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The recent development in machine learning, especially in deep neural networks (DNN), has enabled learning-based end-to-end communication systems, where DNNs are employed to substitute all modules at the transmitter and receiver. In this article, two end-to-end frameworks for frequency-selective channels and multi-input and multi-output (MIMO) channels are developed, where the wireless channel effects are modeled with an untrainable stochastic convolutional layer. The end-to-end framework is trained with mini-batches of input data and channel samples. Instead of using pilot information to implicitly or explicitly estimate the unknown channel parameters as in current communication systems, the transmitter DNN learns to transform the input data in a way that is robust to various channel conditions. The receiver consists of two DNN modules used for channel information extraction and data recovery, respectively. A bilinear production operation is employed to combine the features extracted from the channel information extraction module and the received signals. The combined features are further utilized in the data recovery module to recover the transmitted data. Compared with the conventional communication systems, performance improvement has been shown for frequency-selective channels and MIMO channels. Furthermore, the end-to-end system can automatically leverage the correlation in the channels and in the source data to improve the overall performance.

A novel GO modified g-C3N4 nanosheets/flower-like BiOBr hybrid photocatalyst is fabricated by a facile method. The characterization results reveal that wrinkled GO is deposited between g-C3N4 nanosheets and flower-like BiOBr forming a Z-scheme heterojunction. As a mediator, plicate GO plays a positive role in prompting photogenerated electrons transferring through its sizeable 2D/2D contact surface area. The g-C3N4/GO/BiOBr hybrid displays a superior photocatalytic ability to g-C3N4 and BiOBr in photodegrading tetracycline (TC), whose removal efficiency could reach 96% within 2 h. Besides, g-C3N4/GO/BiOBr composite can reduce Cr(VI), and simultaneously treat TC and Cr(VI) combination contaminant under the visible light. The g-C3N4/GO/BiOBr ternary composite also exhibits satisfactory stability and reusability after four cycling experiments. Further, a feasible mechanism related to the photocatalytic process of g-C3N4/GO/BiOBr is put forward. This study offers a ternary hybrid photocatalyst with eco-friendliness and hopeful application in water pollution.

Brain tumor segmentation is one of the most challenging tasks for radiomics analysis. Manual segmentation by radiologists is both laborious and subjective, which limits the number of study cases and the reproductivity of each clinical study. Hence, the automatic tumor segmentation method is in high demand, not only to segment the tumor, but also divide it into several fine-grained specific categories in voxel level. To this end, we propose a 3D Center-crop Dense Block for medical images by introducing high-level supervision into the lower layers of neural networks and adopt a parallel pathway network architecture of attention and context pathways. The attention pathway is of normal resolution and mainly concentrate on the detailed information of each voxel, while the context pathway is of low resolution and focus on the surrounding information. Furthermore, we also involve cross-pathway connections from attention pathway to context pathway with weighted fusion structure to compensate the missing detailed information caused by downsampling. The segmentation performance on the BraTS 2015 and BraTS 2017 datasets corroborate the effectiveness of the proposed deep learning architecture over the state-of-the-art results.

Head orientation prediction is one of the solutions to reduce end-to-end latency on Virtual Reality (VR) systems and is important since it can alleviate negative effects like motion sickness. This study compared head orientation prediction models from two different electromyography (EMG) systems: surface EMG (sEMG) and High-Density EMG (HD-EMG). The deep learning method was used to train the prediction model, and the results showed that the model with input from the pre-processed sEMG + IMU sensor outperformed the model with input from the HD-EMG + IMU sensor. However, the decreasing performance from HD-EMG was compensated by its comfort and the ease of use of its electrode. This tradeoff between performance and usability with sEMG compared to HD-EMG should be a consideration for users who want to choose between performance and ease of use for head orientation prediction purposes. Comparison with state-of-the-art head prediction methods proved that the sEMG-based model offers better performance in predictions when users change their head directions, which was quantified by calculating the dt peaks. In other words, our sEMG-based prediction model is suitable for VR applications, which require the user to perform high-intensity or abrupt movements, such as in FPS games or exercise/sports games.

Railway turnouts require high-performance condition monitoring to prevent disastrous railway accidents. In industrial practice, turnouts’ monitoring is usually done by railway workers who visually inspect the operating current curves. This results in huge labor costs and prone to human mistakes. Thus, automating the process of turnouts’ monitoring via fault-detection algorithms is imperative. The available turnout field data bring three difficulties to fault detection: 1) large amounts of data do not have any labels; 2) data collected in normal condition have multiple unknown modes; and 3) there are only a small number of samples in some modes. To address these difficulties, this article develops a novel unsupervised fault-detection method by using deep autoencoders, which is composed of an unknown modes’ mining stage and a multimode fault-detection stage. First, unknown modes are identified through clustering and employing engineer expertise. Then, an ensemble monitoring model, consisting of local monitoring models developed with individual fault-free modes and a global monitoring model developed by merging the data in all fault-free modes, is proposed to improve the overall fault-detection performance. In addition, to construct local models for the modes with a small number of samples, a one-class transfer learning algorithm is presented. In online monitoring, the decision of a newly arrived sample exploits both local models and the global model. Using both the simulated turnout data and the field data collected from a high-speed railway in China, the efficacy and robustness of the proposed approach are demonstrated by comparisons with other methods.

Crowd counting, i.e. count the number of people in a crowded visual space, is emerging as an essential research problem with public security. A key in the design of the crowd counting system is to create a stable and accurate robust model, which requires to process on the feature channels of the counting network. In this study, the authors present a featured channel enhancement (FCE) block for crowd counting. First, they use a feature extraction unit to obtain the information of each channel and encodes the information of each channel. Then use a non-linear variation unit to deal with the encoded channel information, finally, normalise the data and affixed to each channel separately. With the use of the FCE, the positive characteristic channel can be enhanced and weak or negative channel information can be suppressed. The authors successfully incorporate the FCE with two compact networks on the standard benchmarks and prove that the proposed FCE achieves promising results.

