Design Of Intelligent System Research Articles

Novel high-safety aeroengine performance predictive control method based on adaptive tracking weight

Increasing attention has been attracted to the dynamic performance and safety of advanced performance predictive control systems of the next-generation aeroengine. The latest research demonstrates that Subspace-based Improved Model Predictive Control (SIMPC) can overcome the difficulty in solving the predictive model in MPC/NMPC applications. However, applying constant design parameters cannot maintain consistent control effects in all states. Meanwhile, the designed system relies too much on sensor-measured data, and thus it is difficult to thoroughly validate the safety of the system because of its high complexity. This means that any potential hardware/software faults will endanger the engine. Therefore, this paper first presents a novel nonlinear mapping relationship to adaptively tune the tracking weight online with the change of Power Lever Angle (PLA) and real-time relative tracking error. Thus, without introducing additional design parameters, an Adaptive Tracking Weight-based SIMPC (ATW-SIMPC) controller is designed to improve the control performance in all operating states effectively. Then, a Primary/Backup Hybrid Control (PBHC) strategy with the ATW-SIMPC controller as the primary system and the traditional speed (Nf) controller as the backup system is proposed to ensure safety. The designed affiliated switching controller and the real-time monitor therein can be used to realize reasonable and smooth switching between primary/backup systems, so as to avoid bump transition. The PBHC system switches to the Nf controller when the ATW-SIMPC controller is wrong because of potential hardware/software faults; otherwise, the ATW-SIMPC controller keeps acting on the engine. The main results prove that the ATW-SIMPC controller with the optimal nonlinear mapping relationship, compared with the existing SIMPC controller, uplifts the dynamic control performance by 32% and reduces overshoots to an allowable limit, resulting in a better control effect in full state. The comparison results consistently indicate that the PBHC can guarantee engine safety in occurrence of hardware/software faults, such as sensor/onboard adaptive model faults. The approach proposed is applicable to the design of a model-based engine intelligent control system.

TorchEEGEMO: A deep learning toolbox towards EEG-based emotion recognition

With deep learning (DL) development, EEG-based emotion recognition has attracted increasing attention. Diverse DL algorithms emerge and intelligently decode human emotion from EEG signals. However, the lack of a toolbox encapsulating these techniques hampers further the design, development, testing, implementation, and management of intelligent systems. To tackle this bottleneck, we propose a Python toolbox, TorchEEGEMO, which divides the workflow into five modules: datasets, transforms, model_selection, models, and trainers. Each module includes plug-and-play functions to construct and manage a stage in the workflow. Recognizing the frequent access to time windows of interest, we introduce a window-centric parallel input/output system, bolstering the efficiency of DL systems. We finally conduct extensive experiments to provide the benchmark results of supported modules. Our extensive experimental results demonstrate the versatility and applicability of TorchEEGEMO across various scenarios.

Theoretical Framework and Research Proposal for Energy Utilization, Conservation, Production, and Intelligent Systems in Tropical Island Zero-Carbon Building

This study aims to theoretically explore the technological systems of tropical island zero-carbon building (TIZCB) to scientifically understand the characteristics of these buildings in terms of energy utilization, energy conservation, energy production, and intelligent system mechanisms. The purpose is to address the inefficiencies and resource wastage caused by the traditional segmented approach to building energy consumption management. Thus, it seeks to achieve a comprehensive understanding and application of the zero-carbon building (ZCB) technology system. This article focuses on the demands for energy-efficient comfort and innovative industrialization in construction. Through an analysis of the characteristics of TIZCB and an explanation of their concepts, it establishes a theoretical framework for examining the system mechanisms of these buildings. Additionally, it delves into the energy utilization, energy conservation, energy production, and intelligent system from macro, meso, and micro perspectives. This approach results in the development of an implementation strategy for studying the mechanisms of energy usage, conservation, and intelligent production systems in TIZCB. The results show that: (1) this study delves into the theoretical underpinnings of TIZCB, emphasizing their evolution from a foundation of low-carbon and near-zero energy consumption. The primary goal is to achieve zero carbon emissions during building operation, with reliance on renewable energy sources. Design considerations prioritize adaptation to high-temperature and high-humidity conditions, integrating regional culture along with the utilization of new materials and technologies. (2) A comprehensive technical framework for TIZCB is proposed, encompassing energy utilization, conservation, production capacity, and intelligent systems. Drawing from systems theory, control theory, and synergy theory, the research employs a macro–meso–micro analytical framework, offering extensive theoretical support for the practical aspects of design and optimization. (3) The research implementation plan establishes parameterized models, unveiling the intricate relationships with building performance. It provides optimized intelligent system design parameters for economically viable zero-carbon operations. This study contributes theoretical and practical support for the sustainable development of TIZCB and aligns with the dual carbon strategy in China and the clean energy free trade zone construction in Hainan.

