• Journal of the Korea Convergence Society
• /
• v.9 no.1
• /
• pp.253-259
• /
• 2018

The Korea Convergence Society. Recently, as the 4th industrial revolution is rising, there are many changes in various field of industries. Among these industries, autonomous vehicles based on artificial intelligence, big data and internet of things is one of the most promising industry. Autonomous vehicle stray from classical car domain of manufacturers and suppliers, IT/Electronics suppliers and communication companies are widen their business area to autonomous vehicle technology. In this paper, we analysis the state of art of autonomous vehicle technology and development direction of industries/research institute. Finally, we discuss the social/economic effects of autonomous vehicle.

• Journal of Broadcast Engineering
• /
• v.26 no.4
• /
• pp.368-376
• /
• 2021

Artificial intelligence clouds help to efficiently develop the autonomous things integrating artificial intelligence technologies and control technologies by sharing the learned models and providing the execution environments. The existing autonomous things development technologies only take into account for the accuracy of artificial intelligence models at the cost of the increment of the complexity of the models including the raise up of the number of the hidden layers and the kernels, and they consequently require a large amount of computation. Since resource-constrained computing environments, could not provide sufficient computing resources for the complex models, they make the autonomous things violate time criticality. In this paper, we propose a digital twin software development framework that selects artificial intelligence models optimized for the computing environments. The proposed framework uses a load estimation DNN model to select the optimal model for the specific computing environments by predicting the load of the artificial intelligence models with digital twin data so that the proposed framework develops the control software. The proposed load estimation DNN model shows up to 20% of error rate compared to the formula-based load estimation scheme by means of the representative CNN models based experiments.

• International journal of advanced smart convergence
• /
• v.11 no.2
• /
• pp.7-12
• /
• 2022

Safety and security are the topmost priority in every environment. With the aid of Artificial Intelligence (AI), many objects are becoming more intelligent, conscious, and curious of their surroundings. The recent scientific breakthroughs in autonomous vehicular designs and development; powered by AI, network of sensors and the rapid increase of Internet of Things (IoTs) could be utilized in maintaining safety and security in our environments. AI based on deep learning architectures and models, such as Deep Neural Networks (DNNs), is being applied worldwide in the automotive design fields like computer vision, natural language processing, sensor fusion, object recognition and autonomous driving projects. These features are well known for their identification, detective and tracking abilities. With the embedment of sensors, cameras, GPS, RADAR, LIDAR, and on-board computers in many of these autonomous vehicles being developed, these vehicles can properly map their positions and proximity to everything around them. In this paper, we explored in detail several ways in which these enormous features embedded in these autonomous vehicles, such as the network of sensors fusion, computer vision and natural image processing, natural language processing, and activity aware capabilities of these automobiles, could be tapped and utilized in safeguarding our lives and environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.4
• /
• pp.81-86
• /
• 2020

With the development of the intelligent Internet of Things AIoT that goes beyond the IoT of the Internet of Things, the industry is changing overall. In addition, with the advent of the 4th Industrial Revolution, revolutionary changes and developments are also taking place in the automobile industry. A representative example is "autonomous driving vehicle". Because the domestic and foreign interests in autonomous vehicles have increased, many developments have been made, and although limited, they have developed into the commercialization stage. However, the structure of the autonomous vehicle that collects, analyzes, and controls data using various sensors installed in the vehicle, not the driver, is often insufficiently exposed to hacking due to the lack of multiplexed devices for security. In this case, as this can be a threat not only to the driver, but also to the surrounding environment, this paper proposes a new data processing function to improve the system security of autonomous vehicles.

• Journal of Internet of Things and Convergence
• /
• v.9 no.1
• /
• pp.65-70
• /
• 2023

In this paper, we propose an autonomous driving system using an end-to-end model to improve lane departure and misrecognition of traffic lights in a vision sensor-based system. End-to-end learning can be extended to a variety of environmental conditions. Driving data is collected using a model car based on a vision sensor. Using the collected data, it is composed of existing data and data with outlayers removed. A class was formed with camera image data as input data and speed and steering data as output data, and data learning was performed using an end-to-end model. The reliability of the trained model was verified. Apply the learned end-to-end model to the model car to predict the steering angle with image data. As a result of the learning of the model car, it can be seen that the model with the outlayer removed is improved than the existing model.

