• Title/Summary/Keyword: signal intelligence

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Wideband Cavity Back Antenna for Signal Intelligence (신호 정보 수집용 광대역 캐비티 백 안테나)

  • Jeoung, Gu-Ho;Lee, Seong-Kyu;Choi, Jae-Hoon
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.27 no.12
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    • pp.1044-1052
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    • 2016
  • In this paper, a cavity back slot antenna with a rotated rectangular patch is proposed. The proposed antenna consists of a ground plane with cavity structure, a microstrip feed line, and a rectangular patch with slot. With a dimension of $55mm{\times}40mm{\times}10mm$, the proposed antenna has the wide bandwidth due to the cavity structure. Measured 10 dB return loss bandwidth and fractional bandwidth of the proposed antenna is 5,030 MHz(3.02~8.05 GHz) and 90.9 % at the center frequency of 5.05 GHz. The proposed antenna is designed and simulated using ANSYS HFSS v.15.0.0. The designed antenna is fabricated and tested to validate its performances.

Localization Estimation Using Artificial Intelligence Technique in Wireless Sensor Networks (WSN기반의 인공지능기술을 이용한 위치 추정기술)

  • Kumar, Shiu;Jeon, Seong Min;Lee, Seong Ro
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.39C no.9
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    • pp.820-827
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    • 2014
  • One of the basic problems in Wireless Sensor Networks (WSNs) is the localization of the sensor nodes based on the known location of numerous anchor nodes. WSNs generally consist of a large number of sensor nodes and recording the location of each sensor nodes becomes a difficult task. On the other hand, based on the application environment, the nodes may be subject to mobility and their location changes with time. Therefore, a scheme that will autonomously estimate or calculate the position of the sensor nodes is desirable. This paper presents an intelligent localization scheme, which is an artificial neural network (ANN) based localization scheme used to estimate the position of the unknown nodes. In the proposed method, three anchors nodes are used. The mobile or deployed sensor nodes request a beacon from the anchor nodes and utilizes the received signal strength indicator (RSSI) of the beacons received. The RSSI values vary depending on the distance between the mobile and the anchor nodes. The three RSSI values are used as the input to the ANN in order to estimate the location of the sensor nodes. A feed-forward artificial neural network with back propagation method for training has been employed. An average Euclidian distance error of 0.70 m has been achieved using a ANN having 3 inputs, two hidden layers, and two outputs (x and y coordinates of the position).

The Design of IoT Device System for Disaster Prevention using Sound Source Detection and Location Estimation Algorithm (음원탐지 및 위치 추정 알고리즘을 이용한 방재용 IoT 디바이스 시스템 설계)

  • Ghil, Min-Sik;Kwak, Dong-Kurl
    • Journal of Convergence for Information Technology
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    • v.10 no.8
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    • pp.53-59
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    • 2020
  • This paper relates to an IoT device system that detects sound source and estimates the sound source location. More specifically, it is a system using a sound source direction detection device that can accurately detect the direction of a sound source by analyzing the difference of arrival time of a sound source signal collected from microphone sensors, and track the generation direction of a sound source using an IoT sensor. As a result of a performance test by generating a sound source, it was confirmed that it operates very accurately within 140dB of the acoustic detection area, within 1 second of response time, and within 1° of directional angle resolution. In the future, based on this design plan, we plan to commercialize it by improving the reliability by reflecting the artificial intelligence algorithm through big data analysis.

Digital Watermarking using ART2 Algorithm (ART2 알고리즘을 이용한 디지털 워터마킹)

  • 김철기;김광백
    • Journal of Intelligence and Information Systems
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    • v.9 no.3
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    • pp.81-97
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    • 2003
  • In this paper, we suggest a method of robust watermarking for protection of multimedia data using the wavelet transform and artificial neural network. for the purpose of implementation, we decompose a original image using wavelet transform at level 3. After we classify transformed coefficients of other subbands using neural network except fur the lowest subband LL$_3$, we apply a calculated threshold about chosen cluster as the biggest. We used binary logo watermarks to make sure that it is true or not on behalf of the Gaussian Random Vector. Besides, we tested a method of dual watermark insertion and extraction. For the purpose of implementation, we decompose a original image using wavelet transform at level 3. After we classify transformed coefficients of other subbands using neural network except for the lowest subband LL$_3$, we apply a above mentioned watermark insert method. In the experimental results, we found that it has a good quality and robust about many attacks.

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Physiological Fuzzy Neural Networks for Image Recognition (영상 인식을 위한 생리학적 퍼지 신경망)

  • Kim, Kwang-Baek;Moon, Yong-Eun;Park, Choong-Shik
    • Journal of Intelligence and Information Systems
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    • v.11 no.2
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    • pp.81-103
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    • 2005
  • The Neuron structure in a nervous system consists of inhibitory neurons and excitory neurons. Both neurons are activated by agonistic neurons and inactivated by antagonist neurons. In this paper, we proposed a physiological fuzzy neural network by analyzing the physiological neuron structure in the nervous system. The proposed structure selectively activates the neurons which go through a state of excitement caused by agonistic neurons and also transmit the signal of these neurons to the output layers. The proposed physiological fuzzy neural networks based on the nervous system consists of a input player, and the hidden layer which classifies features of learning data, and output layer. The proposed fuzzy neural network is applied to recognize bronchial squamous cell carcinoma images and car plate images. The result of the experiments shows that the learning time, the convergence, and the recognition rate of the proposed physiological fuzzy neural networks outperform the conventional neural networks.