Nowadays the most intriguing features of wave-particle complementarity of single-photons are exemplified by the famous Wheeler’s delayed-choice experiment with linear optics, nuclear magnetic resonance, and integrated photonic device systems in the optical platform. Until now, the delayed-choice experiments are demonstrated by either massless photons or massive particles, such as atoms, however, there is no report demonstrating Wheeler’s ideas in a hybrid system which consists of photons and atoms simultaneously. Here, we demonstrate a Wheeler’s delayed-choice experiment in an interface of light and atomic memory, in which the cold atomic memory makes the heralded single-photon divided into a superposition of atomic collective excitation and leaked pulse, thus acting as memory-based beam-splitters. We observe the intermediate states between particle and wave behavior by changing the relative proportion of the quantum random number generator, the second memory efficiency, and the relative storage time of two memories. The reported results confirm Bohr’s view that it makes no sense to illustrate the wave-like or particle-like behavior of light and matter before the measurement happens, and are helpful for improving our comprehension of the complementarity principle under the interface of light-atom interaction.

The task of crowd counting is to estimate the number of pedestrian in crowd images. Due to camera perspective and physical barriers among dense crowds, how to construct a robust counting model for varying densities and various scenarios has become a challenging problem. In this paper, we propose an adaptive scenario discovery framework for counting crowds with varying densities. The framework is structured with two parallel pathways that are trained to represent different crowd densities and present in the proper geometric configuration using different sizes of the receptive field. A third adaption branch is designed to adaptively recalibrate the pathway-wise responses by discovering and modeling the dynamic scenarios implicitly. We conduct experiments using the adaptive scenario discovery framework on five challenging crowd counting datasets and demonstrate its superiority in terms of effectiveness and efficiency over previous approaches.

In this article, we develop an end-to-end wireless communication system using deep neural networks (DNNs), where DNNs are employed to perform several key functions, including encoding, decoding, modulation, and demodulation. However, an accurate estimation of instantaneous channel transfer function, i.e. , channel state information (CSI), is needed in order for the transmitter DNN to learn to optimize the receiver gain in decoding. This is very much a challenge since CSI varies with time and location in wireless communications and is hard to obtain when designing transceivers. We propose to use a conditional generative adversarial net (GAN) to represent channel effects and to bridge the transmitter DNN and the receiver DNN so that the gradient of the transmitter DNN can be back-propagated from the receiver DNN. In particular, a conditional GAN is employed to model the channel effects in a data-driven way, where the received signal corresponding to the pilot symbols is added as a part of the conditioning information of the GAN. To address the curse of dimensionality when the transmit symbol sequence is long, convolutional layers are utilized. From the simulation results, the proposed method is effective on additive white Gaussian noise (AWGN) channels, Rayleigh fading channels, and frequency-selective channels, which opens a new door for building data-driven DNNs for end-to-end communication systems.

Four-dimensional (4D) printing of swellable materials have been viewed as an ideal approach to build shape morphing architectures. However, there is less variety in high-performance swellable materials, limiting its development. To address this challenge, we proposed a new strategy for designing high-performance thermal-responsive swellable materials. The reversible liquid-vapor phase change of embedded low boiling point liquid chambers and functional liquid metal fillers endows the designed elastomer with the reversible thermal-responsive swellable property with high stability, fast response speed, and large equilibrium deformation. Notably, liquid metal fillers play a crucial role in improving the thermal-responsive property via improving the thermal conductivity and fracture toughness and decreasing the stiffness. To demonstrate the feasibility of constructing shape morphing architectures with proposed thermal-responsive liquid metal elastomers, typical bilayer structures were printed and investigated. By altering the key design parameters, the response speed and equilibrium deformation can be adjusted as needed. Therefore, complex shape morphing architectures can be printed. This study could provide a new avenue to design swellable material systems for 4D printing of shape morphing architectures.

In recent years, key performance indicator (KPI) detection has attracted much attention in large-scale process plants. Several methods have been developed to solve this issue. However, further studies find that post-processing methods have relatively high false alarm rates (FARs) for quality-unrelated faults. Also, methods combined with preprocessing, like orthogonal signal correction-modified partial least squares (OSC-MPLS), sometimes lack robustness. To deal with this problem, this paper proposes an enhanced pretreatment method, namely sparse direct orthogonalization (SDO), and a novel KPI-related fault detection approach called SDO-MPLS is developed. Compared with OSC-MPLS, the proposed approach has more robust performance and better interpretability, while a numerical case and the Tennessee Eastman process (TEP) are used to demonstrate the effectiveness of the proposed approach.

Fabrication of p-n heterojunction materials is an efficient strategy for improving the photocatalytic activity by suppressing the recombination of photogenerated electrons and holes. In this study, a p-n heterojunction consisting of Cu₂O and In₂O₃ was synthesized using the hydrothermal in situ deposition method. The Cu₂O/In₂O₃ hybrid exhibited enhanced performance in the photocatalytic hydrogen evolution reaction and degradation of organic pollutants compared with pure Cu₂O and In₂O₃. The sensitization of In₂O₃ by Cu₂O considerably enhanced the light response of the Cu₂O/In₂O₃ hybrid, and the close contact between In₂O₃ and Cu₂O led to the generation of the p-n heterojunction. The photoelectrochemical properties test proved that the successful fabrication of the p-n heterojunction in the Cu₂O/In₂O₃ hybrid promoted the transfer of photoinduced electrons and holes and inhibited the recombination of photogenerated carriers efficiently. The present strategy provides an efficient approach to improve the performance of photocatalysts through the fabrication of p-n heterojunctions.

