Showing papers by "Northeastern University (China) published in 2020"

Present and Future of Surface-Enhanced Raman Scattering

Abstract: The discovery of the enhancement of Raman scattering by molecules adsorbed on nanostructured metal surfaces is a landmark in the history of spectroscopic and analytical techniques. Significant experimental and theoretical effort has been directed toward understanding the surface-enhanced Raman scattering (SERS) effect and demonstrating its potential in various types of ultrasensitive sensing applications in a wide variety of fields. In the 45 years since its discovery, SERS has blossomed into a rich area of research and technology, but additional efforts are still needed before it can be routinely used analytically and in commercial products. In this Review, prominent authors from around the world joined together to summarize the state of the art in understanding and using SERS and to predict what can be expected in the near future in terms of research, applications, and technological development. This Review is dedicated to SERS pioneer and our coauthor, the late Prof. Richard Van Duyne, whom we lost during the preparation of this article.

An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features

Abstract: A complete defect detection task aims to achieve the specific class and precise location of each defect in an image, which makes it still challenging for applying this task in practice. The defect detection is a composite task of classification and location, leading to related methods is often hard to take into account the accuracy of both. The implementation of defect detection depends on a special detection data set that contains expensive manual annotations. In this paper, we proposed a novel defect detection system based on deep learning and focused on a practical industrial application: steel plate defect inspection. In order to achieve strong classification ability, this system employs a baseline convolution neural network (CNN) to generate feature maps at each stage, and then the proposed multilevel feature fusion network (MFN) combines multiple hierarchical features into one feature, which can include more location details of defects. Based on these multilevel features, a region proposal network (RPN) is adopted to generate regions of interest (ROIs). For each ROI, a detector, consisting of a classifier and a bounding box regressor, produces the final detection results. Finally, we set up a defect detection data set NEU-DET for training and evaluating our method. On the NEU-DET, our method achieves 74.8/82.3 mAP with baseline networks ResNet34/50 by using 300 proposals. In addition, by using only 50 proposals, our method can detect at 20 ft/s on a single GPU and reach 92% of the above performance, hence the potential for real-time detection.

A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis.

Abstract: Confounding variation, such as batch effects, are a pervasive issue in single-cell RNA sequencing experiments. While methods exist for aligning cells across batches, it is yet unclear how to correct for other types of confounding variation which may be observed at the subject level, such as age and sex, and at the cell level, such as library size and other measures of cell quality. On the specific problem of batch alignment, many questions still persist despite recent advances: Existing methods can effectively align batches in low-dimensional representations of cells, yet their effectiveness in aligning the original gene expression matrices is unclear. Nor is it clear how batch correction can be performed alongside data denoising, the former treating technical biases due to experimental stratification while the latter treating technical variation due inherently to the random sampling that occurs during library construction and sequencing. Here, we propose SAVERCAT, a method for dimension reduction and denoising of single-cell gene expression data that can flexibly adjust for arbitrary observed covariates. We benchmark SAVERCAT against existing single-cell batch correction methods and show that while it matches the best of the field in low-dimensional cell alignment, it significantly improves upon existing methods on the task of batch correction in the high-dimensional expression matrix. We also demonstrate the ability of SAVERCAT to effectively integrate batch correction and denoising through a data down-sampling experiment. Finally, we apply SAVERCAT to a single cell study of Alzheimer’s disease where batch is confounded with the contrast of interest, and demonstrate how adjusting for covariates other than batch allows for more interpretable analysis.

