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Jian Shi

Publications and source records attributed to Jian Shi.

At least 73 records · Page 4Linked to original sources
arXiv2020

Multi-year Long-term Load Forecast for Area Distribution Feeders based on Selective Sequence Learning

Long-term load forecast (LTLF) for area distribution feeders is one of the most critical tasks frequently performed in electric distribution utility companies. For a specific planning area, cost-effective system upgrades can only be planned out based on accurate feeder LTLF results. In our previous research, we established a unique sequence prediction method which has the tremendous advantage of combining area top-down, feeder bottom-up and multi-year historical data all together for forecast and achieved a superior performance over various traditional methods by real-world tests. However, the previous method only focused on the forecast of the next one-year. In our current work, we significantly improved this method: the forecast can now be extended to a multi-year forecast window in the future; unsupervised learning techniques are used to group feeders by their load composition features to improve accuracy; we also propose a novel selective sequence learning mechanism which uses Gated Recurrent Unit network to not only learn how to predict sequence values but also learn to select the best-performing sequential configuration for each individual feeder. The proposed method was tested on an actual urban distribution system in West Canada. It was compared with traditional methods and our previous sequence prediction method. It demonstrates the best forecasting performance as well as the possibility of using sequence prediction models for multi-year component-level load forecast.

eess.SY↗
arXiv2019

In-hand Sliding Regrasp with Spring-Sliding Compliance

We investigate in-hand regrasping by pushing an object against an external constraint and allowing sliding at the fingertips. Each fingertip is modeled as attached to a multidimensional spring mounted to a position-controlled anchor. Spring compliance maps contact forces to spring compressions, ensuring the fingers remain in contact, and sliding "compliance" governs the relationship between sliding motions and tangential contact forces. A spring-sliding compliant regrasp is achieved by controlling the finger anchor motions. We derive the fingertip sliding mechanics for multifingered sliding regrasps and analyze robust regrasping conditions in the presence of finger contact wrench uncertainties. The results are verified in simulation and experiment with a two-fingered sliding regrasp designed to maximize robustness of the operation.

cs.RO↗
arXiv2019

HyperService: Interoperability and Programmability Across Heterogeneous Blockchains

Blockchain interoperability, which allows state transitions across different blockchain networks, is critical functionality to facilitate major blockchain adoption. Existing interoperability protocols mostly focus on atomic token exchange between blockchains. However, as blockchains have been upgraded from passive distributed ledgers into programmable state machines (thanks to smart contracts), the scope of blockchain interoperability goes beyond just token exchange. In this paper, we present HyperService, the first platform that delivers interoperability and programmability across heterogeneous blockchains. HyperService is powered by two innovative designs: (i) a developer-facing programming framework that allows developers to build cross-chain applications in a unified programming model; and (ii) a secure blockchain-facing cryptography protocol that provably realizes those applications on blockchains. We implement a prototype of HyperService in about 35,000 lines of code to demonstrate its practicality. Our experiment results show that HyperService imposes reasonable latency, in order of seconds, on the end-to-end execution of cross-chain applications

cs.CR↗
arXiv2019

Electron mobility in graphene without invoking the Dirac equation

The Dirac point and linear band structure in Graphene bestow it with remarkable electronic and optical properties, a subject of intense ongoing research. Explanations of high electronic mobility in graphene, often invoke the masslessness of electrons based on the effective relativistic Dirac-equation behavior, which are inaccessible to most undergraduate students and are not intuitive for non-physics researchers unfamiliar with relativity. Here, we show how to use only basic concepts from semiconductor theory and the linear band structure of graphene to explain its unusual effective mass and mobility, and compare them with conventional metals and semiconductors. We discuss the more intuitive concept of transverse effective mass that emerges naturally from these basic derivations, which approaches zero in the limit of undoped graphene at low temperature and is responsible for its extremely high mobility.

cond-mat.mes-hall↗
arXiv2019

Smoothing Spline Semiparametric Density Models

Density estimation plays a fundamental role in many areas of statistics and machine learning. Parametric, nonparametric and semiparametric density estimation methods have been proposed in the literature. Semiparametric density models are flexible in incorporating domain knowledge and uncertainty regarding the shape of the density function. Existing literature on semiparametric density models is scattered and lacks a systematic framework. In this paper, we consider a unified framework based on the reproducing kernel Hilbert space for modeling, estimation, computation and theory. We propose general semiparametric density models for both a single sample and multiple samples which include many existing semiparametric density models as special cases. We develop penalized likelihood based estimation methods and computational methods under different situations. We establish joint consistency and derive convergence rates of the proposed estimators for both the finite dimensional Euclidean parameters and an infinite-dimensional functional parameter. We validate our estimation methods empirically through simulations and an application.

