The Experts below are selected from a list of 231 Experts worldwide ranked by ideXlab platform
Stefanie Kuenzel - One of the best experts on this subject based on the ideXlab platform.
-
wide area monitoring of power systems using principal component Analysis and k Nearest Neighbor Analysis
IEEE Transactions on Power Systems, 2018Co-Authors: Nina F. Thornhill, Stefanie KuenzelAbstract:Wide-area monitoring of power systems is important for system security and stability. It involves the detection and localization of power system disturbances. However, the oscillatory trends and noise in electrical measurements often mask disturbances, making wide-area monitoring a challenging task. This paper presents a wide-area monitoring method to detect and locate power system disturbances by combining multivariate Analysis known as Principal Component Analysis (PCA) and time series Analysis known as $k$ -Nearest Neighbor ( $k{\text{NN}}$ ) Analysis. Advantages of this method are that it can not only analyze a large number of wide-area variables in real time but also can reduce the masking effect of the oscillatory trends and noise on disturbances. Case studies conducted on data from a four-variable numerical model and the New England power system model demonstrate the effectiveness of this method.
-
Wide-Area Monitoring of Power Systems Using Principal Component Analysis and $k$-Nearest Neighbor Analysis
IEEE Transactions on Power Systems, 2018Co-Authors: Nina F. Thornhill, Stefanie KuenzelAbstract:Wide-area monitoring of power systems is important for system security and stability. It involves the detection and localization of power system disturbances. However, the oscillatory trends and noise in electrical measurements often mask disturbances, making wide-area monitoring a challenging task. This paper presents a wide-area monitoring method to detect and locate power system disturbances by combining multivariate Analysis known as Principal Component Analysis (PCA) and time series Analysis known as k-Nearest Neighbor (kNN) Analysis. Advantages of this method are that it can not only analyze a large number of wide-area variables in real time but also can reduce the masking effect of the oscillatory trends and noise on disturbances. Case studies conducted on data from a four-variable numerical model and the New England power system model demonstrate the effectiveness of this method.
-
Real-Time Detection of Power System Disturbances Based on $k$ -Nearest Neighbor Analysis
IEEE Access, 2017Co-Authors: Nina F. Thornhill, Stefanie KuenzelAbstract:Efficient disturbance detection is important for power system security and stability. In this paper, a new detection method is proposed based on a time series Analysis technique known as k-Nearest Neighbor (kNN) Analysis. Advantages of this method are that it can deal with the electrical measurements with oscillatory trends and can be implemented in real time. The method consists of two stages, which are the off-line modeling and the on-line detection. The off-line stage calculates a sequence of anomaly index values using kNN on the historical ambient data and then determines the detection threshold. Afterward, the online stage calculates the anomaly index value of presently measured data by readopting kNN and compares it with the established threshold for detecting disturbances. To meet the real-time requirement, strategies for recursively calculating the distance metrics of kNN and for rapidly picking out the kth smallest metric are built. Case studies conducted on simulation data from the reduced equivalent model of the Great Britain power system and measurements from an actual power system in Europe demonstrate the effectiveness of the proposed method.
Nina F. Thornhill - One of the best experts on this subject based on the ideXlab platform.
-
wide area monitoring of power systems using principal component Analysis and k Nearest Neighbor Analysis
IEEE Transactions on Power Systems, 2018Co-Authors: Nina F. Thornhill, Stefanie KuenzelAbstract:Wide-area monitoring of power systems is important for system security and stability. It involves the detection and localization of power system disturbances. However, the oscillatory trends and noise in electrical measurements often mask disturbances, making wide-area monitoring a challenging task. This paper presents a wide-area monitoring method to detect and locate power system disturbances by combining multivariate Analysis known as Principal Component Analysis (PCA) and time series Analysis known as $k$ -Nearest Neighbor ( $k{\text{NN}}$ ) Analysis. Advantages of this method are that it can not only analyze a large number of wide-area variables in real time but also can reduce the masking effect of the oscillatory trends and noise on disturbances. Case studies conducted on data from a four-variable numerical model and the New England power system model demonstrate the effectiveness of this method.
-
Wide-Area Monitoring of Power Systems Using Principal Component Analysis and $k$-Nearest Neighbor Analysis
IEEE Transactions on Power Systems, 2018Co-Authors: Nina F. Thornhill, Stefanie KuenzelAbstract:Wide-area monitoring of power systems is important for system security and stability. It involves the detection and localization of power system disturbances. However, the oscillatory trends and noise in electrical measurements often mask disturbances, making wide-area monitoring a challenging task. This paper presents a wide-area monitoring method to detect and locate power system disturbances by combining multivariate Analysis known as Principal Component Analysis (PCA) and time series Analysis known as k-Nearest Neighbor (kNN) Analysis. Advantages of this method are that it can not only analyze a large number of wide-area variables in real time but also can reduce the masking effect of the oscillatory trends and noise on disturbances. Case studies conducted on data from a four-variable numerical model and the New England power system model demonstrate the effectiveness of this method.
