Intelligent Circuit Research Articles

Research on the Development Trend of Intelligent Circuit Breaker in Building Installation

With the continuous progress of science and technology, intelligent circuit breakers are more and more widely used in building installation. Today, the construction industry on the safety, stability and intelligence of the electrical system requirements continue to improve, intelligent circuit breakers with its advanced technology and superior performance, has become a key equipment in the field of building installation. This paper discusses the development trend of intelligent circuit breaker in building installation. The continuous improvement of the level of intelligence means that it will have more powerful data analysis and autonomous decision-making capabilities, and can monitor the operating status of the building electrical system in real time and predict potential failures in advance. The diversified expansion of functions makes it not only realize the traditional circuit breaker protection, but also integrate power quality monitoring, equipment condition monitoring and other functions. The deep integration with the Internet of Things technology will build an intelligent building energy management system to achieve interconnection and collaborative control between devices. At the same time, the outstanding contribution of intelligent circuit breakers to energy conservation and environmental protection can not be ignored, and reduce energy consumption by optimizing control strategies to help the green development of buildings. The continuous strengthening of safety performance provides a solid guarantee for building electrical safety. Through the analysis of these trends, it points out the direction of the future development of intelligent circuit breakers in the field of building installation, and provides useful reference and guidance.

Design and optimization of CMOS based 4-bit comparator

The paper primarily focuses on optimizing circuit delay and energy consumption, specifically in gate-level circuits. Three methods are employed for circuit optimization. The first method aims to minimize transistor usage to reduce both delay and energy consumption. The second method involves prioritizing logic gates based on underlying hardware, favoring simpler circuit structures whenever possible, given that our design primarily revolves around logic gates. The third method entails adjusting the number of stages to enhance delay optimization. To validate these rules, three distinct circuits were designed to implement a 4-bit absolute value comparator, each corresponding to one of the rules. Through simulation, calculation, and comparison, the best circuit was identified, providing validation for the rules. The second part of the paper shifts its focus towards optimizing delay and energy consumption by adjusting the sizing of logic gates and the supply voltage to achieve optimal overall performance. In conclusion, further research is needed to corroborate these three rules and identify additional rules, laying the foundation for intelligent circuit optimization.

Neural Network Circuits for Bionic Associative Memory and Temporal Order Memory Based on DNA Strand Displacement.

Pavlovian associative memory plays an important role in our daily life and work. The realization of Pavlovian associative memory at the deoxyribonucleic acid (DNA) molecular level will promote the development of biological computing and broaden the application scenarios of neural networks. In this article, bionic associative memory and temporal order memory circuits are constructed by DNA strand displacement (DSD) reactions. First, a temporal logic gate is constructed on the basis of DSD circuit and extended to a three-input temporal logic gate. The output of temporal logic gate is used for the weight species of associative memory. Second, the forgetting module and output module based on the DSD circuit are constructed to realize some functions of associative memory, including associative memory with simultaneous stimulus, associative memory with interstimulus interval effect, and the facilitation by intermittent stimulus. In addition, the coding, storage, and retrieval modules are designed based on the analysis and memory capabilities of temporal logic gate for temporal information. The temporal order memory circuit is constructed, demonstrating the temporal order memory ability of DNA circuit. Finally, the reliability of the circuit is verified through Visual DSD software simulation. Our work provides ideas and inspiration to construct more complex DNA bionic circuits and intelligent circuits by using DSD technology.

Advancements in Artificial Intelligence Circuits and Systems (AICAS)

In the rapidly evolving landscape of electronics, Artificial Intelligence Circuits and Systems (AICAS) stand out as a groundbreaking frontier. This review provides an exhaustive examination of the advancements in AICAS, tracing its development from inception to its modern-day applications. Beginning with the foundational principles that underpin AICAS, we delve into the state-of-the-art architectures and design paradigms that are propelling the field forward. This review also sheds light on the multifaceted applications of AICAS, from optimizing energy efficiency in electronic devices to empowering next-generation cognitive computing systems. Key challenges, such as scalability and robustness, are discussed in depth, along with potential solutions and emerging trends that promise to shape the future of AICAS. By offering a comprehensive overview of the current state and potential trajectory of AICAS, this review serves as a valuable resource for researchers, engineers, and industry professionals looking to harness the power of AI in electronics.

