American Factories Continue to Increase Equipment Investment——Competitors in Asia also increased their consumption of machine tools.

去年美国金属加工工厂对新机床产品的需求量较 2004 年和 2003 年都有增加。消沉中的美国资本投资发生了一个转变,排在德国之前成为第三大消费国。然而对于美国机床业来说,不太好的消息是亚洲竞争对手同样增加了他们在机床的设备投资。来自第 41 次世界机床生产和消费的调查显示,中国、日本、韩国、印度以及中国台湾地区的机床消费都有显著增长。在 28 个国家和地区的调查中,四分之三的国家和地区消费都在增长,这同时也诠释了经济“表面消费”的含义。这一切表明美国的机床消费量还会继续保持增加,订购机器的数量会维持向上的势头,最新的数据也证明了这一点。此外,调查数据还罗列了其它国家,尤其是亚洲国家的机床生产情况(本刊2006 年第一期环球瞭望栏目文章图表中显示了世界主要机床生产状况):德国和日本领导着世界机床的生产;中国超过意大利成为第三大机床生产国;中国台湾地区排在第五位;美国机床的生产和出口排在台湾之后,保持小的增长;韩国与瑞士紧追其后;印度也毫不示弱。这28个被调查国家和地区的机床产量都比前一年平均增长了14% 。中国、日本、韩国、印度以及中国台湾地区占据了整个调查地区机床产量的 47.6% ,作为CECIMO 成员的由 15 个国家组成的西欧联盟占据了机床产量的 4 2 . 3 % 。

Empirical investigation of the cooling performance of a new designed Trombe wall in combination with solar chimney and water spraying system

This paper presents an experimental study of a new designed Trombe wall in combination with solar chimney and water spraying system in a test room under Yazd(Iran) desert climate.The Trombe wall area is 50% of that of the southern wall of the building that occupies less space and reduces the implementation costs. The new design of the channel has caused the absorber to receive the solar radiation from three directions. Based on the results, the optimum mass flow rate and the nozzle diameter of the water spraying system has been obtained 10 l/h and 30 μm, respectively. The results indicate that the water spraying system decreases indoor temperature and increases indoor relative humidity by about 8 ℃ and 17%, respectively. The most effect of outdoor relative humidity variation is on indoor relative humidity, rather than indoor temperature. When outdoor temperature increases, both indoor relative humidity and the difference between indoor and outdoor relative humidity decreases. The results also showed that theTrombe wall; Solar chimney; Water spraying system(2) Prediction of energy performance of residential buildings:A genetic programming approach, P67-74, by Mauro Castelli,Leonardo Trujillo, Leonardo Vanneschi, Ale觢 Popovic Abstract: Energy consumption has long been emphasized as an important policy issue in today's economies. In particular, the energy efficiency of residential buildings is considered a top priority of a country's energy policy. The paper proposes a genetic programming-based framework for estimating the energy performance of residential buildings. The objective is to build a model able to predict the heating load and the cooling load of residential buildings. An accurate prediction of these parameters facilitates a better control of energy consumption and, moreover, it helps choosing the energy supplier that better fits the energy needs,which is considered an important issue in the deregulated energy market. The proposed framework blends a recently developed version of genetic programming with a local search method and linear scaling. The resulting system enables us to build a model that produces an accurate estimation of both considered parameters. Extensive simulations on 768 diverse residential buildings confirm the suitability of the proposed method in predicting heating load and cooling load. In particular, the proposed method is more accurate than the existing state-of-the-art techniques.

Minimization of Air Consumption and Potential Savings of Textile Denim Fabric Manufacturing Process

