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The future distribution systems expose to an unprecedented level of uncertainties due to renewable resources, nontraditional loads, aging infrastructure, etc., posing potential risks to secure operation of the system.This article proposes a new technique to estimate the voltage stability margin of the distribution systems with high penetration of renewables.Its convergence and robustness under complex and stressed working conditions are guaranteed in theory. This technique is handy for the operation as it features self-adaptive step size and is applicable to general system topology. It leverages a newly derived analytical solvability certificate based on the Kantorovich fixed-point theorem. A fast version of the proposed technique is duly proposed to speed up the computation up to 8 times while maintaining high accuracy, which lends itself to online and time-sensitive emergency tasks. Numerical simulations with various IEEE test feeders verify the performance of the techniques. Ministry of Education (MOE) Nanyang Technological University National Research Foundation (NRF) This work was supported in part by NTU SUG, MOE AcRF TIER 1- 2019-T1-001-119 (RG 79/19), and in part by EMA & NRFEMA-EP004-EKJGC-0003.

In this paper, an optimal steady-state voltage control framework is developed based on a novel linear Voltage-Power dependence deducted from Gaussian Process (GP) learning. Different from other point-based linearization techniques, this GP-based linear relationship is valid over a subspace of operating points and thus suitable for a system with uncertainties such as those in power injections due to renewables. The proposed optimal voltage control algorithms, therefore, perform well over a wide range of operating conditions. Both centralized and distributed optimal control schemes are introduced in this framework. The least-squares estimation is employed to provide analytical forms of the optimal control which offer great computational benefits. Moreover, unlike many existing voltage control approaches deploying fixed voltage references, the proposed control schemes not only minimize the control efforts but also optimize the voltage reference setpoints that lead to the least voltage deviation errors with respect to such setpoints. The control algorithms are also extended to handle uncertain power injections with robust optimal solutions which guarantee compliance with the voltage regulation standards. As for the distributed control scheme, a new network partition problem is cast, based on the concept of Effective Voltage Control Source (EVCS), as an optimization problem which is further solved using convex relaxation. Various simulations on the IEEE 33-bus and 69-bus test feeders are presented to illustrate the performance of the proposed voltage control algorithms and EVCS-based network partition. Energy Market Authority (EMA) Nanyang Technological University National Research Foundation (NRF) Accepted version This work was supported in part by the Nanyang Technological University SUG, in part by the Academic Research Fund TIER 1 2019-T1-001-119 (RG 79/19), and in part by the Energy Market Authority (EMA) and National Research Foundation (NRF) Singapore under Grant EMA-EP004-EKJGC-0003.

Examples of long-range electronic conductivity are rare in biological systems. The observation of micrometer-scale electronic transport through protein wires produced by bacteria is therefore notable, providing an opportunity to study fundamental aspects of conduction through protein-based materials and natural inspiration for bioelectronics materials. Borrowing sequence and structural motifs from these conductive protein fibers, we designed self-assembling peptides that form electronically conductive nanofibers under aqueous conditions. Conductivity in these nanofibers is distinct for two reasons: first, they support electron transport over distances orders of magnitude greater than expected for proteins, and second, the conductivity is mediated entirely by amino acids lacking extended conjugation, π-stacking, or redox centers typical of existing organic and biohybrid semiconductors. Electrochemical transport measurements show that the fibers support ohmic electronic transport and a metallic-like temperature dependence of conductance in aqueous buffer. At higher solution concentrations, the peptide monomers form hydrogels, and comparisons of the structure and electronic properties of the nanofibers and gels highlight the critical roles of α-helical secondary structure and supramolecular ordering in supporting electronic conductivity in these materials. These findings suggest a structural basis for long-range electronic conduction mechanisms in peptide and protein biomaterials.

