The Experts below are selected from a list of 117 Experts worldwide ranked by ideXlab platform

Yu Ding - One of the best experts on this subject based on the ideXlab platform.

  • Unsupervised Anomaly Detection Based on Minimum Spanning Tree Approximated Distance Measures and its Application to Hydropower Turbines
    IEEE Transactions on Automation Science and Engineering, 2019
    Co-Authors: Imtiaz Ahmed, Aldo Dagnino, Yu Ding
    Abstract:

    Anomalies are data points or a cluster of data points that lie away from the neighboring points or clusters and are inconsistent with the overall pattern of the data. Anomaly detection techniques help distinguish the anomalous observations from the regular ones, and thus provide the basis for developing a standard Performance Guideline for process control. The process of identifying anomalies becomes complicated in the absence of labeled training data as in supervised learning. Moreover, Euclidean distance between two points is less likely able to reflect the intrinsic structural distance imposed by the underlying manifold structure. In this paper, the authors propose a minimum spanning tree (MST)-based anomaly detection method. The merit of the method is that an MST provides a new distance measure, capable of capturing the relative connectedness of data points/clusters in a complicated manifold, and could be a better (dis)similarity metric, than the simple Euclidean distance, to identify anomalies in unsupervised learning settings. The proposed method is compared with 13 popular anomaly detection methods on 20 benchmark data sets, demonstrating a considerable improvement in its ability of identifying anomalies. Furthermore, the MST-based anomaly detection is applied to the data set from a hydropower turbine and demonstrates remarkable detection competence. Note to Practitioners —This paper is motivated by the problem of unsupervised anomaly detection in a hydropower generation plant, which operates with turbine systems that are instrumented with dozens of sensors. Each turbine has subcomponents or functional areas such as several bearing systems, a generator, and so on. Sensors collect various types of data in real time such as temperature of oil inside the bearing systems, temperature of the bearings, ambient temperature, vibrations in each functional areas, a variety of harmonics in functional areas, temperature of the coil in the generator, and many more. In total, each turbine collects more than 200 attributes from its sensors. The sensor data are then stored in a control system and kept as time stamped historical data points. When a service/maintenance engineer suspects that there is a malfunction in a turbine, she/he extracts a data set from the control system that contains the collected sensor data for that turbine for the selected period of time (few weeks to few months), and then stores this data in a relational databases or simply in a comma separate value (csv) file for further analysis. The objective is to efficiently identify and isolate anomalies in the turbines. Toward this goal, we propose a new solution for tackling this challenging problem, which is an unsupervised method based on the concept of MST. The proposed method can be used as a competitive tool to aid the practitioners in their search of anomalies for making their systems better.

  • Unsupervised Anomaly Detection Based on Minimum Spanning Tree Approximated Distance Measures and its Application to Hydropower Turbines
    IEEE Transactions on Automation Science and Engineering, 2019
    Co-Authors: Imtiaz Ahmed, Aldo Dagnino, Yu Ding
    Abstract:

    Anomalies are data points or a cluster of data points that lie away from the neighboring points or clusters and are inconsistent with the overall pattern of the data. Anomaly detection techniques help distinguish the anomalous observations from the regular ones, and thus provide the basis for developing a standard Performance Guideline for process control. The process of identifying anomalies becomes complicated in the absence of labeled training data as in supervised learning. Moreover, Euclidean distance between two points is less likely able to reflect the intrinsic structural distance imposed by the underlying manifold structure. In this paper, the authors propose a minimum spanning tree (MST)-based anomaly detection method. The merit of the method is that an MST provides a new distance measure, capable of capturing the relative connectedness of data points/clusters in a complicated manifold, and could be a better (dis)similarity metric, than the simple Euclidean distance, to identify anomalies in unsupervised learning settings. The proposed method is compared with 13 popular anomaly detection methods on 20 benchmark data sets, demonstrating a considerable improvement in its ability of identifying anomalies. Furthermore, the MST-based anomaly detection is applied to the data set from a hydropower turbine and demonstrates remarkable detection competence.

