• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.230-236
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• 2022

Objectives: The primary goal of this article is to compare the multiple algorithms used for deadlock handling methods and then outline the common method in deadlock handling methods. Methods: The article methodology begins with introducing a literature review studying different algorithms used in deadlock detection and many algorithms for deadlocks prevented, recovered, and avoided. Discussion and analysis of the literature review were done to classify and compare the studied algorithms. Findings: The results showed that the deadlock detection method solves the deadlock. As soon as the real-time deadlock detection algorithm is identified and indicated, it performs better than the non-real-time deadlock detection algorithm. Our novelty the statistics that we get from the percentages of reviewing outcomes that show the most effective rate of 47% is in deadlock prevention. Then deadlock detection and recovery with 28% finally, a rate of 25% for deadlock avoidance.

• 한국BIM학회 논문집
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• 제7권1호
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• pp.18-27
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• 2017

Although Industry Foundation Classes (IFC) provide standards for exchanging Building Information Modeling (BIM) data, authoring tools still require manual mapping between BIM entities and IFC classes. This leads to errors and omissions, which results in corrupted data exchanges that are unreliable and thus compromise the validity of IFC. This research explored precedent work by Krijnen and Tamke, who suggested ways to automate the mapping of IFC classes using a machine learning technique, namely anomaly detection. The technique incorporates geometric features of individual components to find outliers among entities in identical IFC classes. This research primarily focused on applying this approach on two architectural BIM models and determining its feasibility as well as limitations. Results indicated that the approach, while effective, misclassified outliers when an IFC class had several dissimilar entities. Another issue was the lack of entities for some specific IFC classes that prohibited the anomaly detection from comparing differences. Future research to improve these issues include the addition of geometric features, using novelty detection and the inclusion of a probabilistic graph model, to improve classification accuracy.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.159-167
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• 2023

Objective: • To detect black hole and warm hole attacks in wireless sensor networks. • To give a solution for energy depletion and security breach in wireless sensor networks. • To address the security problem using strategic decision support system. Methods: The proposed stackelberg game is used to make the spirited relations between multi leaders and multi followers. In this game, all cluster heads are acts as leaders, whereas agent nodes are acts as followers. The game is initially modeled as Quadratic Programming and also use backtracking search optimization algorithm for getting threshold value to determine the optimal strategies of both defender and attacker. Findings: To find optimal payoffs of multi leaders and multi followers are based on their utility functions. The attacks are easily detected based on some defined rules and optimum results of the game. Finally, the simulations are executed in matlab and the impacts of detection of black hole and warm hole attacks are also presented in this paper. Novelty: The novelty of this study is to considering the stackelberg game with backtracking search optimization algorithm (BSOA). BSOA is based on iterative process which tries to minimize the objective function. Thus we obtain the better optimization results than the earlier approaches.

• 인터넷정보학회논문지
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• 제12권5호
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• pp.87-100
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• 2011

본 논문에서는 비디오 요약을 시각적으로 특징이 있고 주관적으로 중요한 비디오 세그먼트 집합을 구하는 새로운 요약 방식을 기술한다. 시각적으로 특징이 있는 데이터 포인트를 찾기 위해 novelty detection으로 잘 알려져 있는 OC-SVM(One-Class Support Vector Machine)을 사용할 수 있다. 그러나 OC-SVM의 처리과정에 비디오 세그먼트에 대한 사용자의 주관적인 중요도를 반영하기는 어렵다. OC-SVM의 처리과정에 사용자의 주관적 중요성을 반영하기 위해서, 본 논문에서는 OC-SVM의 퍼지 버전을 유도한다. IFOC-SVM(Importance-based Fuzzy One-Class Support Vector Machine)은 비디오 세그먼트의 중요도에 따라 각 데이터 포인트에 가중치를 부여하고 데이터 분포의 서포트를 측정한다. 이때, 구해진 서포트 벡터는 비 오 세그먼트의 중요도와 시각적 특징 관점에서 비디오의 내용을 축약하여 표현한다. 제안된 알고리즘의 성능을 증명하기 위하여 가상의 데이터들과 다양한 종류의 비디오들을 가지고 실험하였다. 실험 결과는 제안하는 방법의 성능이 다른 비디오 요약의 성능보다 우수함을 보여주었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권3호
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• pp.755-778
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• 2024

In recent years, the number of devices being connected to the internet has grown enormously, as has the intrusive behavior in the network. Thus, it is important for intrusion detection systems to report all intrusive behavior. Using deep learning and machine learning algorithms, intrusion detection systems are able to perform well in identifying attacks. However, the concern with these deep learning algorithms is their inability to identify a suitable network based on traffic volume, which requires manual changing of hyperparameters, which consumes a lot of time and effort. So, to address this, this paper offers a solution using the extended compact genetic algorithm for the automatic tuning of the hyperparameters. The novelty in this work comes in the form of modeling the problem of identifying attacks as a multi-objective optimization problem and the usage of linkage learning for solving the optimization problem. The solution is obtained using the feature map-based Convolutional Neural Network that gets encoded into genes, and using the extended compact genetic algorithm the model is optimized for the detection accuracy and latency. The CIC-IDS-2017 and 2018 datasets are used to verify the hypothesis, and the most recent analysis yielded a substantial F1 score of 99.23%. Response time, CPU, and memory consumption evaluations are done to demonstrate the suitability of this model in a fog environment.

