• Proceedings of the KSR Conference
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• 2003.10c
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• pp.204-209
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• 2003

Recently, The TCN(Train Communication Network} as the distributed control systems for electric vehicles, which is the international standard of the intra vehicle communication, actively recognizes variations and supports reconfiguration of the train network when a vehicle is separated or recombined. The technique of reconfiguration to take variety and interoperability of a vehicle constitution is used when the vehicle constitution is changed. At the time, each node making up vehicle network shares the information about the variation of vehicle constitutions and the state of nodes. In the hierarchical TCN structure, an exchange of data becomes available as a work to transmit information between components is performed at the node playing a role of gateway. This paper proposes a protocol to transmit the information of the train reconfiguration. The protocol gives an application to renew a list for transmitting information and to perform the transmission that can guarantee periodic and non-periodic data transmission between nodes when the network nodes changed by a variation of the network state are reconfigured. If use this protocol, can use functions that are offered in the electric railcar at the same time that composition of vehicles is completed without delay. And when driver of the electric railcar inspect before running of vehicles, can confirm state of vehicles visually through monitor in driver's room.

• Convergence Security Journal
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• v.2 no.1
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• pp.1-15
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• 2002

Network management system exchanges information between management servers to manage other network as well as local network for MSP(Management Service Provider) business. Because communication line that is used here is communication line that several users use, other user can steal a important information that are passed through communication line. For these information include important information of administrator or equipments, These information should be protected. Usually it is use much SSL to security of communication and it wish to protect information using SSL in communication between management servers' or communication between server and client at network management system to use much SSL for security of communication.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.35-57
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• 2014

Multi-criteria decision making (MCDM) algorithms play an important role in ensuring quality of service in an integrated HetNets (Heterogeneous Networks). The primary objective of this paper is to develop a multi-criteria vertical handoff decision algorithm (VHDA) for best access network selection in an integrated Wireless Local Area Network (WLAN)/Universal Mobile Telecommunications System (UMTS)/Worldwide Interoperability for Microwave Access (WiMAX) system. The proposed design consists of two parts, the first part is the evaluation of an Analytic Hierarchy Process (AHP) to decide the relative weights of handoff decision criteria and the second part computes the final score of the weights to rank network alternatives using Simple Additive Weighting (SAW). SAW ranks the network alternatives in a faster and simpler manner than AHP. The AHP-SAW mathematical model has been designed, evaluated and simulated for streaming video type of traffic. For other traffic type, such as conversational, background and interactive, only simulation results have been discussed and presented in brief. Simulation results reveal that the hierarchical modelling and computing provides optimum solution for access network selection in an integrated environment as obtained results prove to be an acceptable solution to what could be expected in real life scenarios.

• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.34-50
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• 2023

Quantum error correction is a key technology for achieving fault-tolerant quantum computation. Finding the best decoding solution to a single error syndrome pattern counteracting multiple errors is an NP-hard problem. Consequently, error decoding is one of the most expensive processes to protect the information in a logical qubit. Recent research on quantum error decoding has been focused on developing conventional and neural-network-based decoding algorithms to satisfy accuracy, speed, and scalability requirements. Although conventional decoding methods have notably improved accuracy in short codes, they face many challenges regarding speed and scalability in long codes. To overcome such problems, machine learning has been extensively applied to neural-network-based error decoding with meaningful results. Nevertheless, when using neural-network-based decoders alone, the learning cost grows exponentially with the code size. To prevent this problem, hierarchical error decoding has been devised by combining conventional and neural-network-based decoders. In addition, research on quantum error decoding is aimed at reducing the spacetime decoding cost and solving the backlog problem caused by decoding delays when using hardware-implemented decoders in cryogenic environments. We review the latest research trends in decoders for quantum error correction with high accuracy, neural-network-based quantum error decoders with high speed and scalability, and hardware-based quantum error decoders implemented in real qubit operating environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2101-2123
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• 2023

Recent studies have shown that the neural network-based binary code similarity detection technology performs well in vulnerability mining, plagiarism detection, and malicious code analysis. However, existing cross-architecture methods still suffer from insufficient feature characterization and low discrimination accuracy. To address these issues, this paper proposes a cross-architecture binary function similarity detection method based on composite feature model (SDCFM). Firstly, the binary function is converted into vector representation according to the proposed composite feature model, which is composed of instruction statistical features, control flow graph structural features, and application program interface calling behavioral features. Then, the composite features are embedded by the proposed hierarchical embedding network based on a graph neural network. In which, the block-level features and the function-level features are processed separately and finally fused into the embedding. In addition, to make the trained model more accurate and stable, our method utilizes the embeddings of predecessor nodes to modify the node embedding in the iterative updating process of the graph neural network. To assess the effectiveness of composite feature model, we contrast SDCFM with the state of art method on benchmark datasets. The experimental results show that SDCFM has good performance both on the area under the curve in the binary function similarity detection task and the vulnerable candidate function ranking in vulnerability search task.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.579-586
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• 2023

A wireless sensor network (WSN) has limited battery power because it is used wirelessly using low-cost small sensors. Since the battery cannot be replaced, the lifespan of the sensor node is directly related to the lifespan of the battery, so power must be used efficiently to maximize the lifespan of the network. In this study, based on PEGASIS, a representative energy-efficient routing protocol, we propose a protocol that classifies layers according to the distance from the sink node and configures multiple chains rather than one chain. The proposed protocol can increase network lifespan by reducing the transmission distance between nodes to prevent unnecessary energy consumption.