In this article, by taking a 3-DOF parallel tool head as an object of study, a dynamics calibration method is investigated to improve the calculation accuracy of driving forces and dynamic parameters. First, the dynamic model of the parallel tool head is established in joint space, and the dynamics prediction problem is described through a typical reciprocating feed motion experiment on a prototype. Next, a decoupled polynomial model is proposed to fit dynamic parameter in workspace, which is used to solve the parameters coupling problem of parallel mechanism, and related simulations are further given to validate the reasonability and determine the optimum order of the polynomial model. Finally, a complete dynamics calibration experiment is designed considering the characteristic of the decoupled fitting model, and all actual coefficients are obtained. Moreover, some verification experiments are performed at the nontest positions, and the results show that the calculation results at these nontest positions are consistent very well with the actual values by using the calibrated model, which proves the effectiveness of the proposed dynamics calibration method.

By applying two donor-acceptor motif molecules, 5,10-di(pyridin-4-yl)-5,10-dihydrophenazine (L1) and 10,10'-di(pyridin-3-yl)-10H,10'H-9,9'-spiroacridine (L2), as ligands and CuI/AgCF3CO2 as metal salt, we synthesized three coordination polymers, namely, {Cu4(L1)2I4} (CP1), {Cu(L2)I·CHCl3} (CP2) and {Ag(L2)CO2CF3·CHCl3} (CP3). X-ray crystallographic analysis revealed that three coordination polymers all feature one-dimensional (1D) linear chains which are consisting of molecular boxlike units. In comparison with low photoluminescence quantum yield (PLQY) of two ligands, three coordination polymers, CP1, CP2 and CP3, present more intense photoluminescence with PLQY of 15%, 46% and 34% at room temperature respectively. The PL emission of CP1 and CP2 at room temperature could be attributed to the fast phosphorescence with lifetime both around 5 μs due to effective intersystem crossing (ISC). Whilst, it is worth noting that CP3 exhibit thermally activated delayed fluorescence (TADF) emission at room temperature.

Multivariate time series (MTS) processing plays an important role in many fields such as industry, finance, medical, etc. However, the presence of missing data in MTS makes data analysis process more complicated. To address this issue, MTS imputation reconstructs missing data with a high accuracy by exploiting a precise model of MTS distribution. Despite being an effective tool for modeling distribution on images, generative adversarial networks (GANs) have limitations in modeling MTS distribution. In this paper, to improve MTS imputation performance, a multivariate time series generative adversarial network (MTS-GAN) is proposed for MTS distribution modeling by introducing the multi-channel convolution into GANs. It is then applied to MTS imputation by formulating a constrained MTS generation task. Experimental results show that MTS-GAN performs well in modeling MTS distribution. Compared with several approaches, the proposed MTS-GAN based imputation method not only achieves a higher imputation accuracy under different missing rates, but also performs more robustly as the missing rate increases.

The large deflection postbuckling behavior of rings under external pressure was studied theoretically and numerically. Rigid linear strain-hardening property was adopted to approximate the plastic behavior of the rings. Analytical models based on different theories were derived to describe the large deflection properties of postbuckling behavior. The influence of material properties and geometric parameter on the postbuckling response was investigated based on the proposed analytical models. Besides, finite element analyses were carried out to verify the validity of the proposed analytical models and the numerical results showed good agreement with the theoretical ones.

Sheath current is one of the key indicators of underground power cable conditions. Considering the limitations of existing model-based methods for sheath current monitoring and difficulty in handling the increasing amount of unlabeled sheath current data accumulated by cable monitoring systems, we propose a data mining method based on unsupervised learning and spatiotemporal analysis of sheath currents for underground power cable monitoring. Tests based on real historical data demonstrate that the proposed method can effectively reveal unknown inherent patterns in unlabeled sheath current data.

Photodynamic therapy (PDT) shows a promising synergy with chemotherapy in the therapeutic outcome of malignant cancers. The minimal invasiveness and nonsystemic toxicity are appealing advantages of PDT, but combination with chemotherapy brings in the nonselective toxicity. We designed a polymeric nanoparticle system that contains both a chemotherapeutic agent and a photosensitizer to seek improvement for chemo-photodynamic therapy. First, to address the challenge of efficient co-delivery, polymer-conjugated doxorubicin (PEG-PBC-TKDOX) was synthesized to load photosensitizer chlorin e6 (Ce6). Ce6 is retained with DOX by a π-π stacking interaction, with high loading (41.9 wt %) and the optimal nanoparticle size (50 nm). Second, light given in PDT treatment not only excites Ce6 to produce cytotoxic reactive oxygen species (ROS) but also spatiotemporally activates a cascade reaction to release the loaded drugs. Finally, we report a self-destructive polymeric carrier (PEG-PBC-TKDOX) that depolymerizes its backbone to facilitate drug release upon ROS stimulus. This is achieved by grafting the ROS-sensitive pendant thioketal to aliphatic polycarbonate. When DOX is covalently modified to this polymer via thioketal, target specificity is controlled by light, and off-target delivery toxicity is mostly avoided. An oral squamous cell carcinoma that is clinically relevant to PDT was used as the cancer model. We put forward a polymeric system with improved efficiency for chemo-photodynamic therapy and reduced off-target toxicity.

Although environment-sensitive prodrug-based nanoparticles (NPs) have developed rapidly, lots of prodrug NPs still show poor selectivity and efficiency of parent drug bioactivation because of tumor heterogeneity. Herein, self-strengthened bioactivating prodrug-based NPs are fabricated via co-encapsulation of oxidation-responsive thioether-linked linoleic acid-paclitaxel conjugates (PTX-S-LA) and β-lapachone (LPC) into polymeric micelles (PMs). Following cellular uptake, PMs first release LPC to significantly elevate the reactive oxidative species (ROS) level through NAD(P)H: quinone oxidoreductase-1 (NQO1) catalysis. Then, NQO1-generated ROS in combination with endogenous high ROS levels in tumor cells could synergistically facilitate PTX-S-LA to release the active cytotoxic agent PTX. Such a novel prodrug nanosystem exhibits self-strengthened prodrug bioactivation, ultraselective release, and cytotoxicity between cancer and normal cells, prolonged circulation time, and enhanced tumor accumulation, leading to high antitumor efficiency and superior biosafety. Our findings pave the new way for the rational design of oxidation-responsive prodrug NPs for high-efficacy cancer chemotherapy.