The load flow problem is fundamental to characterize the equilibrium behavior of a power system. Uncertain power injections such as those due to demand variations and intermittent renewable resources will change the system's equilibrium unexpectedly, and thus potentially jeopardizing the system's reliability and stability. Understanding load flow solutions under uncertainty becomes imperative to ensure the seamless operation of a power system. In this work, we propose a non-parametric probabilistic load flow (NP-PLF) technique based on the Gaussian Process (GP) learning to understand the power system behavior under uncertainty for better operational decisions. The technique can provide ``\textit{semi-explicit}'' form of load flow solutions by implementing the learning and testing steps that map control variables to inputs. The proposed NP-PLF leverages upon GP upper confidence bound (GP-UCB) sampling algorithm. The salient features of this NP-PLF method are: i) applicable for power flow problem having power injection uncertainty with an unknown class of distribution; ii) providing probabilistic learning bound (PLB) which further provides control over the error and convergence; iii) capable of handling intermittent distributed generation as well as load uncertainties. The simulation results performed on the IEEE 30-bus and IEEE 118-bus system show that the proposed method can learn the voltage function over the power injection subspace using a small number of training samples. Further, the testing with different input uncertainty distributions indicates that complete statistical information can be obtained for the probabilistic load flow problem with an average percentage relative error of the order of 10-3% on 50,000 test points. Ministry of Education (MOE) Accepted version

Wireless dynamic charging technologies are becoming a promising alternative solution to plug-in ones as they allow on-the-move charging for electric vehicles. From a power network point of view, this type of charging makes electric vehicles a new type of loads--the moving loads. Such moving loads are different from the traditional loads as they may change their locations constantly in the grids. To study the effect of these moving loads on power distribution grids, this work focuses on the steady-state analysis of electrified roads equipped with wireless dynamic charging. In particular, the voltage profile and the long-term voltage stability of the electrified roads are considered. Unusual shapes of the voltage profile are observed such as the half-leaf veins for a one-way road and the harp-like shape for a two-way road. Voltage swings are also detected while the vehicles move in the two-way road configuration. As for the long-term voltage stability, continuation power flow is used to characterize the maximum length of a road as well as the maximum number of vehicles that the road can accommodate., 6 pages, 8 figures, accepted to present at eenergy19, ACM19, Phoenix, AZ, USA, June 2019

Peer-to-peer (P2P) energy trading allows prosumers to trade energy directly without intermediaries. To provide a payment system and record transaction information, public blockchain is designed to match the decentralized feature of the P2P market. The incentive for nodes outside the microgrid is removed but it is maintained for the prosumers within the microgrid. Therefore the number of miner competitors is limited to decrease the mining difficulty and its power consumption. Proof-of-Stake (PoS) consensus protocol defines the function of blockchain with its mining mechanism. Miners sacrifice part of their stake to compensate for the power losses and reduce the price gap from the traditional prosumer-to-grid trading (Feed-in-tariff). Moreover, the proposed model also contributes to increase the social welfare by improving producers’ income and consumers’ cost-saving through the designed pricing scheme, which eliminates the price gap between buying and selling. Successful mining is encouraged by rewards accordingly. A case study is introduced where a microgrid model with 27 prosumers is tested with the PoS public blockchain-based pricing scheme. The process of model implementation and smart contract creation are specifically demonstrated. Numerical results prove the feasibility and effectiveness of the proposed method.

The increase in distributed generation (DG) and variable load mandates system operators to perform decision-making considering uncertainties. This paper introduces a novel state-aware stochastic optimal power flow (SA-SOPF) problem formulation. The proposed SA-SOPF has objective to find a day-ahead base-solution that minimizes the generation cost and expectation of deviations in generation and node voltage set-points during real-time operation. We formulate SA-SOPF for a given affine policy and employ Gaussian process learning to obtain a distributionally robust (DR) affine policy for generation and voltage set-point change in real-time. In simulations, the GP-based affine policy has shown distributional robustness over three different uncertainty distributions for IEEE 14-bus system. The results also depict that the proposed SA-OPF formulation can reduce the expectation in voltage and generation deviation more than 60% in real-time operation with an additional day-ahead scheduling cost of 4.68% only for 14-bus system. For, in a 30-bus system, the reduction in generation and voltage deviation, the expectation is achieved to be greater than 90% for 1.195% extra generation cost. These results are strong indicators of possibility of achieving the day-ahead solution which lead to lower real-time deviation with minimal cost increase.

