• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.540-542
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• 2014

The Universal Plug and Play (UPnP) specification permits networked devices to discover each other and to provide diverse services in the home network environment. Recently, new paradigms such as mobile connected computing, cloud-based service delivery, smart device content sharing, and Internet of Things (IoT) have emerged, but the home network based UPnP shows functional limitations in supporting such paradigms. To support them, the UPnP Forum has recently extended the capabilities of the existing UPnP, calling it UPnP+. In this paper, the UPnP Device Architecture V2.0 (UDA 2.0), which forms the basis of UPnP+, is presented. We present how UDA 2.0 enables the expansion of the home network to wide-area networks and non-IP device domains.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 1999.10a
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• pp.59-59
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• 1999

In this study, planer inductively coupled $Cl_2$ based plasmas were used to etch InGaN and the effects of plasma conditions on the InGaN etch properties have been characterized using quadrupole mass spectrometry(QMS) and optical emission spectroscopy(OES). As process conditions used to study the effects of plasma characteristics on the InGaN etch properties, $Cl_2$ was used as the main etch gas and $CHF_3,{\;}CH_4$, and Ar were used as additive gases. Operational pressure was varied from SmTorr to 3OmTorr, inductive power and bias voltage were varied from 400W to 800W and -50V to -250V, respectively while the substrate temperature was fixed at 50 centigrade. For the $Cl_2$ plasmas, selective etching of GaN to InGaN was obtained regardless of plasma conditions. The small addition of $CHF_3$ or Ar to $Cl_2$ and the decrease of pressure generally increased InGaN etch rates. The selective etching of InGaN to GaN could be obtained by the reduction of pressure to l5mTorr in $CI_2/IO%CHF_3{\;}or{\;}CI_2/IO%Ar$ plasma. The enhancement of InGaN etch rates was related to the ion bombardment for $CI_2/Ar$ plasmas and the formation of $CH_x$ radicals for $CI_2/CHF_3(CH_4)$ plasmas.

• Journal of the Korean Society of Food Science and Nutrition
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• v.35 no.5
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• pp.516-523
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• 2006

The mushroom Inonotus obliquue (IO) has been traditionally used for the treatment of gastrointestinal cancer in Russia, Poland, and most of Baltic countries. To explore the possibility that IO has chemoprevention effects, we examined whether or not the aqueous extract of IO inhibits HT-29 cell growth and investigated tile mechanism for this effect. Cells were incubated in the presence of increasing concentrations of the aqueous extract of IO. The extract substantially inhibited the viable HT-29 cell number in a dose-dependent manner and inhibited 5-bromo-2'-deoxyuridine incorporation into DNA of HT-29 cells. Annexin-V staining followed by flow cytometry revealed that the extract induced apoptosis of HT-29 cells in a dose-dependent manner. Western blot analysis of total cell lysates revealed that the extract induced cleavage of caspase-8, -9 and -3 and poly (ADP-ribose) polymerase, but did not affect the protein levels of Bax and Bcl-2. In addition, the extract dose-dependently increased the activity of caspase-8, -9 and -3. We have demonstrated that the aqueous extract of IO inhibits cell proliferation and induces apoptosis in HT-29 cells, which may be mediated by its ability to activate the caspase pathway.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1478-1499
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• 2024

Coronavirus disease (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. COVID-19 become an active epidemic disease due to its spread around the globe. The main causes of the spread are through interaction and transmission of the droplets through coughing and sneezing. The spread can be minimized by isolating the susceptible patients. However, it necessitates remote monitoring to check the breathing issues of the patient remotely to minimize the interactions for spread minimization. Thus, in this article, we offer a wearable-IoTs-centered framework for remote monitoring and recognition of the breathing pattern and abnormal breath detection for timely providing the proper oxygen level required. We propose wearable sensors accelerometer and gyroscope-based breathing time-series data acquisition, temporal features extraction, and machine learning algorithms for pattern detection and abnormality identification. The sensors provide the data through Bluetooth and receive it at the server for further processing and recognition. We collect the six breathing patterns from the twenty subjects and each pattern is recorded for about five minutes. We match prediction accuracies of all machine learning models under study (i.e. Random forest, Gradient boosting tree, Decision tree, and K-nearest neighbor. Our results show that normal breathing and Bradypnea are the most correctly recognized breathing patterns. However, in some cases, algorithm recognizes kussmaul well also. Collectively, the classification outcomes of Random Forest and Gradient Boost Trees are better than the other two algorithms.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.4
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• pp.187-194
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• 2018

