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WF-Velo: A New Method for RNA Velocity Analysis Based on Single-Cell data

WF-Velo: A New Method for RNA Velocity Analysis Based on Sin...

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data driven control and learning systems (ddcls)

作者： Cheng Li Wenjie Cao Zihao Zhang Wenbo Tang Yazhou Shi Ben-gong Zhang School of Mathematical & Physical Science Wuhan Textile University Wuhan China Sun Yat-Sen University Guangzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the direction of cell differentiation is very important to study the process of cell differentiation. there was a strong hypothesis that the direction was fixed in previous studies until the RNA velocity method appearance. It is a powerful tool to analyze cell differentiation. However, there is still some problem of it due to the low expression of unspliced mRNA readings and high randomness. this paper intends to propose a new method called WF-velo which means the whole feature (WF) are used during the process of RNA velocity estimations, while the part feature was used by the traditional method, to overcome the problem of the existed methods. By using the characteristics of the gene in the whole transcription process, the RNA velocity of cells can be estimated more realistically, and the cell differentiation process is more in line with biological phenomena compared with the cellDancer, scvelo, Deepvelo and other RNA velocity estimation algorithms. the pancreatic endocrine development dataset and the mouse hippocampus development dataset are tested by WF-velo. the cell differentiation trajectory shows that it is a good method to estimate the RNA velocity.

关键词： learning systems RNA Estimation control systems Biology Trajectory Hippocampus

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Informer-LSTM-EWMA for Panax Notoginseng with Prediction of Early Intervention Time

Informer-LSTM-EWMA for Panax Notoginseng with Prediction of ...

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data driven control and learning systems (ddcls)

作者： Jiahui Ye Chunxi Yang Xiufeng Zhang Jin Zhang Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Panax notoginseng is one of economic crop with significant medicinal value in southwest China. However, traditional extensive fertilization and irrigation method usually damages the soil, making it difficult to achieve sustainable cultivation of Panax notoginseng. In order to assess the growth status of Panax notoginseng and take timely action, this paper proposed an Informer-LSTM-EWMA model to predict the growth trend of Panax notoginseng in cultivation fields. In the experimental greenhouse, we set up two experimental fields A and B to verify the feasibility of the proposed method. Firstly, wavelet denoising is utilized on the date collected by the sensor. then, Informer and Long short-term Memory (LSTM) are used to train the denoised data to obtain data prediction. Further process the predicted data through Exponential Weighted Moving Average (EWMA) for trend prediction. Finally, the early intervention time is obtained by searching for the maximum value. the results indicate that the model can predict early intervention time and its predictive performance is far better than that of a single model.

关键词： learning systems Economics Irrigation Noise reduction Crops Predictive models Soil

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Optimal Load Frequency control for Heterogeneous Frequency Regulation systems Based on Distributed Model Predictive control

Optimal Load Frequency Control for Heterogeneous Frequency R...

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data driven control and learning systems (ddcls)

作者： Zhongwen Li Ningning Bai Zhiping Cheng Zhengzhou University Zhengzhou P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

With the increasing penetration rate of renewable energy and the diversified coupling relationship between generation cost and unit emission cost, the control problem of power system becomes more difficult. In order to solve the problems of load frequency control and economic load dispatch in complex power systems, this paper proposes an automatic generation control (AGC) method based on distributed adaptive model predictive control (DAMPC). Firstly, a two-area interconnected AGC system containing heterogeneous power sources is constructed. then, considering that heterogeneous generators have different generation characteristics and generation costs, changes in unit output during the current control period are bound to have an impact on the subsequent control period, so DAMPC is used to optimize the unit output in short time scale. In addition, to ensure the economic load dispatch of the power system while ensuring the frequency stability of the power system, the generation cost and emission cost of heterogeneous power sources are considered in the DAMPC optimization objective. Finally, by comparing with other AGC methods, the effectiveness of the proposed optimal load frequency control method based on DAMPC is verified.

关键词： Renewable energy sources Costs Adaptive systems Uncertainty Stability criteria Automatic generation control Power system stability

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Reinforcement learning with Neighborhood Search and Self-learned Rules

Reinforcement Learning with Neighborhood Search and Self-lea...

