• 경영과학
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• 제28권3호
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• pp.1-13
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• 2011

Hub-and-spoke transportation network is a powerful and useful network structure that takes full advantage of economies of scale on routes between hubs. In recent studies, the network structure is extended to hybrid hub-andspoke that allows direct transportation between spokes. In this study, we considered more extended network structure which is called hybrid multiple hub-and-spoke that has multiple hubs and allows direct transportation between spokes. We developed a mathematical optimization model for automotive service parts transportation planning under hybrid multiple hub-and-spoke network structure. The model suggests a long-term transportation route planning and a short-term vehicle assignment planning. The model is verified by simulation and validated in real world application to Hyundai Mobis automotive service parts transportation planning. From the simulation result, the model reduced the transportation cost about 24.7%, the total distance about 6.8% and the CO2 emissions about 8.8%. In real world application for 6 months from July to December 2010, the model reduced the transportation cost about 9.1% by changing the long-term transportation route without daily vehicle assignment planning.

• 경영과학
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• 제30권1호
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• pp.25-41
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• 2013

This paper presents a supply chain framework with the ODM (Original Design Manufacturing)-OEM (Original Equipment Manufacturing) hybrid production of textile industry in FTA (Free Trade Agreements) environments between Korea and other countries. The proposed supply chain framework with ODM-OEM hybrid production is a unique supply chain that has both domestic production with non-tariff advantages in FTA environment and oversea production with low labor costs. To investigate the validity of the proposed supply chain, we first construct its strategic profit model and supply chain planning and then show that each member of supply chain network-yarn manufacturer, fabric manufacturer, and apparel manufacturer-can maximize their own profits without conflicts among the members. The efficiency of the ODM-OEM hybrid production system is analytically verified in comparison with the general OEM and ODM production model using profit models. Comprehensive numerical examples are provided to illustrate the advantages of the proposed system.

• 반도체디스플레이기술학회지
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• 제13권1호
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• pp.73-81
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• 2014

In this paper, high-speed Hybrid MAC (Medium Access Control layer) implementation suitable for smart grid applications is researched. MB-OFDM (Multi-Band Orthogonal Frequency Division Multiplexing) is considered for high-speed communication method in smart grid application. In this paper, the MAC adopts the distributed network managing method. Also, the MB-OFDM merit of high-speed transfer rate of up to 480Mbps must be supported. Hence, this paper presents an efficient hardware-software integration (co-design) method in order to realize a high- speed transmission, and a realizing method of distribution network. Finally, MAC performance and reliability based on MB-OFDM PHY (PHYsical layer) are confirmed through simulation and emulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1281-1285
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• 2004

There are numerous emotions in the human world. Human expresses and recognizes their emotion using various channels. The example is an eye, nose and mouse. Particularly, in the emotion recognition from facial expression they can perform the very flexible and robust emotion recognition because of utilization of various channels. Hybrid-feature extraction algorithm is based on this human process. It uses the geometrical feature extraction and the color distributed histogram. And then, through the independently parallel learning of the neural-network, input emotion is classified. Also, for the natural classification of the emotion, advancing two-dimensional emotion space is introduced and used in this paper. Advancing twodimensional emotion space performs a flexible and smooth classification of emotion.

• ETRI Journal
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• 제36권6호
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• pp.1016-1022
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• 2014

This paper presents a hybrid model solution for user motion recognition. The use of a single classifier in motion recognition models does not guarantee a high recognition rate. To enhance the motion recognition rate, a hybrid model consisting of decision trees and artificial neural networks is proposed. We define six user motions commonly performed in an indoor environment. To demonstrate the performance of the proposed model, we conduct a real field test with ten subjects (five males and five females). Experimental results show that the proposed model provides a more accurate recognition rate compared to that of other single classifiers.