In this paper, we develop a decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communications based on deep reinforcement learning, which can be applied to both unicast and broadcast scenarios. According to the decentralized resource allocation mechanism, an autonomous agent', a V2V link or a vehicle, makes its decisions to find the optimal sub-band and power level for transmission without requiring or having to wait for global information. Since the proposed method is decentralized, it incurs only limited transmission overhead. From the simulation results, each agent can effectively learn to satisfy the stringent latency constraints on V2V links while minimizing the interference to vehicle-to-infrastructure (V2I) communications., arXiv admin note: text overlap with arXiv:1711.00968

As wireless networks evolve towards high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular environments and thus motive rethinking of traditional wireless design methodologies. Future intelligent vehicles, which are at the heart of high mobility networks, are increasingly equipped with multiple advanced onboard sensors and keep generating large volumes of data. Machine learning, as an effective approach to artificial intelligence, can provide a rich set of tools to exploit such data for the benefit of the networks. In this article, we first identify the distinctive characteristics of high mobility vehicular networks and motivate the use of machine learning to address the resulting challenges. After a brief introduction of the major concepts of machine learning, we discuss its applications to learn the dynamics of vehicular networks and make informed decisions to optimize network performance. In particular, we discuss in greater detail the application of reinforcement learning in managing network resources as an alternative to the prevalent optimization approach. Finally, some open issues worth further investigation are highlighted., Updated title. Accepted by IEEE Internet of Things Journal

Modifying environmentally harmful waste polystyrene foam as an efficient recyclable adsorbent for organic dyes is important. Amidation modified polystyrene (PS-SD) was prepared by the Friedel-Crafts reaction and N,N'-dicyclohexylcarbodiimide (DCC) dehydration condensation reaction of waste polystyrene foam. PS-SD had highly efficient removal performance for organic dyes in large volume water sample solutions, and equilibrium was achieved in 0.5 h. The maximum adsorption capacities for Methylene blue (MB) and Congo red (CR) were 881.62 and 1,880.91 mg/g, respectively, at room temperature according to the Langmuir adsorption isotherm (R2 > 0.99). The kinetic data of the two dyes followed pseudo-second-order kinetics. The removal percentage remained high (>85%) after eight filtration-regeneration cycles. Experimental results showed that PS-SD was an excellent adsorbent for water treatment with high recyclability and long life. HIGHLIGHTS Modification of waste materials.; The reusability, and high adsorption capacity of amidated PS-SD.; High value-added products, which can be used for wastewater treatment.

As an efficient data fusion method, over-the-air computation integrates computation and communication by exploiting the superposition property of multiple access channels. In this paper, a framework on deep learning enabled over-the-air computation is proposed, where both the pre-processing and post-processing functions are represented by deep neural networks (DNNs). In this way, the over-the-air computation can approximate any function via learning through the data. The deep over-the-air framework is useful to a variety of machine learning applications on the Internet-of-Things (IoT). The experiments on distribution regression and anomaly detection have shown the effectiveness of the proposed method.

Phototherapy has been intensively investigated as a non-invasive cancer treatment option. However, its clinical translation is still impeded by unsatisfactory therapeutic efficacy and severe phototoxicity. To achieve high therapeutic efficiency and high security, a nanoassembly of Forster Resonance Energy Transfer (FRET) photosensitizer pairs is developed on basis of dual-mode photosensitizer co-loading and photocaging strategy. For proof-of-concept, an erythrocyte-camouflaged FRET pair co-assembly of chlorine e6 (Ce6, FRET donor) and 1,1′-dioctadecyl-3,3,3′,3′-tetramethylindotricarbocyanine iodide (DiR, FRET acceptor) is investigated for breast cancer treatment. Notably, Ce6 in the nanoassemby is quenched by DiR and could be unlocked for photodynamic therapy (PDT) only when DiR is photobleached by 808-nm laser. As a result, Ce6-caused phototoxicity could be well controlled. Under cascaded laser irradiation (808–660 nm), tumor-localizing temperature rise following laser irradiation on DiR not only induces tumor cell apoptosis but also facilitates the tumor penetration of NPs, relieves tumor hypoxia, and promotes the PDT efficacy of Ce6. Such FRET pair-based nanoassembly provides a new strategy for developing multimodal phototherapy nanomedicines with high efficiency and good security., Graphical abstract Image 1, Highlights • Biomimetic carrier-free nanoassembly developed by FRET photosensitizer pairs. • Dual-mode co-loading and photocaging strategy for programmed cancer synergistic phototherapy. • Avoiding the ROS-induced off-target phototoxicity by the intelligently controlled ROS activation.

A spin-flip from a triplet to a singlet excited state, that is, reverse intersystem crossing (RISC), is an attractive route for improving light emission in organic light-emitting diodes, as shown by devices using thermally activated delayed fluorescence (TADF). However, device stability and efficiency roll-off remain challenging issues that originate from a slow RISC rate (kRISC). Here, we report a TADF molecule with multiple donor units that form charge-resonance-type hybrid triplet states leading to a small singlet–triplet energy splitting, large spin–orbit couplings, and a dense manifold of triplet states energetically close to the singlets. The kRISC in our TADF molecule is as fast as 1.5 × 107 s−1, a value some two orders of magnitude higher than typical TADF emitters. Organic light-emitting diodes based on this molecule exhibit good stability (estimated T90 about 600 h for 1,000 cd m−2), high maximum external quantum efficiency (>29.3%) and low efficiency roll-off (

Magnetic-free optical nonreciprocity has been experimentally demonstrated for a genuine single photon at room temperature., Nonreciprocal devices operating at the single-photon level are fundamental elements for quantum technologies. Because magneto-optical nonreciprocal devices are incompatible for magnetic-sensitive or on-chip quantum information processing, all-optical nonreciprocal isolation is highly desired, but its realization at the quantum level is yet to be accomplished at room temperature. Here, we propose and experimentally demonstrate two regimes, using electromagnetically induced transparency (EIT) or a Raman transition, for all-optical isolation with warm atoms. We achieve an isolation of 22.52 ± 0.10 dB and an insertion loss of about 1.95 dB for a genuine single photon, with bandwidth up to hundreds of megahertz. The Raman regime realized in the same experimental setup enables us to achieve high isolation and low insertion loss for coherent optical fields with reversed isolation direction. These realizations of single-photon isolation and coherent light isolation at room temperature are promising for simpler reconfiguration of high-speed classical and quantum information processing.

