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.

Advanced Optimized Anomaly Detection System for IoT Cyberattacks Using Artificial Intelligence

While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.

Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part geopolymer.However,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported.Therefore,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part geopolymer.The database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing temperature.The performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed GEP.Sensitivity and parametric analysis were also performed to identify the significant contributor to strength.According to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 days.The water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.

Design of AI-Enhanced and Hardware-Supported Multimodal E-Skin for Environmental Object Recognition and Wireless Toxic Gas Alarm

Post-earthquake rescue missions are full of challenges due to the unstable structure of ruins and successive aftershocks.Most of the current rescue robots lack the ability to interact with environments,leading to low rescue efficiency.The multimodal electronic skin(e-skin)proposed not only reproduces the pressure,temperature,and humidity sensing capabilities of natural skin but also develops sensing functions beyond it—perceiving object proximity and NO2 gas.Its multilayer stacked structure based on Ecoflex and organohydrogel endows the e-skin with mechanical properties similar to natural skin.Rescue robots integrated with multimodal e-skin and artificial intelligence(AI)algorithms show strong environmental perception capabilities and can accurately distinguish objects and identify human limbs through grasping,laying the foundation for automated post-earthquake rescue.Besides,the combination of e-skin and NO2 wireless alarm circuits allows robots to sense toxic gases in the environment in real time,thereby adopting appropriate measures to protect trapped people from the toxic environment.Multimodal e-skin powered by AI algorithms and hardware circuits exhibits powerful environmental perception and information processing capabilities,which,as an interface for interaction with the physical world,dramatically expands intelligent robots’application scenarios.

Trustworthy semi-supervised anomaly detection for online‐tooffline logistics business in merchant identification

The rise of online-to-offline(O2O)e-commerce business has brought tremendous opportunities to the logistics industry.In the online-to-offline logistics business,it is essential to detect anomaly merchants with fraudulent shipping behaviours,such as sending other merchants'packages for profit with their low discounts.This can help reduce the financial losses of platforms and ensure a healthy environment.Existing anomaly detection studies have mainly focused on online fraud behaviour detection,such as fraudulent purchase and comment behaviours in e-commerce.However,these methods are not suitable for anomaly merchant detection in logistics due to the more complex online and offline operation of package-sending behaviours and the interpretable requirements of offline deployment in logistics.MultiDet,a semi-supervised multiview fusion-based Anomaly Detection framework in online-to-offline logistics is proposed,which consists of a basic version SemiDet and an attention-enhanced multi-view fusion model.In SemiDet,pair-wise data augmentation is first conducted to promote model robustness and address the challenge of limited labelled anomaly instances.Then,SemiDet calculates the anomaly scoring of each merchant with an auto-encoder framework.Considering the multi-relationships among logistics merchants,a multi-view attention fusion-based anomaly detection network is further designed to capture merchants'mutual influences and improve the anomaly merchant detection performance.A post-hoc perturbation-based interpretation model is designed to output the importance of different views and ensure the trustworthiness of end-to-end anomaly detection.The framework based on an eight-month real-world dataset collected from one of the largest logistics platforms in China is evaluated,involving 6128 merchants and 16 million historical order consignor records in Beijing.Experimental results show that the proposed model outperforms other baselines in both AUC-ROC and AUC-PR metrics.

Intelligent Medical Diagnostic System for Hepatitis B

The hepatitis B virus is the most deadly virus,which significantly affects the human liver.The termination of the hepatitis B virus is mandatory and can be done by taking precautions as well as a suitable cure in its introductory stage;otherwise,it will become a severe problem and make a human liver suffer from the most dangerous diseases,such as liver cancer.In this paper,two medical diagnostic systems are developed for the diagnosis of this life-threatening virus.The methodologies used to develop thesemodels are fuzzy logic and the neuro-fuzzy technique.The diverse parameters that assist in the evaluation of performance are also determined by using the observed values from the proposed system for both developedmodels.The classification accuracy of a multilayered fuzzy inference system is 94%.The accuracy with which the developed medical diagnostic system by using Adaptive Network based Fuzzy Interference System(ANFIS)classifies the result corresponding to the given input is 95.55%.The comparison of both developed models on the basis of their performance parameters has been made.It is observed that the neuro-fuzzy technique-based diagnostic system has better accuracy in classifying the infected and non-infected patients as compared to the fuzzy diagnostic system.Furthermore,the performance evaluation concluded that the outcome given by the developed medical diagnostic system by using ANFIS is accurate and correct as compared to the developed fuzzy inference system and also can be used in hospitals for the diagnosis of Hepatitis B disease.In other words,the adaptive neuro-fuzzy inference system has more capability to classify the provided inputs adequately than the fuzzy inference system.

