[{"name":"Intelligent Control and Optimization of Complex Systems","link":"","figures":[],"children":[{"name":"New energy system based on artificial intelligence","link":"","figures":[],"description":"

Research on new energy systems based on artificial intelligence is mainly carried out from the aspects of power grid control and power market transactions.

Power grid control aims to monitor, dispatch and manage power systems to ensure safe, stable and efficient power supply. Traditional power grid control faces challenges such as low data processing efficiency, slow response speed and poor adaptability. Power grid control technology based on artificial intelligence, including machine learning, deep learning, reinforcement learning and big data analysis, can significantly improve the efficiency and reliability of power grid operation. Specific research content covers load forecasting and power generation forecasting, intelligent dispatching optimization, demand response management, distributed energy management and safety and stability analysis.

Power market transactions are the process of buying and selling electricity through market mechanisms, aiming to optimize resource allocation through free transactions between supply and demand parties, and achieve efficient production and reasonable distribution of electricity. Traditional power market transactions rely on manual analysis and experience judgment, and face problems such as large market fluctuations, low transaction efficiency and difficulty in risk management. Power market transactions based on artificial intelligence use AI technology to analyze market dynamics and historical data, optimize transaction decisions and improve market efficiency. This study predicts power price trends through machine learning and deep learning algorithms to help power generation companies and consumers formulate accurate trading strategies. Specific contents include algorithmic trading, demand forecasting, price forecasting, risk management, support for new trading models, etc.

"},{"name":"Intelligent cluster control of unmanned systems","link":"","figures":[],"description":"

Intelligent cluster control of unmanned systems uses advanced artificial intelligence and collaborative control technologies to achieve intelligent, automated and collaborative management and control of multiple unmanned systems (such as drones, unmanned vehicles, unmanned ships, etc.). The research content mainly includes task allocation and collaboration, path planning, environmental perception and obstacle avoidance, autonomous decision-making, communication and coordination, etc. This research uses technologies such as reinforcement learning, deep learning, distributed algorithms and multi-agent systems to achieve autonomous navigation, real-time environmental perception and dynamic obstacle avoidance of unmanned systems. Specifically, it includes efficient task allocation algorithms, path planning technology based on deep neural networks, environmental perception systems that integrate multi-sensor data, and network protocols that support efficient and low-latency communication. The research aims to enable multiple unmanned systems to complete complex tasks autonomously and efficiently, and is widely used in military, agriculture, logistics, environmental monitoring and disaster relief, so as to improve the efficiency and effectiveness of task execution and reduce labor costs and risks.

"},{"name":"Multi-agent reinforcement learning","link":"","figures":[],"description":"

The research on multi-agent reinforcement learning focuses on studying the techniques and methods of how multiple agents learn optimal decision-making strategies through interaction in complex dynamic environments. The research includes collaborative and competitive behavior modeling, strategy coordination and conflict resolution, and multi-agent system optimization. The purpose of the research is to develop algorithms that can achieve effective cooperation or competition between agents to improve the performance and efficiency of the overall system. The main research contents include the design and optimization of multi-agent reinforcement learning algorithms, the development of distributed decision-making coordination mechanisms, adaptive learning and adaptive control in complex environments, etc. Through advanced technologies such as deep reinforcement learning, evolutionary algorithms, and game theory, this research explores the application potential in the fields of autonomous driving, robot collaboration, the Internet of Things, and social networks, and provides a theoretical basis and innovative solutions for promoting the intelligence level and effect of multi-agent systems in practical applications.

"}]},{"name":"Artificial Intelligence","link":"","figures":[],"children":[{"name":"Data-driven industrial intelligence","link":"","figures":[],"description":"

The research direction of data-driven industrial intelligence focuses on using big data analysis and artificial intelligence technology to improve the intelligence level of industrial production and operation processes. The research includes industrial data collection and cleaning, data mining and predictive analysis, intelligent manufacturing process optimization, and industrial equipment health management. The research goal is to develop algorithms and systems that can effectively use industrial data to achieve accurate monitoring and prediction, so as to optimize production efficiency, reduce costs and resource consumption. The main research contents include real-time data stream processing, large-scale data storage and management, the application of machine learning models in industrial prediction and control, and the integration of intelligent sensor networks and edge computing technologies. The research uses deep learning, neural networks, Internet of Things technology and cloud computing platforms to explore data-driven solutions in multiple industrial application scenarios such as manufacturing, energy, logistics and supply chain management. This technology not only helps to improve the intelligence and flexibility of production lines, but also provides data support for corporate decision-making, promotes industrial intelligent transformation, and improves competitiveness and sustainable development capabilities.

