• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.47-57
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• 2021

Neuromorphic technology is proposed to complement the shortcomings of existing artificial intelligence technology by mimicking the human brain structure and computational process with hardware. NA-IDE has also been proposed for developing neuromorphic hardware-based IoT applications. To implement an SNN model in NA-IDE, commonly used input data must be transformed for use in the SNN model. In this paper, we implemented a neural coding method encoder component that converts image data into a spike train signal and uses it as an SNN input. The decoder component is implemented to convert the output back to image data when the SNN model generates a spike train signal. If the decoder component uses the same parameters as the encoding process, it can generate static data similar to the original data. It can be used in fields such as image-to-image and speech-to-speech to transform and regenerate input data using the proposed encoder and decoder.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.173-178
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• 2019

Recently, the types and amount of data generated, collected, and measured in IoT such as smart home, security, and factory are increasing. The technologies for IoT service include sensor devices to measure desired data, embedded software to control the devices such as signal processing, wireless network protocol to transmit and receive the measured data, and big data and AI-based analysis. In this paper, we focused on developing a gateway for interfacing heterogeneous sensor network protocols that are used in various IoT devices and propose a heterogeneous network interface IoT gateway. We utilized a OpenWrt-based wireless routers and used 6LoWAN stack for IP-based communication via BLE and IEEE 802.15.4 adapters. We developed a software to convert Z-Wave and LoRa packets into IP packet using our Python-based middleware. We expect the IoT gateway to be used as an effective device for collecting IoT big data.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.17-23
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• 2020

Biometric information indicating measurement items related to human characteristics has attracted great attention as security technology with high reliability since there is no fear of theft or loss. Among these biometric information, fingerprints are mainly used in fields such as identity verification and identification. If there is a problem such as a wound, wrinkle, or moisture that is difficult to authenticate to the fingerprint image when identifying the identity, the fingerprint expert can identify the problem with the fingerprint directly through the preprocessing step, and apply the image processing algorithm appropriate to the problem. Solve the problem. In this case, by implementing artificial intelligence software that distinguishes fingerprint images with cuts and wrinkles on the fingerprint, it is easy to check whether there are cuts or wrinkles, and by selecting an appropriate algorithm, the fingerprint image can be easily improved. In this study, we developed a total of 17,080 fingerprint databases by acquiring all finger prints of 1,010 students from the Royal University of Cambodia, 600 Sokoto open data sets, and 98 Korean students. In order to determine if there are any injuries or wrinkles in the built database, criteria were established, and the data were validated by experts. The training and test datasets consisted of Cambodian data and Sokoto data, and the ratio was set to 8: 2. The data of 98 Korean students were set up as a validation data set. Using the constructed data set, five CNN-based architectures such as Classic CNN, AlexNet, VGG-16, Resnet50, and Yolo v3 were implemented. A study was conducted to find the model that performed best on the readings. Among the five architectures, ResNet50 showed the best performance with 81.51%.

• Tunnel and Underground Space
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• v.33 no.4
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• pp.281-298
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• 2023

Continuous research efforts are being devoted to unmanned mobile platforms for lunar exploration. There is an ongoing demand for real-time information processing to accurately determine the positioning and mapping of areas of interest on the lunar surface. To apply deep learning processing and analysis techniques to practical rovers, research on software integration and optimization is imperative. In this study, a foundational investigation has been conducted on real-time analysis of virtual lunar base construction site images, aimed at automatically quantifying spatial information of key objects. This study involved transitioning from an existing region-based object recognition algorithm to a boundary box-based algorithm, thus enhancing object recognition accuracy and inference speed. To facilitate extensive data-based object matching training, the Batch Hard Triplet Mining technique was introduced, and research was conducted to optimize both training and inference processes. Furthermore, an improved software system for object recognition and identical object matching was integrated, accompanied by the development of visualization software for the automatic matching of identical objects within input images. Leveraging satellite simulative captured video data for training objects and moving object-captured video data for inference, training and inference for identical object matching were successfully executed. The outcomes of this research suggest the feasibility of implementing 3D spatial information based on continuous-capture video data of mobile platforms and utilizing it for positioning objects within regions of interest. As a result, these findings are expected to contribute to the integration of an automated on-site system for video-based construction monitoring and control of significant target objects within future lunar base construction sites.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.29-37
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• 2024

