Aquaponics is essentially a compound agricultural farming model that combines aquaculture and hydroponics, which is used to improve resource utilization and promote sustainable development. It can be used to solve foo...
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Aiming at the problems that the existing computer room environment monitoring system has a low communication efficiency and can't access to Ethernet, a new system that can monitor environmental parameters in real ...
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ISBN:
(纸本)9798400709166
Aiming at the problems that the existing computer room environment monitoring system has a low communication efficiency and can't access to Ethernet, a new system that can monitor environmental parameters in real time and has the ability to remotely control the equipment is designed. The system mainly consists of a controller and multiple monitors. The controller and the monitor both use STM32 chips based on the ARM core. The monitors are loaded with various sensors to collect environmental parameters. The controller obtains the data of the monitor and responds to the abnormal situation, alarms and automatically opens the safety equipment in the computer room, so that environmental parameters are restored to normal. The controller is connected to the W5500 chip through the SPI interface. The W5500 driver is combined with FreeModbus protocol stack in the software to complete the Modbus TCP protocol communication function between the controller and the host computer. After testing, the intelligentcomputer room environment monitoring system can monitor the environmental parameters of the computer room in real time. And it can communicate with the host computer via Ethernet to complete the functions of data storage, viewing and controlling the device to meet the expected requirements.
Due to the limitations of traditional monitoring methods in environmental adaptability, the intelligent video surveillance system has garnered significant attention. The evolution of computer vision technology has fac...
ISBN:
(数字)9781837242160
Due to the limitations of traditional monitoring methods in environmental adaptability, the intelligent video surveillance system has garnered significant attention. The evolution of computer vision technology has facilitated the emergence of this intelligent surveillance system. Specifically, the utilization of deep learning technology in intelligent video surveillance has demonstrated immense potential in analyzing video behaviors and addressing real-world challenges. At present, several deep learning-based video surveillance architectures have been proposed, including hybrid CNN-RNN networks, two-stream networks, attention mechanisms, and transformers. These architectures have found robust applications in various fields, such as action identification, behavior comprehension, and detection of aberrant conduct. In this paper, the author first delves into the differences between traditional video behavior analyses and those based on deep learning. Subsequently, the author elaborates on the applications of the deep learning in video surveillance, highlighting its advantages and capabilities. After that, the author introduces several architectures of intelligent video surveillance based on deep learning, providing insights into their workings and potential impact. Finally, the author puts forward the improvement direction of intelligent video surveillance.
Battery degradation has an impact on the safety and sustain ability of energy storage systems, which is a consequence of multiple coupled ageing mechanisms. The caused factors include battery chemistry and manufacturi...
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Inefficient garbage collection not only leads to overflowing bins and unpleasant but also poses significant environmental and health risks. This paper proposes an effective garbage monitoring system utilizing a GSM mo...
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ISBN:
(数字)9798350361186
ISBN:
(纸本)9798350361193
Inefficient garbage collection not only leads to overflowing bins and unpleasant but also poses significant environmental and health risks. This paper proposes an effective garbage monitoring system utilizing a GSM module for real-time fill-level detection and automated alerts. The system employs ultrasonic sensors to measure garbage level within the bin, triggering an alert (SMS or data transmission) to designated authorities via the GSM module when a predefined threshold is reached. This enables optimized collection schedules, reducing unnecessary trips, fuel consumption, and associated emissions. Additionally, the system can be integrated with GPS for location tracking, facilitating targeted waste management in geographically dispersed areas. The proposed system offers a cost-effective and scalable solution for enhancing waste management efficiency, promoting environmental sustainability, and improving public health.
Automated diagnosis has always been a challenging task to AI. When model-based diagnosis is adopted, a model of the system is required in order to generate a set of diagnoses based on a collection of observations, whe...
