A photovoltaic anomaly data identification method based on image feature detection by QIU Yutao, ZHANG Lei, ZHOU Kaiyun, YAN Min, SUN Jintong, LONG Huan

“…This method maps numerical data to images， transforming the anomaly detection problem into an image processing problem. …”
Get full text

Optimized Demand Forecasting for Bike-Sharing Stations Through Multi-Method Fusion and Gated Graph Convolutional Neural Networks by Hebin Guo, Kexin Li, Yutong Rou

Get full text

Application of statistical methods for predicting udp-flood attacks by M. V. Tumbinskaya, V. V. Volkov, B. G. Zagidullin

“…The aim of this study was to increase the level of security of web resources by means of timely detection of anomalies in their work, detection of information security threats based on analysis and forecasting methods. …”
Get full text

System analysis and information processing to solve the problem of detecting breakdowns of computer information storage by N. M. Kodatsky, E. A. Revyakina, A. R. Gazizov

Get full text

Intelligent Data Processing Methods for the Atypical Values Correction of Stock Quotes by T. V. Zolotova, D. A. Volkova

“…The practical implementation of the methods for detecting and eliminating outliers used in this work can be a tool for calculating more accurate indicators in any area, for example, to improve forecasting the stock price. …”
Get full text

Research on real-time abnormal voltage detection and prediction method based on the linear tracking differentiator by XIAO Xiong, ZHANG Yong-jun, WANG Jing, SU Hong-yue

“…One kind of detection and prediction method for abnormal grid voltage has been designed due to the case that modern power electronic equipment is sensitive to non-stationary time-varying voltage signal. …”
Get full text

Seasonal footprints on ecological time series and jumps in dynamic states of protein configurations from a nonlinear forecasting method characterization by Leonardo Reyes, Kilver Campos, Gilberto D Avendaño, Lenin González-Paz, Alejandro Vivas, Ysaías J Alvarado, Saúl Flores

“…We have analyzed phenology data and protein configurations from molecular dynamics simulations with the nonlinear forecasting method proposed by May and Sugihara. Our primary focus in this work is to characterize the dynamic state of a system by quantifying prediction quality from data. …”
Get full text

Explaining the Earnings Management Prediction Model Using the Hybrid of Machine Learning Methods by Hassan Hassani, Esfandiar Malekian Kallehbasti, Yahya Kamyabi

“…Based on the obtained results, the hybrid method based on deep learning and relief feature selection has the highest prediction accuracy (89/62) among other hybrid methods for forecasting accruals earnings management, and the hybrid method based on deep learning and principal component analysis feature selection has the highest prediction accuracy (82/65) among other hybrid methods for forecasting real earnings management.The findings of this study can expedite earnings management detection for financial statement users by improving earnings management prediction accuracy. …”
Get full text

Sensor Fusion Enhances Anomaly Detection in a Flood Forecasting System by Andrew Ma, Abhir Karande, Natalie Dahlquist, Fabien Ferrero, N. Rich Nguyen

“…Our method uses time-series anomaly detection to identify incorrect readings. …”
Get full text

Evaluation and Anomaly Detection Methods for Broadcast Ephemeris Time Series in the BeiDou Navigation Satellite System by Jiawei Cai, Jianwen Li, Shengda Xie, Hao Jin

“…Evaluation results show that the IF-TEA-LSTM model reduces the RMSE by up to 20.80% for orbital parameters and improves clock deviation prediction accuracy for MEO satellites by 68.37% in short-term forecasts, outperforming baseline models. This method significantly enhances anomaly detection accuracy across GEO, IGSO, and MEO satellite orbits, demonstrating its superiority in long-term data processing and its capacity to improve the reliability of satellite operations within the BDS.…”
Get full text

Retrospective Medium-Term Forecast of a Catastrophic Earthquake in Turkey in 2023 (02.06.2023, MW=7.7) Using the LURR Method by A. S. Zakupin, N. A. Sycheva