The operating system for IoT should have a small memory footprint and provide low power state, real-time, multitasking, various network protocols, and security. Although the Zephyr kernel, an operating system for IoT, released by the Linux Foundation in February 2016, has these features but errors generated by the user code can generate fatal problems in the system because the Zephyr kernel adopts a single-space method that both the user code and kernel code execute in the same space. In this research, we propose a space separation method, which separates kernel space and user space, to solve this problem. The space separation that we propose consists of three modifications in Zephyr kernel. The first is the code separation that kernel code and user code execute in each space while using different stacks. The second is the kernel space protection that generates an exception by using the MPU (Memory Protection Unit) when the user code accesses the kernel space. The third is the SVC based system call that executes the system call using the SVC instruction that generates the exception. In this research, we implemented the space separation in Zephyr v1.8.0 and evaluated safety through abnormal execution of the user code. As the result, the kernel was not crashed by the errors generated by the user code and was normally executed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.44-46
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• 2018

In this paper, we design a system that can control unmanned mobile objects through communication between unmanned mobile object and control server system using LoRa network which is a dedicated IoT network. It is more efficient when the unmanned mobile object performs the special work by installing the LoRa network applied to the unmanned mobile object control system from the small space house or office hospital to the factory. In this paper, we will discuss the design of a system that can improve the social utilization of unmanned mobile objects by making it possible to communicate the events that occur around other mobile objects from the simplification of the navigation path.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.229-238
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• 2022

Due to digitization, data has become a tsunami in almost every data-driven business sector. The information wave has been greatly boosted by man-to-machine (M2M) digital data management. An explosion in the use of ICT for farm management has pushed technical solutions into rural areas and benefited farmers and customers alike. This study discusses the benefits and possible pitfalls of using information and communication technology (ICT) in conventional farming. Information technology (IT), the Internet of Things (IoT), and robotics are discussed, along with the roles of Machine learning (ML), Artificial intelligence (AI), and sensors in farming. Drones are also being studied for crop surveillance and yield optimization management. Global and state-of-the-art Internet of Things (IoT) agricultural platforms are emphasized when relevant. This article analyse the most current publications pertaining to precision agriculture using ML and AI techniques. This study further details about current and future developments in AI and identify existing and prospective research concerns in AI for agriculture based on this thorough extensive literature evaluation.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.3
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• pp.373-379
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• 2019

Nowadays, the IoT portable electronic devices have become more useful and diverse, so they require various supply voltage levels to operate. This paper presents a DC-DC buck converter with pulse width modulation (PWM) for portable electronic devices. The proposed step-down DC-DC converter consists of passive elements such as capacitors, inductors, and resistors and an integrated chip (IC) for signal control to reduce power consumption and improves ripple voltage with the resolution. The proposed DC-DC converter is simulated and analyzed in PSPICE circuit design platform, and implemented on the prototype PCB board with a Texas Instruments LM5165 IC. The proposed buck converter is showed 92.6% of peak efficiency including a load current range of 4-10 mA, 3.29 mV of the voltage ripple at 5 V output voltage for the supply voltage 12 V. Measured and Simulated power efficiency are made good agreement with each other.

• Journal of IKEEE
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• v.22 no.4
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• pp.1012-1018
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• 2018

In this paper, CMOS SAR (Successive Approximation Register) A/D converter with 1.8V supply voltage is designed for IoT sensor processing. This paper proposes design of a 12-bit SAR A/D converter with two A / D converters in parallel to improve the sampling rate. A/D converter1 of the two A/D converters determines all the 12-bit bits, and another A/D converter2 uses the upper six bits of the other A/D converters to minimize power consumption and switching energy. Since the second A/D converter2 does not determine the upper 6 bits, the control circuits and SAR Logic are not needed and the area is minimized. In addition, the switching energy increases as the large capacitor capacity and the large voltage change in the C-DAC, and the second A/D converter does not determine the upper 6 bits, thereby reducing the switching energy. It is also possible to reduce the process variation in the C-DAC by proposed structure by the split capacitor capacity in the C-DAC equals the unit capacitor capacity. The proposed SAR A/D converter was designed using 0.18um CMOS process, and the supply voltage of 1.8V, the conversion speed of 10MS/s, and the Effective Number of Bit (ENOB) of 10.2 bits were measured. The area of core block is $600{\times}900um^2$, the total power consumption is $79.58{\mu}W$, and the FOM (Figure of Merit) is 6.716fJ / step.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.495-505
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• 2019

Typical algorithms of deep learning include CNN(Convolutional Neural Networks), which are mainly used for image recognition, and RNN(Recurrent Neural Networks), which are used mainly for speech recognition and natural language processing. Among them, CNN is able to learn from filters that generate feature maps with algorithms that automatically learn features from data, making it mainstream with excellent performance in image recognition. Since then, various algorithms such as R-CNN and others have appeared in object detection to improve performance of CNN, and algorithms such as YOLO(You Only Look Once) and SSD(Single Shot Multi-box Detector) have been proposed recently. However, since these deep learning-based detection algorithms determine the success of the detection in the still images, stable object tracking and detection in the video requires separate tracking capabilities. Therefore, this paper proposes a method of combining Kalman filters into deep learning-based detection networks for improved object tracking and detection performance in the video. The detection network used YOLO v2, which is capable of real-time processing, and the proposed method resulted in 7.7% IoU performance improvement over the existing YOLO v2 network and 20 fps processing speed in FHD images.