A Longitudinal Nonparametric Classification Method for Assessing Change in Latent Attributes Over Time

<p>This study proposed a longitudinal Hamming distance discrimination (Long-HDD) method to improve the application of longitudinal cognitive diagnosis in practical teaching by introducing a simple computation and less time-consuming nonparametric classification method—HDD—into longitudinal diagnostic data processing. Compared with the HDD, the proposed method represents correlation or dependence between adjacent time points of the same student using Hamming distance in anticipation of using information from the previous time point to improve the classification accuracy at the current time point. A simulation study was conducted to explore the performance of the proposed method in longitudinal diagnostic data analysis and to compare the performance of the proposed method with the HDD and a parametric longitudinal diagnostic classification model. The findings suggest that (1) the Long-HDD can provide high classification accuracy in longitudinal diagnostic data analysis; (2) compared with the parametric model, the Long-HDD is almost unaffected by sample size and performs better than the parametric model in small sample sizes; and (3) the Long-HDD consumes much less computing time than the parametric model. Overall, the Long-HDD is well suited to analyzing longitudinal diagnostic data and can provide speedy diagnostic feedback due to its convenient computation, which is especially significant in small-scale assessments at the classroom and school levels.</p>

A Longitudinal Nonparametric Classification Method for Assessing Change in Latent Attributes Over Time

<p>This study proposed a longitudinal Hamming distance discrimination (Long-HDD) method to improve the application of longitudinal cognitive diagnosis in practical teaching by introducing a simple computation and less time-consuming nonparametric classification method—HDD—into longitudinal diagnostic data processing. Compared with the HDD, the proposed method represents correlation or dependence between adjacent time points of the same student using Hamming distance in anticipation of using information from the previous time point to improve the classification accuracy at the current time point. A simulation study was conducted to explore the performance of the proposed method in longitudinal diagnostic data analysis and to compare the performance of the proposed method with the HDD and a parametric longitudinal diagnostic classification model. The findings suggest that (1) the Long-HDD can provide high classification accuracy in longitudinal diagnostic data analysis; (2) compared with the parametric model, the Long-HDD is almost unaffected by sample size and performs better than the parametric model in small sample sizes; and (3) the Long-HDD consumes much less computing time than the parametric model. Overall, the Long-HDD is well suited to analyzing longitudinal diagnostic data and can provide speedy diagnostic feedback due to its convenient computation, which is especially significant in small-scale assessments at the classroom and school levels.</p>

Study of distance metrics on k - nearest neighbor algorithm for star categorization

Classification of stars is essential to investigate the characteristics and behavior of stars. Performing classifications manually is error-prone and time-consuming. Machine learning provides a computerized solution to handle huge volumes of data with minimal human input. k-Nearest Neighbor (kNN) is one of the simplest supervised learning approaches in machine learning. This paper aims at studying and analyzing the performance of the kNN algorithm on the star dataset. In this paper, we have analyzed the accuracy of the kNN algorithm by considering various distance metrics and the range of k values. Minkowski, Euclidean, Manhattan, Chebyshev, Cosine, Jaccard, and Hamming distance were applied on kNN classifiers for different k values. It is observed that Cosine distance works better than the other distance metrics on star categorization.

Fault-Tolerant Secure Routing of B H n -Based Data Center Networks

As the core infrastructure of cloud computing, a large scale of the data center networks (DCNs), which consist of millions of servers with high capacity, suffer from node failure such that the reliability is deteriorated. Malicious group could inevitably compromise the quality and reliability of data; thus, how to ensure the security routing of data is an urgent practical problem. As models for large-scale DCNs, it is worth mentioning the balanced hypercube, which is well-known for its strong connectivity, regularity, and a smaller diameter. Each of which makes a balanced hypercube a trustworthy model to deal with data traffic and provides a certain degree of fault-tolerance as well. In this paper, we use the balanced hypercube as a model for the data center networks and design a reliable safety level by referring to different safety levels of related subgraph. This subgraph contains the source and destination nodes, and the shortest feasible paths are located so that the reliable transmission is achieved. Then, we get that the length of fault-tolerant safety routing of data center networks based on balanced hypercube is always no greater than the Hamming distance plus two. Experiment shows that our fault-tolerant security routing scheme is more effective in the same reliable network environment of DCNs.

BIODIVERSITY OF TREES AND SHRUBS OF URBAN PLANTATIONS IN ARID REGIONS

Trees and shrubs are essential components in the production of ecosystem services. The aim of this study is to evaluate the biodiversity of plantations in arid regions. A floristic inventory was carried out in three biotopes using an exhaustive sampling approach. Diversity parameters were calculated to assess phytodiversity in each biotope. A total of 23 plants species belonging to 17 families, with a dominance of eudicots at 95.65% were recorded. The dominant families were Fabaceae (17.38%), Arecaceae, Lythraceae and Tamaricaceae (8.69%). The flora was mixed with 56.52% of exotic plant species. The most predominant plant species were Phoenix dactylifera L, Tamarix aphylla (L.) H. Karst, Dodonaea viscosa (L.) Jacq, and Leucaena leucocephala (Lam.) de Wit, these species accounted for 72.74% of all individuals inventoried. Shannon’s diversity index and Piélou index of evenness range from 2.68 to 2.95 bits and 0.64 to 0.69, respectively. Simpson’s index range from 0.78 to 0.82. Hamming distance range from 21 to 43. Plantations provide ecosystem services with great importance on ornamental interest. The data collected in this study should be used for creating a floristic database. This database will be regularly updated for monitoring urban plantations. The information resulting from the monitoring will help to improve the urban forest management projects in the development plan.

