Showing papers by "Northeastern University (China) published in 2018"
TL;DR: A new data-driven approach for prognostics using deep convolution neural networks (DCNN) using time window approach is employed for sample preparation in order for better feature extraction by DCNN.
948 citations
TL;DR: In this article, an amorphous ordered mesoporous carbon (OMC) is proposed as an anode material for high-performance KIBs, which can accommodate the increase of the interlayer spacing and tolerate the volume expansion.
Abstract: The adequate potassium resource on the earth has driven the researchers to explore new-concept potassium-ion batteries (KIBs) with high energy density. Graphite is a common anode for KIBs; however, the main challenge faced by KIBs is that K ions have the larger size than Li and Na ions, hindering the intercalation of K ions into electrodes and thus leading to poor rate performance, low capacity, and cycle stability during the potassiation and depotassiation process. Herein, an amorphous ordered mesoporous carbon (OMC) is reported as a new anode material for high-performance KIBs. Unlike the well-crystallized graphite, in which the K ions are squeezed into the restricted interlayer spacing, it is found that the amorphous OMC possesses larger interlayer spacing in short range and fewer carbon atoms in one carbon-layers cluster, making it more flexible to the deformation of carbon layers. The larger interlayer spacing and the unique layered structure in short range can intercalate more K ions into the carbon layer, accommodate the increase of the interlayer spacing, and tolerate the volume expansion, resulting in a battery behavior with high capacity, high rate capability, and long cycle life.
461 citations
01 Jun 2018
TL;DR: A global Recurrent Localization Network (RLN) is proposed which exploits contextual information by the weighted response map in order to localize salient objects more accurately and performs favorably against all existing methods in terms of the popular evaluation metrics.
Abstract: Effective integration of contextual information is crucial for salient object detection. To achieve this, most existing methods based on 'skip' architecture mainly focus on how to integrate hierarchical features of Convolutional Neural Networks (CNNs). They simply apply concatenation or element-wise operation to incorporate high-level semantic cues and low-level detailed information. However, this can degrade the quality of predictions because cluttered and noisy information can also be passed through. To address this problem, we proposes a global Recurrent Localization Network (RLN) which exploits contextual information by the weighted response map in order to localize salient objects more accurately. Particularly, a recurrent module is employed to progressively refine the inner structure of the CNN over multiple time steps. Moreover, to effectively recover object boundaries, we propose a local Boundary Refinement Network (BRN) to adaptively learn the local contextual information for each spatial position. The learned propagation coefficients can be used to optimally capture relations between each pixel and its neighbors. Experiments on five challenging datasets show that our approach performs favorably against all existing methods in terms of the popular evaluation metrics.
422 citations
TL;DR: A morphology and phase-controlled electrodeposition of MnO2 with ultrahigh mass loading of 10 mg cm-2 on a carbon cloth substrate is performed to achieve high overall capacitance without sacrificing the electrochemical performance.
Abstract: Metal oxides have attracted renewed interest as promising electrode materials for high energy density supercapacitors. However, the electrochemical performance of metal oxide materials deteriorates significantly with the increase of mass loading due to their moderate electronic and ionic conductivities. This limits their practical energy. Herein, we perform a morphology and phase-controlled electrodeposition of MnO2 with ultrahigh mass loading of 10 mg cm–2 on a carbon cloth substrate to achieve high overall capacitance without sacrificing the electrochemical performance. Under optimum conditions, a hierarchical nanostructured architecture was constructed by interconnection of primary two-dimensional e-MnO2 nanosheets and secondary one-dimensional α-MnO2 nanorod arrays. The specific hetero-nanostructures ensure facile ionic and electric transport in the entire electrode and maintain the structure stability during cycling. The hierarchically structured MnO2 electrode with high mass loading yields an outsta...
405 citations
TL;DR: The present review for the first time introduces the state of the art in the progress of the IFE-based fluorescent sensing systems, including sensing strategy, essential conditions, materials option, and their applications for the detection of various target analytes, e.g., ionic species, small molecules, and macromolecules.
