• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.123-131
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• 2015

Training neural networks is a complex task with great importance in the field of supervised learning. In the training process, a set of input-output patterns is repeated to an artificial neural network (ANN). From those patterns weights of all the interconnections between neurons are adjusted until the specified input yields the desired output. In this paper, a new hybrid algorithm is proposed for global optimization of connection weights in an ANN. Dynamic swarms are shown to converge rapidly during the initial stages of a global search, but around the global optimum, the search process becomes very slow. In contrast, the gradient descent method can achieve faster convergence speed around the global optimum, and at the same time, the convergence accuracy can be relatively high. Therefore, the proposed hybrid algorithm combines the dynamic particle swarm optimization (DPSO) algorithm with the backpropagation (BP) algorithm, also referred to as the DPSO-BP algorithm, to train the weights of an ANN. In this paper, we intend to show the superiority (time performance and quality of solution) of the proposed hybrid algorithm (DPSO-BP) over other more standard algorithms in neural network training. The algorithms are compared using two different datasets, and the results are simulated.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.673-676
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• 2018

4차 산업혁명이 도래하면서 정보 통신 기술 및 융합 기술의 발전에 힘입어 소리 데이터를 이용한 연구가 활발하게 진행되고 있다. 소리 데이터를 이용한 학술적 프로토타입 연구들을 실제 환경에서 운용하기 위해서는 소리 취득 시 발생하는 다양한 잡음 환경에서도 원시 데이터(raw data)에 근접한 정보를 취득할 수 있는 시스템의 강인함이 보장되어야 한다. 본 논문에서는 SEGAN(Speech Enhancement Generative Adversarial Network) 모델을 활용하여, 전처리 및 후처리 과정이 필요 없이 원시 데이터를 대상으로 하는 end-to-end 방식의 소리 음질 향상 시스템을 제안한다. 제안하는 시스템은, 축산업 분야의 돼지 호흡기 질병 소리 데이터를 이용하여 실험하였으며, 여러 가지 잡음 상황(인위적인 잡음, 실제 환경 잡음)에서 소리 음질이 개선됨을 실험적으로 검증하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.12
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• pp.2265-2270
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• 2007

This paper discusses a tester of functionality and call processing performance, based on the MEGACO/H.248 protocol that both IETF and ITU-T recommend as a media gateway control protocol, of both a media gateway controller and an access gateway which constitute a next generation convergence network. Effective methods, a functional architecture and implementation for such testification are provided. Especially included are not only a virtual emulation function of analog subscriber lines connecting to an access gateway, but also a tester emulated as a counter system of the protocol for the testifying a media gateway controller and an access gateway system.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.1-6
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• 2021

The purpose of this study is to improve the performance of the artificial neural network system for facial image analysis through the image landmark selection technique. For landmark selection, a CNN-based multi-layer ResNet model for classification of facial image age is required. From the configured ResNet model, a heat map that detects the change of the output node according to the change of the input node is extracted. By combining a plurality of extracted heat maps, facial landmarks related to age classification prediction are created. The importance of each pixel location can be analyzed through facial landmarks. In addition, by removing the pixels with low weights, a significant amount of input data can be reduced.

• Journal of information and communication convergence engineering
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• v.22 no.2
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• pp.109-120
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• 2024

Trash or garbage is one of the most dangerous health and environmental problems that affect pollution. Pollution affects nature, human life, and wildlife. In this paper, we propose modern solutions for cleaning the environment of trash pollution by enforcing strict action against people who dump trash inappropriately on streets, outside the home, and in unnecessary places. Artificial Intelligence (AI), especially Deep Learning (DL), has been used to automate and solve issues in the world. We availed this as an excellent opportunity to develop a system that identifies trash using a deep convolutional neural network (CNN). This paper proposes a real-time garbage identification system based on a deep CNN architecture with eight distinct classes for the training dataset. After identifying the garbage, the CCTV camera captures a video of the individual placing the trash in the incorrect location and sends an alert notice to the relevant authority.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.5
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• pp.325-330
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• 2014

The era of convergence is being applied to all areas of Information and Communication Technology (ICT). The convergence of broadcasting service and communication service almost occurs on smart devices including smartphone. The smart hybrid Digital Multimedia Broadcasting (DMB) is a typical example of the convergence of broadcasting and wireless communication service. The hybrid mobile broadcasting service can support seamless video, 3D, high quality, and additional data services based on network connection between the broadcasting and wireless network. The gateway and terminal (including apps on the smartphone) take the role of the main components on the hybrid service. This paper presents the service concept, main components structure, the implementation of gateway and terminals, and field test to the urban areas for the mobile hybrid system.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.237-240
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• 2005

We propose a fully converged Ethernet and TDM transport system. Developed Ethernet and TDM convergence system can support not only L2VPN service and premium multimedia service based on MPLS protocol but also TDM leased line service, simultaneously. Developed convergence system can provide high reliability for Ethernet data due to support protection and restoration function of circuit based networks. Evaluation for Ethernet and TDM path was successfully performed to show the typical application of the proposed system in the legacy networks.

• Journal of Information Technology Services
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• v.4 no.1
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• pp.107-115
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• 2005

As Real Time Enterprise (RTE) has been spotlighted since the year of 2002, some advanced companies have been implementing the paradigm of RTE to its legacy systems and the number of the companies has been growing. However, the network infrastructures mainly based on fixed telecommunications network make the results of the RTE implementations fall short of their expectations. In this paper, we propose to construct the fixed-mobile convergence (FMC) environment to achieve RTE. The convergence infrastructure will improve user accessibility to all kinds of data and will encourage the users to interact with the systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.507-509
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• 2018

Recently, the research for IoT technologies has been established actively, however the structure of centralized network has been pointed out as the vulnerable points. To solve these problems such as system load and security vulnerability, the research to introduce block chain technology is needed. In this paper, we propose the network domain for convergence of block chain and IoT platform, and describe the advantages from the convergence and various and applicable fields.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.853-858
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• 1993

The error back-propagation(BP) algorithm is widely used for finding optimum weights of multi-layer neural networks. However, the critical drawback of the BP algorithm is its slow convergence of error. The major reason for this slow convergence is the premature saturation which is a phenomenon that the error of a neural network stays almost constant for some period time during learning. An inappropriate selections of initial weights cause each neuron to be trapped in the premature saturation state, which brings in slow convergence speed of the multi-layer neural network. In this paper, to overcome the above problem, Micro-Genetic algorithms(.mu.-GAs) which can allow to find the near-optimal values, are used to select the proper weights and slopes of activation function of neurons. The effectiveness of the proposed algorithms will be demonstrated by some computer simulations of two d.o.f planar robot manipulator.