• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2086-2098
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• 2018

Security plays a vital role and is the key challenge in Mobile Ad-hoc Networks (MANET). Infrastructure-less nature of MANET makes it arduous to envisage the genre of topology. Due to its inexhaustible access, information disseminated by roaming nodes to other nodes is susceptible to many hazardous attacks. Intrusion Detection and Prevention System (IDPS) is undoubtedly a defense structure to address threats in MANET. Many IDPS methods have been developed to ascertain the exceptional behavior in these networks. Key issue in such IDPS is lack of fast self-organized learning engine that facilitates comprehensive situation awareness for optimum decision making. Proposed "Intelligent Behavioral Hybridized Intrusion Detection and Prevention System (IBH_IDPS)" is built with computational intelligence to detect complex multistage attacks making the system robust and reliable. The System comprises of an Intelligent Client Agent and a Smart Server empowered with fuzzy inference rule-based service engine to ensure confidentiality and integrity of network. Distributed Intelligent Client Agents incorporated with centralized Smart Server makes it capable of analyzing and categorizing unethical incidents appropriately through unsupervised learning mechanism. Experimental analysis proves the proposed model is highly attack resistant, reliable and secure on devices and shows promising gains with assured delivery ratio, low end-to-end delay compared to existing approach.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.732-736
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• 2008

The traditional method to calculate the gravity feed is to assume that only one tank in fuel system supplies the needed fuel to the engine, and then calculated for the single branch. Actually, all fuel tanks compete for supplying oil. Our method takes into consideration all fuel tanks and therefore, we believe, our method is intrinsically superior to traditional methods and is closer to understanding the real seriousness of the oil supply situation. Firstly, the thesis gives the mathematical model for fuel flow pipe, pump, check valve and the simulation model for fuel tank. On the basis of flow network theory and time difference method, we established a new calculation method for gravity feed oil of aeroplane fuel system, secondly. This model can solve the multiple-branch and transient process simulation of gravity feed oil. Finally, we give a numerical example for a certain type of aircraft, achieved the variations of oil level and flow mass per second of each oil tanks. In addition, we also obtained the variations of the oil pressure of the engine inlet, and predicted the maximum time that the aeroplane could fly safely under gravity feed. These variations show that our proposed method of calculations is satisfactory.

• International Journal of Naval Architecture and Ocean Engineering
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• v.2 no.3
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• pp.132-138
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• 2010

In the present study, the real-time monitoring system is developed based on the wireless sensor network (WSN) and power line communication (PLC) employed in the 3,000-ton-class training ship. The WSN consists of sensor nodes, router, gateway and middleware. The PLC is composed of power lines, modems, Ethernet gateway and phase-coupler. The basic tests show that the ship has rather good environments for the wired and wireless communications. The developed real-time monitoring system is applied to recognize the thermal environments of main-engine room and one cabin in the ship. The main-engine room has lots of heat sources and needs careful monitoring to satisfy safe operation condition or detect any human errors beforehand. The monitoring is performed in two regions near the turbocharger and cascade tank, considered as heat sources. The cabin on the second deck is selected to monitor the thermal environments because it is close to the heat source of main engine. The monitoring results of the cabin show the thermal environment is varied by the human activity. The real-time monitoring for the thermal environment would be useful for the planning of the ventilation strategy based on the traces of the human activity against inconvenient thermal environments as well as the recognizing the temperature itself in each cabin.

• JSTS:Journal of Semiconductor Technology and Science
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• v.3 no.4
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• pp.167-174
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• 2003

This paper proposes a network processor especially optimized for content switching. With 2Gbps port capability, it integrates packet processor cluster, content-based classification engine and traffic manager on a single chip. A switch fabric architecture is also designed for scale-up of the network processor's capability over hundreds gigabit bandwidth. Applied in real network systems, the network processor shows wire-speed network address translator (NAT) and content-based switching performance.

• Journal of KIISE:Computing Practices and Letters
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• v.6 no.2
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• pp.244-253
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• 2000

Since no single routing-switching combination performs the best under various different types of applications, a flexible network is required to support a range of polices. This paper introduces an implementation of a look-up table routing engine offering flexible routing and switching polices without performance degradation unlike those based on microprocessors. By deciding contents of look-up tables, the engine can implement wormhole routing, virtual cut-through routing, and packet switching, as well as hybrid switching, under a variety of routing algorithms. Since the routing engine has a piplelined look-up table architecture, the routing delay is as small as one flit, and thus it can overlap multiple routing actions without performance degradation in comparison with hardwired routers dedicated to a specific policy. Because four pipeline stages do not induce a hazard, expensive forwarding logic is not required. The routing engine can accommodate four physical links with a time shared cut-through bus or single link with a cross-bar switch. It is implemented using Xilinx 4000 series FPGA.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.9
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• pp.459-466
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• 2007

