16 research outputs found
Recent Advances in Single-Particle Tracking: Experiment and Analysis
This Special Issue of Entropy, titled âRecent Advances in Single-Particle Tracking: Experiment and Analysisâ, contains a collection of 13 papers concerning different aspects of single-particle tracking, a popular experimental technique that has deeply penetrated molecular biology and statistical and chemical physics. Presenting original research, yet written in an accessible style, this collection will be useful for both newcomers to the field and more experienced researchers looking for some reference. Several papers are written by authorities in the field, and the topics cover aspects of experimental setups, analytical methods of tracking data analysis, a machine learning approach to data and, finally, some more general issues related to diffusion
Transitioning power distribution grid into nanostructured ecosystem : prosumer-centric sovereignty
PhD ThesisGrowing acceptance for in-house Distributed Energy Resource (DER) installations at lowvoltage
level have gained much significance in recent years due to electricity market liberalisations
and opportunities in reduced energy billings through personalised utilisation
management for targeted business model. In consequence, modelling of passive customersâ
electric power system are progressively transitioned into Prosumer-based settings where presidency
for Transactive Energy (TE) system framework is favoured. It amplifies Prosumersâ
commitments into annexing TE values during market participations and optimised energy
management to earn larger rebates and incentives from TE programs. However, when dealing
with mass Behind-The-Meter DER administrations, Utility foresee managerial challenges
when dealing with distribution network analysis, planning, protection, and power quality
security based on Prosumersâ flexibility in optimising their energy needs.
This dissertation contributes prepositions into modelling Distributed Energy Resources
Management System (DERMS) as an aggregator designed for Prosumer-centered cooperation,
interoperating TE control and coordination as key parameters to market for both
optimised energy trading and ancillary services in a Community setting. However, Prosumers
are primarily driven to create a profitable business model when modelling their
DERMS aggregator. Greedy-optimisation exploitations are negative concerns when decisions
made resulted in detrimental-uncoordinated outcomes on Demand-Side Response (DSR)
and capacity market engagements. This calls for policy decision makers to contract safe (i.e.
cooperative yet competitive tendency) business models for Prosumers to maximise TE values
while enhancing networkâs power quality metrics and reliability performances.
Firstly, digitalisation and nanostructuring of distribution network is suggested to identify
Prosumer as a sole energy citizen while extending bilateral trading between Prosumer-to-
Prosumer (PtP) with the involvements of other grid operatorsâTE system. Modelling of
Nanogrid environment for DER integrations and establishment of local area network infrastructure
for IoT security (i.e. personal computing solutions and data protection) are committed
for communal engagements in a decentralise setting. Secondly, a multi-layered Distributed
Control Framework (DCF) is proposed using Microsoft Azure cloud-edge platform that cascades energy actors into respective layers of TE control and coordination. Furthermore,
modelling of flexi-edge computing architecture is proposed, comprising of Contract-Oriented
Sensor-based Application Platform (COSAP) employing Multi-Agent System (MAS) to
enhance data-sharing privacy and contract coalition agreements during PtP engagements.
Lastly, the Agents of MAS are programmed with cooperative yet competitive intelligences
attributed to Reinforcement Learning (RL) and Neural Networks (NN) algorithms to solve
multimodal socio-economical and uncertainty problems that corresponded to Prosumersâ
dynamic energy priorities within the TE framework. To verify the DERMS aggregator
operations, three business models were proposed (i.e. greedy-profit margin, collegial-peak
demand, reserved-standalone) to analyse comparative technical/physical and economic/social
dimensions. Results showed that the proposed TE-valued DERMS aggregator provides
participation versatility in the electricity market that enables competitive edginess when utilising
Behind-The-Meter DERs in view of Prosumerâs asset scheduling, bidding strategy, and
corroborative ancillary services. Performance metrics were evaluated on both domestic and
industrial NG environments against IEEE Standard 2030.7-2017 & 2030.8-2018 compliances
to ensure deployment practicability.