In this paper, we propose an efficient image processing system to detect and track the movement of specific objects such as patients. The proposed system extracts the outline area of an object from a binarized difference image by applying a thinning algorithm that enables more precise detection compared to previous algorithms and is advantageous for mixed-mode design. The binarization and thinning steps, which require a lot of computation, are designed based on RTL (Register Transfer Level) and replaced with optimized hardware blocks through logic circuit synthesis. The designed binarization and thinning block was synthesized into a logic circuit using the standard 180n CMOS library and its operation was verified through simulation. To compare software-based performance, performance analysis of binary and thinning operations was also performed by applying sample images with 640 × 360 resolution in a 32-bit FPGA embedded system environment. As a result of verification, it was confirmed that the mixed-mode design can improve the processing speed by 93.8% in the binary and thinning stages compared to the previous software-only processing speed. The proposed mixed-mode system for object recognition is expected to be able to efficiently monitor patient movements even in an edge computing environment where artificial intelligence networks are not applied.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.51-61
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• 2021

In the era of the 4th industrial revolution symbolized by the Internet of Things, big data and artificial intelligence, various embedded devices are increasing exponentially. These devices have communication functions despite their low specifications, so the possibility of personal information leakage is increasing, and security threats are also increasing. Embedded devices can have security issues at most levels, from hardware to services over the network. In addition, it is difficult to apply general security techniques because it has characteristics of resource constraints such as low specifications and low power, and the related technology has not been standardized. In this study, we present vulnerabilities and possible problems and considerations in applying SDN to embedded devices in consideration of structural characteristics and real-world discovered cases. This study presents vulnerabilities and possible problems and considerations when applying SDN to embedded devices. From a hardware perspective, we consider the problems of Wi-Fi chips and Bluetooth, the problems of open flow implementation, SDN controllers, and examples of structural properties. SDN separates the data plane and the control plane, and provides a standardized interface between the two, enabling efficient communication control. It can respond to the security limitations of existing network technologies that are difficult to respond to rapid changes.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.91-96
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• 2022

At the current camera-based technology level, sensor-based basic life pattern recognition technology has to suffer inconvenience to obtain accurate data, and commercial band products are difficult to collect accurate data, and cannot take into account the motive, cause, and psychological effect of behavior. the current situation. In this paper, radar technology for life pattern recognition is a technology that measures the distance, speed, and angle with an object by transmitting a waveform designed to detect nearby people or objects in daily life and processing the reflected received signal. It was designed to supplement issues such as privacy protection in the existing image-based service by applying it. For the implementation of the proposed system, based on TI IWR1642 chip, RF chipset control for 60GHz band millimeter wave FMCW transmission/reception, module development for distance/speed/angle detection, and technology including signal processing software were implemented. It is expected that analysis of individual life patterns will be possible by calculating self-management and behavior sequences by extracting personalized life patterns through quantitative analysis of life patterns as meta-analysis of living information in security and safe guards application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1331-1340
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• 2020

Object detection in maritime means that the captain detects floating objects that has a risk of colliding with the ship using the computer automatically and as accurately as human eyes. In conventional ships, the presence and distance of objects are determined through radar waves. However, it cannot identify the shape and type. In contrast, with the development of AI, cameras help accurately identify obstacles on the sea route with excellent performance in detecting or recognizing objects. The computer must calculate high-volume pixels to analyze digital images. However, the CPU is specialized for sequential processing; the processing speed is very slow, and smooth service support or security is not guaranteed. Accordingly, this study developed maritime object detection software and implemented it with FPGA to accelerate the processing of large-scale computations. Additionally, the system implementation was improved through embedded boards and FPGA interface, achieving 30 times faster performance than the existing algorithm and a three-times faster entire system.

• Smart Media Journal
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• v.13 no.6
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• pp.9-15
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• 2024

In recent trends, there has been an increase in 'Qshing' attacks, a hybrid form of phishing that exploits fake QR (Quick Response) codes impersonating government agencies to steal personal and financial information. Particularly, this attack method is characterized by its stealthiness, as victims can be redirected to phishing pages or led to download malicious software simply by scanning a QR code, making it difficult for them to realize they have been targeted. In this paper, we have developed a classification technique utilizing machine learning algorithms to identify the maliciousness of URLs embedded in QR codes, and we have explored ways to integrate this with existing QR code readers. To this end, we constructed a dataset from 128,587 malicious URLs and 428,102 benign URLs, extracting 35 different features such as protocol and parameters, and used AutoML to identify the optimal algorithm and hyperparameters, achieving an accuracy of approximately 87.37%. Following this, we designed the integration of the trained classification model with existing QR code readers to implement a service capable of countering Qshing attacks. In conclusion, our findings confirm that deriving an optimized algorithm for classifying malicious URLs in QR codes and integrating it with existing QR code readers presents a viable solution to combat Qshing attacks.