math.ST↗
arXiv2018

Lattice-mismatched semiconductor heterostructures

Semiconductor heterostructure is a critical building block for modern semiconductor devices. However, forming semiconductor heterostructures of lattice-mismatch has been a great challenge for several decades. Epitaxial growth is infeasible to form abrupt heterostructures with large lattice-mismatch while mechanical-thermal bonding results in a high density of interface defects and therefore severely limits device applications. Here we show an ultra-thin oxide-interfaced approach for the successful formation of lattice-mismatched semiconductor heterostructures. Following the depiction of a theory on the role of interface oxide in forming the heterostructures, we describe experimental demonstrations of Ge/Si (diamond lattices), Si/GaAs (zinc blende lattice), GaAs/GaN (hexagon lattice), and Si/GaN heterostructures. Extraordinary electrical performances in terms of ideality factor, current on/off ratio, and reverse breakdown voltage are measured from p-n diodes fabricated from the four types of heterostructures, significantly outperforming diodes derived from other methods. Our demonstrations indicate the versatility of the ultra-thin-oxide-interface approach in forming lattice-mismatched heterostructures, open up a much larger possibility for material combinations for heterostructures, and pave the way toward broader applications in electronic and optoelectronic realms.

physics.app-ph↗
arXiv2018

A Relaxation-based Network Decomposition Algorithm for Parallel Transient Stability Simulation with Improved Convergence

Transient stability simulation of a large-scale and interconnected electric power system involves solving a large set of differential algebraic equations (DAEs) at every simulation time-step. With the ever-growing size and complexity of power grids, dynamic simulation becomes more time-consuming and computationally difficult using conventional sequential simulation techniques. To cope with this challenge, this paper aims to develop a fully distributed approach intended for implementation on High Performance Computer (HPC) clusters. A novel, relaxation-based domain decomposition algorithm known as Parallel-General-Norton with Multiple-port Equivalent (PGNME) is proposed as the core technique of a two-stage decomposition approach to divide the overall dynamic simulation problem into a set of subproblems that can be solved concurrently to exploit parallelism and scalability. While the convergence property has traditionally been a concern for relaxation-based decomposition, an estimation mechanism based on multiple-port network equivalent is adopted as the preconditioner to enhance the convergence of the proposed algorithm. The proposed algorithm is illustrated using rigorous mathematics and validated both in terms of speed-up and capability. Moreover, a complexity analysis is performed to support the observation that PGNME scales well when the size of the subproblems are sufficiently large.

cs.DC↗
arXiv2016

Learning Non-Lambertian Object Intrinsics across ShapeNet Categories

We consider the non-Lambertian object intrinsic problem of recovering diffuse albedo, shading, and specular highlights from a single image of an object. We build a large-scale object intrinsics database based on existing 3D models in the ShapeNet database. Rendered with realistic environment maps, millions of synthetic images of objects and their corresponding albedo, shading, and specular ground-truth images are used to train an encoder-decoder CNN. Once trained, the network can decompose an image into the product of albedo and shading components, along with an additive specular component. Our CNN delivers accurate and sharp results in this classical inverse problem of computer vision, sharp details attributed to skip layer connections at corresponding resolutions from the encoder to the decoder. Benchmarked on our ShapeNet and MIT intrinsics datasets, our model consistently outperforms the state-of-the-art by a large margin. We train and test our CNN on different object categories. Perhaps surprising especially from the CNN classification perspective, our intrinsics CNN generalizes very well across categories. Our analysis shows that feature learning at the encoder stage is more crucial for developing a universal representation across categories. We apply our synthetic data trained model to images and videos downloaded from the internet, and observe robust and realistic intrinsics results. Quality non-Lambertian intrinsics could open up many interesting applications such as image-based albedo and specular editing.

cs.CV↗
arXiv2016

Spinodal Decomposition-Enabled Halide Perovskite Double Heterostructure with Reduced Fröhlich Electron-Phonon Coupling

Epitaxial III-V semiconductor heterostructures are key components in modern microelectronics, electro-optics and optoelectronics. With superior semiconducting properties, halide perovskite materials are rising as promising candidates for coherent heterostructure devices. In this report, spinodal decomposition is proposed and experimentally implemented to produce epitaxial double heterostructures in halide perovskite system. Pristine epitaxial mixed halide perovskites rods and films were synthesized via Van der Waals epitaxy by chemical vapor deposition method. At room temperature, photon was applied as a knob to regulate the kinetics of spinodal decomposition and classic coarsening. By this approach, halide perovskite double heterostructures were created carrying epitaxial interfaces and outstanding optical properties. Reduced Fröhlich electron-phonon coupling was discovered in coherent halide double heterostructure, which is hypothetically attributed to the classic phonon confinement effect widely existing in III-V double heterostructures. The ability to develop coherent double heterostructures in halide perovskites paves an avenue to exploring halide perovskite-based quantum wells and superlattices for high-performance and low-cost optoelectronics, electro-optics and microelectronics.

cond-mat.mtrl-sci↗
arXiv2016

Injection method of barrier bucket supported by off-aligned electron cooling for CRing of HIAF

