Enabling Live Data Controlled Manual Assembly Processes by Worker Information System and Nearfield Localization System

AbstractExisting localization solutions cannot be directly integrated into production systems. This article describes a nearfield localization system which can be installed on tools due to its small dimensions. Live data controlled manual assembly processes are enabled. In combination with worker information systems, the manual assembly process can be supported more precisely compared to common systems. The benefits are shown within product-specific assembly scenarios. One benefit is enabling work out of sight (non-visible range) guided through a virtual model on a screen. Error prevention (zero-defect assembly) can be realized by monitoring and matching the actual position to the assembly location. Even without augmented reality devices, comparative 3-D representations of real and virtual world are feasible, supporting employees in mobile workshop with complex repairs. In particular, difficult accessibility can be easily determined when carrying out maintenance work by knowing the complete product structure

Safe reinforcement learning with self-improving hard constraints for multi-energy management systems

Safe reinforcement learning (RL) with hard constraint guarantees is a promising optimal control direction for multi-energy management systems. It only requires the environment-specific constraint functions itself a prior and not a complete model (i.e. plant, disturbance and noise models, and prediction models for states not included in the plant model - e.g. demand, weather, and price forecasts). The project-specific upfront and ongoing engineering efforts are therefore still reduced, better representations of the underlying system dynamics can still be learned and modeling bias is kept to a minimum (no model-based objective function). However, even the constraint functions alone are not always trivial to accurately provide in advance (e.g. an energy balance constraint requires the detailed determination of all energy inputs and outputs), leading to potentially unsafe behavior. In this paper, we present two novel advancements: (I) combining the Optlayer and SafeFallback method, named OptLayerPolicy, to increase the initial utility while keeping a high sample efficiency. (II) introducing self-improving hard constraints, to increase the accuracy of the constraint functions as more data becomes available so that better policies can be learned. Both advancements keep the constraint formulation decoupled from the RL formulation, so that new (presumably better) RL algorithms can act as drop-in replacements. We have shown that, in a simulated multi-energy system case study, the initial utility is increased to 92.4% (OptLayerPolicy) compared to 86.1% (OptLayer) and that the policy after training is increased to 104.9% (GreyOptLayerPolicy) compared to 103.4% (OptLayer) - all relative to a vanilla RL benchmark. While introducing surrogate functions into the optimization problem requires special attention, we do conclude that the newly presented GreyOptLayerPolicy method is the most advantageous.Comment: 4579 words. arXiv admin note: text overlap with arXiv:2207.0383

Maxwell consideration of polaritonic quasi-particle Hamiltonians in multi-level systems

We address the problem of the correct description of light-matter coupling for excitons and cavity photons in the case of systems with multiple photon modes or excitons, respectively. In the literature, two different approaches for the phenomenological coupling Hamiltonian can be found: Either one single Hamiltonian with a basis whose dimension equals the sum of photonic modes and excitonic resonances is used. Or a set of independent Hamiltonians, one for each photon mode, is chosen. Both are usually used equivalently for the same kind of multi-photonic systems which cannot be correct. However, identifying the suitable Hamiltonian is difficult when modeling experimental data. By means of numerical transfer matrix calculations, we demonstrate the scope of application of each approach: The first one holds only for the coupling of a single photon state to several excitons, while in the case of multiple photon modes, separate Hamiltonians must be used for each photon mode

Safe reinforcement learning for multi-energy management systems with known constraint functions

Reinforcement learning (RL) is a promising optimal control technique for multi-energy management systems. It does not require a model a priori - reducing the upfront and ongoing project-specific engineering effort and is capable of learning better representations of the underlying system dynamics. However, vanilla RL does not provide constraint satisfaction guarantees — resulting in various potentially unsafe interactions within its environment. In this paper, we present two novel online model-free safe RL methods, namely SafeFallback and GiveSafe, where the safety constraint formulation is decoupled from the RL formulation. These provide hard-constraint satisfaction guarantees both during training and deployment of the (near) optimal policy. This is without the need of solving a mathematical program, resulting in less computational power requirements and more flexible constraint function formulations. In a simulated multi-energy systems case study we have shown that both methods start with a significantly higher utility compared to a vanilla RL benchmark and Optlayer benchmark (94,6% and 82,8% compared to 35,5% and 77,8%) and that the proposed SafeFallback method even can outperform the vanilla RL benchmark (102,9% to 100%). We conclude that both methods are viably safety constraint handling techniques applicable beyond RL, as demonstrated with random policies while still providing hard-constraint guarantees

Safe reinforcement learning for multi-energy management systems with known constraint functions

