In recent years, research on smart factory wireless mobile communication technology that wirelessly remotely controls utilities is being actively conducted. The Wi-SUN(Wireless Smart Utility Network) Alliance proposed Wi-SUN protocol structure suitable for building a platform such as a smart factory as a new wireless communication standardization standard based on EEE802.15.4g/e. It analyzes the performance of the IEEE802.15.4e CSL(Coordinated Sampled Listening) Mode MAC(Media Access Control) in terms of latency and looks at considerations for efficient operation.
Leakage and Bragg condition of optical waveguide with asymmetric rectangular grating profile are evaluated in detail by using novel and rigorous modal transmission-line theory (MTLT) based on eigenvalue problem. The optical waveguide composed by asymmetric rectangular grating occur leaky-wave stop-bands at Bragg conditions, and anomalies based on Rayleigh-Wood condition near Bragg conditions. Furthermore, DFB properties of the guiding structure at Bragg conditions are analyzed by applying longitudinal equivalent transmission-line with characteristic impedance of periodic grating. The numerical results show that filtering characteristics that maximize the reflected power of DFB waveguide are activated near Bragg conditions, in which leaky-wave stop-bands occur.
Millimeter-wave tracking radars should be operated in various environmental restrictions, thus they demand more computing power and smaller size compared to conventional tracking radars. This paper presents the design and implementation of the compact power supply for millimeter-wave tracking radar applications. To meet requirements of low voltage/high current and voltage accuracy for FPGA/DSP digital circuits, Point of Load (POL) converters are used in order to enhance power density and system efficiency. LDO (Low Dropout) is applied for the output voltage under the light load condition, then the single-input-multi-output power supply with max power of 375 W and 8 outputs is developed. The proposed power supply achieves output voltage accuracy of ±2 % and noise level of <50 mVpp % under full load conditions.
In recent years, permanent magnets such as IPM (Interior Permanent Magnet) motors or SPM (Surface Permanent Magnet) motors that can obtain high efficiency and power density by inserting rare earth permanent magnets into the rotor are used. Research on the used electric motor is being actively conducted. Since it uses a permanent magnet, it has the advantage of high efficiency and high power density compared to reluctance motors and induction motors, but by inserting a permanent magnet into the rotor, it operates at high speeds and decreases reliability due to demagnetization of the permanent magnets, and increases the cost of rare earth metals. In this paper, in accordance with the development of future technology that can replace rare-earth permanent magnet motors and technological preoccupation of rare-earth reduction type motors and de-rare-earth motors, switched reluctance motors that do not require permanent magnets (Switched Reluvtance Motors) Motor, SRM) to drive driving control. Using the 3-phase SRM library provided by the PSIM simulation program, we will study the driving and control system modeling of SRM using the rotor position information sensor.
The strip binary is a binary from which debug symbol information has been deleted, and therefore it is difficult to analyze the binary through techniques such as reverse engineering. Traditional binary analysis tools rely on debug symbolic information to analyze binaries, making it difficult to detect or analyze malicious code with features of these strip binaries. In order to solve this problem, the need for a technology capable of effectively extracting the information of the strip binary has emerged. In paper, focusing the fact that the byte code of the binary file is generated very differently depending on compiler version, optimazer level, etc. For effective compiler version extraction, the entire byte code is read and imaged as the target of the stripped binaries and this is applied to the convolution neural network. Finally, we achieve an accuracy of 93.5%, and we provide an opportunity to analyze stripped binary more effectively than before.
Scientific software requires a development process different from conventional application software due to its unique characteristics, such as expert-level deep domain knowledge requirements. In this study
In addition to the OTT video production service represented by Nexflix and YouTube, a personalized recommendation system for content with artificial intelligence has become common. YouTube's personalized recommendation service system consists of two neural networks, one neural network consisting of a recommendation candidate generation model and the other consisting of a ranking network. Netflix's video recommendation system consists of two data classification systems, divided into content-based filtering and collaborative filtering. As the online platform-led content production is activated by the Corona Pandemic, the field of virtual influencers using artificial intelligence is emerging. Virtual influencers are produced with GAN (Generative Adversarial Networks) artificial intelligence, and are unsupervised learning algorithms in which two opposing systems compete with each other. This study also researched the possibility of developing AI platform based on individual recommendation and virtual influencer (metabus) as a core content of OTT in the future.
Humans mainly recognize surrounding objects using visual and auditory information among the five senses (sight, hearing, smell, touch, taste). Major research related to the latest object recognition mainly focuses on analysis using image sensor information. In this paper, after emitting various chirp audio signals into the observation space, collecting echoes through a 2-channel receiving sensor, converting them into spectral images, an object recognition experiment in 3D space was conducted using an image learning algorithm based on deep learning. Through this experiment, the experiment was conducted in a situation where there is noise and echo generated in a general indoor environment, not in the ideal condition of an anechoic room, and the object recognition through echo was able to estimate the position of the object with 83% accuracy. In addition, it was possible to obtain visual information through sound through learning of 3D sound by mapping the inference result to the observation space and the 3D sound spatial signal and outputting it as sound. This means that the use of various echo information along with image information is required for object recognition research, and it is thought that this technology can be used for augmented reality through 3D sound
Determining the percentage of negligence between the parties in the event of road traffic accidents is a significant problem. In order to provide users with more accurate criteria for determining the percentage of negligence, several companies are providing services. However, services currently available are limited to immediate use at the scene of an accident. Generally, the service that determines the percentage of negligence can be used after all accident handling procedures have been completed. This paper provides a real-time traffic accident type and fault rate information provision service utilizing a deep learning-based predictive model to overcome these limitations. Users can immediately identify accident types and fault information by taking pictures at the accident site and check actual precedents of the same accident type. Users will be able to use the service to more accurately and reliably determine the percentage of negligence and handle incidents.