• 한국지능시스템학회논문지
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• 제15권3호
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• pp.282-288
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• 2005

본 논문에서는 기존의 침입탐지 모델인 오용탐지 모델과 비정상 탐지 모델의 장점은 유지하되 단점은 보완하는 견지에서 새로운 침입탐지 모델을 제안한다. MMIDS로 명명된 새로운 침입탐지시스템은 다음의 평가 기준들을 모두 만족하는 차원에서 설계되었다: 1) 시스템에서 학습되지 않은 새로운 공격 유형의 신속한 발견; 2) 탐지된 공격 유형에 대한 세부적 정보의 제공; 3) 빠르고 효율적인 학습 및 갱신으로 인한 경제적인 시스템의 유지/보수; 4) 시스템의 점증성(incrementality) 및 확장성. MMIDS의 핵심 구성요소로 새롭게 제안된 다중 클래스 SVM은 빠르고 효율적인 학습 및 갱신이 가능하여 침입탐지 시스템의 유지보수 비용을 절감할 수 있다. 실험을 통해 유사한 공격 패턴에 대한 분류성능 및 각 공격 유형별 세분화 능력이 우수함을 보인다.

• Journal of Power Electronics
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• 제16권3호
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• pp.1012-1024
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• 2016

This study deals with an on-line software fault detection, localization, and system reconfiguration method for electrical system drives composed of three-phase AC/DC/AC converters and three-phase permanent magnet synchronous machine (PMSM) drives. Current sensor failure (outage), speed/position sensor loss (disconnection), and damaged DC-link voltage sensor are considered faults. The occurrence of these faults in PMSM drive systems degrades system performance and affects the safety, maintenance, and service continuity of the electrical system drives. The proposed method is based on the monitoring signals of "abc" currents, DC-link voltage, and rotor speed/position using a measurement chain. The listed signals are analyzed and evaluated with the generated residuals and threshold values obtained from a Sliding Mode Current-Speed-DC-link Voltage Observer (SMCSVO) to acquire an on-line fault decision. The novelty of the method is the faults diagnosis algorithm that combines the use of SMCSVO and adaptive thresholds; thus, the number of false alarms is reduced, and the reliability and robustness of the fault detection system are guaranteed. Furthermore, the proposed algorithm's performance is experimentally analyzed and tested in real time using a dSPACE DS 1104 digital signal processor board.

• Structural Monitoring and Maintenance
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• 제9권2호
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• pp.179-200
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• 2022

This paper proposes a data-driven methodology for online early damage identification under changing environmental conditions. The proposed method relies on two data analysis methods: feature-based method and hybrid principal component analysis (PCA) and kernel PCA to separate damage from environmental influences. First, spectral sub-band features, namely, spectral sub-band centroids (SSCs) and log spectral sub-band energies (LSSEs), are proposed as damage-sensitive features to extract damage information from measured structural responses. Second, hybrid modeling by integrating PCA and kernel PCA is performed on the spectral sub-band feature matrix for data normalization to extract both linear and nonlinear features for nonlinear procedure monitoring. After feature normalization, suppressing environmental effects, the control charts (Hotelling T² and SPE statistics) is implemented to novelty detection and distinguish damage in structures. The hybrid PCA-KPCA technique is compared to KPCA by applying support vector machine (SVM) to evaluate the effectiveness of its performance in detecting damage. The proposed method is verified through numerical and full-scale studies (a Bridge Health Monitoring (BHM) Benchmark Problem and a cable-stayed bridge in China). The results demonstrate that the proposed method can detect the structural damage accurately and reduce false alarms by suppressing the effects and interference of environmental variations.

• 정보관리학회지
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• 제22권1호
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• pp.191-208
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• 2005

새로운 주제의 탐지를 위한 여러 접근법들을 지식 구조 표현 방법 중 하나인 디스크립터의 부여 및 갱신 과정에 적용하였다. 새로운 주제 탐지는, 특히 특정 학문 분야에서 새로운 주제의 출현 및 성장으로 인하여 지식구조상의 변화가 발생하는 경우에, 기존의 색인어로는 이를 표현할 수 없거나 표현상의 제한이 따르는 문제를 해결하는 데 응용할 수 있다. 실험 결과, 정보학 내에서 긍정적 측면의 변화가 발생한 것으로 식별된 신흥 주제들은 상당수가 서로 밀접하게 연관되어 있으면서 동시에 성장${\cdot}$발전의 단계에 있는 주제임을 확인하였다. 또한, 새로운 주제 탐지를 통한 후보 디스크립터 리스트의 사용이 색인자의 색인작업을 지원하는 효율적인 도구가 될 수 있다는 가능성을 보여 주었다. 특히, 적절한 디스크립터의 선정과 부여를 위한 후보 디스크립터 리스트의 제공은 색인작업의 효율성과 정확성을 향상시키는 데 기여할 수 있을 것이다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제3권2호
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• pp.227-232
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• 2003

Recently, support vector learning attracts an enormous amount of interest in the areas of function approximation, pattern classification, and novelty detection. One of the main reasons for the success of the support vector machines(SVMs) seems to be the availability of global and sparse solutions. Among the approaches sharing the same reasons for success and exhibiting a similarly good performance, we have KFD(kernel Fisher discriminant) approach. In this paper, we consider the problem of function approximation utilizing both predetermined basis functions and the KFD approach for regression. After reviewing support vector regression, semi-parametric approach for including predetermined basis functions, and the KFD regression, this paper presents an extension of the conventional KFD approach for regression toward the direction that can utilize predetermined basis functions. The applicability of the presented method is illustrated via a regression example.