Railway turnouts monitoring is critical for ensuring the safety of railway systems. Owing to the scarcity of faulty samples, many approaches focus on the fault detection of railway turnouts only using normal samples. However, although the faulty samples are insufficient, they can still provide useful information for fault detection. To improve the fault detection performance of railway turnouts, this paper proposes an enhanced fault detection method incorporating prior faulty information, which deals with the limitation of existing fault detection methods of turnouts that they fail to take insufficient faulty samples into account. In our method, a novel model which shares the architecture of deep autoencoders but has a different training objective is presented. By minimizing the difference between the averaged reconstruction error of normal samples and that of faulty samples, the proposed model enlarges the gap between the normal and faulty classes and making them more separable. The field data collected from a real high-speed railway in China is used to evaluate the proposed method, and a comparative study with several existing approaches is conducted. Experimental results show the effectiveness of the proposed method.

Automated Valet Parking is foreseen as a highly automated function to be implemented on large scale in mass production in the near future. For such function, The authors have developed an innovative system concept using both real time kinematic sensing system and infrastructure IoT (Internet of things) camera to perform AVP function. As the driver activates the AVP function, the infrastructure identifies available parking spots by processing in real time camera frames and as the path is computed, the vehicle executes automated parking maneuvers. This paper introduces the system scheme and system engineering approach as well as key technology bricks to realize AVP function and proves the effectiveness and accuracy of the system through extensive vehicle testing. From system definition to final prototype validation, through Motion control algorithm description or deep learning algorithm tuning method, this paper covers a large spectrum of the engineering domains required to implement such function. Finally a specific Pursuit Model development and calibration approach is elicited for the motion control function and proven in use by AVL Vehicle Prototype platform.

As the environmental problems increasing seriously, governments have to take environmental protection as one of the most important development directions in the future. Large emissions of greenhouse gases (GHGs) will accelerate the greenhouse effect. Comparing with the traditional fuel vehicles, electric vehicle (EV) is a cleaner technology with lower emissions, which can slow the pace of global warming effectively and it has been promoted by governments around the world vigorously. However, there are still some challenges in the development of EV, such as range anxiety, battery safety and so on which will influence consumers’ choices. In order to promote the promotion of new energy vehicles, the government has promulgated a series of incentive policies on EV area. Scholars from all over the world have also studied the policy impact in the field of new energy vehicles. By using CiteSpace literature visualization tool, this paper analyzes relevant literature on the web of science (WOS) to determine the policy influence in EV area. The results indicate the research background of this field. In another hand, costumer and energy supply are the two important impact factors in this area.

High-dimensional entangled states and quantum repeaters are important elements in efficient long-range quantum communications. The high-dimensional property associated with the orbital angular momentum (OAM) of each photon improves the bandwidth of the quantum communication network. However, the generation of high-dimensional entangled states by the concentration method reduces the brightness of the entangled light source, making extensions to these higher dimensions difficult. To overcome this difficulty, we propose to generate entangled qutrits in the OAM space by loading the pump light with OAM. Compared with the concentration method, our experimental results show that the rate of generation of photon pairs improves significantly with an observed 5.5-fold increase. The increased generation rate provides the system with the ability to resist the noise and improve the fidelity of the state. The S value of the Clauser–Horne–Shimony–Holt inequality increases from 2.48 ± 0.07 to 2.69 ± 0.04 under the same background noise, and the fidelity of the reconstructed density matrix improves from 57.8 ± 0.14% to 70 ± 0.17%. These achievements exhibit the enormous advantages of high-dimensional entanglement generation.

Re-utilization of industrial wastes or by-products in construction, including dredged river sediments (DRS), iron tailing slag (ITS) and calcium carbide slag (CCS) etc., can reduce the construction cost, decrease the storage requirement and also be of great benefit to the environment. In this research, the aforementioned wastes (i.e., DRS, ITS and CCS) were synthetically recycled and used as the backfilling materials in the mine goaf to solve the problem of subsidence. Systematic laboratory experiments were conducted to ensure that these recycled materials can fulfill the requirement on the slump value and unconfined compressive strength after 7-day’s curing. The results show that despite that Ordinary Portland cement (OPC) suppresses the slump value, CCS elevates the flowability, and then the maximal flowability is achieved when the mass ratio of DRS to ITS was 70:30 with a cement content of 16.7%. ITS addition into DRS upgrades the strength of the mixtures of DRS and ITS stabilized by OPC and CCS for the skeleton effect and water content reduction. The optimal proportion between DRS, ITS, OPC and CCS in this case is 60:40:16:4 in mass, with a slump value of about 160, and unconfined compression strength after 7-day’s curing about 2.8 MPa. At last, a simple expression to predict the strength of the backfilling materials was proposed referring the gel-space theory and the concept of volumetric solid content, where just two variables (i.e., the binder content and the volumetric solid content) can characterize this complex matrix.