Performance Analysis of Iteratively Decoded Convergent Source Mapping with Sphere Packing-Assisted Differential Space-Time Spreading Technique for Efficient Video Transmission

With the substantial growth in number of wireless devices, future communication demands overarching research to design high-throughput and efficient systems. We propose an intelligent Convergent Source Mapping (CSM) approach incorporating Differential Space-Time Spreading (DSTS) technique with Sphere Packing (SP) modulation. The crux of CSM process is assured convergence by attaining an infinitesimal Bit-Error Rate (BER). Data Partitioning (DP) H.264 video codec is deployed to gauge the performance of our intelligent and efficient system. For the purpose of efficient and higher data rates, we have incorporated compression efficient source encoding along with error resiliency and transmission robustness features. The proposed system follows the concept of iterations between the Soft-Bit Source-Decoder (SBSD) and Recursive Systematic Convolutional (RSC) decoder. Simulations of the DSTS-SP-assisted CSM system are presented for the correlated narrowband Rayleigh channel, using different CSM rates but constant overall bit-rate budget. The SP-assisted DSTS systems are mainly useful in decoding algorithms that operate without requiring Channel State Information (CSI). The effects of incorporating redundancy via different CSM schemes on the attainable performance and convergence of the proposed system are investigated using EXtrinsic Information Transfer (EXIT) charts. The effectiveness of the proposed system is demonstrated through IT++ based proof-of-concept simulations. The Peak Signal-to-Noise Ratio (PSNR) analysis shows that using Rate-2/6 CSM with minimum Hamming distance ( d H , min ) of 4 offers about 5 dB gain, compared to an identical overall system code rate but with Rate-2/3 CSM and d H , min of 2. Furthermore, for a consistent value of d H , min and overall rate, the Rate-2/3 CSM scheme beats the Rate-5/6 CSM by about 2 dB at the PSNR degradation point of 2 dB. Moreover, the proposed system with Rate-2/3 CSM scheme furnishes an E b / N 0 gain of 20 dB when compared with the uniform-rate benchmarker. Clearly, we can say that higher d H , min and lower CSM values are favourable for our proposed setup.

A Combined Interval Type-2 Fuzzy MCDM Framework for the Resilient Supplier Selection Problem

Selecting the most resilient supplier is a crucial problem for organizations and managers in the supply chain. However, due to the inherited high degree of uncertainty in real-life projects, developing a decision-making framework in a crisp or fuzzy environment may not present accurate or reliable results for the managers. For this reason, it is better to evaluate the potential suppliers in an Interval Type-2 Fuzzy (IT2F) environment for better dealing with this ambiguity. This study developed an improved combined IT2F Best Worst Method (BWM) and IT2F technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model “Atieh Sazan” Co. as a case study, such that the IT2FBWM was employed for obtaining the weight of criteria. The IT2FTOPSIS was utilized for ranking the potential suppliers based on Hamming distance measure. In both phases, the opinions of experts as IT2F linguistic terms were employed for weighting the criteria and obtaining the relative importance of the alternatives in terms of the evaluative criteria. After obtaining the final results, the proposed model was validated by replacing Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) approaches separately instead of BWM for weighting the criteria. After executing both new models, it was found that the final ranking was similar to the final ranking of the proposed model, representing the reliability and accuracy of the obtained results. Moreover, it was concluded that the resilient criteria of “Reorganization” and “Redundancy” are the most determinant measures for selecting the best supplier rather than measures in the Iranian Construction Industry.

Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms Analysis

Wind farm operators are overwhelmed by a large amount of supervisory control and data acquisition (SCADA) alarms when faults occur. This paper presents an online root fault identification method for SCADA alarms to assist operators in wind turbine fault diagnosis. The proposed method is based on the similarity analysis between an unknown alarm vector and the feature vectors of known faults. The alarm vector is obtained from segmented alarm lists, which are filtered and simplified. The feature vector, which is a unique signature representing the occurrence of a fault, is extracted from the alarm lists belonging to the same fault. To mine the coupling correspondence between alarms and faults, we define the weights of the alarms in each fault. The similarities is measured by the weighted Euclidean distance and the weighted Hamming distance, respectively. One year of SCADA alarms and maintenance records are used to verify the proposed method. The results show that the performance of the weighted Hamming distance is better than that of the weighted Euclidean distance; 84.1% of alarm lists are labeled with the right root fault.

Integrated Semi-Supervised Clustering Method and Its Application in Rock-Typing in AA Reservoir

Rock-typing is complicated and critical for numerical simulation. Therefore, some researchers proposed several clustering methods to make classification automatic and convenient. However, traditional methods only focus in specific area such as lithofacies or petrophysical data instead of integrated clustering. Besides, all the clustering method are related to classification interval determined subjectively. Therefore, a new clustering method for rock-typing integrated different disciplines is critical for modelling and reservoir simulation. In this paper, we proposed a novel semi-supervised clustering method integrated with data from different disciplines, which can divide rock type automatically and precisely. Considering AA reservoir is a porous carbonate reservoir with seldom fracture and vug, FZI (Flow Zone Indicator) and RQI (Reservoir Quality Index) is utilized as the corner stone of the clustering method after collection and plotting for porosity and permeability data for cores from AA reservoir. Then lithofacies, sedimentary facies and petrophysical data are applied as constraints to improve FZI method. Hamming distance and earth mover distance are imported to build integrated function for clustering method. Finally, based on output results of integrated clustering method from experimental data, grid properties of model in Petrel software are imported as the input parameter for further procession. Therefore, saturation region for numerical simulation built by rock-typing is constructed. The results show that new method could make classification accurately and easily. History matching results for watercut indicate that new saturation regions improve the numerical simulation performance.