The rise of big data analytics on top of NLP increasing the computational burden for text processing at scale. The problems faced in NLP are very high dimensional text, so it takes a high computation resource. The Map...
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This book provides an accessible introduction to the foundations of machine learning and deep learning in medicine for medical students, researchers, and professionals who are not necessarily initiated in advanced mat...
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ISBN:
(数字)9783031195020
ISBN:
(纸本)9783031195013
This book provides an accessible introduction to the foundations of machine learning and deep learning in medicine for medical students, researchers, and professionals who are not necessarily initiated in advanced mathematics but yearn for a better understanding of this disruptive technology and its impact on medicine. Once an esoteric subject known to few outside of computer science and engineering departments, today artificial intelligence (AI) is a widely popular technology used by scholars from all across the academic universe. In particular, recent years have seen a great deal of interest in the AI subfields of machine learning and deep learning from researchers in medicine and life sciences, evidenced by the rapid growth in the number of articles published on the topic in peer-reviewed medical journals over the last decade. The demand for high-quality educational resources in this area has never been greater than it is today, and will only continue to grow at a rapid *** authors remove the veil of unnecessary complexity that often surrounds machine learning and deep learning by employing a narrative style that emphasizes intuition in place of abstract mathematical formalisms, allowing them to strike a delicate balance between practicality and theoretical rigor in service of facilitating the reader’s learning experience. Topics covered in the book include: mathematical encoding of medical data, linear regression and classification, nonlinear feature engineering, deep learning, convolutional and recurrent neural networks, and reinforcement learning. Each chapter ends with a collection of exercises for readers to practice and test their *** is an ideal introduction for medical students, professionals, and researchers interested in learning more about machine learning and deep learning. Readers who have taken at least one introductory mathematics course at the undergraduate-level (e.g., biostatistics or calculus) will be well-equipped to use thi
When implementing zero-trust edge computing, offloading computational tasks and data access through traditional model training and usage approaches can lead to increased latency. Since the traditional methods often in...
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Unveiling the underlying control principles of complex networks is one of the ultimate goals of network science. We introduce a novel concept, control hub, to reveal a cornerstone of the control structure of a network...
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Stop-skipping strategy can benefit both bus operators and passengers if the control is intelligent enough to adapt to the changes in passenger demands and traffic conditions. This is possible via deep reinforcement le...
Stop-skipping strategy can benefit both bus operators and passengers if the control is intelligent enough to adapt to the changes in passenger demands and traffic conditions. This is possible via deep reinforcement learning (DRL), where an agent can learn the optimal policy by continuously interacting with the dynamic bus operating environment. In this paper, one express bus lane followed by one no-skip flow is treated as one episode for bus route optimization. The objective is to maximize the passenger satisfaction level while minimizing the bus operator expenditures. To this end, a reward function is formulated as a function of passenger waiting time, passenger in-vehicle time, and total bus travel time. By training an agent of a double deep Q-network (DDQN), simulation results show that the agent can intelligently skip the stations and outperform the noskip method, under different passenger distribution patterns.
The present paper studies the use of genetic algorithm to optimize the tuning of the Proportional, Integral and Derivative (PID) controller. Two control criteria were considered, the integral of the time multiplied by...
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The present paper studies the use of genetic algorithm to optimize the tuning of the Proportional, Integral and Derivative (PID) controller. Two control criteria were considered, the integral of the time multiplied by the absolute error (ITAE), and the integral of the time multiplied by the absolute output (ITAY). The time variant plant tested is a first-order plant with time delay. We aim at a real time implementation inside a digital board, so, the previous continuous approach was discretized and tested;the corresponding control algorithm is presented in this paper. The genetic algorithms and the PID controller are executed using the soft processor NIOS II in the Field Programmable Gate Array (FPGA). The computational results show the robustness and versatility of this technology.
This paper discusses the advantages of orchestrating distribution energy resources (DERs) within the distribution network and integrating third generation (Gen3) blockchain technology among a diverse range of customer...
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ISBN:
(数字)9798350364965
ISBN:
(纸本)9798350364972
This paper discusses the advantages of orchestrating distribution energy resources (DERs) within the distribution network and integrating third generation (Gen3) blockchain technology among a diverse range of customers. The implementation of peer-to-peer (P2P) energy trading aims to optimize local energy utilization, reduce energy costs for participants, and provide insights into profit margins for aggregators and the distribution system operator (DSO). The study focuses on three types of DERs, namely electric vehicles (EVs), photovoltaic (PV) systems, and battery energy storage systems (BESSs), to facilitate transparent and secure P2P energy trading. The results underscore the importance of Gen3 blockchain technology in expediting and enhancing energy trading processes, thereby benefiting a wide range of participants.
An optimization computational grid algorithm and the results of its application for estimating the velocity characteristics of complex medium based on experimentally obtained seismic wave arrival times are presented. ...
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ISBN:
(纸本)9781665464819
An optimization computational grid algorithm and the results of its application for estimating the velocity characteristics of complex medium based on experimentally obtained seismic wave arrival times are presented. An increase in the resolution of the algorithm for detecting inhomogeneities is achieved by adaptively selecting the grid step in areas with a pronounced heterogeneity of the medium structure. The algorithm automatically identifies zones of inhomogeneity, taking into account non-linear variations in the hodograph of seismic waves. The restriction on the minimum allowable grid step is deter-mined taking into account the upper limiting frequency of sounding oscillations generated by the vibration source and the number of intersections of seismic wave fronts at the grid nodes. The proposed algorithm has been tested on experimental data obtained from vibration sounding of mud volcano zones and the Baikal Rift Zone (BRZ). The consistency of the reconstructed theoretical velocity hodograph with the geological structure of the heterogeneity of the structure of medium in the areas of field works is shown.
An autonomous service robot should be able to interact with its environment safely and robustly without requiring human assistance. Unstructured environments are challenging for robots since the exact prediction of ou...
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