Automated electron-optical system optimization through switching Levenberg–Marquardt algorithms

Koh, Jin; Cheong, Kang Hao

doi:10.1016/j.elspec.2018.05.009

Cited by 28 publications

(8 citation statements)

References 67 publications

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“…We have seen applications of artificial intelligence and machine learning techniques across different disciplines [36][37][38][39]. Our work here focuses on health services research, which has not gained much attention until now.…”

Section: Discussionmentioning

confidence: 99%

Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction

Lin

Cheong

et al. 2020

IJERPH

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The accurate prediction of ambulance demand provides great value to emergency service providers and people living within a city. It supports the rational and dynamic allocation of ambulances and hospital staffing, and ensures patients have timely access to such resources. However, this task has been challenging due to complex multi-nature dependencies and nonlinear dynamics within ambulance demand, such as spatial characteristics involving the region of the city at which the demand is estimated, short and long-term historical demands, as well as the demographics of a region. Machine learning techniques are thus useful to quantify these characteristics of ambulance demand. However, there is generally a lack of studies that use machine learning tools for a comprehensive modeling of the important demand dependencies to predict ambulance demands. In this paper, an original and novel approach that leverages machine learning tools and extraction of features based on the multi-nature insights of ambulance demands is proposed. We experimentally evaluate the performance of next-day demand prediction across several state-of-the-art machine learning techniques and ambulance demand prediction methods, using real-world ambulatory and demographical datasets obtained from Singapore. We also provide an analysis of this ambulatory dataset and demonstrate the accuracy in modeling dependencies of different natures using various machine learning techniques.

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Section: Discussionmentioning

confidence: 99%

Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction

Lin

Cheong

et al. 2020

IJERPH

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“…[30] Parrondo's paradox was first conceptualized as an abstraction of flashing Brownian ratchets, [31][32][33] wherein diffusive particles exhibit unexpected drift when exposed to alternating periodic potentials. It has since been applied across a wide range of disciplines in the physical sciences and engineering-related fields, [34,35] such as diffusive and granular flow dynamics, [36,37] information thermodynamics, [38][39][40] chaos theory, [41][42][43][44][45][46][47] switching problems, [48][49][50] and quantum phenomena. [51][52][53][54][55][56][57] The paradox has also found numerous applications in life science, [58][59][60][61][62] ecology and evolutionary biology, [63][64][65] social dynamics, [66][67][68][69][70] and interdisciplinary work.…”

Section: Introductionmentioning

confidence: 99%

Relieving Cost of Epidemic by Parrondo's Paradox: A COVID‐19 Case Study

2020

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COVID‐19, also known as SARS‐CoV‐2, is a coronavirus that is highly pathogenic and virulent. It spreads very quickly through close contact, and so in response to growing numbers of cases, many countries have imposed lockdown measures to slow its spread around the globe. The purpose of a lockdown is to reduce reproduction, that is, the number of people each confirmed case infects. Lockdown measures have worked to varying extents but they come with a massive price. Nearly every individual, community, business, and economy has been affected. In this paper, switching strategies that take into account the total “cost” borne by a community in response to COVID‐19 are proposed. The proposed cost function takes into account the health and well‐being of the population, as well as the economic impact due to the lockdown. The model allows for a comparative study to investigate the effectiveness of various COVID‐19 suppression strategies. It reveals that both the strategy to implement a lockdown and the strategy to maintain an open community are individually losing in terms of the total “cost” per day. However, switching between these two strategies in a certain manner can paradoxically lead to a winning outcome—a phenomenon attributed to Parrondo's paradox.

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“…The agitation from Game A can lead to increased likelihood of landing in favourable branches when Game B is subsequently invoked, thus manifesting a ratcheting mechanism and enabling the characteristic paradoxical winning outcomes. There have been many examples of such counter-intuitive dynamics studied to date, for instance, in ecological populations [3][4][5][6][7][8][9][10], population genetics [11][12][13], physical quantum systems [14][15][16][17][18][19], reliability theory [20], system design optimization [21,22], and the Allison mixture in information thermodynamics [23].…”

Section: Introductionmentioning

confidence: 99%

Passive network evolution promotes group welfare in complex networks

Hang

Koh

et al. 2020

Chaos, Solitons & Fractals

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The Parrondo's paradox is a counter-intuitive phenomenon in which individually losing strategies, canonically termed Game A and Game B, are combined to produce winning outcomes. In this paper, a co-evolution of game dynamics and network structure is adopted to study adaptability and survivability in multi-agent dynamics. The model includes Action A, representing a rewiring process on the network, and a two-branch Game B, representing redistributive interactions between agents. Simulation results indicate that stochastically mixing Action A and Game B can produce enhanced, and even winning outcomes, despite Game B being individually losing. In other words, a Parrondo-type paradox can be achieved, but unlike canonical variants, the source of agitation is provided by passive network evolution instead of an active second game. The underlying paradoxical mechanism is analyzed, revealing that the rewiring process drives a topology shift from initial regular lattices towards scale-free characteristics, and enables exploitative behavior that grants enhanced access to the favourable branch of Game B.

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Automated electron-optical system optimization through switching Levenberg–Marquardt algorithms

Cited by 28 publications

References 67 publications

Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction

Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction

Relieving Cost of Epidemic by Parrondo's Paradox: A COVID‐19 Case Study

Passive network evolution promotes group welfare in complex networks

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