• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1216-1233
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• 2023

Conventional occupational therapy (OT) is conducted under the observation of an occupational therapist, and there are limitations in measuring and analyzing details such as degree of hand tremor and movement tendency, so this important information may be lost. It is therefore difficult to identify quantitative performance indicators, and the presence of observers during performance sometimes makes the subjects feel that they have to achieve good results. In this study, by using the Unity3D and artificial intelligence (AI) speaker, we propose a system that allows the subjects to steadily use it by themselves and helps the occupational therapist objectively evaluate through quantitative data. This system is based on the OT of the sensory integration approach. And the purpose of this system is to improve children's activities of daily living by providing various feedback to induce sensory integration, which allows them to develop the ability to effectively use their bodies. A dynamic OT cognitive assessment tool for children used in clinical practice was implemented in Unity3D to create an OT environment of virtual space. The Leap Motion Controller allows users to track and record hand motion data in real time. Occupational therapists can control the user's performance environment remotely by connecting Unity3D and AI speaker. The experiment with the conventional OT tool and the system we proposed was conducted. As a result, it was found that when the system was performed without an observer, users can perform spontaneously and several times feeling ease and active mind.

• Electronics and Telecommunications Trends
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• v.36 no.1
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• pp.89-98
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• 2021

The Ministry of National Defense aims to create an environment in which transparent and reasonable defense policies can be implemented in real time by establishing the vision of smart defense innovation based on the Fourth Industrial Revolution and promoting innovation in technology-based defense operation systems. Artificial intelligence (AI) based defense technology is at the level of basic research worldwide, includes no domestic tasks, and involves classified military operation data and command control/decision information. Further, it is needed to secure independent technologies specialized for our military. In the army, military power continues to decline due to aging and declining population. In addition, it is expected that there will be more than 500,000 units should be managed simultaneously, to recognize the battle situation in real time on the future battlefields. Such a complex battlefield, command decisions will be limited by the experience and expertise of individual commanders. Accordingly, the study of AI core technologies supporting real-time combat command is actively pursued at home and abroad. It is necessary to strengthen future defense capabilities by identifying potential threats that commanders are likely to miss, improving the viability of the combat system, ensuring smart commanders always win conflicts and providing reasonable AI digital staff based on data science. This paper describes the recent research trends in AI military staff technology supporting commander decision-making, broken down into five key areas.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.127-133
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• 2020

As the usages of artificial intelligence is increased, the demand to algorithms for small portable devices increases. Also as the embedded system becomes high-performance, it is possible to implement algorithms for high-speed computation and machine learning as well as operating systems. As the machine learning algorithms process repetitive calculations, it depend on the cloud environment by network connection. For an stand alone system, low power consumption and fast execution by optimized algorithm are required. In this study, for the purpose of smart control, an energy measurement sensor is connected to an embedded system, and a real-time monitoring system is implemented to store measurement information as a database. Continuously measured and stored data is applied to a learning algorithm, which can be utilized for optimal power control, and a system interfacing various sensors required for energy measurement was constructed.

• Journal of Korean Neurosurgical Society
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• v.67 no.5
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• pp.493-509
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• 2024

In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

• Smart Media Journal
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• v.13 no.2
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• pp.136-144
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• 2024

In recent years, with the rapid development of IT technology and the aging of the population, various solutions to the labor shortage have emerged. In the livestock industry, there are an increasing number of management systems that utilize artificial intelligence technology. The Metaverse Smart Farm is a system that combines the digital virtual world with advanced agricultural technology. With this system, farmers can monitor the health of their animals in real time without having to visit the barns, and analyze the data collected through sensors and cameras for more efficient agricultural management. In addition, the barn environment can be adjusted through a remote control function, which is expected to reduce labor and revitalize the livestock industry.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.17-22
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• 2022

In this paper, we design a lightweight embedded device that can support intelligent edge computing, and show that the device quickly detects an object in an image input from a camera device in real time. The proposed system can be applied to environments without pre-installed infrastructure, such as an intelligent video control system for industrial sites or military areas, or video security systems mounted on autonomous vehicles such as drones. The On-Device AI(Artificial intelligence) technology is increasingly required for the widespread application of intelligent vision recognition systems. Computing offloading from an image data acquisition device to a nearby edge device enables fast service with less network and system resources than AI services performed in the cloud. In addition, it is expected to be safely applied to various industries as it can reduce the attack surface vulnerable to various hacking attacks and minimize the disclosure of sensitive data.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1023-1026
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• 2005

