• Korean Management Science Review
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• v.28 no.3
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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.

• Korean Management Science Review
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• v.30 no.1
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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.

• Journal of the Semiconductor & Display Technology
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• v.13 no.1
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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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• 2004.08a
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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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• v.36 no.6
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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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• v.30 no.5
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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.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.2
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• pp.65-72
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• 2021

Recently, attacks on the network environment have been rapidly escalating and intelligent. Thus, the signature-based network intrusion detection system is becoming clear about its limitations. To solve these problems, research on machine learning-based intrusion detection systems is being conducted in many ways, but two problems are encountered to use machine learning for intrusion detection. The first is to find important features associated with learning for real-time detection, and the second is the imbalance of data used in learning. This problem is fatal because the performance of machine learning algorithms is data-dependent. In this paper, we propose the HSF-DNN, a network intrusion detection model based on a deep neural network to solve the problems presented above. The proposed HFS-DNN was learned through the NSL-KDD data set and performs performance comparisons with existing classification models. Experiments have confirmed that the proposed Hybrid Feature Selection algorithm does not degrade performance, and in an experiment between learning models that solved the imbalance problem, the model proposed in this paper showed the best performance.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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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.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.6
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• pp.106-113
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• 2017

In this paper, we propose a suitable network topology for the Ethernet based Train Communication Network (TCN) for control system in a autonomous multi-articulated vehicle. We propose a network topology considering the structural constraints such as the number of cables and ports, and the performance constraints such as network response time and maximum throughput. We compare the network performances of star topology and daisy chain topology as well as hybrid topology, which is proposed in previous studies and a compromise between daisy chain and star topology. Here, the appropriate number of nodes in a group is obtained for the configuration of the hybrid topology. We first derive estimates of the network performance through simulation with different topologies, and then, implement the network by connecting the actual devices with each network topology. The performance of each topology is measured using various network performance measurement programs and the superiority of the proposed topology is described through comparison.

• ETRI Journal
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• v.33 no.4
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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.