• 한국생물공학회:학술대회논문집
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• 2000.04a
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• pp.51-52
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• 2000

생물공정의 운전에 있어서 적절한 공정변수가 부족한 경우가 많다. 이것은 멸균과정을 견딜 수 있는 신뢰성 높은 센서가 부족하기 때문이다[1]. 생물공정에 주로 사용되는 센서로서는 온도, pH, D.O., rpm, viscosoty 등이 있으나 이 센서들은 배양액의 물리적 혹은 화학적 상태를 측정할 수 있는 경우가 대부분이다[2]. 미생물의 대사활동과 관련이 있는 공정 변수로는 배출가스의 성분을 측정하여 얻을 수 있는 Oxygen uptake rate, Carbon dioxide evolution rate 및 Respiratory quotient가 있으며 현재 생물공정의 운전에 사용되고 있다[3]. 그러나 반복적인 센서의 보정과 연결관의 잦은 청소 및 보수를 필요로 하여 제한적으로 사용되고있는 실정이다. 자동화된 습식분석장치, Gas chromatograph, High Performace Liquid Chromatograph 혹은 Mass spectrophtometry 등을 온라인 샘플 처리장치와 연결하여 발효조의 배양액의 성분을 온라인으로 분석하고 공정의 운전에 응용하는 사례가 많이 발표되었다[4-6]. 고가의 장비 및 운전의 번거러움이나 추가적인 인력이 필요하므로 역시 특별한 경우에만 사용되고 있다. 이외에도 여러 종류의 온라인 센서 및 바이오 센서등이 개발되어 사용되고 있으나 역시 그 사용범위는 특수한 영역에 한정되어있다. 이와 같이 새로운 센서를 개발하여 공정변수를 측정하려는 시도중의 하나가 소프트웨어 센서의 개발이다. 이 것은 공정상에서 발생하는 1차 공정변수를 이용하여 배양액의 상태 혹은 2차적인 공정 변수를 추측해내는 것이다. 대부분의 경우 기존의 공정 변수를 사용하므로 추가적인 비용이 들지 않고 소프트웨어의 형태로 구현되므로 센서의 보정과 설치 및 유지관리의 노력이 매우 적은 장점이 있다. 본 연구에서는 생물공정에서 자동제어 과정에서 발생하는 여러 가지 공정상의 제어 신호로부터 새로운 공정 변수를 얻어내고자 시도하였다. 대부분의 생물공정에서는 pH의 자동제어가 필수적인데 자동제어 과정에서 발생하는 pH 제어 신호 및 pH의 변화 응답신호를 이용하여 배지의 완충용량의 변화와 알칼리의 소비속도를 온라인으로 측정할 수 있었다. 여기에 인공지능망을 설계하여 균체의 량을 온라인으로 추정하는 방법을 개발하였다 [7].산업용 발효조의 운전 온도는 주로 냉각수의 단속적인 공급에 의하여 항상 일정하게 조절된다. 따라서 냉각수의 냉각량을 측정하면 미생물의 배양시 발생하는 대사열량을 측정할 수 있게 된다. 본 연구에서는 실험실의 발효조를 냉각수의 단속적인 공급에 의하여 자동온도 조절이 되도록 개조하고 여기에 냉각수의 유출입 지점에 온도센서를 부착하여 냉각수의 온도를 측정하고 냉각수의 공급량과 대기의 온도 등을 측정하여 대사열의 발생을 추정할 수 있었다. 동시에 이를 이용하여 유가배양시 기질을 공급하는 공정변수로 사용하였다 [8]. 생물학적인 폐수처리장치인 활성 슬러지법에서 미생물의 활성을 측정하는 방법은 아직 그다지 개발되어있지 않다. 본 연구에서는 슬러지의 주 구성원이 미생물인 점에 착안하여 침전시 슬러지층과 상등액의 온도차를 측정하여 대사열량의 발생량을 측정하고 슬러지의 활성을 측정할 수 있는 방법을 개발하였다.