403 citations
TL;DR: A comprehensive survey on NFV is presented, which starts from the introduction of NFV motivations, and provides an extensive and in-depth discussion on state-of-the-art VNF algorithms including VNF placement, scheduling, migration, chaining and multicast.
361 citations
TL;DR: This study synthesized a sulfo-self-doped PANI cathode by a facile electrochemical copolymerization process and opens a door for the use of conducting polymers as cathode materials for high-performance rechargeable zinc batteries.
Abstract: Rechargeable aqueous zinc batteries are promising energy-storage systems for grid applications. Highly conductive polyaniline (PANI) is a potential cathode, but it tends to deactivate in electrolytes with low acidity (i.e. pH >1) owing to deprotonation of the polymer. In this study, we synthesized a sulfo-self-doped PANI electrode by a facile electrochemical copolymerization process. The -SO3 - self-dopant functions as an internal proton reservoir to ensure a highly acidic local environment and facilitate the redox process in the weakly acidic ZnSO4 electrolyte. In a full zinc cell, the self-doped PANI cathode provided a high capacity of 180 mAh g-1 , excellent rate performance of 70 % capacity retention with a 50-fold current-density increase, and a long cycle life of over 2000 cycles with coulombic efficiency close to 100 %. Our study opens a door for the use of conducting polymers as cathode materials for high-performance rechargeable zinc batteries.
305 citations
TL;DR: A deep learning-based approach for RUL prediction of rotating components with big data is presented and tested and validated using data collected from a gear test rig and bearing run-to-failure tests and compared with existing PHM methods.
Abstract: In the age of Internet of Things and Industrial 4.0, prognostic and health management (PHM) systems are used to collect massive real-time data from mechanical equipment. PHM big data has the characteristics of large-volume, diversity, and high-velocity. Effectively mining features from such data and accurately predicting the remaining useful life (RUL) of the rotating components with new advanced methods become issues in PHM. Traditional data driven prognostics is based on shallow learning architectures, requires establishing explicit model equations and much prior knowledge about signal processing techniques and prognostic expertise, and therefore is limited in the age of big data. This paper presents a deep learning-based approach for RUL prediction of rotating components with big data. The presented approach is tested and validated using data collected from a gear test rig and bearing run-to-failure tests and compared with existing PHM methods. The test results show the promising RUL prediction performance of the deep learning-based approach.
288 citations
TL;DR: A brief review of the mechanisms of MIC provides a state of the art insight into MIC mechanisms and it helps the diagnosis and prediction of occurrences of MIC under anaerobic conditions in the oil and gas industry.
279 citations
TL;DR: This paper is concerned with the input-to-state stabilizing control problem for cyber-physical systems (CPSs) with multiple transmission channels under denial-of-service (DoS) attacks, and the proposed LMI-based method is more flexible.
Abstract: This paper is concerned with the input-to-state stabilizing control problem for cyber-physical systems (CPSs) with multiple transmission channels under denial-of-service (DoS) attacks. Under the data update policy with bounded update interval, a new control scheme that discards the outdated information is proposed, and the stability analysis of CPSs under DoS attacks is transformed into analyzing the stability of the system under a switched controller with the help of a class of linear matrix inequalities (LMIs). Then, inspired by the techniques for switched systems, sufficient conditions on the duration and frequency of the DoS attacks, under which the stability of the closed-loop systems is still guaranteed, are proposed. Compared with the existing method for the single-channel case, the considered multiple-channel case is more challenging, and the proposed LMI-based method is more flexible.
273 citations
TL;DR: A solution for secure and efficient image encryption with the help of self-adaptive permutation–diffusion and DNA random encoding and the reusability of the random variables can dramatically promote the efficiency of the cryptosystem, which renders great potential for real-time secure image applications.
TL;DR: A review of the salinity measurement technology based on the optical fiber sensor is presented in this article, where the authors compare the performance of various sensing structures and analyses the advantages and disadvantages of different sensors.
