Capsizing is one of the worst scenarios in oceangoing vessels. It could lead to a high number of fatalities. A considerable number of studies have been conducted until the 1980s, and one of the discoveries is the weather criterion established by the International Maritime Organization (IMO). In the past, one of the biggest difficulties in revealing the behavior of ship-roll motion was the nonlinearity of the governing equation. On the other hand, after the mid-1980s, the complexity of the capsizing problem was uncovered with the aid of computers. In this study, we present the theoretical backgrounds of the capsizing problem from the viewpoint of nonlinear dynamics. Then, we discuss the theoretical conditions and mechanisms of the bifurcations of periodic solutions and numerical attempts for the bifurcations and capsizing.

Driven by the remarkable breakthroughs during the past decade, photonics neural networks have experienced a revival. Here, we provide a general overview of progress over the past decade, and sketch a roadmap of important future developments. We focus on photonic implementations of the reservoir computing machine learning paradigm, which offers a conceptually simple approach that is amenable to hardware implementations. In particular, we provide an overview of photonic reservoir computing implemented via either spatio temporal or delay dynamical systems. Going beyond reservoir computing, we discuss recent advances and future challenges of photonic implementations of deep neural networks, of the quest for learning methods that are hardware-friendly as well as realizing autonomous photonic neural networks, i.e. with minimal digital electronic auxiliary hardware.

We propose and experimentally demonstrate a method of generating wideband and flat-spectrum chaos from a simple device configuration composed of a semiconductor laser subject to strong dispersive light feedback from a chirped fiber Bragg grating (CFBG). The dispersive feedback light induces external-cavity modes with irregular mode separations which beat with the internal modes of laser. This physical process of beating introduces more high-frequency oscillations and thus removes the domination of relaxation oscillation, widening and flattening the radio-frequency spectrum. Experimental results show that laser chaos with a 3-dB bandwidth of 24GHz can be obtained at a feedback strength of 1.60, which is three times the bandwidth at a feedback strength of 0.35. Effects of the dispersive feedback strength and the wavelength detuning between the laser and the CFBG on the 3-dB bandwidth are studied.

Communication privacy is one of the key requirements for always expanding networks. Furthermore, fibre systems are becoming saturated and many remote areas do not have access to broadband connection because current systems are too difficult or expensive to deploy. In this work, we experimentally demonstrate a mid-infrared free-space cryptosystem that is based on chaos synchronization between two quantum cascade lasers. Optimal amplitude conditions to ensure both privacy and acceptable deciphering are described, paving the way towards a wide adoption of quantum cascade lasers for future communication systems.

A bistable system with a spatial expanse can support traveling wavefront which is an interface between different stable states such as “on” and “off”. In this work, a two-dimensional thermo-optical bistable device is investigated in terms of wavefront propagation property and its application to maze exploration. It is shown that wavefront velocity is controllable by light intensity and can take even a negative value, i.e. reduction or retreat of the “on”-state area. Utilizing this feature, an application of this device to maze exploration is demonstrated, where extension of the “on” state area explores the whole paths of the maze, and then the device is switched to reduction mode in which the “on” state area retreats from dead-end paths, resulting in the collect path of the maze.

This study numerically investigates two different noise-induced transitions of nonlinear dynamics in optically injected semiconductor lasers based on the Lang-Kobayashi laser model. Spontaneous emission noise is observed to induce dynamical transitions from stable injection locking to period-one dynamics and from period-one to period-two dynamics when operating points are close to the corresponding dynamical boundaries, respectively. Such transitions follow a smooth dynamical evolution as the noise level increases. The resulting noise-induced dynamical states exhibit features highly similar to their adjacent dynamical states that keep the same dynamical behaviors when subject to noise. Regions where the noise-induced dynamical transitions occur are identified.

Non-Orthogonal Multiple Access is one of the most important technologies in 5G and Beyond 5G wireless communications, which improve system performance by power domain multiplexing. In realizing Non-Orthogonal Multiple Access, the pairing of multiple users is necessary where efficient principles are highly demanded in dynamically changing electromagnetic environments. In the meantime, ultrafast methods of solving multi-armed bandit problems have been developed using chaotic laser time series. In this paper, we consider the user pairing problem in Non-Orthogonal Multiple Access as a multi-armed bandit problem and propose an ultra-fast user pairing algorithm based on the laser chaos decision maker. We numerically demonstrate that the proposed scheme accomplishes higher throughputs compared with traditional user pairing algorithms, especially in cases with lower user density.

