• Proceedings of the Korea Information Processing Society Conference
• /
• 2007.05a
• /
• pp.1288-1290
• /
• 2007

Congestion in WSN increases energy dissipation rates of sensor nodes as well as loss of packets and thereby hinders fair and reliable event detections. We find that one of the key reasons of congestion in WSN is allowing sensing nodes to transfer as many packets as possible. This is due to the use of CSMA/CA that gives opportunistic media access control. In this paper, we propose an energy efficient congestion avoidance protocol that includes source count based hierarchical and load adaptive medium access control. Our proposed mechanism ensures load adaptive media access to the nodes and thus achieves fairness in event detection. The results of simulation show our scheme exhibits more than 90% delivery ratio with retry limit 1, even under bursty traffic condition which is good enough for reliable event perception.

• Industrial Engineering and Management Systems
• /
• v.14 no.2
• /
• pp.196-209
• /
• 2015

Retail sales forecast is a special area of forecasting. Its unique characteristics call for unique data models and treatment, and unique forecasting processes. In this paper, we will address lessons learned and challenges encountered in retail sales forecast from a practical and technical perspective. In particular, starting with the data models of retail sales data, we proceed to address issues existing in estimating and processing each component in the data model. We will discuss how to estimate the multi-seasonal cycles in retail sales data, and the limitations of the existing methodologies. In addition, we will talk about the distinction between business events and forecast events, the methodologies used in event detection and event effect estimation, and the difficulties in compound event detection and effect estimation. For each of the issues and challenges, we will present our solution strategy. Some of the solution strategies can be generalized and could be helpful in solving similar forecast problems in different areas.

• Journal of KIISE:Databases
• /
• v.37 no.6
• /
• pp.324-332
• /
• 2010

The majority of event detection in real-time embedded sensor network systems is based on data fusion that uses noisy sensor data collected from complicated real-world environments. Current research has produced several excellent low-level mechanisms to collect sensor data and perform aggregation. However, solutions that enable these systems to provide real-time data processing using readings from heterogeneous sensors and subsequently detect complex events of interest in real-time fashion need further research. We are developing real-time event detection approaches which allow light-weight data fusion and do not require significant computing resources. Underlying the event detection framework is a collection of real-time monitoring and fusion mechanisms that are invoked upon the arrival of sensor data. The combination of these mechanisms and the framework has the potential to significantly improve the timeliness and reduce the resource requirements of embedded sensor networks. In addition to that, we discuss about a privacy that is foundation technique for trusted embedded sensor network system and explain anonymization technique to ensure privacy.

• Journal of Korea Multimedia Society
• /
• v.7 no.11
• /
• pp.1540-1549
• /
• 2004

In this paper, we propose an algorithm to detect semantic events in golf video using Hidden Markov Model. The purpose of this paper is to identify and classify the golf events to facilitate highlight-based video indexing and summarization. In this paper we first define 4 semantic events, and then design HMM model with states made up of each event. We also use 10 multiple visual features based on MPEG-7 visual descriptors to acquire parameters of HMM for each event. Experimental results showed that the proposed algorithm provided reasonable detection performance for identifying a variety of golf events.

• International Journal of Computer Science & Network Security
• /
• v.24 no.4
• /
• pp.179-191
• /
• 2024

With the advancement of modern technology, cyber-attacks are always rising. Specialized defense systems are needed to protect organizations against these threats. Malicious behavior in the network is discovered using security tools like intrusion detection systems (IDS), firewall, antimalware systems, security information and event management (SIEM). It aids in defending businesses from attacks. Delivering advance threat feeds for precise attack detection in intrusion detection systems is the role of cyber-threat intelligence (CTI) in the study is being presented. In this proposed work CTI feeds are utilized in the detection of assaults accurately in intrusion detection system. The ultimate objective is to identify the attacker behind the attack. Several data sets had been analyzed for attack detection. With the proposed study the ability to identify network attacks has improved by using machine learning algorithms. The proposed model provides 98% accuracy, 97% precision, and 96% recall respectively.