One of the most important aspects of Bangladesh’s textile industry is denim. Bangladesh now has a new opportunity thanks to the global demand for denim among fashion industry professionals. Entrepreneurs from Bangladesh provide denim products to well-known international merchants all over the world. The worldwide denim market is predicted to expand by roughly 8% through the year 2020. We must raise the standard of denim if we are to keep up with the expanding industry. In contrast to projectile and rapier systems, air-jet weaving machines nowadays can weave practically all types of yarns without any issues and at higher rates. Due to this, air-jet looms are an excellent substitute for other weft insertion techniques. This kind of device still has one significant flaw, though, and that is the enormous power consumption brought on by the creation of compressed air. Researchers and manufacturers of air-jet looms have therefore worked very hard to find a solution to this issue and achieve a huge reduction in air consumption without compromising loom performance or fabric quality. Therefore, the purpose of this project is to look into ways to decrease air consumption and reduce auxiliary selvedge waste without any decrease in loom performance and fabric quality on existing air-jet weaving looms which reduce the manufacturing costs with process improvement. Just updating the air pressure allowed a weaving mill to reduce air usage by 11 cfm. So, with just almost no cost, a company with 100 looms could save $0.15 M each year, on compressed air. Two new methods for decreasing process costs on air jet looms have also been developed by this project work.

Simulation of Energy Consumption of Machine Tool Motion for 3-Axis Machining

In recent years, the shortage of the energy source is a serious problem in the world. Thus, the reduction of the energy consumption in manufacturing fields has been demanded. The energy consumption of NC machine tools has been also focused on. However, the energy consumption of the machine tool motion of each control axis during machining process has not been considered. In this study, we focus on the energy consumption during the machining process and we proposed the simulation model of the energy consumption of the feed drive systems of NC machine tool. Based on the proposed model, the energy consumption during the machining motion was simulated and evaluated. From these results, if the CAD/CAM systems can generate the tool paths considering about the energy consumption of NC machine tools, the energy consumption will be reduced without replacing or overhaul the machine tools.

Machinability Investigation and Optimization of Process Parameters in Cryogenic Assisted Sustainable Turning of AISI‑L6 Tool Steel

The application of cutting fluid is significantly increased in the machining sector to improve productivity.However,the inherent characteristics of cutting fluids on ecology,environment,and society shift the interest of researchers to work on environmentally friendly cooling conditions such as cryogenic cooling.Here,the effect of cutting speed and feed rate on the machining performance of the AISI‑L6 tool steel is investigated under cryogenic cooling conditions.Then,the L9 Taguchi based grey relational analysis(GRA)is conducted to investigate the essential machining indices such as cutting energy,surface roughness,tool wear,and material removal rate(MRR).The results indicate that the cutting speed of 160 m/min and feed rate of 0.16 mm/r are the optimum parameters that significantly improves the machining performance of AISI‑L6 tool steel.

Implication of machine learning techniques to forecast the electricity price and carbon emission:Evidence from a hot region

The current study examines the significant determinants of electricity consumption and identifies an appropriate model to forecast the electricity price accurately.The main contribution is focused on eastern region of Saudi Arabia,a relatively hottest geographical area full of energy resources but with different electricity consumption patterns.The relative irrelevance of temperature as predicting factor of electricity consumption is quite surprising and contradicts the previous studies.In the eastern region,electricity price has negative association with electricity consumption.While comparing traditional and machine learning,it is found that machine learning techniques offer better predictability.Amongst the machine learning techniques,the support vector machine has the lowest errors in forecasting the electricity price.Additionally,the support vector machine approach is used to forecast the trend of carbon emissions caused by electricity consumption.The findings have policy implications and offer valuable suggestions to policymakers while addressing the determinants of electricity consumption and forecasting electricity prices.

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

Energy is essential to practically all exercises and is imperative for the development of personal satisfaction.So,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current innovations.However,there is a shortage of energy,as the energy required is higher than that produced.Many new plans are being designed to meet the consumer’s energy requirements.In many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future demands.To overcome the challenges of energy consumption optimization,in this study,we apply an energy management technique.Data fusion has recently attracted much energy efficiency in buildings,where numerous types of information are processed.The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate.The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

Evaluating performance of cutting machines during sawing dimension stones

The performance of cutting machines in terms of energy consumption and vibration directly affects the production costs. In this work, our aim was to evaluate the performance of cutting machines using hybrid intelligent models. For this purpose, a systematic experimental work was performed. A database of the carbonate and granite rocks was established, in which the physical and mechanical properties of these rocks (i.e., UCS, elastic modulus, Mohs hardness, and Schmiazek abrasivity factor) and the operational parameters (i.e., depth of cut and feed rate) were considered as the input parameters. The predictive models were developed incorporating a combination of the multi-layered perceptron artificial neural networks and genetic algorithm (GANN-BP) and the support vector regression method and Cuckoo optimization algorithm (COA-SVR). The results obtained indicated that the performance of the developed GANN-BP and COA-SVR models was close to each other and that these models had good agreements with the measured values. These results also showed that these proposed models were suitable tools in evaluating the performance of cutting machines.