Abstract: A review of the salinity measurement technology based on the optical fiber sensor is presented. The principles of optical fiber measurement, the structures of probes and the characteristics of various sensing structures are concerned. Firstly, this paper discusses the relationship between the salinity and refractive index, and the effect of ion pairs on the refractive index. Secondly, four methods of direct or non-direct measurements of salinity are summarized, including optical refraction method, optical fiber grating, optical interference and surface plasmon effect. Subsequently, the article compares performances of various sensing structures and analyses the advantages and disadvantages of different sensors. Finally, a prospect of salinity measurement requirement and the development direction of fiber-optic sensors in this area are addressed.
TL;DR: In the proposed method, the feedback signals and the NN weights are aperiodically updated only when the event-triggered condition is violated, and the number of transmissions can be significantly reduced.
Abstract: This paper is concerned with the adaptive event-triggered control problem of nonlinear continuous-time systems in strict-feedback form. By using the event-sampled neural network (NN) to approximate the unknown nonlinear function, an adaptive model and an associated event-triggered controller are designed by exploiting the backstepping method. In the proposed method, the feedback signals and the NN weights are aperiodically updated only when the event-triggered condition is violated. A positive lower bound on the minimum intersample time is guaranteed to avoid accumulation point. The closed-loop stability of the resulting nonlinear impulsive dynamical system is rigorously proved via Lyapunov analysis under an adaptive event sampling condition. In comparing with the traditional adaptive backstepping design with a fixed sample period, the event-triggered method samples the state and updates the NN weights only when it is necessary. Therefore, the number of transmissions can be significantly reduced. Finally, two simulation examples are presented to show the effectiveness of the proposed control method.
TL;DR: In this paper, an effective and sustainable approach for selective leaching of lithium from spent LiFePO4 batteries was demonstrated, and the results showed that high purity Li2CO3 (99.95 wt%) could be obtained with a high recovery rate.
TL;DR: An energy-aware multi-objective optimization algorithm for solving the hybrid flow shop (HFS) scheduling problem with consideration of the setup energy consumptions with the highly effective proposed EA-MOA algorithm compared with several efficient algorithms from the literature.
TL;DR: The role of biogenic H 2 S in the microbiologically influenced corrosion (MIC) of carbon steel was investigated in this article, where Desulfovibrio vulgaris (ATCC 7757), a sulfate reducing bacterium, was tested against C1018 carbon steel in anaerobic vials with three different sizes, each filled with 40mL of ATCC 1249 culture medium, providing headspace volumes of 10mL, 85mL and 160mL, respectively for H 2 s to escape.
TL;DR: This paper investigates the problem of event-triggered fault detection (FD) filter design for nonlinear networked systems in the framework of interval type-2 fuzzy systems and proposes an augmented FD system with imperfectly matched MFs, which hampers the stability analysis and FD.
Abstract: This paper investigates the problem of event-triggered fault detection (FD) filter design for nonlinear networked systems in the framework of interval type-2 fuzzy systems. In the system model, the parameter uncertainty is captured effectively by the membership functions (MFs) with upper and lower bounds. For reducing the utilization of limited communication bandwidth, an event-triggered communication mechanism is applied. A novel FD filter subject to event-triggered communication mechanism, data quantization, and communication delay is designed to generate a residual signal and detect system faults, where the premise variables are different from those of the system model. Consequently, the augmented FD system is with imperfectly matched MFs, which hampers the stability analysis and FD. To relax the stability analysis and achieve a better FD performance, the information of MFs and slack matrices are utilized in the stability analysis. Finally, two examples are employed to demonstrate the effectiveness of the proposed scheme.
TL;DR: This paper jointly considers multiple decision factors to facilitate vehicle-to-infrastructure networking, where the energy efficiency of the networks is adopted as an important factor in the network selection process.