Application of power electronic circuit design based on BP artificial neural network in environmental monitoring

Abstract Our natural resources have played an important role in building our economy. In addition to the number one energy resource, oil, electric energy is also the best quality and much in demand by society in the history of human civilization. It is because electrical energy can be fully developed and applied by society that the construction of human spiritual civilization has entered industrialization and high-tech information technology. The transmission and application of electric energy has been recognized by society, but how can we make it more reasonable and efficient for easy utilization. In order to solve the problem of how complex power electronic circuit devices can be more widely used with improved technology. This paper establishes the error estimation model of intelligent circuit from data collection and data prediction preprocessing, then proposes to optimize the number of nodes in the hidden layer by using particle swarm optimization algorithm to address the limitations of the traditional BP neural network, and uses the optimized number of nodes in the hidden layer to build a BP neural network structure to train the training sample data, and calculates the error data of the intelligent circuit based on the trained BP neural network to test the sample data. The method established in this paper is used to perform the evaluation of intelligent circuit operation errors. The simulation example shows that the established model can effectively evaluate the smart circuit operation error, and the evaluation accuracy is significantly improved compared with the traditional evaluation method.

AICAS—PAST, PRESENT, AND FUTURE

Artificial intelligence circuits and systems (AICAS) are electronic circuits and systems designed to solve artificial intelligence (AI) problems and perform tasks [...]

Electrical design problems and improvement countermeasures of transmission lines under the background of big data and artificial intelligence

This paper studies the feasibility of intelligent power design mode, and proves that the intelligent power design mode has significant advantages over the traditional mode by using the method of comparative study. Comparative studies are more distinctive in the breadth and depth of content. With the further acceleration of the urbanization process and the popularization of electronic equipment, people’s demand for electricity is gradually increasing. The overall operation of the transmission line is actually highly related to the design mode of the line. The quality of electrical design will be closely related to people’s electricity consumption. The stability of the electrical design is directly linked to the sustainability, and it will also be linked to the reputation of the power design company and the safety of public life. The use of a variety of new technologies in the electrical design process to guide the design scheme and identify design problems as soon as possible is the latest trend in the electrical design of current transmission lines. In the context of the rapid development of data science, the functions of comprehensive sorting and providing suggestions provided by big data technology have become the mainstream choice for solving problems in various industries. At present, big data technology has begun to be associated with the power industry, and big data technology has been applied to transmission lines. Electrical design can also solve various dilemmas that exist in current design. Artificial intelligence technology enables computers to have the ability to think like ordinary people through algorithms and training data. Applying artificial intelligence technology to electrical design of transmission lines can make scheme design more intelligent. In order to analyze the current status of electrical design of transmission lines, this paper conducts a questionnaire survey on professionals and practitioners of electrical design of transmission lines. Through a detailed analysis of the questionnaire data, it is found that the electrical design of current transmission lines mainly includes unreasonable line path arrangement, line support towers unreasonable models and insufficient consideration in safety protection design, and combined with big data and artificial intelligence technology, the intelligent arrangement of line paths, tower-shaped intelligent matching and intelligent safety protection strategies are designed. The respondents of the questionnaire conducted a second survey and found that the efficiency, cost, intelligence, sustainability and safety of the electrical design of transmission lines were improved after combining big data and artificial intelligence technology. The intelligent circuit design scheme constructed by big data and artificial intelligence technology can improve the quality of circuit transportation, solve the problems of unreasonable route layout, unreasonable line support tower type and insufficient consideration of safety protection design. Intelligent circuit design scheme compared to the traditional circuit design scheme in the design efficiency, cost, sustainability is significantly improved.

Scalable functionalized liquid crystal elastomer fiber soft actuators with multi-stimulus responses and photoelectric conversion.

Liquid crystal elastomer (LCE) fibers exhibit large deformation and reversibility, making them an ideal candidate for soft actuators. It is still challenging to develop a scalable strategy and endow fiber actuators with photoelectric functions to achieve tailorable photo-electro-thermal responsiveness and rapid large actuation deformation. Herein, we fabricated a multiresponsive actuator that consists of LCE long fibers obtained by continuous dry spinning and further coated it with polydopamine (PDA)-modified MXene ink. The designed PDA@MXene-integrated LCE fiber is used for shape-deformable and multi-trigger actuators that can be photo- and electro-thermally actuated. The proposed LCE fiber actuator combines an excellent photothermal and long-term electrically conductive PDA@MXene and a shape-morphing LCE fiber, enabling their robust mechanical flexibility, multiple fast responses (∼0.4 s), and stable and large actuation deformation (∼60%). As a proof-of-concept, we present near-infrared light-driven artificial muscle that can lift 1000 times the weight and an intelligent circuit switch with stable controllability and fast responsiveness (∼0.1 s). Importantly, an adaptive smart window system that integrates light-driven energy harvesting/conversion functions is ingeniously constructed by the integration of a propellable curtain woven by the designed fiber and solar cells. This work can provide insights into the development of advanced intelligent materials toward soft robotics, sustainable energy savings and beyond.

