• 한국전자통신학회논문지
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• 제17권1호
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• pp.105-110
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• 2022

교통 표지 인식은 교통 관련 문제를 해결하는 데 중요한 역할을 한다. 교통 표지 인식 및 분류 시스템은 교통안전, 교통 모니터링, 자율주행 서비스 및 자율주행 차의 핵심 구성 요소이다. 휴대용 장치에 적용할 수 있는 경량 모델은 설계 의제의 필수 측면이다. 우리는 교통 표지 인식 시스템을 위한 잔여 블록이 있는 경량 합성곱 신경망 모델을 제안한다. 제안된 모델은 공개적으로 사용 가능한 벤치마크 데이터에서 매우 경쟁력 있는 결과를 보여준다.

• 인터넷정보학회논문지
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• 제23권2호
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• pp.1-7
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• 2022

Machine learning (ML) algorithms have been intended to seamlessly collaborate for enabling intelligent networking in terms of massive service differentiation, prediction, and provides high-accuracy recommendation systems. Mobile edge computing (MEC) servers are located close to the edge networks to overcome the responsibility for massive requests from user devices and perform local service offloading. Moreover, there are required lightweight methods for handling real-time Internet of Things (IoT) communication perspectives, especially for ultra-reliable low-latency communication (URLLC) and optimal resource utilization. To overcome the abovementioned issues, this paper proposed an intelligent scheme for traffic steering based on the integration of MEC and lightweight ML, namely support vector machine (SVM) for effectively routing for lightweight and resource constraint networks. The scheme provides dynamic resource handling for the real-time IoT user systems based on the awareness of obvious network statues. The system evaluations were conducted by utillizing computer software simulations, and the proposed approach is remarkably outperformed the conventional schemes in terms of significant QoS metrics, including communication latency, reliability, and communication throughput.

• International Journal of Contents
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• 제18권2호
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• pp.68-80
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• 2022

The health of the human heart is commonly measured using ECG (Electrocardiography) signals. To identify any anomaly in the human heart, the time-sequence of ECG signals is examined manually by a cardiologist or cardiac electrophysiologist. Lightweight anomaly detection on ECG signals in an embedded system is expected to be popular in the near future, because of the increasing number of heart disease symptoms. Some previous research uses deep learning networks such as LSTM and BiLSTM to detect anomaly signals without any handcrafted feature. Unfortunately, lightweight LSTMs show low precision and heavy LSTMs require heavy computing powers and volumes of labeled dataset for symptom classification. This paper proposes an ECG anomaly detection system based on two level BiLSTM for acceptable precision with lightweight networks, which is lightweight and usable at home. Also, this paper presents a new threshold technique which considers statistics of the current ECG pattern. This paper's proposed model with BiLSTM detects ECG signal anomaly in 0.467 ~ 1.0 F₁ score, compared to 0.426 ~ 0.978 F₁ score of the similar model with LSTM except one highly noisy dataset.

• Journal of Platform Technology
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• 제11권1호
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• pp.72-84
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• 2023

With the rise of the Internet of Things, the security of such lightweight computing environments has become a hot topic. Lightweight block ciphers that can provide efficient performance and security by having a relatively simpler structure and smaller key and block sizes are drawing attention. Due to these characteristics, they can become a target for new attack techniques. One of the new cryptanalytic attacks that have been attracting interest is Neural cryptanalysis, which is a cryptanalytic technique based on neural networks. It showed interesting results with better results than the conventional cryptanalysis method without a great amount of time and cryptographic knowledge. The first work that showed good results was carried out by Aron Gohr in CRYPTO'19, the attack was conducted on the lightweight block cipher SPECK-/32/64 and showed better results than conventional differential cryptanalysis. In this paper, we first apply the Differential Neural Distinguisher proposed by Aron Gohr to the block ciphers HIGHT and GOST to test the applicability of the attack to ciphers with different structures. The performance of the Differential Neural Distinguisher is then analyzed by replacing the neural network attack model with five different models (Multi-Layer Perceptron, AlexNet, ResNext, SE-ResNet, SE-ResNext). We then propose a Related-key Neural Distinguisher and apply it to the SPECK-/32/64, HIGHT, and GOST block ciphers. The proposed Related-key Neural Distinguisher was constructed using the relationship between keys, and this made it possible to distinguish more rounds than the differential distinguisher.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권9호
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• pp.2505-2528
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• 2023

The field of facial expression recognition (FER) has been actively researched to improve human-computer interaction. In recent years, deep learning techniques have gained popularity for addressing FER, with numerous studies proposing end-to-end frameworks that stack or widen significant convolutional neural network layers. While this has led to improved performance, it has also resulted in larger model sizes and longer inference times. To overcome this challenge, our work introduces a novel lightweight model architecture. The architecture incorporates three key factors: Depth-wise Separable Convolution, Residual Block, and Attention Modules. By doing so, we aim to strike a balance between model size, inference speed, and accuracy in FER tasks. Through extensive experimentation on popular benchmark FER datasets, our proposed method has demonstrated promising results. Notably, it stands out due to its substantial reduction in parameter count and faster inference time, while maintaining accuracy levels comparable to other lightweight models discussed in the existing literature.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제13권3호
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• pp.141-152
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• 2007

