Classification of Domestic Water Consumption Using an ANFIS Model

This work presents classification results of different water outputs in a house. Input variables are time and flow measurements in a point of the network distribution, and the identified classes are relevant consumptions as sink consumption, shower consumption, etc. Due to human influence on consumption data, we selected a classifier based on an interpretable model; that allows the incorporation of knowledge provided by users or experts. Thus, this study is based on the well known Anfis model and AGUA (real data taken for a project being developed in Guadalajara, Mexico) the data set corresponding to a supervised case. The result shows that the proposed algorithm works well, with recognition above 91%, and it could be used for a better profit of domestic water management

An improvement to the possibilistic fuzzy c-means clustering algorithm

In this work we propose to use the Gustafson-Kessel (GK) algorithm within the PFCM (Possibilistic Fuzzy c-Means), such that the cluster distributions have a better adaptation with the natural distribution of the data. The PFCM, proposed by Pal et al. on 2005, is founded on the fuzzy membership degrees of the FCM and the typicality values of the PCM. Nevertheless, this algorithm uses the Euclidian distance which gives circular clusters. So, incorporating the GK algorithm and the Mahalanobis measure for the calculus of the distance, we have the possibility to,get ellipsoidal forms as well, allowing a better representation of the clusters. Copyright - World Automation Congress (WAC) 2006

Classification of domestic water consumption using an Anfis model

This work presents classification results of different water outputs in a house. Input variables are time and flow measurements in a point of the network distribution, and the identified classes are relevant consumptions as sink consumption, shower consumption, etc. Due to human influence on consumption data, we selected a classifier based on an interpretable model; that allows the incorporation of knowledge provided by users or experts. Thus, this study is based on the well known Anfis model and AGUA (real data taken for a project being developed in Guadalajara, Mexico) the data set corresponding to a supervised case. The result shows that the proposed algorithm works well, with recognition above 91 %, and it could be used for a better profit of domestic water management

Identification of domestic water consumption in a house based on fuzzy clustering algorithms

This work presents the classification of different types of consumptions of water in a house (sinks, showers, washing machines etc.). This classification takes into account the measured flow and the duration of the flow at a particular point in the water distribution network. The classifier uses the FCM and Gustafson-Kessel algorithms. The data set is called AGUA and it corresponds to real data gathered for a research project in Guadalajara, Mexico. The classifier was trained in an unsupervised way. As such, it learns the patterns for the flow and duration of flow, for each type of consumption. The identified classes are relevant consumption types such as using the sink, using the shower, etc. The results show that the proposed approach gives good results, with 91.6 % of the examples classified correctly, and it could be used in the future as part of a supervisory system in order to make better use of water in households. �2009 IEEE

Classification of domestic water consumption using an Anfis model

This work presents classification results of different water outputs in a house. Input variables are time and flow measurements in a point of the network distribution, and the identified classes are relevant consumptions as sink consumption, shower consumption, etc. Due to human influence on consumption data, we selected a classifier based on an interpretable model; that allows the incorporation of knowledge provided by users or experts. Thus, this study is based on the well known Anfis model and AGUA (real data taken for a project being developed in Guadalajara, Mexico) the data set corresponding to a supervised case. The result shows that the proposed algorithm works well, with recognition above 91 %, and it could be used for a better profit of domestic water management

Classification of domestic water consumption using an Anfis model

This work presents classification results of different water outputs in a house. Input variables are time and flow measurements in a point of the network distribution, and the identified classes are relevant consumptions as sink consumption, shower consumption, etc. Due to human influence on consumption data, we selected a classifier based on an interpretable model; that allows the incorporation of knowledge provided by users or experts. Thus, this study is based on the well known Anfis model and AGUA (real data taken for a project being developed in Guadalajara, Mexico) the data set corresponding to a supervised case. The result shows that the proposed algorithm works well, with recognition above 91 %, and it could be used for a better profit of domestic water management

Identification of masses in mammograms by image sub-segmentation

Mass detection in mammography is a complex and challenge problem for digital image processing. Partitional clustering algorithms are a good alternative for automatic detection of such elements, but have the disadvantage of having to segment an image into a number of regions, the number of which is unknown in advance, in addition to discrete approximations of the regions of interest. In this work we use a method of image sub-segmentation to identify possible masses in mammography. The advantage of this method is that the number of regions to segment the image is a known value so the algorithm is applied only once. Additionally, there is a parameter ? that can change between 1 and 0 in a continuous way, offering the possibility of a continuous and more accurate approximation of the region of interest. Finally, since the identification of masses is based on the internal similarity of a group data, this method offers the possibility to identify such objects even from a small number of pixels in digital images. This paper presents an illustrative example using the traditional segmentation of images and the sub-segmentation method, which highlights the potential of the alternative we propose for such problems. � 2011 Springer-Verlag Berlin Heidelberg

Antiinflammatory therapy with canakinumab for atherosclerotic disease

BACKGROUND: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. METHODS: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P=0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P=0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P=0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P=0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P=0.31). CONCLUSIONS: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. Copyright © 2017 Massachusetts Medical Society