Electronic medical records (EMR) have emerged as an important source of data for research in medicine andinformation technology, as they contain much of valuable human medical knowledge in healthcare and patienttreatment. This paper tackles the problem of coreference resolution in Vietnamese EMRs. Unlike in English ones,in Vietnamese clinical texts, verbs are often used to describe disease symptoms. So we first define rules to annotateverbs as mentions and consider coreference between verbs and other noun or adjective mentions possible. Thenwe propose a support vector machine classifier on bag-of-words vector representation of mentions that takes intoaccount the special characteristics of Vietnamese language to resolve their coreference. The achieved F1 scoreon our dataset of real Vietnamese EMRs provided by a hospital in Ho Chi Minh city is 91.4%. To the best of ourknowledge, this is the first research work in coreference resolution on Vietnamese clinical texts.Keywords: Clinical text, support vector machine, bag-of-words vector, lexical similarity, unrestricted coreference

The incidence of chronic graft-versus-host disease (cGVHD) is on the rise and still the major cause of morbidity and mortality among patients after allogeneic hematopoietic stem cell transplantation (HCT). Both donor T and B cells contribute to the pathogenesis of cGVHD. Inducible T-cell co-stimulator (ICOS), a potent co-stimulatory receptor, plays a key role in T-cell activation and differentiation. Yet, how ICOS regulates the development of cGVHD is not well understood. Here, we investigated the role of ICOS in cGVHD pathogenesis using mice with germline or regulatory T cell (Treg)-specific ICOS deficiency. The recipients of ICOS−/− donor grafts had reduced cGVHD compared with wild-type controls. In recipients of ICOS−/− donor grafts, we observed significant reductions in donor T follicular helper (Tfh), Th17, germinal center B-cell, and plasma cell differentiation, coupled with lower antibody production. Interestingly, Tregs, including follicular regulatory T (Tfr) cells, were also impaired in the absence of ICOS. Using ICOS conditional knockout specific for Foxp3+ cells, we found that ICOS was indispensable for optimal survival and homeostasis of induced Tregs during cGVHD. Furthermore, administration of anti-ICOS alleviated cGVHD severity via suppressing T effector cells without affecting Treg generation. Taken together, ICOS promotes T- and B-cell activation and differentiation, which can promote cGVHD development; however, ICOS is critical for the survival and homeostasis of iTregs, which can suppress cGVHD. Hence, ICOS balances the development of cGVHD and could offer a potential target after allo-HCT in the clinic.

The slow development of green building (GB) practices in emerging economies has resulted in these countries remaining dominated by uncertified conventional buildings, which has caused negat...

We propose a scalable optimization framework for estimating convex inner approximations of the steady-state security sets. The framework is based on Brouwer fixed point theorem applied to a fixed-point form of the power flow equations. It establishes a certificate for the self-mapping of a polytope region constructed around a given feasible operating point. This certificate is based on the explicit bounds on the nonlinear terms that hold within the self-mapped polytope. The shape of the polytope is adapted to find the largest approximation of the steady-state security region. While the corresponding optimization problem is nonlinear and non-convex, every feasible solution found by local search defines a valid inner approximation. The number of variables scales linearly with the system size, and the general framework can naturally be applied to other nonlinear equations with affine dependence on inputs. Test cases, with the system sizes up to $1354$ buses, are used to illustrate the scalability of the approach. The results show that the approximated regions are not unreasonably conservative and that they cover substantial fractions of the true steady-state security regions for most medium-sized test cases., Comment: 11 pages, 5 figures

In modern power systems, the operating point, at which the demand and supply are balanced, may take different values due to changes in loads and renewable generation levels. Understanding the dynamics of stressed power systems with a range of operating points would be essential to assuring their reliable operation, and possibly allow higher integration of renewable resources. This letter introduces a non-traditional way to think about the stability assessment problem of power systems. Instead of estimating the set of initial states leading to a given operating condition, we characterize the set of operating conditions that a power grid converges to from a given initial state under changes in power injections and lines. We term this problem as "inverse stability," a problem which is rarely addressed in the control and systems literature, and hence, poorly understood. Exploiting quadratic approximations of the system's energy function, we introduce an estimate of the inverse stability region. Also, we briefly describe three important applications of the inverse stability notion: 1) robust stability assessment of power systems with respect to different renewable generation levels; 2) stability-constrained optimal power flow; and 3) stability-guaranteed corrective action design. ©2017 IEEE., MIT/Skoltech, Ministry of Education and Science of Russian Federation (Grant no.14.615.21.0001.), NSF (1508666), NSF (1550015)

