Hydrogen economy has emerged as a very promising alternative to the current hydrocarbon economy, which involves the process of harvesting renewable energy to split water into hydrogen and oxygen and ...then further utilization of clean hydrogen fuel. The production of hydrogen by water electrolysis is an essential prerequisite of the hydrogen economy with zero carbon emission. Among various water electrolysis technologies, alkaline water splitting has been commercialized for more than 100 years, representing the most mature and economic technology. Here, the historic development of water electrolysis is overviewed, and several critical electrochemical parameters are discussed. After that, advanced nonprecious metal electrocatalysts that emerged recently for negotiating the alkaline oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) are discussed, including transition metal oxides, (oxy)hydroxides, chalcogenides, phosphides, and nitrides for the OER, as well as transition metal alloys, chalcogenides, phosphides, and carbides for the HER. In this section, particular attention is paid to the catalyst synthesis, activity and stability challenges, performance improvement, and industry‐relevant developments. Some recent works about scaled‐up catalyst synthesis, novel electrode designs, and alkaline seawater electrolysis are also spotlighted. Finally, an outlook on future challenges and opportunities for alkaline water splitting is offered, and potential future directions are speculated.
The hydrogen economy has emerged as a very promising alternative to the current hydrocarbon economy, which involves the process of harvesting renewable energy to split water into hydrogen and oxygen and then further utilization of hydrogen fuel. Alkaline water splitting represents the most mature and economic technology for clean hydrogen production, making high potential for successful implementation of hydrogen economy.
Feature extraction for fault signals is critical and difficult in all kinds of fault detection schemes. A novel simple and effective method of faulty feeder detection in resonant grounding ...distribution systems based on the continuous wavelet transform (CWT) and convolutional neural network (CNN) is presented in this paper. The time-frequency gray scale images are acquired by applying the CWT to the collected transient zero-sequence current signals of the faulty feeder and sound feeders. The features of the gray scale image will be extracted adaptively by the CNN, which is trained by a large number of gray scale images under various kinds of fault conditions and factors. The features extraction and the faulty feeder detection can be implemented by the trained CNN simultaneously. As a comparison, two faulty feeder detection methods based on artificial feature extraction and traditional machine learning are introduced. A practical resonant grounding distribution system is simulated in power systems computer aided design/electromagnetic transients including DC, the effectiveness and performance of the proposed faulty feeder detection method is compared and verified under different fault circumstances.
Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, long short-term memory (LSTM) networks have shown promising performance in this task due to ...their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, global context-aware attention LSTM, for skeleton-based action recognition, which is capable of selectively focusing on the informative joints in each frame by using a global context memory cell. To further improve the attention capability, we also introduce a recurrent attention mechanism, with which the attention performance of our network can be enhanced progressively. Besides, a two-stream framework, which leverages coarse-grained attention and fine-grained attention, is also introduced. The proposed method achieves state-of-the-art performance on five challenging datasets for skeleton-based action recognition.
Feature Boosting Network For 3D Pose Estimation Liu, Jun; Ding, Henghui; Shahroudy, Amir ...
IEEE transactions on pattern analysis and machine intelligence,
02/2020, Letnik:
42, Številka:
2
Journal Article
Recenzirano
Odprti dostop
In this paper, a feature boosting network is proposed for estimating 3D hand pose and 3D body pose from a single RGB image. In this method, the features learned by the convolutional layers are ...boosted with a new long short-term dependence-aware (LSTD) module, which enables the intermediate convolutional feature maps to perceive the graphical long short-term dependency among different hand (or body) parts using the designed Graphical ConvLSTM. Learning a set of features that are reliable and discriminatively representative of the pose of a hand (or body) part is difficult due to the ambiguities, texture and illumination variation, and self-occlusion in the real application of 3D pose estimation. To improve the reliability of the features for representing each body part and enhance the LSTD module, we further introduce a context consistency gate (CCG) in this paper, with which the convolutional feature maps are modulated according to their consistency with the context representations. We evaluate the proposed method on challenging benchmark datasets for 3D hand pose estimation and 3D full body pose estimation. Experimental results show the effectiveness of our method that achieves state-of-the-art performance on both of the tasks.
The high similarities of different real-world vehicles and great diversities of the acquisition views pose grand challenges to vehicle re-identification (ReID), which traditionally maps the vehicle ...images into a high-dimensional embedding space for distance optimization, vehicle discrimination, and identification. To improve the discriminative capability and robustness of the ReID algorithm, we propose a novel end-to-end embedding adversarial learning network (EALN) that is capable of generating samples localized in the embedding space. Instead of selecting abundant hard negatives from the training set, which is extremely difficult if not impossible, with our embedding adversarial learning scheme, the automatically generated hard negative samples in the specified embedding space can greatly improve the capability of the network for discriminating similar vehicles. Moreover, the more challenging cross-view vehicle ReID problem, which requires the ReID algorithm to be robust with different query views, can also benefit from such a scheme based on the artificially generated cross-view samples. We demonstrate the promise of EALN through extensive experiments and show the effectiveness of hard negative and cross-view generation in facilitating vehicle ReID based on the comparisons with the state-of-the-art schemes.