Review of ultrasonic vibration-assisted machining in advanced materials

Abstract: Compared to conventional machining (CM), ultrasonic vibration-assisted machining (UVAM) with high-frequency and small-amplitude has exhibited good cutting performances for advanced materials. In recent years, advances in ultrasonic generator, ultrasonic transducer, and horn structures have led to the rapid progress in the development of UVAM. Following this trend, numerous new design requirements and theoretical concepts have been proposed and studied successively, however, very few studies have been conducted from a comprehensive perspective. To address this gap in the literature and understanding the development trend of UVAM, a critical overview of UVAM is presented in this study, covering different vibration-assisted machining styles, device architectures, and theoretical analysis. This overview covers the evolution of typical hardware systems used to achieve vibratory motions from the one-dimensional UVAM to three-dimensional UVAM, the discussion of cutting characteristics with periodic separation between the tools and workpiece and the analysis of processing properties. Challenges for UVAM include ultrasonic vibration systems with high power, large amplitude, and high efficiency, as well as theoretical research on the dynamics and cutting characteristics of UVAM. Consequently, based on the current limitations and challenges, device improvement and theoretical breakthrough play a significant role in future research on UVAM.

Superhigh Electromagnetic Interference Shielding of Ultrathin Aligned Pristine Graphene Nanosheets Film

Abstract: Ultrathin, lightweight, high-strength, and thermally conductive electromagnetic interference (EMI) shielding materials with high shielding effectiveness (SE) are highly desired for next-generation portable and wearable electronics. Pristine graphene (PG) has a great potential to meet all the above requirements, but the poor processability of PG nanosheets hinders its applications. Here, efficient synthesis of highly aligned laminated PG films and nacre-like PG/polymer composites with a superhigh PG loading up to 90 wt% by a scanning centrifugal casting method is reported. Due to the PG-nanosheets-alignment-induced high electrical conductivity and multiple internal reflections, such films show superhigh EMI SE comparable to the reported best synthetic material, MXene films, at an ultralow thickness. An EMI SE of 93 dB is obtained for the PG film at a thickness of ≈100 µm, and 63 dB is achieved for the PG/polyimide composite film at a thickness of ≈60 µm. Furthermore, such PG-nanosheets-based films show much higher mechanical strength (up to 145 MPa) and thermal conductivity (up to 190 W m-1 K-1 ) than those of their MXene counterparts. These excellent comprehensive properties, along with ease of mass production, pave the way for practical applications of PG nanosheets in EMI shielding.

Bimetallic Sulfide Sb2S3@FeS2 Hollow Nanorods as High-Performance Anode Materials for Sodium-Ion Batteries

Abstract: Constructing heterojunction and introducing interfacial interaction by designing ideal structures have the inherent advantages of optimizing electronic structure and macroscopic mechanical property...

Porous spherical NiO@NiMoO4@PPy nanoarchitectures as advanced electrochemical pseudocapacitor materials

Abstract: In this work, a rational design and construction of porous spherical NiO@NiMoO4 wrapped with PPy was reported for the application of high-performance supercapacitor (SC). The results show that the NiMoO4 modification changes the morphology of NiO, and the hollow internal morphology combined with porous outer shell of NiO@NiMoO4 and NiO@NiMoO4@PPy hybrids shows an increased specific surface area (SSA), and then promotes the transfer of ions and electrons. The shell of NiMoO4 and PPy with high electronic conductivity decreases the charge-transfer reaction resistance of NiO, and then improves the electrochemical kinetics of NiO. At 20 A g−1, the initial capacitances of NiO, NiMoO4, NiO@NiMoO4 and NiO@NiMoO4@PPy are 456.0, 803.2, 764.4 and 941.6 F g−1, respectively. After 10,000 cycles, the corresponding capacitances are 346.8, 510.8, 641.2 and 904.8 F g−1, respectively. Especially, the initial capacitance of NiO@NiMoO4@PPy is 850.2 F g−1, and remains 655.2 F g−1 with a high retention of 77.1% at 30 A g−1 even after 30,000 cycles. The calculation result based on density function theory shows that the much stronger Mo-O bonds are crucial for stabilizing the NiO@NiMoO4 composite, resulting in a good cycling stability of these materials.