Design and development of an IoT-based intelligent water quality management system for aquaculture

Water quality is generally known to directly affect the health and growth rate of aquatic organisms and determines the success of any aquaculture fish production. However, water quality problems are difficult to detect early in aquaculture production facilities, largely because it requires a high level of technical understanding of the physio-chemical properties of water. In this research, an IoT-based intelligent water quality management system for aquaculture was designed and developed to monitor temperature, pH, and turbidity. ESP32 Microcontroller programmed with the C programming language was used to implement the smart control module which received data from the sensors and transmitted to a cloud database. A web application was also developed which enabled real-time monitoring and control of the system by a user from anywhere in the world, via any internet-connected device. Alarms and notifications could be received via WhatsApp Messenger. The system demonstrated capacity to improve the efficiency and productivity of aquaculture production.

Design and Application of Intelligent Scheduling System Based on Business and Labor Forecasting

Design and Application of Intelligent Scheduling System Based on Business and Labor Forecasting

Design and implementation of facility intelligent carbon dioxide incubator control system based on POA optimized fuzzy PID

To solve the problem that traditional carbon dioxide incubators are difficult to accurately control the required carbon dioxide environment to the detriment of experimental cultivation, and given the difficulties of time lag, nonlinearity, time-varying, and poor adaptive ability of the incubator control system, this study designs a set of intelligent carbon dioxide incubator control system suitable for laboratory cultivation. The system is based on the POA (Pelican optimization algorithm) to optimize the fuzzy PID (Proportional Integral Derivative) control to regulate the opening degree of the solenoid valve to accurately control the carbon dioxide concentration in the incubator to the set range. In this study, the simulation and comparison tests of POA-based optimized fuzzy PID control, PID control, and fuzzy PID control are carried out by MATLAB/Simulink, and it is found that the overshooting amount of the system is reduced by 11.6%, the regulating time is shortened by 69.3 s, and the steady state error is reduced by 0.1% compared with that of the traditional fuzzy PID control. It can be obtained through practical tests that the accuracy, response, and stability of the optimized fuzzy PID control of the pelican algorithm are better than those of the fuzzy PID control method, and it has better robustness. Therefore, the carbon dioxide incubator control system has high practical application value.

CONVOLUTIONAL NEURAL NETWORKS: AN APPROACH FOR VISUAL OBSTRUCTION DETECTION IN AUTOMOTIVE REVERSING CAMERAS

In recent years, the study of Artificial Intelligence in the automotive industry has led to the design of intelligent systems applied to road safety, highlighting the importance of improving road safety worldwide, and thus reducing the number of accidents annually. One of the main functions of these systems is, for example, pedestrian detection, which is performed by cameras and radar-type sensors, among others. However, environmental factors cause visibility problems and obstructions that make pedestrian detection difficult and lead to collisions. With the purpose of contributing to the solution of the exposed problem, two case studies using Convolutional Neural Networks are applied in this research. The first using a pre-trained model (Inception V3) and the second, a proposed model (RvlsNet) to detect dirt on the lens of a vehicle's reverse camera. These types of factors directly affect visibility, which leads to an increased risk of collision when reversing the vehicle. Applying a general data mining methodology, we obtained a result of 0.9549 and 0.9416 accuracy, respectively, for the models used. Keywords: Convolutional Neural Networks, Classification, Obstruction, Detection, Reversing camera, Inception V3

The intersection of mechatronics and precision engineering: Synergies and future directions