  • CASE - Outlier Detection for Hydropower Generation Plant
    2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 2018
    Co-Authors: Imtiaz Ahmed, Aldo Dagnino, Alessandro Bongiovi, Yu Ding
    Abstract:

    A hydropower generation plant is a complex system and composed of numerous physical components. To monitor the health of different components it is necessary to detect anomalous behavior in time. Establishing a Performance Guideline along with identification of the critical variables causing anomalous behavior can help the maintenance personnel to detect any potential shift in the process timely. To establish any Guideline for future control, at first a mechanism is needed to differentiate anomalous observations from the normal ones. In our work we have employed three different approaches to detect the anomalous observations and compared their Performances using a historical data set received from a hydropower plant. The outliers detected are verified by the domain experts. Making use of a decision tree and feature selection process, we have identified some critical variables which are potentially linked to the presence of the outliers. We further developed a one-class classifier using the outlier cleaned dataset, which defines the normal working condition, and therefore, violation of the normal conditions could identify anomalous observations in future operations.

  • Outlier Detection for Hydropower Generation Plant
    2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 2018
    Co-Authors: Imtiaz Ahmed, Aldo Dagnino, Alessandro Bongiovi, Yu Ding
    Abstract:

    A hydropower generation plant is a complex system and composed of numerous physical components. To monitor the health of different components it is necessary to detect anomalous behavior in time. Establishing a Performance Guideline along with identification of the critical variables causing anomalous behavior can help the maintenance personnel to detect any potential shift in the process timely. To establish any Guideline for future control, at first a mechanism is needed to differentiate anomalous observations from the normal ones. In our work we have employed three different approaches to detect the anomalous observations and compared their Performances using a historical data set received from a hydropower plant. The outliers detected are verified by the domain experts. Making use of a decision tree and feature selection process, we have identified some critical variables which are potentially linked to the presence of the outliers. We further developed a one-class classifier using the outlier cleaned dataset, which defines the normal working condition, and therefore, violation of the normal conditions could identify anomalous observations in future operations.

Imtiaz Ahmed - One of the best experts on this subject based on the ideXlab platform.

  • Unsupervised Anomaly Detection Based on Minimum Spanning Tree Approximated Distance Measures and its Application to Hydropower Turbines
    IEEE Transactions on Automation Science and Engineering, 2019
    Co-Authors: Imtiaz Ahmed, Aldo Dagnino, Yu Ding
    Abstract:

    Anomalies are data points or a cluster of data points that lie away from the neighboring points or clusters and are inconsistent with the overall pattern of the data. Anomaly detection techniques help distinguish the anomalous observations from the regular ones, and thus provide the basis for developing a standard Performance Guideline for process control. The process of identifying anomalies becomes complicated in the absence of labeled training data as in supervised learning. Moreover, Euclidean distance between two points is less likely able to reflect the intrinsic structural distance imposed by the underlying manifold structure. In this paper, the authors propose a minimum spanning tree (MST)-based anomaly detection method. The merit of the method is that an MST provides a new distance measure, capable of capturing the relative connectedness of data points/clusters in a complicated manifold, and could be a better (dis)similarity metric, than the simple Euclidean distance, to identify anomalies in unsupervised learning settings. The proposed method is compared with 13 popular anomaly detection methods on 20 benchmark data sets, demonstrating a considerable improvement in its ability of identifying anomalies. Furthermore, the MST-based anomaly detection is applied to the data set from a hydropower turbine and demonstrates remarkable detection competence. Note to Practitioners —This paper is motivated by the problem of unsupervised anomaly detection in a hydropower generation plant, which operates with turbine systems that are instrumented with dozens of sensors. Each turbine has subcomponents or functional areas such as several bearing systems, a generator, and so on. Sensors collect various types of data in real time such as temperature of oil inside the bearing systems, temperature of the bearings, ambient temperature, vibrations in each functional areas, a variety of harmonics in functional areas, temperature of the coil in the generator, and many more. In total, each turbine collects more than 200 attributes from its sensors. The sensor data are then stored in a control system and kept as time stamped historical data points. When a service/maintenance engineer suspects that there is a malfunction in a turbine, she/he extracts a data set from the control system that contains the collected sensor data for that turbine for the selected period of time (few weeks to few months), and then stores this data in a relational databases or simply in a comma separate value (csv) file for further analysis. The objective is to efficiently identify and isolate anomalies in the turbines. Toward this goal, we propose a new solution for tackling this challenging problem, which is an unsupervised method based on the concept of MST. The proposed method can be used as a competitive tool to aid the practitioners in their search of anomalies for making their systems better.

