Design of Intelligent Fire Alarm System for Large Storage Places Based on AVRmega128 Single Chip Microcomputer

With principles of reliability, independence, practicality and economical effi- ciency, a set of intelligent fire alarm system based on AVRmega128 single chip microcomputer was designed to solve problems of fire alarm system in many large- scale warehouses. Using advanced flame sensor, 485 bus communication, computer interactive software and related peripheral devices, this intelligent fire alarm system has functions of sound-light alarm and intelligent fire extinguishing. The human-com- puter interactive software was adopted for the remote control of the alarm main control panel through the 485 bus communication. This design of intelligent fire alarm system shows high reference and practical value to the development of intel- ligent alarm products with high integration and high reliability.

Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

With the increasing and rapid growth rate of COVID-19 cases,the healthcare scheme of several developed countries have reached the point of collapse.An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients,in such a way that positive patient can be treated and isolated.A chest radiology image-based diagnosis scheme might have several benefits over traditional approach.The accomplishment of artificial intelligence(AI)based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems.This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19Monitoring System(IFFA-DTLMS).The proposed IFFADTLMSmodelmajorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs.To attain this,the presented IFFA-DTLMS model primarily applies densely connected networks(DenseNet121)model to generate a collection of feature vectors.In addition,the firefly algorithm(FFA)is applied for the hyper parameter optimization of DenseNet121 model.Moreover,autoencoder-long short term memory(AE-LSTM)model is exploited for the classification and identification of COVID19.For ensuring the enhanced performance of the IFFA-DTLMS model,a wide-ranging experiments were performed and the results are reviewed under distinctive aspects.The experimental value reports the betterment of IFFA-DTLMS model over recent approaches.

System Design and Implementation of Intelligent Fire Engine Path Planning based on SAT Algorithm

In this paper, in order to make intelligent fi re car complete autonomy path planning in simulation map. Proposed system design of intelligent fi re car path planning based on SAT. The system includes a planning module, a communication module, a control module. Control module via the communication module upload the initial state and the goal state to planning module. Planning module solve this planning solution,and then download planning solution to control module, control the movement of the car fi re. Experiments show this the system is tracking short time, higher planning effi ciency.

Unlocking Intrinsic Conductive Dynamics of Ionogel Microneedle Arrays as Wearable Electronics for Intelligent Fire Safety

Ionogels have enabled flexible electronic devices for wide-ranging innovative applications in wearable electronics,soft robotics,and intelligent systems.Ionogels for flexible electronics need to essentially tolerate stress,temperature,humidity,and solvents that may cause their electrical conductivity,structural stability,processing compatibility and sensibility failure.Herein,we developed a novel in-situ photopolymerization protocol to fabricate intrinsically conductive,self-gated ionogels via ion-restriction dual effects.Highly sensitive and intelligent safety sensors with tunable stretchability,robust chemical stability,favorable printability,and complete recyclability,are programmed from defined microneedle arrays printed by the intrinsically conductive ionogel.Ultrahigh elasticity(~794%elongation),high compression tolerance(~90%deformation),improved mechanical strength(tensile and compressive strength of~2.0 MPa and~16.3 MPa,respectively)and remark-able transparency(>91.1%transmittance),as well as high-temperature sensitivity(-2.07%℃^(-1))and a wide working range(-40 to200℃)can be achieved.In particular,the intrinsic sensing mechanisms of ion-restriction dual effects are unlocked based on DFT calculations and MD simulations,and operando temperature-dependent FTIR,and Raman technolo-gies.Moreover,the real-time intelligent monitoring systems toward physical signals and precise temperature based on the microneedle array-structures sensors are also presented and demonstrate great potential applications for extreme environ-ments,e.g.,fire,deep-sea or aerospace.

