Article
Engineering, Chemical
Yuhan Wang, Dan Yang, Xin Peng, Weimin Zhong, Hui Cheng
Summary: This paper proposes a framework for evaluating the operating performance of complex industrial processes and identifying non-optimal root causes, including establishing a multiblock operating performance assessment model, simplifying the network structure through PLS-GC to establish a Bayesian network structure, and identifying non-optimal root causes and transmission paths through Bayesian inference.
Article
Business, Finance
Xudong Lin, Yiqun Meng, Hao Zhu
Summary: In this study, we examine the connection between investor sentiment and crypto market risk using the decomposed and partial connectedness framework. By identifying interconnectedness and bi-directional causal relationship between online investor sentiment, bitcoin investor sentiment, and 12 dominant cryptos, we find strong correlations between investor sentiment and volatility spillovers in the crypto market, highlighting the importance of considering sentiment contagion in analyzing crypto market movements. This study provides valuable insights for market participants and regulators, and contributes to the existing literature on sentiment analysis and market dynamics in the crypto domain.
FINANCE RESEARCH LETTERS
(2023)
Article
Computer Science, Information Systems
Shyamal Shivneel Chand, Ravneel Prasad, Hiye K. Mudaliar, Dhirendran Munith Kumar, Adriano Fagiolini, Marco Di Benedetto, Maurizio Cirrincione
Summary: This paper proposes a method to improve the dynamic performance of on-grid induction machine-based wind generators. The method uses an online mechanical parameter estimation technique and an adaptive feedforward neural controller to enhance the system's stability during wind speed variations and weak grid connections. The proposed approach achieves good control performance in experiments.
Article
Operations Research & Management Science
Mahmoud Hassan, Marc Kouzez, Ji-Yong Lee, Badreddine Msolli, Hatem Rjiba
Summary: This study empirically examines the impact of environmental policy stringency on financial development in OECD countries. The results suggest a positive association between environmental policy stringency and financial development, indicating that financial development can be promoted through increasing environmental policy stringency. Additionally, the study finds that past values of environmental policy stringency can predict financial institutions development.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Energy & Fuels
Yiying Wang, Runjie Shen, Ming Ma
Summary: This study emphasizes the importance of wind power generation and the methodology of establishing a wind farm output prediction model. By considering various meteorological factors comprehensively, the accuracy of the predictions can be improved.
Article
Economics
Erdinc Akyildirim, Oguzhan Cepni, Linh Pham, Gazi Salah Uddin
Summary: This paper examines the connectedness and directional spillovers between agricultural commodity futures markets and sentiment indices, and finds that the spillovers are influenced by economic and financial uncertainty, including the global COVID-19 pandemic.
Article
Engineering, Mechanical
Ayad G. Baziyad, Adnan S. Nouh, Irfan Ahmad, Abdulaziz Alkuhayli
Summary: This paper proposes a kernel-based learning method to enhance the learning and generalization capabilities of models for describing and compensating the hysteresis phenomenon in piezoelectric actuators. Two control schemes are evaluated through real-time experiments, showing improved tracking performance.
Article
Engineering, Electrical & Electronic
H. E. Keshta, A. A. Ali, O. P. Malik, E. M. Saied, F. M. Bendary
Summary: This study investigates an adaptive self-tuning controller based on pole-shifting control for micro-grid systems, which is optimized using an advanced global Porcellio Scaber algorithm. The system response based on the proposed pole-shift controller is better than the classical PI controller under various operating conditions, as verified by simulation results.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Energy & Fuels
Wenxin Wang, Yun Xu, Xiaoliang Han, Mingliang Gao, Qi Zhang, Jianwen Wang
Summary: This paper establishes testing stations and collects real-time data in the suburban area of Hohhot to study the optimal composite function form of the wind speed profile. The physical significance of each parameter is analyzed, providing a basis for accurate micro-siting of small wind turbines.
Article
Engineering, Electrical & Electronic
Po Li, Ying He, Jingrui Zhang
Summary: This paper presents a real-time detection method for harmonic extraction that is able to handle sudden fluctuations in amplitude, phase, and frequency of the fundamental wave. The method utilizes the Least Squares method with forgetting factor to extract the phase and frequency of the fundamental wave, and then uses this information to design a linear time-varying observer for the extraction of DC bias and harmonics. The proposed method is shown to be robust and accurate in various scenarios such as distorted power grid signals, white noise, inter-harmonics, and high-order harmonics.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Green & Sustainable Science & Technology
T. M. Riayatsyah, T. A. Geumpana, I. M. Rizwanul Fattah, Samsul Rizal, T. M. Indra Mahlia
Summary: This study analyzed and optimized the techno-economic performance of solar PV, wind turbines, and battery packs for Syiah Kuala University. The results showed that by connecting solar PV and wind turbines to the grid, the renewable energy system can meet a large portion of the electricity demand.
Article
Forestry
Yiqing Xu, Dianjing Li, Hao Ma, Rong Lin, Fuquan Zhang
Summary: The quantitative simulation of forest fire spread is significant for risk management and fire fighting strategies. A new method called LSSVM-CA, combining cellular automaton model and LSSVM, is proposed for modeling fire spread, considering the effects of adjacent wind. The proposed model performs well in simulating forest fire spread and determining fire probability.
Article
Engineering, Mechanical
Ayad G. Baziyad, Irfan Ahmad, Yasser Bin Salamah, Abdulaziz Alkuhayli
Summary: Nanopositioning technology is commonly used in high-resolution applications, but the hysteresis effect of piezoelectric actuators limits their positioning accuracy. Developing an accurate hysteresis model and compensation method is essential, which can be achieved using a machine learning-based model combined with a feedback controller. The proposed hybrid controller is capable of compensating for hysteresis and rejecting disturbances, resulting in superior tracking performance compared to the PID-LSSVM controller.
Article
Engineering, Multidisciplinary
Qiwu Luo, Bingxing Zhou, Jingxuan Geng, Zihuai Liu, Jiaojiao Su, Chunhua Yang
Summary: Inspired by structural sparse learning, the paper proposes a novel prediction model called triple sparse least squares support vector machine (TS-LSSVM), which realizes feature, sample, and structure sparsity simultaneously by redefining the objective function. The selection of support vectors can be adjusted adaptively according to the time-varying environmental noise to ensure long-term reliability.
Article
Engineering, Geological
Wenping Gong, Shan Tian, Lei Wang, Zhibin Li, Huiming Tang, Tianzheng Li, Liang Zhang
Summary: This paper presents a new method for interval prediction of landslide displacement, integrating dual-output least squares support vector machine and particle swarm optimization algorithms. Case studies demonstrate that the proposed method has the best overall performance compared with other existing methods and provides accurate and reliable results.