Machine Learning Empowered Electricity Consumption Prediction

Electricity,being the most efficient secondary energy,contributes for a larger proportion of overall energy usage.Due to a lack of storage for energy resources,over supply will result in energy dissipation and substantial investment waste.Accurate electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as:smart distributed grids,assessing the degree of socioeconomic growth,distributed system design,tariff plans,demand-side management,power generation planning,and providing electricity supply stability by balancing the amount of electricity produced and consumed.This paper proposes amedium-termprediction model that can predict electricity consumption for a given location in Saudi Arabia.Hence,this study implemented a standalone ArtificialNeuralNetwork(ANN)model and bagging ensemble for predicting total monthly electricity consumption in 18 locations across Saudi Arabia.The dataset used in this research is gathered exclusively from the Saudi Electric Company.The pre-processing phase included normalizing the data using min-max method and mapping the cyclical attribute to its sine and cosine facets.The number of neurons and learning rate of the standalone model were optimized using hyperparameter tuning.Finally,the standalone model was tested against the bagging ensemble using the optimized ANN.The bagging ensemble with an optimized ANN as the chosen classifier outperformed the standalone ANN model.The results for the proposed model produced 0.9116 Correlation Coefficient(CC),0.2836 Mean Absolute Percentage Error(MAPE),0.4578,Root Mean Squared Percentage Error(RMSPE),0.0298 MAE,and 0.069 Root Mean Squared Error(RMSE),respectively.

Scheduling an Energy-Aware Parallel Machine System with Deteriorating and Learning Effects Considering Multiple Optimization Objectives and Stochastic Processing Time

Currently,energy conservation draws wide attention in industrial manufacturing systems.In recent years,many studies have aimed at saving energy consumption in the process of manufacturing and scheduling is regarded as an effective approach.This paper puts forwards a multi-objective stochastic parallel machine scheduling problem with the consideration of deteriorating and learning effects.In it,the real processing time of jobs is calculated by using their processing speed and normal processing time.To describe this problem in a mathematical way,amultiobjective stochastic programming model aiming at realizing makespan and energy consumption minimization is formulated.Furthermore,we develop a multi-objective multi-verse optimization combined with a stochastic simulation method to deal with it.In this approach,the multi-verse optimization is adopted to find favorable solutions from the huge solution domain,while the stochastic simulation method is employed to assess them.By conducting comparison experiments on test problems,it can be verified that the developed approach has better performance in coping with the considered problem,compared to two classic multi-objective evolutionary algorithms.

A hybrid agent⁃based machine learning method for human⁃centred energy consumption prediction

Occupant behaviour has significant impacts on the performance of machine learning algorithms when predicting building energy consumption.Due to a variety of reasons(e.g.,underperforming building energy management systems or restrictions due to privacy policies),the availability of occupational data has long been an obstacle that hinders the performance of machine learning algorithms in predicting building energy consumption.Therefore,this study proposed an agent⁃based machine learning model whereby agent⁃based modelling was employed to generate simulated occupational data as input features for machine learning algorithms for building energy consumption prediction.Boruta feature selection was also introduced in this study to select all relevant features.The results indicated that the performances of machine learning algorithms in predicting building energy consumption were significantly improved when using simulated occupational data,with even greater improvements after conducting Boruta feature selection.

A Virtual Machine Placement Strategy Based on Virtual Machine Selection and Integration

Cloud data centers face the largest energy consumption.In order to save energy consumption in cloud data centers,cloud service providers adopt a virtual machine migration strategy.In this paper,we propose an efficient virtual machine placement strategy(VMP-SI)based on virtual machine selection and integration.Our proposed VMP-SI strategy divides the migration process into three phases:physical host state detection,virtual machine selection and virtual machine placement.The local regression robust(LRR)algorithm and minimum migration time(MMT)policy are individual used in the first and section phase,respectively.Then we design a virtual machine migration strategy that integrates the process of virtual machine selection and placement,which can ensure a satisfactory utilization efficiency of the hardware resources of the active physical host.Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics.

