Uncovering Hidden Factors in Electricity Consumption Based on Gaussian Mixture Estimation

Load characteristics play an essential role in the planning of power generation and distribution. Various undiscovered factors, which could be socioeconomic, geographic, or climatic, make it possible to describe the electricity demand by a multimodal distribution. This letter proposes a novel method based on multimodal distributions to characterize the hidden factors in electricity consumption. Consequently, a new approach is developed to evaluate the impact of the underlying factors of electricity consumption. Some quantifiable and predictable factors are analyzed in developing multimodal distribution to describe the expected demand. Simulations based on synthetic and real-world data have been conducted to demonstrate the usefulness and robustness of the proposed method.

IOT MODEL WITH MULTIMODAL NODE ALLOCATION AND THE METHOD OF USING MOBILE HEAD NODES FOR DATA COLLECTION

Рассмотрена задача организации движения головных узлов (ГУ) в сети интернета вещей (ИВ) при неоднородном (мультимодальном) распределении узлов в зоне обслуживания. Предложен метод кластеризации неоднородной сети, позволяющий выделить кластеры (отличающиеся плотностью узлов) и выбирать скорость движения ГУ в соответствии с плотностью в каждом кластере. Метод основан на использовании алгоритма кластеризации DBSCAN, позволяет повысить эффективность использования подвижных ГУ и может быть применен при организации сбора данных в сети ИВ. The paper is devoted to the problem of organizing the movement of head nodes in the Internet of Things (IoT) network with a heterogeneous (multimodal) distribution of nodes in the service area. A method for clustering a heterogeneous network is proposed, which makes it possible to distinguish clusters that differ in the density of nodes and select the speed of movement of the head node in accordance with the density in each cluster. The proposed method is based on the use of the DBSCAN clustering algorithm and makes it possible to increase the efficiency of the use of mobile head nodes. The method can be applied in organizing data collection in the IoT network.

Angular offset distributions during fixation are, more often than not, multimodal

Typically, the position error of an eye-tracking device is measured as the distance of the eye-position from the target position in two-dimensional space (angular offset). Accuracy is the mean angular offset. The mean is a highly interpretable measure of central tendency if the underlying error distribution is unimodal and normal. However, in the context of an underlying multimodal distribution, the mean is less interpretable. We will present evidence that the majority of such distributions are multimodal. Only 14.7% of fixation angular offset distributions were unimodal, and of these, only 11.5% were normally distributed. (Of the entire dataset, 1.7% were unimodal and normal.) This multimodality is true even if there is only a single, continuous tracking fixation segment per trial. We present several approaches to measure accuracy in the face of multimodality. We also address the role of fixation drift in partially explaining multimodality.

Comparison of Pattern Discrimination Mechanisms of Hebbian and Spatiotemporal Learning Rules in Self-Organization

The spatiotemporal learning rule (STLR) proposed based on hippocampal neurophysiological experiments is essentially different from the Hebbian learning rule (HEBLR) in terms of the self-organization mechanism. The difference is the self-organization of information from the external world by firing (HEBLR) or not firing (STLR) output neurons. Here, we describe the differences of the self-organization mechanism between the two learning rules by simulating neural network models trained on relatively similar spatiotemporal context information. Comparing the weight distributions after training, the HEBLR shows a unimodal distribution near the training vector, whereas the STLR shows a multimodal distribution. We analyzed the shape of the weight distribution in response to temporal changes in contextual information and found that the HEBLR does not change the shape of the weight distribution for time-varying spatiotemporal contextual information, whereas the STLR is sensitive to slight differences in spatiotemporal contexts and produces a multimodal distribution. These results suggest a critical difference in the dynamic change of synaptic weight distributions between the HEBLR and STLR in contextual learning. They also capture the characteristics of the pattern completion in the HEBLR and the pattern discrimination in the STLR, which adequately explain the self-organization mechanism of contextual information learning.

Accessing the Frank-Kasper Phase of Block Copolymer in the Fuzzy Colloid Regime

The discovery of Frank-Kasper (FK) phase in block copolymer (bcp) has prompted the progress of the field of soft quasicrystals. In principle, the formation of FK phase from the supercooled liquid phase of the bcp micelles should involve the mass transport of constituent molecules to transform the unimodal distribution of micelle size into the multimodal distribution prescribed by the volume asymmetry of the Voronoi cells in the FK phase. Here we present a new regime in which the Laves C14 phase of bcp developed below the glass transition temperature of the micelle core, where the mass transport was inhibited by the immobile block chains forming the core. The bcp micelle comprising a glassy core and a soft corona resembles the fuzzy colloid and the strong van der Waals attraction between the cores directs their organization into C14 phase to minimize the interparticle interaction energy under the metastable condition.

Development of Hartigan’s Dip Statistic with Bimodality Coefficient to Assess Multimodality of Distributions

In general, although some random variables such as wind speed, temperature, and load are known to have multimodal distributions, input or output random variables are considered to follow unimodal distributions without assessing the unimodality or multimodality of distributions from samples. In uncertainty analysis, estimating unimodal distribution as multimodal distribution or vice versa can lead to erroneous analysis results. Thus, whether a distribution is unimodal or multimodal must be assessed before the estimation of distributions. In this paper, the bimodality coefficient (BC) and Hartigan’s dip statistic (HDS), which are representative methods for assessing multimodality, are introduced and compared. Then, a combined HDS with BC method is proposed. The proposed method has the advantages of both BC and HDS by using the skewness and kurtosis of samples as well as the dip statistic through a link function between the BC values in BC and significance level in HDS. To verify the performance of the proposed method, statistical simulation tests were conducted to evaluate the multimodality for various unimodal, bimodal, and trimodal models. The implementation of the proposed method to real engineering data is shown through case studies. The results demonstrate that the proposed method is more accurate, robust, and reliable than the BC and original HDS alone.

Stochastic modelling reveals mechanisms of metabolic heterogeneity

Phenotypic variation is a hallmark of cellular physiology. Metabolic heterogeneity, in particular, underpins single-cell phenomena such as microbial drug tolerance and growth variability. Much research has focussed on transcriptomic and proteomic heterogeneity, yet it remains unclear if such variation permeates to the metabolic state of a cell. Here we propose a stochastic model to show that complex forms of metabolic heterogeneity emerge from fluctuations in enzyme expression and catalysis. The analysis predicts clonal populations to split into two or more metabolically distinct subpopulations. We reveal mechanisms not seen in deterministic models, in which enzymes with unimodal expression distributions lead to metabolites with a bimodal or multimodal distribution across the population. Based on published data, the results suggest that metabolite heterogeneity may be more pervasive than previously thought. Our work casts light on links between gene expression and metabolism, and provides a theory to probe the sources of metabolite heterogeneity.