Sportive Fashion Trend Reports: A Hybrid Style Analysis Based on Deep Learning Techniques

An, Hyosun; Kim, Sung Hoon; Choi, Yerim

doi:10.3390/su13179530

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…It belongs to an important research direction in machine learning under artificial intelligence, and is also an extension of representation learning theory. Deep learning differs from other learning methods and general system building in that it increases the depth of the hidden layer and extracts more abstract feature information from the hidden layer of the previous layer, effectively avoiding the interference factors brought about between different features [6][7][8]. Interaction between measurements is an important issue in poor data recognition for state estimation, and determining the interaction between measurements directly from the network topology may result in areas that are too large or too small, missing measurements that may interact with each other or covering non-interactive measurements.…”

Section: Deep Learning-based Assessment Of the Operational Status Of ...mentioning

confidence: 99%

A deep learning-based approach for identifying bad data in power systems

Dong

Chen

et al. 2023

Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023)

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In the actual operation process, some of the power system bad data identification methods have the problem of low accuracy, for this reason, a deep learning-based power system bad data identification method is designed to improve this defect. The data is collected from power system users, the phase deviation caused by non-integer sampling is reduced by high sampling rate, the measurement signal period is obtained, the operational state of the distribution network is evaluated based on deep learning, the state vector is calculated, the maximum standard residual value is found, the location of the bad data is obtained, and the bad data identification method is designed. Experimental results: The mean accuracy of the power system bad data identification method in the paper is: 78.26%, which indicates that the designed power system bad data identification method performs better after fully integrating the deep learning.

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Section: Deep Learning-based Assessment Of the Operational Status Of ...mentioning

confidence: 99%

A deep learning-based approach for identifying bad data in power systems

Dong

Chen

et al. 2023

Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023)

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“…DL has enabled AI systems to achieve significant performance improvements in many important problems such as CV, ASR, and NLP. It has become the key to the current breakthrough of AI [14]. Fig.…”

Section: A Ai and Nnsmentioning

confidence: 99%

Damage Security Intelligent Identification of Wharf Concrete Structures under Deep Learning and Digital Image Technology

Zhu¹,

Li²,

Zhu³

2023

IJACSA

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Intelligence (AI) technology has quickly developed under the mighty computing power of computers. At this stage, there are many mature non-destructive testing methods in civil engineering, but they are generally only suitable for simple structures and evident damage characteristics. Therefore, it's necessary for us to investigate the damage identification of wharf concrete structures under deep learning and digital image technology. The article propose a damage detection and localization method based on Neural Network (NN) technology in deep learning and Digital Image Correlation (DIC) to identify internal damage to concrete used for wharf construction. Firstly, the identification model of concrete structure is constructed using NN technology. Then, structural damage identification of concrete is further investigated using DIC. Finally, relevant experiments are designed to verify the effect of the model. The results show that: (1) The damage model of concrete structure constructed by NN technology has high convergence and stability and can control the test error well. (2) The image output by the DIC equipment is processed and input into the NN. The errors of the various parameters of different concretes can be within the acceptable range. This paper aims to provide good ideas and references for follow-up structural health monitoring and other topics and has significant engineering application value.

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“…The patients were treated with Bushen-Liyan Decoction and their symptoms were evaluated. The results showed that the total effective rate was 83.33% [4][5][6]. Qiyan Decoction can effectively treat patients with dysphagia after stroke with internal obstruction of blood stasis.…”

Section: Introductionmentioning

confidence: 99%

Antistroke Network Pharmacological Prediction of Xiaoshuan Tongluo Recipe Based on Drug-Target Interaction Based on Deep Learning

Zhou

2022

Computational and Mathematical Methods in Medicine

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Stroke is a common cerebrovascular disease that threatens human health, and the search for therapeutic drugs is the key to treatment. New drug discovery was driven by many accidental factors in the early stage. With the deepening of research, disease-related target discovery and computer-aided drug design constitute a more rational drug discovery process. The deep learning model was constructed by using recurrent neural network, and then, the classification and prediction of compound-protein interactions were studied. In this study, the network pharmacological prediction of stroke based on deep learning is obtained. (1) In the case of discrete time, a distributed optimization algorithm with finite time convergence is applied. A distributed exact first-order algorithm for the case where the objective function is smooth. On the basis of the DGD algorithm, an additional cumulative correction term is added to correct the error caused by the fixed step size of DGD. Solve multiple optimization problems with equality constraints by using Lagrangian functions. Alternately update the original variable and the dual variable to get the solution of a large global problem. It converges to the optimal solution in an asymptotic or exponential way; that is, the node can reach the optimal solution more accurately when the time tends to infinity. (2) Deep learning, also sometimes called representation learning, has a set of algorithms that can automatically discover the desired classification or detection by feeding it into a machine using raw datasets. Multiple levels of abstraction are abstracted through the use of nonlinear models. This simplifies finding solutions to complex and nonlinear functions. Based on the automatic learning function, it provides the functions of modularization and transfer learning. Deep architectures, which usually contain hidden layers, differ from traditional machine learning, which requires a large amount of data to train the network. There are many levels of modules that are nonlinear and transform the information present on the first level into higher levels which are more abstract in nature and are basically used for feature extraction and transformation. (3) The accuracy rate of the framework based on the multitask deep learning algorithm is 91.73%, and the recall rate reaches 96.13%. The final model was predicted and analyzed using real sample data. In the inference problem, it has the advantages of fast training and low cost; in the generation problem, it also has the advantages of fast training, high stability, high diversity, and high quality of image reconstruction.

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Sportive Fashion Trend Reports: A Hybrid Style Analysis Based on Deep Learning Techniques

Cited by 6 publications

References 31 publications

A deep learning-based approach for identifying bad data in power systems

A deep learning-based approach for identifying bad data in power systems

Damage Security Intelligent Identification of Wharf Concrete Structures under Deep Learning and Digital Image Technology

Antistroke Network Pharmacological Prediction of Xiaoshuan Tongluo Recipe Based on Drug-Target Interaction Based on Deep Learning

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