The Li metal battery with ultrahigh-nickel cathode(LiNi_(x)M_(1-x)O_(2),M=Mn,Co,and x≥0.9)under high-voltage is regarded as one of the most promising approaches to fulfill the ambitious target of 400 Wh/***,the pract...
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The Li metal battery with ultrahigh-nickel cathode(LiNi_(x)M_(1-x)O_(2),M=Mn,Co,and x≥0.9)under high-voltage is regarded as one of the most promising approaches to fulfill the ambitious target of 400 Wh/***,the practical application is impeded by the instability of electrode/electrolyte interface and Ni-rich cathode *** we proposed an electron-defect electrolyte additive trimethyl borate(TMB)which is paired with the commercial carbonate electrolyte to construct highly conductive fluorine-and boron-rich cathode electrolyte interface(CEI)on LiNi_(0.9)Co_(0.05)Mn_(0.05)O_(2)(NCM90)surface and solid electrolyte interphase(SEI)on lithium metal *** modified CEI effectively mitigates the structural transformation from layered to disordered rock-salt phase,and consequently alleviate the dissolution of transition metal ions(TMs)and its“cross-talk”effect,while the enhanced SEI enables stable lithium plating/striping and thus demonstrated good compatibility between electrolyte and lithium metal *** a result,the common electrolyte with 1 wt%TMB enables 4.7 V NCM90/Li cell cycle stably over 100 cycles with 70%capacity *** work highlights the significance of the electron-defect boron compounds for designing desirable interfacial chemistries to achieve high performance NCM90/Li battery under high voltage operation.
The Vehicle-to-Grid (V2G) network is a smart grid technology generated under the background of the rapid development of new energy technology, which allows mobile energy storage vehicles (MESVs) to realize bidirection...
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Nowadays, the proliferation of open Internet of Things (IoT) devices has made IoT systems increasingly vulnerable to cyber attacks. It is of great practical significance to solve the security issues of IoT systems. Dr...
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The development of dual functional material for cyclic CO_(2)capture and hydrogenation is of great significance for converting diluted CO_(2)into valuable fuels,but suffers from kinetic limitation and deactivation of ...
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The development of dual functional material for cyclic CO_(2)capture and hydrogenation is of great significance for converting diluted CO_(2)into valuable fuels,but suffers from kinetic limitation and deactivation of adsorbent and ***,we engineered a series of RuNa/γ-Al_(2)O_(3)materials,varying the size of ruthenium from single atoms to clusters/*** coordination environment and structure sensitivity of ruthenium were quantitatively investigated at atomic *** findings reveal that the reduced Ru nanoparticles,approximately 7.1 nm in diameter with a Ru-Ru coordination number of 5.9,exhibit high methane formation activity and selectivity at 340°*** Ru-Na interfacial sites facilitate CO_(2)migration through a deoxygenation pathway,involving carbonate dissociation,carbonyl formation,and ***-situ experiments and theoretical calculations show that stable carbonyl intermediates on metallic Ru nanoparticles facilitate heterolytic C–O scission and C–H bonding,significantly lowering the energy barrier for activating stored CO_(2).
Iris recognition is a secure biometric technology known for its stability and privacy. With no two irises being identical and little change throughout a person’s lifetime, iris recognition is considered more reliable...
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With the rapid development of information technologies,industrial Internet has become more open,and security issues have become more *** endogenous security mechanism can achieve the autonomous immune mechanism withou...
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With the rapid development of information technologies,industrial Internet has become more open,and security issues have become more *** endogenous security mechanism can achieve the autonomous immune mechanism without prior ***,endogenous security lacks a scientific and formal definition in industrial ***,firstly we give a formal definition of endogenous security in industrial Internet and propose a new industrial Internet endogenous security architecture with cost ***,the endogenous security innovation mechanism is clearly ***,an improved clone selection algorithm based on federated learning is ***,we analyze the threat model of the industrial Internet identity authentication scenario,and propose cross-domain authentication mechanism based on endogenous key and zero-knowledge *** conduct identity authentication experiments based on two types of blockchains and compare their experimental *** on the experimental analysis,Ethereum alliance blockchain can be used to provide the identity resolution services on the industrial *** of Things Application(IOTA)public blockchain can be used for data aggregation analysis of Internet of Things(IoT)edge ***,we propose three core challenges and solutions of endogenous security in industrial Internet and give future development directions.
Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone...
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Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone to serious intra-class and inter-class imbalance problems, which can significantly degrade the classification performance. To address the above issues, we propose the multi-label weighted broad learning system(MLW-BLS) from the perspective of label imbalance weighting and label correlation mining. Further, we propose the multi-label adaptive weighted broad learning system(MLAW-BLS) to adaptively adjust the specific weights and values of labels of MLW-BLS and construct an efficient imbalanced classifier set. Extensive experiments are conducted on various datasets to evaluate the effectiveness of the proposed model, and the results demonstrate its superiority over other advanced approaches.
Instance co-segmentation aims to segment the co-occurrent instances among two *** task heavily relies on instance-related cues provided by co-peaks,which are generally estimated by exhaustively exploiting all paired c...
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Instance co-segmentation aims to segment the co-occurrent instances among two *** task heavily relies on instance-related cues provided by co-peaks,which are generally estimated by exhaustively exploiting all paired candidates in point-to-point ***,such patterns could yield a high number of false-positive co-peaks,resulting in over-segmentation whenever there are mutual *** tackle with this issue,this paper proposes an instance co-segmentation method via tensor-based salient co-peak search(TSCPS-ICS).The proposed method explores high-order correlations via triple-to-triple matching among feature maps to find reliable co-peaks with the help of co-saliency *** proposed method is shown to capture more accurate intra-peaks and inter-peaks among feature maps,reducing the false-positive rate of co-peak *** having accurate co-peaks,one can efficiently infer responses of the targeted *** on four benchmark datasets validate the superior performance of the proposed method.
In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior(human standing) for the construction of a smart campus. Based on deep learning, we propose an intellige...
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In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior(human standing) for the construction of a smart campus. Based on deep learning, we propose an intelligentstanding human detection (ISHD) method based on an improved single shot multibox detector to detect thetarget of standing human posture in the scene frame of exam room video surveillance at a specific examinationstage. ISHD combines the MobileNet network in a single shot multibox detector network, improves the posturefeature extractor of a standing person, merges prior knowledge, and introduces transfer learning in the trainingstrategy, which greatly reduces the computation amount, improves the detection accuracy, and reduces the trainingdifficulty. The experiment proves that the model proposed in this paper has a better detection ability for the smalland medium-sized standing human body posture in video test scenes on the EMV-2 dataset.
Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In thi...
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Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In this paper, we conducted a comprehensive evaluation of large multimodal models, such as GPT4V and Gemini, in various text-related visual tasks including text recognition, scene text-centric visual question answering(VQA), document-oriented VQA, key information extraction(KIE), and handwritten mathematical expression recognition(HMER). To facilitate the assessment of optical character recognition(OCR) capabilities in large multimodal models, we propose OCRBench, a comprehensive evaluation benchmark. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available. Furthermore, our study reveals both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression *** importantly, the baseline results presented in this study could provide a foundational framework for the conception and assessment of innovative strategies targeted at enhancing zero-shot multimodal *** evaluation pipeline and benchmark are available at https://***/Yuliang-Liu/Multimodal OCR.
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