• Applied Microscopy
• /
• v.49
• /
• pp.1.1-1.10
• /
• 2019

Joints of three-dimensional (3D) rutile-type (r) tin dioxide ($SnO_2$) nanowire networks, produced by the flame transport synthesis (FTS), are formed by coherent twin boundaries at $(101)^r$ serving for the interpenetration of the nanowires. Transmission electron microscopy (TEM) methods, i.e. high resolution and (precession) electron diffraction (PED), were utilized to collect information of the atomic interface structure along the edge-on zone axes $[010]^r$, $[111]^r$ and superposition directions $[001]^r$, $[101]^r$. A model of the twin boundary is generated by a supercell approach, serving as base for simulations of all given real and reciprocal space data as for the elaboration of three-dimensional, i.e. relrod and higher order Laue zones (HOLZ), contributions to the intensity distribution of PED patterns. Confirmed by the comparison of simulated and experimental findings, details of the structural distortion at the twin boundary can be demonstrated.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.556-557
• /
• 2019

The current video surveillance system is the third generation, and the video device has developed from low image quality to high image quality. The video surveillance solution has improved from the simple type to the intelligent type. However, as the equipment and technology for these video surveillance systems become more complicated and diversified, they are increasingly dependent on infrastructure, such as faster network speed and stable power supply. On the other hand, there is a growing need for video surveillance in areas where basic infrastructure is limited, such as power and communications. In this paper, we propose a system that can support intelligent video surveillance in a region where basic infrastructure is limited.

• Journal of Information Technology Services
• /
• v.20 no.5
• /
• pp.105-118
• /
• 2021

As we enter the era of the 4th industrial revolution, information and communication technologies, including artificial intelligence and big data, are converging throughout society. Especially, as the importance of the social foundation of hyper-connection grows, the social influence of IoT, a network of connecting objects, people, and various entities, is also gradually expanding. In addition, as a pandemic, COVID-19, continues, interests in untact-oriented technology and service development are growing more than ever, and each company is trying to establish a core competency strategy to gain an edge in competition in the changing society. This study is a case study centered on Dawon DNS, a company that provides an IoT-based AI smart plug platform. Dawon DNS is broadening its services while developing products by applying advanced technologies, and this study is aiming to investigate the core competencies of the business evolution process. The obtained result of this study will provide implications for companies to become more competitive by suggesting the attitudes and strategies that startups should have during the transforming business environment.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.3
• /
• pp.894-907
• /
• 2022

The features extracted by convolutional neural networks are more descriptive of images than traditional features, and their convolutional layers are more suitable for retrieving images than are fully connected layers. The convolutional layer features will consume considerable time and memory if used directly to match an image. Therefore, this paper proposes a feature weighting and region integration method for convolutional layer features to form global feature vectors and subsequently use them for image matching. First, the 3D feature of the last convolutional layer is extracted, and the convolutional feature is subsequently weighted again to highlight the edge information and position information of the image. Next, we integrate several regional eigenvectors that are processed by sliding windows into a global eigenvector. Finally, the initial ranking of the retrieval is obtained by measuring the similarity of the query image and the test image using the cosine distance, and the final mean Average Precision (mAP) is obtained by using the extended query method for rearrangement. We conduct experiments using the Oxford5k and Paris6k datasets and their extended datasets, Paris106k and Oxford105k. These experimental results indicate that the global feature extracted by the new method can better describe an image.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.11
• /
• pp.3761-3779
• /
• 2022

The performance of existing recognition algorithms for binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals degrade under conditions of low signal-to-noise ratios (SNR). Hence, a novel recognition algorithm based on features in the graph domain is proposed in this study. First, the power spectrum of the squared candidate signal is truncated by a rectangular window. Thereafter, the graph representation of the truncated spectrum is obtained via normalization, quantization, and edge construction. Based on the analysis of the connectivity difference of the graphs under different hypotheses, the sum of degree (SD) of the graphs is utilized as a discriminate feature to classify BPSK and QPSK signals. Moreover, we prove that the SD is a Schur-concave function with respect to the probability vector of the vertices (PVV). Extensive simulations confirm the effectiveness of the proposed algorithm, and its superiority to the listed model-driven-based (MDB) algorithms in terms of recognition performance under low SNRs and computational complexity. As it is confirmed that the proposed method reduces the computational complexity of existing graph-based algorithms, it can be applied in modulation recognition of radar or communication signals in real-time processing, and does not require any prior knowledge about the training sets, channel coefficients, or noise power.

