The Experts below are selected from a list of 8520 Experts worldwide ranked by ideXlab platform

Quiroga Jabid - One of the best experts on this subject based on the ideXlab platform.

  • Features of cross-correlation analysis in a data-driven approach for structural damage assessment
    'MDPI AG', 2018
    Co-Authors: Camacho Navarro Jhonatan, Ruiz Ordóñez Magda, Villamizar Mejía Rodolfo, Mujica Delgado, Luis Eduardo, Quiroga Jabid
    Abstract:

    This work discusses the advantage of using cross-correlation analysis in a data-driven approach based on principal component analysis (PCA) and piezodiagnostics to obtain successful diagnosis of events in structural health monitoring (SHM). In this sense, the identification of noisy data and outliers, as well as the management of data cleansing Stages can be facilitated through the implementation of a Preprocessing Stage based on cross-correlation functions. Additionally, this work evidences an improvement in damage detection when the cross-correlation is included as part of the whole damage assessment approach. The proposed methodology is validated by processing data measurements from piezoelectric devices (PZT), which are used in a piezodiagnostics approach based on PCA and baseline modeling. Thus, the influence of cross-correlation analysis used in the Preprocessing Stage is evaluated for damage detection by means of statistical plots and self-organizing maps. Three laboratory specimens were used as test structures in order to demonstrate the validity of the methodology: (i) a carbon steel pipe section with leak and mass damage types, (ii) an aircraft wing specimen, and (iii) a blade of a commercial aircraft turbine, where damages are specified as mass-added. As the main concluding remark, the suitability of cross-correlation features combined with a PCA-based piezodiagnostic approach in order to achieve a more robust damage assessment algorithm is verified for SHM tasks.Peer ReviewedPostprint (published version

  • Features of cross-correlation analysis in a data-driven approach for structural damage assessment
    Multidisciplinary Digital Publishing Institute (MDPI), 2018
    Co-Authors: Camacho Navarro Jhonatan, Ruiz Ordóñez Magda, Villamizar Mejía Rodolfo, Mujica Delgado, Luis Eduardo, Quiroga Jabid
    Abstract:

    This work discusses the advantage of using cross-correlation analysis in a data-driven approach based on principal component analysis (PCA) and piezodiagnostics to obtain successful diagnosis of events in structural health monitoring (SHM). In this sense, the identification of noisy data and outliers, as well as the management of data cleansing Stages can be facilitated through the implementation of a Preprocessing Stage based on cross-correlation functions. Additionally, this work evidences an improvement in damage detection when the cross-correlation is included as part of the whole damage assessment approach. The proposed methodology is validated by processing data measurements from piezoelectric devices (PZT), which are used in a piezodiagnostics approach based on PCA and baseline modeling. Thus, the influence of cross-correlation analysis used in the Preprocessing Stage is evaluated for damage detection by means of statistical plots and self-organizing maps. Three laboratory specimens were used as test structures in order to demonstrate the validity of the methodology: (i) a carbon steel pipe section with leak and mass damage types, (ii) an aircraft wing specimen, and (iii) a blade of a commercial aircraft turbine, where damages are specified as mass-added. As the main concluding remark, the suitability of cross-correlation features combined with a PCA-based piezodiagnostic approach in order to achieve a more robust damage assessment algorithm is verified for SHM tasks.Peer Reviewe

Jhingfa Wang - One of the best experts on this subject based on the ideXlab platform.

  • intensity gradient technique for efficient intra prediction in h 264 avc
    IEEE Transactions on Circuits and Systems for Video Technology, 2008
    Co-Authors: Anchao Tsai, Anand Paul, Jiaching Wang, Jhingfa Wang
    Abstract:

    This study presents an intensity gradient approach for intra-prediction in H.264 encoding system, which enhances the performance and efficiency of previous fast algorithms. We propose a Preprocessing Stage in which eight orientation features are extracted from a macro block by the intensity gradient filters. The orientation features are utilized to select a subset of prediction modes to be involved in the rate-distortion calculation so that the encoding time can be reduced. The simulation results indicate that the intensity gradient based algorithm for intra-prediction contributes better tradeoff between rate-distorion performance and encoding complexity than the previous algorithms. Compared to H.264 reference software, the proposed algorithm introduces slight PSNR degradation and bit rate increase but saves around 76% of the total encoding time with all intra-frame coding.

Nasser M Nasrabadi - One of the best experts on this subject based on the ideXlab platform.

  • a modular clutter rejection technique for flir imagery using region based principal component analysis
    International Conference on Image Processing, 1999
    Co-Authors: Syed A Rizvi, T N Saadawi, Nasser M Nasrabadi
    Abstract:

    The Preprocessing Stage of an automatic target recognition system extracts areas containing potential targets from a battlefield scene. These potential target images are then sent to the classification Stage to identify the targets. It is highly desirable at the Preprocessing Stage to minimize the incorrect rejection rate. This, however, results in a high false alarm rate. The high false alarm rate, in turn, makes subsequent target classification decisions unreliable. We present a new technique to reject false alarms (clutter images) produced by the Preprocessing Stage. Our technique, which we call region-based principal component analysis (PCA), uses topological features of the targets to reject false alarms. In this technique a potential target is divided into several regions and a PCA is performed on each region to extract regional feature vectors. We propose to use regional feature vectors of arbitrary shapes and dimensions that are optimized for the topology of a target in a particular region. These regional feature vectors are then used by a two-class classifier based on the learning vector quantization to decide whether a potential target is a false alarm or a real target.