High levels of penetration of distributed generation and aggressive reactive power compensation may result in the reversal of power flows in future distribution grids. The voltage stability of these operating conditions may be very different from the more traditional power consumption regime. This paper focused on demonstration of multistability phenomenon in radial distribution systems with reversed power flow, where multiple stable equilibria co-exist at the given set of parameters. The system may experience transitions between different equilibria after being subjected to disturbances such as short-term losses of distributed generation or transient faults. Convergence to an undesirable equilibrium places the system in an emergency or \textit{in extremis} state. Traditional emergency control schemes are not capable of restoring the system if it gets entrapped in one of the low voltage equilibria. Moreover, undervoltage load shedding may have a reverse action on the system and can induce voltage collapse. We propose a novel pulse emergency control strategy that restores the system to the normal state without any interruption of power delivery. The results are validated with dynamic simulations of IEEE $13$-bus feeder performed with SystemModeler software. The dynamic models can be also used for characterization of the solution branches via a novel approach so-called the admittance homotopy power flow method., Comment: 13 pages, 22 figures. IEEE Transactions on Smart Grid 2015, in press

In this paper, we address a development of a glove-based gesture recognition system for Vietnamese sign language. A sensor glove is attached ten flex sensors and one accelerometer. Here, flex sensors are used for sensing the curvature of fingers and the accelerometer is used in detecting a movement of a hand. Depending on the hand's postures, i.e., vertical, horizontal, and movement, sign language of letters of the Vietnamese alphabets can be divided to category 1, 2, and 3, respectively. Firstly, the hand's posture is recognized. Next, if the hand's posture belongs to either category 1 or category 2, a matching algorithm is used to detect a letter. If the posture belongs to category 3, a dynamic time warping algorithm is applied. The use of our system in recognizing Vietnamese sign language is illustrated. In addition, experimental results are provided.

Dynamic response of loads has a significant effect on system stability and directly determines the stability margin of the operating point. Inherent uncertainty and natural variability of load models make the stability assessment especially difficult and may compromise the security of the system. We propose a novel mathematical "robust stability" criterion for the assessment of small-signal stability of operating points. Whenever the criterion is satisfied for a given operating point, it provides mathematical guarantees that the operating point will be stable with respect to small disturbances for any dynamic response of the loads. The criterion can be naturally used for identification of operating regions secure from the occurrence of Hopf bifurcation. Several possible applications of the criterion are discussed, most importantly the concept of Robust Stability Assessment (RSA) that could be integrated in dynamic security assessment packages and used in contingency screening and other planning and operational studies., Comment: 16 pages, 20 figures, IEEE Transactions on Power Systems 2015, in press

This paper describes the use of multiple-input, multiple-output (MIMO) communication technology as a radio frequency (RF) sensor. We suggest some possible measures for determining how the changes in MIMO channel are related to objects moving through the MIMO channel. Initially, we examine the singular values of the channel matrix. We further demonstrate the effects of the signal-to-noise ratio (SNR) in conjunction with the target physical properties in the creation of eigenchannels. These eigenchannels represent the key factor in the ability of a MIMO system to perform as an effective sensor. Another important feature of MIMO technology is that it allows us to capture spatial information about the target, beyond the typical time and frequency information. Preliminary experimental results at 750 MHz demonstrate that targets can be detected and distinguished based on these simple measures. For example, a vehicular target is distinguishable from a person or groups of people. Our concept is closely related to a MIMO radar approach. However, a key difference is that we make use of the natural process of establishing a MIMO communication link rather than interrogate a specific physical region via a pulsed RF waveform. MIMO communications requires sounding of the physical environment and the creation of a channel matrix in order to maximize data throughput. We leverage this information about the area of interest already captured by the communication system. This allows the use of a MIMO system for both sensing and communication.

