scholarly journals Data Stream Harmonization For Heterogeneous Workflows

2021 ◽  
Author(s):  
Eleftherios Bandis ◽  
Nikolaos Polatidis ◽  
Maria Diapouli ◽  
Stelios Kapetanakis
Keyword(s):  
Transport Infrastructure ◽  
Sensor Data ◽  
Case Based Reasoning ◽  
Data Preparation ◽  
Standard Data ◽  
Efficient Performance ◽  
Rail Industry ◽  
Workflow Pattern ◽  
Workflow Integration ◽  
Workflow Monitoring

Transport infrastructure relies heavily on extended multi sensor networks and data streams to support its advanced real time monitoring and decision making. All relevant stakeholders are highly concerned on how travel patterns, infrastructure capacity and other internal / external factors (such as weather) affect, deteriorate or improve performance. Usually new network infrastructure can be remarkably expensive to build thus the focus is constantly in improving existing workflows, reduce overheads and enforce lean processes. We propose suitable graph-based workflow monitoring met­hods for developing efficient performance measures for the rail industry using extensive business process workflow pattern analysis based on Case-based Reasoning (CBR) combined with standard Data Mining methods. The approach focuses on both data preparation, cleaning and workflow integration of real network data. Preliminary results of this work are promising since workflow integration seems efficient against data complexity and domain peculiarities as well as scale on demand whilst demonstrating efficient accuracy. A number of modelling experiments are presented, that show that the approach proposed here can provide a sound basis for the effective and useful analysis of operational sensor data from train Journeys.

The Negative Information Problem in Mechanical Diagnostics

1996 ◽  
Author(s):  
David L. Hall ◽  
Robert J. Hansen ◽  
Derek C. Lang
Keyword(s):  
Automated Reasoning ◽  
Negative Information ◽  
Remaining Useful Life ◽  
Approximate Reasoning ◽  
Condition Based Maintenance ◽  
Sensor Data ◽  
Case Based Reasoning ◽  
Operational Environment ◽  
Real Time Processing ◽  
Failure Conditions

Condition-based maintenance (CBM) is an emerging technology which seeks to develop sensors and processing systems aimed at monitoring the operation of complex machinery such as turbine engines, rotor craft drive trains, or industrial equipment. The goal of CBM systems is to determine the state of the equipment (i.e., the mechanical health and status), and to predict the remaining useful life for the system being monitored. The success of such systems depends upon a number of factors including: (1) the ability to design or use robust sensors for measuring relevant phenomena such as vibration, acoustic spectra, infrared emissions, oil debris, etc.; (2) real time processing of the sensor data to extract useful information (such as features or data characteristics) in a noisy environment and to detect parametric changes which might be indicative of impending failure conditions; (3) fusion of multi-sensor data to obtain improved information beyond that available to a single sensor; (4) micro and macro level models which predict the temporal evolution of failure phenomena; and finally, (5) the capability to perform automated approximate reasoning to interpret the results of the sensor measurements, processed data, and model predictions in the context of an operational environment. The latter capability is the focus of this paper. Although numerous techniques have emerged from the discipline of artificial intelligence for automated reasoning (e.g., rule-based expert systems, blackboard systems, case-based reasoning, neural networks, etc.), none of these techniques are able to satisfy all of the requirements for reasoning about condition-based maintenance. This paper provides an assessment of automated reasoning techniques for CBM and identifies a particular problem for CBM, namely, the ability to reason with negative information (viz., data which by it’s absence is indicative of mechanical status and health). A general architecture is introduced for CBM automated reasoning, which hierarchically combines implicit and explicit reasoning techniques. Initial experiments with fuzzy logic are also described.

scholarly journals Multilayer Perceptron Integrated Fuzzy Nearest Neighbor to Improve the Proficiency of CBR-Retrieval

2020 ◽  
Vol 2 (2) ◽  
pp. 101-110
Author(s):  
Dr. Suma V.
Keyword(s):  
Multilayer Perceptron ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Medical Field ◽  
Data Set ◽  
Standard Data ◽  
Wide Range ◽  
Training Examples ◽  
Case Based

