A Distributed Power Trading Scheme Based on Blockchain and Artificial Intelligence in Smart Grids

As an emerging hot technology,smart grids(SGs)are being employed in many fields,such as smart homes and smart cities.Moreover,the application of artificial intelligence(AI)in SGs has promoted the development of the power industry.However,as users’demands for electricity increase,traditional centralized power trading is unable to well meet the user demands and an increasing number of small distributed generators are being employed in trading activities.This not only leads to numerous security risks for the trading data but also has a negative impact on the cost of power generation,electrical security,and other aspects.Accordingly,this study proposes a distributed power trading scheme based on blockchain and AI.To protect the legitimate rights and interests of consumers and producers,credibility is used as an indicator to restrict untrustworthy behavior.Simultaneously,the reliability and communication capabilities of nodes are considered in block verification to improve the transaction confirmation efficiency,and a weighted communication tree construction algorithm is designed to achieve superior data forwarding.Finally,AI sensors are set up in power equipment to detect electricity generation and transmission,which alert users when security hazards occur,such as thunderstorms or typhoons.The experimental results show that the proposed scheme can not only improve the trading security but also reduce system communication delays.

Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods

Statistical distributions are used to model wind speed,and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind speed.Accurate estimation of Weibull parameters,the scale(c)and shape(k),is crucial in describing the actual wind speed data and evaluating the wind energy potential.Therefore,this study compares the most common conventional numerical(CN)estimation methods and the recent intelligent optimization algorithms(IOA)to show how precise estimation of c and k affects the wind energy resource assessments.In addition,this study conducts technical and economic feasibility studies for five sites in the northern part of Saudi Arabia,namely Aljouf,Rafha,Tabuk,Turaif,and Yanbo.Results exhibit that IOAs have better performance in attaining optimal Weibull parameters and provided an adequate description of the observed wind speed data.Also,with six wind turbine technologies rating between 1 and 3MW,the technical and economic assessment results reveal that the CN methods tend to overestimate the energy output and underestimate the cost of energy($/kWh)compared to the assessments by IOAs.The energy cost analyses show that Turaif is the windiest site,with an electricity cost of$0.016906/kWh.The highest wind energy output is obtained with the wind turbine having a rated power of 2.5 MW at all considered sites with electricity costs not exceeding$0.02739/kWh.Finally,the outcomes of this study exhibit the potential of wind energy in Saudi Arabia,and its environmental goals can be acquired by harvesting wind energy.

Spatial distribution modeling of subsurface bedrock using a developed automated intelligence deep learning procedure:A case study in Sweden

Due to associated uncertainties,modelling the spatial distribution of depth to bedrock(DTB) is an important and challenging concern in many geo-engineering applications.The association between DTB,the safety and economy of design structures implies that generating more precise predictive models can be of vital interest.In the present study,the challenge of applying an optimally predictive threedimensional(3D) spatial DTB model for an area in Stockholm,Sweden was addressed using an automated intelligent computing design procedure.The process was developed and programmed in both C++and Python to track their performance in specified tasks and also to cover a wide variety of diffe rent internal characteristics and libraries.In comparison to the ordinary Kriging(OK) geostatistical tool,the superiority of the developed automated intelligence system was demonstrated through the analysis of confusion matrices and the ranked accuracies of different statistical errors.The re sults showed that in the absence of measured data,the intelligence models as a flexible and efficient alternative approach can account for associated uncertainties,thus creating more accurate spatial 3D models and providing an appropriate prediction at any point in the subsurface of the study area.

A review of artificial intelligence applications in high-speed railway systems

In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,emergency communication,and real-time scheduling,demands advanced capabilities in real-time perception,automated driving,and digitized services,which accelerate the integration and application of Artificial Intelligence(AI)in the HSR system.This paper first provides a brief overview of AI,covering its origin,evolution,and breakthrough applications.A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system:mechanical manufacturing and electrical control,communication and signal control,and transportation management.The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components,forecast of railroad maintenance,optimization of energy consumption in railroads and trains,communication security,communication dependability,channel modeling and estimation,passenger scheduling,traffic flow forecasting,high-speed railway smart platform.Finally,challenges associated with the application of AI are discussed,offering insights for future research directions.

