Institute of Automation

A universal but simple procedure for identifying the α, β and γ phases in PVDF using FTIR is proposed and validated. An integrated quantification methodology for individual β and γ phase in mixed systems is also proposed.

Abstract A test census of Tampa, Florida and an independent postenumeration survey (PES) were conducted by the U.S. Census Bureau in 1985. The PES was a stratified block sample with heavy emphasis placed on hard-to-count population groups. Matching the individuals in the census to the individuals in the PES is an important aspect of census coverage evaluation and consequently a very important process for any census adjustment operations that might be planned. For such an adjustment to be feasible, record-linkage software had to be developed that could perform matches with a high degree of accuracy and that was based on an underlying mathematical theory. A principal purpose of the PES was to provide an opportunity to evaluate the newly implemented record-linkage system and associated methodology. This article discusses the theoretical and practical issues encountered in conducting the matching operation and presents the results of that operation. A review of the theoretical background of the record-linkage problem provides a framework for discussions of the decision procedure, file blocking, and the independence assumption. The estimation of the parameters required by the decision procedure is an important aspect of the methodology, and the techniques presented provide a practical system that is easily implemented. The matching algorithm (discussed in detail) uses the linear sum assignment model to "pair" the records. The Tampa, Florida, matching methodology is described in the final sections of the article. Included in the discussion are the results of the matching itself, an independent clerical review of the matches and nonmatches, conclusions, problem areas, and future work required. Key Words: Census adjustmentCensus coverage evaluationEM algorithmPostenumeration survey

The Multi-Parametric Toolbox is a collection of algorithms for modeling, control, analysis, and deployment of constrained optimal controllers developed under Matlab. It features a powerful geometric library that extends the application of the toolbox beyond optimal control to various problems arising in computational geometry. The new version 3.0 is a complete rewrite of the original toolbox with a more flexible structure that offers faster integration of new algorithms. The numerical side of the toolbox has been improved by adding interfaces to state of the art solvers and by incorporation of a new parametric solver that relies on solving linear-complementarity problems. The toolbox provides algorithms for design and implementation of real-time model predictive controllers that have been extensively tested.

A Brain-Computer Interface (BCI) is a specific type of human-computer interface that enables the direct communication between human and computers by analyzing brain measurements. Oddball paradigms are used in BCI to generate event-related potentials (ERPs), like the P300 wave, on targets selected by the user. A P300 speller is based on this principle, where the detection of P300 waves allows the user to write characters. The P300 speller is composed of two classification problems. The first classification is to detect the presence of a P300 in the electroencephalogram (EEG). The second one corresponds to the combination of different P300 responses for determining the right character to spell. A new method for the detection of P300 waves is presented. This model is based on a convolutional neural network (CNN). The topology of the network is adapted to the detection of P300 waves in the time domain. Seven classifiers based on the CNN are proposed: four single classifiers with different features set and three multiclassifiers. These models are tested and compared on the Data set II of the third BCI competition. The best result is obtained with a multiclassifier solution with a recognition rate of 95.5 percent, without channel selection before the classification. The proposed approach provides also a new way for analyzing brain activities due to the receptive field of the CNN models.

This study gives the mathematic model of a quadrotor unmanned aerial vehicle (UAV) and then proposes a robust nonlinear controller which combines the sliding-mode control technique and the backstepping control technique. To achieve Cartesian position trajectory tracking capability, the construction of the controller can be divided into two stages: a regular SMC controller for attitude subsystem (inner loop) is first developed to guarantee fast convergence rapidity of Euler angles and the backstepping technique is applied to the position loop until desired attitudes are obtained and then the ultimate control laws. The stability of the closed-loop system is guaranteed by stabilizing each of the subsystems step by step and the robustness of the controller against model uncertainty and external disturbances is investigated. In addition, an adaptive observer-based fault estimation scheme is also considered for taking off mode. Simulations are conducted to demonstrate the effectiveness of the designed robust nonlinear controller and the fault estimation scheme.

