Improving connectivity and accelerating multiscale topology optimization using deep neural network techniques

Patel, Darshil; Bielecki, Dustin; Rai, Rahul; Dargush, Gary F.

doi:10.1007/s00158-022-03223-y

Cited by 28 publications

(6 citation statements)

References 32 publications

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“…The neurons could be assumed to be processing units connected through "synaptic weights" and form the network architecture [39]. This paper used a multilayered architecture of NN with three layers, as seen in Figure 3 [40]. The input or the first layer received input in the problem's context.…”

Section: Figure 1 Average Solar Radiationmentioning

confidence: 99%

Enhancing infrastructure sustainability: reliability and sensitivity analysis of localized integrated renewable energy systems using feed forward backpropagation neural network

2024

IJATEE

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Establishing robust infrastructure and securing a sustainable power supply can be costly and time-consuming. Localized power generation from natural resources through integrated renewable energy systems (IRESs) offers a solution. This study explores the reliability and key statistics of an IRES incorporating solar photovoltaic modules, wind turbines, and battery banks. The failure and repair rates follow an exponential distribution due to the increased risk in integrated structures. Neural networks (NN), particularly the feed forward back propagation neural network (FFBPNN), enhance the consistency and precision of reliability parameters. The learning process of FFBPNN adjusts neural weights, improving parameter values. Utilizing the MATLAB algorithm, this study iterated until achieving accuracy close to 0.0001. The proposed system's real-time operations can be effectively managed by analyzing operational costs and system sensitivity to different parameters.

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Section: Figure 1 Average Solar Radiationmentioning

confidence: 99%

Enhancing infrastructure sustainability: reliability and sensitivity analysis of localized integrated renewable energy systems using feed forward backpropagation neural network

2024

IJATEE

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“…After the data have been processed, an LSTM is trained on it. LSTMs are Recurrent Neural Networks (RNNs) that can learn order dependency in sequence prediction challenges [31,32]. RNNs are a form of neural network that predicts sequential or timeseries data.…”

Section: Lstmmentioning

confidence: 99%

A Trustworthy Healthcare Management Framework Using Amalgamation of AI and Blockchain Network

Jadav

Gupta

et al. 2023

Mathematics

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Over the last few decades, the healthcare industry has continuously grown, with hundreds of thousands of patients obtaining treatment remotely using smart devices. Data security becomes a prime concern with such a massive increase in the number of patients. Numerous attacks on healthcare data have recently been identified that can put the patient’s identity at stake. For example, the private data of millions of patients have been published online, posing a severe risk to patients’ data privacy. However, with the advent of Industry 4.0, medical practitioners can digitally assess the patient’s condition and administer prompt prescriptions. However, wearable devices are also vulnerable to numerous security threats, such as session hijacking, data manipulation, and spoofing attacks. Attackers can tamper with the patient’s wearable device and relays the tampered data to the concerned doctor. This can put the patient’s life at high risk. Since blockchain is a transparent and immutable decentralized system, it can be utilized for securely storing patient’s wearable data. Artificial Intelligence (AI), on the other hand, utilizes different machine learning techniques to classify malicious data from an oncoming stream of patient’s wearable data. An amalgamation of these two technologies would make the possibility of tampering the patient’s data extremely difficult. To mitigate the aforementioned issues, this paper proposes a blockchain and AI-envisioned secure and trusted framework (HEART). Here, Long-Short Term Model (LSTM) is used to classify wearable devices as malicious or non-malicious. Then, we design a smart contract that allows only of those patients’ data having a wearable device to be classified as non-malicious to the public blockchain network. This information is then accessible to all involved in the patient’s care. We then evaluate the HEART’s performance considering various evaluation metrics such as accuracy, recall, precision, scalability, and network latency. On the training and testing sets, the model achieves accuracies of 93% and 92.92%, respectively.

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“…The test results show that the optimized UAV reduces its self-weight by 15.7% compared to the preoptimized UAV, which effectively improves the UAV's endurance [20]. The current additive design capability of Patel D's research team could not meet their production requirements well, so they designed an intelligent additive design system using two neural network architectures, and the test found that the system effectively improved the design efficiency of additive materials [21]. Kien DN et al constructed a structural defect detection method for mechanical components using Alex neural network and tested that the method resulted in an 8.2% improvement in the accuracy of detecting production defects in mechanical structure design [22].…”

Section: Related Workmentioning

confidence: 99%

Optimization Design of Bridge Inspection Vehicle Boom Structure Based on Improved Genetic Algorithm

Xue¹,

Lv²,

Qiu³

2023

IJACSA

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Excessive self-weight of bridge inspection vehicles increases the safety risk of the inspected bridge structures. In this study, a bridge inspection vehicle arm structure self-weight optimization design model is proposed to improve the efficiency and safety of bridge structure inspection. The model uses a finite element model of the arm structure to generate force data to validate and train a back propagation (BP) neural network-based self-weight prediction model of the arm structure, and uses an improved genetic algorithm to assist the prediction model in searching for the optimal solution. The experimental results show that the maximum stress and maximum deformation of the optimal solution from the optimization model designed in this study are lower than the allowable values of the material, and the total weight of the structure from the optimal solution is the lowest, 4687.5 kg. The computational time of the optimization model designed in this study is lower than all the comparison models. The experimental data show that the optimized model for the self-weight optimization of the bridge inspection vehicle arm structure designed in this study has good optimization effect and has some application potential.

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Improving connectivity and accelerating multiscale topology optimization using deep neural network techniques

Cited by 28 publications

References 32 publications

Enhancing infrastructure sustainability: reliability and sensitivity analysis of localized integrated renewable energy systems using feed forward backpropagation neural network

Enhancing infrastructure sustainability: reliability and sensitivity analysis of localized integrated renewable energy systems using feed forward backpropagation neural network

A Trustworthy Healthcare Management Framework Using Amalgamation of AI and Blockchain Network

Optimization Design of Bridge Inspection Vehicle Boom Structure Based on Improved Genetic Algorithm

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