PGA-Net: Pyramid Feature Fusion and Global Context Attention Network for Automated Surface Defect Detection

Abstract: Surface defect detection is a critical task in industrial production process. Nowadays, there are lots of detection methods based on computer vision and have been successfully applied in industry, they also achieved good results. However, achieving full automation of surface defect detection remains a challenge, due to the complexity of surface defect, in intraclass. While the defects between interclass contain similar parts, there are large differences in appearance of the defects. To address these issues, this article proposes a pyramid feature fusion and global context attention network for pixel-wise detection of surface defect, called PGA-Net. In the framework, the multiscale features are extracted at first from backbone network. Then the pyramid feature fusion module is used to fuse these features into five resolutions through some efficient dense skip connections. Finally, the global context attention module is applied to the fusion feature maps of adjacent resolution, which allows effective information propagate from low-resolution fusion feature maps to high-resolution fusion ones. In addition, the boundary refinement block is added to the framework to refine the boundary of defect and improve the result of the prediction. The final prediction is the fusion of the five resolutions fusion feature maps. The results of evaluation on four real-world defect datasets demonstrate that the proposed method outperforms the state-of-the-art methods on mean intersection of union and mean pixel accuracy (NEU-Seg: 82.15%, DAGM 2007: 74.78%, MT_defect: 71.31%, Road_defect: 79.54%).

Fault-Tolerant Consensus Tracking Control for Linear Multiagent Systems Under Switching Directed Network

Abstract: In this paper, for linear leader–follower networks with multiple heterogeneous actuator faults, including partial loss of effectiveness fault and actuator bias fault, a cooperative fault-tolerant control (CFTC) approach is developed. Assume that the interaction network topology among all nodes is a switching directed graph. To address the difficulty of designing the distributed compensation control laws under the time-varying asymmetrical network structure, a novel distributed-reference-observer-based fault-tolerant tracking control approach is established, under which the global tracking errors are proved to be asymptotically convergent in the presence of actuator failures. First, by constructing a group of distributed reference observers based on neighborhood state information, all followers can estimate the leader’s state trajectories directly. Second, a decentralized adaptive fault-tolerant tracking controller via local estimation is designed to achieve the global synchronization. Furthermore, the reliable coordination problem under switching directed topology with intermittent communications is solved by utilizing the presented CFTC approach. Finally, the effectiveness of the proposed coordination control protocol is illustrated by its applications to a networked aircraft system.

Diagnosing Rotating Machines With Weakly Supervised Data Using Deep Transfer Learning

Abstract: Rotating machinery fault diagnosis problems have been well-addressed when sufficient supervised data of the tested machine are available using the latest data-driven methods. However, it is still challenging to develop effective diagnostic method with insufficient training data, which is highly demanded in real-industrial scenarios, since high-quality data are usually difficult and expensive to collect. Considering the underlying similarities of rotating machines, data mining on different but related equipments potentially benefit the diagnostic performance on the target machine. Therefore, a novel transfer learning method for diagnostics based on deep learning is proposed in this article, where the diagnostic knowledge learned from sufficient supervised data of multiple rotating machines is transferred to the target equipment with domain adversarial training. Different from the existing studies, a more generalized transfer learning problem with different label spaces of domains is investigated, and different fault severities are also considered in fault diagnostics. The experimental results on four datasets validate the effectiveness of the proposed method, and show it is feasible and promising to explore different datasets to improve diagnostic performance.

Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation

Abstract: Intelligent machinery fault diagnosis system has been receiving increasing attention recently due to the potential large benefits of maintenance cost reduction, enhanced operation safety and reliability. This paper proposes a novel deep learning method for rotating machinery fault diagnosis. Since accurately labeled data are usually difficult to obtain in real industries, data augmentation techniques are proposed to artificially create additional valid samples for model training, and the proposed method manages to achieve high diagnosis accuracy with small original training dataset. Two augmentation methods are investigated including sample-based and dataset-based methods, and five augmentation techniques are considered in general, i.e. additional Gaussian noise, masking noise, signal translation, amplitude shifting and time stretching. The effectiveness of the proposed method is validated by carrying out experiments on two popular rolling bearing datasets. Fairly high diagnosis accuracy up to 99.9% can be obtained using limited training data. By comparing with the latest advanced researches on the same datasets, the superiority of the proposed method is demonstrated. Furthermore, the diagnostic performance of the deep neural network is extensively evaluated with respect to data augmentation strength, network depth and so forth. The results of this study suggest that the proposed intelligent fault diagnosis method offers a new and promising approach.

Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches.

Abstract: Background The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significant step to fight against this virus as well as release pressure off the healthcare system. Objective One of the critical factors behind the rapid spread of COVID-19 pandemic is a lengthy clinical testing time. The imaging tool, such as Chest X-ray (CXR), can speed up the identification process. Therefore, our objective is to develop an automated CAD system for the detection of COVID-19 samples from healthy and pneumonia cases using CXR images. Methods Due to the scarcity of the COVID-19 benchmark dataset, we have employed deep transfer learning techniques, where we examined 15 different pre-trained CNN models to find the most suitable one for this task. Results A total of 860 images (260 COVID-19 cases, 300 healthy and 300 pneumonia cases) have been employed to investigate the performance of the proposed algorithm, where 70% images of each class are accepted for training, 15% is used for validation, and rest is for testing. It is observed that the VGG19 obtains the highest classification accuracy of 89.3% with an average precision, recall, and F1 score of 0.90, 0.89, 0.90, respectively. Conclusion This study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images.

Porous Two-Dimensional Materials for Photocatalytic and Electrocatalytic Applications

Abstract: Summary Two-dimensional materials with abundant in-plane pores (porous 2D materials) have shown high performances as catalysts, especially for photocatalysis and electrocatalysis, owing to their distinct microstructural advantages originating from both 2D materials and porous materials. Here, the recent progress in porous 2D materials in photocatalysis and electrocatalysis is reviewed. We first highlight the influence of their special structural merits on the processes of photocatalysis and electrocatalysis, including transport of ion and/or charge carriers, surface active sites, stability, modifications, electronic band structure, and light absorption properties. Representative synthetic methods for porous 2D materials are also introduced classified by top-down and bottom-up routes. In addition, their applications in different aspects of photocatalysis and electrocatalysis are presented systematically. In conclusion, we propose some opportunities and challenges for the development of porous 2D materials, with the hope of further facilitating the applications of these emerging advanced materials in photocatalysis and electrolysis.

Summation Detector for False Data-Injection Attack in Cyber-Physical Systems

Abstract: In this paper, from the perspectives of defenders, we consider the detection problems of false data-injection attacks in cyber-physical systems (CPSs) with white noise. The false data-injection attacks usually modify the sensor data to make CPSs unstable and keep stealth for the $\chi ^{2}$ detector. To guarantee system security, a novel detector, that is, the summation (SUM) detector, is proposed to detect the false data-injection attacks. Different from the $\chi ^{2}$ detector, the SUM detector not only utilizes the current compromise information but also collects all historical information to reveal the threat. Its evaluation value also satisfies $\chi ^{2}$ distribution when no attacks compromise the systems, and the false alarm rate can be restricted to less than any given value by choosing the proper threshold value. Furthermore, an improved false data-injection attack with a time-variable increment coefficient is introduced based on the existing approaches. The effects of the SUM detector are also verified for the traditional and the improved false data-injection attacks, respectively. Finally, some simulation results are given to demonstrate the effectiveness and superiority of the SUM detector.

Lexicographic Multiobjective Scatter Search for the Optimization of Sequence-Dependent Selective Disassembly Subject to Multiresource Constraints

Abstract: Industrial products’ reuse, recovery, and recycling are very important because of their environmental and economic benefits. Effective product disassembly planning methods can improve their recovery efficiency and reduce their bad environmental impact. However, the existing approaches pay little attention to sequence-dependent disassembly with resource constraints, such as limited disassembly operators and tools, which makes the current planning methods ineffective in practice. This paper considers a multiobjective resource-constrained and sequence-dependent disassembly optimization problem with disassembly precedence constraints. Energy consumption is adopted to evaluate the disassembly efficiency. Its use with traditional optimization criterion leads to a novel multiobjective optimization model such that the energy consumption and disassembly time are minimized while disassembly profit is maximized. Since the problem complexity increases with the number of components in a product, a lexicographic multiobjective scatter search (SS) method is proposed to solve the proposed multiobjective optimization problem. Its effectiveness is verified by comparing the results of linear weight SS and genetic algorithms. The results show that it is able to provide a better solution in a short execution time and fulfills the precedence requirement in a product structure and resource constraints.

