• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2930-2942
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• 1998

POTS(Plain Old Telephone Service) is the most popular telecommunication service and should be supported in the future BUSDN that is based on the ATM technology. Therefore, the interwoking system between ATM network and PSTN is needed, which provides telephone service between client of ATM network and subscriber of PSTN. In this paper, the interwoking system between ATM network and PSTN is designed and implemented. The interwoking system consists of PSTN I/F module, signaling processing module, voice sampling module, voice regeneration module, transmission module and ATM I/F module. The PSTN I/F module and ATM I/F module are implemented using the existing commercial H/W products. However, the other 4 modules are implemented for the WIN95 environments by software. The test environment is also implemented and the interworking system is operated without problems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.5
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• pp.1008-1013
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• 2009

In this paper, we proposed the biological signal (body heat, pulse, breathe rate, and blood pressure) analysis system using wireless sensor. In order to analyze, we designed a back-propagation neural network system using expert group system. The proposed system is consist of hardware patt such as UStar-2400 ISP and Wireless sensor and software part such as Knowledge Base module, Inference Engine module and User Interface module which is inserted in Host PC. To improve the accuracy of the system, we implement a FEC (Forward Error Correction) block. For conducting simulation, we chose 100 data sets from Knowledge Base module to train the neural network. As a result, we obtained about 95% accuracy using 128 data sets from Knowledge Base module and acquired about 85% accuracy which experiments 13 students using wireless sensor.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.3
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• pp.209-215
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• 2006

This paper was proposed Intelligent and wireless USN/RFID module system that can overcome disadvantage of existing RFID system with no sensing module and wire communication. The proposed USN/RFID system was designed and fabricated. After fabricating new system, we analyzed the characteristics of USN/RFID module. After design VCO block that is point circuit to develop next generation system one chip of RFID system, we were carried out simulation and verified the validity. this paper was showed that VCO system was enough usable in wireless network module. USN/RFID Reader module shows superior result that validity awareness distance corresponds to 30 M in the case of USN and to 5 M in RFID Reader's case and 900 MHz of commercial frequency does practical use enoughly in range of high frequency. The USN/RFID Reader module is considered to act big role to Ubiqitous industry offering computing surrounding of new concept that is intelligence type service and that was associated to real time location system(RTLS), environment improvement/supervision, national defense, traffic administration etc.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.898-910
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• 2020

Single image-based super-resolution has been studied for a long time in computer vision because of various applications. Various deep learning-based super-resolution algorithms are introduced recently to improve the performance by reducing side effects like blurring and staircase effects. Most deep learning-based approaches have focused on how to implement the network architecture, loss function, and training strategy to improve performance. Meanwhile, Several approaches using Attention Module, which emphasizes the extracted features, are introduced to enhance the performance of the network without any additional layer. Attention module emphasizes or scales the feature map for the purpose of the network from various perspectives. In this paper, we propose the various channel attention and spatial attention in single image-based super-resolution and analyze the results and performance according to the architecture of the attention module. Also, we explore that designing multi-attention module to emphasize features efficiently from various perspectives.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.654-660
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• 2004

In this paper, we define that pervasive home network, which provides necessary services for user properties and removes distractions to improve the quality of human life. So, user can enjoy home network technology including devices and softwares at any place with no knowledge of networked home, devices, and softwares. In this home network, a mobile agent, called LAFA, can migrate to unfamiliar home network and control the necessary devices. For this environment, we design security management module for authenticating user and home server that access some other home networks, and for protecting text, multimedia data, and mobile agent that are transferred between home networks. The security management module is composed of a key exchange management module and an access control management module, for key exchange management module, we propose a key exchange protocol, which provides multimode of authentication mode and key exchange mode. One of these two modes is selected according to the data type.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.809-817
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• 2020

In the design and development process of Small-Sat power distribution and transmission module, the stability of dynamic resources was evaluated by a deep learning algorithm. The requirements for the stability evaluation consisted of the power distribution function of the power distribution module and demand module to the SAR radar in Small-Sat. To verify the performance of the switching power components constituting the power module PDM, the reliability was verified using a dynamic neural network. The adoption material of deep learning for reliability verification is the power distribution function of the payload to the power supplied from the small satellite main body. Modeling targets for verifying the performance of this function are output voltage (slew rate control), voltage error, and load power characteristics. First, to this end, the Coefficient Structure area was defined by modeling, and PCB modules were fabricated to compare stability and reliability. Second, Levenberg-Marquare based Two-Way NARX neural network Sigmoid Transfer was used as a deep learning algorithm.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.21-29
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• 2020