Photo/electrochemical splitting of water to hydrogen (H 2 ) fuel is a sustainable way of meeting our energy demands at no environmental cost, but significant challenges remain: for example, the ...sluggish anodic reaction imposes a considerable overpotential requirement. By contrast, urea electrolysis offers the prospect of energy-saving H 2 production together with urea-rich wastewater purification, whereas the lack of inexpensive and efficient urea oxidation reaction (UOR) catalysts places constraints on the development of this technique. Here we report a porous rod-like NiMoO 4 with high oxidation states of the metal elements enabling highly efficient UOR electrocatalysis, which can be readily produced through annealing solid NiMoO 4 · x H 2 O as a starting precursor in Ar. This precursor gives the derived Ni/NiO/MoO x nanocomposite when switching the shielding gas from Ar to H 2 /Ar, exhibiting platinum-like activity for the hydrogen evolution reaction (HER) in alkaline electrolytes. Assembling an electrolytic cell using our developed UOR and HER catalysts as the anode and cathode can provide a current density of 10 milliamperes per square centimeter at a cell voltage of mere 1.38 volts, as well as remarkable operational stability, representing the best yet reported noble-metal-free urea electrolyser. Our results demonstrate the potential of nickel–molybdenum-based materials as efficient electrode catalysts for urea electrolysers that promises cost-effective and energy-saving H 2 production.
Background
As a distributed technology, blockchain has attracted increasing attention from stakeholders in the medical industry. Although previous studies have analyzed blockchain applications from ...the perspectives of technology, business, or patient care, few studies have focused on actual use-case scenarios of blockchain in health care. In particular, the outbreak of COVID-19 has led to some new ideas for the application of blockchain in medical practice.
Objective
This paper aims to provide a systematic review of the current and projected uses of blockchain technology in health care, as well as directions for future research. In addition to the framework structure of blockchain and application scenarios, its integration with other emerging technologies in health care is discussed.
Methods
We searched databases such as PubMed, EMBASE, Scopus, IEEE, and Springer using a combination of terms related to blockchain and health care. Potentially relevant papers were then compared to determine their relevance and reviewed independently for inclusion. Through a literature review, we summarize the key medical scenarios using blockchain technology.
Results
We found a total of 1647 relevant studies, 60 of which were unique studies that were included in this review. These studies report a variety of uses for blockchain and their emphasis differs. According to the different technical characteristics and application scenarios of blockchain, we summarize some medical scenarios closely related to blockchain from the perspective of technical classification. Moreover, potential challenges are mentioned, including the confidentiality of privacy, the efficiency of the system, security issues, and regulatory policy.
Conclusions
Blockchain technology can improve health care services in a decentralized, tamper-proof, transparent, and secure manner. With the development of this technology and its integration with other emerging technologies, blockchain has the potential to offer long-term benefits. Not only can it be a mechanism to secure electronic health records, but blockchain also provides a powerful tool that can empower users to control their own health data, enabling a foolproof health data history and establishing medical responsibility.
The widespread use of surveillance cameras toward smart and safe cities poses the critical but challenging problem of vehicle reidentification (Re-ID). The state-of-the-art research work performed ...vehicle Re-ID relying on deep metric learning with a triplet network. However, most existing methods basically ignore the impact of intraclass variance-incorporated embedding on the performance of vehicle reidentification, in which robust fine-grained features for large-scale vehicle Re-ID have not been fully studied. In this paper, we propose a deep metric learning method, group-sensitive-triplet embedding (GS-TRE), to recognize and retrieve vehicles, in which intraclass variance is elegantly modeled by incorporating an intermediate representation "group" between samples and each individual vehicle in the triplet network learning. To capture the intraclass variance attributes of each individual vehicle, we utilize an online grouping method to partition samples within each vehicle ID into a few groups, and build up the triplet samples at multiple granularities across different vehicle IDs as well as different groups within the same vehicle ID to learn fine-grained features. In particular, we construct a large-scale vehicle database "PKU-Vehicle," consisting of 10 million vehicle images captured by different surveillance cameras in several cities, to evaluate the vehicle Re-ID performance in real-world video surveillance applications. Extensive experiments over benchmark datasets VehicleID, VeRI, and CompCar have shown that the proposed GS-TRE significantly outperforms the state-of-the-art approaches for vehicle Re-ID.
Due to the difficulty in locating high-resistance grounding faults, this paper proposes a novel fault location method for HVdc transmission lines by considering double-end unsynchronized using ...Hilbert-Huang transform and one-dimensional convolutional neural network (1D-CNN). After the fault signal is collected at both ends, the proposed method can achieve high-precision fault location, requiring only the two ends data transmission without time synchronization. After Empirical Mode Decomposition (EMD), the high-frequency components of the double-terminal fault signals are connected in series to make a characteristic waveform. This waveform contains characteristics of different fault types and distances, which can be learned by CNN. The trained CNN can then be used to achieve fault location effectively. As a comparison, two fault location methods based on traditional traveling wave and machine learning are introduced. Electromagnetic transient simulation software PSCAD/EMTDC has been used to carry out various types of fault simulation on the ± 500 kV HVdc transmission system. The results show that the proposed method can reliably and accurately locate line faults under fault resistance up to 5200 Ω.
In this review, the factors influencing the power conversion efficiency (PCE) of perovskite solar cells (PSCs) is emphasized. The PCE of PSCs has remarkably increased from 3.8% to 23.7%, but on the ...other hand, poor stability is one of the main facets that creates a huge barrier in the commercialization of PSCs. Herein, a concise overview of the current efforts to enhance the stability of PSCs is provided; moreover, the degradation causes and mechanisms are summarized. The strategies to improve device stability are portrayed in terms of structural effects, a photoactive layer, hole‐ and electron‐transporting layers, electrode materials, and device encapsulation. Last but not least, the economic feasibility of PSCs is also vividly discussed.
In parallel with the tremendous progress in the efficiency of perovskite solar cells, research on the issue of instability has attracted enormous attention. In this review, the strategies to enhance the stability from the perspectives of the device structure, the photoactive layer, hole‐ and electron‐transporting layers, electrode materials, and device encapsulation are portrayed.
PDF
