• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.357-363
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• 2023

In underwater signal processing, separating individual signals from mixed signals has long been a challenge due to low signal quality. The common method using Short-time Fourier transform for spectrogram analysis has faced criticism for its complex parameter optimization and loss of phase data. We propose a Triple-path Recurrent Neural Network, based on the Dual-path Recurrent Neural Network's success in long time series signal processing, to handle three-dimensional tensors from multi-channel sensor input signals. By dividing input signals into short chunks and creating a 3D tensor, the method accounts for relationships within and between chunks and channels, enabling local and global feature learning. The proposed technique demonstrates improved Root Mean Square Error and Scale Invariant Signal to Noise Ratio compared to the existing method.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.108-113
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• 2012

Routing paths are mightily important for the network security in WSNs. To maintain such routing paths, sustained path re-selection and path management are needed. Region segmentation based path selection method (RSPSM) provides a path selection method that a sensor network is divided into several subregions, so that the regional path selection and path management are available. Therefore, RSPSM can reduce energy consumption when the path re-selection process is executed. However, it is hard to guarantee optimized secure routing path at all times since the information using the path re-selection process is limited in scope. In this paper, we propose partial path selection method in each subregion using preselected partial paths made by RSPSM for routing path optimization in SEF based sensor networks. In the proposed method, the base station collects the information of the all partial paths from every subregion and then, evaluates all the candidates that can be the optimized routing path for each node using a evaluation function. After the evaluation process is done, the result is sent to each super DN using the global routing path information (GPI) message. Thus, each super DN provides the optimized secure routing paths using the GPI. We show the effectiveness of the proposed method via the simulation results. We expect that our method can be useful for the improvement of RSPSM.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.211-221
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• 2012

The development of wireless sensor networks (WSNs) is progressed slowly due to limited resources, but it is in progress to the development of the latest IP-based IP-WSN by the development of hardware and power management technology. IPv6 over Low power WPAN (6LoWPAN) is capable of IPv6-built low-power devices. In these IP-based WSNs, existing IP-based techniques which was impossible in WSNs becomes possible. 6LoWPAN is based on the IEEE 802.15.4 sensor networks and is a IPv6-supported technology. Host-based mobility management scheme in IP-WSNs are not suitable due to the additional signaling, network-based mobility management scheme is more suitable. In this paper, we propose an enhanced PMIPv6-based route optimization scheme which consider multi-6LoWPAN network environments. All SLMA (Sensor Local Mobility Anchor) of the 6LoWPAN domain are connected with the SPIG (Sensor Proxy Internetworking Gateway) and performs distributed mobility control for the 6LoWPAN-based inter-domain operations. All information of SLMA in 6LoWPAN domain is maintained by SMAG (Sensor Mobile Access Gateway), and then is performed the route optimization quickly. The status information of the route optimization from SPIG is stored to SLMA and it is supported without additional signaling.

• Korean Chemical Engineering Research
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• v.52 no.5
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• pp.603-612
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• 2014

Lots of networks are constructed in a large scale industrial complex. Each network meet their demands through production or transportation of materials which are needed to companies in a network. Network directly produces materials for satisfying demands in a company or purchase form outside due to demand uncertainty, financial factor, and so on. Especially utility network and hydrogen network are typical and major networks in a large scale industrial complex. Many studies have been done mainly with focusing on minimizing the total cost or optimizing the network structure. But, few research tries to make an integrated network model by connecting utility network and hydrogen network In this study, deterministic mixed integer linear programming model is developed for integrating utility network and hydrogen network. Steam Methane Reforming process is necessary for combining two networks. After producing hydrogen from Steam-Methane Reforming process whose raw material is steam vents from utility network, produced hydrogen go into hydrogen network and fulfill own needs. Proposed model can suggest optimized case in integrated network model, optimized blueprint, and calculate optimal total cost. The capability of the proposed model is tested by applying it to Yeosu industrial complex in Korea. Yeosu industrial complex has the one of the biggest petrochemical complex and various papers are based in data of Yeosu industrial complex. From a case study, the integrated network model suggests more optimal conclusions compared with previous results obtained by individually researching utility network and hydrogen network.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.11
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• pp.2199-2214
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• 1994

