Article
Computer Science, Artificial Intelligence
Zichen Zhang, Wei -Chiang Hong
Summary: Accurate electric load forecasting is crucial for the efficiency of power system operation. Hybrid intelligent computing methods and swarm-based algorithms, along with the SVR model, show promising results in solving convergence issues. The proposed VMD-SVR-CGWO model outperforms other models in forecasting accuracy based on numerical examples from two electric load data sets.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Energy & Fuels
Mengran Zhou, Tianyu Hu, Kai Bian, Wenhao Lai, Feng Hu, Oumaima Hamrani, Ziwei Zhu
Summary: Short-term electric load forecasting is crucial for the safe and stable operation of power systems and transactions in the power market. This paper proposes an approach using decomposition and ensemble framework with verification on load data from Oslo and surrounding areas in Norway, achieving optimal evaluation metrics for short-term electric load forecasting and showing good application prospects.
Article
Economics
Jian Luo, Tao Hong, Zheming Gao, Shu-Cherng Fang
Summary: Electric load forecasting is crucial in the energy industry, and this study proposes a robust support vector regression (SVR) model to forecast electricity demand under data integrity attacks. By introducing a weight function to calculate the relative importance of each observation in the load history, a weighted quadratic surface SVR model is constructed, and some theoretical properties are derived. Computational experiments based on publicly available data demonstrate the superior accuracy of the proposed robust model compared to other recent robust models in load forecasting literature.
INTERNATIONAL JOURNAL OF FORECASTING
(2023)
Article
Engineering, Chemical
Tong Lu, Sizu Hou, Yan Xu
Summary: This study proposes a method using a composite VTDS model to address the challenging issue of load prediction in user-level integrated energy systems (IESs). The IES multi-dimensional load time series is decomposed into intrinsic mode functions (IMFs) using variational mode decomposition (VMD). Various techniques such as data dimensionality reduction, clustering denoising, and artificial neural network are employed for feature selection and load prediction, resulting in higher accuracy in short-term forecasting.
Article
Energy & Fuels
M. Zulfiqar, M. Kamran, M. B. Rasheed, T. Alquthami, A. H. Milyani
Summary: Peak load forecasting is crucial for energy plants planning and operation. The paper proposes a hybrid model that integrates MEMD, ADE, and SVM to accurately estimate the demand capacity. The model shows good accuracy, stability, and convergence rate in forecasting the electricity load profile.
Article
Computer Science, Artificial Intelligence
Tao Wang
Summary: The study proposed a new wind speed forecasting model that combines multiple techniques, with experimental results showing better performance in multi-step wind speed forecasting.
PEERJ COMPUTER SCIENCE
(2021)
Article
Thermodynamics
Lean Yu, Yueming Ma, Mengyao Ma
Summary: This paper proposes an effective rolling decomposition-ensemble model for quarterly gasoline consumption forecasting in China, involving data decomposition, component prediction, and ensemble output. By utilizing wavelet decomposition and support vector regression, the model addresses data scarcity issue and improves prediction accuracy.
Article
Engineering, Electrical & Electronic
Guo-Feng Fan, Yan-Rong Liu, Hui-Zhen Wei, Meng Yu, Yin-He Li
Summary: This paper proposes a hybrid model based on EEMD-RF-SVR-RR algorithm for electric load forecasting. Numerical experiments have shown that the model outperforms other models in terms of forecasting accuracy, confirming its feasibility and effectiveness in short-term load forecasting.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Automation & Control Systems
Weijie Zhou, Yuke Cheng, Song Ding, Li Chen, Ruojin Li
Summary: The article introduces a grey seasonal least square support vector regression model that reflects seasonal variations by combining dummy variables and grey accumulation generation operation, with the introduction of a regulation method to enhance model stability and generalization. Experimental results demonstrate the model's superiority in seasonal time series analysis.
Article
Energy & Fuels
Xin-yue Fu, Zhong-kai Feng, Hui Cao, Bao-fei Feng, Zheng-yu Tan, Yin-shan Xu, Wen-jing Niu
Summary: This paper proposes an enhanced machine learning model, combining twin support vector regression, singular spectrum analysis, and grey wolf optimizer, for streamflow time series forecasting. The results show that the proposed model can yield superior results compared with traditional forecasting models.
Article
Energy & Fuels
Rui Wang, Xiaoyi Xia, Yanping Li, Wenming Cao
Summary: In this study, a Clifford fuzzy support vector machine for regression (CFSVR) based on geometric algebra is proposed for electric load forecasting. Through fuzzy membership, different input points have different contributions to deciding the optimal regression hyperplane. The experiment results show that CFSVR outperforms CSVR and other SVR algorithms in improving the accuracy of electric load forecasting and achieving multistep forecasting.