본 논문에서 우리는 ELOS(Embedded Lightweight Operating System)라 불리는 이벤트 기반의 운영체제와 멀티흡 에드혹 라우팅 프로토콜로 구성된 네트워크 기반의 소형 실시간 시스템의 구조를 제시한다. 효율적인 실시간 프로세싱을 위하여 보장된 시간 슬롯을 가진 조건적 선점형 FCFS 스케줄러가 개발되었다. 보다 정교한 네트워크 구성을 위하여 무선 에이전트 노드를 통한 반자동 구성(semi-auto configuration) 방식을 사용한다. 개발된 소프트웨어 시스템은 자체 개발한 소형 하드웨어 프로토타입에서 구현되었다. 또한, 제안된 시스템의 성능을 평가하기 위해서, 우리는 유비쿼터스 네트워크 테스트 베드를 개발했고, 다양한 환경에서의 실험이 이루어 졌다. 실험 결과를 통하여 제안된 ELOS 시스템은 실시간 제약을 가진 네트워크 기반의 소형 유비쿼터스 시스템에 상당히 알맞은 시스템이라는 것을 확인한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권12호
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• pp.4411-4431
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• 2014

Mobile ad hoc networks (MANETs) have recently gained increased interest due to the widespread use of smart mobile devices. Group communication applications, serving for better cooperation between subsets of business members, become more significant in the context of MANETs. Multicast routing mechanisms are very useful communication techniques for such group-oriented applications. This paper deals with multicast routing problems in terms of stability and scalability, using the concept of stable core. We propose LMRSC (Lightweight Multicast Routing Based on Stable Core), a lightweight multicast routing technique for MANETs, in order to avoid periodic flooding of the source messages throughout the network, and to increase the duration of multicast routes. LMRSC establishes and maintains mesh architecture for each multicast group member by dividing the network into several zones, where each zone elects the most stable node as its core. Node residual energy and node velocity are used to calculate the node stability factor. The proposed algorithm is simulated by using NS-2 simulation, and is compared with other multicast routing mechanisms: ODMRP and PUMA. Packet delivery ratio, multicast route lifetime, and control packet overhead are used as performance metrics. These metrics are measured by gradual increase of the node mobility, the number of sources, the group size and the number of groups. The simulation performance results indicate that the proposed algorithm outperforms other mechanisms in terms of routes stability and network density.

• 한국구조물진단유지관리공학회 논문집
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• 제24권3호
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• pp.18-25
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• 2020

경량혼합토의 일축압축강도는 배합비에 크게 의존한다. 경량혼합토와 다양한 경량혼합토의 구성성분들의 관계를 특징짓기 위한 기존연구에서는 시험을 통한 회귀모델을 사용하여 정규화계수를 제안하였다. 그러나 실내시험에서 얻은 결과는 재료와 배합비사이의 관계가 복잡하기 때문에 일정한 예측의 정확도를 기대할 수 없다. 이 연구에서는 다양한 배합조건에서 수행된 실내시험결과를 바탕으로 심층신경망 모델을 적용함으로써 경량혼합토의 일축압축강도를 예측하였다. 제안된 심층신경망 모델을 사용함으로써 설계 배합조건으로 구성된 경량혼합토의 일축압축강도 값을 합리적으로 산정할 수 있다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2007년도 춘계학술발표대회
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• pp.1100-1101
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• 2007

This paper proposes an efficient mechanism of node membership verification within the groups of sensors in a wireless sensor network (WSN). We utilize one-way accumulator to check the memberships of the legitimate nodes in a secure way. Our scheme also supports the addition and deletion of nodes in the groups in the network. Our analysis shows that, our scheme could be well-suited for the resource constrained sensors in a sensor network and it provides a lightweight mechanism for secure node membership verification in WSN.

• Journal of Information Processing Systems
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• 제19권5호
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• pp.614-630
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• 2023

Dead trees significantly impact forest production and the ecological environment and pose constraints to the sustainable development of forests. A lightweight YOLOv4 dead tree detection algorithm based on unmanned aerial vehicle images is proposed to address current limitations in dead tree detection that rely mainly on inefficient, unsafe and easy-to-miss manual inspections. An improved logarithmic transformation method was developed in data pre-processing to display tree features in the shadows. For the model structure, the original CSPDarkNet-53 backbone feature extraction network was replaced by MobileNetV3. Some of the standard convolutional blocks in the original extraction network were replaced by depthwise separable convolution blocks. The new ReLU6 activation function replaced the original LeakyReLU activation function to make the network more robust for low-precision computations. The K-means++ clustering method was also integrated to generate anchor boxes that are more suitable for the dataset. The experimental results show that the improved algorithm achieved an accuracy of 97.33%, higher than other methods. The detection speed of the proposed approach is higher than that of YOLOv4, improving the efficiency and accuracy of the detection process.