Voltage Stability Monitoring based on Adaptive Dynamic Mode Decomposition

This paper develops a new voltage stability monitoring method using dynamic mode decomposition (DMD) and its adaptive variance. First, state estimation (SE) is used to estimate the voltage in the system. Then, the measured voltages from the phasor measurement units (PMU) and estimations from SE are used as the inputs for DMD to predict the long-term voltage dynamic. Furthermore, to improve the prediction performance, the normal DMD is improved by adaptively changing the size of input samples based on the error in the training phase, named adaptive DMD (ADMD). The effectiveness of the proposed method is validated on the Nordic32 test system, which is recommended as the test system for voltage stability studies. Different contingency scenarios are used, and the results show that the proposed method is able to monitor the voltage stability after a disturbance (i.e., 4.3x10-4 MAPE for a stable case and 0.0041 MAPE for an unstable case). Furthermore, the results from a scenario in which a real-world oscillation event is used show high accuracy in voltage stability monitoring of the proposed ADMD method

Disturbance decoupled observers for systems with unknown inputs

This note deals with the design of reduced-order disturbance decoupled scalar functional observers for linear systems with unknown inputs. Based on a parametric approach, existence conditions are derived and a design procedure for finding reduced-order scalar functional observers is given. The derived existence conditions are relaxed and the procedure can find first-order disturbance decoupled scalar functional observers for some cases where the number of unknown inputs is more than the number of outputs. Also, the observer matching condition, which is the necessary requirement for the design of state observers for linear systems with unknown inputs, is not required. Numerical examples are given to illustrate the attractiveness of the proposed design method.<br /

Self-adaptive Controllers for Renewable Energy Communities Based on Transformer Loading Estimation

In this paper, self-adaptive controllers for renewable energy communities based on data-driven approach are proposed to mitigate the voltage rise and transformer congestion at the community level. In the proposed approach, the transformer loading percentage is estimated by the trained data-driven model, which uses the extreme gradient boosting regression algorithm based on a measurement set acquired from critical coupling points of the communities. To avoid voltage rise issues, the droop control parameters (i.e., voltage threshold for P - V, Q - V curves) are adaptively tuned based on the solar irradiance availability and estimated transformer loading. The proposed approach has been tested in the IEEE European LV distribution network. Results showed that the control approach could effectively reduce 22.2 % of the total overloaded instances, while still keeping voltage magnitude in the operation range. This method can help DSOs manage voltage violation and congestion without further communication

Edge of Infinity: The Clash between Edge Effect and Infinity Assumption for the Distribution of Charge on a Conducting Plate

We re-examine a familiar problem given in introductory physics courses, about determining the induced charge distribution on an uncharged ``infinitely-large'' conducting plate when placing parallel to it a uniform charged dielectric plate of the same size. We show that, no matter how large the plates are, the edge effect will always be strong enough to influence the charge distribution deep in the central region, which totally destroyed the infinity assumption (that the surface charge densities on the two sides are uniform and of opposite magnitudes). For a more detailed analysis, we solve Poisson's equation for a similar setting in two-dimensional space and obtain the exact charge distribution, helping us to understand what happens how charge distributes at the central, the asymptotic, and the edge regions

A Schrödinger Equation for Evolutionary Dynamics

We establish an analogy between the Fokker–Planck equation describing evolutionary landscape dynamics and the Schrödinger equation which characterizes quantum mechanical particles, showing that a population with multiple genetic traits evolves analogously to a wavefunction under a multi-dimensional energy potential in imaginary time. Furthermore, we discover within this analogy that the stationary population distribution on the landscape corresponds exactly to the ground-state wavefunction. This mathematical equivalence grants entry to a wide range of analytical tools developed by the quantum mechanics community, such as the Rayleigh–Ritz variational method and the Rayleigh–Schrödinger perturbation theory, allowing us not only the conduct of reasonable quantitative assessments but also exploration of fundamental biological inquiries. We demonstrate the effectiveness of these tools by estimating the population success on landscapes where precise answers are elusive, and unveiling the ecological consequences of stress-induced mutagenesis—a prevalent evolutionary mechanism in pathogenic and neoplastic systems. We show that, even in an unchanging environment, a sharp mutational burst resulting from stress can always be advantageous, while a gradual increase only enhances population size when the number of relevant evolving traits is limited. Our interdisciplinary approach offers novel insights, opening up new avenues for deeper understanding and predictive capability regarding the complex dynamics of evolving populations

