• Journal of KIISE:Computer Systems and Theory
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• v.29 no.10
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• pp.561-572
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• 2002

Deadlock detection in distributed systems is considered difficult since no single site knows the exact information on the whole system state. This paper proposes a time-efficient algorithm for distributed deadlock detection and resolution. The initiator of the algorithm propagates a deadlock detection message and builds a reduced wait-for graph from the information carried by the replies. The time required for deadlock detection is reduced to half of that of the other algorithms. Moreover, any deadlock reachable from the initiator is detected whereas most previous algorithms only find out whether the initiator is involved in deadlock. This feature accelerates the detection of deadlock. Resolution of the detected deadlock is also simplified and precisely specified, while the current algorithms either present no resolution scheme or simply abort the initiator of the algorithm upon detecting deadlock.

• Journal of Sensor Science and Technology
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• v.24 no.1
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• pp.1-5
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• 2015

The V-grooved single-mode fiber in which a surface part of the core was removed was investigated as a quasi-distributed water detection sensor. In the normal state, the V-grooved region is filled and covered with a specific RI (Refractive Index)-matched medium, and the sensor experiences minimal optical loss. As water invades the V-grooved region, the material is dissolved and removed, and a considerable optical loss occurs owing to the large RI difference between the fiber core and water. The experimental results showed the feasibility of the device as a sensor element of the quasi-distributed water detection sensor system based on general optical time domain reflectometry (OTDR).

• Journal of Power Electronics
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• v.16 no.4
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• pp.1526-1540
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• 2016

This work mainly discusses an accurate and fast islanding detection based on fractional wavelet packet transform (FRWPT)for multibus microgrid systems. The proposed protection scheme uses combined desirable features retrieved from discrete fractional Fourier transform (FRFT) and wavelet packet transform (WPT) techniques, which provides precise time-frequency information on minute perturbation signals introduced in the system. Moreover, this study focuses on the design of decoupling control with a distributed controller based on state feedback for the efficient operation of microgrid systems that are transitioning from the grid-connected mode to the islanded mode. An IEEE 9-bus test system with inverter based distributed generation (DG) units is considered for islanding assessment and smooth operation. Finally, tracking errors are greatly reduced with stability improvement based on the proposed controller. FRWPT based islanding detection is demonstrated via a time domain simulation of the system. Simulated results show an improvement in system stability with the application of the proposed controller and accurate islanding detection based on the FRWPT technique in comparison with the results obtained by applying the wavelet transform (WT) and WPT.

• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.3
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• pp.247-254
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• 2015

Unintentional islanding results in safety hazards, power quality degradation, and many other issues. Thus, islanding detection of grid-connected distributed generation system is a key function for standards compliance. Many anti-islanding schemes are currently being studied; however, existing anti-islanding schemes used in inverters have power quality degradation and non-detection zone issues. Therefore, this paper analyzes existing anti-islanding schemes by using frequency drift in accordance with both islanding detection performance and power quality. This paper also proposes a new anti-islanding scheme by using frequency drift. Both simulation and experimental results show that the proposed scheme has negligible power quality degradation and no non-detection zones compared with other existing schemes.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.279-284
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• 2011

Harmonic distortions in current waveform may cause significant problems in electric power system facility and operation. This paper presents an adaptive parameter estimation method to detect harmonic current components caused by nonlinear loads. In addition, a coordination strategy for multiple inverter-interfaced distributed generators to compensate the harmonic currents is discussed. The coordination strategy is realized by distributing the harmonic compensation participation index to individual distributed generators. The harmonic compensation participation index can be determined by the amount of remaining power generation capacity of each distributed generator. Simulation results based on switching-level inverter models show that the proposed harmonic detection method has good performance and the coordination strategy can improve harmonic problems efficiently.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.139-147
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• 2009