• Journal of Korea Multimedia Society
• /
• v.9 no.12
• /
• pp.1572-1579
• /
• 2006

An usual acoustic emission(AE) event has two widely characterized parameters in time domain, peak amplitude and event duration. But noise in AE measuring may disturb the signals with its parameters and aggrandize the signal incertitude. Experiment activity of detection of the nick inside of porcelain with AE was made and study on AE signal processing with statistic be presented in this paper in order to pick-up information expected from the signal with noise. Effort is concentrated on developing a novel arithmetic to improve extraction of the characteristic from stochastic signal and to enhance the voracity of detection. The main purpose discussed in this paper is to treat with signals on amplitudes with statistic mutuality and power density spectrum in frequency domain, and farther more to select samples for neural networks training by means of least-squares algorithm between real measuring signal and deterministic signals under laboratory condition. By seeking optimization with the algorithm, the parameters representing characteristic of the porcelain object are selected, while the stochastic interfere be weakened, then study for detection on neural networks is developed based on processing above.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.61 no.8
• /
• pp.1179-1185
• /
• 2012

This paper presents a new approach for the detection of seismic events using accumulated changes on time-frequency domain and variable threshold. To detect seismic P-wave arrivals with rapidness and accuracy, it is that the changes on the time and the frequency domains are simultaneously used. Their changes are parameters appropriated to reflect characteristics of earthquakes over moderate magnitude(${\geq}$ magnitude 4.0) and microearthquakes. In addition, adaptively controlled threshold values can prevent false P-wave detections due to low SNR. We tested our method on real earthquakes those have various magnitudes. The proposed algorithm gives a good detection performance and it is also comparable to STA/LTA algorithm in computational complexity. Computer simulation results shows that the proposed algorithm is superior to the conventional popular algorithm (STA/LTA) in the seismic P-wave detection.

• Journal of KIISE:Databases
• /
• v.33 no.6
• /
• pp.620-630
• /
• 2006

Event-detection is an important application of sensor networks. Join operations can facilitate event-detection with a condition table predefined by a user. When join operations are used for event-detection, it is desirable, if possible, to do in-network join processing to reduce communication costs. In this paper, we propose an energy-efficient in-network join algorithm, called PBA. In PBA, each partition of a condition table is stored along the path from each node to the base station, and then in-network joins are performed on the path. Since each node can identify the parts to store in its storage by its level, PBA reduces the cost of disseminating a condition table considerably Moreover, while the existing method does not work well when the ratio of the size of the condition table to the density of the network is a little bit large, our proposed method PBA does not have such a restriction and works efficiently in most cases. The results of experiments show that PBA is efficient usually and especially provides significant cost reduction over existing one when a condition table is relatively large in comparison with the density of the network, or the routing tree of the network is high.

• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.3
• /
• pp.204-211
• /
• 2011

Embedded systems require intensively complex and high power dissipating modules that have the capability of real-time high performance data processing, wide bandwidth communication, and low power consumption. However, the current battery technology has not been developed as much as meeting the requirements of portable embedded systems for long system lifetime. In this paper, new approach that operates with low energy consumption is proposed to overcome the situation while guaranteeing detection accuracy. The designed method associates a variety of detection algorithms hierarchically to detect events happening around the system. The Change for energy consumption characteristics is shown with change for probabilistic characteristics and those relationships are analytically explained from experiments. Furthermore, several techniques to consume low energy and achieve high detection accuracy are described, depending on the event static or dynamic characteristics.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.1
• /
• pp.355-363
• /
• 2012

The Integration of multi-agent architecture and blackboard architecture may lead to a new architecture to cope with new application areas which need some good and strong points of both the architectures. This paper suggests an integrated architecture of blackboard architecture and multi-agent architecture by using event-based implicit invocation pattern and a blackboard event detection mechanism based on Rete network. From the viewpoints of weak couplings of system components and flexible control of knowledge source agents, it is desirable to use the event-based implicit invocation pattern in the integrated architecture. But the pattern itself does not concern the performance of the architecture, and it is very critical to the performance of the integrated architecture to detect efficiently the blackboard events which can activate knowledge source agents which can contribute to the problem-solving processes of the integrated architecture. The integrated architecture suggested in this paper uses a blackboard event detection mechanism based on Rete network to detect efficiently blackboard events which can activate knowledge source agents.