Reinforcement learning (RL) is a promising optimal control technique for multi-energy management systems. It does not require a model a priori - reducing the upfront and ongoing project-specific engineering effort and is capable of learning better representations of the underlying system dynamics. However, vanilla RL does not provide constraint satisfaction guarantees - resulting in various unsafe interactions within its safety-critical environment. In this paper, we present two novel safe RL methods, namely SafeFallback and GiveSafe, where the safety constraint formulation is decoupled from the RL formulation and which provides hard-constraint satisfaction guarantees both during training (exploration) and exploitation of the (close-to) optimal policy. In a simulated multi-energy systems case study we have shown that both methods start with a significantly higher utility (i.e. useful policy) compared to a vanilla RL benchmark (94,6% and 82,8% compared to 35,5%) and that the proposed SafeFallback method even can outperform the vanilla RL benchmark (102,9% to 100%). We conclude that both methods are viably safety constraint handling techniques capable beyond RL, as demonstrated with random agents while still providing hard-constraint guarantees. Finally, we propose fundamental future work to i.a. improve the constraint functions itself as more data becomes available

Cavity Polariton Condensate in a Disordered Environment

We report on the influence of disorder on an exciton-polariton condensate in a ZnO based bulk planar microcavity and compare experimental results with a theoretical model for a non-equilibrium condensate. Experimentally, we detect intensity fluctuations within the far-field emission pattern even at high condensate densities which indicates a significant impact of disorder. We show that these effects rely on the driven dissipative nature of the condensate and argue that they can be accounted for by spatial phase inhomogeneities induced by disorder, which occur even for increasing condensate densities realized in the regime of high excitation power. Thus, non-equilibrium effects strongly suppress the stabilization of the condensate against disorder, contrarily to what is expected for equilibrium condensates in the high density limit. Numerical simulations based on our theoretical model reproduce the experimental data.Comment: main article and supplementary, 13 pages, 8 figures (main article

Epidermal Growth Factor Receptor-Related Tumor Markers and Clinical Outcomes with Erlotinib in Non-small Cell Lung Cancer: An Analysis of Patients from German Centers in the TRUST Study

IntroductionRelationships between clinical outcomes and epidermal growth factor receptor (EGFR)-related tumor markers were investigated in patients with advanced non-small cell lung cancer.MethodsPatients with stage IIIB/IV non-small cell lung cancer (0–2 prior regimens) received erlotinib (150 mg PO per day). Response and survival were evaluated, and tumor samples were assessed by immunohistochemistry (EGFR, phosphorylated mitogen-activated protein kinase, and phosphorylated AKT protein expression), fluorescence in situ hybridization (FISH; EGFR gene copy number), and DNA sequencing (EGFR, KRAS gene mutations).ResultsAmong 311 patients, 8% had a complete/partial response; the disease control rate was 66%. Median Overall survival (OS) was 6.1 months; 1-year survival rate was 27.2%. Two of 4 patients with EGFR mutations had tumor responses, versus 2/68 with wild-type EGFR (p = 0.014). Progression-free survival (PFS) (HR = 0.31) and OS (HR = 0.33) were significantly prolonged in patients with EGFR mutations. Response rate was significantly higher in patients with EGFR FISH-positive (17%) than FISH-negative tumors (6%), and both PFS (HR = 0.58) and OS (HR = 0.63) significantly favored patients with EGFR FISH-positive tumors; median OS was 8.6 months in the EGFR FISH-positive group. None of 17 patients with a KRAS mutation had a tumor response, but the impact of KRAS mutation status on survival outcomes was of borderline statistical significance. Neither phosphorylated mitogen-activated protein kinase nor phosphorylated AKT immunohistochemistry status had a significant effect on PFS and OS with erlotinib.ConclusionsThe presence of EGFR mutations and EGFR FISH-positive tumors may predispose patients to achieving better outcomes on erlotinib, but may have a beneficial impact on prognosis (irrespective of treatment). Prospective, placebo-controlled studies are needed to determine the predictive value of the putative biomarkers

Exploration of pathomechanisms triggered by a single-nucleotide polymorphism in titin\u27s I-band: the cardiomyopathy-linked mutation T2580I

Missense single-nucleotide polymorphisms (mSNPs) in titin are emerging as a main causative factor of heart failure. However, distinguishing between benign and disease-causing mSNPs is a substantial challenge. Here, we research the question of whether a single mSNP in a generic domain of titin can affect heart function as a whole and, if so, how. For this, we studied the mSNP T2850I, seemingly linked to arrhythmogenic right ventricular cardiomyopathy (ARVC). We used structural biology, computational simulations and transgenic muscle in vivo methods to track the effect of the mutation from the molecular to the organismal level. The data show that the T2850I exchange is compatible with the domain three-dimensional fold, but that it strongly destabilizes it. Further, it induces a change in the conformational dynamics of the titin chain that alters its reactivity, causing the formation of aberrant interactions in the sarcomere. Echocardiography of knock-in mice indicated a mild diastolic dysfunction arising from increased myocardial stiffness. In conclusion, our data provide evidence that single mSNPs in titin\u27s I-band can alter overall muscle behaviour. Our suggested mechanisms of disease are the development of non-native sarcomeric interactions and titin instability leading to a reduced I-band compliance. However, understanding the T2850I-induced ARVC pathology mechanistically remains a complex problem and will require a deeper understanding of the sarcomeric context of the titin region affected