A new accelerator complex, HIAF (the High Intensity Heavy Ion Accelerator Facility), has been approved in China. It is designed to provide intense primary and radioactive ion beams for research in high energy density physics, nuclear physics, atomic physics as well as other applications. In order to achieve a high intensity of up to 5e11 ppp 238U34+, the Compression Ring (CRing) needs to stack more than 5 bunches transferred from the Booster Ring (BRing). However, the normal bucket to bucket injection scheme can only achieve an intensity gain of 2, so an injection method, fixed barrier bucket (BB) supported by electron cooling, is proposed. To suppress the severe space charge effect during the stacking process, off-alignment is adopted in the cooler to control the transverse emittance. In this paper, simulation and optimization with the BETACOOL program are presented.

physics.acc-ph↗
arXiv2015

Two-Dimensional Van der Waals Epitaxy Kinetics in a Three-Dimensional Perovskite Halide

The exploration of emerging materials physics and prospective applications of two-dimensional materials greatly relies on the growth control of their thickness, phases, morphologies and film-substrate interactions. Though substantial progresses have been made for the development of two-dimensional films from conventional layered bulky materials, particular challenges remain on obtaining ultrathin, single crystalline, dislocation-free films from intrinsically non-Van der Waals-type three-dimensional materials. In this report, with the successful demonstration of single crystalline ultrathin large scale perovskite halide material, we reveal and identify the favorable role of weak Van der Waals film-substrate interaction on the nucleation and growth of the two-dimensional morphology out of non-layered materials compared to conventional epitaxy. We also show how the bonding nature of the three-dimensional material itself affects the kinetic energy landscape of ultrathin films growth. By studying the formation of fractal perovskites assisted with Monte Carlo simulations, we demonstrate that the competition between the Van der Waals diffusion and surface free energy of the perovskite leads to film thickening, suggesting extra strategies such as surface passivation may be needed for the growth of monolayer and a few layers films.

cond-mat.mtrl-sci↗
arXiv2015

Closed orbit correction of HIMM synchrotron

The correction of closed orbit has great influence on the operation of synchrotron. The design of correction system is one significant component of lattice design. It is suggested to set BPMs at the peaks of betatron oscillation. The correctors need to be located at the positions where \b{eta} function is large or the sources of large errors. In the simulation of the closed orbit correction of HIMM synchrotron, one important reason affecting the result of horizontal correction is the longitudinal alignment error of dipole magnet. It is advisable to decrease this kind of alignment error while the deflection angle of dipole magnet is large.

physics.acc-ph↗
arXiv2014

Neuromimetic Circuits with Synaptic Devices based on Strongly Correlated Electron Systems

Strongly correlated electron systems such as the rare-earth nickelates (RNiO3, R = rare-earth element) can exhibit synapse-like continuous long term potentiation and depression when gated with ionic liquids; exploiting the extreme sensitivity of coupled charge, spin, orbital, and lattice degrees of freedom to stoichiometry. We present experimental real-time, device-level classical conditioning and unlearning using nickelate-based synaptic devices in an electronic circuit compatible with both excitatory and inhibitory neurons. We establish a physical model for the device behavior based on electric-field driven coupled ionic-electronic diffusion that can be utilized for design of more complex systems. We use the model to simulate a variety of associate and non-associative learning mechanisms, as well as a feedforward recurrent network for storing memory. Our circuit intuitively parallels biological neural architectures, and it can be readily generalized to other forms of cellular learning and extinction. The simulation of neural function with electronic device analogues may provide insight into biological processes such as decision making, learning and adaptation, while facilitating advanced parallel information processing in hardware.

cond-mat.str-el↗
arXiv2013

Electrostatic gating of metallic and insulating phases in SmNiO3 ultrathin films

The correlated electron system SmNiO3 exhibits a metal-insulator phase transition at 130 °C. Using an ionic liquid as an electric double layer (EDL) gate on three-terminal ultrathin SmNiO3 devices, we investigate gate control of the channel resistance and transition temperature. Resistance reduction is observed across both insulating and metallic phases with ~25% modulation at room temperature. We show that resistance modulation is predominantly due to electrostatic charge accumulation and not electrochemical doping by control experiments in inert and air en-vironments. We model the resistance behavior and estimate the accumulated sheet density (~1-2 x 10^14 cm^-2) and EDL capacitance (~12 μF/cm^2).

cond-mat.mtrl-sci↗
arXiv2011

Characterizations of operator order for k strictly positive operators

Let $A_{i}\ (i=1, 2, ..., k)$ be bounded linear operators on a Hilbert space. This paper aims to show characterizations of operator order $A_{k}\geq A_{k-1}\geq...\geq A_{2}\geq A_{1}>0$ in terms of operator inequalities. Afterwards, an application of the characterizations is given to operator equalities due to Douglas's majorization and factorization theorem.

math.FA↗