IoT System for Intelligent Firefighting in the Electric Power Industry

Traditional fire safety management in the electric power industry has significant drawbacks,including a lack of data,difficulty of maintenance,lack of supervision,and lack of interaction.This type of management lags behind current advanced safety management concepts such as“gate advancement”and“full process man-agement”,and it fails to meet the needs of future energy internet construction and development.In response to these problems,an internet of things system for smart firefighting in the electric power industry was constructed in this study.This system defines a centralized information window,trains a power intelligent firefighting brain,establishes a firefighting cloud management and control system,constructs a power firefighting interaction mech-anism,and performs multi-party coordination of firefighting mechanisms to realize concept of“a whole network on one screen and everything in one network”for managing fires.

Intelligent Recognition Using Ultralight Multifunctional Nano‑Layered Carbon Aerogel Sensors with Human‑Like Tactile Perception

Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.

A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

Product quality prediction based on RBF optimized by firefly algorithm

With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.

Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks:A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.

Cooperative User-Scheduling and Resource Allocation Optimization for Intelligent Reflecting Surface Enhanced LEO Satellite Communication

Lower Earth Orbit(LEO) satellite becomes an important part of complementing terrestrial communication due to its lower orbital altitude and smaller propagation delay than Geostationary satellite. However, the LEO satellite communication system cannot meet the requirements of users when the satellite-terrestrial link is blocked by obstacles. To solve this problem, we introduce Intelligent reflect surface(IRS) for improving the achievable rate of terrestrial users in LEO satellite communication. We investigated joint IRS scheduling, user scheduling, power and bandwidth allocation(JIRPB) optimization algorithm for improving LEO satellite system throughput.The optimization problem of joint user scheduling and resource allocation is formulated as a non-convex optimization problem. To cope with this problem, the nonconvex optimization problem is divided into resource allocation optimization sub-problem and scheduling optimization sub-problem firstly. Second, we optimize the resource allocation sub-problem via alternating direction multiplier method(ADMM) and scheduling sub-problem via Lagrangian dual method repeatedly.Third, we prove that the proposed resource allocation algorithm based ADMM approaches sublinear convergence theoretically. Finally, we demonstrate that the proposed JIRPB optimization algorithm improves the LEO satellite communication system throughput.

Short-term effectiveness of intelligent navigated laser photocoagulation versus subthreshold micropulse laser in patients with chronic central serous chorioretinopathy

AIM:To compare the short-term effectiveness of intelligent navigated laser photocoagulation and 577-nm subthreshold micropulse laser(SML)treatment in patients with chronic central serous chorioretinopathy(cCSC).METHODS:This observational retrospective cohort study included 60 consecutive patients who underwent intelligent navigated laser photocoagulation(n=30)or 577-nm SML treatment(n=30)for cCSC between Jan.2021 and Oct.2022.During 3mo follow-up,all patients underwent assessments of best correct visual acuity(BCVA)and optical coherence tomography(OCT).RESULTS:The operation of laser treatment was successful in all cases.At 1mo,BCVA improved significantly more in the intelligent navigated laser photocoagulation group compared to the SML group(P<0.05).The change was not significantly different at 3mo(P>0.05).Central macular thickness(CMT)in the intelligent navigated laser photocoagulation group was lower than in the SML group at 1mo(P<0.05).The subfoveal choroidal thickness(SFCT)in two groups were all significantly improved at 3mo(all P<0.05).The change between two groups was not significantly different at 1mo or at 3mo(P>0.05).CONCLUSION:Intelligent navigated laser photocoagulation is superior to SML for treating cCSC,leading to better improvements in vision and CMT for short term.