A Schr\"odinger Equation for Evolutionary Dynamics

We establish an analogy between the Fokker-Planck equation describing evolutionary landscape dynamics and the Schr\"{o}dinger equation which characterizes quantum mechanical particles, showing how a population with multiple genetic traits evolves analogously to a wavefunction under a multi-dimensional energy potential in imaginary time. Furthermore, we discover within this analogy that the stationary population distribution on the landscape corresponds exactly to the ground-state wavefunction. This mathematical equivalence grants entry to a wide range of analytical tools developed by the quantum mechanics community, such as the Rayleigh-Ritz variational method and the Rayleigh-Schr\"{o}dinger perturbation theory, allowing us to not only make reasonable quantitative assessments but also explore fundamental biological inquiries. We demonstrate the effectiveness of these tools by estimating the population success on landscapes where precise answers are elusive, and unveiling the ecological consequences of stress-induced mutagenesis -- a prevalent evolutionary mechanism in pathogenic and neoplastic systems. We show that, even in a unchanging environment, a sharp mutational burst resulting from stress can always be advantageous, while a gradual increase only enhances population size when the number of relevant evolving traits is limited. Our interdisciplinary approach offers novel insights, opening up new avenues for deeper understanding and predictive capability regarding the complex dynamics of evolving populations

Addressing the Scalability Bottleneck of Semantic Technologies at Bosch

At the heart of smart manufacturing is real-time semi-automatic decision-making. Such decisions are vital for optimizing production lines, e.g., reducing resource consumption, improving the quality of discrete manufacturing operations, and optimizing the actual products, e.g., optimizing the sampling rate for measuring product dimensions during production. Such decision-making relies on massive industrial data thus posing a real-time processing bottleneck

A design of higher-level control based genetic algorithms for wastewater treatment plants

A wastewater treatment plant facilitates various processes (e.g., physical, chemical and biological) to treat industrial wastewater and remove pollutants. This topic recently encourages much attention in different fields to explore suitable methods to be able to remove chemical or biological elements from wastewater. This paper presents a novel genetic based control algorithm for biological wastewater treatment plants, intending to improve the quality of the effluent, and reduce the costs of operation. The proposed controller allows adjusting the dissolved oxygen in the last basin, , according to the operational conditions, instead of maintaining it at a constant value. genetic algorithm (GA) is used in the higher-level control design to verify the desired value of the lower level based on the Ammonium and ammonia nitrogen concentration in the fourth tank, , concentration values in the fourth tank. In order to modify the tuning parameters of the higher level, an adjustment region is determined. Consequently, the effluent quality is improved, help to decrease the total operational cost. Simulation results demonstrate the advantages of the proposed method

Seasonal variation of phytoplankton in My Thanh River, Mekong delta, Vietnam

A study on the seasonal variation of phytoplankton composition was conducted at the upper, middle, and lower parts of the My Thanh River, which supplies an important source of water for aquaculture. Qualitative and quantitative samples of phytoplankton were collected monthly at both high and low tide. The results showed that a total of 171 phytoplankton (algae) species were recorded, belonging to 59 genera and 5 phyla. Diatoms were the most abundant group with the highest species number, followed by green algae. The other phyla possessed a lower number of species. The species composition was more diverse in the rainy season and at high tide at most of the sampling sites. The mean density of algae varied from 30,900-43,521 ind.L^-1^. The density of diatoms was higher in the middle and lower parts. At the same time, euglenoids displayed the highest density in the upper part, showing a difference in the dominant algae group under the influence of salinity. Salinity was found to be significantly positively correlated (p<0.01) with diatoms, whereas it was negatively correlated (p<0.05) with blue-green algae and euglenoids. The algae composition was quite diverse, with the H' index ranging from 2.0-3.3, showing the water quality was slightly to moderately polluted

Recommender Systems with Generative Retrieval

Modern recommender systems perform large-scale retrieval by first embedding queries and item candidates in the same unified space, followed by approximate nearest neighbor search to select top candidates given a query embedding. In this paper, we propose a novel generative retrieval approach, where the retrieval model autoregressively decodes the identifiers of the target candidates. To that end, we create semantically meaningful tuple of codewords to serve as a Semantic ID for each item. Given Semantic IDs for items in a user session, a Transformer-based sequence-to-sequence model is trained to predict the Semantic ID of the next item that the user will interact with. To the best of our knowledge, this is the first Semantic ID-based generative model for recommendation tasks. We show that recommender systems trained with the proposed paradigm significantly outperform the current SOTA models on various datasets. In addition, we show that incorporating Semantic IDs into the sequence-to-sequence model enhances its ability to generalize, as evidenced by the improved retrieval performance observed for items with no prior interaction history.Comment: Preprint versio