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data driven control and learning systems (ddcls)

作者： Xiao Yin Yuanyang Zhu Chunlin Chen Department of Control Science and Intelligent Engineering Nanjing University Nanjing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Reinforcement learning (RL), induced by the exploration-exploitation dilemma, usually requires enough interaction experiences with the environment to improve performance. Rule-based methods utilize the internal expertise of tasks to benefit the learning process of the target task and can be shared with similar goal-setting tasks, which is also one form of knowledge transfer with better interpretability. However, symbolic rules or prior knowledge of the task may not be obtained in advance for most control tasks. In this paper, we propose an effective approach named neighborhood search and self-learned rules (RL-NSSR), where the neighborhood search library and the rules can be learned from the samples of a set of collected trajectories. In the RL- NSSR, first, the expert policy is obtained by training the RL agent in the discretized continuous state-action space task for collecting the trajectories data. Second, we extract the rules using the collected data via ensemble decision tree methods, and select the states of the trajectories with higher cumulative rewards as the neighborhood states to speed up the learning process. third, we employ both the neighborhood search and the self-learned symbolic rules in a fine-grained way and a rough way, respectively, to guide the exploration strategy for accelerating the learning process. Experiments conducted on several groups of locomotion tasks by the MuJoCo physics engine demonstrate that the proposed RL-NSSR approach can achieve improved control performances for both simple and complex tasks.

关键词： Training learning systems Reinforcement learning Libraries Trajectory Decision trees Task analysis

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Enhancing Autonomous Driving: A Scalable Multi-Layered DRL Approach for Decision and control

Enhancing Autonomous Driving: A Scalable Multi-Layered DRL A...

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data driven control and learning systems (ddcls)

作者： Liangguang Wu Kailong Zhou Ting Fei Maojia Yao Hao Han Honglin Li Dongfeng Motor Corporation Research and Development Institute Wuhan P. R. China School of Automotive Engineering Wuhan University of Technology Wuhan P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Autonomous driving technology is rapidly progressing, promising significant societal benefits. this paper presents an advanced DRL-based framework for end-to-end autonomous vehicle decision-making and control. Our technical proposal is articulated through a scalable, multi-layered architecture that integrates sensory data aggregation, trajectory prediction, and dynamic control layers. the framework is distinctive for its Actor-Critic approach, where the Critic network evaluates potential trajectories and the Actor network implements the control commands, ensuring safety and compliance with traffic norms. the adaptability and robustness of our system are validated in extensive simulations and real-world trials, demonstrating its efficacy across various sensory system configurations. this research contributes to the autonomous driving domain by providing a versatile and reliable framework for intelligent vehicle navigation. the methodologies and systems presented in this paper have undergone rigorous validation through simulation experiments and practical real-vehicle tests, confirming the viability and effectiveness of our advanced DRL-based framework for autonomous vehicle decision-making and control.

关键词： Adaptation models Navigation Decision making Robustness Regulation Trajectory Safety

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DA-TCN Based Temperature Prediction Algorithm for Heating Furnace

DA-TCN Based Temperature Prediction Algorithm for Heating Fu...

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data driven control and learning systems (ddcls)

作者： Yidan Xin Shaolin Hu Guotao Yang Xi'an University of Technology Xi'an China Guangdong University of Petrochemical Technology Maoming China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

During the operation of the heating furnace, accurate perception and advance prediction of the furnace temperature are of great significance for preventing safety hazards in the petrochemical process and ensuring the safe operation of the heating furnace. Traditional prediction models have low accuracy in predicting actual working condition data. this paper proposes a Dual-Stage Attention-Based Temporal Convolutional Network (short as DA-TCN) prediction model based on the dual-stage attention mechanism (short as DA). the input variable attention mechanism and time attention mechanism are used to weight sequence features, and then the furnace temperature of the heating furnace is predicted based on the temporal convolutional neural network. the effectiveness of the proposed method is validated based on the sampled data. Measured data are used to validate the effectiveness of this method. the prediction results t of the DA-TCN model, CNN-LSTM model, and DA-LSTM model are compared, which shows that the proposed prediction model can effectively improve the prediction accuracy. Compared with the latter two, the root mean square error of the DA-TCN model decreased by 55% and 89%, and the mean absolute error decreased by 74% and 85%, respectively, which further demonstrates the feasibility and effectiveness of the proposed method.

关键词： Heating systems Temperature measurement Accuracy Attention mechanisms Furnaces Neural networks Predictive models

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Establishment and Analysis of Passive Localization Model for a Class of Unmanned Aerial Vehicle systems

Establishment and Analysis of Passive Localization Model for...

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data driven control and learning systems (ddcls)

作者： Liwen Yu Qianru Yang Yuqian Li Ruikun Zhang School of Mathematics and Physics Qingdao University of Science and Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In order to avoid the interference of external magnetic field, UAV swarms should keep electromagnetic silence while flying, and using pure azimuth passive localization to adjust the position of UAVs is an effective method. the pure azimuth passive localization problem for UAVs includes two scenarios: known and unknown positions and quantities of UAVs emitting signals. For the case that the number and position of the three UAVs transmitting signals are known, a localization model based on the sine-cosine theorem, iteration and other methods for passively receiving signal UAVs is established for adjusting the position of the UAVs in order to realize the effective adjustment of the position of the UAVs. For the case that the number and quantity of some UAVs transmitting signals are unknown, this paper proposes an effective localization model of UAVs with a slight deviation of a certain position based on the polar coordinate localization method and the characteristics of the hidden circle model. Meanwhile, the relevant properties of the sine-cosine function are utilized to determine the uniqueness of the solution. Finally, the accuracy of the model is verified by numerical simulation. the results show that the theoretical credibility of the method proposed in this paper is strong and provides a more reliable theoretical support for the research in the field of UAV technology.