Energy-efficient virtual machine consolidation algorithm in cloud data centers

Cloud data centers consume a multitude of power leading to the problem of high energy consumption. In order to solve this problem, an energy-efficient virtual machine(VM) consolidation algorithm named PVDE(prediction-based VM deployment algorithm for energy efficiency) is presented. The proposed algorithm uses linear weighted method to predict the load of a host and classifies the hosts in the data center, based on the predicted host load, into four classes for the purpose of VMs migration. We also propose four types of VM selection algorithms for the purpose of determining potential VMs to be migrated. We performed extensive performance analysis of the proposed algorithms. Experimental results show that, in contrast to other energy-saving algorithms, the algorithm proposed in this work significantly reduces the energy consumption and maintains low service level agreement(SLA) violations.

Support vector machine and ROC curves for modeling of aircraft fuel consumption

To estimate the fuel consumption of a civil aircraft,we propose to use the receiver operating characteristic(ROC)curve to optimize a support vector machine(SVM)model.The new method and procedure has been developed to build,train,validate,and apply an SVM model.A conceptual support vector network is proposed to model fuel consumption,and the flight data collected from routes are used as the inputs to train an SVM model.During the training phase,an ROC curve is defined to evaluate the performance of the model.To validate the applicability of the trained model,a case study is developed to compare the data from an aircraft performance manual and from the implemented simulation model.The investigated aircraft in the case study is a Boeing 737-800 powered by CFM-56 engines.The comparison has shown that the trained SVM model from the proposed procedure is capable of representing a complex fuel consumption function accurately for all phases during the flight.The proposed methodology is generic,and can be extended to reliably model the fuel consumption of other types of aircraft,such as piston engine aircraft or turboprop engine aircraft.

Low-power emerging memristive designs towards secure hardware systems for applications in internet of things

Emerging memristive devices offer enormous advantages for applications such as non-volatile memories and inmemory computing(IMC),but there is a rising interest in using memristive technologies for security applications in the era of internet of things(IoT).In this review article,for achieving secure hardware systems in IoT,lowpower design techniques based on emerging memristive technology for hardware security primitives/systems are presented.By reviewing the state-of-the-art in three highlighted memristive application areas,i.e.memristive non-volatile memory,memristive reconfigurable logic computing and memristive artificial intelligent computing,their application-level impacts on the novel implementations of secret key generation,crypto functions and machine learning attacks are explored,respectively.For the low-power security applications in IoT,it is essential to understand how to best realize cryptographic circuitry using memristive circuitries,and to assess the implications of memristive crypto implementations on security and to develop novel computing paradigms that will enhance their security.This review article aims to help researchers to explore security solutions,to analyze new possible threats and to develop corresponding protections for the secure hardware systems based on low-cost memristive circuit designs.

A hardware-based algorithm for virtual machine provisioning in a private cloud

Cloud computing is becoming a key factor in the market day by day. Therefore, many companies are investing or going to invest in this sector for development of large data centers. These data centers not only consume more energy but also produce greenhouse gases. Because of large amount of power consumption, data center providers go for different types of power generator to increase the profit margin which indirectly affects the environment. Several studies are carried out to reduce the power consumption of a data center. One of the techniques to reduce power consumption is virtualization. After several studies, it is stated that hardware plays a very important role. As the load increases, the power consumption of the CPU is also increased. Therefore, by extending the study of virtualization to reduce the power consumption, a hardware-based algorithm for virtual machine provisioning in a private cloud can significantly improve the performance by considering hardware as one of the important factors.

