• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2595-2618
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• 2018

In this paper, we investigate secure communication with the presence of multiple eavesdroppers (Eves) in a two-tier downlink dense heterogeneous network, wherein there is a macrocell base station (MBS) and multiple femtocell base stations (FBSs). Each base station (BS) has multiple users. And Eves attempt to wiretap a macrocell user (MU). To keep Eves ignorant of the confidential message, we propose a physical-layer security scheme based on cross-layer cooperation to exploit interference in the considered network. Under the constraints on the quality of service (QoS) of other legitimate users and transmit power, the secrecy rate of system can be maximized through jointly optimizing the beamforming vectors of MBS and cooperative FBSs. We explore the problem of maximizing secrecy rate in both non-colluding and colluding Eves scenarios, respectively. Firstly, in non-colluding Eves scenario, we approximate the original non-convex problem into a few semi-definite programs (SDPs) by employing the semi-definite relaxation (SDR) technique and conservative convex approximation under perfect channel state information (CSI) case. Furthermore, we extend the frame to imperfect CSI case and use the Lagrangian dual theory to cope with uncertain constraints on CSI. Secondly, in colluding Eves scenario, we transform the original problem into a two-tier optimization problem equivalently. Among them, the outer layer problem is a single variable optimization problem and can be solved by one-dimensional linear search. While the inner-layer optimization problem is transformed into a convex SDP problem with SDR technique and Charnes-Cooper transformation. In the perfect CSI case of both non-colluding and colluding Eves scenarios, we prove that the relaxation of SDR is tight and analyze the complexity of proposed algorithms. Finally, simulation results validate the effectiveness and robustness of proposed scheme.

• Journal of the Korea Computer Graphics Society
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• v.24 no.5
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• pp.41-48
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• 2018

The most natural way to increase immersion and provide free interaction in a virtual environment is to provide a gesture interface using the user's hand. However, most studies about hand gesture recognition require specialized sensors or equipment, or show low recognition rates. This paper proposes a three-dimensional DenseNet Convolutional Neural Network that enables recognition of hand gestures with no sensors or equipment other than an RGB camera for hand gesture input and introduces a virtual reality game based on it. Experimental results on 4 static hand gestures and 6 dynamic hand gestures showed that they could be used as real-time user interfaces for virtual reality games with an average recognition rate of 94.2% at 50ms. Results of this research can be used as a hand gesture interface not only for games but also for education, medicine, and shopping.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.385-398
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• 2021

The complexity of deep learning models affects the real-time performance of gesture recognition, thereby limiting the application of gesture recognition algorithms in actual scenarios. Hence, a residual learning neural network based on a deep convolutional neural network is proposed. First, small convolution kernels are used to extract the local details of gesture images. Subsequently, a shallow residual structure is built to share weights, thereby avoiding gradient disappearance or gradient explosion as the network layer deepens; consequently, the difficulty of model optimisation is simplified. Additional convolutional neural networks are used to accelerate the refinement of deep abstract features based on the spatial importance of the gesture feature distribution. Finally, a fully connected cascade softmax classifier is used to complete the gesture recognition. Compared with the dense connection multiplexing feature information network, the proposed algorithm is optimised in feature multiplexing to avoid performance fluctuations caused by feature redundancy. Experimental results from the ISOGD gesture dataset and Gesture dataset prove that the proposed algorithm affords a fast convergence speed and high accuracy.

• Korean Journal of Microbiology
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• v.22 no.1
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• pp.19-28
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• 1984

The nematode-trapping process by Arthrobotrys conoides was investigated with the aid of scanning and transmission electron microscopy. 1. A. conoides captures nematode by means of three-dimensional network. 2. The wall of trap cell was thicker than that of vegetative hypha and the trap cell was more rich in cell organelles such as endoplasmic reticulum, mitochondria and electrondense granule. 3. The electron-dense granule, which could be found only in trap organs, gradually disappeared during its penetration into nematode cuticle. 4. The osmiophilic area was found at adhering site between the trap organ and nematode cuticle. 5. In some cases, any appressorium was not found at the site of penetration. 6. When the fungal-nematode culture was conserved for 2~3 weeks, numerous young nematodes were found to be adhered to spores, resulting in death.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2341-2347
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• 2021

Because single-photon emission computed tomography (SPECT) is one of the widely used nuclear medicine imaging systems, it is extremely important to acquire high-quality images for diagnosis. In this study, we designed a super-resolution (SR) technique using dense block-based deep convolutional neural network (CNN) and evaluated the algorithm on real SPECT phantom images. To acquire the phantom images, a real SPECT system using a^99mTc source and two physical phantoms was used. To confirm the image quality, the noise properties and visual quality metric evaluation parameters were calculated. The results demonstrate that our proposed method delivers a more valid SR improvement by using dense block-based deep CNNs as compared to conventional reconstruction techniques. In particular, when the proposed method was used, the quantitative performance was improved from 1.2 to 5.0 times compared to the result of using the conventional iterative reconstruction. Here, we confirmed the effects on the image quality of the resulting SR image, and our proposed technique was shown to be effective for nuclear medicine imaging.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.15-17
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• 2018