In this paper, optimum design of energy-aware distributed detection is considered for a parallel sensor network system consisting of a fusion center and two passive sonar nodes. AND rule and OR rule are employed as the fusion rules of the sensor network. For the fusion rules, it is shown that a threshold rule of each sensor node has uniformly most powerful properties. Optimum threshold for each sensor is investigated that maximizes the probability of detection under a constraint on energy consumption due to false alarms. It is also investigated through numerical experiments how signal strength, an energy constraint, and the distance between two sensor nodes affect the system detection performances.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4774-4796
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• 2018

Wireless sensor networks are composed of a large number of inexpensive and tiny sensors used in different areas including military, industry, agriculture, space, and environment. Fault tolerance, which is considered a challenging task in these networks, is defined as the ability of the system to offer an appropriate level of functionality in the event of failures. The present study proposed an intelligent throughput descent and distributed energy-efficient mechanism in order to improve fault tolerance of the system against soft and permanent faults. This mechanism includes determining the intelligent neighborhood radius threshold, the intelligent neighborhood nodes number threshold, customizing the base paper algorithm for distributed systems, redefining the base paper scenarios for failure detection procedure to predict network behavior when running into soft and permanent faults, and some cases have been described for handling failure exception procedures. The experimental results from simulation indicate that the proposed mechanism was able to improve network throughput, fault detection accuracy, reliability, and network lifetime with respect to the base paper.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.1
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• pp.212-218
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• 2002

This research is the development of a distributed tactile sensor using PVDF film far the detection of the contact state. The prototype of the tactile sensor with 8$\times$8 taxels was fabricated using PVDF film and flexible circuitry. In the fabrication procedure, the electrode and the common electrode patterns are attached to the both side of the 28${\mu}m$ thickness PVDF film. The sensor is covered with polyester film for insulation. The signals of a contact pressure to the tactile sensor are sensed and processed in the DSP system in which the signals are digitalized and filtered. And the signals are integrated for taking the force profile. The processed signals of the output of the sensor are visualized to take the shape and force distribution of the contact object in personal computer. The usefulness of the sensor system is verified through the sensing examples.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.12
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• pp.287-296
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• 2019

Due to the development and increased usage of Internet services such as IoT and cloud computing, a large number of packets are being generated on the Internet. In order to create a safe Internet environment, malicious data that may exist among these packets must be processed and detected quickly. In this paper, we apply MongoDB, which is specialized for unstructured data analysis and big data processing, to intrusion detection system for rapid processing of big data security events. In addition, building the intrusion detection system(IDS) using some of the private cloud resources which is the target of protection, elastic and dynamic reconfiguration of the IDS is made possible as the number of security events increase or decrease. In order to evaluate the performance of MongoDB - based IDS proposed in this paper, we constructed prototype systems of IDS based on MongoDB as well as existing relational database, and compared their performance. Moreover, the number of virtual machine has been increased to find out the performance change as the IDS is distributed. As a result, it is shown that the performance is improved as the number of virtual machine is increased to make IDS distributed in MongoDB environment but keeping the overall system performance unchanged. The security event input rate based on distributed MongoDB was faster as much as 60%, and distributed MongoDB-based intrusion detection rate was faster up to 100% comparing to the IDS based on relational database.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.35-40
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• 2019

Traditional Malware Detection is susceptible for detecting malware which is modified by polymorphism or obfuscation technology. By learning patterns that are embedded in malware code, machine learning algorithms can detect similar behaviors and replace the current detection methods. Data must collected continuously in order to learn malicious code patterns that change over time. However, the process of storing and processing a large amount of malware files is accompanied by high space and time complexity. In this paper, an HDFS-based distributed processing system is designed to reduce space complexity and accelerate feature extraction time. Using a distributed processing system, we extract two API features based on filtering basis, 2-gram feature and APICFG feature and the generalization performance of ensemble learning models is compared. In experiments, the time complexity of the feature extraction was improved about 3.75 times faster than the processing time of a single computer, and the space complexity was about 5 times more efficient. The 2-gram feature was the best when comparing the classification performance by feature, but the learning time was long due to high dimensionality.