-
Real-Time Detection of Power System Disturbances Based on $k$ -Nearest Neighbor Analysis
IEEE Access, 2017Co-Authors: Nina F. Thornhill, Stefanie KuenzelAbstract:Efficient disturbance detection is important for power system security and stability. In this paper, a new detection method is proposed based on a time series Analysis technique known as k-Nearest Neighbor (kNN) Analysis. Advantages of this method are that it can deal with the electrical measurements with oscillatory trends and can be implemented in real time. The method consists of two stages, which are the off-line modeling and the on-line detection. The off-line stage calculates a sequence of anomaly index values using kNN on the historical ambient data and then determines the detection threshold. Afterward, the online stage calculates the anomaly index value of presently measured data by readopting kNN and compares it with the established threshold for detecting disturbances. To meet the real-time requirement, strategies for recursively calculating the distance metrics of kNN and for rapidly picking out the kth smallest metric are built. Case studies conducted on simulation data from the reduced equivalent model of the Great Britain power system and measurements from an actual power system in Europe demonstrate the effectiveness of the proposed method.
Hjalmar Brismar - One of the best experts on this subject based on the ideXlab platform.
-
Nearest Neighbor Analysis of Dopamine D1 Receptors and Na 1 -K 1 -ATPases in Dendritic Spines Dissected by STED
2020Co-Authors: Hans Blom, Lena Scott, Zuzana Spicarova, Ville Rantanen, Jerker Widengren, Anita Aperia, Hjalmar BrismarAbstract:Protein localization in dendritic spines is the focus of intense investigations within neuroscience. Applications of super-resolution microscopy to dissect nanoscale protein distribu- tions, as shown in this work with dual-color STED, generate spatial correlation coefficients having quite small values. This means that colocalization Analysis to some extent looses part of its correla- tive impact. In this study we thus introduced Nearest Neighbor Analysis to quantify the spatial rela- tions between two important proteins in neurons, the dopamine D1 receptor and Na 1 ,K 1 -ATPase. The Analysis gave new information on how dense the D1 receptor and Na 1 ,K 1 -ATPase constituting nanoclusters are located both with respect to the homogenous (self to same) and the heterogeneous (same to other) topology. The STED dissected nanoscale topologies provide evidence for both a joint as well as a separated confinement of the D1 receptor and the Na 1 ,K 1 -ATPase in the postsynaptic areas of dendritic spines. This confined topology may have implications for generation of local sodium gradients and for structural and functional interactions modulating slow synaptic trans- mission processes. Microsc. Res. Tech. 00:000-000, 2011. V C 2011 Wiley-Liss, Inc.
-
Nearest Neighbor Analysis of dopamine d1 receptors and na k atpases in dendritic spines dissected by sted microscopy
Microscopy Research and Technique, 2012Co-Authors: Hans Blom, Lena Scott, Zuzana Spicarova, Ville Rantanen, Jerker Widengren, Anita Aperia, Hjalmar Brismar, Daniel RonnlundAbstract:Protein localization in dendritic spines is the focus of intense investigations within neuroscience. Applications of super-resolution microscopy to dissect nanoscale protein distributions, as shown ...
-
Nearest Neighbor Analysis of dopamine D1 receptors and Na+-K+-ATPases in dendritic spines dissected by STED microscopy
Microscopy Research and Technique, 2011Co-Authors: Hans Blom, Lena Scott, Zuzana Spicarova, Ville Rantanen, Jerker Widengren, Anita Aperia, Daniel Ronnlund, Hjalmar BrismarAbstract:Protein localization in dendritic spines is the focus of intense investigations within neuroscience. Applications of super-resolution microscopy to dissect nanoscale protein distributions, as shown ...
Francisco Gutierrez - One of the best experts on this subject based on the ideXlab platform.
-
improving sinkhole hazard models incorporating magnitude frequency relationships and Nearest Neighbor Analysis
Geomorphology, 2011Co-Authors: J P Galve, Juan Remondo, Francisco GutierrezAbstract:Abstract This work presents a methodology for elaborating sinkhole hazard models that incorporate the magnitude and frequency relationships of the subsidence process. The proposed approach has been tested in a sector of the Ebro valley mantled evaporite karst, where sinkholes, largely induced by irrigation practices, have a very high occurrence rate (>50 sinkholes/km 2 /yr). In this area, covering 10 km 2 , a total of 943 new cover collapse sinkholes were inventoried in 2005 and 2006. Multiple susceptibility models have been generated analyzing the statistical relationships between the 2005 sinkholes and different sets of variables, including the Nearest sinkhole distance . The quantitative evaluation of the prediction capability of these models using the 2006 sinkhole population has allowed the identification of the method and variables that produce the most reliable predictions. The incorporation of the indirect variable Nearest sinkhole distance has contributed significantly to increase the quality of the models, despite simplifying the modeling process by using categorical rather than continuous variables. The best susceptibility model, generated with the total sinkhole population and the selected method and variables, has been transformed into a hazard model that provides minimum estimates of the spatial–temporal probability of each pixel to be affected by sinkholes of different diameter ranges. This transformation has been carried out combining two equations derived from the more complete 2006 sinkhole population; one of them expressing the expected spatial–temporal probability of sinkhole occurrence and the other the empirical magnitude and frequency relationships generated for two different types of land surfaces, which control the strength of the surface layer and the size of the sinkholes. The presented method could be applied to predict the spatial–temporal probability of events with different magnitudes related to other geomorphic processes (e.g. landslides).