  • Unsupervised Anomaly Detection Based on Minimum Spanning Tree Approximated Distance Measures and its Application to Hydropower Turbines
    IEEE Transactions on Automation Science and Engineering, 2019
    Co-Authors: Imtiaz Ahmed, Aldo Dagnino, Yu Ding
    Abstract:

    Anomalies are data points or a cluster of data points that lie away from the neighboring points or clusters and are inconsistent with the overall pattern of the data. Anomaly detection techniques help distinguish the anomalous observations from the regular ones, and thus provide the basis for developing a standard Performance Guideline for process control. The process of identifying anomalies becomes complicated in the absence of labeled training data as in supervised learning. Moreover, Euclidean distance between two points is less likely able to reflect the intrinsic structural distance imposed by the underlying manifold structure. In this paper, the authors propose a minimum spanning tree (MST)-based anomaly detection method. The merit of the method is that an MST provides a new distance measure, capable of capturing the relative connectedness of data points/clusters in a complicated manifold, and could be a better (dis)similarity metric, than the simple Euclidean distance, to identify anomalies in unsupervised learning settings. The proposed method is compared with 13 popular anomaly detection methods on 20 benchmark data sets, demonstrating a considerable improvement in its ability of identifying anomalies. Furthermore, the MST-based anomaly detection is applied to the data set from a hydropower turbine and demonstrates remarkable detection competence.

  • CASE - Outlier Detection for Hydropower Generation Plant
    2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 2018
    Co-Authors: Imtiaz Ahmed, Aldo Dagnino, Alessandro Bongiovi, Yu Ding
    Abstract:

    A hydropower generation plant is a complex system and composed of numerous physical components. To monitor the health of different components it is necessary to detect anomalous behavior in time. Establishing a Performance Guideline along with identification of the critical variables causing anomalous behavior can help the maintenance personnel to detect any potential shift in the process timely. To establish any Guideline for future control, at first a mechanism is needed to differentiate anomalous observations from the normal ones. In our work we have employed three different approaches to detect the anomalous observations and compared their Performances using a historical data set received from a hydropower plant. The outliers detected are verified by the domain experts. Making use of a decision tree and feature selection process, we have identified some critical variables which are potentially linked to the presence of the outliers. We further developed a one-class classifier using the outlier cleaned dataset, which defines the normal working condition, and therefore, violation of the normal conditions could identify anomalous observations in future operations.

  • Outlier Detection for Hydropower Generation Plant
    2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 2018
    Co-Authors: Imtiaz Ahmed, Aldo Dagnino, Alessandro Bongiovi, Yu Ding
    Abstract:

    A hydropower generation plant is a complex system and composed of numerous physical components. To monitor the health of different components it is necessary to detect anomalous behavior in time. Establishing a Performance Guideline along with identification of the critical variables causing anomalous behavior can help the maintenance personnel to detect any potential shift in the process timely. To establish any Guideline for future control, at first a mechanism is needed to differentiate anomalous observations from the normal ones. In our work we have employed three different approaches to detect the anomalous observations and compared their Performances using a historical data set received from a hydropower plant. The outliers detected are verified by the domain experts. Making use of a decision tree and feature selection process, we have identified some critical variables which are potentially linked to the presence of the outliers. We further developed a one-class classifier using the outlier cleaned dataset, which defines the normal working condition, and therefore, violation of the normal conditions could identify anomalous observations in future operations.

Aldo Dagnino - One of the best experts on this subject based on the ideXlab platform.

  • Unsupervised Anomaly Detection Based on Minimum Spanning Tree Approximated Distance Measures and its Application to Hydropower Turbines
    IEEE Transactions on Automation Science and Engineering, 2019
    Co-Authors: Imtiaz Ahmed, Aldo Dagnino, Yu Ding
    Abstract:

