Showing papers by "Northeastern University (China) published in 2021"

New CTEQ global analysis of quantum chromodynamics with high-precision data from the LHC

Abstract: We present the new parton distribution functions (PDFs) from the CTEQ-TEA collaboration, obtained using a wide variety of high-precision Large Hadron Collider (LHC) data, in addition to the combined HERA I+II deep-inelastic scattering dataset, along with the datasets present in the CT14 global QCD analysis. New LHC measurements in single-inclusive jet production with the full rapidity coverage, as well as production of Drell-Yan pairs, top-quark pairs, and high-pT Z bosons, are included to achieve the greatest sensitivity to the PDFs. The parton distributions are determined at next-to-leading order and next-to-next-to-leading order, with each of these PDFs accompanied by error sets determined using the Hessian method. Fast PDF survey techniques, based on the Hessian representation and the Lagrange multiplier method, are used to quantify the preference of each data set to quantities such as αs(mZ), and the gluon and strange quark distributions. We designate the main resulting PDF set as CT18. The ATLAS 7 TeV precision W/Z data are not included in CT18, due to their tension with other datasets in the global fit. Alternate PDF sets are generated including the ATLAS precision 7 TeV W/Z data (CT18A), a new scale choice for low-x DIS data (CT18X), or all of the above with a slightly higher choice for the charm mass (CT18Z). Theoretical calculations of standard candle cross sections at the LHC (such as the gg fusion Higgs boson cross section) are presented.

Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties

Abstract: This article addresses the adaptive event-triggered neural control problem for nonaffine pure-feedback nonlinear multiagent systems with dynamic disturbance, unmodeled dynamics, and dead-zone input. Radial basis function neural networks are applied to approximate the unknown nonlinear function. A dynamic signal is constructed to deal with the design difficulties in the unmodeled dynamics. Moreover, to reduce the communication burden, we propose an event-triggered strategy with a varying threshold. Based on the Lyapunov function method and adaptive neural control approach, a novel event-triggered control protocol is constructed, which realizes that the outputs of all followers converge to a neighborhood of the leader’s output and ensures that all signals are bounded in the closed-loop system. An illustrative simulation example is applied to verify the usefulness of the proposed algorithms.

The Role of Imaging in the Detection and Management of COVID-19: A Review

Abstract: Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading rapidly around the world, resulting in a massive death toll. Lung infection or pneumonia is the common complication of COVID-19, and imaging techniques, especially computed tomography (CT), have played an important role in diagnosis and treatment assessment of the disease. Herein, we review the imaging characteristics and computing models that have been applied for the management of COVID-19. CT, positron emission tomography - CT (PET/CT), lung ultrasound, and magnetic resonance imaging (MRI) have been used for detection, treatment, and follow-up. The quantitative analysis of imaging data using artificial intelligence (AI) is also explored. Our findings indicate that typical imaging characteristics and their changes can play crucial roles in the detection and management of COVID-19. In addition, AI or other quantitative image analysis methods are urgently needed to maximize the value of imaging in the management of COVID-19.

Recent research and development status of laser cladding: A review

Abstract: In industries such as aerospace, petrochemistry and automobile, many parts of different machines are under environment which shows high temperature and high pressure, and have their proneness to wear and corrosion. Therefore, the wear resistibility and stability under high temperature need to be further improved. Nowadays, Laser cladding (LC) is widely used in machine parts repairing and functional coating due to its advantages such as lower dilution rate, small heat-affected zone and good metallurgical bonding between coating and substrate. In this paper, LC is introduced in detail from aspects of process simulation, monitoring and parameter optimization. At the same time, the paper gives a comprehensive review over LC material system as high entropy alloys (HEAs), amorphous alloy and single crystal alloy have been gradually showing their advantages over traditional metal materials in LC. In addition, the applications of LC in functional coatings and in maintenance of machine parts are also outlined. Also, the existing problems and the development trend of LC is discussed then.

