• Annual Conference of KIPS
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• 2008.05a
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• pp.958-961
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• 2008

유비쿼터스 컴퓨팅 기술의 진보로 무선 센서 네트워크의 관심이 높아지고 있다. 센서 네트워크는 MAC 프로토콜에서 정해진 전송 스케줄에 따라 주기적인 수면(sleep)을 통해 에너지를 절약하는 연구가 진행되어 왔다. 그리고 센서 노드가 센싱한 데이터를 BS(Base Station)로 전달하기 위한 라우팅 경로 기법과 클러스터링 기법 등이 제시되고 있다. 센서 네트워크에서 가장 중요한 이슈중의 하나는 제한된 자원 즉, 센서 노드에 주어진 에너지를 활용하여 네트워크의 수명을 최대로 연장 하는 것이다. 본 논문에서는 S-MAC 프로토콜의 스케줄관리 기법을 이용하여 무선 센서 네트워크에서 서로 다른 그룹 간의 통신을 원활하게 하기 위한 통신기법을 제시한다.

• ETRI Journal
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• v.45 no.5
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• pp.758-767
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• 2023

In this study, we introduce a loosely coupled relative position estimation method that utilizes a decentralized ultrawideband (UWB), Global Navigation Support System and inertial navigation system for flight controllers (FCs). Key obstacles to multidrone collaboration include relative position errors and the absence of communication devices. To address this, we provide an extended Kalman filter-based algorithm and module that correct distance errors by fusing UWB data acquired through random communications. Via simulations, we confirm the feasibility of the algorithm and verify its distance error correction performance according to the amount of communications. Real-world tests confirm the algorithm's effectiveness on FCs and the potential for multidrone collaboration in real environments. This method can be used to correct relative multidrone positions during collaborative transportation and simultaneous localization and mapping applications.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.817-829
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• 2023

Variable wireless channel is a big challenge for real-time video applications, and the rate adaptation of realtime video streaming becomes a hot topic. Intra-video coding is important for high-quality video communication and industrial video applications. In this paper, we proposed a novel adaptive scheme for real-time video transmission with intra-only coding over a wireless network. The key idea of this scheme is to estimate the instantaneous remaining capacity of the network to adjust the quality of the next several video frames, which not only can keep low queuing delay and ensure video quality, but also can respond to bandwidth changes quickly. We compare our scheme with three different schemes in the video transmission system. The experimental results show that our scheme has higher bandwidth utilization and faster bandwidth change response, while maintaining low queuing delay.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.55-62
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• 2023

Slotted-Aloha (S-Aloha) has been widely employed in random access networks owing to its simple implementation in a distributed manner. To enhance the throughput performance of the S-Aloha, connection-based slotted-Aloha (CS-Aloha) has been proposed in recent years. The fundamental principle of the CS-Aloha is to establish a connection with a short-sized request packet before transmitting data packets. Subsequently, the connected node transmits long-sized data packets in a batch of size M. This approach efficiently reduces collisions, resulting in improved throughput compared to the S-Aloha, particularly for a large M. In this paper, we address the short-term fairness of the CS-Aloha, as quantified by Jain's fairness index. Specifically, we evaluate how equitably the CS-Aloha allocatestime slots to all nodes in the network within a finite time interval. Through simulation studies, we identify the impact of system parameters on the short-term fairness of the CS-Aloha and propose an optimal transmission probability to support short-term fairness.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.20-25
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• 2023

Increasing demand for increasing higher data rate in order to solve computationally tasks timely and connecting many user equipment simultaneously have requested researchers to develop novel technology in the area of mobile communications. Intelligent reflecting surface (IRS) have been enabling technologies for commercialization of the fifth generation (5G) networks and the sixth generation (6G) systems. In this paper, we investigate a bit-error rate (BER) analysis on IRS technologies for non-orthogonal multiple access (NOMA) systems. First, we derive a BER expression for IRS-NOMA systems with Rician fading channels. Then, we validate the BER expression by Monte Carlo simulations, and show numerically that BER expressions are in good agreement with simulations. Moreover, we investigate the BER of IRS-NOMA systems with Rician fading channels for various numbers of IRS elements, and show that the BERs improve as the number of IRS elements increases.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.171-176
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• 2023

