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Yokogawa announcement to Windows XP users regarding Systems products OpreX Laboratory Information Management System Non-Dispersive Infrared Gas Analyzers (NDIR)įourier Transform Near-Infrared Analyzer (FTNIR)Īdvanced Control Bioreactor System BR1000 Industrial Temperature Assemblies - Product of Thermo Electric Instrumentation B.V. Moore Industries International – HIM Smart HART Loop Interface and Monitor Moore Industries International – HART Concentrator System HART to Modbus RTU Partner Products for Pressure TransmittersĪrmstrong Veris - Verabar Averaging Pitot Tubes Wireless Differential Pressure/Pressure Transmitters Touch Screen Paperless Recorder GX10/GX20 Manufacturing Data Exchange (Exaquantum/MDX) Synchronous OPC DA Manager (Exaquantum/SDM) Remote Data Synchronization (Exaquantum/RDS) Operator Trending Module (Exaquantum/OTM) Visual MESA Supply Chain Scheduling (VM-SCS) Terminal Logistics Suite VP (Terminal Automation) Safety Performance Indicator and Monitoring (Exaquantum/SFM) Safety Function Monitoring (Exaquantum/SFM) Mobile Field Device Management (FieldMate)Īdvanced Analytical Instrument Management System (AMADAS) Lab-Aid (Lab Information Management System) Integrated Production Information (Exaquantum/mPower) Platform for Advanced Control and Estimation (Advanced Process Control)
Low Power Consumption Autonomous Controller (FCN-RTU)Īlarm Reporting and Analytics (Exaquantum/ARA) The Enterprise Pipeline Management Solution (EPMS) Solid-state SIS (Up to SIL 4) ProSafe-SLS OpreX Operation and Maintenance Improvement Special Solutions of OpreX Transformation Solutions for Biological Contamination ManagementĪbout OpreX Enterprise Business Optimization Predictive Maintenance of Pump Cavitation (Cavitation Detection System) Also, the convergence guarantee has been validated in the experiment.Operational Technology Architecture Design Experimental results demonstrate superior performance in terms of classification against all the compared approaches. Extensive experiments have been conducted on a number of real-world data sets. Since the proposed objective function is nonsmooth and difficult to solve directly, we propose an iterative algorithm for effective optimization. In this way, our algorithm can exploit potential correlations among views as supplementary information that further improves the performance result. Multiple transformation matrices for different views are simultaneously learned in a joint framework. To achieve this goal, we propose a feature learning algorithm in a batch mode, by which the correlations among different views are taken into account. In this letter, we propose a new multiview feature learning algorithm, aiming to exploit common features shared by different views. Therefore, it is assumed that multiviews share subspaces from which common knowledge can be discovered. Different views of features may have some intrinsic correlations that might be beneficial to feature learning. Most of the existing multiview feature analysis approaches separately learn features in each view, ignoring knowledge shared by multiple views. Since combining features from heterogeneous data sources can significantly boost classification performance in many applications, it has attracted much research attention over the past few years.
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