• Journal of Intelligence and Information Systems
• /
• v.17 no.1
• /
• pp.111-125
• /
• 2011

Clustering is a process of grouping similar or relevant documents into a cluster and assigning a meaningful concept to the cluster. By this process, clustering facilitates fast and correct search for the relevant documents by narrowing down the range of searching only to the collection of documents belonging to related clusters. For effective clustering, techniques are required for identifying similar documents and grouping them into a cluster, and discovering a concept that is most relevant to the cluster. One of the problems often appearing in this context is the detection of a complex concept that overlaps with several simple concepts at the same hierarchical level. Previous clustering methods were unable to identify and represent a complex concept that belongs to several different clusters at the same level in the concept hierarchy, and also could not validate the semantic hierarchical relationship between a complex concept and each of simple concepts. In order to solve these problems, this paper proposes a new clustering method that identifies and represents complex concepts efficiently. We developed the Hierarchical Overlapping Clustering (HOC) algorithm that modified the traditional Agglomerative Hierarchical Clustering algorithm to allow overlapped clusters at the same level in the concept hierarchy. The HOC algorithm represents the clustering result not by a tree but by a lattice to detect complex concepts. We developed a system that employs the HOC algorithm to carry out the goal of complex concept detection. This system operates in three phases; 1) the preprocessing of documents, 2) the clustering using the HOC algorithm, and 3) the validation of semantic hierarchical relationships among the concepts in the lattice obtained as a result of clustering. The preprocessing phase represents the documents as x-y coordinate values in a 2-dimensional space by considering the weights of terms appearing in the documents. First, it goes through some refinement process by applying stopwords removal and stemming to extract index terms. Then, each index term is assigned a TF-IDF weight value and the x-y coordinate value for each document is determined by combining the TF-IDF values of the terms in it. The clustering phase uses the HOC algorithm in which the similarity between the documents is calculated by applying the Euclidean distance method. Initially, a cluster is generated for each document by grouping those documents that are closest to it. Then, the distance between any two clusters is measured, grouping the closest clusters as a new cluster. This process is repeated until the root cluster is generated. In the validation phase, the feature selection method is applied to validate the appropriateness of the cluster concepts built by the HOC algorithm to see if they have meaningful hierarchical relationships. Feature selection is a method of extracting key features from a document by identifying and assigning weight values to important and representative terms in the document. In order to correctly select key features, a method is needed to determine how each term contributes to the class of the document. Among several methods achieving this goal, this paper adopted the $x^2$�� statistics, which measures the dependency degree of a term t to a class c, and represents the relationship between t and c by a numerical value. To demonstrate the effectiveness of the HOC algorithm, a series of performance evaluation is carried out by using a well-known Reuter-21578 news collection. The result of performance evaluation showed that the HOC algorithm greatly contributes to detecting and producing complex concepts by generating the concept hierarchy in a lattice structure.

• The Journal of the Acoustical Society of Korea
• /
• v.31 no.8
• /
• pp.551-560
• /
• 2012

Target detection by acoustic barrier method includes active and passive sonar technique and time reversal process whose theoretical background is already well defined. In this paper, the concept and theory of underwater detection by passive ocean time reversal is established. Also, the reason that this study was conducted was to investigate feasibility of complex mathematical modeling to provide some predictive capability for underwater acoustic barrier with passive time reversal. It may eventually lead to a useful predictive tool when designing underwater acoustic barrier detection system using the passive time reversal concept.

• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.4
• /
• pp.341-347
• /
• 2011

This paper presents the performance evaluation. optimal design. and complex obstacle detection of an overlapped ultrasonic sensor ring by introducing a new concept of effective beam width. It is assumed that a set of ultrasonic sensors of the same type are arranged along a circle of nonzero radius at regular spacings with their beams overlapped. First, the global positional uncertainty of an overlapped ultrasonic sensor ring is expressed by the average value of local positional uncertainty over the entire obstacle detection range. The effective beam width of an overlapped ultrasonic sensor ring is assessed as the beam width of a single ultrasonic sensor having the same amount of global positional uncertainty, from which a normalized obstacle detection performance index is defined. Second. using the defined index, the design parameters of an overlapped ultrasonic sensor ring are optimized for minimal positional uncertainty in obstacle detection. For a given number of ultrasonic sensors, the optimal radius of an overlapped ultrasonic sensor ring is determined, and for a given radius of an overlapped ultrasonic sensor ring, the optimal number of ultrasonic sensors is determined. Third, the decision rules of positional uncertainty zone for multiple obstacle detection are provided based on the inequality relationships among obstacle distances by three adjacent ultrasonic sensors. Using the provided rules, the obstacle outline detection is performed in a rather complex environment consisting of several obstacles of different shapes.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2015.07a
• /
• pp.279-281
• /
• 2015

Location awareness is an important capability for mobile-based indoor services. Those indoor services have motivated the implementation of methods that need high computational load cost and complex mechanisms for positioning prediction. These mechanisms, such as opportunistic sensing and machine learning, require more energy consumption to achieve accuracy. In this paper, we propose the Bluetooth Low Energy indoor zone detection (BLEIZOD) technique. This method exploits the concept of proximity zone to reduce the load cost and complexity. Our proposed method implements the received signal strength indicator (RSSI) function more effectively to gain accuracy and reduce energy consumption.

