Implementation of Machine-to-Machine Solutions Using MQTT Protocol in Internet of Things (IoT) Environment to Improve Automation Process for Electrical Distribution Substations in Colombia

In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of MQTT protocol with an intensive use of Internet of things (IoT) environment which guarantees the following properties within the automation process: Advanced reports and statistics, remote command execution on one or more units (groups of units), detailed monitoring of remote units and custom alarm mechanism and firmware upgrade on one or more units (groups of units). This kind of implementation is the first one in Colombia and it is able to automatically recover from an N-1 fault.

Using Support Vector Machine to Predict Eco-environment Burden:A Case Study of Wuhan,Hubei Province,China

Objective The human socio-economic development depends on the planet＇s natural capital. Humans have had a considerable impact on the earth, such as resources depression and environment deterioration. The objective of this study was to assess the impact of socio-economic development on the ecological environment of Wuhan, Hubei Province, China, during the general planning period 2006-2020. Methods Support vector machine （SVM） model was constructed to simulate the process of eco-economic system of Wuhan. Socio-economic factors of urban total ecological footprint （TEF） were selected by partial least squares （PLS） and leave-one-out cross validation （LOOCV）. Historical data of socio-economic factors as inputs, and corresponding historical data of TEF as target outputs, were presented to identify and validate the SVM model. When predicted input data after 2005 were presented to trained model as generalization sets, TEFs of 2005, 2006,…, till 2020 were simulated as output in succession. Results Up to 2020, the district would have suffered an accumulative TEF of 28.374 million gha, which was over 1.5 times that of 2004 and nearly 3 times that of 1988. The per capita EF would be up to 3.019 gha in 2020. Contusions The simulation indicated that although the increase rate of GDP would be restricted in a lower level during the general planning period, urban ecological environment burden could not respond to the socio-economic circumstances promptly. SVM provides tools for dynamic assessment of regional eco-environment. However, there still exist limitations and disadvantages in the model. We believe that the next logical step in deriving better dynamic models of ecosystem is to integrate SVM and other algorithms or technologies.

Predict the evolution of mechanical property of Al-Li alloys in a marine environment

The ocean is one of the essential fields of national defense in the future,and more and more attention is paid to the lightweight research of Marine equipment and materials.This study it is to develop a Machine learning(ML)-based prediction method to study the evolution of the mechanical properties of Al-Li alloys in the marine environment.We obtained the mechanical properties of Al-Li alloy samples under uniaxial tensile deformation at different exposure times through Marine exposure experiments.We obtained the strain evolution by digital image correlation(DIC).The strain field images are voxelized using 2D-Convolutional Neural Networks(CNN)autoencoders as input data for Long Short-Term Memory(LSTM)neural networks.Then,the output data of LSTM neural networks combined with corrosion features were input into the Back Propagation(BP)neural network to predict the mechanical properties of Al-Li alloys.The main conclusions are as follows:1.The variation law of mechanical properties of2297-T8 in the Marine atmosphere is revealed.With the increase in outdoor exposure test time,the tensile elastic model of 2297-T8 changes slowly,within 10%,and the tensile yield stress changes significantly,with a maximum attenuation of 23.6%.2.The prediction model can predict the strain evolution and mechanical response simultaneously with an error of less than 5%.3.This study shows that a CNN/LSTM system based on machine learning can be built to capture the corrosion characteristics of Marine exposure experiments.The results show that the relationship between corrosion characteristics and mechanical response can be predicted without considering the microstructure evolution of metal materials.

Developing an atmospheric aging evaluation model of acrylic coatings:A semi-supervised machine learning algorithm

To study the atmospheric aging of acrylic coatings,a two-year aging exposure experiment was conducted in 13 representative climatic environments in China.An atmospheric aging evaluation model of acrylic coatings was developed based on aging data including11 environmental factors from 567 cities.A hybrid method of random forest and Spearman correlation analysis was used to reduce the redundancy and multicollinearity of the data set by dimensionality reduction.A semi-supervised collaborative trained regression model was developed with the environmental factors as input and the low-frequency impedance modulus values of the electrochemical impedance spectra of acrylic coatings in 3.5wt%NaCl solution as output.The model improves accuracy compared to supervised learning algorithms model(support vector machines model).The model provides a new method for the rapid evaluation of the aging performance of acrylic coatings,and may also serve as a reference to evaluate the aging performance of other organic coatings.