Classification of Multi-User Chirp Modulation Signals Using Wavelet Higher-Order-Statistics Features and Artificial Intelligence Techniques

Higher order statistical features have been recently proved to be very efficient in the classification of wideband communications and radar signals with great accuracy. On the other hand, the denoising properties of the wavelet transform make WT an efficient signal processing tool in noisy environments. A novel technique for the classification of multi-user chirp modulation signals is presented in this paper. A combination of the higher order moments and cumulants of the wavelet coefficients as well as the peaks of the bispectrum and its bi-frequencies are proposed as effective features. Different types of artificial intelligence based classifiers and clustering techniques are used to identify the chirp signals of the different users. In particular, neural networks (NN), maximum likelihood (ML), k-nearest neighbor (KNN) and support vector machine (SVMs) classifiers as well as fuzzy c-means (FCM) and fuzzy k-means (FKM) clustering techniques are tested. The Simulation results show that the proposed technique is able to efficiently classify the different chirp signals in additive white Gaussian noise (AWGN) channels with high accuracy. It is shown that the NN classifier outperforms other classifiers. Also, the simulations prove that the classification based on features extracted from wavelet transform results in more accurate results than that using features directly extracted from the chirp signals, especially at low values of signal-to-noise ratios.

美育视阈下的蜀绣AIGC创新设计与数字化推广研究

目的世代相传的蜀绣凝聚了悠久的历史价值、丰富的文化价值与极高的艺术价值。基于“美育”的视角结合AIGC技术进行创新设计有助于蜀绣的传承与发展。以人工智能的设计融合蜀绣的文化特色来开发文创产品及周边,使美育融入现代年轻人的时尚生活,成为触手可及的生活美学。这既为传统文化的创新发展注入新的生机,也在传承创新中保护了非遗蜀绣的原真性。方法通过文献研究法与图像分析研究梳理蜀绣的历史与现状,并对蜀绣的传承进行实地调研,掌握目前蜀绣的纹样技艺、创新传播等情况并进行系统梳理。使用AIGC进行创新设计,并将创新设计应用于文创产品研发,最后进行数字化推广。结果通过AIGC为蜀绣带来了形式与内容上的创新,再灵活运用以线带面、绣画结合的方式,使人工智能美学助力非遗传承。结论AIGC融合蜀绣的创新设计提升了文创产品的科技精神与内涵,赋予了蜀绣传承与发展重要的时代革新意义,数字化推广也使蜀绣的传播方式与受众群体得到了有效的拓展。

基于代理训练集的属性推理攻击防御方法

本文首次提出针对属性推理攻击的有效防御方法.属性推理攻击可以揭示出用于训练公开模型的原始私有数据集中的隐私属性信息.现有研究已经针对不同的机器学习算法提出了多种属性推理攻击.这些攻击很难防御,一方面原因是训练有素的模型总是会记住训练数据集中的显性和隐性全局属性,另一方面原因在于模型提供者无法事先知道哪些属性将受到攻击从而难以有针对性地进行防御.为了解决这个问题,本文提出了一种通用的隐私保护模型训练方法,名为PPMT(Privacy Preserving Model Training).它以迭代的方式工作.在每次迭代中,PPMT构建一个代理数据集,并在该数据集而不是私有数据集上训练模型.虽然每次迭代会同时导致隐私性的提升和功能性的降低,但隐私性的提升呈快速指数级,而功能性的降低则是缓慢线性的.经过多次迭代,PPMT在模型功能性的约束下最大化全局属性的隐私性,并生成最终的模型.本文选择了两种代表性的机器学习算法和三个典型的数据集来进行实验评估PPMT所训练出模型的功能性、隐私性和鲁棒性.结果显示,使用PPMT训练出的模型,在全局属性上会以不同速度朝不同方向改变,在功能性上的平均损失为1.28%,在超参数α保密的情况下被可能攻击倒推的成功率仅有22%~33%.这说明,PPMT不仅能保护私有数据集的全局属性隐私性,而且能保证模型有足够的功能性,以及面对可能攻击的鲁棒性.

