Multi-Agent Coordinated Dispatch of Power Grid and Pumped Hydro Storage with Embedded Market Game Model by LOU Wei, HU Rong, YU Jinming, ZHANG Xipeng, FAN Feilong, LIU Songyuan

“…The test results show that the proposed method can effectively coordinate the decision-making of direct grid dispatching and pumped storage participation in the electric energy spot market, enhancing the economic benefits of pumped storage, reducing the operating cost of the grid, and improving the consumption of renewable energy.…”
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A 5G heterogeneous network deployment strategy for power distribution network by Zhang Ping, Qiu Chao, Zhang Linjuan, Zheng Zheng, Fan Bing, Xin Yanan

“…Simulation results show that TI-BSDS is superior to the comparison algorithms in terms of weighted signal-to-noise ratio and deployment cost, and TI-BSDS can effectively improve the deployment benefit of 5G networks in power distribution networks.…”
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Adaptive Anomaly Detection in Network Flows With Low-Rank Tensor Decompositions and Deep Unrolling by Lukas Schynol, Marius Pesavento

“…An augmentation of the objective is introduced to decrease the computational cost. We apply deep unrolling to derive a novel deep network architecture based on our proposed algorithm, treating the regularization parameters as learnable weights. …”
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Simple and High-Precision Hamiltonian Simulation by Compensating Trotter Error with Linear Combination of Unitary Operations by Pei Zeng, Jinzhao Sun, Liang Jiang, Qi Zhao

“…For the lattice system, the second algorithm can achieve 3 to 4 orders of magnitude higher accuracy with the same gate costs as the optimal Trotter algorithm. …”
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Pig behavior recognition and disease warning based on compressed sensing and long-short term memory network by Ren Wang, Mingdong Zhao

“…By means of ellipse fitting based on the minimum cost function and target tracking based on the shortest distance matching, we design four motion information detection algorithms including motion displacement, motion velocity, motion acceleration and motion angular velocity. …”
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Driving Pattern Analysis, Gear Shift Classification, and Fuel Efficiency in Light-Duty Vehicles: A Machine Learning Approach Using GPS and OBD II PID Signals by Juan José Molina-Campoverde, Juan Zurita-Jara, Paúl Molina-Campoverde

“…Such integration could optimize gear shift timing based on dynamic factors like road conditions, traffic density, and driver behavior, ultimately contributing to improved fuel efficiency and overall vehicle performance.…”
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Methods to Quantitatively Evaluate the Effect of Shale Gas Fracturing Stimulation Based on Least Squares by DENG Cai, SUN Kexin, WEN Huan, HU Chaolang

“…Its significance to the industry lay in addressing the major challenge of the absence of a cost-effective, quantifiable assessment method. It offered detailed insights into the effectiveness of fracturing (in terms of the number and quality of perforations), enabling engineers to improve the fracturing stimulation process for improved production results. …”
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A Hybrid Method Based on the Iterative Fourier Transform and the Differential Evolution for Pattern Synthesis of Sparse Linear Arrays by Xin-Kuan Wang, Gui-Bao Wang

“…Therefore, the SLA with improved sidelobe performance can be obtained at relative low hardware cost. …”
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Estimation of Current RMS for DC Link Capacitor of S-PMSM Drive System by ZHANG Zhigang, CHANG Jiamian, ZHANG Pengcheng

“…The Cotes method eliminates numerous integration calculations, thus improving calculation accuracy. The proposed technique simplifies the tedious calculation process of traditional algorithms and guarantees high calculation accuracy, providing guidance for optimizing the selection of DC link capacitors and the design of life monitoring controllers. …”
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Robust Planning for Hydrogen-Based Multienergy System Considering P2HH and Seasonal Hydrogen Storage by Shufan Wang, Dong Yang, Linglu Zhang, Lingzhi Chenmei

“…To tackle the unstable factors introduced by renewable sources and varying loads, we apply robust optimization and stochastic programming theory to improve the robustness of the planning results. …”
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AIBPO: Combine the Intrinsic Reward and Auxiliary Task for 3D Strategy Game by Huale Li, Rui Cao, Xuan Wang, Xiaohan Hou, Tao Qian, Fengwei Jia, Jiajia Zhang, Shuhan Qi