Large integer multiplication has been widely used in fully homomorphic encryption (FHE). Implementing feasible large integer multiplication hardware is thus critical for accelerating the FHE evaluation process. In this paper, a novel and efficient operand reduction scheme is proposed to reduce the area requirement of radix-r butterfly units. We also extend the single-port, merged-bank memory structure to the design of number theoretic transform (NTT) and inverse NTT (INTT) for further area minimization. In addition, an efficient memory addressing scheme is developed to support both NTT/INTT and resolving carries computations. Experimental results reveal that significant area reductions can be achieved for the targeted 786432- and 1179648-bit NTT-based multipliers designed using the proposed schemes in comparison with the related works. Moreover, the two multiplications can be accomplished in 0.196 and 2.21 ms, respectively, based on 90-nm CMOS technology. The low-complexity feature of the proposed large integer multiplier designs is thus obtained without sacrificing the time performance.

Jurassic coal-bearing strata are widely distributed in the North China Craton (NCC) and other areas of northern China. These coal-bearing strata were previously considered to be Early–Middle Jurassic in age based on plant fossils, particularly the fossil assemblage of Coniopteris–Phoenicopsis. Since coal-bearing strata are interbedded with volcanic units in the basins of the Yanshan Fold-and-Thrust Belt (YFTB), northern NCC, isotopic dating of the volcanic units can therefore provide age constraints on the coal-bearing strata and the Coniopteris‒Phoenicopsis assemblage. In this paper, we performed a systematic geological survey and present the results of zircon U–Pb dating of the volcanic units and a pluton in typical basins of the YFTB. These data, combined with the results of previous studies, indicate that the ages of the Nandaling/Xinglonggou, Haifanggou, Jiulongshan, and Tiaojishan/Lanqi formations are 180–168, 169–161, 161–157, and 161–153 Ma, respectively. The ages of the interbedded coal-bearing Yaopo and Beipiao formations are constrained to be 169–161 and 177–169 Ma, respectively. Our results demonstrate that both the coal-bearing strata and the Coniopteris‒Phoenicopsis assemblage are Middle Jurassic in age, which is younger than that previously considered. This fossil assemblage plays a critical role in age constraints on the Jurassic coal-bearing strata. The refinement of its age permits a more precise dating of the coal-bearing strata, especially in northwestern China, where datable interbedded volcanic units are lacking. Stratigraphical framework for the Jurassic strata of Yanshan region is established.The fossil assemblage of Coniopteris‒Phoenicopsis occurred in Middle Jurassic.The Jurassic coal-bearing strata in northern China are mainly Middle Jurassic. Stratigraphical framework for the Jurassic strata of Yanshan region is established. The fossil assemblage of Coniopteris‒Phoenicopsis occurred in Middle Jurassic. The Jurassic coal-bearing strata in northern China are mainly Middle Jurassic.

The emerging vehicular networks are expected to make everyday vehicular operation safer, greener, and more efficient and pave the path to autonomous driving in the advent of the fifth-generation (5G) cellular system. Machine learning, as a major branch of artificial intelligence, has been recently applied to wireless networks to provide a data-driven approach to solve traditionally challenging problems. In this article, we review recent advances in applying machine learning in vehicular networks and attempt to bring more attention to this upcoming area.

This study deals with the problem of fault detection (FD) for linear discrete time-varying (LDTV) systems by combining the parity space-based method with the stationary wavelet transform (SWT). For the purpose of achieving good FD performance and simultaneously reducing the online computational burden resulted by high parity space order, SWT on residual signal is the first step in this study. This allows the formulation of residual generation as the selection of wavelet basis for SWT and the design of time-varying parity space vectors in a framework of multiscale optimisation. A recursive algorithm is developed for the computation of optimal parity space vectors and, based on this, a bank of residual signals can be obtained. Moreover, a weighted multiscale residual evaluation strategy is presented to detect the occurrence of a fault. The major contribution of the study lies in the development of a recursive design of SWT aided parity space vectors, which delivers an improvement of FD performance for a lower parity space order and an easy online implementation. A numerical example is demonstrated to illustrate the effectiveness of the proposed method.

The influences of annealing prior to solution treatment on the grain structure, subsequent aging precipitates, and intergranular corrosion (IGC) of Al-Cu-Li alloy (AA2050) sheet with T6 aging at 448 K (175 °C) were investigated. Annealing impedes the full recrystallization during solution treatment, increasing the population density of T1 (Al2CuLi) precipitates, but decreasing that of θ′ (Al2Cu) precipitates, of the aged alloy. Meanwhile, annealing leads to the heterogeneous distribution of T1 precipitates, increasing the alloy hardness, and decreasing the open-circuit potential of the aged alloy. With prolonged aging time, the corrosion mode of the aged AA2050 samples with and without annealing evolved in a similar manner. The corrosion mode as a function of aging may be summarized as local IGC with pitting and general IGC with pitting (following initial aging and under the underaged condition), pitting corrosion (later in the under-aging stage), pitting with slight IGC (near the peak-aged condition), and pitting with local IGC (under the overaging condition). The annealing treatment hinders IGC propagation on the rolling surface while accelerating the IGC on transverse surfaces.

This article presents our initial results in deep learning for channel estimation and signal detection in orthogonal frequency-division multiplexing (OFDM). OFDM has been widely adopted in wireless broadband communications to combat frequency-selective fading in wireless channels. In this article, we take advantage of deep learning in handling wireless OFDM channels in an end-to-end approach. Different from existing OFDM receivers that first estimate CSI explicitly and then detect/recover the transmitted symbols with the estimated CSI, our deep learning based approach estimates CSI implicitly and recovers the transmitted symbols directly. To address channel distortion, a deep learning model is first trained offline using the data generated from the simulation based on the channel statistics and then used for recovering the online transmitted data directly. From our simulation results, the deep learning based approach has the ability to address channel distortions and detect the transmitted symbols with performance comparable to minimum mean-square error (MMSE) estimator. Furthermore, the deep learning based approach is more robust than conventional methods when fewer training pilots are used, the cyclic prefix (CP) is omitted, and nonlinear clipping noise is presented. In summary, deep learning is a promising tool for channel estimation and signal detection in wireless communications with complicated channel distortions and interferences.

