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  • Open Access


    Defect Detection Model Using Time Series Data Augmentation and Transformation

    Gyu-Il Kim1, Hyun Yoo2, Han-Jin Cho3, Kyungyong Chung4,*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1713-1730, 2024, DOI:10.32604/cmc.2023.046324

    Abstract Time-series data provide important information in many fields, and their processing and analysis have been the focus of much research. However, detecting anomalies is very difficult due to data imbalance, temporal dependence, and noise. Therefore, methodologies for data augmentation and conversion of time series data into images for analysis have been studied. This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance, temporal dependence, and robustness to noise. The method of data augmentation is set as the addition of noise. It involves adding… More >

  • Open Access


    An Innovative Deep Architecture for Flight Safety Risk Assessment Based on Time Series Data

    Hong Sun1, Fangquan Yang2, Peiwen Zhang3,*, Yang Jiao4, Yunxiang Zhao5

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2549-2569, 2024, DOI:10.32604/cmes.2023.030131

    Abstract With the development of the integration of aviation safety and artificial intelligence, research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management, but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry. Therefore, an improved risk assessment algorithm (PS-AE-LSTM) based on long short-term memory network (LSTM) with autoencoder (AE) is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels. Firstly, based on… More >

  • Open Access


    CT-NET: A Novel Convolutional Transformer-Based Network for Short-Term Solar Energy Forecasting Using Climatic Information

    Muhammad Munsif1,2, Fath U Min Ullah1,2, Samee Ullah Khan1,2, Noman Khan1,2, Sung Wook Baik1,2,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1751-1773, 2023, DOI:10.32604/csse.2023.038514

    Abstract Photovoltaic (PV) systems are environmentally friendly, generate green energy, and receive support from policies and organizations. However, weather fluctuations make large-scale PV power integration and management challenging despite the economic benefits. Existing PV forecasting techniques (sequential and convolutional neural networks (CNN)) are sensitive to environmental conditions, reducing energy distribution system performance. To handle these issues, this article proposes an efficient, weather-resilient convolutional-transformer-based network (CT-NET) for accurate and efficient PV power forecasting. The network consists of three main modules. First, the acquired PV generation data are forwarded to the pre-processing module for data refinement. Next, to… More >

  • Open Access


    Generating Synthetic Data to Reduce Prediction Error of Energy Consumption

    Debapriya Hazra, Wafa Shafqat, Yung-Cheol Byun*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3151-3167, 2022, DOI:10.32604/cmc.2022.020143

    Abstract Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions. Energy industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary financing. Machine learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy companies. The basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of data. Another critical factor is balancing the data for enhanced prediction. Data Augmentation is a… More >

  • Open Access


    Forecast of LSTM-XGBoost in Stock Price Based on Bayesian Optimization

    Tian Liwei1,2,*, Feng Li1, Sun Yu3, Guo Yuankai4

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 855-868, 2021, DOI:10.32604/iasc.2021.016805

    Abstract The prediction of the “ups and downs” of stock market prices is one of the important undertakings of the financial market. Since accurate prediction helps foster considerable economic benefits, stock market prediction has attracted significant interest by both investors and researchers. Efforts into building an accurate, stable and effective model to predict stock prices’ movements have been proliferating at a fast pace, to meet such a challenge. Firstly, this paper uses a correlation analysis to analyze the attributes of a stock dataset, processing missing values, determining the data attributes to be retained data, then divide… More >

  • Open Access


    Time-Series Data and Analysis Software of Connected Vehicles

    Jaekyu Lee1,2, Sangyub Lee1, Hyosub Choi1, Hyeonjoong Cho2,*

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2709-2727, 2021, DOI:10.32604/cmc.2021.015174

    Abstract In this study, we developed software for vehicle big data analysis to analyze the time-series data of connected vehicles. We designed two software modules: The first to derive the Pearson correlation coefficients to analyze the collected data and the second to conduct exploratory data analysis of the collected vehicle data. In particular, we analyzed the dangerous driving patterns of motorists based on the safety standards of the Korea Transportation Safety Authority. We also analyzed seasonal fuel efficiency (four seasons) and mileage of vehicles, and identified rapid acceleration, rapid deceleration, sudden stopping (harsh braking), quick starting,… More >

  • Open Access


    Causality Learning from Time Series Data for the Industrial Finance Analysis via the Multi-Dimensional Point Process

    Liangliang Shi1,2, Peili Lu3, Junchi Yan4,5,*

    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 873-885, 2020, DOI:10.32604/iasc.2020.010121

    Abstract Causality learning has been an important tool for decision making, especially for financial analytics. Given the time series data, most existing works construct the causality network with the traditional regression models and estimate the causality by pairs. To fulfil a holistic one-shot inference procedure over the whole network, we propose a new causal inference method for the multidimensional time series data, specifically related to some case studies for the industrial finance analytics. Specifically, the time series are first converted to the event sequences with timestamps by fluctuation the detection, and then a multidimensional point process More >

  • Open Access


    A Parallel Approach to Discords Discovery in Massive Time Series Data

    Mikhail Zymbler*, Alexander Grents, Yana Kraeva, Sachin Kumar

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1867-1878, 2021, DOI:10.32604/cmc.2020.014232

    Abstract A discord is a refinement of the concept of an anomalous subsequence of a time series. Being one of the topical issues of time series mining, discords discovery is applied in a wide range of real-world areas (medicine, astronomy, economics, climate modeling, predictive maintenance, energy consumption, etc.). In this article, we propose a novel parallel algorithm for discords discovery on high-performance cluster with nodes based on many-core accelerators in the case when time series cannot fit in the main memory. We assumed that the time series is partitioned across the cluster nodes and achieved parallelization… More >

  • Open Access


    A Survey of Time Series Data Visualization Methods

    Wangdong Jiang1, Jie Wu1,*, Guang Sun1,2, Yuxin Ouyang3, Jing Li3, Shuang Zhou2

    Journal of Quantum Computing, Vol.2, No.2, pp. 105-117, 2020, DOI:10.32604/jqc.2020.07242

    Abstract In the era of big data, the general public is more likely to access big data, but they wouldn’t like to analyze the data. Therefore, the traditional data visualization with certain professionalism is not easy to be accepted by the general public living in the fast pace. Under this background, a new general visualization method for dynamic time series data emerges as the times require. Time series data visualization organizes abstract and hard-to-understand data into a form that is easily understood by the public. This method integrates data visualization into short videos, which is more More >

  • Open Access


    A Self-Organizing Memory Neural Network for Aerosol Concentration Prediction

    Qiang Liu1,*, Yanyun Zou2,3, Xiaodong Liu4

    CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 617-637, 2019, DOI:10.32604/cmes.2019.06272

    Abstract Haze-fog, which is an atmospheric aerosol caused by natural or man-made factors, seriously affects the physical and mental health of human beings. PM2.5 (a particulate matter whose diameter is smaller than or equal to 2.5 microns) is the chief culprit causing aerosol. To forecast the condition of PM2.5, this paper adopts the related the meteorological data and air pollutes data to predict the concentration of PM2.5. Since the meteorological data and air pollutes data are typical time series data, it is reasonable to adopt a machine learning method called Single Hidden-Layer Long Short-Term Memory Neural Network (SSHL-LSTMNN)… More >

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