Subsequently, proposed in-house protection system for DER installation serves as an
add-on monitoring service which can be incorporated into existing Advance Distribution
Management System (ADMS) for Distribution Service Operator (DSO) and field engineers
use, ADMS aggregator. It provides early fault detections and isolation processes from allowing
fault current to propagate upstream causing cascading power quality issues across
the feeder line. In addition, ADMS aggregator also serves as islanding indicator that distinguishes
Nanogridâs islanding state from unintentional or intentional operations. Therefore, a
Overcurrent Current Relay (OCR) is proposed using Fuzzy Logic (FL) algorithm to detect,
profile, and provide decisional isolation processes using specified OCRs. Moreover, the
proposed expert knowledge in FL is programmed to detect fault crises despite insufficient
fault current level contributed by DER (i.e. solar PV system) which conventional OCR fails
to trigger
Efficient Decision Support Systems
This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers
Advances in System Identification and Stochastic Optimization
This work studies the framework of systems with subsystems, which has numerous practical applications, including system reliability estimation, sensor networks, and object detection. Consider a stochastic system composed of multiple subsystems, where the outputs are distributed according to many of the most common distributions, such as Gaussian, exponential and multinomial. In Chapter 1, we aim to identify the parameters of the system based on the structural knowledge of the system and the integration of data independently collected from multiple sources. Using the principles of maximum likelihood estimation, we provide the formal conditions for the convergence of the estimates to the true full system and subsystem parameters. The asymptotic normalities for the estimates and their connections to Fisher information matrices are also established, which are useful in providing the asymptotic or finite-sample confidence bounds.
The maximum likelihood approach is then connected to general stochastic optimization via the recursive least squares estimation in Chapter 2. For stochastic optimization, we consider minimizing a loss function with only noisy function measurements and propose two general-purpose algorithms. In Chapter 3, the mixed simultaneous perturbation stochastic approximation (MSPSA) is introduced, which is designed for mixed variable (mixture of continuous and discrete variables) problems. The proposed MSPSA bridges the gap of dealing with mixed variables in the SPSA family, and unifies the framework of simultaneous perturbation as both the standard SPSA and discrete SPSA can now be deemed as two special cases of MSPSA. The almost sure convergence and rate of convergence of the MSPSA iterates are also derived. The convergence results reveal that the finite-sample bound of MSPSA is identical to discrete SPSA when the problem contains only discrete variables, and the asymptotic bound of MSPSA has the same order of magnitude as SPSA when the problem contains only continuous variables. In Chapter 4, the complex-step SPSA (CS-SPSA) is introduced, which utilizes the complex-valued perturbations to improve the efficiency of the standard SPSA. We prove that the CS-SPSA iterates converge almost surely to the optimum and achieve an accelerated convergence rate, which is faster than the standard convergence rate in derivative-free stochastic optimization algorithms
Advancing Human Assessment: The Methodological, Psychological and Policy Contributions of ETS
âThis book describes the extensive contributions made toward the advancement of human assessment by scientists from one of the worldâs leading research institutions, Educational Testing Service. The bookâs four major sections detail research and development in measurement and statistics, education policy analysis and evaluation, scientific psychology, and validity. Many of the developments presented have become de-facto standards in educational and psychological measurement, including in item response theory (IRT), linking and equating, differential item functioning (DIF), and educational surveys like the National Assessment of Educational Progress (NAEP), the Programme of international Student Assessment (PISA), the Progress of International Reading Literacy Study (PIRLS) and the Trends in Mathematics and Science Study (TIMSS). In addition to its comprehensive coverage of contributions to the theory and methodology of educational and psychological measurement and statistics, the book gives significant attention to ETS work in cognitive, personality, developmental, and social psychology, and to education policy analysis and program evaluation. The chapter authors are long-standing experts who provide broad coverage and thoughtful insights that build upon decades of experience in research and best practices for measurement, evaluation, scientific psychology, and education policy analysis. Opening with a chapter on the genesis of ETS and closing with a synthesis of the enormously diverse set of contributions made over its 70-year history, the book is a useful resource for all interested in the improvement of human assessment