    Anomalies are data points or a cluster of data points that lie away from the neighboring points or clusters and are inconsistent with the overall pattern of the data. Anomaly detection techniques help distinguish the anomalous observations from the regular ones, and thus provide the basis for developing a standard Performance Guideline for process control. The process of identifying anomalies becomes complicated in the absence of labeled training data as in supervised learning. Moreover, Euclidean distance between two points is less likely able to reflect the intrinsic structural distance imposed by the underlying manifold structure. In this paper, the authors propose a minimum spanning tree (MST)-based anomaly detection method. The merit of the method is that an MST provides a new distance measure, capable of capturing the relative connectedness of data points/clusters in a complicated manifold, and could be a better (dis)similarity metric, than the simple Euclidean distance, to identify anomalies in unsupervised learning settings. The proposed method is compared with 13 popular anomaly detection methods on 20 benchmark data sets, demonstrating a considerable improvement in its ability of identifying anomalies. Furthermore, the MST-based anomaly detection is applied to the data set from a hydropower turbine and demonstrates remarkable detection competence. Note to Practitioners —This paper is motivated by the problem of unsupervised anomaly detection in a hydropower generation plant, which operates with turbine systems that are instrumented with dozens of sensors. Each turbine has subcomponents or functional areas such as several bearing systems, a generator, and so on. Sensors collect various types of data in real time such as temperature of oil inside the bearing systems, temperature of the bearings, ambient temperature, vibrations in each functional areas, a variety of harmonics in functional areas, temperature of the coil in the generator, and many more. In total, each turbine collects more than 200 attributes from its sensors. The sensor data are then stored in a control system and kept as time stamped historical data points. When a service/maintenance engineer suspects that there is a malfunction in a turbine, she/he extracts a data set from the control system that contains the collected sensor data for that turbine for the selected period of time (few weeks to few months), and then stores this data in a relational databases or simply in a comma separate value (csv) file for further analysis. The objective is to efficiently identify and isolate anomalies in the turbines. Toward this goal, we propose a new solution for tackling this challenging problem, which is an unsupervised method based on the concept of MST. The proposed method can be used as a competitive tool to aid the practitioners in their search of anomalies for making their systems better.

  • Unsupervised Anomaly Detection Based on Minimum Spanning Tree Approximated Distance Measures and its Application to Hydropower Turbines
    IEEE Transactions on Automation Science and Engineering, 2019
    Co-Authors: Imtiaz Ahmed, Aldo Dagnino, Yu Ding
    Abstract:

    Anomalies are data points or a cluster of data points that lie away from the neighboring points or clusters and are inconsistent with the overall pattern of the data. Anomaly detection techniques help distinguish the anomalous observations from the regular ones, and thus provide the basis for developing a standard Performance Guideline for process control. The process of identifying anomalies becomes complicated in the absence of labeled training data as in supervised learning. Moreover, Euclidean distance between two points is less likely able to reflect the intrinsic structural distance imposed by the underlying manifold structure. In this paper, the authors propose a minimum spanning tree (MST)-based anomaly detection method. The merit of the method is that an MST provides a new distance measure, capable of capturing the relative connectedness of data points/clusters in a complicated manifold, and could be a better (dis)similarity metric, than the simple Euclidean distance, to identify anomalies in unsupervised learning settings. The proposed method is compared with 13 popular anomaly detection methods on 20 benchmark data sets, demonstrating a considerable improvement in its ability of identifying anomalies. Furthermore, the MST-based anomaly detection is applied to the data set from a hydropower turbine and demonstrates remarkable detection competence.

  • CASE - Outlier Detection for Hydropower Generation Plant
    2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 2018
    Co-Authors: Imtiaz Ahmed, Aldo Dagnino, Alessandro Bongiovi, Yu Ding
    Abstract:

    A hydropower generation plant is a complex system and composed of numerous physical components. To monitor the health of different components it is necessary to detect anomalous behavior in time. Establishing a Performance Guideline along with identification of the critical variables causing anomalous behavior can help the maintenance personnel to detect any potential shift in the process timely. To establish any Guideline for future control, at first a mechanism is needed to differentiate anomalous observations from the normal ones. In our work we have employed three different approaches to detect the anomalous observations and compared their Performances using a historical data set received from a hydropower plant. The outliers detected are verified by the domain experts. Making use of a decision tree and feature selection process, we have identified some critical variables which are potentially linked to the presence of the outliers. We further developed a one-class classifier using the outlier cleaned dataset, which defines the normal working condition, and therefore, violation of the normal conditions could identify anomalous observations in future operations.

  • Outlier Detection for Hydropower Generation Plant
    2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 2018
    Co-Authors: Imtiaz Ahmed, Aldo Dagnino, Alessandro Bongiovi, Yu Ding
    Abstract:

    A hydropower generation plant is a complex system and composed of numerous physical components. To monitor the health of different components it is necessary to detect anomalous behavior in time. Establishing a Performance Guideline along with identification of the critical variables causing anomalous behavior can help the maintenance personnel to detect any potential shift in the process timely. To establish any Guideline for future control, at first a mechanism is needed to differentiate anomalous observations from the normal ones. In our work we have employed three different approaches to detect the anomalous observations and compared their Performances using a historical data set received from a hydropower plant. The outliers detected are verified by the domain experts. Making use of a decision tree and feature selection process, we have identified some critical variables which are potentially linked to the presence of the outliers. We further developed a one-class classifier using the outlier cleaned dataset, which defines the normal working condition, and therefore, violation of the normal conditions could identify anomalous observations in future operations.