Integrating linear and nonlinear models to assess the built environment’s impact on older adults’behaviors in China

China is experiencing rapid population aging.The one contributing factor affecting senior citizens’lives is the disconnect between the built environment in urban and rural areas and the behavioral preferences of older adults.However,research on the relation between the built environment and the behavior of older individuals has been limited.Thus,this paper uses the most recent health tracking data on factors influencing aging in China released in 2020(China Senior Health Survey Tracking Survey).Applying traditional regression,least absolute shrinkage and selection operator regression,and two decision tree optimization models from machine learning,a comprehensive comparative study is carried out to investigate the correlation between the built environment and the physical activity,dietary habits,and social interactions of older age groups.The findings reveal that built environment variables most significantly impact physical activity,accounting for 52.525%,followed by social interaction behaviors at 50.202%and dietary intake at 47.991%.Furthermore,the authors identify population density and greenness rate as the built environment factors having considerable effects on the behavior of older adults.Thus,this study establishes a theoretical foundation for developing age-friendly community environments for older adults.

基于MachineWorks的复杂数控加工环境仿真的研究

当前存在的一些商品化数控加工仿真系统通常作为一个独立于数控系统的程序而很难集成入当前数控系统中,为此有些情况下需要依据数控系统的实际情况开发数控加工仿真系统,但是当前存在的一些开发方案都或多或少存在一些问题,如开发周期长、灵活性差。本文仔细研究了数控加工仿真系统的几个关键技术和几类常用开发方案,采用了基于专用的数控加工仿真系统库——MachineWorks的开发方案,结果显示这种方案对开发量过大和灵活性不足的问题做出相应折衷,能方便的集成入当前的数控系统中,同时能够减少开发工作量。并且本文针对一般CAM或加工仿真软件只进行零件刀具轨迹或中间文件的模拟,无法满足复杂加工环境下的加工仿真需求的问题,在此开发方案基础上对复杂数控加工环境仿真的开发思路、过程及相关关键技术做了阐述。

Green Project System Design of Machine Process Factory

As the specialty of the product and the dim conscio us ness of environmental protection, the status of dirty, chaos and difference is l ong-term existed in the machine process factory. It seriously affects workers’ work and living environment, and restricts the total level of the environment p rotection in our country. The project is the fatal scientific research task of H enan province in 2001. As the members’ endeavor of task group, we have finished the total plan of green project system and some other key equipment to the mach ine process factory, such as the design of conveyer of chip, hydraulic former of chip, rough conveyer and dirt collector. And the green project system is made i nto model that the manufacturer can select. This item is a fire-new work. We ho pe that the expert of machine, environment protection and government official ca n put forward some advices by lodging this article. We contribute for our countr y’ environment protection and make it attain a new level.

Tackling Faceless Killers: Toxic Comment Detection to Maintain a Healthy Internet Environment

According to BBC News,online hate speech increased by 20%during the COVID-19 pandemic.Hate speech from anonymous users can result in psychological harm,including depression and trauma,and can even lead to suicide.Malicious online comments are increasingly becoming a social and cultural problem.It is therefore critical to detect such comments at the national level and detect malicious users at the corporate level.To achieve a healthy and safe Internet environment,studies should focus on institutional and technical topics.The detection of toxic comments can create a safe online environment.In this study,to detect malicious comments,we used approxi-mately 9,400 examples of hate speech from a Korean corpus of entertainment news comments.We developed toxic comment classification models using supervised learning algorithms,including decision trees,random forest,a support vector machine,and K-nearest neighbors.The proposed model uses random forests to classify toxic words,achieving an F1-score of 0.94.We analyzed the trained model using the permutation feature importance,which is an explanatory machine learning method.Our experimental results confirmed that the toxic comment classifier properly classified hate words used in Korea.Using this research methodology,the proposed method can create a healthy Internet environment by detecting malicious comments written in Korean.

