• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1487-1497
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• 2016

Explosively growing service of digital multimedia data increases the need for highly scalable low-cost storage. This paper proposes the new storage architecture based on torus network which does not need network switch and erasure coding for efficient storage usage for high scalability and efficient disk utilization. The proposed model has to compensate for the disadvantage of long network latency and network processing overhead of torus network. The proposed storage model was compared to two most popular distributed file system, GlusterFS and Ceph distributed file systems through a prototype implementation. The performance of prototype system shows outstanding results than erasure coding policy of two file systems and mostly even better results than replication policy of them.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.993-996
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• 1993

This paper addresses the structure and its associated learning algorithms of a feedforward multi-layered connectionist network, which has distributed learning abilities, for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed neural-network-based fuzzy logic control system (NN-FLCS) can be contrasted with the traditional fuzzy logic control system in their network structure and learning ability. An on-line supervised structure/parameter learning algorithm dynamic learning algorithm can find proper fuzzy logic rules, membership functions, and the size of output fuzzy partitions simultaneously. Next, a Reinforcement Neural-Network-Based Fuzzy Logic Control System (RNN-FLCS) is proposed which consists of two closely integrated Neural-Network-Based Fuzzy Logic Controllers (NN-FLCS) for solving various reinforcement learning problems in fuzzy logic systems. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. As ociated with the proposed RNN-FLCS is the reinforcement structure/parameter learning algorithm which dynamically determines the proper network size, connections, and parameters of the RNN-FLCS through an external reinforcement signal. Furthermore, learning can proceed even in the period without any external reinforcement feedback.

• Journal of the Korea Society of Computer and Information
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• v.18 no.10
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• pp.121-128
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• 2013

Existing sensor network studies have only emphasized energy aspects that sensors themselves had. But when an actual sensor network is established, biased use of a specific sensor may cause a partial disconnection of the sensor network. It becomes an disadvantage to fail efficient operation of the sensor network for a long time and energy efficiency of specific sensor energy causes to drop the efficiency of the sensor network. When a sensor network is composed of many clusters or made up of a large network, sensor network's disconnection cannot be avoided because they emphasize sensor's energy efficiency. Therefore, it was tried to lengthen the lifespan of the sensor network by making sensors in the sensor network avoid disconnection through even use of all the sensors composing of the sensor network. This article proposes a protocol to maintain a sensor network for a long time by preventing a sensor networks' disconnection through efficient management of sensor network energy composed of the protocols composing of the sensor network in ubiquitous environments.

• The KIPS Transactions:PartC
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• v.19C no.2
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• pp.149-152
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• 2012

In the roaming case, the cost of using the visited network is larger than that of home network. So, if a mobile terminal is connected to the visited network, even though it actually came back to the home network, the user may unduly pay for communication. Such a problem frequently occurs when many networks are overlapped in the same region, as shown in the case of Poland. In this paper, we propose a network search algorithm to support the fast comeback to home network in the roaming environment. In the proposed scheme, which is based on the 3GPP specification, the mobile terminal tries to search the home network by using a database of network information, as fast as possible. For performance evaluation, we construct a virtual testbed with real terminal and network equipment to emulate the service providers in Poland. From the experimental results, we can see that the proposed scheme can reduce the time of comeback to the home network by 3~60 minutes, compared the existing 3GPP scheme.

• Journal of Convergence Society for SMB
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• v.6 no.3
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• pp.29-35
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• 2016

This paper presents the design of a user authentication system (DCIAS) using the device constant information. Defined design a new password using the access device constant information to be used for user authentication during system access on the network, and design a new concept the user authentication system so that it can cope with the threat required from passive replay attacks to re-use the password obtained in other applications offer. In addition, by storing a password defined by the design of the encrypted random locations in the server and designed to neutralize the illegal access to the system through the network. Therefore proposed using the present system, even if access to the system through any of the network can not know whether any where the password is stored, and if all right even stored information is not easy to crack's encrypted to neutralize any replay attacks on the network to that has strong security features.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.159-162
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• 2006

In ubiquitous computing environments, which can be connected to the networks any time any where, wearable computers frequently will be changed their network connection point. Therefore, the demand of the mobility support service becomes more important. The mobility support mechanism allows a wearable computer to continue the existing services without the modification for the network configuration of wearable computer even if wearable computer changes the network connection point during its moving. In this paper, we design the component based middlewear for the mobility supports of wearable computers, propose the method of the mobility support service. This method tunnels the existing data to wearable computer using Mobile IP protocol even if a wearable computer moves to other network after recomposing dynamically the mobility support component in wearable middlewear.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.69-77
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• 2019

Today, most of elevators have an emergency call facility for emergency situations. However, if the network installed in the elevator is also out of power, it cannot be used for the elevator remote monitoring and management. So, we develop an integrated and unified emergency call system, which can transmit not only telephone call but also data signals using PSTN(Public Switched Telephone Network) in order to remote monitoring and management of elevators, even though a power outage occurs. The proposed integrated emergency call system to process multiple data such as voice and operational information is a multi-channel board system which is composed of an emergency phone signal processing module and an operational information processing module in the control box of elevator. In addition, the RMS(remote management server) systems based on the Web consist of a dial-up server and a remote monitoring server where manages the elevator's operating information, status records, and operational faults received via the proposed integrated and unified emergency call system in real time. So even if there's a catastrophic emergency, the proposed RMS systems shall ensure and maintain the safety of passengers inside the elevator. Also, remote control of the elevator by this system should be more efficient and secure. In near future, all elevator emergency call system need to support multifunctional capabilities to transmit operational data as well as phone calls for the safety of passengers. In addition, for safer elevators, it is necessary to improve them more efficiently by combining them with high-tech technologies such as the Internet of Things and artificial intelligence.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.269-274
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• 2022

An artificial neural network (ANN) that identifies radionuclides from low-count gamma spectra of a NaI scintillator is proposed. The ANN was trained and tested using simulated spectra. 14 target nuclides were considered corresponding to the requisite radionuclide library of a radionuclide identification device mentioned in IEC 62327-2017. The network shows an average identification accuracy of 98.63% on the validation dataset, with the gross counts in each spectrum N_c = 100~10000 and the signal to noise ratio SNR = 0.05-1. Most of the false predictions come from nuclides with low branching ratio and/or similar decay energies. If the N_c>1000 and SNR>0.3, which is defined as the minimum identifiable condition, the averaged identification accuracy is 99.87%. Even when the source and the detector are covered with lead bricks and the response function of the detector thus varies, the ANN which was trained using non-shielding spectra still shows high accuracy as long as the minimum identifiable condition is satisfied. Among all the considered nuclides, only the identification accuracy of ²³⁵U is seriously affected by the shielding. Identification of other nuclides shows high accuracy even the shielding condition is changed, which indicates that the ANN has good generalization performance.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• ETRI Journal
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• v.31 no.3
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• pp.330-332
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• 2009

Broadcasting is one of the most important communication primitives used in multiprocessor networks. In this letter, we demonstrate that the broadcasting algorithm proposed by Madabhushi and others is incorrect. We introduce efficient one-to-all broadcasting schemes of even networks for one-port and all-port models. The broadcasting time of the one-port model is 2d-3 and that of the all-port model is d-1. The total time steps taken by the proposed algorithms are optimal.