In this paper, novel methods for detecting steady-state visual evoked potentials using multiple electroencephalogram (EEG) signals are presented. The methods are tailored for brain-computer interfacing, where fast and accurate detection is of vital importance for achieving high information transfer rates. High detection accuracy using short time segments is obtained by finding combinations of electrode signals that cancel strong interference signals in the EEG data. Data from a test group consisting of 10 subjects are used to evaluate the new methods and to compare them to standard techniques. Using 1-s signal segments, six different visual stimulation frequencies could be discriminated with an average classification accuracy of 84%. An additional advantage of the presented methodology is that it is fully online, i.e., no calibration data for noise estimation, feature extraction, or electrode selection is needed.

Plasma protein binding can be an effective means of improving the pharmacokinetic properties of otherwise short lived molecules. Using peptide phage display, we identified a series of peptides having the core sequence DICLPRWGCLW that specifically bind serum albumin from multiple species with high affinity. These peptides bind to albumin with 1:1 stoichiometry at a site distinct from known small molecule binding sites. Using surface plasmon resonance, the dissociation equilibrium constant of peptide SA21 (Ac-RLIEDICLPRWGCLWEDD-NH(2)) was determined to be 266 +/- 8, 320 +/- 22, and 467 +/- 47 nm for rat, rabbit, and human albumin, respectively. SA21 has an unusually long half-life of 2.3 h when injected by intravenous bolus into rabbits. A related sequence, fused to the anti-tissue factor Fab of D3H44 (Presta, L., Sims, P., Meng, Y. G., Moran, P., Bullens, S., Bunting, S., Schoenfeld, J., Lowe, D., Lai, J., Rancatore, P., Iverson, M., Lim, A., Chisholm, V., Kelley, R. F., Riederer, M., and Kirchhofer, D. (2001) Thromb. Haemost. 85, 379-389), enabled the Fab to bind albumin with similar affinity to that of SA21 while retaining the ability of the Fab to bind tissue factor. This interaction with albumin resulted in reduced in vivo clearance of 25- and 58-fold in mice and rabbits, respectively, when compared with the wild-type D3H44 Fab. The half-life was extended 37-fold to 32.4 h in rabbits and 26-fold to 10.4 h in mice, achieving 25-43% of the albumin half-life in these animals. These half-lives exceed those of a Fab'(2) and are comparable with those seen for polyethylene glycol-conjugated Fab molecules, immunoadhesins, and albumin fusions, suggesting a novel and generic method for improving the pharmacokinetic properties of rapidly cleared proteins.

Occluded person re-identification (ReID) aims to match occluded person images to holistic ones across dis-joint cameras. In this paper, we propose a novel framework by learning high-order relation and topology information for discriminative features and robust alignment. At first, we use a CNN backbone to learn feature maps and key-points estimation model to extract semantic local features. Even so, occluded images still suffer from occlusion and outliers. Then, we view the extracted local features of an image as nodes of a graph and propose an adaptive direction graph convolutional (ADGC) layer to pass relation information between nodes. The proposed ADGC layer can automatically suppress the message passing of meaningless features by dynamically learning direction and degree of linkage. When aligning two groups of local features, we view it as a graph matching problem and propose a cross-graph embedded-alignment (CGEA) layer to joint learn and embed topology information to local features, and straightly predict similarity score. The proposed CGEA layer can both take full use of alignment learned by graph matching and replace sensitive one-to-one alignment with a robust soft one. Finally, extensive experiments on occluded, partial, and holistic ReID tasks show the effectiveness of our proposed method. Specifically, our framework significantly outperforms state-of-the-art by $6.5\%$ mAP scores on Occluded-Duke dataset.

This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were learnable through provided pairs of low and high resolution train images. The tracks had 145, 114, 101, and 113 registered participants, resp., and 31 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.