关键词： Location awareness Machine learning algorithms Accuracy Azimuth Reliability theory Sensor fusion Autonomous aerial vehicles

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Application-Oriented State-of-Charge Estimation of Lithium-ion Batteries Based on Appropriate Modeling and EKF

Application-Oriented State-of-Charge Estimation of Lithium-i...

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data driven control and learning systems (ddcls)

作者： Chang Luo Yan Li Tong Yang Jin You Huiqin Xu School of Control Science and Engineering Shandong University Jinan P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the state of charge (SOC) estimation is essential for battery management systems (BMS), necessitating proper modeling and filtering approaches. this study zeroes in on enhancing the accuracy of SOC estimation of battery thanks to an appropriate modeling technique. Considering comprehensive criteria for model structure identification (AIC, BIC, CIC), a second-order RC equivalent circuit model has been chosen. Whose estimation of parameters is transformed into a nonlinear optimization challenge. the cuckoo search algorithm, acclaimed for multi-objective optimization, is employed with a well-defined fitness function, facilitating the determination of optimized parameters. Subsequently, in view of real-world applications, the extended Kalman filter (EKF) algorithm is applied to estimate the SOC with the above optimized second order model. Experimental data analysis affirms that, with appropriate dimensions and iterations, this algorithm efficiently and accurately solves for modeling and SOC estimation of lithium-ion batteries, as well as bolstering its advantages in BMS applications.

关键词： Lithium-ion batteries Heuristic algorithms Estimation Battery management systems Transforms State of charge Kalman filters

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Dynamic Programming Based Trajectory Optimization of Electric Multiple Units during Emergency Self-Rescue

Dynamic Programming Based Trajectory Optimization of Electri...

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data driven control and learning systems (ddcls)

作者： Xi Yang Deqing Huang Qingyuan Wang Tangshan Institute of Southwest Jiaotong University Tangshan China School of Electrical Engineering Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this study investigates an optimization algorithm for emergency self-rescue of electric multiple units (EMUs) in the event of short or long-term train power supply interruptions caused by power system failures, natural disasters, contact wire failures, and other incidental factors. Firstly, a model is established to facilitate the emergency self-propelled running of trains while considering special situations such as power outages and incorporating the utilization conditions for regenerative braking energy. Secondly, an optimization algorithm based on dynamic programming (DP) is developed to address operational challenges during train power outages by efficiently utilizing regenerative braking energy through rapid decision-making. this approach minimizes emergencies' impact by initially determining the train's operational direction and subsequently optimizing its safe operation within limited energy resources. the proposed method enables quick computation of speed curve and control strategy, ensuring safe and efficient operation towards the nearest station. Finally, simulation results validate the efficacy of this algorithm in emergency train operations.

关键词： learning systems Power supplies Heuristic algorithms Simulation Decision making Dynamic programming Power system reliability

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A Brief Survey of Deep Reinforcement learning for Intersection Navigation of Autonomous Vehicles

A Brief Survey of Deep Reinforcement Learning for Intersecti...

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data driven control and learning systems (ddcls)

作者： Yuqi Liu Qichao Zhang Dongbin Zhao Institute of Automation Chinese Academy of Sciences Beijing P. R. China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper presents a brief survey of deep reinforcement learning (DRL) for intersection navigation in autonomous driving. Intersection navigation poses significant challenges for autonomous driving (AD), considering the dynamic environment, high complexity, and safety requirements. DRL has emerged as a powerful learning framework in the field of AD, enabling agents to learn interactive driving policies in complex intersection scenarios. the paper begins by outlining the architecture of a general AD system. It then elaborates on the concepts of DRL. Additionally, the paper discusses emerging technologies such as social attention and graph attention mechanisms, which enhance the performance of DRL-based AD driving policies. Finally, the challenges and future prospects are also given. Challenges include complex intersection scenarios, generalization across different layouts, and the lack of standardized benchmarks and evaluation metrics. Overcoming these challenges and leveraging future advancements can lead to improved safety and efficiency in road traffic.

关键词： Surveys Measurement Attention mechanisms Navigation Layout Benchmark testing Safety

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