TCP/IP processing imposes a heavy load on the host CPU when it is processed by the host CPU on a very high-speed network. Recently the TCP/IP Offload Engine (TOE), which processes TCP/IP on a network adapter instead of the host CPU, has become an attractive solution to reduce the load in the host CPU. There have been two approaches to implement TOE. One is the software TOE in which TCP/IP is processed by an embedded processor and the other is the hardware TOE in which TCP/IP is processed by a dedicated ASIC. The software TOE has poor performance and the hardware TOE is neither flexible nor expandable enough to add new features. In this paper we designed and implemented a hybrid TOE architecture, in which TCP/IP is processed by cooperation of hardware and software, based on an FPGA that has two embedded processor cores. The hybrid TOE can have high performance by processing time-critical operations such as making and processing data packets in hardware. The software based on the embedded Linux performs operations that are not time-critical such as connection establishment, flow control and congestions, thus the hybrid TOE can have enough flexibility and expandability. To improve the performance of the hybrid TOE, we developed a hardware-based transmission/reception accelerator that processes important operations such as creating data packets. In the experiments the hybrid TOE shows the minimum latency of about $19{\mu}s$. The CPU utilization of the hybrid TOE is below 6 % and the maximum bandwidth of the hybrid TOE is about 675 Mbps.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.9
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• pp.691-698
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• 2006

TCP/IP Offload Engine (TOE) is a technology that processes TCP/IP on a network adapter instead of a host CPU to reduce protocol processing overhead from the host CPU. There have been some approaches to implementing TOE: software TOE based on an embedded processor; hardware TOE based on ASIC implementation; and hybrid TOE in which software and hardware functions are combined. In this paper, we designed software modules and hardware modules for a hybrid TOE on an FPGA that had two processor cores. Software modules are based on the embedded Linux. Hardware modules are for data transmission (TX) and reception (RX). One core controls the TX path and the other controls the RX path of the Linux. This TX/RX path separation mechanism can reduce task switching overheads between processes and overcome poor performance of single embedded processor. Hardware modules deal with creating headers for outgoing packets, processing headers of incoming packets, and fetching or storing data from or to the host memory by DMA. These can make it possible to improve the performance of data transmission and reception. We proved performance of the TOE with separated transmission and reception paths by performing experiments with a TOE network adapter that was equipped with the FPGA having processor cores.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.4
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• pp.483-488
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• 2015

This paper implements a long-term monitoring system (LMS) for ship's engine performance analysis (SEPA) based on the web, for the purpose of the communication speed and engine maintenance. This system is composed of a simulator, monitoring module with a multi channel A/D converter, monitoring computer, network attached storage (NAS), RS485 serial and wireless internet communication system. The existing products monitor the information transmitted from pressure sensors installed in the upper parts of each of engines in the local or web computer, but have a delay in the communication speed and errors in long-term monitoring due to the large volume of sampling pressure data. To improve these problems, the monitoring computer saves the sampling pressure data received from the pressure sensors in NAS, monitors the long-term sampling data generated by the sectional down sampling method on a local computer, and transmits them to the web for long-term monitoring. Because this method has one tenth of the original sampling data, it will use memory with small capacity, save communication cost, monitor the long-term sampling data for 30 days, and as a result, make a great contribution to engine maintenance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10a
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• pp.124-140
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• 1995

The merit of fuzzy rule based systems stems from their capability of encoding qualitative knowledge of experts into quantitative rules. Recent advancement in automatic tuning or self-organization of fuzzy rules from experimental data further enhances their power, allowing the integration of the top-down encoding of knowledge with the bottom-up learning of rules. In this paper, methods of self-organizing fuzzy rules and of performing defuzzification and inference is presented based on a multi-resolution radial basis function network. The network learns an arbitrary input-output mapping from sample distribution as the union of hyper-ellipsoidal clusters of various locations, sizes and shapes. The hyper-ellipsoidal clusters, representing fuzzy rules, are self-organized based of global competition in such a way as to ensute uniform mapping errors. The cooperative interpolation among the multiple clusters associated with a mapping allows the network to perform a bidirectional many-to-many mapping, representing a particular from of defuzzification. Finally, an inference engine is constructed for the network to search for an optimal chain of rules or situation transitions under the constraint of transition feasibilities imposed by the learned mapping. Applications of the proposed network to skill acquisition are shown.

• 전자공학회논문지 IE
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• v.48 no.2
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• pp.19-25
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• 2011

This paper presented neural network modeling method using vehicle data to predict fuel consumption. To acquire data for training and testing the proposed neural network, medium-class gasoline vehicle drove at downtown and parameters measured include speed, engine rpm, throttle position sensor (TPS), and mass air flow (MAF) as input data, and fuel consumption as target data from OBD-II port. Multi layer perception network was used for nonlinear mapping between the input and the output data. It was observed that the neural network model can predict the vehicle quite well with mean squared error was $1.306{\times}10^{-6}$ for the fuel consumption.