Study on Electrochemical Properties of Carbon Submicron Fibers Loaded with Cobalt-Ferro Alloy and Compounds

Pang, Xu; Yuan, Qinghui; Ji, Wendong; Zhao, Yuling; Yu, Ruitao; Su, Yu-De; Huo, Ningbo

doi:10.3390/cryst13020282

Cited by 18 publications

(5 citation statements)

References 32 publications

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“…This experiment conducted simulation experiments on analog circuits using the Iris dataset as experimental data. A stable power supply voltage was input to the analog circuit, and the complete circuit was subjected to static scanning observation and classification [21,22]. Specifically, the highest voltage output value of Iris flowers is represented as the classification and recognition results of the experiment.…”

Section: Simulation Results Of Analog Circuits Based On Nvfmumentioning

confidence: 99%

In-Memory Computing Integrated Structure Circuit Based on Nonvolatile Flash Memory Unit

et al. 2023

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Artificial intelligence has made people’s demands for computer computing efficiency increasingly high. The traditional hardware circuit simulation method for neural morphology computation has problems of unstable performance and excessive power consumption. This research will use non-volatile flash memory cells that are easy to read and write to build a convolutional neural network structure to improve the performance of neural morphological computing. In the experiment, floating-gate transistors were used to simulate neural network synapses to design core cross-array circuits. A voltage subtractor, voltage follower and ReLU activation function are designed based on a differential amplifier. An Iris dataset was introduced in this experiment to conduct simulation experiments on the research circuit. The IMC circuit designed for this experiment has high performance, with an accuracy rate of 96.2% and a recall rate of 60.2%. The overall current power consumption of the hardware circuit is small, and the current power consumption of the subtractor circuit and ReLU circuit does not exceed 100 µA, while the power consumption of the negative feedback circuit is about 440 mA. The accuracy of analog circuits under the IMC architecture is above 93%, the energy consumption is only about 360 nJ, and the recognition rate is about 12 μs. Compared with the classic von Neumann architecture, it reduces the circuit recognition rate and power consumption while meeting accuracy requirements.

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Section: Simulation Results Of Analog Circuits Based On Nvfmumentioning

confidence: 99%

In-Memory Computing Integrated Structure Circuit Based on Nonvolatile Flash Memory Unit

et al. 2023

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“…To optimize the computational performance and resource allocation capacity of a fog computing wireless access network, Jo et al proposed to construct a computational task offloading and resource allocation strategy, validated the effectiveness of the proposed optimization strategy, and found that this strategy significantly improved the system processing efficiency and resource allocation compared with the traditional processing methods [ 14 ]. In the transportation system, the logistics path-planning performance is insufficient and there is the problem of long computation times, so Yu et al proposed the deep reinforcement learning mechanism to optimize the logistics path-planning model to verify the model’s effectiveness, and found that the computation time of the model compared with the traditional model was significantly shortened, and the phase of the computation time in the path planning was better [ 15 ]. To further improve the precision and accuracy of fluid flow control, Rabault et al proposed to apply deep reinforcement learning techniques to the training of the flow control to construct an active control model of the flow and validated the effectiveness of this model, which was found to successfully stabilize the vortex channel and reduce the resistance by approximately 8%, opening the way for the execution of active flow control [ 16 , 17 , 18 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Motor Interaction Control Based on Muscle Force Model and Depth Reinforcement Strategy

Liu,

Zhang,

Lee

et al. 2024

Biomimetics

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The current motion interaction model has the problems of insufficient motion fidelity and lack of self-adaptation to complex environments. To address this problem, this study proposed to construct a human motion control model based on the muscle force model and stage particle swarm, and based on this, this study utilized the deep deterministic gradient strategy algorithm to construct a motion interaction control model based on the muscle force model and the deep reinforcement strategy. Empirical analysis of the human motion control model proposed in this study revealed that the joint trajectory correlation and muscle activity correlation of the model were higher than those of other comparative models, and its joint trajectory correlation was up to 0.90, and its muscle activity correlation was up to 0.84. In addition, this study validated the effectiveness of the motion interaction control model using the depth reinforcement strategy and found that in the mixed-obstacle environment, the model’s desired results were obtained by training 1.1 × 103 times, and the walking distance was 423 m, which was better than other models. In summary, the proposed motor interaction control model using the muscle force model and deep reinforcement strategy has higher motion fidelity and can realize autonomous decision making and adaptive control in the face of complex environments. It can provide a theoretical reference for improving the effect of motion control and realizing intelligent motion interaction.

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“…Among them, green visibility mainly refers to the actual proportion of green vegetation perceived by human vision, which characterizes human activity space rather than urban surface space and focuses more on the restoration of the actual vitality space of the landscape. Higher green visibility can relax people physically and mentally and enhance life satisfaction [15][16][17][18]. The corresponding expression is shown in Equation (5).…”

Section: Deeplab V3+ Model Analysis and Quantitative Measurement Fram...mentioning

confidence: 99%

Intentional Characteristics and Public Perceived Preferences of Lake Parks Based on Machine Learning Models

Wang,

Min,

Rui

2024

Land

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This research aimed to analyze and understand the perceived landscape preferences of lake parks (LPs) and how the public perceives and prefers these elements within the context of lake parks. The objective was to provide insights beneficial for landscape design, urban planning, and the creation of more appealing and sustainable lake parks. To achieve this, two primary methods were employed in this study: the Automated Machine Learning (Auto ML) model and the DeepLab v3+ model. To gather data for the research, 46,444 images were collected from 20 different lake parks from 2019 to 2022. Social media platforms such as Instagram, Flickr, and specific lake park community groups were tapped to source photographs from both professional photographers and the general public. According to the experimental findings, the perceived frequency of natural landscapes was 69.27%, which was higher than that of humanistic landscapes by 30.73%. The perceived intensity was also maintained between 0.09 and 0.25. The perceived frequency of water body landscapes was much greater on a macro-scale, at 73.02%, and the public had various plant preferences throughout the year. Aquatic plant landscapes with low-to-medium green visibility were preferred by the public, according to the landscape share characterization, while amusement rides with medium-to-high openness were preferred. The sky visibility of amusement rides was between 0 and 0.1 and between 0.3 and 0.5, indicating that the public preferred amusement rides with medium-to-high openness. In lake parks, the populace chose settings with less obvious architectural features. When combined, the two models used in this study are useful for identifying and analyzing the intended traits and preferences of lake parks among the general public. They also have theoretical and practical application value for directing the development of lake parks and urban landscapes.

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Study on Electrochemical Properties of Carbon Submicron Fibers Loaded with Cobalt-Ferro Alloy and Compounds

Cited by 18 publications

References 32 publications

In-Memory Computing Integrated Structure Circuit Based on Nonvolatile Flash Memory Unit

In-Memory Computing Integrated Structure Circuit Based on Nonvolatile Flash Memory Unit

Motor Interaction Control Based on Muscle Force Model and Depth Reinforcement Strategy

Intentional Characteristics and Public Perceived Preferences of Lake Parks Based on Machine Learning Models

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