Application of Distributed Intelligent Power Supply Technology in Expressway Tunnels

With the coordinated development of today's social economy with science and technology,various advanced technologies are being used in highway engineering,especially the distributed intelligent power supply technology in expressway tunnels,which has a very significant advantage.In order to realize the effective application of this technology and promote the power supply effect in expressway tunnel,this study analyzes the advantages of this technology and its application in expressway tunnel,hoping to provide scientific reference for the application of distributed intelligent power supply technology and the engineering development of expressway tunnels.

Investigation on full distribution CNC system based on SERCOS bus

A full distribution CNC system based on SERCOS bus is studied in accordance with the limitations of traditional PC-based motion card. The conventional PC-based motion control card is dispersed into several autonomous intelligent servo-control units with the function of servo driver. The autonomous intelligent servocontrol units realize the loop control of position, velocity and current. Interpolation computation is completed in PC and the computational results are transferred to every autonomous intelligent servo-control unit by high speed SERCOS bus. Software or hardware synchronization technology is used to ensure all servomotors are successive and synchronously running. The communication and synchronization technology of SERCOS are also researched and the autonomous intelligent servo-control card is developed byself. Finally, the experiment of circle contour process on a prototype system proves the feasibility.

Double-Edge Intelligent Integrated Satellite Terrestrial Networks

The efficient integration of satellite and terrestrial networks has become an important component for 6 G wireless architectures to provide highly reliable and secure connectivity over a wide geographical area.As the satellite and cellular networks are developed separately these years,the integrated network should synergize the communication,storage,computation capabilities of both sides towards an intelligent system more than mere consideration of coexistence.This has motivated us to develop double-edge intelligent integrated satellite and terrestrial networks(DILIGENT).Leveraging the boost development of multi-access edge computing(MEC)technology and artificial intelligence(AI),the framework is entitled with the systematic learning and adaptive network management of satellite and cellular networks.In this article,we provide a brief review of the state-of-art contributions from the perspective of academic research and standardization.Then we present the overall design of the proposed DILIGENT architecture,where the advantages are discussed and summarized.Strategies of task offloading,content caching and distribution are presented.Numerical results show that the proposed network architecture outperforms the existing integrated networks.

A Homogeneous Cloud Task Distribution Method Based on an Improved Leapfrog Algorithm

Cloud manufacturing is a new manufacturing model with crowd-sourcing characteristics,where a cloud alliance composed of multiple enterprises,completes tasks that a single enterprise cannot accomplish by itself.However,compared with heterogeneous cloud tasks,there are relatively few studies on cloud alliance formation for homogeneous tasks.To bridge this gap,a novel method is presented in this paper.First,a homogeneous cloud task distribution model under cloud environment was constructed,where services description,selection and combination were modeled.An improved leapfrog algorithm for cloud task distribution(ILA-CTD)was designed to solve the proposed model.Different from the current alternatives,the initialization operator and the leapfrog operator in ILA-CTD can ensure that the algorithm always searches the optimal solution in the feasible space.Finally,the processing of task allocation for 1000 pieces of medical labeling machine bottom plates was studied as a case to show the feasibility of the proposed method.The superiority of ILA-CTD was also proven based on more optimal solutions found,compared with the three other methods.

Data-driven intelligent monitoring system for key variables in wastewater treatment process

In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous(TP) and ammonia nitrogen(NH_4-N). In this intelligent monitoring system, a fuzzy neural network(FNN) is applied for designing the soft sensor model, and a principal component analysis(PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition(SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.

Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

Artificial intelligence plays an essential role in the medical and health industries.Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis.However,convolution networks examine medical images effectively;such systems require high computational complexity when recognizing the same disease-affected region.Therefore,an optimized deep convolution network is utilized for analyzing disease-affected regions in this work.Different disease-relatedmedical images are selected and examined pixel by pixel;this analysis uses the gray wolf optimized deep learning network.This method identifies affected pixels by the gray wolf hunting process.The convolution network uses an automatic learning function that predicts the disease affected by previous imaging analysis.The optimized algorithm-based selected regions are further examined using the distribution pattern-matching rule.The pattern-matching process recognizes the disease effectively,and the system’s efficiency is evaluated using theMATLAB implementation process.This process ensures high accuracy of up to 99.02%to 99.37%and reduces computational complexity.

