Spiking Neural Network(SNN) is a next-generation neural network called a third-generation neural network. It aims to simulate neurons more realistically than Perceptron and communicates with 1-bit spikes, unlike deep learning.
Therefore, SNN has the advantage of model compression while protecting biological plausability. It is also an area that is actively being studied in connection with the recently proposed neuromorphic hardware.
In our lab, we analyze bio-signals such as ECG through SNN and improve the network topology based on prior studies of neuroscience.
Deep Learning is an advanced form of artificial neural network, and it is the most outstanding technology in AI field that the market demands.
As a result, models have been developed and improved in various research fields, such as computer vision, signal and natural language processing, and are widely used.
Our lab also has access through deep learning models in many studies, and has studied the estimation of sleep state and the analysis of ECG Signals through deep learning.
Based on various machine learning algorithms, especially deep learning models, Artificial Intelligence has recently been developed by many computer science experts through various approaches, along with rapid growth.
Neural networks, which are designed based on various artificial neural network algorithms, are actually offering groundbreaking solutions to a variety of problems, and practical applications to gain an upper hand in a number of markets are being proposed.
Among them, our lab is conducting research on the next generation of Neural Networks called the Spicking Neural Network (SNN), a more biologically accurate and advanced model compared to the conventional artificial neural network, and focusing on the creation of artificial intelligence models from a Neural Morphic perspective.
Data mining is a major data-based technology that meets the needs of recent markets, which extracts significant information by processing and selecting data from large data sets.
A big data generation and utilization model is required from the market and academia, which preprocesses data from various sensors and user environments using data-mining.
Our lab is conducting research by extracting interested data from documents and by using to catch the main signals or features of the biometric signals.
Healthcare, based on the rapid evolution of hardware and the growing IOT-based wearable equipment, is currently the most sought-after health care sector and is evolving rapidly amid many areas of interest and expectations.
Among them, various healthcare applications proposed through the application of artificial intelligence models have been making rapid progress in recent years with a lot of interest in the market.
Our lab conducts research on the development of healthcare applications using machine learning model suggestions for signals from various wearable devices, including biometric measurements and analysis based on user data, measurement of user physical conditions, and suggestion of decision system applications.
The development of wearable devices based on low-power micro models is drawing attention as one of the key technologies to lead the new technology industry in the near future.
Research is mainly conducted on the collection and processing of various biometric data that can be measured based on various equipment, data transmission and reception, data analysis through artificial intelligence model design, and proposal of decision-making service application.
And our lab is conducting research from the development of small electronic devices to the application of artificial intelligence algorithms to signals generated by wearable devices to abnormal detection and user biometric analysis, user detection, etc.
Internet of Things
Internet Of Things (IOT) is a next-generation technology tailored to the software aspect centered on evolving embedded hardware, the first step in technological progress toward a future society in which humans and technology are positively connected.
Our lab is conducting IOT research focusing on its approach as a network-based control technology through close connectivity between various wearable devices and users for the next-generation healthcare industry.
We have been researching healthcare solutions by applying artificial intelligence algorithms around various healthcare devices, and have studied and proposed optimization models for IOT devices.