We have developed algorithms to automatically learn a detection map of a deployed sensor field for a virtual presence and extended defense (VPED) system without apriori knowledge of the local terrain. The VPED system is an unattended network of sensor pods, with each pod containing acoustic and seismic sensors. Each pod has the ability to detect and classify moving targets at a limited range. By using a network of pods we can form a virtual perimeter with each pod responsible for a certain section of the perimeter. The site's geography and soil conditions can affect the detection performance of the pods. Thus, a network in the field may not have the same performance as a network designed in the lab. To solve this problem we automatically estimate a network's detection performance as it is being installed at a site by a mobile deployment unit (MDU). The MDU will wear a GPS unit, so the system not only knows when it can detect the MDU, but also the MDU's location. In this paper, we demonstrate how to handle anisotropic sensor-configurations, geography, and soil conditions.

We have developed algorithms for a virtual presence and extended defense (VPED) system that automatically learns the detection map of a deployed sensor field without a-priori knowledge of the local terrain. The VPED system is a network of sensor pods, with each pod containing acoustic and seismic sensors. Each pod has a limited detection range, but a network of pods can form a virtual perimeter. The site's geography and soil conditions can affect the detection performance of the pods. Thus a network in the field may not have the same performance as a network designed in the lab. To solve this problem we automatically estimate a network's detection performance as it is being constructed. We demonstrate results using simulated and real data.

An improved biasing scheme for NMOS cascode structures for linear voltage regulators is proposed for mixed-signal communication systems. The proposed regulator achieves approximately 40 dB power supply rejection over wide frequency range, 15 mA load current regulation, and a regulated voltage temperature coefficient of 6 ppm/°C. This paper also presents a modified Gilbert cell SiGe-based HBT voltage reference circuit for the proposed regulator. The bandgap circuit demonstrates a simulated line regulation of 95 dB at DC, 70 dB at 1 MHz, and a reference voltage temperature coefficient of 2 ppm/°C. The proposed regulator has been designed for the IBM SiGe BiCMOS 0.35-μm process.

This study developed a distributed vehicle target detection and estimation capability using two algorithmic approaches designed to take advantage of the capabilities of networked sensor systems. The primary interest was on small, quiet vehicles, such as personally owned SUVs and light trucks. The first algorithm approach utilized arrayed sensor beamforming techniques. In addition, it demonstrated a capability to find locations of unknown roads by extending code developed by the Army Acoustic Center for Excellence at Picatinny Arsenal. The second approach utilized single (non-array) sensors and employed generalized correlation techniques. Modifications to both techniques were suggested that, if implemented, could yield robust methods for target classification and tracking using two different types of networked sensor systems.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper will describe an algorithm for detecting and classifying seismic and acoustic signals for unattended ground sensors. The algorithm must be computationally efficient and continuously process a data stream in order to establish whether or not a desired signal has changed state (turned-on or off). The paper will focus on describing a Fourier based technique that compares the running power spectral density estimate of the data to a predetermined signature in order to determine if the desired signal has changed state. How to establish the signature and the detection thresholds will be discussed as well as the theoretical statistics of the algorithm for the Gaussian noise case with results from simulated data. Actual seismic data results will also be discussed along with techniques used to reduce false alarms due to the inherent nonstationary noise environments found with actual data.

We have investigated a promising class of polyimide materials for both passive and active electro-optic devices, namely crosslinkable polyimides. These fluorinated polyimides are soluble in the imidized form and are both thermally and photo-crosslinkable leading to easy processability into waveguide structures and the possibility of stable electro-optic properties. We have fabricated channel and slab waveguides and investigated the mechanism of optical propagation loss using photothermal deflection spectroscopy and waveguide loss spectroscopy, and found the losses to arise from residual absorption due to the formation of charge transfer states. The absorption is inhibited by fluorination leading to propagation losses as low as 0.3 dB/cm in the near infrared. Because of the ability to photocrosslink, channel waveguides are fabricated using a simple wet-etch process. Channel waveguides so formed are observed to have no excess loss over slab structures. Solubility followed by thermal cross-linking allows the formation of multilayer structures. We have produced electro-optic polymers by doping with the nonlinear optical chromophores, DCM and DADC; and a process of concurrent poling and thermal crosslinking. Multilayer structures have been investigated and poling fields optimized in the active layer by doping the cladding with an anti-static agent. The high glass-transition temperature and cross-linking leads to very stable electro-optic properties. We are currently building electro-optic modulators based on these materials. Progress and results in this area also are reported.