Sureka Sivavelu, Venkatesh Palanisamy^*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3469-3487, 2024, DOI:10.32604/cmc.2024.047407

Abstract The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric… More >

Wenlu Ji¹, Yingqi Liao^1,*, Liudong Zhang²

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2825-2848, 2023, DOI:10.32604/iasc.2023.039618

Abstract Existing power anomaly detection is mainly based on a pattern matching algorithm. However, this method requires a lot of manual work, is time-consuming, and cannot detect unknown anomalies. Moreover, a large amount of labeled anomaly data is required in machine learning-based anomaly detection. Therefore, this paper proposes the application of a generative adversarial network (GAN) to massive data stream anomaly identification, diagnosis, and prediction in power dispatching automation systems. Firstly, to address the problem of the small amount of anomaly data, a GAN is used to obtain reliable labeled datasets for fault diagnosis model training based on a few labeled… More >

Feiyan Zhou^1,*, Hui Yin¹, Chen Luo², Haixin Tong², Kun Yu², Zewen Li², Xiangjun Zeng²

Energy Engineering, Vol.120, No.9, pp. 1979-1990, 2023, DOI:10.32604/ee.2023.029480

Abstract The load types in low-voltage distribution systems are diverse. Some loads have current signals that are similar to series fault arcs, making it difficult to effectively detect fault arcs during their occurrence and sustained combustion, which can easily lead to serious electrical fire accidents. To address this issue, this paper establishes a fault arc prototype experimental platform, selects multiple commonly used loads for fault arc experiments, and collects data in both normal and fault states. By analyzing waveform characteristics and selecting fault discrimination feature indicators, corresponding feature values are extracted for qualitative analysis to explore changes in time-frequency characteristics of… More > Graphic Abstract

Hongqiao Wang^1,*, Yanning Cai²

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 187-203, 2022, DOI:10.32604/cmes.2022.018000

Abstract Inertial system platforms are a kind of important precision devices, which have the characteristics of difficult acquisition for state data and small sample scale. Focusing on the model optimization for data-driven fault state prediction and quantitative degree measurement, a fast small-sample supersphere one-class SVM modeling method using support vectors pre-selection is systematically studied in this paper. By theorem-proving the irrelevance between the model's learning result and the non-support vectors (NSVs), the distribution characters of the support vectors are analyzed. On this basis, a modeling method with selected samples having specific geometry character from the training sets is also proposed. The… More >

Nasir Mahmood¹, Yaser Hafeez¹, Khalid Iqbal², Shariq Hussain³, Muhammad Aqib¹, Muhammad Jamal⁴, Oh-Young Song^5,*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 873-893, 2021, DOI:10.32604/cmc.2021.016614

Abstract Despite advances in technological complexity and efforts, software repository maintenance requires reusing the data to reduce the effort and complexity. However, increasing ambiguity, irrelevance, and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories. Thus, there is a need for a repository mining technique for relevant and bug-free data prediction. This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software. To predict errors in mining data, the Apriori algorithm was used to discover association rules by fixing confidence at more… More >