This paper investigates the problem of spacing control between adjacent trains in train formation and proposes a distributed train-formation speed-convergence cooperative-control algorithm based on ...barrier Lyapunov function. Considering practical limitations such as communication distance and bandwidth constraints during operation, not all trains can directly communicate with the leader and obtain the expected trajectory it sends, making it difficult to maintain formation consistency as per the predetermined ideal state. Furthermore, to address the challenge of unknown external disturbances encountered by trains during operation, this paper designs a distributed observer deployed on each train in the formation. This observer can estimate and dynamically compensate for unknown reference trajectories and disturbances solely based on the states of adjacent trains. Additionally, to ensure that the spacing between adjacent trains remains within a predefined range, a safety hard constraint, this paper encodes the spacing hard constraint using barrier Lyapunov function. By integrating nonlinear adaptive control theory to handle model parameter uncertainties, a barrier Lyapunov function-based adaptive control method is proposed, which enables all trains to track the reference trajectory while ensuring that the spacing between them remains within the preset interval, therefore guaranteeing the asymptotic stability of the closed-loop system. Finally, a practical example using data from the Guangzhou Metro Line 22, specifically the route from Shiguang Road Station to Chentougang Station over three stations and two sections, is utilized to validate the effectiveness and robustness of the proposed algorithm.
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In this work, rotating bending fatigue, scanning electron microscope in-situ fatigue, pre-fatigue (and post-fatigue) electron backscatter diffraction tests were carried out for rolled Ti–6Al–4V ...alloys. The macrozone sizes and orientations of the materials were classified and the effect of macrozones on fatigue cracking behavior and fracture mechanisms was studied. The results indicated that intra-granular fracture was dominated in the process of crack propagation. Basal and prismatic slip systems were favored for the macrozones whose crack paths were straight and zigzag, respectively. Multiple active slip systems can also induce the fatigue crack deflections inside grains. Moreover, fatigue crack propagation rate, threshold stress intensity factor range (ΔKth) and fracture toughness (KIC) were measured for different kinds of macrozones. For the macrozones favorably orientated for prismatic slip, their crack propagation resistance and ΔKth were excellent. However, the KIC values for different macrozones were similar. Finally, the effect of macrozone orientations on fracture mechanisms (cleavage and plastic fracture) was discussed through a combination of Schmid factor, active slip systems and the Δθ angle (between ɑ-Ti phase (0001) plane normals (c-axes) and cyclic load directions). The Δθ is an adequate parameter to predict the fatigue fracture modes.
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GEOZS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
An ideal anti-counterfeiting technique has to be inexpensive, mass-producible, nondestructive, unclonable and convenient for authentication. Although many anti-counterfeiting technologies have been ...developed, very few of them fulfill all the above requirements. Here we report a non-destructive, inkjet-printable, artificial intelligence (AI)-decodable and unclonable security label. The stochastic pinning points at the three-phase contact line of the ink droplets is crucial for the successful inkjet printing of the unclonable security labels. Upon the solvent evaporation, the three-phase contact lines are pinned around the pinning points, where the quantum dots in the ink droplets deposited on, forming physically unclonable flower-like patterns. By utilizing the RGB emission quantum dots, full-color fluorescence security labels can be produced. A convenient and reliable AI-based authentication strategy is developed, allowing for the fast authentication of the covert, unclonable flower-like dot patterns with different sharpness, brightness, rotations, amplifications and the mixture of these parameters.
Simulating the human brain for neuromorphic computing has attractive prospects in the field of artificial intelligence. Optoelectronic synapses have been considered to be important cornerstones of ...neuromorphic computing due to their ability to process optoelectronic input signals intelligently. In this work, optoelectronic synapses based on all‐inorganic perovskite nanoplates are fabricated, and the electronic and photonic synaptic plasticity is investigated. Versatile synaptic functions of the nervous system, including paired‐pulse facilitation, short‐term plasticity, long‐term plasticity, transition from short‐ to long‐term memory, and learning‐experience behavior, are successfully emulated. Furthermore, the synapses exhibit a unique memory backtracking function that can extract historical optoelectronic information. This work could be conducive to the development of artificial intelligence and inspire more research on optoelectronic synapses.
Artificial optoelectronic synapses are considered to be essential cornerstones of visual‐related artificial intelligence. A two‐terminal optoelectronic synapse employing CsPbBr3 perovskite nanoplates, which implement electronic synaptic plasticity and photonic synaptic plasticity simultaneously, is fabricated. In‐depth research shows that these devices have a unique memory backtracking function that can extract historical optoelectronic information to emulate the biological synapse.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Quantum dot light emitting diodes (QLEDs) are increasingly attractive owing to their compatibility with the inkjet printing process and potential application in low-cost large-area full-color ...pixelated display. The strategy for controlling the morphology of the quantum dot layer is definitely critical for realizing all-solution processed QLEDs with high performance, which certainly requires in-depth thinking regarding the design of ink composition and their optimization in the printing process. Herein, by carefully controlling the quantum dot ink composition and physicochemical properties, we demonstrate that the viscosity, contact angle, and the three-phase contact line moving would affect the final morphology of the quantum dot film formed by inkjet printing. We achieved coffee ring-free and low-roughness quantum dot film, and all-solution processed QLEDs with normal structure were fabricated for the first time. The devices have a low turn-on voltage of 2.0 V, a luminance of 12100 cd/m2 at the voltage of 12 V, and a maximum current efficiency of 4.44 cd/A at the luminance of 1974 cd/m2, which is the best result to date for inkjet-printed red QLEDs. The results will pave the way for future application of inkjet printing in solution processed pixelated QLED display.
