter svmParam = null;
svmParams.TryPop(out svmParam);
if (svmParam == null)
{
break;
}
anomali = new List<OneClassFV>();
occ.CreateModel(svmParam);
anomali.AddRange(occ.PredictOutliers(featureVectors[sensor].Where(x => start < x.TimeStamp && x.TimeStamp < end).ToList()));
PointsOfInterest dPointsOfInterest = new PointsOfInterest(anomali);
foreach (Events evt in eventResult)
{
evt.SetPointOfInterest(dPointsOfInterest);
}
NoveltyResult tempResult = new NoveltyResult(dPointsOfInterest, eventResult, start, end, svmParam, anomali);
/*NoveltyResult.ConfusionMatrix cfm = tempResult.CalculateConfusionMatrix();
decimal tpr = ((decimal)cfm.TruePostive) / ((decimal)cfm.TruePostive + cfm.FalseNegative);
decimal fpr = 1 - ((decimal)cfm.TrueNegative / ((decimal)cfm.TrueNegative + cfm.FalsePostive));
*/
mutex.WaitOne();
if (bestCoveredResult == null)
{
bestCoveredResult = new NoveltyResult(dPointsOfInterest, eventResult, start, end, svmParam, anomali);
}
else if (tempResult.CalculateCoveredScore() > bestCoveredResult.CalculateCoveredScore())
{
//bestResult = new NoveltyResult(dPointsOfInterest, eventResult, start, end, svmParam, anomali); ;
bestCoveredResult = tempResult;
}
count++;
SetProgress(count, sensor);
mutex.ReleaseMutex();
}
Log.LogMessage(sensor + " done!");
}
// private int numberOfTasks = 10;
private async Task<NoveltyResult> DoNoveltyDetection(SENSOR sensor, int start, int end)
{
string sensorPath = path + "/" + sensor.ToString();
var data = featureVectors[sensor].Select(x => x.Features).ToList();
ConcurrentStack<SVMParameter> svmParams = new ConcurrentStack<SVMParameter>();
//Debug purpose
/*for (int i = 0; i < 10; i++)
{
SVMParameter s = new SVMParameter();
s.C = 100;
s.Gamma = 0.01;
s.Kernel = SVMKernelType.RBF;
s.Type = SVMType.ONE_CLASS;
s.Nu = 0.01;
svmParams.Push(s);
}*/
svmParams.PushRange(GenerateOneClassSVMParameters().ToArray());
SetProgressMax(svmParams.Count + 1);
NoveltyResult bestCoveredResult = null;
Mutex bestResultMu = new Mutex(false, sensor.ToString());
int count = 1;
List<Task> tasks = new List<Task>();
for (int i = 0; i < threadMAX.Value; i++)
{
Task task = Task.Run(() => PredictionThread(ref count, sensor, start, end, ref svmParams, data, svmParams.Count, ref bestCoveredResult, ref bestResultMu));
tasks.Add(task);
}
await Task.WhenAll(tasks);
List<int> integers = Enumerable.Range(1, 100).ToList();
List <double> nus = integers.Select(x => ((double)x) / 100).ToList();
//Cover
foreach (double d in nus)
{
SVMParameter para = new SVMParameter();
para.Gamma = bestCoveredResult.parameter.Gamma;
para.Nu = d;
para.Kernel = bestCoveredResult.parameter.Kernel;
svmParams.Push(para);
}
count = 1;
SetProgressMax(svmParams.Count + 1);
bestResultMu.Dispose();
bestResultMu = new Mutex(false, sensor.ToString());
List<Task> nuCov = new List<Task>();
ConcurrentBag<string> covNu = new ConcurrentBag<string>();
for (int i = 0; i < threadMAX.Value; i++)
{
Task task = Task.Run(() => PredictionNuThread(ref count, sensor, start, end, ref svmParams, data, svmParams.Count, ref bestCoveredResult, ref bestResultMu, ref covNu));
tasks.Add(task);
}
await Task.WhenAll(tasks);
File.WriteAllLines(path + "/Params.txt", new List<string> { bestCoveredResult.parameter.Gamma.ToString(), bestCoveredResult.parameter.Kernel.ToString() });
File.WriteAllLines(path + $"/{sensor.ToString()}CovNu.txt", covNu.ToList());
bestResultMu.Dispose();
return bestCoveredResult;
}
private List<OneClassFV> PredictSlice(SENSOR machine, List<OneClassFV> data)
{
return machines[machine].PredictOutliers(data);
}
private void CreateSVM(SENSOR machine, List<OneClassFV> trainingSet)
{
var data = trainingSet.Select(x => x.Features).ToList();
OneClassClassifier occ = new OneClassClassifier(data);
SVMParameter svmP = new SVMParameter();
svmP.Kernel = SVMKernelType.RBF;
svmP.C = 100;
svmP.Gamma = 0.01;
svmP.Nu = 0.01;
svmP.Type = SVMType.ONE_CLASS;
occ.CreateModel(svmP);
machines.Add(machine, occ);
}
