Search Results for author: Song-Ju Kim

Found 10 papers, 0 papers with code

A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN

no code implementations3 Aug 2022 Aohan Li, Ikumi Urabe, Minoru Fujisawa, So Hasegawa, Hiroyuki Yasuda, Song-Ju Kim, Mikio Hasegawa

(1) Compared to other lightweight transmission-parameter selection schemes, collisions between LoRa devices can be efficiently avoided by our proposed scheme in LoRaWAN irrespective of changes in the available channels.

Fairness reinforcement-learning +1

Resource allocation method using tug-of-war-based synchronization

no code implementations19 Aug 2021 Song-Ju Kim, Hiroyuki Yasuda, Ryoma Kitagawa, Mikio Hasegawa

We propose a simple channel-allocation method based on tug-of-war (TOW) dynamics, combined with the time scheduling based on nonlinear oscillator synchronization to efficiently use of the space (channel) and time resources in wireless communications.


Ultrafast photonic reinforcement learning based on laser chaos

no code implementations14 Apr 2017 Makoto Naruse, Yuta Terashima, Atsushi Uchida, Song-Ju Kim

Reinforcement learning involves decision making in dynamic and uncertain environments, and constitutes one important element of artificial intelligence (AI).

Decision Making reinforcement-learning +1

Single photon in hierarchical architecture for physical reinforcement learning: Photon intelligence

no code implementations1 Sep 2016 Makoto Naruse, Martin Berthel, Aurélien Drezet, Serge Huant, Hirokazu Hori, Song-Ju Kim

In a past study, we successfully used the wave-particle duality of single photons to solve the two-armed bandit problem, which constitutes the foundation of reinforcement learning and decision making.

Decision Making reinforcement-learning +1

Category Theoretic Analysis of Photon-based Decision Making

no code implementations26 Feb 2016 Makoto Naruse, Song-Ju Kim, Masashi Aono, Martin Berthel, Aurélien Drezet, Serge Huant, Hirokazu Hori

Decision making is a vital function in this age of machine learning and artificial intelligence, yet its physical realization and theoretical fundamentals are still not completely understood.

BIG-bench Machine Learning Decision Making

Decision Maker based on Atomic Switches

no code implementations21 Jul 2015 Song-Ju Kim, Tohru Tsuruoka, Tsuyoshi Hasegawa, Masakazu Aono

We propose a simple model for an atomic switch-based decision maker (ASDM), and show that, as long as its total volume of precipitated Ag atoms is conserved when coupled with suitable operations, an atomic switch system provides a sophisticated "decision-making" capability that is known to be one of the most important intellectual abilities in human beings.

Decision Making

Harnessing Natural Fluctuations: Analogue Computer for Efficient Socially Maximal Decision Making

no code implementations14 Apr 2015 Song-Ju Kim, Makoto Naruse, Masashi Aono

Our society comprises a collection of such individuals, and the society is expected to maximise the total rewards, while the individuals compete for common rewards.

Decision Making

Decision Maker using Coupled Incompressible-Fluid Cylinders

no code implementations13 Feb 2015 Song-Ju Kim, Masashi Aono

The multi-armed bandit problem (MBP) is the problem of finding, as accurately and quickly as possible, the most profitable option from a set of options that gives stochastic rewards by referring to past experiences.

Efficient Decision-Making by Volume-Conserving Physical Object

no code implementations30 Oct 2014 Song-Ju Kim, Masashi Aono, Etsushi Nameda

We demonstrate that any physical object, as long as its volume is conserved when coupled with suitable operations, provides a sophisticated decision-making capability.

Decision Making Object

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