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A Study on the Application of AI and Linkage System for Safety in the Autonomous Driving (자율주행시 안전을 위한 AI와 연계 시스템 적용연구)

  • Seo, Dae-Sung
    • Journal of the Korea Convergence Society
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    • v.10 no.11
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    • pp.95-100
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    • 2019
  • In this paper, autonomous vehicles of service with existing vehicle accident for the prevention of the vehicle communication technology, self-driving techniques, brakes automatic control technology, artificial intelligence technologies such as well and developed the vehicle accident this occur to death or has been techniques, can prepare various safety cases intended to minimize the injury. In this paper, it is a study to secure safety in autonomous vehicles. This is determined according to spatial factors such as chip signals for general low-power short-range wireless communication and micro road AI. On the other hand, in this paper, the safety of boarding is improved by checking the signal from the electronic chip, up to "recognition of the emotion from residence time in the sensing area" to the biological electronic chip. As a result of demonstrating the reliability of the world countries the world, inducing safety autonomous system of all passengers in terms of safety. Unmanned autonomous vehicle riding and commercialization will lead to AI systems and biochips (Verification), linked IoT on the road in the near future, and the safety technology reliability of the world will be highlighted.

Non-Profiling Power Analysis Attacks Using Continuous Wavelet Transform Method (연속 웨이블릿 변환을 사용한 비프로파일링 기반 전력 분석 공격)

  • Bae, Daehyeon;Lee, Jaewook;Ha, Jaecheol
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.31 no.6
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    • pp.1127-1136
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    • 2021
  • In the field of power analysis attacks, electrical noise and misalignment of the power consumption trace are the major factors that determine the success of the attack. Therefore, several studies have been conducted to overcome this problem, and one of them is a signal processing method based on wavelet transform. Up to now, discrete wavelet transform, which can compress the trace, has been mostly used for power side-channel power analysis because continuous wavelet transform techniques increase data size and analysis time, and there is no efficient scale selection method. In this paper, we propose an efficient scale selection method optimized for power analysis attacks. Furthermore, we show that the analysis performance can be greatly improved when using the proposed method. As a result of the CPA(Correlation Power Analysis) and DDLA(Differential Deep Learning Analysis) experiments, which are non-profiling attacks, we confirmed that the proposed method is effective for noise reduction and trace alignment.

A Survey of Genetic Programming and Its Applications

  • Ahvanooey, Milad Taleby;Li, Qianmu;Wu, Ming;Wang, Shuo
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.4
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    • pp.1765-1794
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    • 2019
  • Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved utilizing a Genetic Algorithm (GA). In other words, the GP employs novel optimization techniques to modify computer programs; imitating the way humans develop programs by progressively re-writing them for solving problems automatically. Trial programs are frequently altered in the search for obtaining superior solutions due to the base is GA. These are evolutionary search techniques inspired by biological evolution such as mutation, reproduction, natural selection, recombination, and survival of the fittest. The power of GAs is being represented by an advancing range of applications; vector processing, quantum computing, VLSI circuit layout, and so on. But one of the most significant uses of GAs is the automatic generation of programs. Technically, the GP solves problems automatically without having to tell the computer specifically how to process it. To meet this requirement, the GP utilizes GAs to a "population" of trial programs, traditionally encoded in memory as tree-structures. Trial programs are estimated using a "fitness function" and the suited solutions picked for re-evaluation and modification such that this sequence is replicated until a "correct" program is generated. GP has represented its power by modifying a simple program for categorizing news stories, executing optical character recognition, medical signal filters, and for target identification, etc. This paper reviews existing literature regarding the GPs and their applications in different scientific fields and aims to provide an easy understanding of various types of GPs for beginners.

Security Threats and Scenarios using Drones on the Battlefield (전장에서 드론을 활용한 보안 위협과 시나리오)

  • Park, Keun-Seog;Cheon, Sang-pil;Kim, Seong-Pyo;Eom, Jung-ho
    • Convergence Security Journal
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    • v.18 no.4
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    • pp.73-79
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    • 2018
  • Since 1910s, the drones were mainly used for military purposes for reconnaissance and attack targets, but they are now being used in various fields such as disaster prevention, exploration, broadcasting, and surveillance of risk areas. As drones are widely used from military to civilian field, hacking into the drones such as radio disturbance, GPS spoofing, hijacking, etc. targeting drones has begun to occur. Recently, the use of drones in hacking into wireless network has been reported. If the artificial intelligence technology is applied to the drones in the military, hacking into unmanned combat system using drones will occur. In addition, a drone with a hacking program may be able to relay a hacking program to the hacking drone located far away, just as a drone serves as a wireless communication station. And the drones will be equipped with a portable GPS jamming device, which will enable signal disturbance to unmanned combat systems. In this paper, we propose security threats and the anticipated hacking scenarios using the drones on the battlespace to know the seriousness of the security threats by hacking drones and prepare for future cyberspace.

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An Integrated Emergency Call System based on Public Switched Telephone Network for Elevators

  • Lee, Guisun;Ryu, Hyunmi;Park, Sunggon;Cho, Sungguk;Jeon, Byungkook
    • 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.