• International Journal of Computer Science & Network Security
• /
• v.24 no.5
• /
• pp.205-211
• /
• 2024

The increasing number of botnet attacks incorporating new evasion techniques making it infeasible to completely secure complex computer network system. The botnet infections are likely to be happen, the timely detection and response to these infections helps to stop attackers before any damage is done. The current practice in traditional IP networks require manual intervention to response to any detected malicious infection. This manual response process is more probable to delay and increase the risk of damage. To automate this manual process, this paper proposes to automatically select relevant countermeasures for detected botnet infection. The propose approach uses the concept of flow trace to detect botnet behavior patterns from current and historical network activity. The approach uses the multiclass machine learning based approach to detect and classify the botnet activity into IRC, HTTP, and P2P botnet. This classification helps to calculate the risk score of the detected botnet infection. The relevant countermeasures selected from available pool based on risk score of detected infection.

• Journal of Communications and Networks
• /
• v.11 no.6
• /
• pp.544-555
• /
• 2009

This work continues a trend of developments aimed at exploiting the physical layer of the open systems interconnection (OSI) model to enhance wireless network security. The goal is to augment activity occurring across other OSI layers and provide improved safeguards against unauthorized access. Relative to intrusion detection and anti-spoofing, this paper provides details for a proof-of-concept investigation involving "air monitor" applications where physical equipment constraints are not overly restrictive. In this case, RF fingerprinting is emerging as a viable security measure for providing device-specific identification (manufacturer, model, and/or serial number). RF fingerprint features can be extracted from various regions of collected bursts, the detection of which has been extensively researched. Given reliable burst detection, the near-term challenge is to find robust fingerprint features to improve device distinguishability. This is addressed here using wavelet domain (WD) RF fingerprinting based on dual-tree complex wavelet transform (DT-$\mathbb{C}WT$) features extracted from the non-transient preamble response of OFDM-based 802.11a signals. Intra-manufacturer classification performance is evaluated using four like-model Cisco devices with dissimilar serial numbers. WD fingerprinting effectiveness is demonstrated using Fisher-based multiple discriminant analysis (MDA) with maximum likelihood (ML) classification. The effects of varying channel SNR, burst detection error and dissimilar SNRs for MDA/ML training and classification are considered. Relative to time domain (TD) RF fingerprinting, WD fingerprinting with DT-$\mathbb{C}WT$ features emerged as the superior alternative for all scenarios at SNRs below 20 dB while achieving performance gains of up to 8 dB at 80% classification accuracy.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.6
• /
• pp.101-109
• /
• 2012

To develop high quality software, quality control plan is required about fault correction that is latent within software. We should describe fault correction profile properly for this. The tank and pipe model performs complex processes to calculate fault that is remove and escapes. Also, we have to know in which phase the faults were inserted, removed and escaped and know the fault detection rate at any phases. To simplify such complex process, this paper presented model to fault filtering concept. Presented model has advantage that can describe fault more shortly because need not to consider whether was involved in fault that escaped fault is inserted at any step at free step. Also, presented effort estimating model that do fetters in function of fault removal quality and productivity measure and is required in fault detection.

• Journal of Communications and Networks
• /
• v.16 no.6
• /
• pp.667-676
• /
• 2014

Connectivity is a crucial quality of service measure in wireless sensor networks. However, the network is always at risk of being split into several disconnected components owing to the sensor failures caused by various factors. To handle the connectivity problem, this paper introduces an in-advance mechanism to prevent network partitioning in the initial deployment phase. The approach is implemented in a distributed manner, and every node only needs to know local information of its 1-hop neighbors, which makes the approach scalable to large networks. The goal of the proposed mechanism is twofold. First, critical nodes are locally detected by the critical node detection (CND) algorithm based on the concept of maximal simplicial complex, and backups are arranged to tolerate their failures. Second, under a greedy rule, topological holes within the maximal simplicial complex as another potential risk to the network connectivity are patched step by step. Finally, we demonstrate the effectiveness of the proposed algorithm through simulation experiments.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.20 no.1
• /
• pp.7-14
• /
• 1983

Linear feature such as lines and curves are one of important features in image processing. In this paper, new method of linear feature detection is suggested. Also, we have studied approximation technique which transforms detected linear feature into data structure for the practical. This method is based on graph theory and principle of this method is based on minimal spanning tree concept which is widely used in edge linking process. By postprocessing, Hairs and inconsistent line segments are removed. To approximate and describe traced linear feature, piecewise linear approximation is adapted. The algorithm is demonstrated through computer simulations.

• Journal of the Korean Society of Systems Engineering
• /
• v.16 no.2
• /
• pp.16-26
• /
• 2020

Improving operator performance in complex and time-critical situations is critical to maintain plant safety and operability. These situations require quick detection, diagnosis, and mitigation actions to recover from the root cause of failure. One of the key challenges for operators in nuclear power plants is information management and following the control procedures and instructions. Nowadays Digital Twin technology can be used for analyzing and fast detection of failures and transient situations with the recommender system to provide the operator or maintenance engineer with recommended action to be carried out. Systems engineering approach (SE) is used in developing a digital twin for the CEDM system to support operator actions when there is a misalignment in the control element assembly group. Systems engineering is introduced for identifying the requirements, operational concept, and associated verification and validation steps required in the development process. The system developed by using a machine learning algorithm with a text mining technique to extract the required actions from limiting conditions for operations (LCO) or procedures that represent certain tasks.