FRONTIERS IN ENERGY RESEARCH
(2021)
Article
Energy & Fuels
Xinyue Fu, Zhongkai Feng, Xinru Yao, Wenjie Liu
Summary: Although the machine-learning model achieves high accuracy in predicting wind speed, it is limited in accurately depicting the fluctuation range of predicted values due to the inherent uncertainty in wind speed sequences. To overcome this limitation and enhance reliability, we propose an effective wind speed interval prediction model that combines twin support vector regression (TSVR), variational mode decomposition (VMD), and the slime mould algorithm (SMA). Our model decomposes the complex wind speed series, predicts interval using TSVR for principal component and residual series, and applies SMA for optimal parameter combinations. The predicted wind speed interval has demonstrated superior performance and practical application value.
Article
Thermodynamics
Yanmei Huang, Najmul Hasan, Changrui Deng, Yukun Bao
Summary: Accurate day-ahead peak load forecasting is crucial for power dispatching and is of great interest to investors, energy policy makers, and government. This study proposes a novel MEMD-PSO-SVR hybrid model for precise electricity peak load prediction, which has been validated using real-world load data sets from Australia.
Article
Computer Science, Information Systems
Hui Hu, Wenquan Xu
Summary: A new hybrid model is proposed in this paper, combining EMD, DBN-AR model, and BP to improve the prediction accuracy for a time series. Experimental results demonstrate the superiority of the proposed model in both prediction accuracy and efficiency.
Article
Construction & Building Technology
Zeyu Wang, Xiaojun Zhou, Jituo Tian, Tingwen Huang
Summary: This paper presents a hybrid support vector regression model for medium and long term power load forecasting, and proposes a hierarchical optimization method based on nested strategy and state transition algorithm to enhance prediction accuracy.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Computer Science, Information Systems
Mehmet Tahir Sandikkaya, Yusuf Yaslan, Cemile Diler Ozdemir
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2020)
Article
Computer Science, Hardware & Architecture
Nazanin Moarref, Yusuf Yaslan
COMPUTERS & ELECTRICAL ENGINEERING
(2019)
Article
Engineering, Biomedical
Deger Ayata, Yusuf Yaslan, Mustafa E. Kamasak
JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING
(2020)
Article
Computer Science, Information Systems
Yusuf Yaslan, Bilge Gunsel
MULTIMEDIA SYSTEMS
(2020)
Article
Computer Science, Hardware & Architecture
Kiymet Kaya, Elif Ak, Yusuf Yaslan, Sema Fatma Oktug
Summary: Waste to energy transformation solutions are crucial in waste disposal, with machine learning models used to predict waste amount for smart energy management. By modeling with real-world data and WTE framework, technical support is provided for Istanbul's waste incineration power plant.
SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Can Berk Saner, Yusuf Yaslan, Istemihan Genc
Summary: Transient stability assessment is crucial in determining if power systems maintain synchronous operation during disturbances. A stacked ensemble model using wide-area synchrophasor measurements is proposed to classify post-contingency transient stability status, with analysis on robustness and performance in various scenarios.
ELECTRICAL ENGINEERING
(2021)
Article
Computer Science, Information Systems
Kiymet Kaya, Yaren Yilmaz, Yusuf Yaslan, Sule Gunduz Oguducu, Furkan Cingi
Summary: Tourism industry is growing rapidly and accurate demand prediction is crucial for economic planning. A new hotel demand forecasting model is proposed in this study, utilizing real-world data to achieve better performance compared to existing machine learning and deep learning models.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Article
Engineering, Electrical & Electronic
Sevda Jafarzadeh, Yusuf Yaslan, Istemihan Genc
Summary: Transient stability prediction is crucial for maintaining power system reliability, but machine learning-based classifiers require carefully chosen training datasets to ensure accuracy. Imbalanced datasets can lead to mispredictions in stability forecasting.
INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS
(2021)
Proceedings Paper
Computer Science, Information Systems
Elif Ak, Kiymet Kaya, Yusuf Yaslan, Sema Fatma Oktug
Summary: The use of sensor networks and machine learning techniques in data analysis has a significant impact on smart cities, especially in the sub-field of waste management and related waste transformations. The prediction of energy from solid waste is crucial for smart city waste disposal strategies, and IoT technologies like LoRaWAN offer new opportunities for data collection and analysis in this area.
PROCEEDINGS OF THE 2021 IEEE INTERNATIONAL CONFERENCE ON COMPUTER, INFORMATION, AND TELECOMMUNICATION SYSTEMS (IEEE CITS 2021)
(2021)
Proceedings Paper
Computer Science, Theory & Methods
Ezgi Tetik Saglam, Yusuf Yaslan, Sema F. Oktug
Summary: Geocast routing is aimed at transmitting messages to a community of vehicles in the same geographical area, utilizing GPS information to create smart routing decisions. The GeoAKOM protocol introduces a method of predicting vehicle movement and making routing decisions based on mobility likelihood values, providing a robust solution for vehicles with limited GPS record history. Simulation results show promising performance compared to other routing algorithms in terms of delivery ratio, average delay, and hop count.
12TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT) / THE 4TH INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40) / AFFILIATED WORKSHOPS
(2021)
Article
Engineering, Electrical & Electronic
Tohid Behdadnia, Yusuf Yaslan, Istemihan Genc
Summary: This paper proposes a new approach that generates realistic dataset through a new simulator, analyzes the distortion of measurement data, identifies intervals of measurement errors, and suggests a new data arrangement method to effectively remove erroneous parts and enhance the accuracy of transient stability prediction.
IET GENERATION TRANSMISSION & DISTRIBUTION
(2021)
Proceedings Paper
Engineering, Electrical & Electronic
Sevda Jafarzadeh, Nazanin Moarref, Yusuf Yaslan, V. M. Istemihan Genc
2019 11TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONICS ENGINEERING (ELECO 2019)
(2019)
Proceedings Paper
Engineering, Electrical & Electronic
Mert Kesici, Can Berk Saner, Yusuf Yaslan, V. M. Istemihan Genc
2019 11TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONICS ENGINEERING (ELECO 2019)
(2019)
Proceedings Paper
Engineering, Electrical & Electronic
Nazanin Moarref, Sevda Jafarzadeh, Yusuf Yaslan, V. M. Istemihan Genc
2019 11TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONICS ENGINEERING (ELECO 2019)
(2019)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Can Berk Saner, Mert Kesici, Mohammed Mahdi, Yusuf Yaslan, V. M. Istemihan Genc
2019 7TH INTERNATIONAL ISTANBUL SMART GRIDS AND CITIES CONGRESS AND FAIR (ICSG ISTANBUL 2019)
(2019)
Article
Engineering, Multidisciplinary
Sicheng Jiao, Shixiang Wang, Minge Gao, Min Xu
Summary: This paper presents a non-contact method of thickness measurement for thin-walled rotary shell parts based on a chromatic confocal sensor. The method involves using a flip method to obtain surface profiles from both sides of the workpiece, measuring the decentration and tilt errors of the workpiece using a centering system, establishing a unified reference coordinate system, reconstructing the external and internal surface profiles, and calculating the thickness. Experimental results show that the method can accurately measure the thickness of a sapphire spherical shell workpiece and is consistent with measurements of other materials.
Article
Engineering, Multidisciplinary
Rajeev Kumar, Sajal Agarwal, Sarika Pal, Alka Verma, Yogendra Kumar Prajapati
Summary: This study evaluated the performance of a CaF2-Ag-MXene-based surface plasmon resonance (SPR) sensor at different wavelengths. The results showed that the sensor achieved the maximum sensitivity at a wavelength of 532 nm, and higher sensitivities were obtained at shorter wavelengths at the expense of detection accuracy.
Article
Engineering, Multidisciplinary
Attilio Di Nisio, Gregorio Andria, Francesco Adamo, Daniel Lotano, Filippo Attivissimo
Summary: Capacitive sensing is a widely used technique for a variety of applications, including avionics. However, current industry standard Capacitive Level Sensors (CLSs) used in helicopters perform poorly in terms of sensitivity and dynamic characteristics. In this study, novel geometries were explored and three prototypes were built and tested. Experimental validation showed that the new design featuring a helicoidal slit along the external electrode of the cylindrical probe improved sensitivity, response time, and linearity.
Article
Engineering, Multidisciplinary
Kai Yang, Huiqin Wang, Ke Wang, Fengchen Chen
Summary: This paper proposes an effective measurement method for dynamic compaction construction based on time series model, which enables real-time monitoring and measurement of anomalies and important construction parameters through simulating motion state transformation and running time estimation.
Article
Engineering, Multidisciplinary
Hui Fu, Qinghua Song, Jixiang Gong, Liping Jiang, Zhanqiang Liu, Qiang Luan, Hongsheng Wang
Summary: An automatic detection and pixel-level quantification model based on joint Mask R-CNN and TransUNet is developed to accurately evaluate microcrack damage on the grinding surfaces of engineering ceramics. The model is effectively trained on actual micrograph image dataset using a joint training strategy. The proposed model achieves reliable automatic detection and fine segmentation of microcracks, and a skeleton-based quantification model is also proposed to provide comprehensive and precise measurements of microcrack size.
Review
Engineering, Multidisciplinary
Sang Yeob Kim, Da Yun Kwon, Arum Jang, Young K. Ju, Jong-Sub Lee, Seungkwan Hong
Summary: This paper reviews the categorization and applications of UAV sensors in forensic engineering, with a focus on geotechnical, structural, and water infrastructure fields. It discusses the advantages and disadvantages of sensors with different wavelengths and addresses the challenges of current UAV technology and recommendations for further research in forensic engineering.