Enhancing Learning Efficiency of Brain Storm Optimization via Orthogonal Learning Design

Abstract: In brain storm optimization (BSO), the convergent operation utilizes a clustering strategy to group the population into multiple clusters, and the divergent operation uses this cluster information to generate new individuals. However, this mechanism is inefficient to regulate the exploration and exploitation search. This article first analyzes the main factors that influence the performance of BSO and then proposes an orthogonal learning framework to improve its learning mechanism. In this framework, two orthogonal design (OD) engines (i.e., exploration OD engine and exploitation OD engine) are introduced to discover and utilize useful search experiences for performance improvements. In addition, a pool of auxiliary transmission vectors with different features is maintained and their biases are also balanced by the OD decision mechanism. Finally, the proposed algorithm is verified on a set of benchmarks and is adopted to resolve the quantitative association rule mining problem considering the support, confidence, comprehensibility, and netconf. The experimental results show that the proposed approach is very powerful in optimizing complex functions. It not only outperforms previous versions of the BSO algorithm but also outperforms several famous OD-based algorithms.

Electron-ion collider in China

Abstract: Lepton scattering is an established ideal tool for studying inner structure of small particles such as nucleons as well as nuclei. As a future high energy nuclear physics project, an Electron-ion collider in China (EicC) has been proposed. It will be constructed based on an upgraded heavy-ion accelerator, High Intensity heavy-ion Accelerator Facility (HIAF) which is currently under construction, together with a new electron ring. The proposed collider will provide highly polarized electrons (with a polarization of ∼80%) and protons (with a polarization of ∼70%) with variable center of mass energies from 15 to 20 GeV and the luminosity of (2–3) × 10$^{33}$ cm$^{−2}$ · s$^{−1}$. Polarized deuterons and Helium-3, as well as unpolarized ion beams from Carbon to Uranium, will be also available at the EicC.The main foci of the EicC will be precision measurements of the structure of the nucleon in the sea quark region, including 3D tomography of nucleon; the partonic structure of nuclei and the parton interaction with the nuclear environment; the exotic states, especially those with heavy flavor quark contents. In addition, issues fundamental to understanding the origin of mass could be addressed by measurements of heavy quarkonia near-threshold production at the EicC. In order to achieve the above-mentioned physics goals, a hermetical detector system will be constructed with cutting-edge technologies.This document is the result of collective contributions and valuable inputs from experts across the globe. The EicC physics program complements the ongoing scientific programs at the Jefferson Laboratory and the future EIC project in the United States. The success of this project will also advance both nuclear and particle physics as well as accelerator and detector technology in China.[graphic not available: see fulltext]

An Adaptive Localized Decision Variable Analysis Approach to Large-Scale Multiobjective and Many-Objective Optimization.

Abstract: This article proposes an adaptive localized decision variable analysis approach under the decomposition-based framework to solve the large-scale multiobjective and many-objective optimization problems (MaOPs). Its main idea is to incorporate the guidance of reference vectors into the control variable analysis and optimize the decision variables using an adaptive strategy. Especially, in the control variable analysis, for each search direction, the convergence relevance degree of each decision variable is measured by a projection-based detection method. In the decision variable optimization, the grouped decision variables are optimized with an adaptive scalarization strategy, which is able to adaptively balance the convergence and diversity of the solutions in the objective space. The proposed algorithm is evaluated with a suite of test problems with 2-10 objectives and 200-1000 variables. Experimental results validate the effectiveness and efficiency of the proposed algorithm on the large-scale multiobjective and MaOPs.

Universal Domain Adaptation in Fault Diagnostics With Hybrid Weighted Deep Adversarial Learning

Abstract: In the past years, the practical cross-domain machinery fault diagnosis problems have been attracting growing attention, where the training and testing data are collected from different operating conditions. The recent advances in closed-set domain adaptation have well addressed the basic problem where the fault mode sets are identical in the source and target domains. While some attempts have also been made on the partial and open-set domain adaptations, no prior information of the target-domain fault modes can be usually available in the real industries, that forms a challenging problem in transfer learning. This article proposes a universal domain adaptation method for fault diagnosis, where no explicit assumption is made on the target label set. A hybrid approach with source class-wise and target instance-wise weighting mechanism is proposed for selective adaptation. By using additional outlier identifier, the proposed method can automatically recognize the unknown fault modes while achieving class-level alignments for the shared health states, without knowing the target label set. Experiments on two rotating machine datasets validate the proposed method, which is promising for practical applications under strong data uncertainties.

Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

Abstract: Group scheduling problems have attracted much attention owing to their many practical applications. This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time, release time, and due time. It is originated from an important industrial process, i.e., wire rod and bar rolling process in steel production systems. Two objective functions, i.e., the number of late jobs and total setup time, are minimized. A mixed integer linear program is established to describe the problem. To obtain its Pareto solutions, we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods, i.e., an insertion-based local search and an iterated greedy algorithm. The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers. Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.

Open-Set Domain Adaptation in Machinery Fault Diagnostics Using Instance-Level Weighted Adversarial Learning

Abstract: Data-driven machinery fault diagnosis methods have been successfully developed in the past decades. However, the cross-domain diagnostic problems have not been well addressed, where the training and testing data are collected under different operating conditions. Recently, domain adaptation approaches have been popularly used to bridge this gap, which extract domain-invariant features for diagnostics. Despite the effectiveness, most existing methods assume the label spaces of training and testing data are identical that indicates the fault mode sets are the same in different scenarios. In practice, new fault modes usually occur in testing, which makes the conventional methods focusing on marginal distribution alignment less effective. In order to address this problem, a deep learning-based open-set domain adaptation method is proposed in this study. Adversarial learning is introduced to extract generalized features, and an instance-level weighted mechanism is proposed to reflect the similarities of testing samples with known health states. The unknown fault mode can be effectively identified, and the known states can be also recognized. Entropy minimization scheme is further adopted to improve generalization. Experiments on two practical rotating machinery datasets validate the proposed method. The results suggest the proposed method is promising for open-set domain adaptation problems, which largely enhances the applicability of data-driven approaches in the real industries.

Observer-Based Finite-Time Adaptive Fuzzy Control for Nontriangular Nonlinear Systems With Full-State Constraints

Abstract: This article focuses on finite-time adaptive fuzzy output-feedback control for a class of nontriangular nonlinear systems with full-state constraints and unmeasurable states. Fuzzy-logic systems and the fuzzy state observer are employed to approximate uncertain nonlinear functions and estimate the unmeasured states, respectively. In order to solve the algebraic loop problem generated by the nontriangular structure, a variable separation approach based on the property of the fuzzy basis function is utilized. The barrier Lyapunov function is incorporated into each step of backstepping, and the condition of the state constraint is satisfied. The dynamic surface technique with an auxiliary first-order linear filter is applied to avoid the problem of an “explosion of complexity.” Based on the finite-time stability theory, an adaptive fuzzy controller is constructed to guarantee that all signals in the closed-loop system are bounded, the tracking error converges to a small neighborhood of the origin in a finite time, and all states are ensured to remain in the predefined sets. Finally, the simulation results reveal the effectiveness of the proposed control design.

Ammonium-Ion Storage Using Electrodeposited Manganese Oxides.

Abstract: NH4 + ions as charge carriers show potential for aqueous rechargeable batteries. Studied here for the first time is the NH4 + -storage chemistry using electrodeposited manganese oxide (MnOx ). MnOx experiences morphology and phase transformations during charge/discharge in dilute ammonium acetate (NH4 Ac) electrolyte. The NH4 Ac concentration plays an important role in NH4 + storage for MnOx . The transformed MnOx with a layered structure delivers a high specific capacity (176 mAh g-1 ) at a current density of 0.5 A g-1 , and exhibits good cycling stability over 10 000 cycles in 0.5 M NH4 Ac, outperforming the state-of-the-art NH4 + hosting materials. Experimental results suggest a solid-solution behavior associated with NH4 + migration in layered MnOx . Spectroscopy studies and theoretical calculations show that the reversible NH4 + insertion/deinsertion is accompanied by hydrogen-bond formation/breaking between NH4 + and the MnOx layers. These findings provide a new prototype (i.e., layered MnOx ) for NH4 + -based energy storage and contributes to the fundamental understanding of the NH4 + -storage mechanism for metal oxides.