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Automated diagnosis has always been a challenging task to AI. When model-based diagnosis is adopted, a model of the system is required in order to generate a set of diagnoses based on a collection of observations, where a diagnosis is a set of faulty components or, more generally, a set of faults ascribed to components. An active system (AS) is an asynchronous, distributed discrete-event system, whose model consists of a topology (how components are connected to one another), and a communicating automaton for each component (the mode in which a component reacts to events). A problem afflicting all model-based approaches to diagnosis is a possibly large number of diagnoses explaining the observations, which may jeopardize the task of a diagnostician in charge of monitoring the system, owing to the cognitive overload raised by an overwhelming number of faulty scenarios to examine. This is exacerbated in critical application domains, where, under uncertain conditions, an artificial agent is supposed to perform recovery actions in real-time, even in the order of milliseconds, to possibly restore the system. To make diagnosis of ASs viable in critical, real-time application domains, a Smart Diagnosis Engine is presented, which is grounded on two heuristics: (1) if a diagnosis δ is a superset of a diagnosis δ′, then δ is ignored (minimality);(2) if the cardinality (number of faults) of a diagnosis δ is lower than the cardinality of a diagnosis δ′, then δ is generated before δ′ (sorting). Consequently, the diagnosis output consists in a sequence of minimal diagnoses that are generated in ascending order by cardinality. As indicated by the experimental results, the overall improvement is twofold: most likely diagnoses are generated upfront, thereby supporting real-time recovery actions;also, the abductive search in the behavior space of the AS is reduced considerably, owing to the pruning of the trajectories that will not generate minimal diagnoses, thereby resulting in an
The Internet of Things (IoT), has significantly impacted several industry sectors in recent years, particularly those that deal with air, soil, and water quality. With the growing diversity of sensors and their proces...
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ISBN:
(数字)9798350368130
ISBN:
(纸本)9798350368147
The Internet of Things (IoT), has significantly impacted several industry sectors in recent years, particularly those that deal with air, soil, and water quality. With the growing diversity of sensors and their processing and communication capabilities, a number of methods have been put up for creating systems that continuously gather environmental data. These data can be further processed for long-term analysis or quick response. Every solution that has been put forth presents a static system, meaning that the sensors are positioned in fixed, designer-determined locations. Our work presents a dynamic and autonomous system in which sensors are free to travel within a given area and, by utilizing intelligent algorithms and distributed systems techniques, they determine the optimal way to employ in order to monitor the target environment by combining intelligent algorithms and distributed systems techniques.
This research work introduces a design paradigm for an electronic wheelchair, incorporating Artificial Intelligence (AI) assisted smart sensors and controllers to enhance overall functionality and user experience. Cor...
This research work introduces a design paradigm for an electronic wheelchair, incorporating Artificial Intelligence (AI) assisted smart sensors and controllers to enhance overall functionality and user experience. Core components include advanced sensors for environmental and health data, an intelligentcontroller driven by AI algorithms, and motorized wheels for achieving seamless mobility. The smart sensors, spanning temperature, ECG, oxygen, and heart rate sensors, contribute to real-time health monitoring and environmental awareness. The AI-assisted controller optimizes wheelchair navigation, providing a responsive and adaptive system. Comparative performance metrics reveal the proposed design's superiority over traditional control methods, demonstrating heightened precision, sensitivity, specificity, and an impressive overall accuracy. User feedback corroborates the success of this innovative design, attaining high ratings for ease of use, health monitoring capabilities, overall comfort, and higher accuracy. This work outlines a transformative approach to electronic wheelchair design, emphasizing cutting-edge technologies to address user needs, enhancing performance standards, and redefine the landscape of mobility assistance and proposed DNN model achieved an accuracy of 0.97.
The current situation of polluting enterprises is that they are numerous and widely distributed. The traditional supervision means only have regular inspection and public reporting, which is difficult to achieve effec...
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Hyperspectral imaging and artificial intelligence (AI) have transformed imaging and data processing through their ability to capture and analyze detailed spectral information. This paper explores the integration of hy...
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ISBN:
(数字)9798350390049
ISBN:
(纸本)9798350390056
Hyperspectral imaging and artificial intelligence (AI) have transformed imaging and data processing through their ability to capture and analyze detailed spectral information. This paper explores the integration of hyperspectral imaging with AI, focusing on its impact on video analytics. Hyperspectral imaging provides comprehensive, multi-dimensional data by capturing a wide range of spectral wavelengths, enabling precise material identification and environmentalmonitoring beyond traditional RGB imaging. The synergy between hyperspectral imaging and AI enhances real-time analysis and decision-making by leveraging deep learning algorithms for pattern recognition and anomaly detection. This study introduces a novel AI-based hyperspectral image analysis approach for video analytics, utilizing convolutional neural networks (CNNs) and hybrid CNN-attention models to improve object recognition and classification. The methodology is validated through experiments that measure classification accuracy, processing speed, and resilience to varying conditions. Results demonstrate significant improvements in accuracy and efficiency over traditional methods, highlighting the potential for advanced applications in surveillance, environmentalmonitoring, and industrial quality control.
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