• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.212-220
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• 2024

A recently the advancement of society, AI technology has made significant strides, especially in the fields of computer vision and voice recognition. This study introduces a system that leverages these technologies to recognize users through a camera and relay commands within a vehicle based on voice commands. The system uses the YOLO (You Only Look Once) machine learning algorithm, widely used for object and entity recognition, to identify specific users. For voice command recognition, a machine learning model based on spectrogram voice analysis is employed to identify specific commands. This design aims to enhance security and convenience by preventing unauthorized access to vehicles and IoT devices by anyone other than registered users. We converts camera input data into YOLO system inputs to determine if it is a person, Additionally, it collects voice data through a microphone embedded in the device or computer, converting it into time-domain spectrogram data to be used as input for the voice recognition machine learning system. The input camera image data and voice data undergo inference tasks through pre-trained models, enabling the recognition of simple commands within a limited space based on the inference results. This study demonstrates the feasibility of constructing a device management system within a confined space that enhances security and user convenience through a simple real-time system model. Finally our work aims to provide practical solutions in various application fields, such as smart homes and autonomous vehicles.

• Journal of Digital Convergence
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• v.13 no.6
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• pp.267-273
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• 2015

Recently, a wide range of products incorporating cutting-edge technology are being introduced in various sectors of design. Belkin's WeMo or Phillips' Hue are representative examples. In this context, the color-responsive lighting bookcase is a design product that would satisfy the needs of contemporary consumers who seek entertainment in their purchases. By installing lightings that change color according to the user's behavior, this design reconceptualizes the bookcase as a source of entertainment rather than a mundane object of household furnishing. The lighting apparatus can be detached and reattached, serving as stand-alone equipment. The lighting bookcase is modularized, comprising extensions equipped with MCU (Micro Controller Unit), RGB LED and color sensors. The bookcase as a whole is extendable towards four directions up to nine units with the lighting bookcase at the center. The extended, multiple lighting bookcases are wired to receive power from the main bookcase, and are equipped with RGB LEDs but not with MCUs or color sensors. Receiving power and color signals from the main lighting bookcase, the sub-bookcases feature changing shades of color. Also, it includes IoT(internet of Things). This study is a proposal of a design product, modularized to control the shades of the bookcase lighting using these sensors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1176-1182
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• 2021

Recently, with the development of artificial intelligence and IoT technology, automation, and unmanned work are progressing in various fields, and the importance of image processing, which is the basis of AI object recognition, is increasing. In particular, in systems that require detailed data processing, noise removal is used as a preprocessing step, but the existing algorithm does not consider the noise level of the image, so it has the disadvantage of blurring in the filtering process. Therefore, in this paper, we propose a modified Gaussian filter that determines the weight by determining the noise level of the image. The proposed algorithm obtains the noise estimate for the AWGN of the image using quadtree segmentation, determines the Gaussian weight and the pixel weight, and obtains the final output by convolution with the local mask. To evaluate the proposed algorithm, it was simulated compared to the existing method, and superior performance was confirmed compared to the existing method.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1077-1082
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• 2019

The development of computing technology and networking has developed into a fundamental technology of the Fourth Industrial Revolution, which provides a ubiquitous environment. In the ubiquitous environment, the IoT environment has become an issue so that various devices and the things can be actively accessed and connected. Also, the RFID system using the wireless identification code attaches an RFID tag to the object, such as the production and distribution of products. It is applied to the management very efficiently. EPCglobal is conducting a research on RFID system standardization and various security studies. Since RFID systems use wireless environment technology, there are more security threats than wire problems. In particular, failure to provide confidentiality, indistinguishability, and forward safety could expose them to various threats in the Fourth Industrial Revolution. Therefore, this study analyzes the standard method of EPCgolbal and proposes RFID security method using hash code that can consider the amount of computation.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.63-68
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• 2022

Due to COVID-19, many paradigm shifts in existing manufacturing facilities and the expansion of non-face-to-face services are accelerating worldwide. A representative technology is digital twin technology. Such digital twin technology, which existed only conceptually in the past, has recently become feasible with the construction of a 5G-based network. Accordingly, this paper designed and implemented a part of the USB process to enable digital twins based on OPC UA communication, which is a standard interlocking structure, between real object objects and virtual reality-based USB process in accordance with this paradigm change. By reflecting the physical characteristics of real objects together, it is possible to simulate real-time synchronization of these with real objects. In the future, this can be applied to various industrial fields, and it is expected that it will be possible to reduce costs for decision-making and prevent dangerous accidents.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.