  • clutter rejection technique for flir imagery using region based principal component analysis
    Automatic target recognition. Conference, 1999
    Co-Authors: Syed A Rizvi, Nasser M Nasrabadi, Sandor Z Der
    Abstract:

    The Preprocessing or detection Stage of an automatic target recognition system extracts areas containing potential targets from a battlefield scene. These potential target images are then sent to the classification Stage to determine the identity of the targets. It is highly desirable at the Preprocessing Stage to minimize incorrect rejection rate. This, however, results in a high false alarm rate. In this paper, we present a new technique to reject false alarms (clutter images) produced by the Preprocessing Stage. Our technique, region-based principal component analysis (PCA), uses topological features of the targets to reject false alarms. A potential target is divided into several regions and a PCA is performed on each region to extract regional feature vectors. We propose to use regional feature vectors of arbitrary shapes and dimensions that are optimized for the topology of a target in a particular region. These regional feature vectors are then used by a two-class classifier based on the learning vector quantization to decide whether a potential target is a false alarm or a real target.

Syed A Rizvi - One of the best experts on this subject based on the ideXlab platform.

  • a modular clutter rejection technique for flir imagery using region based principal component analysis
    International Conference on Image Processing, 1999
    Co-Authors: Syed A Rizvi, T N Saadawi, Nasser M Nasrabadi
    Abstract:

    The Preprocessing Stage of an automatic target recognition system extracts areas containing potential targets from a battlefield scene. These potential target images are then sent to the classification Stage to identify the targets. It is highly desirable at the Preprocessing Stage to minimize the incorrect rejection rate. This, however, results in a high false alarm rate. The high false alarm rate, in turn, makes subsequent target classification decisions unreliable. We present a new technique to reject false alarms (clutter images) produced by the Preprocessing Stage. Our technique, which we call region-based principal component analysis (PCA), uses topological features of the targets to reject false alarms. In this technique a potential target is divided into several regions and a PCA is performed on each region to extract regional feature vectors. We propose to use regional feature vectors of arbitrary shapes and dimensions that are optimized for the topology of a target in a particular region. These regional feature vectors are then used by a two-class classifier based on the learning vector quantization to decide whether a potential target is a false alarm or a real target.

  • clutter rejection technique for flir imagery using region based principal component analysis
    Automatic target recognition. Conference, 1999
    Co-Authors: Syed A Rizvi, Nasser M Nasrabadi, Sandor Z Der
    Abstract:

    The Preprocessing or detection Stage of an automatic target recognition system extracts areas containing potential targets from a battlefield scene. These potential target images are then sent to the classification Stage to determine the identity of the targets. It is highly desirable at the Preprocessing Stage to minimize incorrect rejection rate. This, however, results in a high false alarm rate. In this paper, we present a new technique to reject false alarms (clutter images) produced by the Preprocessing Stage. Our technique, region-based principal component analysis (PCA), uses topological features of the targets to reject false alarms. A potential target is divided into several regions and a PCA is performed on each region to extract regional feature vectors. We propose to use regional feature vectors of arbitrary shapes and dimensions that are optimized for the topology of a target in a particular region. These regional feature vectors are then used by a two-class classifier based on the learning vector quantization to decide whether a potential target is a false alarm or a real target.

Yuliant Sibaroni - One of the best experts on this subject based on the ideXlab platform.

  • multi aspect sentiment of beauty product reviews using svm and semantic similarity
    Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2021
    Co-Authors: Irbah Salsabila, Yuliant Sibaroni
    Abstract:

    Beauty products are an important requirement for people, especially women. But, not all beauty products give the expected results. A review in the form of opinion can help the consumers to know the overview of the product. The reviews were analyzed using a multi-aspect-based approach to determine the aspects of the beauty category based on the reviews written on femaledaily.com. First, the review goes through the Preprocessing Stage to make it easier to be processed, and then it used the Support Vector Machine (SVM) method with the addition of Semantic Similarity and TF-IDF weighting. From the test result using semantic, get an accuracy of 93% on the price aspect, 92% on the packaging aspect, and 86% on the scent aspect.

  • Multi Aspect Sentiment of Beauty Product Reviews using SVM and Semantic Similarity
    'Ikatan Ahli Informatika Indonesia (IAII)', 2021
    Co-Authors: Irbah Salsabila, Yuliant Sibaroni
    Abstract:

    Beauty products are an important requirement for people, especially women. But, not all beauty products give the expected results. A review in the form of opinion can help the consumers to know the overview of the product. The reviews were analyzed using a multi-aspect-based approach to determine the aspects of the beauty category based on the reviews written on femaledaily.com. First, the review goes through the Preprocessing Stage to make it easier to be processed, and then it used the Support Vector Machine (SVM) method with the addition of Semantic Similarity and TF-IDF weighting. From the test result using semantic, get an accuracy of 93% on the price aspect, 92% on the packaging aspect, and 86% on the scent aspect.Beauty products are an important requirement for people, especially women. But, not all beauty products give the expected results. A review in the form of opinion can help the consumers to know the overview of the product. The reviews were analyzed using a multi-aspect-based approach to determine the aspects of the beauty category based on the reviews written on femaledaily.com. First, the review goes through the Preprocessing Stage to make it easier to be processed, and then it used the Support Vector Machine (SVM) method with the addition of Semantic Similarity and TF-IDF weighting. From the test result using semantic, get an accuracy of 93% on the price aspect, 92% on the packaging aspect, and 86% on the scent aspect