A novel virtual machine deployment algorithm with energy efficiency in cloud computing

In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the energy consumption and(processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is,host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load; VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies(minimization of migrations policy based on TESA(MIMT), maximization of migrations policy based on TESA(MAMT), highest potential growth policy based on TESA(HPGT), lowest potential growth policy based on TESA(LPGT) and random choice policy based on TESA(RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold(ST) algorithm and minimization of migrations(MM) algorithm, MIMT significantly improves the energy efficiency in data centers.

Future Vision on Artificial Intelligence Assisted Green Energy Efficiency Network

To meet the key performance requirement of the 5G network and the demand of the growing number of mobile subscribers,millions of base stations are being constructed.5G New Radio is designed to enable denser network deployments,which raises significant concerns about network energy consumption.Machine learning(ML),as a kind of artificial intelligence(AI)technologies,can enhance network optimization performance and energy efficiency.In this paper,we propose AI/ML-assisted energy-saving strategies to achieve optimal performance in terms of cell shutdown duration and energy efficiency.To realize network intelligence,we put forward the concept of intrinsic AI,which integrates AI into every aspect of wireless communication networks.

A Regular Study on Yarn Count, Size Box Temperature and Machine Speed to Increase Weaving Efficiency of Cotton Fabric

Sizing is an inherent part of weaving works, consisting in the coating of the warp yarn with a polymeric adhesive, such as starch, in order to assist efficient weaving. The study is aimed to assess the effects of squeezed roller pressure, dryer temperature, yarn count, machine speed (rpm) on cotton fabric weaving. Coarser and finer cotton yarn samples were prepared using sizing solution BENSIZE 850. Different size box temperature, yarn count, fabric construction, machine speed, squeeze roller pressure were considered to construct different weaving designs to study yarn breakages parameter. A warping plan was designed on TAROKO V5.4 (190325) software. The results established that size box lower temperature and higher machine speed provide the smallest amount yarn break during weaving for coarser cotton yarn and the highest for finer cotton yarn. Size box higher temperature and lower machine speed provide maximum yarn breakage during weaving coarser cotton yarn and minimum for fine yarn. Size penetration is uniform, which provides a higher strength of the yarn to less breakage. This aspect of the research suggested that higher yarn strength gives a lesser amount of breakage.

Design and Experimentation of Multi-Rod Grain Sampling Machine

In order to enhance grain sampling efficiency, in this work a truss type multi-rod grain sampling machine is designed and tested. The sampling machine primarily consists of truss support mechanism, main carriage mechanism, auxiliary carriage mechanism, sampling rods, and a PLC controller. The movement of the main carriage on the truss, the auxiliary carriage on the main carriage, and the vertical movement of the sampling rods on the auxiliary carriage are controlled through PLC programming. The sampling machine accurately controls the position of the sampling rods, enabling random sampling with six rods to ensure comprehensive and random sampling. Additionally, sampling experiments were conducted, and the results showed that the multi-rod grain sampling machine simultaneously samples with six rods, achieving a sampling frequency of 38 times per hour. The round trip time for the sampling rods is 33 seconds per cycle, and the sampling length direction reaches 18 m. This study provides valuable insights for the design of multi-rod grain sampling machines.

Parallel Inference for Real-Time Machine Learning Applications

Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes the performance gains from parallel versus sequential hyperparameter optimization. Using scikit-learn’s Randomized SearchCV, this project tuned a Random Forest classifier for fake news detection via randomized grid search. Setting n_jobs to -1 enabled full parallelization across CPU cores. Results show the parallel implementation achieved over 5× faster CPU times and 3× faster total run times compared to sequential tuning. However, test accuracy slightly dropped from 99.26% sequentially to 99.15% with parallelism, indicating a trade-off between evaluation efficiency and model performance. Still, the significant computational gains allow more extensive hyperparameter exploration within reasonable timeframes, outweighing the small accuracy decrease. Further analysis could better quantify this trade-off across different models, tuning techniques, tasks, and hardware.