The CBR (case based reasoning) is a problem solving technique following different strategy compared to the major approaches of the artificial intelligence. It develops remedies to certain problem based on the pre-existing solutions of similar nature. So the problem using the CBR is handled by retrieving and reusing the similar previously solved problems and available solutions respectively. This makes the process functioning alike based on the human activities is instinctively attractive and more beneficial compared to the Conventional_AI as begins to reason out the possible solutions form the shallow base. The CBR due to the exceeding performance are popular among a wide range of applications such as the weather fore casting, medical and engineering diagnosis, aerospace etc. Identification or sorting out or classification take a significant role in cases that is the training examples retrieval as the perfect identification results in perfect case retrieval, this further enables the case based reasoning to arrive to at a perfect remedy for the problem. The retrieval of cases are mostly based on the similarity and utilizes the KNN (K-Nearest Neighbor). The proposed method in the paper integrates the multilayer perceptron with the fuzzy nearest neighbor (MLP-NFF) system with the help of WEKA to deliver a perfect classification to make the CBR-retrieval efficient. The evaluation of the proposed method and its comparison with the KNN is done using the standard data set obtained from the medical field.

scholarly journals A benchmark dataset for individual tree crown delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network

2020 ◽  
Author(s):  
Ben. G. Weinstein ◽  
Sarah J. Graves ◽  
Sergio Marconi ◽  
Aditya Singh ◽  
Alina Zare ◽  
...  
Keyword(s):  
Forest Type ◽  
R Package ◽  
Benchmark Dataset ◽  
Sensor Data ◽  
Evaluation Metrics ◽  
Forest Types ◽  
Individual Tree ◽  
Standard Data ◽  
Wide Range ◽  
Crown Delineation

AbstractBroad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is designing individual tree segmentation algorithms to associate pixels into delineated tree crowns. While dozens of tree delineation algorithms have been proposed, their performance is typically not compared based on standard data or evaluation metrics, making it difficult to understand which algorithms perform best under what circumstances. There is a need for an open evaluation benchmark to minimize differences in reported results due to data quality, forest type and evaluation metrics, and to support evaluation of algorithms across a broad range of forest types. Combining RGB, LiDAR and hyperspectral sensor data from the National Ecological Observatory Network’s Airborne Observation Platform with multiple types of evaluation data, we created a novel benchmark dataset to assess individual tree delineation methods. This benchmark dataset includes an R package to standardize evaluation metrics and simplify comparisons between methods. The benchmark dataset contains over 6,000 image-annotated crowns, 424 field-annotated crowns, and 3,777 overstory stem points from a wide range of forest types. In addition, we include over 10,000 training crowns for optional use. We discuss the different evaluation sources and assess the accuracy of the image-annotated crowns by comparing annotations among multiple annotators as well as to overlapping field-annotated crowns. We provide an example submission and score for an open-source baseline for future methods.

scholarly journals A benchmark dataset for canopy crown detection and delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network

2021 ◽  
Vol 17 (7) ◽  
pp. e1009180
Author(s):  
Ben. G. Weinstein ◽  
Sarah J. Graves ◽  
Sergio Marconi ◽  
Aditya Singh ◽  
Alina Zare ◽  
...  
Keyword(s):  
R Package ◽  
The United States ◽  
Benchmark Dataset ◽  
Sensor Data ◽  
Evaluation Metrics ◽  
Forest Types ◽  
Evaluation Data ◽  
Standard Data ◽  
Wide Range ◽  
The Usa

Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is to associate sensor data into individual crowns. While dozens of crown detection algorithms have been proposed, their performance is typically not compared based on standard data or evaluation metrics. There is a need for a benchmark dataset to minimize differences in reported results as well as support evaluation of algorithms across a broad range of forest types. Combining RGB, LiDAR and hyperspectral sensor data from the USA National Ecological Observatory Network’s Airborne Observation Platform with multiple types of evaluation data, we created a benchmark dataset to assess crown detection and delineation methods for canopy trees covering dominant forest types in the United States. This benchmark dataset includes an R package to standardize evaluation metrics and simplify comparisons between methods. The benchmark dataset contains over 6,000 image-annotated crowns, 400 field-annotated crowns, and 3,000 canopy stem points from a wide range of forest types. In addition, we include over 10,000 training crowns for optional use. We discuss the different evaluation data sources and assess the accuracy of the image-annotated crowns by comparing annotations among multiple annotators as well as overlapping field-annotated crowns. We provide an example submission and score for an open-source algorithm that can serve as a baseline for future methods.