Barrier Lyapunov Function-Based Adaptive Fuzzy FTC for Switched Systems and Its Applications to Resistance–Inductance–Capacitance Circuit System

Abstract: In this article, the adaptive fault-tolerant control (FTC) problem is solved for a switched resistance–inductance–capacitance (RLC) circuit system. Due to the existence of faults which may lead to instability of subsystems, the innovation of this article is that the unstable subsystems are taken into account in the frame of output constraint and unmeasurable states. Obviously, there are not any unstable subsystems in unswitched systems. The unstable subsystems will involve many serious consequences and difficulties. Since the system states are unavailable, a switched state observer is designed. In addition, the fuzzy-logic systems (FLSs) are employed to approximate unknown internal dynamics in the controller design procedure. Then, the barrier Lyapunov function (BLF) is exploited to guarantee that the system output satisfy its constrained interval. Moreover, by using the average dwell-time method, all signals in the resulting systems are proofed to be bounded even when faults occur. Finally, the proposed strategy is carried out on the switched RLC circuit system to show the effectiveness and practicability.

Deep Learning-Based Machinery Fault Diagnostics With Domain Adaptation Across Sensors at Different Places

Abstract: In the recent years, data-driven machinery fault diagnostic methods have been successfully developed, and the tasks where the training and testing data are from the same distribution have been well addressed. However, due to sensor malfunctions, the training and testing data can be collected at different places of machines, resulting in the feature space with significant distribution discrepancy. This challenging issue has received less attention in the current literature, and the existing approaches generally fail in such scenarios. This article proposes a domain adaptation method for machinery fault diagnostics based on deep learning. Adversarial training is introduced for marginal domain fusion, and unsupervised parallel data are explored to achieve conditional distribution alignments with respect to different machine health conditions. Experiments on two rotating machinery datasets are carried out for validations. The results suggest the proposed method is promising to address the fault diagnostic tasks with data from different places of machines, further enhancing applicability of data-driven methods in real industries.

Pressure-Induced Blue-Shifted and Enhanced Emission: A Cooperative Effect between Aggregation-Induced Emission and Energy-Transfer Suppression

Abstract: Most organic piezochromic materials exhibit red-shifted and quenched emission as pressure increases. However, an abnormal phenomenon of pressure-induced blue-shifted and enhanced emission is observed in a 9-(3-(1,2,2-triphenylvinyl)phenyl)anthracene crystal, which is based on discrete π–π anthracene (AN) dimers stacking with tetraphenylethylene (TPE) as spacer. A blue-shifted emission appears and strengthens when the pressure is more than 1.23 GPa, and it reaches the maximum when the pressure is 4.28 GPa. This phenomenon is ascribed to the cooperative effect between the aggregation-induced emission of TPE units and energy-transfer suppression from TPE to an AN excimer. This work reports a new concept in the piezochromic field and provides a novel strategy to achieve luminescence from a high-lying excited state.

Subterahertz spin pumping from an insulating antiferromagnet

Abstract: Spin-transfer torque and spin Hall effects combined with their reciprocal phenomena, spin pumping and inverse spin Hall effects (ISHEs), enable the reading and control of magnetic moments in spintronics. The direct observation of these effects remains elusive in antiferromagnetic-based devices. We report subterahertz spin pumping at the interface of the uniaxial insulating antiferromagnet manganese difluoride and platinum. The measured ISHE voltage arising from spin-charge conversion in the platinum layer depends on the chirality of the dynamical modes of the antiferromagnet, which is selectively excited and modulated by the handedness of the circularly polarized subterahertz irradiation. Our results open the door to the controlled generation of coherent, pure spin currents at terahertz frequencies.