An improved method for the real time sensitivity analysis in large scale complex systems is proposed in this paper. The method borrows principles from the event tracking of interrelated causal events and deploys clustering methods to automatically measure the relevance and contribution made by each input event data (ED) on system outputs. The ethos of the proposed event modeling (EM) technique is that the behavior or the state of a system is a function of the knowledge acquired about events occurring in the system and its wider operational environment. As such it builds on the theoretical and the practical foundation for the engineering of knowledge and data in modern and complex systems. The proposed EM platform EventiC filters noncontributory ED sources and has the potential to include information that was initially thought irrelevant or simply not considered at the design stage. The real-time ability to group and rank relevant input-output ED in order of its importance and relevance will not only improve the data quality, but leads to an improved higher level of mathematical formulization in the modern complex systems. The contribution of the approach to systems' modeling is in the automation of data analysis, control, and plant process modeling. EventiC has been validated as the monitoring and the control system for a cement factory. In addition to the previously known parameters, the proposed EventiC identified new influential parameters that were previously unknown. It also filtered 18% of the input data without compromising the data quality or the integrity. The solution has improved the quality of input variable selection and simplify plant control strategies.

This paper proposes a unique and novel approach for real time input variable selection (IVS) sensitivity analysis (SA) applicable to large scale complex systems. Borrowed from the EventTracker [1] principle of interrelation of causal events, it deploys the Rank Order Clustering (ROC) method to automatically group every relevant system input (e.g. sensor and actuation) within the known boundaries of the system to parameters that define the state of the systems (e.g. Performance Indicators or status).The proposed event modelling technique removes all the logical boundaries of isolation that exits in complex systems with the principle that every acquirable knowledge or data (input) affects the output unless proven otherwise. In addition to being able to filter unwanted data, it is capable of including information that was thought irrelevant at the outset. This feature is unique and novel.The underpinning logic of the proposed event clustering (EC) technique is building an event cause-effect relationship between the inputs and outputs of the system the technique is not only capable of group inputs with relevant corresponding output, but also in short spans of time (relative real-time) measure the weight of each input variable on the output variables. The proposed method will become the foundation for control and stability operations in large and complex systems.Our motivation is that components of current complex and organised systems are capable of generating and sharing information within their known domain (network of interrelated devices and systems).Normally monitoring and control systems are equipped with sensors and actuators that allow for the monitoring and control of isolable systems. The purpose of isolating control system into smaller components is to simplify functionality. The isolation allows for mathematical solutions to work. However, modern interrelated complex systems do not necessarily lend themselves to the classical control engineering solutions. The knowledge of systems has improved, thanks to sensor and actuation, communication and overall computer and electronic engineering. Such systems combined with mechanical parts require better models. The authors believe that the proposed event clustering and sensitivity analysis technique allows monitoring and control systems to become more flexible and responsive in dealing with real-time events. By removing the boundaries of the systems a more accurate representation of the cause-effect relationship is thus generated. This 1 Morad DANISHVAR is with School of Engineering and Design, Brunel University Kingston Lane , UB8 3PH, UK (e-mail: Morad.Danishvar@brunel.ac.uk 2 Ali MOUSAVI is with School of Engineering and Design, Brunel University Kingston Lane,UB8 3PH, The United Kingdom

Movement-based brain–computer Interfaces (BCI) rely significantly on the automatic identification of movement intent. They also allow patients with motor disorders to communicate with external devices. The extraction and selection of discriminative characteristics, which often boosts computer complexity, is one of the issues with automatically discovered movement intentions. This research introduces a novel method for automatically categorizing two-class and three-class movement-intention situations utilizing EEG data. In the suggested technique, the raw EEG input is applied directly to a convolutional neural network (CNN) without feature extraction or selection. According to previous research, this is a complex approach. Ten convolutional layers are included in the suggested network design, followed by two fully connected layers. The suggested approach could be employed in BCI applications due to its high accuracy.

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This paper proposes EventCluster, a novel approach in real-time data modeling. It deploys the Rank Order Clustering (ROC) method to automatically group all existing data sensors and actuators of the system to the Key Performance Indicators of the system. EventCluster (EC) is a cause-effect relationship data clustering tool that detects the interrelationship between field data and system performance parameters in real-time. Through its simple data filtering mechanism it can be used as a precursor to real-time sensitivity analysis. The underpinning logic of the technique is that the raw data can be obtained from field data acquisition devices and the degree of their influence on key system performance indicators can be measured in realtime with minimum computational effort.Normally monitoring and control systems are equipped with sensors and actuators that provide information for a pre-specified function regardless of other parts of the system. The global assumption of method is that a system performance or state is a function of all the inputs of the system, unless proven otherwise. In the proposed method all the inputs and outputs of the system are assumed to affect one another unless proven otherwise.In this paper, an experiment in Cement Kiln operation case demonstrates the suitability and applicability of EventClustering modeling method in industrial applications. We use the Supervisory Control and Data Acquisition (SCADA) sensors and actuators installed to monitor the operations of Kilns in Cement manufacturing process and its contagious operations as a case study for proof of concept. The sensors and actuators data collected builds the input data for measuring the performance (output) of the Kiln. The EventCluster algorithm resides within the control center of the SCADA system to assess the contribution of each input to the overall key performance indicators (output) of the process. This method improves the quality of data analysis and reduces computation overhead on the control system.