A Preparation Framework for EHR Data to Construct CBR Case-Base

2017 ◽  
pp. 345-378 ◽  
Author(s):  
Shaker El-Sappagh ◽  
Mohammed Mahfouz Elmogy ◽  
Alaa M. Riad ◽  
Hosam Zaghloul ◽  
Farid A. Badria
Keyword(s):  
Machine Learning Algorithms ◽  
Case Based Reasoning ◽  
Diabetes Diagnosis ◽  
Data Preparation ◽  
Snomed Ct ◽  
Starting Point ◽  
Diabetes Mellitus Diagnosis ◽  
Diagnosis Accuracy ◽  
Case Base ◽  
Two Phases

Diabetes mellitus diagnosis is an experience-based problem. Case-Based Reasoning (CBR) is the first choice for these problems. CBR depends on the quality of its case-base structure and contents; however, building a case-base is a challenge. Electronic Health Record (EHR) data can be used as a starting point for building case-bases, but it needs a set of preparation steps. This chapter proposes an EHR-based case-base preparation framework. It has three phases: data-preparation, coding, and fuzzification. The first two phases will be discussed in this chapter using a diabetes diagnosis dataset collected from EHRs of 60 patients. The result is the case-base knowledge. The first phase uses some machine-learning algorithms for case-base data preparation. For encoding phase, we propose and apply an encoding methodology based on SNOMED-CT. We will build an OWL2 ontology from collected SNOMED-CT concepts. A CBR prototype has been designed, and results show enhancements to the diagnosis accuracy.

The Negative Information Problem in Mechanical Diagnostics

1997 ◽  
Vol 119 (2) ◽  
pp. 370-377 ◽  
Author(s):  
D. L. Hall ◽  
R. J. Hansen ◽  
D. C. Lang
Keyword(s):  
Automated Reasoning ◽  
Negative Information ◽  
Remaining Useful Life ◽  
Approximate Reasoning ◽  
Condition Based Maintenance ◽  
Sensor Data ◽  
Case Based Reasoning ◽  
Operational Environment ◽  
Real Time Processing ◽  
Failure Conditions

Condition-based maintenance (CBM) is an emerging technology, which seeks to develop sensors and processing systems aimed at monitoring the operation of complex machinery such as turbine engines, rotor craft drivetrains, and industrial equipment. The goal of CBM systems is to determine the state of the equipment (i.e., the mechanical health and status), and to predict the remaining useful life for the system being monitored. The success of such systems depends upon a number of factors, including: (1) the ability to design or use robust sensors for measuring relevant phenomena such as vibration, acoustic spectra, infrared emissions, oil debris, etc.; (2) real-time processing of the sensor data to extract useful information (such as features or data characteristics) in a noisy environment and to detect parametric changes that might be indicative of impending failure conditions; (3) fusion of multi-sensor data to obtain improved information beyond that available to a single sensor; (4) micro and macro level models, which predict the temporal evolution of failure phenomena; and finally, (5) the capability to perform automated approximate reasoning to interpret the results of the sensor measurements, processed data, and model predictions in the context of an operational environment. The latter capability is the focus of this paper. Although numerous techniques have emerged from the discipline of artificial intelligence for automated reasoning (e.g., rule-based expert systems, blackboard systems, case-based reasoning, neural networks, etc.), none of these techniques are able to satisfy all of the requirements for reasoning about condition-based maintenance. This paper provides an assessment of automated reasoning techniques for CBM and identifies a particular problem for CBM, namely, the ability to reason with negative information (viz., data which by their absence are indicative of mechanical status and health). A general architecture is introduced for CBM automated reasoning, which hierarchically combines implicit and explicit reasoning techniques. Initial experiments with fuzzy logic are also described.

EHR Data Preparation for Case Based Reasoning Construction

2014 ◽  
pp. 483-497 ◽  
Author(s):  
Shaker El-Sappagh ◽  
Mohammed Elmogy ◽  
A. M. Riad ◽  
Hosam Zaghlol ◽  
Farid A. Badria
Keyword(s):  
Case Based Reasoning ◽  
Data Preparation ◽  
Case Based
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