High-Performance Breaking and Intelligent of Miniature Circuit Breakers.

The exploitation and utilization of clean energy such as wind and photovoltaic power plays an important role in the reduction in carbon emissions to achieve the goal of “emission peak and carbon neutral”, but such a quantity of clean energy accessing the electric system will foster the transition of the electric power system structure. The intelligentization of power equipment will be an inevitable trend of development. High breaking performance, remote control and a digital detection platform of miniature circuit breaker, a protective equipment of a power distribution system, have also been inevitable requirements of the power Iot system. Based on the above, this paper studies three aspects: high-performance AC and DC general switching technology, remote control technology and operation status’ digital monitoring. A new DC non-polar breaking technology is proposed, which improves the short circuit breaking ability. An experimental prototype using the above techniques was fabricated and passed the DC 1000 V/10 kA short-circuit breaking test. On the basis of the above, an intelligent circuit breaker is developed, which contains multiple functions: remote switching, real-time temperature detection, energy metering and fault warning. Moreover, a software for digital condition monitoring and remote control is developed. This work has certain theoretical and practical significance for the development of the power Internet of things.

Preface

The ICICS 2022 (International Conference on Intelligent Circuits and Systems ’2022) is the 4th series of the conference which is organised biannually since 2016. ICICS 2022 was organised (Virtually) by School of Electronics and Electrical Engineering of Lovely Professional University, Punjab, India on 08th -09th April 2022.The 4th International Conference on Intelligent Circuits and Systems ’2022 was organized by the Organizing Committee ICICS 2022, initially scheduled offline on 08th -09th April 2022 at the Lovely Professional University, Punjab, India. Considering the criticalities induced by the spread of COVID-19, difficulties in travelling, social distancing, and strict rules to follow, the Organizing Committee made the difficult decision to organize the 4th International Conference on Intelligent Circuits and Systems ’2022 completely online on 08th -09th April 2022, using the Zoom platform, to give the opportunity to the authors to introduce their valuable papers in 11 online parallel technical sessions. A total of 107 research papers were provisionally accepted and presented, out of which 78 research papers are finally accepted for publication in the 4th International Conference on Intelligent Circuits and Systems ’2022.List of COMMITTEE MEMBERS, ORGANIZING COMMITTEE, TECHNICAL PROGRAM COMMITTEE, LOCAL COMMITTEE are available in this pdf.

Radical Hybrids with Multiple Responses to Thermal Stimuli via Electron Transfer

AbstractIt is desirable to develop semiconductors with simultaneously multiple responses to single stimuli. Herein, a thermally responsive hybrid semiconductor (AEV)[Pb2Br8] (AEV4+ = 1,1′‐bis(2‐ammonioethyl)‐[4,4′‐ bipyridine]‐1,1′‐diium) is successfully prepared by introducing functionalized organic cations into 1D lead(II) chains. The lead(II) chain contains an edge‐sharing PbBr6 octahedron with highly electron‐deficient cations distributed in the interspace of the inorganic framework. Interestingly, the as‐formed hybrid exhibits extraordinary thermochromic behavior along with intriguing temperature‐dependent conductivity and thermal conductivity due to the thermal motion of the electrons in the hybrid. Specifically, the color of the pristine hybrid changes from yellow to brown when the temperature rises to 70 °C, and gradually deepens as the temperature continues to rise, accompanied by the continuous decline in electric conductivity and thermal conductivity. Once heating is stopped, the color and conductivity recover to the original state in a few minutes. As demonstrated by electron paramagnetic resonance and X‐ray photoelectron spectroscopy studies, the thermal‐induced electron transfer is likely responsible for the intriguing thermally responsive behaviors. This work provides a paradigm for the modular and functional design of hybrid materials that manifest potential applications in semiconductor refrigeration, thermal power generation, intelligent circuit, and other opt‐electronic devices.