Fall detection system in enclosed environments based on single Gaussian model

In this paper,we propose an efficient fall detection system in enclosed environments based on single Gaussian model using the maximum likelihood method.Online video clips are used to extract the features from two cameras.After the model is constructed,a threshold is set,and the probability for an incoming sample under the single Gaussian model is compared with that threshold to make a decision.Experimental results show that if a proper threshold is set,a good recognition rate for fall activities can be achieved.

Explainable Artificial Intelligence-Based Model Drift Detection Applicable to Unsupervised Environments

Cybersecurity increasingly relies on machine learning(ML)models to respond to and detect attacks.However,the rapidly changing data environment makes model life-cycle management after deployment essential.Real-time detection of drift signals from various threats is fundamental for effectively managing deployed models.However,detecting drift in unsupervised environments can be challenging.This study introduces a novel approach leveraging Shapley additive explanations(SHAP),a widely recognized explainability technique in ML,to address drift detection in unsupervised settings.The proposed method incorporates a range of plots and statistical techniques to enhance drift detection reliability and introduces a drift suspicion metric that considers the explanatory aspects absent in the current approaches.To validate the effectiveness of the proposed approach in a real-world scenario,we applied it to an environment designed to detect domain generation algorithms(DGAs).The dataset was obtained from various types of DGAs provided by NetLab.Based on this dataset composition,we sought to validate the proposed SHAP-based approach through drift scenarios that occur when a previously deployed model detects new data types in an environment that detects real-world DGAs.The results revealed that more than 90%of the drift data exceeded the threshold,demonstrating the high reliability of the approach to detect drift in an unsupervised environment.The proposed method distinguishes itself fromexisting approaches by employing explainable artificial intelligence(XAI)-based detection,which is not limited by model or system environment constraints.In conclusion,this paper proposes a novel approach to detect drift in unsupervised ML settings for cybersecurity.The proposed method employs SHAP-based XAI and a drift suspicion metric to improve drift detection reliability.It is versatile and suitable for various realtime data analysis contexts beyond DGA detection environments.This study significantly contributes to theMLcommunity by addressing the critical issue of managing ML models in real-world cybersecurity settings.Our approach is distinguishable from existing techniques by employing XAI-based detection,which is not limited by model or system environment constraints.As a result,our method can be applied in critical domains that require adaptation to continuous changes,such as cybersecurity.Through extensive validation across diverse settings beyond DGA detection environments,the proposed method will emerge as a versatile drift detection technique suitable for a wide range of real-time data analysis contexts.It is also anticipated to emerge as a new approach to protect essential systems and infrastructures from attacks.

Hash Tree Based Trustworthiness Verification Mechanism in Virtual Environment

The trustworthiness of virtual machines is a big security issue in cloud computing. In this paper, we aimed at designing a practical trustworthiness mechanism in virtual environment. With the assist of a third certificate agent, the cloud user generates a trust base and extends it to its VMs. For each service running on the VM, a hash value is generated from all the necessary modules, and these hash values are organized and maintained with a specially designed hash tree whose root is extended from the user's trust base. Before the VM loads a service, the hash tree is verified from the coordinated hash value to check the trustworthiness of the service.

基于电子商务环境的农机管理系统设计

介绍了电子商务环境的背景和现状,详细描述了农机管理系统的实际需求和业务流程,并根据系统设计原则设计了系统总体架构,最后从人机管理、作业管理及综合信息等3个方面实现了农机管理系统。系统在测试过程中取得了较好的应用效果,是一种具有广泛应用前景的高效农机管理系统。

A sleep staging model for the sleep environment control based on machine learning

To date,dynamic sleep environment has been attracted the focus of researchers.Owing to the individual difference on sleep phase and thermal comfort,changes in sleep environment should be occupant-centered,and precise regulation of the environment required current sleep stages.However,few studies connected occupants and the environment through physiological signal-based model of sleep staging.Therefore,this study tried to develop a data driven sleep staging model with higher accuracy through sleep experiments collecting information.Raw database was processed and selected efficiently according to the characteristics of physiological signals.Finally,the sleep staging model with an average accuracy of 93.9%was built,and other mean indicators(precision:82.5%,recall:83.1%,F1 score:82.8%)performed well.The features adopted by model were found to come from different brain regions,and the global brain signals were suggested to play an important role in the construction of sleep staging model.Moreover,the computational processing of physiology signals should consider their characteristics,i.e.,time domain,frequency domain,time-frequency domain and nonlinear characteristics.The model obtained in this study may deliver a credible reference to advance the research on control of sleep environment.