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets,and used to train,verify and test the diagnostic model of RVO.All the images were divided into four categories[normal,central retinal vein occlusion(CRVO),branch retinal vein occlusion(BRVO),and macular retinal vein occlusion(MRVO)]by three fundus disease experts.Swin Transformer was used to build the RVO diagnosis model,and different types of RVO diagnosis experiments were conducted.The model’s performance was compared to that of the experts.RESULTS:The accuracy of the model in the diagnosis of normal,CRVO,BRVO,and MRVO reached 1.000,0.978,0.957,and 0.978;the specificity reached 1.000,0.986,0.982,and 0.976;the sensitivity reached 1.000,0.955,0.917,and 1.000;the F1-Sore reached 1.000,0.9550.943,and 0.887 respectively.In addition,the area under curve of normal,CRVO,BRVO,and MRVO diagnosed by the diagnostic model were 1.000,0.900,0.959 and 0.970,respectively.The diagnostic results were highly consistent with those of fundus disease experts,and the diagnostic performance was superior.CONCLUSION:The diagnostic model developed in this study can well diagnose different types of RVO,effectively relieve the work pressure of clinicians,and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

Intelligent Deep Learning Enabled Wild Forest Fire Detection System

The latest advancements in computer vision and deep learning(DL)techniques pave the way to design novel tools for the detection and monitoring of forestfires.In this view,this paper presents an intelligent wild forestfire detec-tion and alarming system using deep learning(IWFFDA-DL)model.The pro-posed IWFFDA-DL technique aims to identify forestfires at earlier stages through integrated sensors.The proposed IWFFDA-DL system includes an Inte-grated sensor system(ISS)combining an array of sensors that acts as the major input source that helps to forecast thefire.Then,the attention based convolution neural network with bidirectional long short term memory(ACNN-BLSTM)model is applied to examine and identify the existence of danger.For hyperpara-meter tuning of the ACNN-BLSTM model,the bacterial foraging optimization(BFO)algorithm is employed and thereby enhances the detection performance.Finally,when thefire is detected,the Global System for Mobiles(GSM)modem transmits messages to the authorities to take required actions.An extensive set of simulations were performed and the results are investigated interms of several aspects.The obtained results highlight the betterment of the IWFFDA-DL techni-que interms of various measures.

Intelligent casting:Empowering the future foundry industry

Emerging technological advances are reshaping the casting sector in latest decades.Casting technology is evolving towards intelligent casting paradigm that involves automation,greenization and intelligentization,which attracts more and more attention from the academic and industry communities.In this paper,the main features of casting technology were briefly summarized and forecasted,and the recent developments of key technologies and the innovative efforts made in promoting intelligent casting process were discussed.Moreover,the technical visions of intelligent casting process were also put forward.The key technologies for intelligent casting process comprise 3D printing technologies,intelligent mold technologies and intelligent process control technologies.In future,the intelligent mold that derived from mold with sensors,control devices and actuators will probably incorporate the Internet of Things,online inspection,embedded simulation,decision-making and control system,and other technologies to form intelligent cyber-physical casting system,which may pave the way to realize intelligent casting.It is promising that the intelligent casting process will eventually achieve the goal of real-time process optimization and full-scale control,with the defects,microstructure,performance,and service life of the fabricated castings can be accurately predicted and tailored.

An intelligent prediction model of epidemic characters based on multi-feature

The epidemic characters of Omicron(e.g.large-scale transmission)are significantly different from the initial variants of COVID-19.The data generated by large-scale transmission is important to predict the trend of epidemic characters.However,the re-sults of current prediction models are inaccurate since they are not closely combined with the actual situation of Omicron transmission.In consequence,these inaccurate results have negative impacts on the process of the manufacturing and the service industry,for example,the production of masks and the recovery of the tourism industry.The authors have studied the epidemic characters in two ways,that is,investigation and prediction.First,a large amount of data is collected by utilising the Baidu index and conduct questionnaire survey concerning epidemic characters.Second,theβ-SEIDR model is established,where the population is classified as Susceptible,Exposed,Infected,Dead andβ-Recovered persons,to intelligently predict the epidemic characters of COVID-19.Note thatβ-Recovered persons denote that the Recovered persons may become Sus-ceptible persons with probabilityβ.The simulation results show that the model can accurately predict the epidemic characters.