DEVELOPMENT AND EXPERIMENT OF NEW AlTiN COATED DRILLS FOR HIGH EFFICIENCY DRY DRILLING OF 40Cr

Two new AlTiN coated cemented carbide drills with Al content of 40% and 55% in weight are developed for high efficiency dry drilling of 40Cr. By studying tool durability, machined hole quality, tool wear mechanism, chip deformation, and lubrication, the dry drilling performance of the two kinds of coated drills is analyzed. Experimental results show that the AlTiN coated drills are suitable for high efficiency dry drilling and can obtain higher quality of machined holes. The tool durability of the drill with 55% Al content is 1. 3 times of that of the drill with 40% Al content at the cutting speed of 90 m/min. The wear mechanism of two AlTiN coatings are studied in experiments. During dry drilling process, oxidative wear appears in both two kinds of drills. The oxide film is formed on the top of the coated drill containing Al content of 55%. And the oxide film helps to increase its high temperature resistance and decrease the coating flaking, thus the drill is failed because of coating subsidence. The drill with less Al content is failed due to peeling and breakage. The lubricated condition in dry drilling is improved by the high Al content coating. It helps to reduce the cutting deformation and benefits to improve the quality of machined holes. The AlTiN coating with higher Al content shows longer tool life and higher quality of machined holes in high efficiency dry drilling. Its tool life increases by 30% compared with that of the coating with less Al content.

Influence of confining stress on fracture characteristics and cutting efficiency of TBM cutters conducted on soft and hard rock

Combined with numerical simulation, the influence of confining stress on cutting process, fracture conditions and cutting efficiencies of soft and hard rock has been conducted on the triaxial testing machine(TRW-3000) designed and manufactured in Central South University(China). Results are obtained by performing analysis on the fracture scopes of cement and granite plates,the characteristics of cutting force in cutting processes and the cutting efficiency. Firstly, the increase of latitude fracture scope and the decrease of longitude fracture scope are both more notable in the tests conducted on cement plates subjected to the increasing confining stresses; secondly, the increase tendency of peak penetration forces obtained from tests conducted on granite plates is more obvious, however, the increase tendencies of average penetration forces achieved from cement and granite plates are close to each other; thirdly, the cutting efficiency could be improved by increasing the spacing between cutters when the confining stress which acts on soft and hard rock increases in a certain degree, and the cutting efficiency of soft rock is more sensitive to the varying confining stresses.

Application Research on Powder Mixed EDM in Rough Machining

Powder Mixed Electric Discharge Machining (PMEDM) has different mechanism from conventional EDM, which can improve the surface roughness and surface quality distinctly and to obtain nearly mirror surface effects. It is a useful finish machining method and is researched and applied by many countries. However there are little research on rough machining of PMEDM. Experiments show that PMEDM machining makes discharge breakdown easier, enlarges the discharge gaps and widens discharge passage, and at last forms even distributed and "large and shadow" shaped etched cavities. Because of much loss of discharge energy in the discharge gaps and reduction of ejecting force on the melted material, the machining efficiency gets lower and the surface roughness gets small in PMEDM machining in comparison with conventional EDM machining. This paper performs experimental research on the machining efficiency and surface roughness of PMEDM in rough machining. The machining efficiency of PMEDM can be highly increased by selecting proper discharge parameters (increasing peak current, reducing pulse width) with approximate surface roughness in comparison with conventional EDM machining. Although PMEDM can improve machining efficiency in rough efficiency, but a series of problems like electrode wear, efficiently separation of machined scraps from the powder mixed working fluid, should be solved before PMEDM machining is really applied in rough machining. Experiments result shows that powder mixed EDM machining can obviously improve machining efficiency at the same surface roughness by selecting proper discharging parameters, and can provide reference accordingly for the application of PMEDM machining technology in rough machining.

Evaluation of the energy efficiency of rotary percussive drilling using dimensionless energy index

This paper sets forth a geomechanics framework for assessing the energy efficiency of rotary percussive drilling using the energy criterion, which has been proposed by Victor Oparin for volumetric destruction of high-stress rocks having nonuniform physico-mechanical properties. We review the long-term research and development in the specified area of science and technology, including research and development projects implemented at the Institute of Mining, Siberian Branch of the Russian Academy of Sciences. A new modified expression of Oparin’s dimensionless energy criterion of volumetric rock destruction k is introduced. The range of in situ values is determined for the energy criterion of volumetric rock destruction at the optimized energy efficiency of rotary percussive drilling. The temporospatial intervals of geotechnical monitoring are found to control pneumatic drilling energy efficiency at subsoil use objects in Russia. The integrated experimental, theoretical and geotechnical approach to the comprehensive investigation of real-time processes of rock fracture in rotary percussive drilling using the energy concept possesses the necessary geomechanical performance-and-technology potential to create the next level geotechnical monitoring of drilling systems for various purposes, including determination of physico-mechanical properties and the stress-strain analysis of rock mass in full-scale drilling.

