Design Of Intelligent System Research Articles

Neuromorphic engineering in GaN HEMTs exploiting dendritic dislocations for neuromodulation behaviors and adaptive intelligent power forecasting systems

Gallium nitride (GaN), a prominent III-V semiconductor, plays a pivotal role in the advancement of sophisticated power systems. This study presents an innovative neuromorphic engineering approach utilizing the unique dendritic dislocations extending from bulk to channel within GaN High Electron Mobility Transistors (HEMTs) for three-dimensional charge modulation. This approach dynamically alters charge states and channel potentials in response to multiple parametric stimuli, effectively mimicking neural processing and transmission functions, as well as emulating direct and indirect neuromodulation behaviors observed in human nervous system. The advanced neuromodulation emulation enhances biomimetic diversity, laying a solid foundation for the design of intelligent systems. Comprehensive material and electrical analyses reveal the charging and discharging behaviors of the defect energy levels, underlying the neuromorphic working mechanisms. Additionally, by incorporating artificial neural network algorithms, an intelligent power forecasting system that leverages the device’s diverse neuromorphic traits is developed for real-time and adaptive power management, optimizing efficiency and accuracy in balancing supply and demand, thereby enhancing the cost-effectiveness and sustainability of power systems. The findings highlight the neuromorphic capabilities of GaN devices and open avenues for further exploration into the intelligent power management.

Construction of Urban Spatial Intelligent Planning and Design System Under the Background of Big Data

Based on the induction of potentially utilisable micro-spaces in typical old residential communities, this study selects the communities located at 19 and 22 Yuhe Street, Taiyuan City, Shanxi Province, as typical research objects. By employing space syntax analysis, the spatial properties of these micro-spaces are examined and matched with the activity needs of various age groups within the community. By leveraging the characteristics of activity periods for all age groups, the study also proposes a composite design for less disruptive activity spaces. Consequently, optimisation suggestions for the renovation of public spaces in these two communities are presented, aiming to specifically meet the activity needs of residents of all ages within the limited space of the community. This research provides an analytical framework and methodological approach for the renovation strategies of old residential communities.

Systematic Research and Optimization Design of Intelligent Electric Furnace Control System

Systematic Research and Optimization Design of Intelligent Electric Furnace Control System

Design of Intelligent Financial System Based on Adaptive Learning Algorithm

The high-frequency trading system in the financial domain has long been a focal point of investigation. This study posits an intelligent financial system design framework predicated on a cross-adaptive self-entropy projection clustering model, aimed at enhancing the efficacy of high-frequency trading systems. A composite distribution model of financial data is formulated to derive sequences of financial data activities. And cross-adaptive learning algorithm is employed to ascertain the interrelated attributes of financial data. Following this, the support vector machine algorithm is applied for the classification processing of these interrelated features, yielding a set of financial data feature vectors, which are then fed into the gray correlation-based information feature extraction model. Through extensive empirical evaluations with authentic trading data, the proposed intelligent financial system design framework exhibits commendable performance, furnishing a viable solution for the intelligent optimization of high-frequency trading systems.

Design and implementation of intelligent control system for non-evaporable getter pumps

Abstract An intelligent control system for Non-Evaporable Getter (NEG) pumps was designed and developed. The main functions of the system include remote monitoring and remote control of key parameters of the NEG power supply, intelligent control of NEG working status according to NEG saturation status and vacuum chamber status, especially intelligent warning and protection of NEG from vacuum leaks and NEG supersaturation. The intelligent control system is proven stable and reliable, which provides a feasible logic architecture and control design for NEG pump application in future fusion devices.

Design of intelligent cruise control system using fuzzy-PID control on autonomous electric vehicles prototypes

Electric vehicles provide a solution for using alternative fuels, namely, electricity. Electric vehicles are used for short distances and intercity travel over long distances, increasing the risk of accidents. Cruise Control is a technology embedded in vehicles to maintain stable speeds; this system will automatically adjust the vehicle's speed when motion changes cause changes in vehicle speed. This study aims to apply lidar sensors to detect distance in the Intelligent Cruise Control (ICC) system using the Fuzzy-PID control method. Testing results were obtained at safe distance inputs of 5, 6, and 7 meters with various object distances. All the tests were carried out; the response systems were obtained with an average settling time of 5 seconds and an average overshoot of 1.53%. Therefore, the proposed Fuzzy-PID method works well for controlling Intelligent Cruise Control systems in autonomous electric vehicle prototypes.