• The KIPS Transactions:PartC
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• v.13C no.5 s.108
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• pp.541-548
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• 2006

Sensor network is a network for realizing the ubiquitous computing circumstances, which aggregates data by means of observation or detection deployed at the inaccessible places with the capacities of sensing and communication. To realize this circumstance, data which sensor nodes gathered from sensor networks are delivered to users, in which it is required to encrypt the data for the guarantee of secure communications. Therefore, it is needed to design key management scheme for encoding appropriate to the sensor nodes which feature continual data transfer, limited capacity of computation and storage and battery usage. We propose a key management scheme which is appropriate to sensor networks organizing hierarchical architecture. Because sensor nodes send data to their parent node, we can reduce routing energy. We assume that sensor nodes have different security levels by their levels in hierarchy. Our key management scheme provides different key establishment protocols according to the security levels of the sensor nodes. We reduce the number of sensor nodes which share the same key for encryption so that we reduce the damage by key exposure. Also, we propose key update protocols which take different terms for each level to update established keys efficiently for secure data encoding.

• Journal of KIISE:Information Networking
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• v.33 no.2
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• pp.175-182
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• 2006

The CGA proposed by IETF working group prevents address spoofing and stealing and provides digital signature to users, but key collision problem arises. To solve this critical problem, the CGA defines the SEC field within address format, which is set to high value when high security is required and vice versa, but the CGA faces a dilemma between security and the processing time. As SEC value increases, the processing time to generate the CGA grows dramatically while key collision ratio increases if low SEC value is applied to the CGA. We propose modified CGA (MCGA) that has shorter processing time than the CGA and offers digital signature with small overheads. To solve key collision problem, we employ hierarchical ad hoc network. The MCGA is applicable to IPv6 networks as well public networks. In this paper, we design a mathematical model to analyze the processing time for MCGA and CGA first and evaluate the processing time via simulations, where the processing time for MCGA is reduced down 3.3 times when SEC value is set to 0 and 68,000 times when SEC value is set to 1. Further, we have proved that the CGA is inappropriate for both ad hoc networks and IPv6 networks when the SEC field is set to more than 3.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.889-900
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• 2006

Even though sensor networks are different in user requests and data characteristics depending on each application area, the existing researches on stream data transmission problem focus on the performance improvement of their methods rather than considering the original characteristic of stream data. In this paper, we introduce a hierarchical or distributed sensor network architecture and data model, and then evaluate the multivariate data reduction methods suitable for user requirements and data features so as to apply reduction methods alternatively. To assess the relative performance of the proposed multivariate data reduction methods, we used the conventional techniques, such as Wavelet, HCL(Hierarchical Clustering), Sampling and SVD (Singular Value Decomposition) as well as the experimental data sets, such as multivariate time series, synthetic data and robot execution failure data. The experimental results shows that SVD and Sampling method are superior to Wavelet and HCL ia respect to the relative error ratio and execution time. Especially, since relative error ratio of each data reduction method is different according to data characteristic, it shows a good performance using the selective data reduction method for the experimental data set. The findings reported in this paper can serve as a useful guideline for sensor network application design and construction including multivariate stream data.

• Journal of Biomedical Engineering Research
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• v.23 no.3
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• pp.197-205
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• 2002

The objectives of this Paper is to implement a diagnostic classifier of differential laryngeal diseases from acoustic signals acquired in a noisy room. For this Purpose, the voice signals of the vowel /a/ were collected from Patients in a soundproof chamber and got mixed with noise. Then, the acoustic Parameters were analyzed, and hierarchical neural networks were applied to the data classification. The classifier had a structure of five-step hierarchical neural networks. The first neural network classified the group into normal and benign or malign laryngeal disease cases. The second network classified the group into normal or benign laryngeal disease cases The following network distinguished polyp. nodule. Palsy from the benign laryngeal cases. Glottic cancer cases were discriminated into T1, T2. T3, T4 by the fourth and fifth networks All the neural networks were based on multilayer perceptron model which classified non-linear Patterns effectively and learned by an error back-propagation algorithm. We chose some acoustic Parameters for classification by investigating the distribution of laryngeal diseases and Pilot classification results of those Parameters derived from MDVP. The classifier was tested by using the chosen parameters to find the optimum ones. Then the networks were improved by including such Pre-Processing steps as linear and z-score transformation. Results showed that 90% of T1, 100% of T2-4 were correctly distinguished. On the other hand. 88.23% of vocal Polyps, 100% of normal cases. vocal nodules. and vocal cord Paralysis were classified from the data collected in a noisy room.