A Stabilized Feedback Episodic Memory (SF-EM) and Home Service Provision Framework for Robot and IoT Collaboration

Kim, Ue-Hwan; Kim, Jong-Hwan

doi:10.1109/tcyb.2018.2882921

Cited by 18 publications

(6 citation statements)

References 33 publications

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“…where β I and β O are the learning rates for the internal division and outer division, respectively. The area of the high effectiveness template k w JH is reduced by (17). On the other hand, according to (18), the low effectiveness template k w JL expands to the low-effectiveness content k x L .…”

Section: Learner Experience Dlnmentioning

confidence: 99%

Individualized AI Tutor Based on Developmental Learning Networks

Kim

2020

IEEE Access

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In recent years, in the field of education technology, artificial intelligence tutors have come to be expected to provide individualized educational services to help learners achieve high levels of academic success. To this end, AI tutors need to be able to understand the current status and preferences of a learner and then suggest appropriate learning contents accordingly. However, it is challenging to monitor learner status and preferences continually and to recommend appropriate educational services. In this paper, we propose an individualized AI tutor as an integrated system of three developmental learning networks (DLNs) by extending a deep adaptive resonance theory (Deep ART) network, a neural network capable of incremental learning. Specifically, the learner status DLN is able to easily add new input channels about learner status without disrupting existing classifiers. The learner preference DLN is to categorize learner preferences based on frequency as well as sequence of events. The learner experience DLN is updated to immediately reflect alteration of the educational effectiveness in the current classification. Our AI tutor is currently embedded in a commercialized mobile application for teaching the Korean language to children. Experimental results show that the AI tutor application efficiently helps children learn the Korean language.

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Section: Learner Experience Dlnmentioning

confidence: 99%

Individualized AI Tutor Based on Developmental Learning Networks

Kim

2020

IEEE Access

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“…So far, the future of ubiquitous home networks will rely on sensors to aggregate various environmental data variables [57]. This work has similar baseline design attributes with robotic smart homes based on stabilized feedback Episodic memory (SF-EM) [58], stewards robot smart homes [59], humanoid defense smart homes [60], Indoor autonomous robots [61], and Smart home activities IoT [62]. These similarities are highlighted below [63], [64], [65]:…”

Section: Summary Of Related Work (Automated Home)mentioning

confidence: 99%

ASM-ROBOT: A Cyber-Physical Home Automation Controller with Memristive Reconfigurable State Machine

Okafor¹,

Longe²

2022

IJACSA

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In the next 5 to 10 years, digital Artificial Intelligence with Machine Circuit Learning Algorithms (MCLA) will become the mainstream in complex automated robots. Its power concerns, ethical perspectives, including the issues of digital sensing, actuation, mobility, efficient processcomputation, and wireless communication will require advanced neuromorphic process variable controls. Existing home automated robots lack memristic associative memory. This work presents Cyber-Physical Home Automation System (CPHAS) using Memristive Reconfigurable Algorithmic State Machine (MRASM) chart. A process control architecture that supports Concurrent Wireless Data Streams and Power-Transfer (CWDSPT) is developed. Unlike legacy systems with powersplitting (PS) and time-switching (TS) controls, the MRASM-ROBOT explores granular wireless signal controls through unmodulated high-power continuous wave (CW). This transmits infinite process variables using Orthogonal Space-Time Block Code (OSTBC) for interference reduction. The CWDSPT transmitter and receiver circuit design for signal processing are implemented with complexity noise-error reduction during telemetry data decoding. Received signals are error-buffered while gathering control variables' status.Transceiver Memristive neuromorphic circuits are introduced for computational acceleration in the design. Hardware circuit design is tested for system reliability considering the derived schematic models for all process variables. Under small range space diversity, the system demonstrated significant memory stabilization at the synchronous iteration of the synaptic circuitry.

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“…As various advanced countries and large enterprises have joined the promotion of the IoT industry, the business opportunities of the IoT have emerged in many areas. Forrester Research, a market research organization, predicts that the business opportunities brought by the IoT in the future will exceed the current Internet output value by more than 30 times [1,2].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Home Using Fuzzy Control Based on AIoT

Hsiao¹,

Sung²

2023

Computer Systems Science and Engineering

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The Internet of Things has grown rapidly in recent years, and the technologies related to it have been widely used in various fields. The idea of this paper is to build a set of Internet of Things systems in a smart home wireless network environment, with the purpose of providing people with a more comfortable, convenient, and safe life. In the sensing layer of the Internet of Things, we discuss the uses of common sensing technologies on the Internet and combine these with Arduino microprocessors to integrate temperature sensing modules, humidity sensing modules, gas sensing modules, and particulate matter 2.5 (PM2.5) sensing modules. In the network layer, we discuss using the Wi-Fi wireless networking function composed of a home router and a wireless Wi-Fi chip Espressif system 8266 (ESP8266) to transmit the collected home-sensing data to the ThingSpeak cloud database. Finally, in the application layer part, the system uses a mobile device with fuzzy calculation optimization software. The system is also connected remotely for home environment monitoring, so the home environment can be optimized anytime, anywhere.

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A Stabilized Feedback Episodic Memory (SF-EM) and Home Service Provision Framework for Robot and IoT Collaboration

Cited by 18 publications

References 33 publications

Individualized AI Tutor Based on Developmental Learning Networks

Individualized AI Tutor Based on Developmental Learning Networks

ASM-ROBOT: A Cyber-Physical Home Automation Controller with Memristive Reconfigurable State Machine

Intelligent Home Using Fuzzy Control Based on AIoT

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