Reconfigurable Sensing Time in Cooperative Cognitive Network Using Machine Learning

A cognitive radio network(CRN)intelligently utilizes the available spectral resources by sensing and learning from the radio environment to maximize spectrum utilization.In CRNs,the secondary users(SUs)opportunistically access the primary users(PUs)spectrum.Therefore,unambiguous detection of the PU channel occupancy is the most critical aspect of the operations of CRNs.Cooperative spectrum sensing(CSS)is rated as the best choice for making reliable sensing decisions.This paper employs machinelearning tools to sense the PU channels reliably in CSS.The sensing parameters are reconfigured to maximize the spectrum utilization while reducing sensing error and cost with improved channel throughput.The fine-k-nearest neighbor algorithm(FKNN),employed in this paper,estimates the number of samples based on the nature of the channel under-specific detection and false alarm probability demands.The simulation results reveal that the sensing cost is suppressed by reducing the sensing time and exploiting the traditional fusion rules,validating the effectiveness of the proposed scheme.Furthermore,the global decision made at the fusion center(FC)based on the modified sensing samples,results low energy consumption,higher throughput,and improved detection with low error probabilities.

一种改进的低功耗NAND Flash控制器设计

介绍了一种低功耗低成本Nand Flash控制器,控制器能够根据输入的命令码产生满足擦除、读写功能的时序完成相应操作。具有功耗低、面积小、电路实现简单的特点,采用中芯国际0.18μm工艺库进行DC综合验证,最高工作频率可达28.5MHz,面积仅需6805μm2。32位命令码仅用到低八位,能够有效实现抗干扰,在命令码判别方面,采用改进的判别电路,将命令码进行分块比较,将命令码分成高16位和低16位,然后再细分进行比较判断,减少了计数器和移位寄存器的使用,从而减小了面积。

基于机器学习的空气源热泵干燥能耗回归预测

为了降低空气源热泵干燥过程能耗,研究了空气源热泵干燥能耗特性,采用多元线性回归模型(multivariate linear regression model, MLRM)和BP神经网络(back propagation neural network, BPNN)模型来预测干燥工艺能耗。在分析干燥能耗影响特征参数的基础上,提出将干燥工艺过程进行切分处理的方法以降低数据获取难度。选取烘房设定温度、烘房设定湿度、烘房初始温度、烘房初始湿度、环境平均温度、环境平均湿度、物料质量和初始含水率8个特征参数作为模型输入,能耗和物料结束含水率作为模型输出。使用MLRM模型、BPNN模型和其他机器学习模型进行能耗预测,MLRM模型对能耗拟合的决定系数为0.739,对物料结束含水率拟合的决定系数为0.931;BPNN模型使用Sigmoid函数作为激活函数时对能耗拟合的决定系数最高,为0.828,使用Identity函数作为激活函数时对物料结束含水率拟合的决定系数最高,为0.942,拟合效果优于其他机器学习模型,能够满足实际生产需求。以复水豌豆为干燥对象设计加载物料65 kg、持续时间4 h的完整变温变湿干燥工艺进行验证试验,结果表明:试验总能耗为15.066 kW·h,MLRM模型和BPNN模型的预测总能耗分别为14.476 kW·h、15.183 kW·h,预测精度分别为96.08%、99.23%;试验结束含水率为8.541%,MLRM模型和BPNN模型的预测结束含水率分别为9.560%、8.889%,预测精度分别为88.07%、95.93%。该研究提出了一种使用MLRM模型和BPNN模型对空气源热泵干燥能耗进行分段精准预测的有效手段,对于优化干燥工艺和降低干燥能耗具有实际意义。

人工智能推动的高技能人才需求与能力结构变迁研究——以人机智能协作的模式差异为视角

人工智能对许多职业产生了明显影响,人机智能协作将成为常态。基于对不同任务类型下人类和当前人工智能技术的优势特征分析,人机智能协作可归纳为智能工具型、协同合作型和重塑补充型三种典型模式。三种模式下的人机分工和协同工作方式差别显著,因此也造成了社会人才需求尤其是高技能应用型人才的需求状况和适应性能力结构要求的巨大差异。智能工具型协作模式下高技能人才的职业能力核心是新型智能化业务实践能力,协同合作型协作模式下的高技能人才的职业能力核心则是以统筹人机智能系统实现职业功能为根本的智能协作能力,重塑补充型协作模式下高技能人才需求则会被人工智能大量替代。