• Journal of the Korea Society for Simulation
• /
• v.18 no.3
• /
• pp.91-102
• /
• 2009

The average window size is most closely related to average throughput. In order to improve fairness, the proposed dropper tries to control the window size of each flow to equal level by intentional packet drop. Intentional packet drop is performed only to the flows that have been occupied bandwidth in a large amount. Because of intentional packet drop, this flow cut down its transmission rate to a half. Accordingly, somewhat capacity of core link comes into existence. And other flow can use this new capacity of this link. Hence other flows have more throughput than before. In this paper, we propose the TRC (Transmission Rate Control) Dropper improving the fairness between individual flows of aggregated sources on DiffServ network. It has the fairness improvement mechanism mentioned above paragraph.

• Journal of Information Processing Systems
• /
• v.19 no.2
• /
• pp.240-257
• /
• 2023

The industrial Internet of Things (IIoT) is characterized by intelligent connection, real-time data processing, collaborative monitoring, and automatic information processing. The heterogeneous IIoT devices require a high data rate, high reliability, high coverage, and low delay, thus posing a significant challenge to information security. High-performance edge and cloud servers are a good backup solution for IIoT devices with limited capabilities. However, privacy leakage and network attack cases may occur in heterogeneous IIoT environments. Cloud-based multi-party computing is a reliable privacy-protecting technology that encourages multiparty participation in joint computing without privacy disclosure. However, the default cloud selection method does not meet the heterogeneous IIoT requirements. The server can be dishonest, significantly increasing the probability of multi-party computation failure or inefficiency. This paper proposes a blockchain and smart contract-based optimized cloud node selection framework. Different participants choose the best server that meets their performance demands, considering the communication delay. Smart contracts provide a progressive request mechanism to increase participation. The simulation results show that our framework improves overall multi-party computing efficiency by up to 44.73%.

• Annual Conference of KIPS
• /
• 2022.11a
• /
• pp.144-146
• /
• 2022

차량 환경에서 발생하는 계산 집약적인 태스크가 증가하면서 모바일 엣지 컴퓨팅(MEC, Mobile Edge Computing)의 필요성이 높아지고 있다. 하지만 지상에 존재하는 MEC 서버는 출퇴근 시간과 같이 태스크가 일시적으로 급증하는 상황에 유동적으로 대처할 수 없으며, 이러한 상황을 대비하기 위해 지상 MEC 서버를 추가로 설치하는 것은 자원의 낭비를 불러온다. 최근 이 문제를 해결하기 위해 UAV(Unmanned Aerial Vehicle)기반 MEC 서버를 추가로 사용해 엣지 서비스를 제공하는 연구가 진행되고 있다. 그러나 UAV MEC 서버는 지상 MEC 서버와 달리 한정적인 배터리 용량으로 인해 서버 간 로드밸런싱을 통해 에너지 사용량을 최소화 하는 것이 필요하다. 본 논문에서는 UAV MEC 서버의 에너지 사용량을 고려한 마이그레이션 기법을 제안한다. 또한 GRU(Gated Recurrent Unit) 모델을 활용한 트래픽 예측을 바탕으로 한 마이그레이션을 통해 지연시간을 최소화할 수 있도록 한다. 제안 시스템의 성능을 평가하기 위해 MEC의 마이그레이션 시점을 결정하는 기준점와 차량의 밀도에 따라 실험을 진행하고, 서버의 로드 편차, UAV MEC 서버의 에너지 사용량 그리고 평균 지연 시간 측면에서 성능을 분석한다.

• Annual Conference of KIPS
• /
• 2023.05a
• /
• pp.389-391
• /
• 2023

In the era of Big Data, humanity is facing a huge overflow of information. To overcome such an obstacle, many new cutting-edge technologies are being introduced. The movie recommendation system is also one such technology. To date, many theoretical and practical kinds of research have been conducted. Our research also focuses on the movie recommendation system by implementing methods from Social Network Analysis(SNA) and Parallel Programming. We applied the Girvan-Newman algorithm to detect communities of users, and a future package to perform the parallelization. This approach not only tries to improve the accuracy of the system but also accelerates the execution time. To do our experiment, we used the MovieLense Dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.1
• /
• pp.147-169
• /
• 2024

In pace with the development of network technology at lightning speed, social networks have been extensively applied in our lives. However, as social networks retain a large number of users' sensitive information, the openness of this information makes social networks vulnerable to attacks by malicious attackers. To preserve the link privacy of individuals in social networks, an uncertain graph method based on node random response is devised, which satisfies differential privacy while maintaining expected data utility. In this method, to achieve privacy preserving, the random response is applied on nodes to achieve edge modification on an original graph and node differential privacy is introduced to inject uncertainty on the edges. Simultaneously, to keep data utility, a divide and conquer strategy is adopted to decompose the original graph into many sub-graphs and each sub-graph is dealt with separately. In particular, only some larger sub-graphs selected by the exponent mechanism are modified, which further reduces the perturbation to the original graph. The presented method is proven to satisfy differential privacy. The performances of experiments demonstrate that this uncertain graph method can effectively provide a strict privacy guarantee and maintain data utility.