An intelligent design system for tailored metamaterial properties

Metamaterials are widely studied for their ability to carry human designs and realize exotic, specific physical properties. However, the existing metamaterial design methods are highly dependent on the experience and expertise of the designers. Unlocking the potential of metamaterials requires efficient design methods accessible beyond expert users. This work presents a novel intelligent design system (IDS) for fabricating metamaterials with designated mechanical properties without extensive expertise. The IDS leverages a combined approach of particle swarm and moving morphable components topology optimization, capitalizing on swarm intelligence for efficient material structure generation. The proposed IDS successfully generates tailored metamaterial matrices exhibiting desired elastic properties like Poisson's ratio and elastic modulus for orthotropic and isotropic materials. Innovative development of fuzzy models is achieved to optimize convergence mapping of isotropic mechanical metamaterials for user guidance. Notably, it achieves this even without requiring an initial design, completing the process within minutes and offering seamless integration with MATLAB for portability. From the pre-determination of convergence before design to the export of fabrication models after design, IDS provides a new path to designing metamaterials without needing research experience. This comprehensive, user-friendly system holds immense potential for expansion into 3D design and various metamaterial classes, acting as a valuable bridge for non-experts to achieve metamaterial design.

Asymmetric flexible graphene oxide papers for moisture-driven actuators and water level indicators

Smart actuators have shown great potential for a range of industries, including artificial muscles, sensors, and smart devices. However, traditional actuators often suffer from small deformation and complex materials and manufacturing processes. Graphene oxide (GO) has emerged as an ideal candidate for moisture-responsive actuators due to its excellent moisture sensitivity. In this study, we present the development of a moisture-responsive actuator using a simple and straightforward approach based on a monolayer graphene oxide (GO) paper. By utilizing the different affinity of its two sides for water, the graphene oxide actuator demonstrates excellent performance in response to humidity gradients, achieving a large bending amplitude with a curvature of 3 cm−1. Furthermore, the graphene oxide actuator was used as a humidity-sensitive switch to perform on and off switch movements, demonstrating its capability for autonomous detection for real-time humidity monitoring. This work provides a new avenue for the development of moisture-responsive materials and their potential applications in the design of intelligent control systems, actuators, soft robots, artificial muscles, and switches.

Design and Application of Intelligent Subject System Based on TPACK Framework

With the continuous advancement of educational informatization deepening of teachers' professional development, educational technology ability has become a necessary quality and skill for college teachers. This article introduces the basic concept of the TPACK framework and the design concept of intelligent disciplinary systems and elaborates in detail on how to apply the TPACK framework to the design and development of intelligent disciplinary systems. Through case analysis, this article demonstrates the advantages of intelligent subject systems in improving teaching quality and promoting active learning among students. Finally, this article discusses the future development direction and application prospects of intelligent disciplinary systems. The intelligent subject system based on the TPACK framework provides a new teaching solution for the education field, which helps to promote the process of educational informatization and personalized teaching. Cultivate students' rigorous scientific literacy and good practice habits, and master standardized experimental analysis methods.

Hybrid PVP/Battery/Fuel Cell Wireless Charging Stations Using High-Frequency Optimized Inverter Technology for Electric Vehicles

The design and integration of intelligent energy management systems in hybrid electric vehicle (EV) charging stations, leveraging industry 4.0 and renewable energy sources, is crucial for advancing sustainability, efficiency, and technological development. The innovative hybrid EV charging station described in this study uses a combination of fuel cells, batteries, and solar panels that run at 14 amps a piece at 240 volts. The system consists of five essential components that work together to transfer power wirelessly: an EV battery bank, a boost converter, an HF inverter, transfer coils, and a power supply. Two crucial phases make up the optimization process. In phase 1, the boost converter’s maximum power point is tracked and optimized to generate the most power possible by varying the duty cycle between 10% and 90%. In phase 2, the HF uses a class ϕ2 inverter at 30 MHz to synchronize with the resonant frequency of wireless power transfer coils. Zero-voltage switching is used by a digital signal processor card to carry out control for effective operations. By utilizing hybrid sources to optimize power transmission, this design improves the sustainability of EV charging options.