The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content. Owing to the locality in convolutional neural networks (CNNs), extracting and maintaining the global information of input images is difficult. Therefore, traditional neural style transfer methods face biased content representation. To address this critical issue, we take long-range dependencies of input images into account for image style transfer by proposing a transformer-based approach called StyTr2. In contrast with visual transformers for other vision tasks, StyTr2 contains two different transformer encoders to generate domain-specific sequences for content and style, respectively. Following the encoders, a multi-layer transformer decoder is adopted to stylize the content sequence according to the style sequence. We also analyze the deficiency of existing positional encoding methods and propose the content-aware positional encoding (CAPE), which is scale-invariant and more suitable for image style transfer tasks. Qualitative and quantitative experiments demonstrate the effectiveness of the proposed StyTr2 compared with state-of-the-art CNN-based and flow-based approaches. Code and models are available at https://github.com/diyiiyiii/StyTR-2.

Due to environmental and fuel cost concerns more and more wind- and solar-based generation units are embedded in distribution networks (DNs). However, a part of such an embedded generation would be curtailed due to system constraints and variations of the energy penetration. This part of energy can be recovered by introducing energy storage systems (ESSs) and an optimal dispatch of both active and reactive powers. Therefore, we propose a combined problem formulation for active-reactive optimal power flow (A-R-OPF) in DNs with embedded wind generation and battery storage. The solution provides an optimal operation strategy which ensures the feasibility and enhances the profit significantly. Results of a 41-bus distribution network are presented. It can be demonstrated that more than 12% of energy losses and a large amount of reactive energy to be imported from the transmission network (TN) can be reduced using the proposed approach in comparison to the operation strategy where only active OPF is considered.

High-frequency simulation models for power cables and motors are key tools to aid a better understanding of the overvoltage problem in pulsewidth modulation drives with long feeders. In this paper, frequency responses of the cable characteristic and the motor input impedances are obtained experimentally and suitable models are developed to match the experimental results. Several lumped segments incorporating a lossy representation of the line are used to model the cable. The cable and induction motor models may be implemented using a computational tool such as MATLAB, thereby providing a convenient method to analyze the overvoltage phenomena. Simulation and experimental results are presented for a typical 3-hp induction motor, showing the suitability of the developed simulation models. The most promising dv/dt filter networks are also investigated through simulation analysis, and a design approach based on a tradeoff between filter losses and motor peak voltage is proposed. Experimental results of an RC filter placed at the motor terminals demonstrate the validity of the simulation models.

Applying lean can boost a firm’s performance significantly by focusing on value-adding activities. Additionally, Industry 4.0 is regarded as another promising trend in industry. Combining these developments resulted in terms like "lean 4.0". However, the existing literature lacks a comprehensive and detailed conjunction of both paradigms. This paper builds upon this research gap with a twofold aim: Firstly, the target is to build upon existing groundwork to conclude whether lean management and Industry 4.0 can complement each other. Secondly, this work considers how Industry 4.0 can support specific lean methods. This is exemplified by an electric drives production use case.

Multiphase motors are usually designed to have the concentrated winding and nonsinusoidal airgap flux density distribution in order to maximize the torque per ampere. This means that the phase voltage of a multiphase motor has the nonsinusoidal waveform. Accordingly, the conventional analysis on a multiphase space vector pulse-width modulation (SVPWM), which is confined to a sinusoidal phase voltage, should be extended to a nonsinusoidal phase voltage. In this paper, based on a multiple d-q spaces concept a novel analysis on a multiphase SVPWM to synthesize an arbitrary nonsinusoidal phase voltage is proposed. Throughout this paper, a five-phase inverter is used as a practical example. The basic concepts can be easily extended to an n-phase inverter.

Point cloud processing is very challenging, as the diverse shapes formed by irregular points are often indistinguishable. A thorough grasp of the elusive shape requires sufficiently contextual semantic information, yet few works devote to this. Here we propose DensePoint, a general architecture to learn densely contextual representation for point cloud processing. Technically, it extends regular grid CNN to irregular point configuration by generalizing a convolution operator, which holds the permutation invariance of points, and achieves efficient inductive learning of local patterns. Architecturally, it finds inspiration from dense connection mode, to repeatedly aggregate multi-level and multi-scale semantics in a deep hierarchy. As a result, densely contextual information along with rich semantics, can be acquired by DensePoint in an organic manner, making it highly effective. Extensive experiments on challenging benchmarks across four tasks, as well as thorough model analysis, verify DensePoint achieves the state of the arts.