INTELLIGENT INTEGRATION CONTROL OF ROTATING DISK VIBRATION

The rotating disk is a basic machine part that is u sed widely in industry. The motion equation is transformed into the dynamic equa tion in real modal space. The personating intelligent integration is introduced to improve the existing control method. These modes that affect the transverse vibration mainly are included to simulate the vibration of rotating disk, and two methods are applied separately on condition that the sensor and the ac tuator are collocated and non collocated. The results obtained by all sided si mulations show that the new method can obtain better control effect, especially when the sensor and the actuator are non collocated.

AN INTELLIGENT METHOD FOR REAL-TIME DETECTION OF DDOS ATTACK BASED ON FUZZY LOGIC

The paper puts forward a variance-time plots method based on slide-window mechanism tocalculate the Hurst parameter to detect Distribute Denial of Service(DDoS)attack in real time.Basedon fuzzy logic technology that can adjust itself dynamically under the fuzzy rules,an intelligent DDoSjudgment mechanism is designed.This new method calculates the Hurst parameter quickly and detectsDDoS attack in real time.Through comparing the detecting technologies based on statistics andfeature-packet respectively under different experiments,it is found that the new method can identifythe change of the Hurst parameter resulting from DDoS attack traffic with different intensities,andintelligently judge DDoS attack self-adaptively in real time.

A New Approach of Intelligent Data Retrieval Paradigm

What is a real time agent,how does it remedy ongoing daily frustrations for users,and how does it improve the retrieval performance in World Wide Web?These are the main question we focus on this manuscript.In many distributed information retrieval systems,information in agents should be ranked based on a combination of multiple criteria.Linear combination of ranks has been the dominant approach due to its simplicity and effectiveness.Such a combination scheme in distributed infrastructure requires that the ranks in resources or agents are comparable to each other before combined.The main challenge is transforming the raw rank values of different criteria appropriately to make them comparable before any combination.Different ways for ranking agents make this strategy difficult.In this research,we will demonstrate how to rank Web documents based on resource-provided information how to combine several resources raking schemas in one time.The proposed system was implemented specifically in data provided by agents to create a comparable combination for different attributes.The proposed approach was tested on the queries provided by Text Retrieval Conference(TREC).Experimental results showed that our approach is effective and robust compared with offline search platforms.

工业机器人应用会加剧中国城乡收入差距吗?

基于IFR数据库中国工业机器人数据,在科学测度2007—2019年中国31个省级区域工业机器人渗透度及城乡收入差距的基础上,细致刻画工业机器人应用对城乡收入差距的作用机制。研究发现:工业机器人应用显著加剧了中国城乡收入差距,因而有效处理机器人应用的收入分配效应能为高质量增长和共同富裕战略的协同共进提供重要支撑;工业机器人应用会通过提升就业技术结构高级化水平和资本利润率两个渠道,加剧中国城乡居民收入差距,且工业机器人应用在高劳动收入份额区域内引致的城乡收入差距效应较为显著;工业机器人应用有利于激发产业结构服务化及合理化的演进动力,并对制造业与生产性服务业协同集聚具有催化剂作用。为此,中国政府应及时和准确地预判工业机器人应用对中国目前及未来较长一段时间内经济与社会发展的动态影响,并在享受人工智能时代红利的同时,平抑其对城乡收入差距造成的负面冲击。

考虑车流速度分布扰动的干道绿波最大带宽协调控制

城市车辆速度受多种因素影响呈现某种分布,鲁棒的干道协调控制模型应考虑不同速度下的车辆带宽需求。针对速度波动变化干道传统协调控制方法适用性差的问题,分析了带干扰的干道车辆速度时空分布特征,以推荐速度带宽最大与期望带宽最大为目标,提出了考虑车流速度分布扰动的干道绿波最大带宽协调控制方法。针对佛山市同济路4交叉口干道,速度分布扰动干道协调方法能够提供更符合车辆分布特性的协调方案,比Maxband模型和改进的Maxband模型提升约70%和14%的期望带宽。研究结果表明:笔者方法能够降低Maxband模型平均停车次数25%,平均延误45%和平均排队长度17%;针对不同速度期望和方差情况下的灵敏度分析实验表明,该方法能够为干道提供更具有鲁棒性的干道协调控制方案。