BSA-SGRU-A Novel Deep Learning Framework for Alleviate the Multiple Attacks in IoT-Cloud Environment

The Internet of Things (IoT) has emanated as an innovative technology that grants users to establish connections to external servers located in the cloud and the uninterrupted Internet, allowing them to live more comfortably. As the number of users increases, security and privacy breaches also increase exponentially, as it invests the greatest threats to the IoT networked devices and cloud storage. Though machine and deep learning algorithms have paved the bright light toward the design of intelligent systems to mitigate the attacks, creating effective defenses against numerous assaults remains a significant design challenge for researchers, and accurately predicting these attacks continues to pose an ongoing hurdle. To solve this problem, this paper proposes a hybrid learning model that ensembles the skip connection-based gated recurrent units (Skip-GRU) and bi-layered self-attention networks to predict and mitigate the different attacks. First, Skip-GRU is constructed using residual connections that remove redundant information and capture only global features. Second, novel bi-layered privacy-preserving networks are combined to obtain the spatial and temporal attributes. Finally, softmax is utilized to obtain the prediction results of sample labels. Performance parameters including accuracy, precision, recall, and F1-score are assessed and evaluated during the comprehensive eradication exploration that is conducted utilizing the NSL-KDD and UNSW datasets. By comparing how it performs to other cutting-edge approaches to learning, the one recommended demonstrates its superiority. The technique has proven its efficacy in providing enough security versus various threats, as seen by the results, which show that it has obtained 0.97 accuracy, 0.96 precision, 0.96 recall, and 0.96 F1-score.

An integrated intelligent intersection management and control system design

While a fair amount of study has been done recently on integrated management and control models for large-scale traffic systems, there is comparatively little information available regarding the design of management and control systems for small-scale intersections. This study aims to create the structural diagram of a management system for intersections and control technologies. It does this by designing three distinct systems: congestion prediction, traffic signal timing, and congestion flow dredging. This study searches the current state of common technology and chooses the intersection integrated management system by means of comparison. Fuzzy TOPSIS algorithm and ADP adaptive dynamic programming are chosen as the calculation techniques of signal lamp timing, while BP neural network and ATT-GCN network are chosen as the key algorithms of congestion prediction after thorough investigation. In the end, conventional traffic management techniques like forbidding left turns are used to direct cars that haven't arrived at the busy crossroads beforehand. This paper aims to select the best model for an intersection management system currently available from the common technologies. The idealized management system and its structure diagram are designed in a straightforward manner, and they will serve as a guide for future road and vehicular intersection design.

Leveraging systems’ non-linearity to tackle the scarcity of data in the design of intelligent fault diagnosis systems

Deep transfer learning (DTL) allows for the efficient building of intelligent fault diagnosis systems (IFDS). On the other hand, DTL methods still heavily rely on large amounts of labelled data. Obtaining such an amount of data can be challenging when dealing with machines or structures faults. This document proposes a novel approach to the design of vibration-based IFDS using DTL in condition of strong data scarcity. A periodic multi-excitation level procedure leveraging intrinsic non-linearities of real-world systems is used to produce images that can be conveniently analysed by pre-trained Convolutional Neural Networks to diagnose faults. A new data visualization method and its augmentation technique are proposed in this paper to tackle the typical lack of data encountered during the design of IFDS. Experimental validation on a railway pantograph structure provides effective support for the proposed method.

Intelligent design and optimization system for shear wall structures based on large language models and generative artificial intelligence

Intelligent design technology for shear wall structures has great potential for enhancing design efficiency and addressing the challenges of tedious and repetitive design tasks. Recently, there has been a surge in the development of this technology. However, existing deep learning-based design methods for shear wall structures suffer from poor quality and usability issues. To address these challenges, this study proposes an intelligent design and optimization system for shear wall structures based on large language models (LLMs) and generative artificial intelligence (AI). The system employs an LLM as the core controller, which interacts with engineers to interpret their language descriptions and translate them into executable computer code. Subsequently, the system utilizes the corresponding structural generation and optimization methods to accomplish intelligent design tasks automatically. Furthermore, the system incorporates such critical factors as the empirical rules, mechanical performance, and material consumption into the structural optimization process. A unique three-level, two-stage optimization method is constructed based on topology, pattern, and size to enhance the overall design quality. Being able to complete the entire workflow of architectural drawing processing, structural scheme generation, and analysis model establishment, the proposed system enables automated and efficient design of shear wall structures. Through the analysis and validation of multiple cases, it was demonstrated that this system can significantly speed up the design by approximately 30 times compared to that of traditional methods whilst ensuring the safety and cost-effectiveness of the design schemes. Consequently, this study provides valuable insights for the advancement of automated structural design undertakings.