A flexible ultrasensitive optoelectronic sensor array for neuromorphic vision systems

Abstract: The challenges of developing neuromorphic vision systems inspired by the human eye come not only from how to recreate the flexibility, sophistication, and adaptability of animal systems, but also how to do so with computational efficiency and elegance. Similar to biological systems, these neuromorphic circuits integrate functions of image sensing, memory and processing into the device, and process continuous analog brightness signal in real-time. High-integration, flexibility and ultra-sensitivity are essential for practical artificial vision systems that attempt to emulate biological processing. Here, we present a flexible optoelectronic sensor array of 1024 pixels using a combination of carbon nanotubes and perovskite quantum dots as active materials for an efficient neuromorphic vision system. The device has an extraordinary sensitivity to light with a responsivity of 5.1x10(7)A/W and a specific detectivity of 2x10(16) Jones, and demonstrates neuromorphic reinforcement learning by training the sensor array with a weak light pulse of 1 mu W/cm(2). To emulate nature biological processing, highly-integrated ultra-sensitive artificial neuromorphic system is highly desirable. Here, the authors report flexible sensor array of 1024 pixels using combination of carbon nanotubes and perovskite QDs as active matetials, achieving highly responsive device for reinforcement learning.

Oxalate enhanced synergistic removal of chromium(VI) and arsenic(III) over ZnFe2O4/g-C3N4: Z-scheme charge transfer pathway and photo-Fenton like reaction

Abstract: In this work, direct Z-Scheme photocatalyst (ZnFe2O4/g-C3N4) was employed to purify Cr(VI) and As(III) in wastewater. Results showed that Cr(VI) and As(III) was completely removed in a wide pH range of 3–7 under visible light irradiation. During photo reaction, oxalate enhanced the generation of Fe(II) and CO2−, which were beneficial for formation of OH and O2−. In photocatalytic system, Fe(II), photogenerated electron, as well as CO2− contributed to Cr(VI) reduction, OH and O2− were responsible for treatment of As(III). Moreover, ZnFe2O4/g-C3N4 was easily recycled by magnetic separation, and presented an excellent reuse ability in treatment of Cr(VI) and As(III).

Perovskite-structured LaCoO3 modified ZnO gas sensor and investigation on its gas sensing mechanism by first principle

Abstract: In this work, LaCoO3 (LCO) nanoparticles were synthesized by sol-gel method and modified on the surface of ZnO. The LaCoO3-modified ZnO (LCO/ZnO) nanometer flake materials were successfully prepared, the microstructure, surface properties and internal composition of which were analyzed by various characterization tools. Compared with the traditional ZnO sensor, LCO/ZnO sensor has been greatly improved in terms of gas-sensitive response, response time and recovery time. At the optimal operating temperature of 320 °C, the maximum response of LCO/ZnO sensor to 100 ppm ethanol gas can reach 55, which is 6 times higher than that of pure ZnO sensor. Meanwhile, the response time and recovery time of LCO/ZnO sensor were reduced to 2.8 and 9.7 s, respectively. All the results demonstrate that LCO is an excellent catalyst for improving the gas-sensitive performance of metal oxide semiconductor sensors. The first principle was used to analyze the surface properties, and study the sensitization mechanism of LCO in detail from the adsorption process of surface oxygen, heterojunction action and LCO catalytic oxidation process for ethanol sensing. The improvement of the sensing performance of LCO/ZnO sensor was attributed to the increase of surface adsorbed oxygen content and the strong catalytic oxidation activity of LCO.

A high capacity small molecule quinone cathode for rechargeable aqueous zinc-organic batteries.

Abstract: Rechargeable aqueous zinc-organic batteries are promising energy storage systems with low-cost aqueous electrolyte and zinc metal anode. The electrochemical properties can be systematically adjusted with molecular design on organic cathode materials. Herein, we use a symmetric small molecule quinone cathode, tetraamino-p-benzoquinone (TABQ), with desirable functional groups to protonate and accomplish dominated proton insertion from weakly acidic zinc electrolyte. The hydrogen bonding network formed with carbonyl and amino groups on the TABQ molecules allows facile proton conduction through the Grotthuss-type mechanism. It guarantees activation energies below 300 meV for charge transfer and proton diffusion. The TABQ cathode delivers a high capacity of 303 mAh g−1 at 0.1 A g−1 in a zinc-organic battery. With the increase of current density to 5 A g−1, 213 mAh g−1 capacity is still preserved with stable cycling for 1000 times. Our work proposes an effective approach towards high performance organic electrode materials. The flexible structural design of organic materials make them promising candidates for cathode in rechargeable batteries. Here, the authors report a tetraamino-p-benzoquinone cathode which realizes facile proton conduction by the Grotthuss-type mechanism and shows excellent electrochemical performance.