Cost Estimate and Input Energy of Floor Systems in Low Seismic Regions

Mahmoud, Sayed; Salman, Alaa

doi:10.32604/cmc.2022.022357

Cited by 6 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lastly, a multi-disciplinary approach could be pivotal. Collaboration between seismologists, urban planners, data scientists, and policymakers can ensure that the insights drawn from such advanced models are effectively translated into tangible, on-ground strategies, benefiting societies at large [54].…”

Section: F Future Prospects and Recommendationsmentioning

confidence: 99%

Deep Convolutional Neural Network for Accurate Prediction of Seismic Events

Turarbek,

Bektemesov,

Ongarbayeva

et al. 2023

IJACSA

View full text Add to dashboard Cite

In recent years, the realm of seismology has witnessed an increased integration of advanced computational techniques, seeking to enhance the precision and timeliness of earthquake predictions. The paper titled "Deep Convolutional Neural Network and Machine Learning Enabled Framework for Analysis and Prediction of Seismic Events" embarks on an ambitious exploration of this interstice, marrying the formidable prowess of Deep Convolutional Neural Networks (CNNs) with an array of machine learning algorithms. At the forefront of our investigation is the Deep CNN, known for its unparalleled capability to process spatial hierarchies and multi-dimensional seismic data. Accompanying this neural behemoth is LightGBM, a gradient boosting framework that offers superior speed and performance, especially with voluminous datasets. Additionally, conventional neural networks, noted for their adeptness in pattern recognition, offer a robust method to gauge the intricacies of seismic data. Our exploration doesn't halt here; the research delves deeper with Random Forest and Support Vector Machines (SVM), both renowned for their resilient performance in classification tasks. By amalgamating these diverse methodologies, this research crafts a multifaceted and synergistic framework. The culmination is a sophisticated tool poised to not only discern the minutiae of seismic activities with heightened accuracy but to predict forthcoming events with a degree of certainty previously deemed elusive. In this era of escalating seismic activities, our research offers a timely beacon, heralding a future where communities are better equipped to respond to the Earth's capricious tremors.

show abstract

Section: F Future Prospects and Recommendationsmentioning

confidence: 99%

Deep Convolutional Neural Network for Accurate Prediction of Seismic Events

Turarbek,

Bektemesov,

Ongarbayeva

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…At the same time, the system will release two platforms, one is VR platform and the other is mobile platform [12]. Students can experience rich chemical experiment [13] interaction functions with the help of VR helmets and mobile phones, which can ensure the safety of experiments and enhance the interest of classroom experiments [14].…”

Section: Pruning Stepmentioning

confidence: 99%

Design of Middle School Chemistry Experiment Simulation System Based on Apriori Algorithm

Li¹,

Zhou²,

Zheng³

et al. 2022

Journal of New Media

View full text Add to dashboard Cite

Aiming at the safety problems of toxic, flammable and explosive chemicals used in middle school chemical experiments, such as human poisoning, skin corrosion, fire or explosion caused by improper experimental operation, a virtual simulation method of chemical experiments based on unity is proposed. Due to the need to analyze and compare the data in chemical experiments, summarize the experimental characteristics and data relevance. Therefore, based on the Apriori algorithm, this method deeply excavates the data obtained in the chemical experiment, uses Maya to model the experimental environment, uses unity to design the interactive functions in the experimental process, and uses visual effect graph technology, shader graph technology and other methods to realize the special effect simulation of various chemical experiments. With this method, the virtual simulation prototype system of middle school chemistry experiment is developed. The example shows that this method can vividly and realistically reproduce the simulation effect of chemical experiment, find the correlation between the experimental data, classification, properties and the overall characteristics of the data, describe and predict the development trend of the data, so as to make better use of these data to provide support for chemical experiment, and not only solve the problems of poisoning, corrosion of skin and It also solves the problems that it is difficult to scientifically compare data and find data relevance in traditional chemical experiments.

show abstract

“…In the software industry and in social assessment of "carbon peaking and carbon neutralization" energy consumption is an important indicator. Many researchers have studied the parameter from different aspects [1][2][3][4]. Timing speculation (TS) was a concept recently proposed to apply to energyefficient microprocessors [5].…”

Section: Introductionmentioning

confidence: 99%

Read-Write Dependency Aware Register Allocation

Xiao¹,

Chen²,

He³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Read-write dependency is an important factor restricting software efficiency. Timing Speculative (TS) is a processing architecture aiming to improve energy efficiency of microprocessors. Timing error rate, influenced by the read-write dependency, bottlenecks the voltage down-scaling and so the energy efficiency of TS processors. We proposed a method called Read-Write Dependency Aware Register Allocation. It is based on the Read-Write Dependency aware Interference Graph (RWDIG) conception. Registers are reallocated to loosen the read-write dependencies, so resulting in a reduction of timing errors. The traditional no operation (Nop) padding method is also redesigned to increase the distance value to above 2. We analyzed the dependencies of registers and maximized the average distance value of read and write dependencies. Experimental results showed that we can reduce all read-write dependency by Nop padding, as well as the overhead timing errors. An energy saving of approximately 7% was achieved.

show abstract

Cost Estimate and Input Energy of Floor Systems in Low Seismic Regions

Cited by 6 publications

References 9 publications

Deep Convolutional Neural Network for Accurate Prediction of Seismic Events

Deep Convolutional Neural Network for Accurate Prediction of Seismic Events

Design of Middle School Chemistry Experiment Simulation System Based on Apriori Algorithm

Read-Write Dependency Aware Register Allocation

Contact Info

Product

Resources

About