Intelligent Correction System of Students’ English Pronunciation Errors Based on Speech Recognition Technology

With the increasingly frequent international exchanges, English has become a common language for communication between countries. Under this research background, in order to correct students’ wrong English pronunciation, an intelligent correction system for students’ English pronunciation errors based on speech recognition technology is designed. In order to provide a relatively stable hardware correction platform for voice data information, the sensor equipment is optimised and combined with the processor and intelligent correction circuit. On this basis, the MLP (Multi-layer Perceptron) error correction function is defined; with the help of the known recognition confusion calculation results, the actual input speech error is processed by gain mismatch; and the software execution environment of the system is built. Combined with the related hardware structure, the intelligent correction system of students’ English pronunciation error based on speech recognition technology is successfully applied, and the comparative experiment is designed and the practical application value of the system is highlighted.

An Intelligent Control-Based Vacuum Circuit Breaker with Permanent Magnet and Dynamic Characteristic Analysis

In the transmission and distribution of energy, circuit breaker plays a very important role. In order to fulfill the current market/user demands, they must be capable of promptly identifying the problem and isolating the problematic areas. According to the extinguishing medium used, circuit breakers are divided into the following categories: air blast, SF6, vacuum, and oil. Among others, permanent magnet (PM) based vacuum circuit breaker is also gaining popularity with each passing day and has become a crucial control and protection equipment in the modern power systems. This study proposes the pulse width modulation (PWM) intelligent control strategy for PM vacuum circuit breaker closing/opening capacitance, group charging, and movement process. For the practical implementation of this idea, dual CPUs are utilized for PM vacuum circuit breaker intelligent control components, by taking STM32F103RBT6 as the core and combining peripheral hardware circuit. PWM pulse output drives IGBT to fulfill the closing/opening capacitance group intelligent charging. On the other hand, PM circuit breaker closing/opening motion displacement signals and coil current feedback achieve closed-loop for PWM intelligent control. The experimental results show that the proposed intelligent control component and PWM intelligent control strategy effectively improve the PM circuit breaker intelligent charging and motion dynamic characteristics.

Analysis of the Influence of the Breaking Radiation Magnetic Field of a 10 kV Intelligent Circuit Breaker on an Electronic Transformer.

The development of the smart grid requires the distribution switch to not be limited to the original breaking function. More functional requirements lead to more complex switch structures, especially the intelligent processing unit on the secondary side. A technology called primary and secondary integration optimizes the structure of the switch, which greatly increases the intelligence level of the switch, but also has disadvantages. The secondary intelligent unit is arranged close to the primary high-voltage electromagnetic environment, and the distribution switch is prone to failure due to electromagnetic interference. In order to explore the influence of electromagnetic interference on it, a transient electromagnetic interference simulation test platform was built for a 10 kV intelligent distribution switch based on the principle of spherical gap arc discharge, and the interference signal of the intelligent distribution switch was measured; the law of the spatial magnetic field near the electronic transformer is mainly studied in this paper. The shielding effectiveness of the distribution terminal of the switch was analyzed, and the interference of the power line of the sensor merging unit circuit board was calculated. The results show that the electronic transformer may have serious faults under continuous strong transient electromagnetic interference. The electromagnetic transient simulation test system studied in this paper can evaluate the anti strong electromagnetic interference ability of the electronic transformer.

Application of Intelligent Iot Circuit Breaker in Construction of Intelligent Platform Area

Application of Intelligent Iot Circuit Breaker in Construction of Intelligent Platform Area

Nonvolatile resistive switching characteristics based on Ni–Al LDHs and its electronic synapse application

Future artificial intelligence circuits will require expandable electronic synapses with extremely high bit density and computing speed. In this regard, the nanostructure of two-dimensional materials achieves the goal and provides device scalability in both horizontal and vertical dimensions. In this work, we report the nonvolatile bipolar resistive switching characteristics of Ni–Al layer double hydroxide (LDH)-adsorbed thiadiazole memristors. The Ni–Al LDH-adsorbed thiadiazole memristors implement a progressive reduction process and can be used to simulate the “learning” and “forgetting” functions of biological synapses. At the same positive and negative voltage pulse width, multiple resistance stages can be observed for continuous pulse number. In addition, the application of pulse train operation scheme is an effective method to control the simulated synaptic devices during the reset process, which helps understand the nature of the evolution of conductive nanofilaments.

