Distributionally Robust Safety Filter for Learning-Based Control in Active Distribution Systems

Operational constraint violations may occur when deep reinforcement learning (DRL) agents interact with real-world active distribution systems to learn their optimal policies during training. This letter presents a universal distributionally robust safety filter (DRSF) using which any DRL agent can reduce the constraint violations of distribution systems significantly during training while maintaining near-optimal solutions. The DRSF is formulated as a distributionally robust optimization problem with chance constraints of operational limits. This problem aims to compute near-optimal actions that are minimally modified from the optimal actions of DRL-based Volt/VAr control by leveraging the distribution system model, thereby providing constraint satisfaction guarantee with a probability level under the model uncertainty. The performance of the proposed DRSF is verified using the IEEE 33-bus and 123-bus systems

Sample-Efficient Learning for a Surrogate Model of Three-Phase Distribution System

A surrogate model that accurately predicts distribution system voltages is crucial for reliable smart grid planning and operation. This letter proposes a fixed-point data-driven surrogate modeling method that employs a limited dataset to learn the power-voltage relationship of an unbalanced three-phase distribution system. The proposed surrogate model is designed using a fixed-point load-flow equation, and the stochastic gradient descent method with an automatic differentiation technique is employed to update the parameters of the surrogate model using complex power and voltage samples. Numerical examples in IEEE 13-bus, 37-bus, and 123-bus systems demonstrate that the proposed surrogate model can outperform surrogate models based on the deep neural network and Gaussian process regarding prediction accuracy and sample efficiencyComment: Under review in IEEE PES Lette

Transduction of Cu, Zn-superoxide dismutase mediated by an HIV-1 Tat protein basic domain into human chondrocytes

This study was performed to investigate the transduction of a full-length superoxide dismutase (SOD) protein fused to transactivator of transcription (Tat) into human chondrocytes, and to determine the regulatory function of transduced Tat-SOD in the inflammatory cytokine induced catabolic pathway. The pTat-SOD expression vector was constructed to express the basic domain of HIV-1 Tat as a fusion protein with Cu, Zn-SOD. We also purified histidine-tagged SOD without an HIV-1 Tat and Tat-GFP as control proteins. Cartilage samples were obtained from patients with osteoarthritis (OA) and chondrocytes were cultured in both a monolayer and an explant. For the transduction of fusion proteins, cells/explants were treated with a variety of concentrations of fusion proteins. The transduced protein was detected by fluorescein labeling, western blotting and SOD activity assay. Effects of transduced Tat-SOD on the regulation of IL-1 induced nitric oxide (NO) production and inducible nitric oxide synthase (iNOS) mRNA expression was assessed by the Griess reaction and reverse transcriptase PCR, respectively. Tat-SOD was successfully delivered into both the monolayer and explant cultured chondrocytes, whereas the control SOD was not. The intracellular transduction of Tat-SOD into cultured chondrocytes was detected after 1 hours, and the amount of transduced protein did not change significantly after further incubation. SOD enzyme activity increased in a dose-dependent manner. NO production and iNOS mRNA expression, in response to IL-1 stimulation, was significantly down-regulated by pretreatment with Tat-SOD fusion proteins. This study shows that protein delivery employing the Tat-protein transduction domain is feasible as a therapeutic modality to regulate catabolic processes in cartilage. Construction of additional Tat-fusion proteins that can regulate cartilage metabolism favorably and application of this technology in in vivo models of arthritis are the subjects of future studies

Characterization of cationic dextrin prepared by ultra high pressure (UHP)-assisted cationization reaction

AbstractCationic dextrins were prepared through substitution reaction of dextrin with low and high addition levels of 2,3-epoxypropyltrimethylammonium chloride (ETMAC), respectively. Conventional cationization reactions were carried out for 5h under continued stirring. UHP-assisted cationization reactions were conducted at three pressurization levels of 100, 300 and 500MPa for a pressure holding time of 30min. Degree of substitution (DS) of UHP-assisted cationic dextrins ranged from 0.58 to 1.51, and in general, their DS values were enhanced with increasing pressure levels. FT-IR and 13C NMR spectra indicated the presence of CN bond, which provided clear evidence about incorporation of cationic moieties onto dextrin molecules. In flocculation test, UHP-assisted cationic dextrin revealed higher flocculating activity. Overall results suggested that UHP-assisted cationization reaction could modulate reactivity and flocculating activity of dextrin by controlling pressure levels and reaction mixture compositions, and cationic dextrins likely possessed a higher potential to replace synthetic polymer-based flocculants

Predictive Coding Strategies for Developmental Neurorobotics

In recent years, predictive coding strategies have been proposed as a possible means by which the brain might make sense of the truly overwhelming amount of sensory data available to the brain at any given moment of time. Instead of the raw data, the brain is hypothesized to guide its actions by assigning causal beliefs to the observed error between what it expects to happen and what actually happens. In this paper, we present a variety of developmental neurorobotics experiments in which minimalist prediction error-based encoding strategies are utilize to elucidate the emergence of infant-like behavior in humanoid robotic platforms. Our approaches will be first naively Piagian, then move onto more Vygotskian ideas. More specifically, we will investigate how simple forms of infant learning, such as motor sequence generation, object permanence, and imitation learning may arise if minimizing prediction errors are used as objective functions

Micro-Segregated Liquid Crystal Haze Films for Photovoltaic Applications: A Novel Strategy to Fabricate Haze Films Employing Liquid Crystal Technology

Herein, a novel strategy to fabricate haze films employing liquid crystal (LC) technology for photovoltaic (PV) applications is reported. We fabricated a high optical haze film composed of low-molecular LCs and polymer and applied the film to improve the energy conversion efficiency of PV module. The technique utilized to fabricate our haze film is based on spontaneous polymerization-induced phase separation between LCs and polymers. With optimized fabrication conditions, the haze film exhibited an optical haze value over 95% at 550 nm. By simply attaching our haze film onto the front surface of a silicon-based PV module, an overall average enhancement of 2.8% in power conversion efficiency was achieved in comparison with a PV module without our haze film

Sleep state classification using power spectral density and residual neural network with multichannel EEG signals.

This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., the pre-frontal, central, and occipital lobes. Without considering any artifact removal approach, the residual neural network (ResNet) architecture is used to automatically learn the distinctive features of different sleep stages from the power spectral density (PSD) of the raw EEG data. The residual block of the ResNet learns the intrinsic features of different sleep stages from the EEG data while avoiding the vanishing gradient problem. The proposed approach is validated using the sleep dataset of the Dreams database, which comprises of EEG signals for 20 healthy human subjects, 16 female and 4 male. Our experimental results demonstrate the effectiveness of the ResNet based approach in identifying different sleep stages in both female and male subjects compared to state-of-the-art methods with classification accuracies of 87.8% and 83.7%, respectively