Marcin Fojcik

Object Oriented Vertical Communication in Distributed Industrial Systems

Low level industrial systems use simple data representation which reflects flat input and output structure of control industrial system. This structure is directly mapped to the flat memory allocation. The structural data organization is used in upper layers of industrial computer system. This article contains compare analysis of tag and object oriented data model used for vertical communication in distributed industrial computer systems. This analysis is based on classical tag approach used in the most of classical visualization systems and object oriented approach introduced in OPC UA architecture.

An Ontology Model for Communicating with an Autonomous Mobile Platform

This document presents an ontology-based communication interface dedicated for an autonomous mobile platform (AMP). All data between the Platform and other controllers such as PCs or AMPs are exchanged using standardized services. This solution not only allows the required measurement information and its states to be received from an AMP but also control of the Platform. The first advantage is that all of the information is available through an XML file. The second advantage is better possibility for controlling the AMP using external machines that can monitor the route of the AMP. In the case of avoiding obstacles, an external machine can, with the existing sensors and services, help the AMP come back via the correct route. The structure for the data for the Platform is described as set of standardized services such as information about the existing configuration and the status of any installed sensors. The XML format helps to structure information by adding metadata. To create a fully functioning system, it is necessary to add a semantic model (relations between the elements and services) of the AMP services. This paper describes one possible solution for creating ontology model, using the current configuration, services for monitor and services for control of the AMP.

Introduction to OPC UA Performance

Applications operating in the control layer of the industrial computer systems are designed not only to perform real-time data exchange between each other, but also to transfer some information to higher levels (SCADA, MES, ERP). OPC Unified Architecture (OPC UA) is an example of a standard that handles this type of communication. It is not a real-time protocol, but is designed rather to gather information about the transferred data with the occurrence time stamp and distribute that information on demand. Nevertheless, it is crucial to be able to estimate the performance of data transfer in this client-server model based protocol. For now, there is no documented research on the impact of number of clients and amount of transferred data on server’s performance. The goal of this article is to find and describe the parameters of the OPC Unified Architecture protocol that are the most important for the system’s performance.

Performance evaluation of redundant OPC UA architecture for process control

Classical control applications with a centralized logic and distributed input/output system are being replaced by dynamic environments of cooperating components. Thus, the OPC (Object Linking and Embedding for Process Control) UA (Unified Architecture) is becoming more popular, because the OPC Data Access substandard is not well suited for distributed systems. Moreover, in many production systems, redundant data servers are preferred, for financial and legal reasons. Providing performance evaluation gives an estimate of the time required (and data samples lost) to switch to a backup data source for redundant OPC UA architecture, depending on the failure detection method, number of variables and redundancy mode.

Identifying a Joint in Medical Ultrasound Images Using Trained Classifiers

A novel learning approach for detecting the joint in ultrasound images is proposed as a first step of an automated method of assessment of synovitis activity. The training and test data sets consist of images with labeled pixels of the joint region. Feature descriptors based on a pixel’s neighborhood, are selected among SURF, SIFT, FAST, ORB, BRISK, FREAK descriptors, and their mixtures, to define the feature vectors for a trainable pixel classifier. Multiple pixel classifiers, including k-nearest neighbor, support vector machine, and decision tree classifier, are constructed by supervised learning. The AUC measure computed from ROC curves is used as the performance criterion for evaluation. The measure is used to compare and select the best mixture of image descriptors, forming a feature vector for the classifier, the best classifier and the best chain of image preprocessing operations. The final joint detector is a result of clustering the pixels classified as ”joint”. The results of experiments using the proposed method on a set of ultrasound images are presented, demonstrating the method’s applicability and usefulness.

OPC UA Based Solutions for Integrated Operations

In all big, industrial systems standardization is necessary. It concerns also offshore systems. There are some ways to realize it: use existing standards and join them together in new functionality or find something new. OPC UA seems to be one of new possibilities. This article presents different offshore standards and how OPC UA can be used in this case, not only in control and supervisory systems, but in sensor and MES/ERP levels as well.

Optimization of Joint Detector for Ultrasound Images Using Mixtures of Image Feature Descriptors

Joint detector is an essential part of an approach towards automated assessment of synovitis activity, which is a subject of the current research work. A recent formulation of the joint detector, that integrates image processing, local image neighborhood descriptors, such as SURF, FAST, ORB, BRISK, FREAK, trainable classification (SVM, NN, CART) and clusterization, results in a large number of possible choices of classifiers, their modes, components of features vectors, and parameter values, and making such choices by experimentation is impractical. This article presents a novel approach, and an implemented environment for the parameter selection process for the joint detector, which automatically choses the best configuration of image processing operators, type of image neighborhood descriptors, the form of a classifier and the clustering method and their parameters. Its implementation uses new scripting tools and generic techniques, such as chain-of-responsibility design pattern and metafunction idiom. Also presented are novel results, comparing the effect of feature vectors composed from multiple SURF descriptors on the performance of the joint detector, which demonstrate the potential of mixture of descriptors for improving the classification results.

Collaborative Tool for Annotation of Synovitis and Assessment in Ultrasound Images

We present a cloud based collaborative tool intended for organization and unification of USG data and annotation of features useful for discovery of synovitis. The Annotation Editor can be used to outline anatomical features in an ultrasound images such as joint and bones, and identify regions of synovitis and level of synovitis activity. The software will be used by medical personnel for building reference database of annotated ultrasound images. This database will be the source of training and testing data in a system of automated assessment of synovitis activity. System supports collaborative use and management of the database from multiple locations. Semiquantitative ultrasound is a reliable and widely used method of assessing synovitis. Presently used manual assessment needs trained medical personnel and the result can be affected by a human error. A proposed system that can automatically assess the activity of synovitis would eliminate human dependent discrepancies and reduce time and the cost of evaluation.

A Machine-Learning Approach to the Automated Assessment of Joint Synovitis Activity

Medical ultrasound imaging is an important tool in diagnosing and monitoring synovitis, which is an inflammation of the synovial membrane that surrounds a joint. Ultrasound images are examined by medical experts to assess the presence and progression of synovitis. Automating image analysis reduces the costs and increases the availability of the ultrasound diagnosis of synovitis and diminishes or eliminates subjective discrepancies. This article describes research that is concerned with the problem of the automatic estimation of the state of the activity of finger joint inflammation using the information that is present in ultrasonography imaging.

Ontology-Based Integrated Monitoring of Hadoop Clusters in Industrial Environments with OPC UA and RESTful Web Services

Contemporary industrial and production systems produce huge amounts of data in various models, used for process monitoring, predictive maintenance of the machines, historical analysis and statistics, and more. Apache Hadoop brings a cost-effective opportunity for Big Data analysis, including the data generated in various industries. Integrating Hadoop into industrial environments creates new possibilities, as well as many challenges. The authors of this paper are involved into commercial and scientific projects utilizing Hadoop for industry as predictive analytics platform. In such initiatives the lack of standardization of monitoring of the industrial process in terms of Hadoop cluster utilization is especially perplexing. In this paper, authors propose the methodology of monitoring Hadoop in industrial environments, based on dedicated ontology and widely adopted standards: OPC Unified Architecture and RESTful Web Services.