Intelligent responsive self-assembled micro-nanocapsules:Used to delay gel gelation time

In the application of polymer gels to profile control and water shutoff,the gelation time will directly determine whether the gel can"go further"in the formation,but the most of the methods for delaying gel gelation time are complicated or have low responsiveness.There is an urgent need for an effective method for delaying gel gelation time with intelligent response.Inspired by the slow-release effect of drug capsules,this paper uses the self-assembly effect of gas-phase hydrophobic SiO_(2) in aqueous solution as a capsule to prepare an intelligent responsive self-assembled micro-nanocapsules.The capsule slowly releases the cross-linking agent under the stimulation of external conditions such as temperature and pH value,thus delaying gel gelation time.When the pH value is 2 and the concentration of gas-phase hydrophobic SiO_(2) particles is 10%,the gelation time of the capsule gel system at 30,60,90,and 120℃is12.5,13.2,15.2,and 21.1 times longer than that of the gel system without containing capsule,respectively.Compared with other methods,the yield stress of the gel without containing capsules was 78 Pa,and the yield stress after the addition of capsules was 322 Pa.The intelligent responsive self-assembled micronanocapsules prepared by gas-phase hydrophobic silica nanoparticles can not only delay the gel gelation time,but also increase the gel strength.The slow release of cross-linking agent from capsule provides an effective method for prolongating the gelation time of polymer gels.

Privacy-Preserving Large-Scale AI Models for Intelligent Railway Transportation Systems:Hierarchical Poisoning Attacks and Defenses in Federated Learning

The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data.However,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s accuracy.In this study,we introduce PMM(Perturbation coefficient Multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated learning.Extensive experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s accuracy.Additionally,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.

Preventing the Immense Increase in the Life-Cycle Energy and Carbon Footprints of LLM-Powered Intelligent Chatbots

Intelligent chatbots powered by large language models(LLMs)have recently been sweeping the world,with potential for a wide variety of industrial applications.Global frontier technology companies are feverishly participating in LLM-powered chatbot design and development,providing several alternatives beyond the famous ChatGPT.However,training,fine-tuning,and updating such intelligent chatbots consume substantial amounts of electricity,resulting in significant carbon emissions.The research and development of all intelligent LLMs and software,hardware manufacturing(e.g.,graphics processing units and supercomputers),related data/operations management,and material recycling supporting chatbot services are associated with carbon emissions to varying extents.Attention should therefore be paid to the entire life-cycle energy and carbon footprints of LLM-powered intelligent chatbots in both the present and future in order to mitigate their climate change impact.In this work,we clarify and highlight the energy consumption and carbon emission implications of eight main phases throughout the life cycle of the development of such intelligent chatbots.Based on a life-cycle and interaction analysis of these phases,we propose a system-level solution with three strategic pathways to optimize the management of this industry and mitigate the related footprints.While anticipating the enormous potential of this advanced technology and its products,we make an appeal for a rethinking of the mitigation pathways and strategies of the life-cycle energy usage and carbon emissions of the LLM-powered intelligent chatbot industry and a reshaping of their energy and environmental implications at this early stage of development.

Vision based intelligent traffic light management system using Faster R‐CNN

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

An intelligent control method based on artificial neural network for numerical flight simulation of the basic finner projectile with pitching maneuver

In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect.Firstly,a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed.In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow,a numerical simulation of the basic finner projectile without control is carried out.The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation.Secondly,combined with the real-time control requirements of aerodynamic,attitude and displacement parameters of the projectile during the flight process,the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative)control strategy and the intelligent PID control strategy respectively.The intelligent PID controller based on BP(Back Propagation)neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters.Compared with the traditional PID controller,the concerned control variable overshoot,rise time,transition time and steady state error and other performance indicators have been greatly improved,and the higher the learning efficiency or the inertia coefficient,the faster the system,the larger the overshoot,and the smaller the stability error.The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application.