Browsing by Author "Yoo, P D"
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Item Open Access Clustering the dominant defective patterns in semiconductor wafer maps(IEEE, 2017-10-30) Taha, Kamal; Salah, K; Yoo, P DIdentifying defect patterns on wafers is crucial for understanding the root causes and for attributing such patterns to specific steps in the fabrication process. We propose in this paper a system called DDPfinder that clusters the patterns of defective chips on wafers based on their spatial dependence across wafer maps. Such clustering enables the identification of the dominant defect patterns. DDPfinder clusters chip defects based on how dominant are their spatial patterns across all wafer maps. A chip defect is considered dominant, if: (1) it has a systematic defect pattern arising from a specific assignable cause, and (2) it displays spatial dependence across a larger number of wafer maps when compared with other defects. The spatial dependence of a chip defect is determined based on the contiguity ratio of the defect pattern across wafer maps. DDPfinder uses the dominant chip defects to serve as seeds for clustering the patterns of defective chips. This clustering procedure allows process engineers to prioritize their investigation of chip defects based on the dominance status of their clusters. It allows them to pay more attention to the ongoing manufacturing processes that caused the dominant defects. We evaluated the quality and performance of DDPfinder by comparing it experimentally with eight existing clustering models. Results showed marked improvement.Item Open Access Multi-Layered clustering for power consumption profiling in smart grids(IEEE, 2017-06-13) Al-Jarrah, O Y; Al-Hammadi, Y; Yoo, P D; Muhaidat, SSmart Grids (SGs) have many advantages over traditional power grids as they enhance the way electricity is generated, distributed, and consumed by adopting advanced sensing, communication and control functionalities that depend on power consumption profiles of consumers. Clustering algorithms (e.g., centralized clustering) are used for profiling individual’s power consumption. Due to the distributed nature and ever growing size of SGs, it is predicted that massive amounts of data will be created. However, conventional clustering algorithms neither efficient enough nor scalable enough to deal with such amount of data. In addition, the cost for transferring and analyzing large amounts of data is expensive high both computationally and communicationally. This paper thus proposes a power consumption profiling model based on two levels of clustering. At the first level, local power consumption profiles are derived, which are then used by the second level in order to create a global power consumption profile. The followed approach reduces the communication and computation complexity of the proposed two level model and improves the privacy of consumers. We point out that having good knowledge of the local power profiles leads to more effective prediction model and cost-effective power pricing scheme, especially in a heterogeneous grid topology. In addition, the correlations between the local and global profiles can be used to localize/identify power consumption outliers. Simulation results illustrate that the proposed model is effective in reducing the computational complexity without much affecting its accuracy. The reduction in computational complexity is about 52% and the reduction in the communicational complexity is about 95% when compared to the centralized clustering approach.Item Open Access Using the spanning tree of a criminal network for identifying its leaders(IEEE, 2016-10-26) Taha, K; Yoo, P DWe introduce a forensic analysis system called ECLfinder that identifies the influential members of a criminal organization as well as the immediate leaders of a given list of lower-level criminals. Criminal investigators usually seek to identify the influential members of criminal organizations, because eliminating them is most likely to hinder and disrupt the operations of these organizations and put them out of business. First, ECLfinder constructs a network representing a criminal organization from either mobile communication data associated with the organization or crime incident reports that include information about the organization. It then constructs a minimum spanning tree (MST) of the network. It identifies the influential members of a criminal organization by determining the important vertices in the network representing the organization, using the concept of existence dependence. Each vertex v is assigned a score, which is the number of other vertices, whose existence in MST is dependent on v. Vertices are ranked based on their scores. Criminals represented by the top ranked vertices are considered the influential members of the criminal organization represented by the network. We evaluated the quality of ECLfinder by comparing it experimentally with three other systems. Results showed marked improvement.