• Journal of Digital Contents Society
• /
• v.19 no.9
• /
• pp.1819-1824
• /
• 2018

This paper proposes a control method of ASMR(autonomous sensory meridian response) contents to relieve user's stress and improve his attention. The proposed method measures EEG(electroencephalography), attention, meditation, and eyeblink data from an EEG device and sends them to an oneM2M-compliant IoT(internet of things) server platform through an Android IoT Application. Then a SVM(support vector machine) model is built to classify user's mental health status by using EEG, attention and meditation data collected in the server platform. The ASMR contents are controlled by the mental health status classified by a SVM model and the eyeblink data. When comparing the SVM models according to types of data used, the SVM model with attention and meditation data showed accuracy of 85.7%. It was verified that the proposed control algorithm of ASMR contents properly worked as the mental health status from the SVM model and the eyeblink data changed.

• Journal of the Korean Society for Precision Engineering
• /
• v.31 no.4
• /
• pp.287-295
• /
• 2014

Smart manufacturing (SM) considered as a new trend of modern manufacturing helps to meet objectives associated with the productivity, quality, cost and competiveness. It is characterized by decentralized, distributed, networked compositions of autonomous systems. The model of SM is inherited from the organization of the living systems in biology and nature such as ant colony, school of fish, bee's foraging behaviors, and so on. In which, the resources of the manufacturing system are considered as biological organisms, which are autonomous entities so that the manufacturing system has the advanced characteristics inspired from biology such as self-adaptation, self-diagnosis, and self-healing. To prove this concept, a cloud machining system is considered as research object in which internet of things and cloud computing are used to integrate, organize and allocate the machining resources. Artificial life tools are used for cooperation among autonomous elements in the cloud machining system.

• ETRI Journal
• /
• v.40 no.1
• /
• pp.122-132
• /
• 2018

This paper proposes a hierarchical dual filtering (HDF) algorithm to estimate the spatial region between a Cloud of Things (CoT) gateway and an Internet of Things (IoT) device. The accuracy of the spatial region estimation is important for autonomous CoT clustering. We conduct spatial region estimation using a hidden Markov model (HMM) with a raw Bluetooth received signal strength indicator (RSSI). However, the accuracy of the region estimation using the validation data is only 53.8%. To increase the accuracy of the spatial region estimation, the HDF algorithm removes the high-frequency signals hierarchically, and alters the parameters according to whether the IoT device moves. The accuracy of spatial region estimation using a raw RSSI, Kalman filter, and HDF are compared to evaluate the effectiveness of the HDF algorithm. The success rate and root mean square error (RMSE) of all regions are 0.538, 0.622, and 0.75, and 0.997, 0.812, and 0.5 when raw RSSI, a Kalman filter, and HDF are used, respectively. The HDF algorithm attains the best results in terms of the success rate and RMSE of spatial region estimation using HMM.

• Journal of Arbitration Studies
• /
• v.31 no.4
• /
• pp.119-136
• /
• 2021

Due to the restriction of movement caused by the Corona epidemic and the expansion of the "big face" through human distance, the "unmanned system" based on artificial intelligence and the Internet of Things has been widely used in modern life. "Self-driving," one of the transportation systems based on artificial technology, has taken the initiative in the transportation system as the spread of Corona has begun. Self-driving technology eliminates unnecessary contact and saves time and manpower, which can significantly impact current and future transportation. Accidents may occur, however, due to the performance of self-driving technology during transportation albeit the U.S. allows ordinary people to drive automatically through experimental operations, and the product liability law will resolve the dispute. Self-driving has become popular in the U.S. after the experimental stage, and in the event of a self-driving accident, product liability should be applied to protect drivers from complicated self-driving disputes. The purpose of this paper is to investigate whether disputes caused by defects in ordinary cars can be resolved through arbitration through U.S. precedents and to investigate whether disputes caused by defects in autonomous cars can be arbitrated.

• Journal of Korean Library and Information Science Society
• /
• v.52 no.1
• /
• pp.27-54
• /
• 2021

This study examines the overall contents of robots, drones, and autonomous driving that can be applied to libraries among autonomous objects, and proposes a plan that can be introduced and applied to libraries in the future based on this. As a result of the study, in the case of the building, robots and drones can be used to apply from collection inspection, collection transport, collection arrangement, collection classification, book location guidance, book recommendation, loan/return, library general guidance, and reference information service. Outside of the building, robots, drones, and autonomous vehicles can be used for book delivery service, book return service, and unmanned mobile libraries. This study is a basic research for the introduction and application of autonomous objects in the library, and follow-up studies such as perception survey and application model development for systematic introduction should be conducted in the future.