In order to sufficiently utilize the potential effectiveness of ATM networks, systematic and effective network management and control systems must be employed. In addition to network design, planning, and call routing techniques used in traditional circuit-switched networks. ATM networks can provide logical VP(Virtual Path) reconfiguration capabilities which can adapt to changes in the network environment. With a proper application of the VP reconfiguration technique, ATM networks can reduce the overhead of frequent network redesign and the overhead of call routing processing. However, ATM VP reconfiguration is a very complex and difficult problem which consists of many facets of optimization subproblems such as VP routing. VP sizing, VP terminating point location and VC routing. Most previous research on the ATM logical network reconfiguration problem dealt with the subset of the problem. This paper mathematically formulates the ATM network reconfiguration problem completely considering all subproblems. Since it is very difficult to derive the optimal solution of the formulated problem, we develop a heuristic method based on a Max min bandwidth allocation principle. We show the feasibility of the proposed heuristic method with a simple example.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5A
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• pp.391-400
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• 2009

Recently, cellular communication networks are migrating from 2G to 3G. Spectrum utilization tends to be inefficient during the transition. Cognitive radio (CR) technology can be a key solution to increase spectrum efficiency by allowing secondary networks to utilize frequency resource of primary networks. However, conventional CR approaches which do not utilize the frequency reuse factor of primary networks may incur degradation of whole network performance. In this paper, we propose a mechanism that a secondary network senses pilot signals of a primary network and select optimum frequency bands. In order to maximize whole network performance, we formulate an optimization problem subject to interference constraints for a primary network and present algorithms. Simulation results compare the proposed method with the conventional method. Our proposed method shows performance gain over the conventional method if channel variation of a primary network is dynamic and the frequency reuse factor of a primary network is high.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.9
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• pp.38-45
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• 2016

For higher efficiency and reliability of railroad trains with many electronic sensors and actuators, a distributed control system with which electronic components communicate with each other in a distributed manner via a data network is considered. This paper considers Ethernet-based Train Communication Network (TCN) for this purpose and proposes a methodology to optimize the topology in terms of transmission latency and reliability, each of which is modeled as the number of traversing backbone nodes and the number of cables between vehicles, respectively. An objective function is derived accordingly and a closed-form optimum is obtained by relaxing the integer constraint of the number of vehicles for a unit network. Then, the final integer optimum is searched around it. Through numerical evaluation, the validity of the proposed methodology and the characteristics of the resulting solutions are shown.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.2892-2913
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• 2016

Network virtualization promises to play a dominant role in shaping the future Internet by overcoming the Internet ossification problem. However, due to the injecting of additional virtualization layers into the network architecture, several new security risks are introduced by the network virtualization. Although traditional protection mechanisms can help in virtualized environment, they are not guaranteed to be successful and may incur high security overheads. By performing the virtual network (VN) embedding in a security-aware way, the risks exposed to both the virtual and substrate networks can be minimized, and the additional techniques adopted to enhance the security of the networks can be reduced. Unfortunately, existing embedding algorithms largely ignore the widespread security risks, making their applicability in a realistic environment rather doubtful. In this paper, we attempt to address the security risks by integrating the security factors into the VN embedding. We first abstract the security requirements and the protection mechanisms as numerical concept of security demands and security levels, and the corresponding security constraints are introduced into the VN embedding. Based on the abstraction, we develop three security-risky modes to model various levels of risky conditions in the virtualized environment, aiming at enabling a more flexible VN embedding. Then, we present a mixed integer linear programming formulation for the VN embedding problem in different security-risky modes. Moreover, we design three heuristic embedding algorithms to solve this problem, which are all based on the same proposed node-ranking approach to quantify the embedding potential of each substrate node and adopt the k-shortest path algorithm to map virtual links. Simulation results demonstrate the effectiveness and efficiency of our algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1477-1483
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• 2020

The stability guarantee of wireless network based control systems is still challenging due to the lossy links and node failures. This paper proposes a hierarchical cluster-based network protocol called robust wireless sensor and actuator network (R-WSAN) by combining time, channel, and space resource diversity. R-WSAN includes a scheduling algorithm to support the network resource allocation and a control task sharing scheme to maintain the control stability of multiple plants. R-WSAN was implemented on a real test-bed using Zolertia RE-Mote embedded hardware platform running the Contiki-NG operating system. Our experimental results demonstrate that R-WSAN provides highly reliable and robust performance against lossy links and node failures. Furthermore, the proposed scheduling algorithm and the task sharing scheme meet the stability requirement of control systems, even if the controller fails to support the control task.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.