In the ubiquitous computing environment, an intelligent vehicle is defined as a sensor node with a capability of intelligence and communication in a wire and wireless network space. To make it real, a lot of problems should be addressed in the aspect of vehicle mobility, in-vehicle communication, common service platform and the connection of heterogeneous networks to provide a driver with several intelligent information services beyond the time and space. In this paper, we present an intelligent information system for managing in-vehicle sensor network and a vehicle gateway for connecting the external networks. The in-vehicle sensor network connected with several sensor nodes is used to collect sensor data and control the vehicle based on CAN protocol. Each sensor node is equipped with a reusable modular node architecture, which contains a common CAN stack, a message manager and an event handler. The vehicle gateway makes vehicle control and diagnosis from a remote host possible by connecting the in-vehicle sensor network with an external network. Specifically, it gives an access to the external mobile communication network such as CDMA. Some experiments was made to find out how long it takes to communicate between a vehicle's intelligent information system and an external server in the various environment. The results show that the average response time amounts to 776ms at fixed place, 707ms at rural area and 910ms at urban area.

• Journal of Korean Society of Water and Wastewater
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• v.14 no.3
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• pp.281-290
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• 2000

This study investigates the prediction of daily water supply, which is a necessary for the efficient management of water distribution system. ANFIS, namely artificial intelligence, is a neural network into which fuzzy information is inputted and then processed. In this study, daily water supply was predicted through an application of network-based fuzzy inference system(ANFIS) for daily water supply prediction. This study was investigated methods for predicting water supply based on data about the amount of water which supplied in Kwangju city. For variables choice, four analyses of input data were conducted: correlation analysis, autocorrelation analysis, partial autocorrelation analysis, and cross-correlation analysis. Input variables were (a) the amount of water supply, (b) the mean temperature, and (c) the population of the area supplied with water. Variables were combined in an integrated model. Data of the amount of daily water supply only was modelled and its validity was verified in the case that the meteorological office of weather forecast is not always reliable. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 18.46% and the average error was lower than 2.36%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.317-319
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• 2021

In Korea, an intelligent transportation system(ITS) is established to efficiently operate traffic congestion on roads and is being used for traffic information collection and speed control systems. Currently, designated and dedicated lanes are in place to ensure traffic circulation and traffic safety, and systematic and accurate illegal vehicle crackdown systems with artificial intelligence technology are needed. In this study, we propose a vehicle number recognition model that can improve the efficiency of the traffic of designated vehicles. By applying computer vision technology, we are going to identify three-lane and four-lane multi-lane roads in real time and detect vehicle numbers by car to suggest ways to crack down on vehicles that violate the designated lane system.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.69-77
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• 2019

Today, most of elevators have an emergency call facility for emergency situations. However, if the network installed in the elevator is also out of power, it cannot be used for the elevator remote monitoring and management. So, we develop an integrated and unified emergency call system, which can transmit not only telephone call but also data signals using PSTN(Public Switched Telephone Network) in order to remote monitoring and management of elevators, even though a power outage occurs. The proposed integrated emergency call system to process multiple data such as voice and operational information is a multi-channel board system which is composed of an emergency phone signal processing module and an operational information processing module in the control box of elevator. In addition, the RMS(remote management server) systems based on the Web consist of a dial-up server and a remote monitoring server where manages the elevator's operating information, status records, and operational faults received via the proposed integrated and unified emergency call system in real time. So even if there's a catastrophic emergency, the proposed RMS systems shall ensure and maintain the safety of passengers inside the elevator. Also, remote control of the elevator by this system should be more efficient and secure. In near future, all elevator emergency call system need to support multifunctional capabilities to transmit operational data as well as phone calls for the safety of passengers. In addition, for safer elevators, it is necessary to improve them more efficiently by combining them with high-tech technologies such as the Internet of Things and artificial intelligence.