Effective visual representation plays an important role in the scene classification systems. While many existing methods are focused on the generic descriptors extracted from the RGB color channels, we argue the importance of depth context, since scenes are composed with spatial variability and depth is an essential component in understanding the geometry. In this letter, we present a novel depth representation for RGB-D scene classification based on a specific designed convolutional neural network (CNN). Contrast to previous deep models that transfer from pretrained RGB CNN models, we harness model by using the multiviewpoint depth image augmentation to overcome the data scarcity problem. The proposed CNN framework contains the dilated convolutions to expand the receptive field and a subsequent spatial pooling to aggregate multiscale contextual information. The combination of contextual design and multiviewpoint depth images are important toward a more compact representation, compared to directly using original depth images or off-the-shelf networks. Through extensive experiments on SUN RGB-D dataset, we demonstrate that the representation outperforms recent state of the arts, and combining it with standard CNN-based RGB features can lead to further improvements.

Organo-metal-halide perovskites are a promising set of materials for optoelectronic applications such as solar cells, light emitting diodes and lasers. Perovskite thin films have demonstrated amplified spontaneous emission thresholds as low as 1.6 μJ cm-2 and lasing thresholds as low as 0.2 μJ cm-2. Recently the performance of perovskite light emitting diodes has rapidly risen due to the formation of quasi 2D films using bulky ligands such as phenylethylammonium. Despite the high photoluminescent yield and external quantum efficiency of quasi 2D perovskites, few reports exist on amplified spontaneous emission. We show within this report that the threshold for amplified spontaneous emission of quasi 2D perovskite films increases with the concentration of phenylethylammonium. We attribute this increasing threshold to a charge transfer state at the PEA interface that competes for excitons with the ASE process. Additionally, the comparatively slow inter-grain charge transfer process cannot significantly contribute to the fast radiative recombination in amplified spontaneous emission. These results suggest that relatively low order PEA based perovskite films that are suitable for LED applications are not well suited for lasing applications. However high order films were able to maintain their low threshold values and may still benefit from improved stability.

In the context of global warming and energy shortages, using photocatalysis technology to reduce the concentration of CO2 and convert it into C1 fuel has been extensively studied. However, the application of this technology is inevitably hindered by low energy conversion efficiency, low selectivity, and the use of sacrificial agents. In this work, we developed a novel flower-like La doped BiOBr nanosheets, and as-prepared catalyst exhibited excellent photocatalytic CO2 conversion of 22.77 μmol g−1h−1 and CH3OH selectivity of 92.41%. The influence of doped La and catalytic mechanism were further investigated, and the results revealed that the introduction of La species extended the photoresponse range of BiOBr photocatalyst and reduced the photoelectron-hole recombination rate, thus improving the photoactivity of pure BiOBr. This work brings a guidance for designing new high performance photocatalysts in future.

Establishing a quantitative similarity between different datasets has gained prevalence and significance in many applications of process control. In industrial practice, process data are usually multi-dimensional, nonlinearly correlated, and with unknown time-varying distribution, which raise immense challenge for reasonably evaluating similarity. To address this issue, a novel similarity metric based on deep autoencoder (DAE) and the Wasserstein distance is proposed in this paper. Specifically, DAE is used to first capture nonlinear relationship embedded in multivariate process data, and the reconstruction error acts as an indicator to reveal discrepancy between two datasets. After that, the similarity is characterized by evaluating the gap between reconstruction error distributions using Wasserstein distance. The proposed similarity metric has wide applicability in a variety of data analytics tasks including pattern matching, fault diagnosis and mode classifications. Both simulated data and industrial data collected from a real iron-making process are utilized to carry out comprehensive case studies. It is shown that the proposed similarity metric not only enjoys better rationality and sensitivity than generic similarity metrics, but also effectively improves the accuracy of fault diagnosis and mode classification based on big process data.

High performance Ni-based porous catalytically activated absorbers (PCAA) was prepared and experimentally studied for methane dry reforming reaction. Effects of critical operating parameters were discussed to disclose their influence on variation trends of outlet composition of each species. With experimental results, kinetic models of PCAAs for the complex methane dry reforming reaction were extracted by using plug flow model, nonlinear least squares fitting method as well as genetic algorithm. It was found that (1) High CO2/CH4 ratio is beneficial to increase CH4 conversion and CO/H2 selectivities; (2) High temperature condition would increase conversions of CH4 and CO2, but decrease CO selectivity; (3) The increase of molar flow rate would decrease CH4 conversion & CO/H2 selectivities, and increase CO2 conversion; (4) Compared to other reported models, the established kinetic models can predict the outlet molar fractions of species well, showing great potential for the design and optimization of PCAA-type solar reactor.

Pipelines are the fifth type of transportation, which has the highest efficiency and the least environmental damage for gas or oil transmission. Recently, the pipeline failures due to corrosion defects have attracted people's attention in maintaining pipeline integrity. Based on the double circular arc (DCA) model and elastic-plastic theory, an analytical solution of the corroded pipelines under internal and external pressures was obtained. And then, a new burst pressure equation of the corroded pipelines with high strength was further provided according to the von Mises criterion and previous equations. Compared with previous equations, this equation greatly extends the application scope and increases the calculation accuracy. The accuracy of our equation is validated by comparing with the full-scale experimental data. The effects of the thickness to diameter ratio, corrosion ratio and yield strength on the burst pressure are carefully investigated. The results reveal that when the thickness to diameter ratio is less than 0.1, the calculated results show a good agreement with the experimental data for the corroded and high strength pipes. Our research can provide a foundation for replacing the corroded pipelines at the bottom of the sea. In addition, our research is beneficial for the engineering design and integrity assessment of oil and gas pipelines.