“…Finally, a framework of auxiliary intrinsic-based policy optimization (AIBPO) is proposed, which improves the performance of the IBPO. …”
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A Multi-Strategy ALNS for the VRP with Flexible Time Windows and Delivery Locations by Xiaomei Zhang, Xinchen Dai, Ping Lou, Jianmin Hu

“…In particular, the last-mile delivery is particularly important because it serves customers directly. Improving customer satisfaction is one of the important factors to ensure the quality of service in delivery and also an important guarantee for improving the market competitiveness of logistics enterprises. …”
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Maximum Energy Absorbed from the Persian Gulf Waves Considering Uncertainty in Power Take off Parameters by Mohammad Jalali, Reihaneh Kardehi Moghaddam, Naser Pariz

“…Compared to particle swarm optimization and conventional black hole algorithm, the results of the proposed method indicate enhancements in reference velocity tracking and absorbed power. …”
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GNSS Precipitable Water Vapor Prediction for Hong Kong Based on ICEEMDAN-SE-LSTM-ARIMA Hybrid Model by Jie Zhao, Xu Lin, Zhengdao Yuan, Nage Du, Xiaolong Cai, Cong Yang, Jun Zhao, Yashi Xu, Lunwei Zhao

“…Enhanced by local mean optimization and adaptive noise regulation, the ICEEMDAN algorithm effectively suppresses pseudo-modes and minimizes residual noise, enabling its decomposed intrinsic mode functions (IMFs) to more accurately capture the multi-scale features of GNSS-PWV. …”
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A Deep Learning Method for Photovoltaic Power Generation Forecasting Based on a Time-Series Dense Encoder by Xingfa Zi, Feiyi Liu, Mingyang Liu, Yang Wang

“…Deep learning has become a widely used approach in photovoltaic (PV) power generation forecasting due to its strong self-learning and parameter optimization capabilities. In this study, we apply a deep learning algorithm, known as the time-series dense encoder (TiDE), which is an MLP-based encoder–decoder model, to forecast PV power generation. …”
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Power Control for Full-Duplex Device-to-Device Underlaid Cellular Networks: A Stackelberg Game Approach by Zhen Yang, Titi Liu, Guobin Chen

“…The simulation results show that the proposed game algorithm improves network performance compared with other existing schemes.…”
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Application of Machine Learning to Statistical Evaluation of Artificial Rainfall Enhancement by Li Dan, Lin Wen, Liu Qun, Feng Hongfang, Hu Shuping, Wang Zhihai

“…Each statistical model is greatly improved after six root square transformations of rainfall data. …”
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Application of Markov processes for analysis and control of aircraft maintainability by Yu. M. Chinyuchin, A. S. Solov'ev

“…In order to reduce the number of the mathematical operations for the analysis of aeronautical engineering maintainability by using non-stationary Markov processes an algorithm for their optimization is presented. The suggested methods of the analysis by means of Markov chains allow to execute comparative assessments of expected maintenance and repair costs for one or several one-type objects taking into account their original conditions and operation time. …”
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High-Resolution and Robust One-Bit Direct-of-Arrival Estimation via Reweighted Atomic Norm Estimation by Rui Li, Jianchao Yang, Zheng Dai, Xingyu Lu, Ke Tan, Weimin Su

“…In recent years, one-bit quantization has attracted widespread attention in the field of direction-of-arrival (DOA) estimation as a low-cost and low-power solution. Many researchers have proposed various estimation algorithms for one-bit DOA estimation, among which atomic norm minimization algorithms exhibit particularly attractive performance as gridless estimation algorithms. …”
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Research on RF Intensity Temperature Sensing based on 1D-CNN by DING Meiqi, GUI Lin, WANG Ziyi, SHANG Disen, QIAN Min, LI Qiankun

“…Compared with the traditional Gaussian fitting algorithm, the demodulation speed of the 1D-CNN-based algorithm is improved by 2.72 times. 1D-CNN shows high stability and low error under different temperature conditions.…”
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