The convergence of mechatronics and precision engineering has sparked significant interest due to its potential to revolutionize various industries, ranging from manufacturing to healthcare. This review explores the synergies between these two domains and outlines potential future directions for research and application. Mechatronics, an interdisciplinary field combining mechanical engineering, electrical engineering, computer science, and control engineering, focuses on the design and development of intelligent systems with integrated sensing, actuation, and control capabilities. Precision engineering, on the other hand, emphasizes the fabrication of high-accuracy components and systems, often at micro or nanoscales, to meet stringent performance requirements. The intersection of mechatronics and precision engineering offers numerous synergistic opportunities. One such opportunity lies in the development of precision actuators and sensors with embedded intelligent control systems, enabling precise motion control and feedback mechanisms in complex systems. Furthermore, advancements in microfabrication techniques allow for the integration of sensors and actuators directly onto precision components, leading to miniaturized and efficient mechatronic systems. In manufacturing, this synergy facilitates the creation of highly automated and flexible production processes capable of producing intricate and high-quality products with minimal human intervention. In healthcare, it enables the development of precision medical devices for diagnostics, surgery, and drug delivery, enhancing patient outcomes and reducing the invasiveness of procedures. Future directions in this interdisciplinary field include exploring novel materials and manufacturing techniques to further improve the precision and performance of mechatronic systems. Additionally, advancements in artificial intelligence and machine learning are poised to enhance the autonomy and adaptability of mechatronic systems, enabling them to learn from data and adapt to changing environments in real-time. the intersection of mechatronics and precision engineering holds great promise for addressing complex engineering challenges and driving innovation across various industries. By leveraging their complementary strengths, researchers and engineers can unlock new opportunities to create smarter, more efficient, and more precise systems with transformative impacts on society.

Exploiting Molecular Orders at the Interface of Microdroplets for Intelligent Materials.

ConspectusThe intrinsic molecular order of liquid crystals (LCs) and liquid crystalline elastomers (LCEs) is the origin of their stimuli-responsive properties. The programmable responsiveness and functionality, such as shape morphing and color change under external stimuli, are the key features that attract interest in designing LC- and LCE-based intelligent material platforms. Methods such as mechanical stretching and shearing, surface alignment, and field-assisted alignment have been exploited to program the order of LC molecules for the desired responsiveness. However, the huge size mismatch between the nanometer-sized LC mesogens and the targeted macroscopic objects calls for questions about how to delicately control molecular order for desired performance. Microparticles that can be synthesized with intrinsic molecular order precisely controlled to micrometer size can be used as building blocks for bulk materials, thus offering opportunities to bridge the gap and transcend molecular orders across scales. By taking advantage of the interfacial anchoring effects, we can control and engineer the molecular orders inside the microdroplets, allowing for the realization of various responsive behaviors. Furthermore, designer LC microparticles with multiple responsiveness can be assembled and confined within a matrix, opening a new pathway to engineering LC-enabled intelligent materials.In this Account, we present our recent work on exploiting the molecular order inside microdroplets for the construction of intelligent materials. We briefly introduce the typical chemicals used in the synthesis and the methods developed to control LC molecular alignment within a microdroplets. We then present examples of microparticles synthesized from microdroplets that can transform into complex morphologies upon cooling from the isotropic to nematic phase or due to phase separation within the droplets coupled with the segregation of LC oligomers (LCOs) with polydisperse chain lengths. Furthermore, we show the synthesis of elliptical LCE microparticles and exploit their thermal and magnetic responsiveness to program shape-morphing behaviors and microarrays with switchable optical polarization. By mixing magnetic nanoparticles in cholesteric liquid crystals (CLCs) and silicone oils, we created Janus microparticles capable of color switching for camouflage and information encryption. Moreover, we can engineer complex molecular orders in LCE microparticles by mixing different surfactants, yielding microparticles of diverse anisotropic, temperature-responsive shapes after photopolymerization and extraction of the template LC molecules with different solvents. We conclude the Account with an outlook on the design of intelligent material systems via the design of unprecedented molecular ordering within the microparticles and their coupling with bulk materials.

Precise Post-Synthetic Modification of Heterometal-Organic Capsules for Selectively Encapsulating Tetrahedral Anions.