智慧学习环境中的人机协同设计

作为教育数字化转型的首要任务,智慧学习环境建设过分强调技术之于教育的能力,而忽略教育主体的价值与地位,涌现出场景割裂、数据孤岛等问题。人机协同旨在充分发挥人与机器的优势,弥补彼此的劣势,成为指导智慧学习环境创设与优化的最优解。研究将人机协同视为智慧学习环境设计的基线思维,构建了由数据模型层、技术支撑层和场景应用层三个层级,包含场景、数据、模型、资源、工具与服务等六个要素的智慧学习环境概念模型。基于普瑞斯的人机功能分配决策矩阵理论,提出了AI讲师、执行型AI+人类助手、伙伴型AI+人类同侪、助教型AI+人类教练、人类导师等五种人机协同模式。在此基础上,研究制定了智慧学习环境各层级的设计原则,分析了数据模型层的决策协同设计、技术支撑层的交互协同设计和场景应用层的流程协同设计,讨论了人机协同模式中人机互信和价值对齐的建构策略,以期指导智慧学习环境中的人机协同设计。

基于改进YOLOX的自然环境下辣椒果实检测方法

针对不同光照、枝叶遮挡和果实遮挡条件下模型适应能力差和检测精度较低的问题,该研究提出了一种基于YOLOX的改进辣椒果实检测模型YOLOX_Pepper。首先,在YOLOX特征融合网络中添加融合高效通道CA(coordinate attention)注意力机制,提升不同光照条件下模型捕捉辣椒果实关键特征的能力;其次,将主干网络特征聚合模块中的卷积模块替换为可变形卷积DCNv2(deformable convnets v2),提升了模型对不同遮挡情况下辣椒多样几何特征的感知能力。试验结果表明,改进的YOLOX_Pepper模型平均检测精度为93.30%,与Faster R-CNN、YOLOv5、YOLOv7以及YOLOX相比,分别提高了3.99、1.58、3.19和2.84个百分点,F1分数为96%。改进的YOLOX_Pepper模型对自然环境不同光照和遮挡条件的辣椒果实均能进行准确快速的检测。该方法可为辣椒智能化生产提供技术基础。

人机环境系统智能与生产方式变革

人机环境系统智能作为新质生产力的重要组成部分,其发展深刻影响人类社会生产方式。人工智能技术不仅能提高生产效率,降低生产成本,还可能在根本上改变劳动的性质和内容,其进步将推动生产关系的调整,并使社会生产力的释放和利用更加高效。人机环境系统智能带来的人机分工不同于以往简单的体力、智力再分配,还需要考虑人、机、环境优势如何融合,这不仅意味着人们工作方式、职业结构和思维方式的改变,也加速推动各个行业的数字化和智能化进程。未来在人机环境系统智能思维下,社会将迎来生产效率更高、公平与环境可持续的新阶段。