“…The results of calculations using the LURR method were compared with the reconstruction data using the STD method. …”
Get full text

AI-Driven Insect Detection, Real-Time Monitoring, and Population Forecasting in Greenhouses by Dimitrios Kapetas, Panagiotis Christakakis, Sofia Faliagka, Nikolaos Katsoulas, Eleftheria Maria Pechlivani

“…This widespread reliance on chemical insecticides has substantial economic, environmental, and human health consequences, highlighting the urgent need for sustainable pest management strategies. Early detection, insect monitoring, and population forecasting through Artificial Intelligence (AI)-based methods, can enable swift responsiveness, allowing for reduced but more effective insecticide use, mitigating traditional labor-intensive and error prone solutions. …”
Get full text

Intelligent Processing Methods by Veronika V. Tolmanova, Denis A. Andrikov

“…The study used the wavelet transform and stochastic methods to detect hidden patterns and trends in data. …”
Get full text

Tunnel Seismic Detection for Tunnel Boring Machine by Joint Active and Passive Source Method and Imaging Advanced Prediction by Xinglin Lu, Jun Wang, Wei Wang, Xuhua Liu, Chao Yang, Yufeng Liu, Zhihong Fu

“…The passive detection method usually produces results of low resolution and with limited forecast precision. …”
Get full text

Short-term prediction method of oblique sounding ionosphere MUF based on robust Kalman filter by Guojun LI, Guangfa ZHENG, Changrong YE, Yinping ZHOU

“…To improve the low accuracy of ionospheric frequency forecasting, poor HF communication connectivity, and the severe impact of observational outliers in engineering on forecast accuracy, a short-term prediction method for oblique detection ionospheric MUF based on robust Kalman filtering was proposed.By studying the variation law of the measured MUF data and the causes as well as characteristics of observation errors, and exploiting the ionospheric reference model as a priori information, the state estimation equation of the Kalman filter model was improved.The cost function mechanism was introduced to realize the measurement update of the forecast state through Huber-M estimation, which reduced the influence of the observation outliers on the forecast results and improves the robustness of the proposed method.The simulation results show that the proposed method can effectively suppress the adverse effects caused by the observation outliers and has good robustness and stability.…”
Get full text

Early Warning Method of Abnormal Electricity Consumption Behavior Based on Data Driven by WAN Wei, LIU Hongqi, SUN Hong-chang, ZHANG Feng, WANG Yang, SUN Wei-qing

Get full text

Detection and Prediction of Wind and Solar Photovoltaic Power Ramp Events Based on Data-Driven Methods: A Critical Review by Jie Zhang, Xinchun Zhu, Yigong Xie, Guo Chen, Shuangquan Liu

Get full text

Predictive control method with conduction mode detection to suppress input current distortion of three‐level power factor correction by Xifeng Xie, Zeliang Shu, Rongxin Chen, Jianghong Yin, Chunxiang Ling

“…Abstract A novel predictive control method with conduction mode detection is proposed to suppress input current distortion of three‐level power factor correction (PFC) converters. …”
Get full text

Method of Diagnostics of Operation Modes of Individual Heat Supply Units, Allowing to Detect Pre-Emergency Situations at an Early Stage by Dvortsevoy A.I., Borush O.V., Khoreva V.A., Yakovina I.N.

“…This was confirmed by the "Elbow Method", which determined the optimal number, which made it possible to significantly improve the forecasting of emergency modes. …”
Get full text

University proceedings. Volga region. Technical sciences by D.V. Artamonov, V.M. Myskin, A.D. Semenov

“…Assuming that the amount of energy emitted by the housing obeys the Stefan-Boltzmann law, a heat balance equation was obtained taking into account the internal reflection of thermal energy, which showed that the occurrence of elementary Arnold disasters of types A1-A4 is theoretically possible. A forecasting method is proposed, using which the disaster detection time is reduced by 200 minutes, compared to the time of emergency detection by a traditional monitoring system. …”