Hardware circuit design of intelligent fast charger control system

Hardware circuit design of intelligent fast charger control system

Intelligent LED lighting control system design and realization

Intelligent LED lighting control system design and realization

Design of Intelligent Street Lamp System Driven by New Energy Sources

The streets, large and small, are full of street lights, which light up the way home when night falls. But tens of thousands of street lamps consume a lot of energy. The non-renewable energy on the earth is limited, so in order to protect our earth and enable the sustainable development of the earth, this paper designs and develops an intelligent street lamp system driven by new energy sources. Different from the traditional single power supply mode, the street lamp mentioned in this paper uses solar energy, wind energy and ceramic piezoelectric machinery to provide electricity to the street lamp. Solar power is generated when the sun is full, and wind power is generated when there is no sun. The mechanical energy of a car passing through piezoelectric ceramics can also be used to generate electricity in special weather conditions when there is no wind or sunshine. A variety of power generation methods have solved the malpractice of new energy source street lamps relying on weather conditions. And the system can display the power of the storage battery in real time, and light it automatically when night falls. It is an efficient multi-source drive intelligent new energy street lamp.

Design, Fabrication, and Evaluation of 3D Biopotential Electrodes and Intelligent Garment System for Sports Monitoring.

This study presents the development and evaluation of an innovative intelligent garment system, incorporating 3D knitted silver biopotential electrodes, designed for long-term sports monitoring. By integrating advanced textile engineering with wearable monitoring technologies, we introduce a novel approach to real-time physiological signal acquisition, focusing on enhancing athletic performance analysis and fatigue detection. Utilizing low-resistance silver fibers, our electrodes demonstrate significantly reduced skin-to-electrode impedance, facilitating improved signal quality and reliability, especially during physical activities. The garment system, embedded with these electrodes, offers a non-invasive, comfortable solution for continuous ECG and EMG monitoring, addressing the limitations of traditional Ag/AgCl electrodes, such as skin irritation and signal degradation over time. Through various experimentation, including impedance measurements and biosignal acquisition during cycling activities, we validate the system's effectiveness in capturing high-quality physiological data. Our findings illustrate the electrodes' superior performance in both dry and wet conditions. This study not only advances the field of intelligent garments and biopotential monitoring, but also provides valuable insights for the application of intelligent sports wearables in the future.

Design and Application of Intelligent Control System for Molten Iron Transportation Based on 5G Technology

Molten transport is an important link in the iron and steel enterprise production, involves many complex factors, artificial management is difficult. Therefore, puts forward a kind of molten iron transport wisdom control system based on 5G technology, which mainly contains the intelligent identification tracking system, equipment status collection information acquisition system, locomotive vehicle terminal system, etc. Combined with the analysis of the actual application situation, the system could integrate all the processes and elements of molten iron production and transportation, realize the integration of operation and management, and also promote the improvement of the turnover efficiency of molten iron tank, reduce the demand for personnel, and reduce the labor cost.

PLANNING OF CONSTRUCTION SITES USING INFORMATION TECHNOLOGIES

Planning construction sites involves determining the layout of temporary facilities on the site and significantly impacts the safety and efficiency of construction activities. However, such planning is a complex combinatorial problem that encompasses multiple objectives and becomes more challenging with an increasing number of objects and constraints on work execution conditions. To reduce the complexity of creating such solutions, the use of intelligent design systems is facilitated. The study analyzed existing information modeling technologies for finding optimal solutions based on adopted efficiency criteria and identified drawbacks in such systems. Through theoretical research, the conclusion was drawn that current information technologies for optimizing construction site planning address issues only in isolated aspects, such as minimizing the areas of administrative or storage facilities or reducing costs for unproductive resource movements.