Abstract: The emerging technologies for connected vehicles have become hot topics. In addition, connected vehicle applications are generally found in heterogeneous wireless networks. In such a context, user terminals face the challenge of access network selection. The method of selecting the appropriate access network is quite important for connected vehicle applications. This paper jointly considers multiple decision factors to facilitate vehicle-to-infrastructure networking, where the energy efficiency of the networks is adopted as an important factor in the network selection process. To effectively characterize users’ preference and network performance, we exploit energy efficiency, signal intensity, network cost, delay, and bandwidth to establish utility functions. Then, these utility functions and multi-criteria utility theory are used to construct an energy-efficient network selection approach. We propose design strategies to establish a joint multi-criteria utility function for network selection. Then, we model network selection in connected vehicle applications as a multi-constraint optimization problem. Finally, a multi-criteria access selection algorithm is presented to solve the built model. Simulation results show that the proposed access network selection approach is feasible and effective.
TL;DR: The experimental results of this study suggest the proposed deep distance metric learning method offers a new and promising tool for intelligent fault diagnosis of rolling bearings.
TL;DR: Findings provide compelling evidence that LA plays a role in inhibiting Tau hyperphosphorylation and neuronal loss, including ferroptosis, through several pathways, suggesting that LA may be a potential therapy for tauopathies.
Abstract: Alzheimer's disease (AD) is the most common neurodegenerative disease and is characterized by neurofibrillary tangles (NFTs) composed of Tau protein. α-Lipoic acid (LA) has been found to stabilize the cognitive function of AD patients, and animal study findings have confirmed its anti-amyloidogenic properties. However, the underlying mechanisms remain unclear, especially with respect to the ability of LA to control Tau pathology and neuronal damage. Here, we found that LA supplementation effectively inhibited the hyperphosphorylation of Tau at several AD-related sites, accompanied by reduced cognitive decline in P301S Tau transgenic mice. Furthermore, we found that LA not only inhibited the activity of calpain1, which has been associated with tauopathy development and neurodegeneration via modulating the activity of several kinases, but also significantly decreased the calcium content of brain tissue in LA-treated mice. Next, we screened for various modes of neural cell death in the brain tissue of LA-treated mice. We found that caspase-dependent apoptosis was potently inhibited, whereas autophagy did not show significant changes after LA supplementation. Interestingly, Tau-induced iron overload, lipid peroxidation, and inflammation, which are involved in ferroptosis, were significantly blocked by LA administration. These results provide compelling evidence that LA plays a role in inhibiting Tau hyperphosphorylation and neuronal loss, including ferroptosis, through several pathways, suggesting that LA may be a potential therapy for tauopathies.
TL;DR: In this paper, a modified power law formulation is employed to depict the material properties of the plates in the thickness direction, and three terms of inertial forces are taken into account due to the translation of plates.
TL;DR: The results demonstrate that the multigroup patch-based learning system is efficient to improve the performance of lung nodule detection and greatly reduce the false positives under a huge amount of image data.
Abstract: High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around this domain for approximately two decades. However, previous computer-aided detection (CADe) schemes are mostly intricate and time-consuming since they may require more image processing modules, such as the computed tomography image transformation, the lung nodule segmentation, and the feature extraction, to construct a whole CADe system. It is difficult for these schemes to process and analyze enormous data when the medical images continue to increase. Besides, some state of the art deep learning schemes may be strict in the standard of database. This study proposes an effective lung nodule detection scheme based on multigroup patches cut out from the lung images, which are enhanced by the Frangi filter. Through combining two groups of images, a four-channel convolution neural networks model is designed to learn the knowledge of radiologists for detecting nodules of four levels. This CADe scheme can acquire the sensitivity of 80.06% with 4.7 false positives per scan and the sensitivity of 94% with 15.1 false positives per scan. The results demonstrate that the multigroup patch-based learning system is efficient to improve the performance of lung nodule detection and greatly reduce the false positives under a huge amount of image data.
TL;DR: It is proven that all the signals of the resulting closed-loop system are semiglobally bounded, and the tracking errors of each subsystem exponentially converge to a compact set, whose radius is adjustable by choosing different controller design parameters.