人工智能对企业收入分配的非线性影响——基于2007—2022年上市公司数据的检验

人工智能带来的新的技术变革促使企业收入分配格局发生变化。基于2007—2022年沪深A股上市公司数据,通过文本分析法对人工智能技术和应用进行科学测算,并就人工智能对企业利润与工资之间收入分配的影响展开分析。研究发现:人工智能技术和应用对企业利润与工资收入分配的影响呈“U”型,即在人工智能技术和应用发展的早期,利润与工资比率不断下降,差距逐渐缩小;但当人工智能技术和应用超过阈值时,利润不断吞噬工资,导致二者之间差距逐渐扩大。该结论在经历了替换被解释变量、分时段检验、剔除直辖市样本等一系列稳健性检验后依然成立。机制分析发现,人工智能技术和应用能够通过就业规模效应、就业结构效应和生产率效应间接影响企业利润与工资之间的分配。进一步研究发现,财务柔性能够正向调节人工智能与利润和工资之间收入分配的“U”型关系,对于财务柔性较高的企业来说,人工智能技术和应用与企业内部分配效应的“U”型关系较为明显,说明人工智能技术和应用对财务柔性较高的企业分配效应的影响相对更大。研究为在人工智能加速发展背景下提高员工工资、抑制企业间资本和劳动差距扩大提供了重要的政策建议。

基于校企合作的燃气输配智慧化实验平台设计与开发

针对燃气企业人才职业能力培养与新工科背景下燃气输配课程教学改革的需要,实施行业指导、校企共建,设计建设了燃气输配智慧化实验平台,开发出多种类型的实验项目,构建了多层次实验教学项目体系并进行了实验教学方法的探索。该平台运用各种信息化先进技术,可模拟燃气输配管网工艺系统及运行工况、燃气管网智能化控制系统运行、燃气管网事故应急处理等任务,具有丰富多样的实验与工程实训功能,有利于实施理实一体化教学,锻炼和提高学生的工程实践能力、智能化能力和创新意识,加强培养学生解决城市燃气输配复杂工程问题的能力和素质。

基于信赖域模型管理技术的永磁同步电机多目标优化

针对永磁同步电机多目标优化过程中要反复调用耗时的仿真分析且在整个设计域构建代理模型精度低的问题,提出基于信赖域模型管理技术的永磁同步电机多目标优化算法,该算法将整个设计域划分为一系列信赖域,在信赖域上构建径向基函数代理模型并采用微型多目标遗传算法近似优化,降低多目标优化中代理模型的精度要求。通过代理模型与电磁仿真模型的近似程度来调节信赖域的大小,通过缩放和平移信赖域,引导各个电机目标性能逐步逼近Pareto最优前沿面,加快收敛速度。为了进一步减少有限元仿真次数,引入遗传智能布点技术,通过样本遗传策略,遗传落在下个信赖域上的样本。通过算例验证了该算法具有较强的求解永磁同步电机多目标优化问题的能力及较高的求解效率。

改进流向算法的无线传感器网络覆盖优化

针对标准流向算法易陷入局部最优和收敛精度低等问题,提出一种融合莱维(Levy)飞行和入侵杂草策略的改进流向算法.首先,该算法在选择水流流向时引入Levy飞行机制,使水流沿最优水流位置方向做Levy飞行运动,避免陷入局部最优;其次,利用入侵杂草策略,对每一代水流进行繁殖、空间扩散和竞争操作,增加水流的多样性,扩大搜索范围,提高全局寻优能力.最后,将改进流向算法应用于无线传感器网络覆盖优化中,并与标准流向算法及其他改进算法进行实验对比.仿真结果表明,相比标准流向算法及其他改进算法,所提改进流向算法的覆盖性能有大幅提升,覆盖率可达98.52%,可实现更均匀的节点分布和更低的部署成本.

基于MR技术的智能配送中心虚拟仿真实训平台教学实践研究

随着信息技术的发展,传统物流管理正在演进到与数字化智能技术深度融合的智能物流新阶段,将混合现实技术(MR)应用于虚拟仿真实训平台建设,解决物流管理专业实训教学三高三难问题,激发学生获取专业知识和技能的兴趣,本研究通过在《配送中心运营管理》课程应用MR技术搭建虚拟仿真实训平台,进行数字化教学改革实践为例,对智慧供应链专业群的实训教学条件建设、教学过程设计、教学资源开发、教学效率的提升进行了有益的探索,希望为高职实训教学提供有效的教学参考。