On Distributed Estimation for Resource Constrained Wireless Sensor Networks
We study Distributed Estimation (DES) problem, where several agents observe a noisy version of an underlying unknown physical phenomena (which is not directly observable), and transmit a compressed version of their observations to a Fusion Center (FC), where collective data is fused to reconstruct the unknown. One of the most important applications of Wireless Sensor Networks (WSNs) is performing DES in a field to estimate an unknown signal source. In a WSN battery powered geographically distributed tiny sensors are tasked with collecting data from the field. Each sensor locally processes its noisy observation (local processing can include compression, dimension reduction, quantization, etc) and transmits the processed observation over communication channels to the FC, where the received data is used to form a global estimate of the unknown source such that the Mean Square Error (MSE) of the DES is minimized. The accuracy of DES depends on many factors such as intensity of observation noises in sensors, quantization errors in sensors, available power and bandwidth of the network, quality of communication channels between sensors and the FC, and the choice of fusion rule in the FC. Taking into account all of these contributing factors and implementing a DES system which minimizes the MSE and satisfies all constraints is a challenging task. In order to probe into different aspects of this challenging task we identify and formulate the following three problems and address them accordingly: 1- Consider an inhomogeneous WSN where the sensors\u27 observations is modeled linear with additive Gaussian noise. The communication channels between sensors and FC are orthogonal power and bandwidth-constrained erroneous wireless fading channels. The unknown to be estimated is a Gaussian vector. Sensors employ uniform multi-bit quantizers and BPSK modulation. Given this setup, we ask: what is the best fusion rule in the FC? what is the best transmit power and quantization rate (measured in bits per sensor) allocation schemes that minimize the MSE? In order to answer these questions, we derive some upper bounds on global MSE and through minimizing those bounds, we propose various resource allocation schemes for the problem, through which we investigate the effect of contributing factors on the MSE. 2- Consider an inhomogeneous WSN with an FC which is tasked with estimating a scalar Gaussian unknown. The sensors are equipped with uniform multi-bit quantizers and the communication channels are modeled as Binary Symmetric Channels (BSC). In contrast to former problem the sensors experience independent multiplicative noises (in addition to additive noise). The natural question in this scenario is: how does multiplicative noise affect the DES system performance? how does it affect the resource allocation for sensors, with respect to the case where there is no multiplicative noise? We propose a linear fusion rule in the FC and derive the associated MSE in closed-form. We propose several rate allocation schemes with different levels of complexity which minimize the MSE. Implementing the proposed schemes lets us study the effect of multiplicative noise on DES system performance and its dynamics. We also derive Bayesian Cramer-Rao Lower Bound (BCRLB) and compare the MSE performance of our porposed methods against the bound. As a dual problem we also answer the question: what is the minimum required bandwidth of the network to satisfy a predetermined target MSE? 3- Assuming the framework of Bayesian DES of a Gaussian unknown with additive and multiplicative Gaussian noises involved, we answer the following question: Can multiplicative noise improve the DES performance in any case/scenario? the answer is yes, and we call the phenomena as \u27enhancement mode\u27 of multiplicative noise. Through deriving different lower bounds, such as BCRLB,Weiss-Weinstein Bound (WWB), Hybrid CRLB (HCRLB), Nayak Bound (NB), Yatarcos Bound (YB) on MSE, we identify and characterize the scenarios that the enhancement happens. We investigate two situations where variance of multiplicative noise is known and unknown. We also compare the performance of well-known estimators with the derived bounds, to ensure practicability of the mentioned enhancement modes
Deep Model for Improved Operator Function State Assessment
A deep learning framework is presented for engagement assessment using EEG signals. Deep learning is a recently developed machine learning technique and has been applied to many applications. In this paper, we proposed a deep learning strategy for operator function state (OFS) assessment. Fifteen pilots participated in a flight simulation from Seattle to Chicago. During the four-hour simulation, EEG signals were recorded for each pilot. We labeled 20- minute data as engaged and disengaged to fine-tune the deep network and utilized the remaining vast amount of unlabeled data to initialize the network. The trained deep network was then used to assess if a pilot was engaged during the four-hour simulation