Self‐Organization of Remote Reservoirs: Transferring Computation to Spatially Distant Locations

Tanaka, Kazutoshi; Tokudome, Yuji; Minami, Yuna; Honda, Satoko; Nakajima, Toshiki; Takei, Kuniharu; Nakajima, Kohei

doi:10.1002/aisy.202100166

Cited by 15 publications

(7 citation statements)

References 47 publications

(65 reference statements)

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“…This system also makes multitasking easy because the target tasks do not interfere during the learning process, unlike conventional backpropagation schemes. [18] However, to date, RC, combined with flexible weather sensors has yet to be reported, although different types of information processing, such as movements of soft bodies, [19,20] organic electrochemical networks, [21,22] and some other physical systems [23][24][25] have been studied.…”

Section: Introductionmentioning

confidence: 99%

A Multitasking Flexible Sensor via Reservoir Computing

et al. 2022

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radar, obtaining detailed local information is still limited by cost and by bulky, complicated instrumentation. For future safety of human life, it is obvious that simple, attachable weather sensors can help to mitigate flood disasters. Currently, acquiring local weather information relies on personal input via websites or weather cams.One approach is to utilize a simple, lightweight, flexible sensor that can be attached conformably to a variety of surfaces, such as building and automobile roofs and even umbrellas. In fact, flexible sensors with multiple functionalities have been already reported for applications such as wearable healthcare devices, [1][2][3][4][5][6] robotics, [7][8][9][10] and plants. [11] Although the concept of attachable sensor sheets to collect local weather information is unique, it is not simple to detect precipitation and wind velocity simultaneously using a single device. It should be noted that power generation using raindrops has been proposed, [12] and this may also be able to estimate precipitation. However, the concept of monitoring local weather information has yet to be suggested, most likely because it is challenging to measure wind velocity due to complexity of signal changes.For simultaneous monitoring of precipitation and wind, waterdrop behavior must be understood to enable further analyses. Waterdrops, including rain, exhibit a variety of dynamics such as spreading, bouncing, and wetting, depending on droplet behavior and surface conditions at the time of surface impact. These behaviors include information at micro-to-macro scales about volume, velocity, etc. If dynamics of water behavior can be monitored in real time, weather information focused on precipitation and wind velocity may be readily extracted. Furthermore, using wireless networks and data sharing on the Internet of Things platform, local weather information can be shared automatically by attaching sensors to various objects. As a result, it will be possible to collect at the edge and analyze more data using cloud computing, to monitor dangerous weather in real time. In general, to gather multiple types of data in real-time, it is necessary to integrate multiple sensors for each information type. However, the use of a large number of sensors to collect information can limit applications, owing to device weight, size, cost, and complexity of the systems. It is important to analyze multiple features simply, with low power consumption.With sensor developments, complicated signal processing may be required regardless of the number of sensors and Natural disasters are reported globally, and one source of severe damage to cities is flooding caused by locally heavy rain. Sharing of local weather information can save lives. However, it is difficult to collect local weather information in real-time because such data collection requires bulky, expensive sensors. For local, real-time monitoring of heavy rain and wind, a sensor system should be simple and low-cost so that it can be attached to a variety of surfaces, includi...

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Section: Introductionmentioning

confidence: 99%

A Multitasking Flexible Sensor via Reservoir Computing

et al. 2022

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“…In addition, they tried to develop autonomous soft robots for scenarios such as the deep sea. [15] Meanwhile, multidisciplinary knowledge such as machine vision, [123,124] machine learning, [125][126][127][128] CFD, [129][130][131] and finite element method (FEM) [73] were combined to complete the modeling and design of the robotic systems. These new soft machines can operate autonomously for longer periods (Figure 4).…”

Section: Research Statusmentioning

confidence: 99%

Recent Advances on Underwater Soft Robots

Qu,

Xu,

et al. 2023

Advanced Intelligent Systems

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The ocean environment has enormous uncertainty due to the influence of complex waves and undercurrents. The human beings are limited in their abilities to detect and utilize marine resources without powerful tools. Soft robots employ soft materials to simplify the complex mechanical structures in rigid robots and adapt their morphology to the environment, making them suitable for performing some challenging tasks in place of manual labor. Due to superior flexible and deformable bodies, underwater soft robots have played significant roles in numerous applications in recent decades. Meanwhile, various technical challenges still need to be tackled to ensure the reliability and practical performance of underwater soft robots in complicated ocean environment. Nowadays, some researchers have developed underwater soft robotic systems based on biomimetics and other disciplines, aiming at comprehensive exploration of ocean and appropriate utilization of unexploited resources. This review presents the recent advances of underwater soft robots in the aspects of intelligent soft materials, fabrication, actuation, locomotion patterns, power storage, sensing, control, and modeling; additionally, the existing challenges and perspectives are analyzed as well.

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“…There is an exponentially growing number of data generated by intelligent edge devices. However, the existing computing system still relies on the traditional von Neumann architecture with separated storage and computing units, resulting in a frequent data transfer brought by limited bandwidth of data path and leading to huge latency and energy consumption [2,3]. Meanwhile, metal-oxide-semiconductor field effect transistors functioning as its basic component struggle to improve integration and performance by further reducing size as Moore's law slows down [4].…”

Section: Introductionmentioning

confidence: 99%

In-materio reservoir computing based on nanowire networks: fundamental, progress, and perspective

Fang

Zhang

et al. 2023

Mater. Futures

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The reservoir computing (RC) system, known for its ability to seamlessly integrate memory and computing functions, is considered as a promising solution to meet the high demands for time and energy-efficient computing in the current big data landscape, compared with traditional silicon-based computing systems that have a noticeable disadvantage of separate storage and computation. This review focuses on in-materio RC based on nanowire networks (NWs) from the perspective of materials, extending to reservoir devices and applications. The common methods used in preparing nanowires-based reservoirs, including the synthesis of nanowires and the construction of networks, are firstly systematically summarized. The physical principles of memristive and memcapacitive junctions are then explained. Afterwards, the dynamic characteristics of nanowires-based reservoirs and their computing capability, as well as the neuromorphic applications of NWs-based RC systems in recognition, classification, and forecasting tasks, are explicated in detail. Lastly, the current challenges and future opportunities facing NWs-based RC are highlighted, aiming to provide guidance for further research.

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Self‐Organization of Remote Reservoirs: Transferring Computation to Spatially Distant Locations

Cited by 15 publications

References 47 publications

A Multitasking Flexible Sensor via Reservoir Computing

A Multitasking Flexible Sensor via Reservoir Computing

Recent Advances on Underwater Soft Robots

In-materio reservoir computing based on nanowire networks: fundamental, progress, and perspective

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