Abstract: This paper is concerned with the adaptive decentralized fault-tolerant tracking control problem for a class of uncertain interconnected nonlinear systems with unknown strong interconnections An algebraic graph theory result is introduced to address the considered interconnections In addition, to achieve the desirable tracking performance, a neural-network-based robust adaptive decentralized fault-tolerant control (FTC) scheme is given to compensate the actuator faults and system uncertainties Furthermore, via the Lyapunov analysis method, it is proven that all the signals of the resulting closed-loop system are semiglobally bounded, and the tracking errors of each subsystem exponentially converge to a compact set, whose radius is adjustable by choosing different controller design parameters Finally, the effectiveness and advantages of the proposed FTC approach are illustrated with two simulated examples
TL;DR: A new database, CAS(ME), which provides both long videos and cropped expression samples, which may aid researchers in developing efficient algorithms for the spotting and recognition of macro-expressions and micro- expressions.
Abstract: Deception is a very common phenomenon and its detection can be beneficial to our daily lives. Compared with other deception cues, micro-expression has shown great potential as a promising cue for deception detection. The spotting and recognition of micro-expression from long videos may significantly aid both law enforcement officers and researchers. However, database that contains both micro-expression and macro-expression in long videos is still not publicly available. To facilitate development in this field, we present a new database, Chinese Academy of Sciences Macro-Expressions and Micro-Expressions (CAS(ME) $^2$ ), which provides both macro-expressions and micro-expressions in two parts (A and B). Part A contains 87 long videos that contain spontaneous macro-expressions and micro-expressions. Part B includes 300 cropped spontaneous macro-expression samples and 57 micro-expression samples. The emotion labels are based on a combination of action units (AUs), self-reported emotion for every facial movement, and the emotion types of emotion-evoking videos. Local Binary Pattern (LBP) was employed for the spotting and recognition of macro-expressions and micro-expressions and the results were reported as a baseline evaluation. The CAS(ME) $^2$ database offers both long videos and cropped expression samples, which may aid researchers in developing efficient algorithms for the spotting and recognition of macro-expressions and micro-expressions.
TL;DR: This paper presents a novel approach to detect the milling chatter based on Variational Mode Decomposition (VMD) and energy entropy and shows that the proposed method can effectively detect the chatter.
TL;DR: A modified cuckoo search algorithm is proposed to solve economic dispatch problems that have non-convex, non-continuous or non-linear solution spaces considering valve-point effects, prohibited operating zones, transmission losses and ramp rate limits.
Abstract: A modified cuckoo search ( CS ) algorithm is proposed to solve economic dispatch ( ED ) problems that have non-convex, non-continuous or non-linear solution spaces considering valve-point effects, prohibited operating zones, transmission losses and ramp rate limits. Comparing with the traditional cuckoo search algorithm, we propose a self-adaptive step size and some neighbor-study strategies to enhance search performance. Moreover, an improved lambda iteration strategy is used to generate new solutions. To show the superiority of the proposed algorithm over several classic algorithms, four systems with different benchmarks are tested. The results show its efficiency to solve economic dispatch problems, especially for large-scale systems.
TL;DR: In this paper, LiNiO2 was synthesized from a commercial Ni(OH)2 precursor and modern synthesis methods showed a specific capacity close to the theoretical specific capacity of 274 mAh/g.
Abstract: Ni-rich transition metal layered oxide materials are of great interest as positive electrode materials for lithium ion batteries. As the popular electrode materials NMC (LiNi1-x-yMnxCoyO2) and NCA (LiNi1-x-yCoxAlyO2) become more and more Ni-rich, they approach LiNiO2. Therefore it is important to benchmark the structure and electrochemistry of state of the art LixNiO2 for the convenience of researchers in the field. In this work, LiNiO2 synthesized from a commercial Ni(OH)2 precursor and modern synthesis methods shows a specific capacity close to the theoretical specific capacity of 274 mAh/g. In-situ X-ray diffraction (XRD) measurements were conducted to obtain accurate structural information versus lithium content, x. The known multiple phase transitions of LixNiO2 during charge and discharge were clearly observed, and the variation in unit cell lattice constants and volume was measured. Differential capacity versus voltage (dQ/dV vs. V) studies were used to investigate the electrochemical properties including regions of composition that show very slow kinetics. It is hoped that this work will be a useful reference for those working on Ni-rich positive electrode materials for Li-ion cells.