• ETRI Journal
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• 제30권5호
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• pp.663-673
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• 2008

The multi-scenario topology of multi-channel hybrid ubiquitous sensor networks (USNs) is studied and a novel link auto-diversity cross-layer cooperative scheduling scheme is proposed in this paper. The proposed scheme integrates the attributes of the new performance evaluation link auto-diversity air-time metric and the topology space in the given multi-scenario. The proposed scheme is compared with other schemes, and its superiority is demonstrated through simulations. The simulation results show that relative energy consumption, link reception probability, and end-to-end blocking probability are improved. The addressing ratio of success with unchanged parameters and external information can be increased. The network can tolerate more hops to support reliable transportation when the proposed scheme is implemented. Moreover, the scheme can make the network stable. Therefore, the proposed scheme can enhance the average rate performance of the hybrid USN and stabilize the outage probability.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권2호
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• pp.65-72
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• 2021

최근 네트워크 환경에 대한 공격이 급속도로 고도화 및 지능화 되고 있기에, 기존의 시그니처 기반 침입탐지 시스템은 한계점이 명확해지고 있다. 이러한 문제를 해결하기 위해서 기계학습 기반의 침입 탐지 시스템에 대한 연구가 활발히 진행되고 있다. 하지만 기계학습을 침입 탐지에 이용하기 위해서는 두 가지 문제에 직면한다. 첫 번째는 실시간 탐지를 위한 학습과 연관된 중요 특징들을 선별하는 문제이며, 두 번째는 학습에 사용되는 데이터의 불균형 문제로, 기계학습 알고리즘들은 데이터에 의존적이기에 이러한 문제는 치명적이다. 본 논문에서는 위 제시된 문제들을 해결하기 위해서 Hybrid Feature Selection과 Data Balancing을 통한 심층 신경망 기반의 네트워크 침입 탐지 모델인 HFS-DNN을 제안한다. NSL-KDD 데이터 셋을 통해 학습을 진행하였으며, 기존 분류 모델들과 성능 비교를 수행한다. 본 연구에서 제안된 Hybrid Feature Selection 알고리즘이 학습 모델의 성능을 왜곡 시키지 않는 것을 확인하였으며, 불균형을 해소한 학습 모델들간 실험에서 본 논문에서 제안한 학습 모델이 가장 좋은 성능을 보였다.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• 전자공학회논문지
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• 제54권6호
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• pp.106-113
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• 2017

본 논문에서는 자율주행형 다관절 차량용 제어 시스템 구축 시 장치 간 네트워크로 이더넷 기반의 Train Communication Network(TCN)를 적용할 경우 적합한 네트워크 토폴로지를 제안하고 실험을 통하여 그 결과를 측정하여 검증한다. 케이블 수, 포트 수 등 구조적인 제한조건과 네트워크 응답시간, 최대 전송량 등 성능적인 제한조건을 고려하여 네트워크 토폴로지를 제안한다. 스타 토폴로지, 데이지체인 토폴로지, 그리고 이들을 절충한 하이브리드 토폴로지를 각각 적용하여 비교하며 본 논문에서는 특히 하이브리드 토폴로지의 적절한 구성을 위해 그룹으로 묶이는 노드 수를 구한다. 적절하게 노드의 그룹이 구성된 하이브리드 토폴로지는 본 논문에서 최적 토폴로지로 제안하는 구조이다. 먼저 시뮬레이션을 통해 각각의 토폴로지 구성 시의 네트워크 성능에 대한 예상치를 도출하며 이 후 실제 장치를 연결하여 네트워크를 구현한다. 다양한 네트워크 성능 측정 프로그램을 이용하여 각 토폴로지에서의 성능을 측정하고 비교를 통해 제안한 방안의 우수성을 기술한다.

• ETRI Journal
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• 제33권4호
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• pp.629-632
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• 2011

This letter presents a hybrid conditional access system (CAS) for advanced terrestrial digital multimedia broadcasting (AT-DMB). The proposed architecture is characterized by its use of a unified CAS channel and various communication networks for CAS message transmissions. We implement a prototype CAS based on the hybrid architecture, which improves the CAS message transmission efficiency greatly compared to the existing T-DMB CAS standard and supports various AT-DMB interlayer services more easily and efficiently.