• Journal of Technology Innovation
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• v.25 no.3
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• pp.1-24
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• 2017

With the rise of internet of things (IoT), there have been several studies to analyze the technological trend and technological convergence. However, previous work have been relied on the qualitative work that investigate the IoT trend and implication for future business. In response, this study considers the patent information as the proxy measure of technology, and conducts a quantitative and analytic approach for analyzing technological convergence using patent co-classification analysis and text mining. First, this study investigate the characteristics of IoT business, and characterize IoT business into four dimensions: device, network, platform, and services. After this process, total 923 patent classes are classified into four types of IoT technology group. Since most of patent classes are classified into device technology, we developed a co-classification network for both device technology and all technologies. Patent keywords are also extracted and these keywords are also classified into four types: device, network, platform, and services. As a result, technologies for several IoT devices such as sensors, healthcare, and energy management are derived as a main convergence group for the device network. For the total IoT network, base network technology plays a key role to characterize technological convergence in the IoT network, mediating the technological convergence in each application area such as smart healthcare, smart home, and smart grid. This work is expected to effectively be utilized in the technology planning of IoT businesses.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.8
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• pp.856-867
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• 1990

In this paper, the convergence and the learning accuracy of the back-propagation (BP) method in neural network are investigated by 1) analyzing the reason for decelerating the convergence of BP method and examining the rapid deceleration of the convergence when the learning is executed on the part of sigmoid activation function with the very small first derivative and 2) proposing the modified logistic activation function by defining, the convergence factor based on the analysis. Learning on the output patterns of binary as well as analog forms are tested by the proposed method. In binary output patter, the test results show that the convergence is accelerated and the learning accuracy is improved, and the weights and thresholds are converged so that the stability of neural network can be enhanced. In analog output patter, the results show that with extensive initial transient phenomena the learning error is decreased according to the convergence factor, subsequently the learning accuracy is enhanced.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.160-162
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• 2006

In this paper, a heterogeneous home network is designed and implemented based on the PLC (power line communications) and the IEEE 802.15.4. This paper presents the need of the heterogeneous home network and the convergence sub-layer. The convergence sub-layer is designed and implemented on the Xelline power line communication modem with IEEE 802.15.4 communication module.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.216-224
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• 2017

Abnormal situation caused by aggressive behavior of pigs adversely affects the growth of pigs, and comes with an economic loss in intensive pigsties. Therefore, IT-based video surveillance system is needed to monitor the abnormal situations in pigsty continuously in order to minimize the economic demage. Recently, some advances have been made in pig monitoring; however, detecting each pig is still challenging problem. In this paper, we propose a new color image-based monitoring system for the detection of the individual pig using a fast region-based convolution neural network with consideration of detecting touching pigs in a crowed pigsty. The experimental results with the color images obtained from a pig farm located in Sejong city illustrate the efficiency of the proposed method.

• ETRI Journal
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• v.30 no.2
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• pp.216-226
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• 2008

This paper proposes a novel transport network architecture for the next generation network (NGN) based on the optical burst switching technology. The proposed architecture aims to provide efficient delivery of various types of network traffic by satisfying their quality-of-service constraints. To this end, we have developed a soft-state bandwidth reservation mechanism, which enables NGN transport nodes to dynamically reserve bandwidth needed for active data burst flows. The performance of the proposed mechanism is evaluated by means of numerical analysis and NS2 simulation. Our results show that the packet delay is kept within the constraint for each traffic flow and the burst loss rate is remarkably improved.

• Journal of Communications and Networks
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• v.8 no.4
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• pp.410-421
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• 2006

In this work, we propose an analytic framework for dimensioning the link capacity of broadband access networks which provide universal broadband access services to a diverse kind of customers such as patient and impatient customers. The proposed framework takes into account the flow-level quality of service (QoS) of a connection as well as the packet-level QoS, via which a simple and systematic provisioning and operation of the network are provided. To that purpose, we first discuss the necessity of flow-aware network dimensioning by reviewing the networking technologies of the current and future access network. Next, we propose an analytic model for dimensioning the link capacity for an access node of broadband convergence networks which takes into account both the flow and packet level QoS requirements. By carrying out extensive numerical experiment for the proposed model assuming typical parameters that represent real network environment, the validity of the proposed method is assessed.

• ETRI Journal
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• v.31 no.2
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• pp.231-233
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• 2009

We propose a nonreciprocal filter based on coarse wavelength division multiplexing (CWDM) that reduces the upstream channel insertion loss in a passive optical network (PON). We also propose a method to increase the number of channels/optical network units (ONUs) in PON systems using the proposed filter to reduce the service cost per subscriber. Experimental results show that the PON system with the proposed 4-channel filter can reduce the power budget of the upstream and increase the number of ONUs by 3 to 4 times that of a conventional time-division multiplexing PON.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.816-839
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• 2023

In a random vector functional link (RVFL) network, shortcomings such as local optimal stagnation and decreased convergence performance cause a reduction in the accuracy of illumination correction by only inputting the weights and biases of hidden neurons. In this study, we proposed an improved regularized random vector functional link (RRVFL) network algorithm with an optimized grey wolf optimizer (GWO). Herein, we first proposed the moth-flame optimization (MFO) algorithm to provide a set of excellent initial populations to improve the convergence rate of GWO. Thereafter, the MFO-GWO algorithm simultaneously optimized the input feature, input weight, hidden node and bias of RRVFL, thereby avoiding local optimal stagnation. Finally, the MFO-GWO-RRVFL algorithm was applied to ameliorate the performance of illumination correction of various test images. The experimental results revealed that the MFO-GWO-RRVFL algorithm was stable, compatible, and exhibited a fast convergence rate.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.4
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• pp.110-118
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• 1997

Multi-layer neural networks with error back-propagation algorithm have a great potential for identifying nonlinear systems with unknown characteristics. However, because they have a demerit that the speed of convergence is too slow, various methods for improving the training characteristics of backpropagition networks have been proposed. In this paper, a fuzzy division method is proposed to improve the convergence speed, which can find out an effective fuzzy division by the tuning of membership function and independently train each neural network after dividing the network model into several parts. In the simulations, the proposed method showed that the optimal fuzzy partitions could be found from the arbitray initial ones and that the convergence speed was faster than the traditional method without the fuzzy division.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.493-495
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• 2023

Deep learning methods and attention mechanisms have been incorporated to improve facial emotion recognition, which has recently attracted much attention. The fusion approaches have improved accuracy by combining various types of information. This research proposes a fusion network with self-attention and local attention mechanisms. It uses a multi-layer perceptron network. The network extracts distinguishing characteristics from facial images using pre-trained models on RAF-DB dataset. We outperform the other fusion methods on RAD-DB dataset with impressive results.