Accurate estimation of human pose relies on backbone method in which its role is to extract feature map. Up to dated, the method of backbone feature extraction is conducted by the plain convolutional neural networks named by CNN and the residual neural networks named by Resnet, both of which have various architectures and performances. The CNN family network such as VGG which is well-known as a multiple stacked hidden layers architecture of deep learning methods, is base and simple while Resnet which is a bottleneck layers architecture yields fewer parameters and outperform. They have achieved inspired results as a backbone network in human pose estimation. However, they were used then followed by different pose estimation networks named by pose parsing module. Therefore, in this paper, we present a comparison between the plain CNN family network (VGG) and bottleneck network (Resnet) as a backbone method in the same pose parsing module. We investigate their performances such as number of parameters, loss score, precision and recall. We experiment them in the bottom-up method of human pose estimation system by adapted the pose parsing module of openpose. Our experimental results show that the backbone method using VGG network outperforms the Resent network with fewer parameter, lower loss score and higher accuracy of precision and recall.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4439-4455
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• 2021

Compared with two-stage object detection algorithms, one-stage algorithms provide a better trade-off between real-time performance and accuracy. However, these methods treat the intermediate features equally, which lacks the flexibility to emphasize meaningful information for classification and location. Besides, they ignore the interaction of contextual information from different scales, which is important for medium and small objects detection. To tackle these problems, we propose an image pyramid network based on dual attention mechanism (DAIPNet), which builds an image pyramid to enrich the spatial information while emphasizing multi-scale informative features based on dual attention mechanisms for one-stage object detection. Our framework utilizes a pre-trained backbone as standard detection network, where the designed image pyramid network (IPN) is used as auxiliary network to provide complementary information. Here, the dual attention mechanism is composed of the adaptive feature fusion module (AFFM) and the progressive attention fusion module (PAFM). AFFM is designed to automatically pay attention to the feature maps with different importance from the backbone and auxiliary network, while PAFM is utilized to adaptively learn the channel attentive information in the context transfer process. Furthermore, in the IPN, we build an image pyramid to extract scale-wise features from downsampled images of different scales, where the features are further fused at different states to enrich scale-wise information and learn more comprehensive feature representations. Experimental results are shown on MS COCO dataset. Our proposed detector with a 300 × 300 input achieves superior performance of 32.6% mAP on the MS COCO test-dev compared with state-of-the-art methods.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.571-575
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• 2004

Having age of ubiquitous ahead, existing IPv4's address space insufficiency phenomenon appears because of increasing network usage as well as multimedia data transmission becomes much, necessity of new IP address system that guarantee QoS are needed. IPv6 was made to solve these problem. IPv6 solves address space insufficiency phenomenon offering by 128bit address space, and also offers hierarchical address layer that support improved QoS. IPv6 defines relation between surrounding node using Neighbor Discovery protocol. Used Neighbor Discovery messages, grasp surrounding node, include important informations about network. These network information outcrops can give rise in network attack and also service that use network will paralysis. Various kinds of security limitation was found in Present Neighbor Discovery protocol therefore security function to supplement tris problem was required. In this thesis, Secure Neighbor Discovery protocol that add with security function was design and embody by CGA module and SEND module.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.6
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• pp.155-163
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• 2003

As vehicles become more intelligent for convenience and safety of drivers, the in-vehicle networking(IVN) systems and smart modules are essential components for intelligent vehicles. However, for wider application of smart modules and IVN's, the following two problems should be overcome. Firstly, because it is very difficult that transducer manufacturers developed the smart module that supports all the existing IVN protocols, the smart module must be independent of the type of networking protocols. Secondly, when the smart module needs to be replaced due to its failure, only the transducer should be replaced these without the replacement of the microprocessor and network transceiver. To solve these problems, this paper investigates the feasibility of an IEEE 1451 based smart module. More specifically, a smart module for DC motor control has been developed. The module has been evaluated for its delay caused by the IEEE 1451 architecture. In addition, the time required for transducer replacement has been measured.