网络服务异常事件告警因果图构造方法

网络服务系统中,异常事件的发生经常导致系统中产生大量告警事件,形成告警风暴。运维人员需要花费大量的时间和精力从这些告警数据中寻找关键信息、确定异常事件的根源。为了减少运维人员所需处理的告警数量,智能化、自动化地提取告警风暴中的根源告警,基于网络服务告警的传播模式分析,提出了一种告警因果图构造方法,并将其应用于提取异常事件发生时的告警风暴关键信息。实验使用运营商现网管理系统的真实数据集,通过告警风暴摘要提取实验,验证了告警因果图生成的效果,并进行了相关案例的物理意义分析。结果表明,使用告警因果图生成的方式进行告警风暴摘要提取,达到了96%的召回率,保留了绝大部分关键信息。同时,使用该方法对系统产生的告警进行压缩,对较难压缩的告警码的压缩率能够达到66.5%。

基于深度神经网络的电厂跑冒滴漏智能识别方法研究

电厂设备复杂,容易发生跑冒滴漏问题,人工巡检存在发现滞后、人为疏忽、不能实时传达异常情况等问题。基于深度学习卷积神经网络、迁移学习和小样本学习技术,设计电厂异常状态智能识别报警系统,利用深度学习模型检测监控系统捕获的现场图片,识别常见的设备跑冒滴漏现象,准确并且及时地发出警告,以此提高电厂的安全监管和对意外事故的应急能力。采用相对成熟的YOLOv5作为目标检测网络基础框架,针对跑冒滴漏数据稀少问题,对网络结构进行优化并采用迁移学习与小样本学习方法来提高网络识别精度。结果表明,基于深度学习卷积神经网络的电厂异常状态智能识别报警系统,能够保持电厂异常状态识别的准确性和实时性。该系统可以实现自主全天候智能检测,及时推送报警信息,减少利用人力关注监控设备排查异常状态可能发生的疏漏,降低电厂运行维护成本,提高电厂的安全监管与对意外事故的应急能力。

人工智能技术用于肺癌放射治疗靶区勾画中的临床效果研究

目的:探讨人工智能(AI)技术在肺癌患者放射治疗中靶区勾画的临床效果。方法:选择2021年9月至2023年3月在萍乡市人民医院行放疗治疗的60例肺癌患者,采用随机信封法将其分为对照组和观察组,每组30例。对照组按照传统方法勾画靶区,观察组采用深度学习技术进行训练,输出和利用UNet网络模型完成患者放疗靶区的自动勾画,比较两组近期疗效、计划靶区体积(PTV)、靶区辐射剂量、危及器官(OAR)体积和剂量、生存期及不良反应发生率。结果:观察组干预后客观缓解率(ORR)为70.0%(21/30),高于对照组的46.67%(14/30),其差异有统计学意义(χ2=5.691,P<0.05);观察组内靶区(ITV)及计划靶区辐射剂量低于对照组,差异有统计学意义(t=4.591、4.934,P<0.05);观察组正常肺组织接受放射剂量20 Gy、5Gy的体积百分比(V20、V5)、双肺接受的平均肺部剂量(MLD)及脊髓1cc体积所受照剂量(D1cc)辐射剂量均低于对照组,差异有统计学意义(t=5.249、4.571、6.092、5.339,P<0.05);两组放疗过程中不良反应发生率比较,差异无统计意义(P>0.05)。结论:AI技术用于肺癌放疗中靶区勾画,能够提高患者ORR,有助于降低PTV、D95及适形指数,减少OAR体积和剂量,且未增加不良反应发生率。

改进的邻近加权合成过采样技术

针对邻近加权合成过采样技术(proximity weighted synthetic oversampling technique,ProWSyn)在合成样例时未删除噪声样例,且当平滑因子在[0,1]区间取值时,权重比例难以覆盖整个搜索空间的缺陷,提出一种改进的邻近加权合成过采样技术(improved proximity weighted synthetic oversampling technique,IProWSyn).改变权重的计算策略,引入底数为(0,1]的普通指数函数,通过动态改变底数令权重覆盖更大范围的搜索空间,进而找到更优的权重.将IProWSyn、ASN-SMOTE和ProWSyn应用在非平衡数据集ada、ecoli1、glass1、haberman、Pima和yeast1上,再使用k近邻(k-nearest neighbors,kNN)分类器和神经网络分类器检验方法的有效性.实验结果表明,在多数数据集上IProWSyn的F1、几何平均值(geometric mean,G-mean)和曲线下面积(area under curve,AUC)指标性能都高于其他过采样方法.IProWSyn过采样技术在这些数据集的综合分类效果更好,有更好的泛化表现.