Article
Engineering, Multidisciplinary
Anton Nunez-Seoane, Joaquin Martinez-Sanchez, Erik Rua, Pedro Arias
Summary: This article compares the use of Mobile Laser Scanners (MLS) and Aerial Laser Scanners (ALS) for digitizing the road environment and detecting road slopes. The study found that ALS data and its corresponding algorithm achieved better detection and delimitation results compared to MLS. Measuring the road from a terrestrial perspective negatively impacted the detection process, while an aerial perspective allowed for scanning of the entire slope structure.
Article
Engineering, Multidisciplinary
Nur Luqman Saleh, Aduwati Sali, Raja Syamsul Azmir Raja Abdullah, Sharifah M. Syed Ahmad, Jiun Terng Liew, Fazirulhisyam Hashim, Fairuz Abdullah, Nur Emileen Abdul Rashid
Summary: This study introduces an enhanced signal processing scheme for detecting mouth-click signals used by blind individuals. By utilizing additional band-pass filtering and other steps, the detection accuracy is improved. Experimental results using artificial signal data showed a 100% success rate in detecting obstacles. The emerging concepts in this research are expected to benefit radar and sonar system applications.
Article
Engineering, Multidisciplinary
Jiqiang Tang, Shengjie Qiu, Lu Zhang, Jinji Sun, Xinxiu Zhou
Summary: This paper studies the magnetic noise level of a compact high-performance magnetically shielded room (MSR) under different operational conditions and establishes a quantitative model for magnetic noise calculation. Verification experiments show the effectiveness of the proposed method.
Review
Engineering, Multidisciplinary
Krzysztof Bartnik, Marcin Koba, Mateusz Smietana
Summary: The demand for miniaturized sensors in the biomedical industry is increasing, and optical fiber sensors (OFSs) are gaining popularity due to their small size, flexibility, and biocompatibility. This study reviews various OFS designs tested in vivo and identifies future perspectives and challenges for OFS technology development from a user perspective.
Article
Engineering, Multidisciplinary
Yue Wang, Lei Zhou, Zihao Li, Jun Wang, Xuangou Wu, Xiangjun Wang, Lei Hu
Summary: This paper presents a 3-D reconstruction method for dynamic stereo vision of metal surface based on line structured light, overcoming the limitation of the measurement range of static stereo vision. The proposed method uses joint calibration and global optimization to accurately reconstruct the 3-D coordinates of the line structured light fringe, improving the reconstruction accuracy.
Article
Engineering, Multidisciplinary
Jaafar Alsalaet
Summary: Order tracking analysis is an effective tool for machinery fault diagnosis and operational modal analysis. This study presents a new formulation for the data equation of the second-generation Vold-Kalman filter, using separated cosine and sine kernels to minimize error and provide smoother envelopes. The proposed method achieves high accuracy even with small weighting factors.
Article
Engineering, Multidisciplinary
Tonglei Cao, Kechen Song, Likun Xu, Hu Feng, Yunhui Yan, Jingbo Guo
Summary: This study constructs a high-resolution dataset for surface defects in ceramic tiles and addresses the scale and quantity differences in defect distribution. An improved approach is proposed by introducing a content-aware feature recombination method and a dynamic attention mechanism. Experimental results demonstrate the superior accuracy and efficiency of the proposed method.
Article
Engineering, Multidisciplinary
Qinghong Fu, Yunxi Lou, Jianghui Deng, Xin Qiu, Xianhua Chen
Summary: Measurement and quantitative characterization of aging-induced gradient properties is crucial for accurate analysis and design of asphalt pavement. This research proposes the composite specimen method to obtain asphalt binders at different depths within the mixture and uses dynamic shear rheometer tests to measure aging-induced gradient properties and reveal internal mechanisms. G* master curves are constructed to investigate gradient aging effects in a wide range. The study finds that the composite specimen method can effectively restore the boundary conditions and that it is feasible to study gradient aging characteristics within the asphalt mixture. The study also observes variations in G* and delta values and the depth range of gradient aging effects for different aging levels.
Article
Engineering, Multidisciplinary
Min Li, Kai Wei, Tianhe Xu, Yali Shi, Dixing Wang
Summary: Due to the limitations of ground monitoring stations in China for the BDS, the accuracy of BDS Medium Earth Orbit (MEO) satellite orbits can be influenced. To overcome this, low Earth orbit (LEO) satellites can be used as additional monitoring stations. In this study, data from two LEO satellites were collected to improve the precise orbit determination of the BDS. By comparing the results with GPS and BDS-2/3 solutions, it was found that including the LEO satellites significantly improved the accuracy of GPS and BDS-2/3 orbits.