"},{"name":"Counterattack and Defense","link":"","figures":[],"description":"

The purpose of adversarial attack and defense research is to improve the security and robustness of artificial intelligence systems. This research uses deep learning, generative adversarial networks (GANs), optimization algorithms, and multimodal learning techniques to evaluate the robustness of AI models in adversarial environments, and to improve the stability and reliability of models in the face of attacks by specifying corresponding defense strategies. This research is widely used in image recognition, natural language processing, autonomous driving, network security, and other fields, providing a solid theoretical foundation and technical support for building safe and reliable AI systems, ensuring their credibility and effectiveness in practical applications.

"},{"name":"Computer Vision","link":"","figures":[],"description":"

Computer vision research focuses on studying and developing technologies and methods that enable computer systems to acquire, understand and process information from images or videos. This research covers key areas such as image recognition and classification, target detection and tracking, image segmentation and semantic understanding, as well as three-dimensional reconstruction and visual SLAM (Simultaneous Localization and Mapping). The research goal is to improve the visual perception ability and analysis accuracy of computer systems in different scenarios through deep learning, traditional computer vision algorithms and multimodal data fusion technology. The main research contents include developing efficient visual feature extraction algorithms, optimizing convolutional neural network structures, exploring multi-task learning and weakly supervised learning technologies, and their applications in practical applications such as medical image analysis, intelligent transportation, and intelligent manufacturing. This research can promote the innovative application of computer vision technology in the fields of autonomous driving, security monitoring, human-computer interaction, etc., and provide society with safer and more intelligent solutions.

"}]},{"name":"Intelligent Robots","link":"","figures":[],"children":[{"name":"Robot navigation based on generative methods","link":"","figures":[],"description":"

Research on robot navigation based on generative methods focuses on using generative models and deep learning techniques to achieve intelligent navigation and path planning of robots in complex environments. The research covers several key areas, including generative adversarial networks (GANs), variational autoencoders (VAEs), reinforcement learning, and multi-agent systems. The purpose of this research is to develop algorithms that can generate high-quality navigation paths while taking into account challenges such as dynamic environmental changes, obstacle avoidance, and real-time positioning. Through the combination of deep learning models and real-time sensor data, the research is committed to improving the accuracy and efficiency of robot navigation to adapt to increasingly complex and changing real-world scenarios. These technologies are not only used in autonomous vehicles and service robots, but can also play an important role in industrial automation, medical and health fields, and provide technical support and innovative solutions for the further development and practical application of robotics.

"},{"name":"Intelligent Robot Environment Exploration","link":"","figures":[],"description":"

The research on intelligent robot environmental exploration focuses on developing and optimizing the autonomous exploration capabilities of robots in unknown or complex environments. The research covers key areas such as robot perception and positioning, path planning and decision-making, environmental modeling and understanding, and multi-sensor fusion technology. The purpose of this research is to explore how to enable robots to efficiently perceive the surrounding environment, build accurate environmental maps, and make intelligent path planning and decisions based on deep learning, reinforcement learning, and adaptive control algorithms. Key technologies include visual SLAM (Simultaneous Localization and Mapping), lidar data processing, and the application of machine learning models in unknown environments. This research can enhance the navigation and task execution capabilities of robots in complex and dynamic environments to cope with increasingly complex and changing application requirements. This research will be applied to commercial applications such as automated warehousing and smart homes, and can also play an important role in rescue missions, expedition science, and other fields, providing cutting-edge technical support and innovative solutions for the promotion and practical application of intelligent robot technology.

"},{"name":"Intelligent robot confrontation/game","link":"","figures":[],"description":"

Intelligent robot confrontation/game research focuses on exploring the intelligent decision-making and behavior of robots in confrontational or game scenarios. This research includes game theory, reinforcement learning, multi-agent systems and robot decision theory. The purpose of this research is to develop algorithms and methods that can make decisions and interact in dynamic and uncertain environments. It mainly includes game and competition strategies between robots, decision optimization in confrontational environments, and analysis of multi-agent collaboration and competition behaviors. This research uses advanced technologies such as deep reinforcement learning, evolutionary algorithms, and game theory models to improve the intelligent performance and adaptability of robots in complex interactive scenarios. This research can be applied to autonomous driving vehicles, behavior optimization of service robots, as well as military confrontation, intelligent games, and social robots, providing a theoretical basis and technical support for promoting cutting-edge research and practical applications of intelligent robot technology.

"}]}]