Post-synthetic modification plays a crucial role in precisely adjusting the structure and functions of advanced materials. Herein, we report the self-assembly of a tubular heterometallic Pd3Cu6L16 capsule that incorporates Pd(II) and CuL1 metalloligands. This capsule undergoes further modification with two tridentate anionic ligands (L2) to afford a bicapped Pd3Cu6L16L22 capsule with an Edshammer polyhedral structure. By employing transition metal ions, acid, and oxidation agents, the bicapped capsule can be converted into an uncapped one. This uncapped form can then revert back to the bicapped structure on the addition of Br- ions and a base. Interestingly, introducing Ag+ ions leads to the removal of one L2 ligand from the bicapped capsule, yielding a mono-capped Pd3Cu6L16L2 structure. Furthermore, the size of the anions critically influences the precise control over the post-synthetic modifications of the capsules. It was demonstrated that these capsules selectively encapsulate tetrahedral anions, offering a novel approach for the design of intelligent molecular delivery systems.

Design of intelligent inspection system for solder paste printing defects based on improved YOLOX

Aiming at the current SPI (solder paste inspection) system for printing solder paste similar defects detection accuracy is not high, the system intelligence degree is low and so on, design a for the solder paste similar defects and combined with phase modulation profile measurement technique and improve the YOLOX intelligent detection system. The core of the system is the improved YOLOX depth model based on s-mosica and kt-iou algorithms proposed in this paper. The experimental results show that the proposed s-mosica and kt-iou algorithms can effectively improve the detection accuracy of printed solder paste, and when combined with the YOLOX model, the best 90.33% detection accuracy is obtained, which is better than the detection performance of the existing algorithms in the same scenario, and it provides an effective and feasible reference program for the design of the SPI high-precision intelligent detection system.

A design of intelligent AGV system combined with a robotic arm for flexible production lines

Unmanned mobile robots have broad application prospects in industrial production. With the continuous deepening of Industry 4.0, the complexity of manufacturing workflows has skyrocketed, and researching robots suitable for flexible production is becoming a focus of development. With the rapid development of computer technology and sensor technology, the ability of robots to obtain their own state and environmental information has also been greatly expanded. Studying the work, path planning, and obstacle avoidance of robots has important practical significance. This article designs a flexible production robot that integrates Automated Guided Vehicle (AGV) with industrial robots. It has the ability to perceive the environment, make optimal decisions, and operate independently. It can achieve functions such as mobile transportation, flexible operation, and human-machine interaction and cooperation in the production line. This article uses Gazebo to construct a production environment and simulate robot movement. In addition, MATLAB is used to conduct simulation experiments on the operation of the UR10 robot. The results and planning path map validate the feasibility and effectiveness of this method. The mobile robot designed in this article combines a robotic arm with an AGV, which has certain application value in future flexible manufacturing factories and provides a certain reference value for flexible production.

Developing an intelligent systems design framework based on multidisciplinary design analysis and multi-agent thinking integration

The design of real-world engineering systems deals with high complexity at the system and subsystem levels, as well as the interaction between the subsystems involved. Multidisciplinary Design Optimization (MDO), which aims to integrate the optimization algorithms in both system and subsystem levels along with exploring mechanisms of synergistic action among subsystems, is a renowned methodology to design complex engineering systems. In Multi-Agent Systems (MASs), as a complex system, to achieve the best system performance, agents have to work their individual tasks in a coordinated manner and in cooperation with other agents. Given some architectural resemblance between MDO and MAS, especially in subsystems interaction (agents, in MAS case), this paper addresses proposing a multi-agent multidisciplinary system design optimization strategy. This strategy by taking advantage of the capabilities of MDO proposes a consolidated framework for the optimal design of real world engineering systems. This strategy is extendable to multi-agent systems in a diverse range of missions and tasks. In this regard to illustrating its application a distributed cooperative search and coverage (CSC) mission in uncertain environments is considered as a case study for designing a MAS. The proposed framework contributes to designing a MAS by applying the history data of previously completed missions and the normalized cross-correlating (NCC) algorithm as well. The effectiveness of the proposed framework is demonstrated via simulations and comparison.

Design and Development of Intelligent Learning System for University Innovation and Entrepreneurship Based on Knowledge Visualisation

To build a comprehensive learning resource system covering learning process, learning users, learning resources and other aspects, an intelligent learning system for innovation and entrepreneurship in universities was constructed and designed combined with knowledge visualisation theory. The system includes student information record module, system recommendation calculation module, learning material module, and knowledge visualisation module. The research focuses on the system recommendation calculation module, including the User-based Collaborative Filtering Algorithm (UCF) and the Joint Recommendation (JR) algorithm of the application content recommendation algorithm. The best neighbour number of JR is 20, and the best transmission path length is 2. The recommendation effect is optimal under this setting. The convergence iterations of the training and test set of the joint recommendation algorithm are 80 times and 100 times, respectively. For the training set and test set, the accuracy, precision, sensitivity, recall, running time, error, and other performance evaluation indicators of the joint recommendation algorithm are better than the corresponding values of other recommendation algorithms. The system has a good design effect and experience effect in the application of the online learning platform.