Laser chaos decision-maker has been demonstrated to enable ultrahigh-speed decision-making in solving multi-armed bandit (MAB) problems in the GHz order. In addition to recent intensive studies of photonic information processing devices and systems, the pursuit of novel applications is important, which is also demanded from future technologies, including Beyond 5G context. In this paper, we examine the applicability to dynamic channel bonding (DCB), which has been introduced in wireless local area networks (WLANs), and demonstrate a method for achieving higher data rate transmissions while avoiding interference. First, we propose a DCB method utilizing laser chaos decision-maker. Second, we design two hierarchical trees for decision making, that is, DCB selection. Third, we experimentally implement our proposed methods in a practical WLAN and confirm its operational ability. We analyze the parameter of the proposed method and compare the proposed method with conventional decision-making algorithms of ε-greedy and UCB1-tuned. We show that our proposed method demonstrates better DCB decisions than the other decision-making algorithms. Furthermore, we demonstrate that in DCB, the design method of the hierarchical trees or the parameter for the proposed method influences the performance of decision making.

High-bandwidth irregular oscillations caused by optical time-delayed feedback subjected to the laser cavity, known as laser chaos, have been investigated for various engineering applications. Recently, a fast decision-making algorithm for a multi-arm bandit problem by utilizing laser chaos time series has been demonstrated. Furthermore, the arms order recognition of the reward expectation for each arm has been successfully developed by incorporating the notion of the confidence interval regarding the reward estimate. However, in previous studies, the verification was limited to numerical experiments; real-world demonstrations were not conducted. This study experimentally demonstrated that the arm-order recognition algorithm is successfully operated in channel order recognition in wireless communications while revising the original strategy to take into account the wireless application requirements. Such accurate arm rank recognition involving non-best arms would be useful for various real-world applications such as channel bonding, among others.

We numerically demonstrate the principle of adaptive decision making for solving multi-armed bandit problems in dynamically changing reward environments. We use the tug-of-war method by comparing a threshold and a chaotic temporal waveform generated from a semiconductor laser observed in an experiment. We propose a method for detecting dynamic changes in hit probabilities by evaluating short-term standard deviations of the estimated hit probabilities. Furthermore, the threshold is forced to be initialized when changes in the hit probabilities are detected. We perform adaptive decision making in time-varying hit probabilities, including cases in which the differences in the hit probabilities are small. The proposed method paves the way for ultrafast photonic decision making in dynamically changing environments for various applications, such as cognitive wireless communications and robot control using reinforcement learning.

Reservoir computing provides superior information processing ability for a time series prediction based on appropriate learning prior to task execution. The performance of reservoir computing, however, may degrade if the characteristics of the input signal drastically change over time because the internal model of reservoir computing deviates from the subjected input signal trains. We propose a method for adaptive model selection using reinforcement learning in electro-optic delay-based reservoir computing. We experimentally show that an adaptive model selection is effective when different dynamical models for the input signals change dynamically over time.

Power packet dispatching system has been proposed for smart power management in the form of discretized packet. In this paper, we discuss the routing optimization of power packets on the network of power routers. We propose a cost metric for the power packet delivery by circuit analysis of the router network. Using the metric, we formulate the optimization problem as a general shortest path problem from a source node to a load node. The result of numerical simulations shows that the proposed algorithm can allocate distributed power sources to load demands and identify the optimal path for the power delivery.

This paper addresses a problem of assessment of voltage phasors in AC power distribution grids. This problem is motivated by the promising increase of Distributed Energy Resources (DERs) such as Electric Vehicles (EVs) and its impact to the power grids. Specifically, we address the nonlinear ODE (Ordinary Differential Equation) model for representing the spatial profile of voltage phasor along a distribution feeder, which has been recently introduced in literature. The assessment problem is then formulated as a two-point boundary value problem of the nonlinear ODE model. In this paper, we derive an asymptotic characterization of solutions of the problem through the standard regular perturbation method. This enables a quantitative and physically interpretable approach to assessing how the introduction of DERs, e.g., charging/discharging of EVs, affects the spatial profile of distribution voltage. Effectiveness of the asymptotic characterization is established with simulations of both simple and practical configurations of the power distribution grid.