How do social groups, such as Facebook groups and Wechat groups, dynamically evolve over time? How do people join the social groups, uniformly or with burst? What is the pattern of people quitting from groups? Is there a simple universal model to depict the come-and-go patterns of various groups? In this article, we examine temporal evolution patterns of more than 100 thousands social groups with more than 10 million users. We surprisingly find that the evolution patterns of real social groups goes far beyond the classic dynamic models like SI and SIR. For example, we observe both diffusion and non-diffusion mechanism in the group joining process, and power-law decay in group quitting process, rather than exponential decay as expected in SIR model. Therefore, we propose a new model come N go , a concise yet flexible dynamic model for group evolution. Our model has the following advantages: (a) Unification power: it generalizes earlier theoretical models and different joining and quitting mechanisms we find from observation. (b) Succinctness and interpretability: it contains only six parameters with clear physical meanings. (c) Accuracy: it can capture various kinds of group evolution patterns preciously, and the goodness of fit increases by 58% over baseline. (d) Usefulness: it can be used in multiple application scenarios, such as forecasting and pattern discovery. Furthermore, our model can provide insights about different evolution patterns of social groups, and we also find that group structure and its evolution has notable relations with temporal patterns of group evolution.

This paper deals with the problem of parity space-based fault detection (FD) for linear discrete time-varying systems. In order to simultaneously ensure the performance of FD and avoid the heavy computational burden of high dimensional parity space, the techniques of stationary wavelet transform (SWT) on residual signals are applied in the paper. Based on this, the design of residual generators can be formulated as generalized optimization problems and a recursive algorithm is developed to a feasible solution of parity vectors. Furthermore, a bank of residual signals are generated at multiscales. The significant advantage of the SWT based parity space approach is the improvement of FD performance with low parity space order and an easy online realization. Finally, a numerical example is given to demonstrate the effectiveness of the proposed approach.

Qualitative trend analysis (QTA) is an effective tool for process data analysis, the applications of which can be found in a variety of fields, such as process monitoring, fault diagnosis, and data mining. Reliable and accurate trend extraction of sensor data is the first and indispensable step in QTA. In this article, a new trend extraction algorithm is developed that is based on global optimization of the polynomial fit of the process data. Different from most existing works, this newly proposed algorithm solves the trend extraction task by simultaneously and globally estimating the episode number, the boundary time points of the episode, and the fitted polynomial coefficients, which shows improved performance over other nonglobally optimal trend extraction algorithms and requires less a priori knowledge than the existing globally optimal trend algorithms. The effectiveness of the algorithm is illustrated by testing on a variety of simulation and real blast furnace data. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3374–3383, 2017

Ocean thermal energy refers to the thermal potential energy produced by the temperature difference between the warm surface seawater and the cold deep seawater. In this paper, a siphon flash evaporation desalination system using ocean thermal energy is proposed. Because it can utilize the ocean thermal energy directly for desalination, siphon flash evaporation desalination system has relatively higher energy efficiency compared with converting ocean thermal energy into electric energy and then using electric energy for desalination. The working principle of this system is introduced firstly. Then, the exergy, exergy loss and exergy efficiency in the flash evaporation, condensation and the whole system are carried out quantitatively. The results show that the exergy efficiency of the system which directly utilizing ocean thermal energy for desalination reaches to 7.81% under design conditions; lower surface seawater temperature, higher deep seawater temperature and higher flash temperature can result in an increasing of system efficiency, while the whole energy consumption shall also be taken into consideration. Then the simulation model of the whole system is created in ASPEN PLUS in order to investigate the influence of some most important parameters, such as surface seawater temperatures, deep seawater temperatures and difference of inlet temperature between surface and deep seawater. Finally, an experimental platform is established based on the working principle and process to verify the validity of the working principle and the simulation model. The siphon flash evaporation desalination system provides a novel method of direct high efficient conversion and utilization of ocean thermal energy and this work can provide the theoretical support for the feasibility of similar engineering applications.

Breast cancer contributes to high mortality rates as a result of metastasis. Tumor-derived exosomes facilitate the development of the premetastatic environment, interacting and inhibiting the normal function of immune cells, thereby forming an immunosuppressive microenvironment for tumor metastasis. Herein, the platelet and neutrophil hybrid cell membrane (PNM) was embellished on a gold nanocage (AuNC) surface called nanosponges and nanokillers (NSKs). NSKs can simultaneously capture and clear the circulating tumor cells (CTCs) and tumor-derived exosomes via high-affinity membrane adhesion receptors, effectively cutting off the connection between exosomes and immune cells. Bionic NSK is loaded with doxorubicin (DOX) and indocyanine green (ICG) for synergic chemo-photothermal therapy. NSKs show greater cellular uptake, deeper tumor penetration, and higher cytotoxicity to tumor cells in comparison to non-coated AuNCs or single-coated AuNCs in vitro. In vivo, the multipurpose NSKs could not only completely ablate the primary tumor but also inhibit breast cancer metastasis with high efficiency in xenograft and orthotopic breast tumor-bearing models. Thus, NSKs could be a promising nanomedicine for the future clinical intervention of breast cancer metastasis.

In this paper, a novel T-channel field effect transistor with three input terminals(Ti-TcFET) is proposed. The channel of a Ti-TcFET consists of horizontal and vertical sections. Thetop gate is above the horizontal channel, while the front gate and back gate are on either side of the vertical channel. The T-shaped channel structure increases the coupling area between the top gate and the front and back gates, which improves the ability of the gate electrodes to control the channel. What&rsquo, s more, it makes the top gate have almost the same control ability for the channel as the front gate and the back gate. This unique structure design brings a unique function in that the device is turned on only when two or three inputs are activated. Silvaco technology computer-aided design (TCAD) simulations are used to verify the current characteristics of the proposed Ti-TcFET. The current characteristics of the device are theoretically analyzed, and the results show that the theoretical analysis agrees with the TCAD simulation results. The proposed Ti-TcFET devices with three input terminals can be used to simplify the complex circuits in a compact style with reduced counts of transistors compared with the traditional complementarymetal&ndash, oxide&ndash, semiconductor/ fin field-effect transistors (CMOS/FinFETs) with a single inputterminal and thus provides a new idea for future circuit designs.