Coping with evolution in automated production systems implies a cross-disciplinary challenge along the system's life-cycle for variant-rich systems of high complexity. The authors from computer science and automation provide an interdisciplinary survey on challenges and state of the art in evolution of automated production systems. Selected challenges are illustrated on the case of a simple pick and place unit. In the first part of the paper, we discuss the development process of automated production systems as well as the different type of evolutions during the system's life-cycle on the case of a pick and place unit. In the second part, we survey the challenges associated with evolution in the different development phases and a couple of cross-cutting areas and review existing approaches addressing the challenges. We close with summarizing future research directions to address the challenges of evolution in automated production systems.

Since the strict separation of working spaces of humans and robots has experienced a softening due to recent robotics research achievements, close interaction of humans and robots comes rapidly into reach. In this context, physical human–robot interaction raises a number of questions regarding a desired intuitive robot behavior. The continuous bilateral information and energy exchange requires an appropriate continuous robot feedback. Investigating a cooperative manipulation task, the desired behavior is a combination of an urge to fulfill the task, a smooth instant reactive behavior to human force inputs and an assignment of the task effort to the cooperating agents. In this paper, a formal analysis of human–robot cooperative load transport is presented. Three different possibilities for the assignment of task effort are proposed. Two proposed dynamic role exchange mechanisms adjust the robot’s urge to complete the task based on the human feedback. For comparison, a static role allocation strategy not relying on the human agreement feedback is investigated as well. All three role allocation mechanisms are evaluated in a user study that involves large-scale kinesthetic interaction and full-body human motion. Results show tradeoffs between subjective and objective performance measures stating a clear objective advantage of the proposed dynamic role allocation scheme.

Batteries are the key technology for enabling further mobile electrification and energy storage. Accurate prediction of the state of the battery is needed not only for safety reasons, but also for better utilization of the battery. In this work we present a state estimation strategy for a detailed electrochemical model of a lithium-ion battery. The benefit of using a detailed model is the additional information obtained about the battery, such as accurate estimates of the internal temperature, the state of charge within the individual electrodes, overpotential, concentration and current distribution across the electrodes, which can be utilized for safety and optimal operation. Based on physical insight, we propose an output error injection observer based on a reduced set of partial differential-algebraic equations. This reduced model has a less complex structure, while it still captures the main dynamics. The observer is extensively studied in simulations and validated in experiments for actual electric-vehicle drive cycles. Experimental results show the observer to be robust with respect to unmodeled dynamics as well as to noisy and biased voltage and current measurements. The available state estimates can be used for monitoring purposes or incorporated into a model based controller to improve the performance of the battery while guaranteeing safe operation.

This paper studies panoptic segmentation, a recently proposed task which segments foreground (FG) objects at the instance level as well as background (BG) contents at the semantic level. Existing methods mostly dealt with these two problems separately, but in this paper, we reveal the underlying relationship between them, in particular, FG objects provide complementary cues to assist BG understanding. Our approach, named the Attention-guided Unified Network (AUNet), is a unified framework with two branches for FG and BG segmentation simultaneously. Two sources of attentions are added to the BG branch, namely, RPN and FG segmentation mask to provide object-level and pixel-level attentions, respectively. Our approach is generalized to different backbones with consistent accuracy gain in both FG and BG segmentation, and also sets new state-of-the-arts both in the MS-COCO (46.5% PQ) and Cityscapes (59.0% PQ) benchmarks.

Batch processes are characterised by a prescribed processing of raw materials into final products for a finite duration and play an important role in many industrial sectors due to the low-volume and high-value products. Process dynamics and stochastic disturbances are inherent characteristics of batch processes, which cause monitoring of batch processes a challenging problem in practice. To solve this problem, a subspace-aided data-driven approach is presented in this article for batch process monitoring. The advantages of the proposed approach lie in its simple form and its abilities to deal with stochastic disturbances and process dynamics existing in the process. The kernel density estimation, which serves as a non-parametric way of estimating the probability density function, is utilised for threshold calculation. An industrial benchmark of fed-batch penicillin production is finally utilised to verify the effectiveness of the proposed approach.