Design of artificial intelligence volleyball teaching intelligent assistant system based on artificial muscle integrated optical equipment

Design of artificial intelligence volleyball teaching intelligent assistant system based on artificial muscle integrated optical equipment

Design of intelligent financial data management system based on higher-order hybrid clustering algorithm

Amid the ever-expanding landscape of financial data, the importance of predicting potential risks through artificial intelligence methodologies has steadily risen. To achieve prudent financial data management, this manuscript delves into the domain of intelligent financial risk forecasting within the scope of system design. It presents a data model based on the variational encoder (VAE) enhanced with an attention mechanism meticulously tailored for forecasting a company’s financial peril. The framework called the ATT-VAE embarks on its journey by encoding and enhancing multidimensional data through VAE. It then employs the attention mechanism to enrich the outputs of the VAE network, thereby demonstrating the apex of the model’s clustering capabilities. In the experimentation, we implemented the model to a battery of training tests using diverse public datasets with multimodal features like AWA and CUB and verified with the local finance dataset. The results conspicuously highlight the model’s commendable performance in comparison to publicly available datasets, surpassing numerous deep clustering networks at this juncture. In the realm of financial data, the ATT-VAE model, as presented within this treatise, achieves a clustering accuracy index exceeding 0.7, a feat demonstrably superior to its counterparts in the realm of deep clustering networks. The method outlined herein provides algorithmic foundations and serves as a pivotal reference for the prospective domain of intelligent financial data governance and scrutiny.

Underwater Vehicle Path Planning Based on Bidirectional Path and Cached Random Tree Star Algorithm

Underwater autonomous path planning is a critical component of intelligent underwater vehicle system design, especially for maritime conservation and monitoring missions. Effective path planning for these robots necessitates considering various constraints related to robot kinematics, optimization objectives, and other pertinent factors. Sample-based strategies have successfully tackled this problem, particularly the rapidly exploring random tree star (RRT*) algorithm. However, conventional path-searching algorithms may face challenges in the marine environment due to unique terrain undulations, sparse and unpredictable obstacles, and inconsistent results across multiple planning iterations. To address these issues, we propose a new approach specifically tailored to the distinct features of the marine environment for navigation path planning of underwater vehicles, named bidirectional cached rapidly exploring random tree star (BCRRT*). By incorporating bidirectional path planning and caching algorithms on top of the RRT*, the search process can be expedited, and an efficient path connection can be achieved. When encountering new obstacles, ineffective portions of the cached path can be efficiently modified and severed, thus minimizing the computational workload while enhancing the algorithm’s adaptability. A certain number of simulation experiments were conducted, demonstrating that our proposed method outperformed cutting-edge techniques like the RRT* in several critical metrics such as the density of path nodes, planning time, and dynamic adaptability.

Intelligent Controller Design for a Sustainable Energy System

This paper discusses an intelligent control system design for a sustainable energy system. This work also described an optimal design and implementation of an efficient selfsustainable energy system with consideration of energy storage options for excess energy produced by renewable energy sources.

Retracted: Design of the Music Intelligent Management System Based on a Deep CNN

Retracted: Design of the Music Intelligent Management System Based on a Deep CNN

Design and Implementation of Intelligent Data Security Risk Assessment and Management System

Data is facing increasingly complex and diverse security risks. To have a better control on these, an Intelligent Data Security Risk Assessment and Management system emerges. This study takes a comprehensive literature research and case analysis method to have an in-depth research on the design and implementation of Intelligent Data Security Risk Assessment and Management System. Based on the collection and analysis of relevant literature, the key features and technical content of the system are introduced. The successful experience and challenges in the practical application of the system are explored through case analysis. The results indicate that the evaluation accuracy of the system is between 91% and 98%. The design and implementation of the Intelligent Data Security Risk Assessment and Management System greatly enhances the risk management capability of the organization. The system, leveraging on intelligent technology, is efficient, accurate in response and a comprehensive management of data security risk.