Activation function and computing performance of spin neuron driven by magnetic field and strain

The spin neuron is an emerging artificial neural device which has many advantages such as ultra-low power consumption, strong nonlinearity, and high integration. Besides, it has ability to remember and calculate at the same time. So it is seen as a suitable and excellent candidate for the new generation of neural network. In this paper, a spin neuron driven by magnetic field and strain is proposed. The micromagnetic model of the device is realized by using the OOMMF micromagnetic simulation software, and the numerical model of the device is also established by using the LLG equation. More importantly, a three-layer neural network is composed of spin neurons constructed respectively using three materials (Terfenol-D, FeGa, Ni). It is used to study the activation functions and the ability to recognize the MNIST handwritten datasets.c Results show that the spin neuron can successfully achieve the random magnetization switching to simulate the activation behavior of the biological neuron. Moreover, the results show that if the ranges of the inputting magnetic fields are different, the three materials' neurons can all reach the saturation accuracy. It is expected to replace the traditional CMOS neuron. And the overall power consumption of intelligent computing can be further reduced by using appropriate materials. If we input the magnetic fields in the same range, the recognition speed of the spin neuron made of Ni is the slowest in the three materials. The results can establish a theoretical foundation for the design and the applications of the new artificial neural networks and the intelligent circuits.

Introduction to the Special Issue on the 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS 2020)

This issue of the IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) includes the highlighted papers from the 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS 2020), which was originally planned to be held at the Centro Congressi Porto Antico in Genoa, Italy, on March 23–25, 2020. However, due to the difficult pandemic situation that the world was living during those days, the conference was postponed to August 31–September 4, 2020, and was finally held as a fully virtual event. Despite those difficulties, the conference was a great success in terms of participation which demonstrated the consolidation of the IEEE Circuits and Systems Society (CASS) community interested in the research and advances of new circuits and systems paradigms and hardware platforms that can boost a new generation of more efficient Artificial Intelligence (AI) systems and may enlarge the AI application scenarios and performances. The AI revolution is promising to incorporate the AI computing power to all industrial and social sectors. To make this trend possible, new computing platforms suited to efficiently support AI emerging algorithms, systems, and applications in different scenarios (from cloud servers to edge and autonomous devices) should be developed at a parallel pace. The IEEE AICAS conference has appeared and it is being consolidated as an international forum to communicate state-of-the-art research, innovation, and development results and advances in AI circuits and systems. It is an ideal forum to join efforts and exchange experiences between different international communities from academia and industry boosting the progress of AI circuits and systems.

Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence

Recent years have seen an increasing interest in the development of artificial intelligence circuits and systems for edge computing applications. In-memory computing mixed-signal neuromorphic architectures provide promising ultra-low-power solutions for edge-computing sensory-processing applications, thanks to their ability to emulate spiking neural networks in real-time. The fine-grain parallelism offered by this approach allows such neural circuits to process the sensory data efficiently by adapting their dynamics to the ones of the sensed signals, without having to resort to the time-multiplexed computing paradigm of von Neumann architectures. To reduce power consumption even further, we present a set of mixed-signal analog/digital circuits that exploit the features of advanced Fully-Depleted Silicon on Insulator (FDSOI) integration processes. Specifically, we explore the options of advanced FDSOI technologies to address analog design issues and optimize the design of the synapse integrator and of the adaptive neuron circuits accordingly. We present circuit simulation results and demonstrate the circuit's ability to produce biologically plausible neural dynamics with compact designs, optimized for the realization of large-scale spiking neural networks in neuromorphic processors.

Programmable electronic synapse and nonvolatile resistive switches using MoS2 quantum dots

Brain-inspired computation that mimics the coordinated functioning of neural networks through multitudes of synaptic connections is deemed to be the future of computation to overcome the classical von Neumann bottleneck. The future artificial intelligence circuits require scalable electronic synapse (e-synapses) with very high bit densities and operational speeds. In this respect, nanostructures of two-dimensional materials serve the purpose and offer the scalability of the devices in lateral and vertical dimensions. In this work, we report the nonvolatile bipolar resistive switching and neuromorphic behavior of molybdenum disulfide (MoS2) quantum dots (QD) synthesized using liquid-phase exfoliation method. The ReRAM devices exhibit good resistive switching with an On–Off ratio of 104, with excellent endurance and data retention at a smaller read voltage as compared to the existing MoS2 based memory devices. Besides, we have demonstrated the e-synapse based on MoS2 QD. Similar to our biological synapse, Paired Pulse Facilitation / Depression of short-term memory has been observed in these MoS2 QD based e-synapse devices. This work suggests that MoS2 QD has potential applications in ultra-high-density storage as well as artificial intelligence circuitry in a cost-effective way.