-
Improving sinkhole hazard models incorporating magnitude–frequency relationships and Nearest Neighbor Analysis
Geomorphology, 2011Co-Authors: J P Galve, Juan Remondo, Francisco GutierrezAbstract:Abstract This work presents a methodology for elaborating sinkhole hazard models that incorporate the magnitude and frequency relationships of the subsidence process. The proposed approach has been tested in a sector of the Ebro valley mantled evaporite karst, where sinkholes, largely induced by irrigation practices, have a very high occurrence rate (>50 sinkholes/km 2 /yr). In this area, covering 10 km 2 , a total of 943 new cover collapse sinkholes were inventoried in 2005 and 2006. Multiple susceptibility models have been generated analyzing the statistical relationships between the 2005 sinkholes and different sets of variables, including the Nearest sinkhole distance . The quantitative evaluation of the prediction capability of these models using the 2006 sinkhole population has allowed the identification of the method and variables that produce the most reliable predictions. The incorporation of the indirect variable Nearest sinkhole distance has contributed significantly to increase the quality of the models, despite simplifying the modeling process by using categorical rather than continuous variables. The best susceptibility model, generated with the total sinkhole population and the selected method and variables, has been transformed into a hazard model that provides minimum estimates of the spatial–temporal probability of each pixel to be affected by sinkholes of different diameter ranges. This transformation has been carried out combining two equations derived from the more complete 2006 sinkhole population; one of them expressing the expected spatial–temporal probability of sinkhole occurrence and the other the empirical magnitude and frequency relationships generated for two different types of land surfaces, which control the strength of the surface layer and the size of the sinkholes. The presented method could be applied to predict the spatial–temporal probability of events with different magnitudes related to other geomorphic processes (e.g. landslides).
B C Bruno - One of the best experts on this subject based on the ideXlab platform.
-
Nearest‐Neighbor Analysis of small features on Mars: Applications to tumuli and rootless cones
Journal of Geophysical Research, 2007Co-Authors: Stephen M Baloga, Lori S Glaze, B C BrunoAbstract:[1] Fields of low relief cones, domes, and mounds on Mars have been interpreted as tumuli, rootless cones, and pingos. The spatial distribution of such features can be characterized by their Nearest-Neighbor (NN) distances. The NN distributions can provide insight into the physical processes that affected field formation and can possibly provide a future diagnostic of the feature type present on Mars. The conventional approach for analyzing the observed NN distribution is based on the Poisson probability distribution and uses one statistic, c. Our simulations of spatial randomness show that there is an unrecognized bias in c that often leads to erroneous conclusions. Three alternative extensions of the Poisson distribution are proposed here. The first accounts for a minimum threshold on the size of features discerned, the second considers the scavenging of the resources necessary to form Neighboring features (e.g., inflation pressurization and subsurface volatiles), and the third models self-limiting growth of the feature population. Three statistical tests are proposed to improve the conventional NN Analysis. We provide the critical regions needed to perform these tests for common sample sizes of these geologic features. Our methodology is applied to tumuli at Mauna Ulu, Hawaii, and conjectured tumuli and rootless cones on Mars. Each example exhibits a different aspect of spatial randomness. These applications suggest avenues for further field and theoretical studies to develop this approach as a remote-sensing diagnostic.
-
Nearest Neighbor Analysis of small features on mars applications to tumuli and rootless cones
Journal of Geophysical Research, 2007Co-Authors: Stephen M Baloga, Lori S Glaze, B C BrunoAbstract:[1] Fields of low relief cones, domes, and mounds on Mars have been interpreted as tumuli, rootless cones, and pingos. The spatial distribution of such features can be characterized by their Nearest-Neighbor (NN) distances. The NN distributions can provide insight into the physical processes that affected field formation and can possibly provide a future diagnostic of the feature type present on Mars. The conventional approach for analyzing the observed NN distribution is based on the Poisson probability distribution and uses one statistic, c. Our simulations of spatial randomness show that there is an unrecognized bias in c that often leads to erroneous conclusions. Three alternative extensions of the Poisson distribution are proposed here. The first accounts for a minimum threshold on the size of features discerned, the second considers the scavenging of the resources necessary to form Neighboring features (e.g., inflation pressurization and subsurface volatiles), and the third models self-limiting growth of the feature population. Three statistical tests are proposed to improve the conventional NN Analysis. We provide the critical regions needed to perform these tests for common sample sizes of these geologic features. Our methodology is applied to tumuli at Mauna Ulu, Hawaii, and conjectured tumuli and rootless cones on Mars. Each example exhibits a different aspect of spatial randomness. These applications suggest avenues for further field and theoretical studies to develop this approach as a remote-sensing diagnostic.