深部矿井覆岩沉积环境–力学特性及冲击地压风险判识

【目的】灾害风险判识和预测是控制和治理灾害的前提,工程地质环境是冲击地压等矿井动力灾害最基本的孕灾条件,其沉积成因机制研究对冲击地压风险预测具有重要意义。【方法】以陕西彬长矿区高家堡煤矿为研究对象,分析不同沉积微相下岩体工程地质特性,探究不同沉积环境岩石变形破坏特征及岩体能量释放规律。【结果和结论】结果表明:沉积环境的差异导致岩石岩性和微观结构的差异,岩石加载破坏中能量演化过程大致分为3个阶段,分别为:能量耗散波动阶段、能量耗散平稳阶段、能量耗散阶段。沉积微相的差异导致岩石在能量耗散阶段能量占比变化具有显著差异,河道沉积的细砂岩、中砂岩以及心滩沉积的粗砂岩岩石破坏时能量占比变化较高,在27%以上,而泛滥平原沉积的泥岩岩石破坏时能量占比变化较小,为14%。在此基础上,以沉积微相、煤层厚度、煤层埋藏深度、顶板岩层厚度特征参数、顶板坚硬岩层厚度及其与煤层的间距、岩体质量评估参数、地质构造容量维、侧压系数和弹性能等工程地质环境因素,采用机器学习算法,构建冲击地压风险非线性判识模型。采用BP神经网络、支持向量机(SVM)、决策树(DT)和袋装树(Bag-ging)等4种机器学习算法进行了对比,各机器学习算法准确率,宏F1分数以及ROC曲线下面积AUC值均在0.7以上,表明各模型准确度高且稳定性较好。其中,Bagging模型性能最优,表明采用工程地质环境因素能够准确、有效地进行冲击地压风险判识和预测,能够为相似地质条件和开采条件的矿井冲击地压危险性评价提供借鉴,为煤矿防冲卸压设计提供指导和依据。

关于智能若干问题的思考

提升人工智能系统的通用性是人类追求的目标。随着科技的发展,人工智能已经从单一任务的解决者发展为能够适应复杂环境、理解人类需求并进行自主决策的系统。但不可否认的是,当前的人工智能水平仍远未实现通用人工智能,智能问题是实现通用人工智能的基础和关键。从智能的发展状况及与其他领域的对比方面介绍了智能的内涵,提出其未来发展趋势——人机环境系统智能。通过分析并实证验证人机环境系统智能的核心概念,揭示了人机环境智能系统中人与机各自的优势、劣势及二者的互补性,为进一步优化智能系统提供了理论支持。最后对智能的发展方向进行展望,指出人机环境系统智能的不断发展将对社会、经济、科技等多个领域产生深远影响,并提出人机环境系统智能发展会面临的终极问题——生态问题。

城市道路网联混驾车辆分阶段动态轨迹控制方法

随着城市的进步和不断发展,智能驾驶车辆逐渐代替路段中的部分人工驾驶车辆,但在未来较长时间内人工驾驶车辆并不会被完全取代,此时出现网联车与人工驾驶车辆的混驾环境,即目前以及未来时间内我们面临的驾驶环境。网联车与人工驾驶车辆驾驶行为在路段内相互干扰,造成混合车流行驶效率低下。为减弱2种车辆间的相互作用,提出一种分离混驾环境下网联车和人工驾驶车辆的分阶段动态车道引导算法(dynamic lane guidance algorithm for separating CAVs and HDVs in mixed traffic environment,SCHME)。通过该算法分离在交叉口上游路段的混合流车辆集合,调整智能驾驶车辆的行驶路线并进行实时动态更新,在满足运动学约束收敛的条件下,人工驾驶车辆根据网联车的动态路线进行相应调整,实现在每辆车广义安全损失成本最小的情况下提高路段内混驾环境下车辆运行效率。通过MATLAB模拟车辆在进入交叉口前的车辆运行状态,结果表明,SCHME算法可在广义安全损失成本最小的情况下提高路段内平均车辆通行效率(17.29%),同时当车辆优化数组越大,车辆集合距离交叉口越远时,智能驾驶车辆渗透率越低,每辆车的道路广义安全损失成本越低。

AGI时代人机共生景观下的媒介化生存:新的延伸、相互形塑与潜在异化

随着Sora的横空出世,人类在某种意义上探索出了AGI时代的入口。当前人类社会已进入深度媒介化阶段,人机协同共生是实现价值最大化的必然路径。本文以媒介环境学派为线索,考察了媒介变迁史与社会结构演变的联系,并从中推演人工智能接入媒介后可能对社会产生的作用。在人机共生景观中,人的媒介化生存需要沿着三重视角展开:一是人与媒介之间超越中介的全新延伸,二是人与媒介彼此塑造共生的复合关系,三是警惕人工智能对人的反向异化。