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TOC Alert for Publication# 8856 2018March 15<![CDATA[Table of contents]]>151C11170<![CDATA[IEEE Transactions on Automation Science and Engineering]]>151C2C2103<![CDATA[Integration of Learning-Based Testing and Supervisory Control for Requirements Conformance of Black-Box Reactive Systems]]>1512152494<![CDATA[Asymptotic Tracking and Robustness of MAS Transitions Under a New Communication Topology]]>15116323054<![CDATA[Optimal Sensor Placement for Monitoring of Spatial Networks]]>15133441272<![CDATA[Image-Guided Nanopositioning Scheme for SEM]]>15145568147<![CDATA[Continuous Tracking Control for a Compliant Actuator With Two-Stage Stiffness]]>15157666164<![CDATA[A Methodological Approach to Model-Driven Design and Development of Automation Systems]]>15167794924<![CDATA[Simultaneous Multiple-Nanowire Motion Control, Planning, and Manipulation Under Electric Fields in Fluid Suspension]]>15180912638<![CDATA[Event-Based Supervisory Control for Energy Efficient Manufacturing Systems]]>151921031507<![CDATA[Design of Vector Field for Different Subphases of Gait and Regeneration of Gait Pattern]]>1511041103086<![CDATA[Curved Reflection Symmetric Axes on Free-Form Surfaces and Their Extraction]]>1511111263762<![CDATA[A Spectral Graph Theoretic Approach for Monitoring Multivariate Time Series Data From Complex Dynamical Processes]]>1)] and with higher fidelity (consistency of detection) when compared with the conventional statistics-based approaches. The presented approach maps a multidimensional sensor data stream X^{N×d} (visualize N as time and d as the number of sensors) as an unweighted and undirected network graph G(V, E), indexed by its vertices V and edges E, i.e., X → G(V, E). The rationale is that the graph-based topological invariants are surrogate representatives of the system state. We compare the monitoring performance of spectral graph theoretic invariants with conventional statistical features in an exponentially weighted moving average control chart setting. The practical utility of the approach is substantiated in the context of process monitoring in two advanced manufacturing scenarios, namely, ultraprecision machining (UPM) and semiconductor chemical mechanical planarization. These studies corroborate the hypothesis that graph theoretic invariants, when used as monitoring statistics, lead to lower ARL_{1} and more consistent detections in contrast to conventional statistical features. For instance, in the UPM case, the fault detection delay using graph theoretic invariants is less than 160 ms, compared with over 8 s of delay with statistical features.]]>1511271443700<![CDATA[Multifeature, Sparse-Based Approach for Defects Detection and Classification in Semiconductor Units]]>1511451593428<![CDATA[Set-Membership-Based Fault Detection and Isolation for Robotic Assembly of Electrical Connectors]]>1511601712548<![CDATA[Analysis and Observations From the First Amazon Picking Challenge]]>1511721883918<![CDATA[Planning and Control for Collision-Free Cooperative Aerial Transportation]]>1511892014360<![CDATA[Machining-Based Coverage Path Planning for Automated Structural Inspection]]>2 carbon steel sample of 10-mm nominal thickness. The potential of this automated approach has benefits in terms of repeatability of area coverage, obstacle avoidance, and reduced path overlap, all of which directly lead to increased task efficiency and reduced inspection time of large structural assets.]]>1512022132550<![CDATA[Using Degradation Messages to Predict Hydraulic System Failures in a Commercial Aircraft]]>1512142241726<![CDATA[Data-Defect Inspection With Kernel-Neighbor-Density-Change Outlier Factor]]>1512252382873<![CDATA[Collision Detection and Signal Recovery for UHF RFID Systems]]>1512392502398<![CDATA[A Noise-Tolerant Algorithm for Robot-Sensor Calibration Using a Planar Disk of Arbitrary 3-D Orientation]]>1512512632344<![CDATA[Census Signal Temporal Logic Inference for Multiagent Group Behavior Analysis]]>1512642771955<![CDATA[Efficient Planar Caging Test Using Space Mapping]]>1512782893112<![CDATA[Steering and Control of Miniaturized Untethered Soft Magnetic Grippers With Haptic Assistance]]>1512903065896<![CDATA[Fast Linear Quaternion Attitude Estimator Using Vector Observations]]>1513073194430<![CDATA[Strategies for Improving and Evaluating Robot Registration Performance]]>1513203282340<![CDATA[Interface Design of a Physical Human–Robot Interaction System for Human Impedance Adaptive Skill Transfer]]>1513293405484<![CDATA[Chiller Plant Operation Optimization: Energy-Efficient Primary-Only and Primary–Secondary Systems]]>1513413552577<![CDATA[A Motion Planning Strategy for the Active Vision-Based Mapping of Ground-Level Structures]]>1513563682915<![CDATA[CrowdGIS: Updating Digital Maps via Mobile Crowdsensing]]>1513693802948<![CDATA[Analysis With Histogram of Connectivity: For Automated Evaluation of Piping Layout]]>1513813925168<![CDATA[Functional Quantitative and Qualitative Models for Quality Modeling in a Fused Deposition Modeling Process]]>1513934031250<![CDATA[Revised Test for Stochastic Diagnosability of Discrete-Event Systems]]>151404408576<![CDATA[IEEE Robotics and Automation Society Information]]>151C3C357<![CDATA[IEEE Transactions on Automation Science and Engineering information for authors]]>151C4C498