Mingda Zhou - One of the best experts on this subject based on the ideXlab platform.

  • an efficient algorithm and hardware architecture for maximum likelihood based carrier frequency offset estimation in mimo systems
    IEEE Access, 2018
    Co-Authors: Mingda Zhou, Zhe Feng, Youjian Liu, Xinming Huang
    Abstract:

    Carrier frequency offset (CFO), which is often caused by the mismatch between the local oscillators in transmitter and receiver, limits the Performance of multiple-input multiple-output (MIMO) wireless communication systems. This paper presents a maximum-likelihood-based CFO estimation algorithm for MIMO systems and its efficient hardware design. The proposed algorithm can accurately estimate the CFO, especially at high signal-to-noise ratio. Moreover, the Cramer–Rao lower bound of the proposed method is derived as a Performance Guideline. In terms of hardware implementation, an efficient pipeline architecture is presented in detail, showing that the architecture only occupies minimal hardware resources so that it can be fitted into a small field-programmable gate array. The proposed architecture can be reconfigured according to different pilot lengths, making it flexible to various frame structures.

  • On the accuracy of in-body TOA ranging inside the gastrointestinal tract using carrier frequency
    2015 Wireless Telecommunications Symposium (WTS), 2015
    Co-Authors: Mingda Zhou, Kaveh Pahlavan
    Abstract:

    Body area network is now on its speedway of development, which stresses the importance of in-body localization. Wireless capsule endoscopy, one major application in BAN, helps physicians to work out better treatments and inspections of gastrointestinal tract if wireless capsule endoscope could be more accurately localized. In this paper we first propose the CRLB of TOA ranging inside homogeneous tissue as Performance Guideline and compare the accuracy of different TOA ranging methods inside homogeneous tissue. Then, simulations inside non-homogeneous tissues are conducted to investigate the impact of non-homogeneity on TOA ranging accuracy qualitatively and quantitatively.

  • WTS - On the accuracy of in-body TOA ranging inside the gastrointestinal tract using carrier frequency
    2015 Wireless Telecommunications Symposium (WTS), 2015
    Co-Authors: Mingda Zhou, Kaveh Pahlavan
    Abstract:

    Body area network is now on its speedway of development, which stresses the importance of in-body localization. Wireless capsule endoscopy, one major application in BAN, helps physicians to work out better treatments and inspections of gastrointestinal tract if wireless capsule endoscope could be more accurately localized. In this paper we first propose the CRLB of TOA ranging inside homogeneous tissue as Performance Guideline and compare the accuracy of different TOA ranging methods inside homogeneous tissue. Then, simulations inside non-homogeneous tissues are conducted to investigate the impact of non-homogeneity on TOA ranging accuracy qualitatively and quantitatively.

Wee Peng Tay - One of the best experts on this subject based on the ideXlab platform.

  • LaIF: A Lane-Level Self-Positioning Scheme for Vehicles in GNSS-Denied Environments
    IEEE Transactions on Intelligent Transportation Systems, 2019
    Co-Authors: Ramtin Rabiee, Yongsheng Yan, Xionghu Zhong, Wee Peng Tay
    Abstract:

    Vehicle self-positioning is of significant importance for intelligent transportation applications. However, accurate positioning (e.g., with lane-level accuracy) is very difficult to obtain due to the lack of measurements with high confidence, especially in an environment without full access to a global navigation satellite system (GNSS). In this paper, a novel information fusion algorithm based on a particle filter is proposed to achieve lane-level tracking accuracy under a GNSS-denied environment. We consider the use of both coarse-scale and fine-scale signal measurements for positioning. Time-of-arrival measurements using the radio frequency signals from known transmitters or roadside units, and acceleration or gyroscope measurements from an inertial measurement unit (IMU) allow us to form a coarse estimate of the vehicle position using an extended Kalman filter. Subsequently, fine-scale measurements, including lane-change detection, radar ranging from the known obstacles (e.g., guardrails), and information from a high-resolution digital map, are incorporated to refine the position estimates. A probabilistic model is introduced to characterize the lane changing behaviors, and a multi-hypothesis model is formulated for the radar range measurements to robustly weigh the particles and refine the tracking results. Moreover, a decision fusion mechanism is proposed to achieve a higher reliability in the lane-change detection as compared to each individual detector using IMU and visual (if available) information. The posterior Cramér-Rao lower bound is also derived to provide a theoretical Performance Guideline. The Performance of the proposed tracking framework is verified by simulations and real measured IMU data in a four-lane highway.