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With the ever-growing demand for a greater number of pixels, next-generation displays have challenging requirements for resolution as well as colour gamut. Here, to meet this need, quantum-dot ...light-emitting diodes (QLEDs) with an ultrahigh pixel resolution of 9,072–25,400 pixels per inch are realized via transfer printing combined with the Langmuir–Blodgett film technology. To reduce the leakage current of the devices, a honeycomb-patterned layer of wide-bandgap quantum dots is embedded between the light-emitting quantum-dot pixels as a non-emitting charge barrier layer. Red and green QLEDs are demonstrated. Notably, the red devices achieve a brightness of up to 262,400 cd m−2 at an applied voltage of 8 V and a peak external quantum efficiency of 14.72%. This work provides a promising way for achieving ultrahigh-resolution QLED devices with high performance.The demonstration of high-resolution quantum-dot light-emitting diodes by transfer printing could prove useful for next-generation displays.
Social media is a real-time social sensor to sense and collect diverse information, which can be combined with sentiment analysis to help IoT sensors provide user-demanded favorable data in smart ...systems. In the case of insufficient data labels, cross-domain sentiment analysis aims to transfer knowledge from the source domain with rich labels to the target domain that lacks labels. Most domain adaptation sentiment analysis methods achieve transfer learning by reducing the domain differences between the source and target domains, but little attention is paid to the negative transfer problem caused by invalid source domains. To address these problems, this paper proposes a cross-domain sentiment analysis method based on feature projection and multi-source attention (FPMA), which not only alleviates the effect of negative transfer through a multi-source selection strategy but also improves the classification performance in terms of feature representation. Specifically, two feature extractors and a domain discriminator are employed to extract shared and private features through adversarial training. The extracted features are optimized by orthogonal projection to help train the classification in multi-source domains. Finally, each text in the target domain is fed into the trained module. The sentiment tendency is predicted in the weighted form of the attention mechanism based on the classification results from the multi-source domains. The experimental results on two commonly used datasets showed that FPMA outperformed baseline models.
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The Boolean Satisfiability problem (SAT) is a prototypical NP-complete problem, which has been widely studied due to its significant importance in both theory and applications. Stochastic local ...search (SLS) algorithms are among the most efficient approximate methods available for solving certain types of SAT instances. The quantitative configuration checking (QCC) heuristic is an effective approach for improving SLS algorithms on solving the SAT problem, resulting in an efficient SLS solver for SAT named Swqcc. In this paper, we focus on combining the QCC heuristic with an aspiration mechanism, and then design a new heuristic called QCCA. On the top of Swqcc, we utilize the QCCA heuristic to develop a new SLS solver dubbed AspiSAT. Through extensive experiments, the results illustrate that, on random 3-SAT instances, the performance of AspiSAT is much better than that of Swqcc and Sparrow, which is an influential and efficient SLS solver for SAT. In addition, we further enhance the original clause weighting schemes employed in Swqcc and AspiSAT, and thus obtain two new SLS solvers called Ptwqcc and AspiPT, respectively. The eperimental results present that both Ptwqcc and AspiPT outperform Swqcc and AspiSAT on random 5-SAT instances, indicating that both QCC and QCCA heuristics are able to cooperate effectively with different clause weighting schemes.
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The switch machine, an essential element of railway infrastructure, is crucial in maintaining the safety of railway operations. Traditional methods for fault diagnosis are constrained by their ...dependence on extensive labeled datasets. Semi-supervised learning (SSL), although a promising solution to the scarcity of samples, faces challenges such as the imbalance of pseudo-labels and inadequate data representation. In response, this paper presents the Semi-Supervised Adaptive Matrix Machine (SAMM) model, designed for the fault diagnosis of switch machine. SAMM amalgamates semi-supervised learning with adaptive technologies, leveraging adaptive low-rank regularizer to discern the fundamental links between the rows and columns of matrix data and applying adaptive penalty items to correct imbalances across sample categories. This model methodically enlarges its labeled dataset using probabilistic outputs and semi-supervised, automatically adjusting parameters to accommodate diverse data distributions and structural nuances. The SAMM model’s optimization process employs the alternating direction method of multipliers (ADMM) to identify solutions efficiently. Experimental evidence from a dataset containing current signals from switch machines indicates that SAMM outperforms existing baseline models, demonstrating its exceptional status diagnostic capabilities in situations where labeled samples are scarce. Consequently, SAMM offers an innovative and effective approach to semi-supervised classification tasks involving matrix data.
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In recent years, interest in aquaculture acoustic signal has risen since the development of precision agriculture technology. Underwater acoustic signals are known to be noisy, especially as they are ...inevitably mixed with a large amount of environmental background noise, causing severe interference in the extraction of signal features and the revelation of internal laws. Furthermore, interference adds a considerable burden on the transmission, storage, and processing of data. A signal recognition curve (SRC) algorithm is proposed based on higher-order cumulants (HOC) and a recognition-sigmoid function for feature extraction of target signals. The signal data of interest can be accurately identified using the SRC. The analysis and verification of the algorithm are carried out in this study. The results show that when the SNR is greater than 7 dB, the SRC algorithm is effective, and the performance improvement is maximized when the SNR is 11 dB. Furthermore, the SRC algorithm has shown better flexibility and robustness in application.
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