Artificial intelligence in andrology-fact or fiction:essential takeaway for busy clinicians

Artificial intelligence (AI) is revolutionizing the current approach to medicine. AI uses machine learning algorithms to predict the success of therapeutic procedures or assist the clinician in the decision-making process. To date, machine learning studies in the andrological field have mainly focused on prostate cancer imaging and management. However, an increasing number of studies are documenting the use of AI to assist clinicians in decision-making and patient management in andrological diseases such as varicocele or sexual dysfunction. Additionally, machine learning applications are being employed to enhance success rates in assisted reproductive techniques (ARTs). This article offers the clinicians as well as the researchers with a brief overview of the current use of AI in andrology, highlighting the current state-of-the-art scientific evidence, the direction in which the research is going, and the strengths and limitations of this approach.

Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents.However,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent systems.To tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration efficiency.These rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment state.In addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent learning.MioDSC was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty levels.The experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

A topic-controllable keywords-to-text generator with knowledge base network

With the introduction of more recent deep learning models such as encoder-decoder,text generation frameworks have gained a lot of popularity.In Natural Language Generation(NLG),controlling the information and style of the output produced is a crucial and challenging task.The purpose of this paper is to develop informative and controllable text using social media language by incorporating topic knowledge into a keyword-to-text framework.A novel Topic-Controllable Key-to-Text(TC-K2T)generator that focuses on the issues of ignoring unordered keywords and utilising subject-controlled information from previous research is presented.TC-K2T is built on the framework of conditional language encoders.In order to guide the model to produce an informative and controllable language,the generator first inputs unordered keywords and uses subjects to simulate prior human knowledge.Using an additional probability term,the model in-creases the likelihood of topic words appearing in the generated text to bias the overall distribution.The proposed TC-K2T can produce more informative and controllable senescence,outperforming state-of-the-art models,according to empirical research on automatic evaluation metrics and human annotations.

基于CAN总线的智能火灾报警系统的设计与实现

本文介绍一种基于 CAN总线的智能大厦智能火灾报警系统，提出了智能火灾报警的概念及形成智能火警的技术思路，给出了系统的整体设计方案。

Rockburst prediction using artificial intelligence techniques:A review

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation process.Rockburst disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict rockburst.Traditional rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a dilemma.With the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst mechanism.In previous research,several scholars have attempted to summarize the application of AI techniques in rockburst prediction.However,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive overview.Drawing on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI tech-niques.Firstly,pertinent definitions of rockburst and its associated hazards are introduced.Subsequently,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each approach.Finally,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

重症监护室智慧化管理措施减少临床虚假警报效果的整合性综述

目的医疗设备的频繁虚假警报可能导致护土警报疲劳,这可能导致反应行动延迟或错过警报,增加患者不良事件的风险。本综述旨在探讨重症监护室临床警报智慧化管理措施对减少临床虚假警报的应用效果。方法根据Whitmore 和Knaf的方法学框架,对6个数据库进行系统检索: PubMed, EMBASE,CINAHL,OVID,CochraneLibrary,Scopus,检索从每个数据库建库到2022年12月以英文或中文发表的临床警报的智慧化管理相关文章。采用乔安娜布里格斯研究所的评价工具(JBI-MAStARI)评估研究的质量,并对文献进行数据提取和分析。该研究已在PROSPERO完成注册(CRD42023411552)。结果共纳入7项研究。不同的警报智慧化管理措施有利于减少虚假警报数量和警报的持续时间,缩短护士对重要警报的应答时间,减轻护士的警报疲劳程度。在实践中,应用新的警报管理方式后取得了积极的效果。结论智慧化管理措施可能是警报减少的有效途径,医院迫切需要进行临床警报的智慧化管理。为确保更有效的患者监测、减少警报给护士带来的困扰,未来需要更多与人工智能相结合的警报管理手段,以实现关键警报的准确识别,确保护士对警报准确响应,并真正改善警报频繁的临床医疗环境。

生成式人工智能赋能网络安全运营降噪能力研究

在数字化时代背景下,网络安全面临的挑战日益增加,告警疲劳问题突出,传统的告警处理方法因难以区分真假威胁而效率低下。通过采用生成式人工智能(Artificial Intelligence,AI)技术,不仅能更准确地识别安全威胁、减少误报,还能提高安全事件处理的效率。此外,AI的数据分析能力也有助于安全团队更有效应对复杂安全事件,提升网络安全运营水平。AI技术在实际应用中面临准确度和可解释性挑战,通过引入大型语言模型代理(Large Language Model Agent,LLM Agent)降噪系统,集成大小模型的能力,结合告警态势感知和知识库数据,能进一步提高降噪的准确率,实现告警降噪的高效处理。