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, No 4

■ Cover page(PDF 3.16 MB)■  Table of Content, April 2021  (PDF 91 KB) 

  • Original article
    EM Approach for Weibull Analysis in a Strongly Censored Data Context - Application to Road Markings
    Redondin Maxime, Bouillaut Laurent, and Daucher Dimitri
    2021, 17(4): 333-342.  doi:10.23940/ijpe.21.04.p1.333342
    Abstract    HTML   PDF (635KB)   
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    Road surface markings organize road traffic and therefore play a major role in road safety. This is one reason why a part of the current guidance system is to model traffic lanes based on pavement marking detection. Consequently, road managers must guarantee detectable marking lines. An adapted maintenance strategy is a potential solution. Today, marking lines are inspected periodically. A direct consequence of this is that the failure moment of a given marking is generally strongly censored (left, interval or right). Sathyanarayanan et al.present the first Weibull approach for road markings [1]. Weibull distributions are estimated by the maximum likelihood estimation (MLE) method adapted to this censored problem. The MLE is computed by a numerical approach such as the Newton-Raphson algorithm. In the context of interval-censored data, Pradhan and Kundu proposed an alternative method based on an Expectation-Maximisation EM algorithm [2]. This paper proposes replacing this EM approach in the road markings context. A case study regarding the broken center line of a section of French National Road 4 illustrates this methodology. Lifetime markings are distributed by the Weibull distribution.

    Comparative Study on the Performance Attributes of NHPP Software Reliability Model based on Weibull Family Distribution
    Yang Tae-Jin
    2021, 17(4): 343-353.  doi:10.23940/ijpe.21.04.p2.343353
    Abstract    PDF (326KB)   
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    In this study, the performance attributes of software reliability are analyzed by applying Weibull family distributions (Lindley, Rayleigh, Type-2 Gumbel) to the finite fault NHPP reliability model. Also, the Weibull family distribution models were compared with the Goel-Okumoto basic model to confirm reliability performance, and the optimal model among the proposed models was presented. For this, software failure time data was used, parametric estimation was applied to the maximum likelihood estimation (MLE) method, and nonlinear equations were calculated using the bisection method. As a result, in the intensity function analysis, the Rayleigh model was effective because the failure occurring rate increased initially with a small value and then decreased significantly as the failure time passed and the mean squared error (MSE) was also small. In the analysis of the mean value function, all the proposed models showed a slightly overestimated value compared to the true value, but the Rayleigh model showed the smallest error to the true value. As a result of comparing reliability by applying future mission time, the Rayleigh model was high and stable together with the Lindley model, but the Type-2 Gumbel model showed a decreasing tendency along with the Goel-Okumoto basic model. In conclusion, we have found that the Rayleigh model has the best performance among the proposed models. In this study, the reliability performance of the Weibull family distribution model without the existing research case was newly analyzed, and it is expected that software developers can use it as a basic guideline to search for an optimal software reliability model.

    Operational Reliability Metric to Characterize Radar Detection Performability
    Tyler D. Ridder and Ram M. Narayanan
    2021, 17(4): 354-363.  doi:10.23940/ijpe.21.04.p3.354363
    Abstract    HTML   PDF (493KB)   
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    Historically, reliability studies of electronic systems, including radar, have primarily focused on the trustworthiness and endurance of the hardware components of the system. While the radar’s hardware is indeed a crucial part of the system, the signal processing aspects of the radar and the actual operating conditions, such as the environment and aging, have been neglected in previous reliability studies. In this paper, the operational reliability paradigm is used to analyze the parameters of the radar’s signal processor and their effects on the radar’s target detection performance while in service. A detection threshold is derived using the operational reliability formulation that can be optimized for given target and clutter statistics.

    Ameliorating Vertically Bundled Electricity Price Prediction Exclusively from ICMLP Network
    S. Anbazhagan and Bhuvaneswari Ramachandran
    2021, 17(4): 364-370.  doi:10.23940/ijpe.21.04.p4.364370
    Abstract    PDF (321KB)   
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    In this paper, an improved complex-valued multi-layer perceptron (ICMLP) network has been proposed with a view to forecast the fluctuating prices of the deregulated market. The existing literature focuses on a hybrid of hard and soft computing models that should be able to capture the nonlinearity associated with those electricity market prices. Apart from individual models, the hybrid algorithms are based on the integration of different computing paradigms, which in turn increases the time complexity. An individual model for estimating electricity prices has yet to be developed. This paper proposes an exclusive approach called ICMLP with logarithmic performance index for forecasting electricity prices. In ICMLP, a novel optimization criterion is used that takes into account minimizing both the errors by magnitude and phase. Efficacy of ICMLP is evaluated using benchmark data sets of Spanish electricity market. The results have been found to be promisingly better than the existing prediction-based models. Simulation results demonstrate that the ICMLP is superior in terms of accuracy, training speed, and structure compactness.


