Artificial intelligence (AI) is one of the most rapidly developing technologies today, and it is gradually playing a role in practical applications. To enhance everyone's understanding of AI, this article will introduce some of its applications so that you can understand how AI can be put into practice. If you are interested in AI, please continue reading.
I. Three-dimensional moving target recognition
Over 20 years ago, in my research lab, there was a group working on 3D moving object recognition. Everyone involved knew that 3D moving object recognition technology was applied to military applications. After the Gulf War in 1991, media reports surfaced that US missile-laden warplanes had fired missiles at a civilian train, but fortunately, they missed. During the Second Gulf War in 2003, media again reported that US missile-laden warplanes had fired missiles at a civilian train, accurately destroying it and causing numerous casualties. Why did the US launch missiles at an unarmed train twice? Researchers working on 3D moving object recognition knew that this was the US demonstrating its technology, because GPS positioning technology can only track fixed targets; for moving targets, 3D moving object recognition technology is essential.
During the first Gulf War in 1991, the United States used 3D moving object recognition technology. This technology involved registering the feature vector values of the image contours of a 3D object in three directions. When identifying a moving 3D object, the feature vector values of the contours of the captured image of the object from any angle were compared with the registered feature vector values to obtain an approximation. Under normal circumstances, this algorithm would yield relatively good recognition results. However, in the real environment of war, with heavy artillery fire and smoke, the images of moving objects were greatly interfered with. In particular, image recognition based on contours violated the principles of information science; using a one-dimensional method to identify a two-dimensional image already lacked sufficient information. If the contour portion of the image was further interfered with, drastically different recognition results would occur. Therefore, in the first Gulf War, the US experiment of striking moving targets did not achieve the expected results.
During the Second Gulf War in 2003, the United States incorporated artificial intelligence algorithms into its moving object recognition technology, which was particularly effective in accurately targeting moving targets in situations where images were severely interfered with by harsh environments.
Currently, the use of unmanned aerial vehicles for terrain mapping and automatic search for missing persons requires artificial intelligence-enabled 3D moving object recognition products, making this high-end technology industry highly commercially valuable.
II. "Driverless" Vehicles on the Streets - Latest Pedestrian Prediction Model Imminent
Currently, in places like Yizhuang, it's possible to hail a Baidu autonomous vehicle. In the future, with technological advancements and policy approvals, safety drivers will be removed from the vehicles, and autonomous vehicles will achieve true driverless operation.
According to Baidu, the core of its autonomous driving technology is the "Baidu Automotive Brain Apollo Platform," which includes four major modules: high-precision maps, localization, perception, and intelligent decision-making and control. The latest version of Apollo has evolved to include multiple deep learning-based models, a low-speed pedestrian prediction model based on semantic maps, and imitation learning based on semantic maps.
At this year's Zhongguancun Forum, Megvii Technology released its self-developed intelligent pallet four-way shuttle system. As a discrete device within a flexible logistics system, Megvii's intelligent pallet four-way shuttle can achieve "one vehicle running throughout the entire warehouse." Why is it called "flexible logistics"? Megvii explains that it's primarily due to its two main characteristics: discrete device design and distributed control. User companies can flexibly combine and deploy these devices as needed, like building blocks. Secondly, flexibility is reflected in the system's "dynamic scalability." User companies can adjust the number of four-way shuttles according to seasonal fluctuations and business growth, thereby increasing the system's capacity.
III. Smart Cities with Lower Carbon – AI “Butler” Manages Everything from Water and Electricity to Air Conditioning
AI is playing an increasingly important role in the construction of smart cities. For example, AI can be used for urban infrastructure management, such as automatically monitoring the structural health of roads, bridges, and buildings, and detecting and repairing cracks and potholes in roads; AI can help cities manage energy, such as by analyzing energy usage data to achieve more efficient energy use and optimize the city's energy system; AI can also help cities protect the environment, such as by improving the city's environmental quality through air quality monitoring, waste disposal, and water resource management.
So, how can AI be used to reduce carbon emissions in buildings and achieve carbon neutrality and peak carbon emissions? HengHua Digital showcased its carbon management platform based on a building brain neural network system. Starting from the perspective of fully utilizing clean energy, it centrally applies cost-effective technology products, covering all end-point sensing nodes and sensing nodes of major energy-consuming equipment in the building. Through unified coordination and management by the building brain edge computing server, it enables the efficient operation of building energy-consuming equipment and minimizes unnecessary energy waste. According to edge computing model analysis, the energy consumption curves of each energy-consuming subsystem in the building are in a stable operating state, and the overall energy consumption is at its lowest.
Among building energy consumption categories, building electricity consumption should be the largest contributor. Targeting the characteristics of building low-voltage systems, a smaller, more accurate, and easier-to-install low-voltage monitoring and AI control system has been developed without increasing renovation work. This system can dynamically monitor the building's power system, ensuring timely power outages in unoccupied areas and avoiding unnecessary energy waste. Building air conditioning systems account for 40% of total building energy consumption. HengHua Digital, through in-depth cooperation with universities to establish industry-academia-research bases, has developed strategy algorithms for optimizing building cooling and heating systems, forming a mature data algorithm model that enables air conditioning systems to achieve energy savings of over 10%. Currently, this project has been implemented in Guangdong, Tianjin, Jiangxi, Sichuan, Hubei, and Anhui provinces. In the future, residential communities, office buildings, and shopping malls will all "evolve" towards a green and low-carbon direction.