VVC(Versatile Video Coding)는 입력된 영상을 CTU(Coding Tree Unit) 단위로 분할하여 코딩하며, 이를 다시 QTBTT(Quadtree plus binary tree and triple tree)로 분할하고, TU(Transform Unit)도 이와 같은 단위로 분할된다. 따라서 TU의 크기는 $4{\times}4$, $4{\times}8$, $4{\times}16$, $4{\times}32$, $8{\times}4$, $16{\times}4$, $32{\times}4$, $8{\times}8$, $8{\times}16$, $8{\times}32$, $16{\times}8$, $32{\times}8$, $16{\times}16$, $16{\times}32$, $32{\times}16$, $32{\times}32$, $64{\times}64$의 17가지 종류가 있다. 기존의 VVC 참조 Software인 VTM에서는 디블록킹필터와 SAO(Sample Adaptive Offset)로 이루어진 인루프필터를 이용하여 에러를 복원하는데, 본 논문은 TU 크기에 따라서 원본블록과 복원블록의 차이(에러)가 통계적으로 다름을 이용하여 서로 다른 CNN(Convolution Neural Network)을 구축하고 에러를 복원하는 방법으로 VTM의 인루프 필터를 대체한다. 복원영상의 에러를 감소시키기 위하여 TU 블록크기에 따라 DenseNet의 Dense Block기반 CNN을 구성하고, Hyper Parameter와 복잡도의 감소를 위해 네트워크 간에 일부 가중치를 공유하는 모양의 Network를 구성하였다.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.2
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• pp.101-107
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• 2021

The objective of this study was to analyze social wasps' urban distribution tendency based on 10 species found in two metropolitan cities (Busan and Daegu) of South Korea. There 10 species included six species (Vespa mandarinia, V. ducalis, V. crabro flavofasciata, Vespula koreensis koreensis, Parapolybia indica, and Polistes snelleni) of forest dwellers that inhabited urban main forests and satellite forests, two species (V. simillima simillima and V. analis parallela) of facultative dwellers that nested at diverse sites of urban areas with greater preference for urban forest, and two species (V. velutina nigrithorax and P. rothneyi koreanus) of urban dwellers that nested at almost all sites, including urban and forest areas. These urban dwellers were found to adapt well to an urban environment based on their far higher rate of urban nesting compared to facultative dwellers. When distribution tendencies of facultative dwellers and urban dwellers in Busan and Daegu were compared, a regular distribution was mostly observed in Busan with a dense forest network. For Daegu that lacked forest connectivity, the greatest distribution of species was found in the nearby urban forest. For Daegu, a city further away from forests, urban dwellers occurred far beyond forest sites compared to Busan with a dense forest network.

• Smart Media Journal
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• v.11 no.2
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• pp.53-62
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• 2022

Due to the accelerated global warming phenomenon after industrialization, the frequency of changes in the existing environment and abnormal climate is increasing. Agriculture is an industry that is very sensitive to climate change, and global warming causes problems such as reducing crop yields and changing growing regions. In addition, environmental changes make the growth period of crops irregular, making it difficult for even experienced farmers to easily estimate the growth stage of crops, thereby causing various problems. Therefore, in this paper, we propose a CNN model for estimating the growth stage of crops. The proposed model was a model that modified the pooling layer of ResNet, and confirmed the accuracy of higher performance than the growth stage estimation of the ResNet and DenseNet models.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.142-148
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• 2023

In Modern days, Self-driving for modern people is an absolute necessity for transportation and many other reasons. Additionally, after the outbreak of COVID-19, driving by oneself is preferred over other means of transportation for the prevention of infection. However, due to the constant exposure to stressful situations and chronic fatigue one experiences from the work or the traffic to and from it, modern drivers often drive under drowsiness which can lead to serious accidents and fatality. To address this problem, we propose a drowsy driving prevention learning model which detects a driver's state of drowsiness. Furthermore, a method to sound a warning message after drowsiness detection is also presented. This is to use MoveNet to quickly and accurately extract the keypoints of the body of the driver and Dense Neural Network(DNN) to train on real-time driving behaviors, which then immediately warns if an abnormal drowsy posture is detected. With this method, we expect reduction in traffic accident and enhancement in overall traffic safety.

• Journal of the Microelectronics and Packaging Society
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• v.24 no.4
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• pp.91-95
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• 2017

Package-On-Package (PoP) technology is developing toward smaller form factors with high-speed data transfer capabilities to cope with high DDR4x memory capacity. The common application processor (AP) used for PoP devices in smartphones has the bottom package as logic and the top package as memory, which requires both thermally and electrically enhanced functions. Therefore, it is imperative that PoP designs consider both thermal and power distribution network (PDN) issues. Stacked packages have poorer thermal dissipation than single packages. Since the bottom package usually has higher power consumption than the top package, the bottom package impacts the thermal budget of the top package (memory). This paper investigates the thermal and electrical characteristics of PoP designs, particularly the bottom package. Findings include that via and dense via-cluster volume have an important role to lower thermal resistance to the motherboard, which can be an effective way to manage chip hot spots and reduce the thermal impact on the memory package. A Cu block and dense via-cluster layout with an optimal location are proposed to drain the heat from the chip hot spots to motherboard which will enhance thermal and electrical performance at the design stage. The analytical thermal results can be used for design guidelines in 3D packaging.