    Performance Analysis of the Impact of Technical Skills on Employability
    D. Laddha Manjushree, T. Lokare Varsha, W. Kiwelekar Arvind, and D. Netak Laxman
    2021, 17(4): 371-378.  doi:10.23940/ijpe.21.04.p5.371378
    Abstract    PDF (336KB)   
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    The competency to predict student success in a course or program generates opportunities to enhance educational outcomes to improve graduate employment. With effective performance prediction techniques, teachers can appropriate resources and instruction more precisely. Research in this area aspires to recognize features that can be used to make predictions with the help of machine learning techniques that can refine predictions and quantify aspects of student performance on employability. Moreover, research in predicting student performance on employability strives to discover interrelated features and to connect the underlying reasons why definite features work better than others. This study is to build the Technical Skills Based Employability Prediction Model (TSBEPM) using machine learning techniques. The technical skills are the scores of the students in various programming courses. The experimental work is based on the predictions obtained by various machine learning classifiers, namely Support Vector Machine, Naive Bayes, Logistic Regression, Decision Tree, Random Forest, AdaBoost, and Artificial Neural Network. To confirm all models used, the experiments were carried out using real data collected from the graduate students at the University. With the help of performance measuring parameters, different models are formulated to be used for predicting whether a student is placed or not. Random Forest gives a maximum accuracy of 70% and F1-Score of 0.85. The model is formulated to be used for predicting whether a student is placed or not.

    Intelligent Optimization of Latent Fingerprint Image Segmentation using Stacked Convolutional Autoencoder
    Chhabra Megha, Shukla Manoj Kumar, and Ravulakolluc Kiran Kumar
    2021, 17(4): 379-393.  doi:10.23940/ijpe.21.04.p6.379393
    Abstract    PDF (870KB)   
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    In recent times, image forensics have contributed to multiple research areas like latent fingerprint forensics. Latent fingerprint forensics is performed by law enforcement agencies. Latent fingerprint matching and identification of criminals depends highly on accurate detection and therefore segmentation of fingermarks. A novel algorithm is designed a) for efficient pre-feature extraction as the early-distinction of the structure of interest using saliency and color-map based information, thereby channeling lesser data for patch-based classification, b) for reliable post-feature extraction-based classification and segmentation task via a dropout-based regularized stack of convolutional autoencoder, which helps in automating optimal feature selection and reliable object detection with reduced overfitting. An intelligent parameter standardization of the proposed technique that improves the efficiency and effectiveness of the segmentation process is proposed to demonstrate the repeatability of the system using cross-validation. The experimentation is performed on the IIIT-D CLF database. By addressing the need for intelligent distinction of the structure of interest in latent fingerprint and impact of patch size in the pre-feature extraction phase along with optimal feature selection and object detection using a stacked convolutional autoencoder, the proposed work improved the latent fingerprint segmentation and detection technique.

    Review of Aerodynamic Design Configurations for Wind Mitigation in High-Rise Buildings: Two Cases from Amman
    F. Al-Najjar Sonia and W. Al-Azhari Wael
    2021, 17(4): 394-403.  doi:10.23940/ijpe.21.04.p7.394403
    Abstract    PDF (971KB)   
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    This study is concerned with reviewing and analyzing methods used in early design stages to mitigate wind effects on high-rise buildings. In order to mitigate wind effects on structures and specifically high-rise buildings, early stage aerodynamic design decisions are made. Architects try to mitigate the wind effects on buildings by choosing the right form configuration like tapering or setbacks, etc., or by making vital decisions in the early design stage. On the other hand, structural engineers utilize the structural system that can best counteract forces acting on the stability of the building. For both architects and engineers, there are many tools to use in early design; including advanced analysis methods, wind tunnel testing,and wind studies combined with Computational Fluid Dynamics (CFD) simulations. This study reviews general architectural and structural design configurations performed in the early phases of the design process, for achieving structural stability, comfort, and cost control. The research methodology depends on the study and analysis of different international building examples, and also on reviewing two local high-rise building cases in Amman, Jordan. The study concludes that there are many architectural aerodynamic configurations for the purpose of mitigating wind loads, which can be used as guidelines in the early design phases.

    Non-Linear Frequency Modulated Radar Echo Signal Cancellation using Interrupted Sampling Repeater Jamming
    Ch Anoosha and B.T.Krishna
    2021, 17(4): 404-410.  doi:10.23940/ijpe.21.04.p8.404410
    Abstract    PDF (602KB)   
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    Jamming is protecting the original target from being detected by the enemy radar. There are different types of jamming methods that can mislead the enemy radar, but they are not as effective and still have a chance to retrieve the original signal from the enemy radar. Thus, a deception jamming is proposed using interrupted sampling repeater jamming called ISRJ. ISRJ generates a train of false targets and also uses an active echo cancellation technique which totally cancels the original target echo signal and generates only a train of false targets. The techniques used before to cancel the targets are based on Linear Frequency Modulation (LFM) radar waveform. A Non-Linear Frequency Modulation (NLFM) waveform is used because it has an advantage of very low peak side lobe level. In this paper, we generated a train of false targets along with real radar target echos to deceive the enemy radar and to protect the target from being detected. In addition, the amplitude of the real target is made very small to confuse enemy radars. However, the fact cannot be ignored that the original target echo is present in the false targets, so there is a chance that the enemy radar can detect the original target. Therefore, using the active cancellation technique cancelled the target echo signal. The results are compared with both LFM and NLFM pulse compression techniques. The simulation results show how the target echo is cancelled and protected by the false targets.

ISSN 0973-1318