This study is about a speech recognition error correction system designed to detect and correct speech recognition errors before natural language processing to increase the success rate of intent analysis in natural language processing with optimal efficiency in various service domains. An encoder is constructed to embedded the correct speech token and one or more error speech tokens corresponding to the correct speech token so that they are all located in a dense vector space for each correct token with similar vector values. One or more utterance tokens within a preset Manhattan distance based on the correct utterance token in the dense vector space for each embedded correct utterance token are detected through an error detector, and the correct answer closest to the detected error utterance token is based on the Manhattan distance. Errors are corrected by extracting the utterance token as the correct answer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2903-2923
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• 2023

Knowledge recommendation is a type of recommendation system that recommends knowledge content to users in order to satisfy their needs. Although using graph neural networks to extract data features is an effective method for solving the recommendation problem, there is information loss when modeling real-world problems because an edge in a graph structure can only be associated with two nodes. Because one super-edge in the hypergraph structure can be connected with several nodes and the effectiveness of knowledge graph for knowledge expression, a dual-channel hypergraph convolutional neural network model (DCHC) based on hypergraph structure and knowledge graph is proposed. The model divides user data and knowledge data into user subhypergraph and knowledge subhypergraph, respectively, and extracts user data features by dual-channel hypergraph convolution and knowledge data features by combining with knowledge graph technology, and finally generates recommendation results based on the obtained user embedding and knowledge embedding. The performance of DCHC model is higher than the comparative model under AUC and F1 evaluation indicators, comparative experiments with the baseline also demonstrate the validity of DCHC model.

• ETRI Journal
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• v.46 no.3
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• pp.513-525
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• 2024

Deep neural networks trained on labeled medical data face major challenges owing to the economic costs of data acquisition through expensive medical imaging devices, expert labor for data annotation, and large datasets to achieve optimal model performance. The heterogeneity of diseases, such as Alzheimer's disease, further complicates deep learning because the test cases may substantially differ from the training data, possibly increasing the rate of false positives. We propose a reconstruction-based self-supervised anomaly detection model to overcome these challenges. It has a dual-subnetwork encoder that enhances feature encoding augmented by skip connections to the decoder for improving the gradient flow. The novel encoder captures local and global features to improve image reconstruction. In addition, we introduce an entropy-based image conversion method. Extensive evaluations show that the proposed model outperforms benchmark models in anomaly detection and classification using an encoder. The supervised and unsupervised models show improved performances when trained with data preprocessed using the proposed image conversion method.

• Annual Conference of KIPS
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• 2007.11a
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• pp.1218-1221
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• 2007

센서 네트워크는 유비쿼터스 컴퓨팅 환경을 실현하기 위한 네트워크로 센싱 및 통신 능력으로 인간이 접근하기 어려운 다양한 곳에 설치되어 감시나 탐지 등을 통하여 데이터를 수집한다. 이러한 환경의 구현을 위하여 센서 네트워크에서 센서 노드가 수집한 데이터는 사용자에게 전달될 때 안전한 통신을 보장하기 위해 센서 노드간 키를 설정하는 것은 보안을 위한 기본적인 요구사항이 되고 있다. 따라서 초소형, 빈번한 데이터 이동, 제한적인 계산 능력 및 저장 능력 그리고 베터리 전력 사용이라는 특성을 갖는 센서 노드에 알맞은 암호화를 위한 키 관리 구조가 요구된다. 따라서 본 논문에서는 센서 네트워크에서의 효율적인 키 설정을 위해 클러스터에 기반한 구조와 다항식을 사용한 pair-wise key설정 방법을 제안 하였다.

• Journal of information and communication convergence engineering
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• v.22 no.2
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• pp.133-138
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• 2024

Dissolved oxygen (DO) is an important factor in ecosystems. However, the analysis of DO is frequently rather complicated because of the nonlinear phenomenon of the river system. Therefore, a convenient model-free algorithm for DO variable is required. In this study, a data-driven algorithm for predicting DO was developed by combining XGBoost and an artificial neural network (ANN), called ANN-XGB. To train the model, two years of ecosystem data were collected in Anyang, Seoul using the Troll 9500 model. One advantage of the proposed algorithm is its ability to capture abrupt changes in climate-related features that arise from sudden events. Moreover, our algorithm can provide a feature importance analysis owing to the use of XGBoost. The results obtained using the ANN-XGB algorithm were compared with those obtained using the ANN algorithm in the Results Section. The predictions made by ANN-XGB were mostly in closer agreement with the measured DO values in the river than those made by the ANN.