TL;DR: Luminescent investigations demonstrate that the stable CH3NH3PbBr3@MOF-5 composites not only featured excellent sensing properties with respect to temperature changes from 30 to 230 °C but also exhibited significant selective luminescent response to several different metal ions in aqueous solution.
Abstract: The stability issue of organometallic halide perovskites remains a great challenge for future research as to their applicability in different functional material fields. Herein, a novel and facile two-step synthesis procedure is reported for encapsulation of CH3NH3PbBr3 perovskite quantum dots (QDs) in MOF-5 microcrystals, where PbBr2 and CH3NH3Br precursors are added stepwise to fabricate stable CH3NH3PbBr3@MOF-5 composites. In comparison to CH3NH3PbBr3 QDs, CH3NH3PbBr3@MOF-5 composites exhibited highly improved water resistance and thermal stability, as well as better pH adaptability over a wide range. Luminescent investigations demonstrate that CH3NH3PbBr3@MOF-5 composites not only featured excellent sensing properties with respect to temperature changes from 30 to 230 °C but also exhibited significant selective luminescent response to several different metal ions in aqueous solution. These outstanding characteristics indicate that the stable CH3NH3PbBr3@MOF-5 composites are potentially interesting for...
TL;DR: NiO-SnO2 heterojunction microflowers assembled by thin porous nanosheets were successfully synthesized through a facile one-step hydrothermal route as discussed by the authors.
Abstract: NiO-SnO2 heterojunction microflowers assembled by thin porous nanosheets were successfully synthesized through a facile one-step hydrothermal route. The structural and composition information were examined by means of X-ray diffractometer, field emission scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, and Brunauer-Emmett-Teller nitrogen adsorption-desorption. The formaldehyde gas sensing properties were systematically investigated between the pure and NiO-SnO2 microflowers. The experiment results showed that NiO-SnO2 microflower sensor displayed the higher response at a lower operating temperature region compared to pure SnO2 microflower sensor. Meanwhile, introducing NiO obviously reduced operating temperature. Especially, the sensor utilizing 5 mol% NiO-SnO2 microflowers showed significantly enhanced sensing performances to formaldehyde including the higher responses, lower operating temperatures, lower detecting limit level, quick response/recovery characteristics, good reproducibility and stability, and superior selectivity. The enhanced sensing properties were probably attributed to the formation of p–n heterojunctions at interface and the catalytic effect of NiO, which significantly enlarges surface depletion region and increases potential barrier. Our studies provide a facile synthesis process, which could be developed to synthesize other semiconductor oxide composites, and provide a potential material for fabricating high performance sensors.
TL;DR: In this article, a comprehensive survey on frequency regulation methods for variable speed wind turbines is presented, including the concepts, principles and control strategies of prevailing frequency controls of VSWTs, including future development trends.
Abstract: With an increasing penetration of wind power in the modern electrical grid, the increasing replacement of large conventional synchronous generators by wind power plants will potentially result in deteriorated frequency regulation performance due to the reduced system inertia and primary frequency response. A series of challenging issues arise from the aspects of power system planning, operation, control and protection. Therefore, it is valuable to develop variable speed wind turbines (VSWTs) equipped with frequency regulation capabilities that allow them to effectively participate in addressing severe frequency contingencies. This paper provides a comprehensive survey on frequency regulation methods for VSWTs. It fully describes the concepts, principles and control strategies of prevailing frequency controls of VSWTs, including future development trends. It concludes with a performance comparison of frequency regulation by the four main types of wind power plants.