Enabling Energy Efficiency in 5G Network

The mobile Internet and Internet of Things are considered the main driving forc⁃es of 5G,as they require an ultra-dense deployment of small base stations to meet the in⁃creasing traffic demands.5G new radio(NR)access is designed to enable denser network deployments,while leading to a significant concern about the network energy consump⁃tion.Energy consumption is a main part of network operational expense(OPEX),and base stations work as the main energy consumption equipment in the radio access network(RAN).In order to achieve RAN energy efficiency(EE),switching off cells is a strategy to reduce the energy consumption of networks during off-peak conditions.This paper intro⁃duces NR cell switching on/off schemes in 3GPP to achieve energy efficiency in 5G RAN,including intra-system energy saving(ES)scheme and inter-system ES scheme.Addition⁃ally,NR architectural features including central unit/distributed unit(CU/DU)split and dual connectivity(DC)are also considered in NR energy saving.How to apply artificial in⁃telligence(AI)into 5G networks is a new topic in 3GPP,and we also propose a machine learning(ML)based scheme to save energy by switching off the cell selected relying on the load prediction.According to the experiment results in the real wireless environment,the ML based ES scheme can reduce more power consumption than the conventional ES scheme without load prediction.

Energy-Efficient Framework for Virtual Machine Consolidation in Cloud Data Centers

With the advent of the era of cloud computing, the high energy consumption of cloud computing data centers has become a prominent problem, and how to reduce the energy consumption of cloud computing data center and improve the efficiency of data center has become the research focus of researchers all the world. In a cloud environment, virtual machine consolidation(VMC) is an effective strategy that can improve the energy efficiency. However, at the same time, in the process of virtual machine consolidation, we need to deal with the tradeoff between energy consumption and excellent service performance to meet service level agreement(SLA). In this paper, we propose a new virtual machine consolidation framework for achieving better energy efficiency-Improved Underloaded Decision(IUD) algorithm and Minimum Average Utilization Difference(MAUD) algorithm. Finally, based on real workload data on Planet Lab, experiments have been done with the cloud simulation platform Cloud Sim. The experimental result shows that the proposed algorithm can reduce the energy consumption and SLA violation of data centers compared with existing algorithms, improving the energy efficiency of data centers.

Predicting the device performance of the perovskite solar cells from the experimental parameters through machine learning of existing experimental results

The performance of the metal halide perovskite solar cells(PSCs)highly relies on the experimental parameters,including the fabrication processes and the compositions of the perovskites;tremendous experimental work has been done to optimize these factors.However,predicting the device performance of the PSCs from the fabrication parameters before experiments is still challenging.Herein,we bridge this gap by machine learning(ML)based on a dataset including 1072 devices from peer-reviewed publications.The optimized ML model accurately predicts the PCE from the experimental parameters with a root mean square error of 1.28%and a Pearson coefficientr of 0.768.Moreover,the factors governing the device performance are ranked by shapley additive explanations(SHAP),among which,A-site cation is crucial to getting highly efficient PSCs.Experiments and density functional theory calculations are employed to validate and help explain the predicting results by the ML model.Our work reveals the feasibility of ML in predicting the device performance from the experimental parameters before experiments,which enables the reverse experimental design toward highly efficient PSCs.

Magnetic Electrochemical Finishing Machining

How to improve the finishing efficiency and surface roughness have been all along the objective of research in electrochemical polishing. However, the research activity, i.e. during electrochemical polishing, directly introduce the magnetic field to study how the magnetic field influences on the finishing efficiency, quality and the electrochemical process in the field of finishing machining technology, is insufficient. When introducing additional magnetic field in the traditional electrochemical polishing, due to the co-action of Lorentz’ force and electric field force, the ions arriving the machined surface by way of a curvilinear motion result in the electric current density distribution on the surface even more non-uniform, then the dissolving velocity of the peak points or side faces and the diffusion velocity of the product are enhanced, and the forcible agitation is happened on the electrodes surface by magnetic field, the removal rate of peak points are still more greater, and efficiency is also still more higher. Compared with the electrochemical polishing, in the magnetic electrochemical finishing machining, the finishing speed at peak points is higher, but at valley points it is lower, therefore after machining, both the highness at peak points and finishing depth at valley points are smaller, the results are propitious to minish initial wear quantity caused by friction and wear when machined workpiece employing in practice, and increase contact stiffness of workpiece, and from the viewpoint of microcosmic theory, this phenomenon is also of advantage to reduce damage to substrate. It can also be seen from the equation presented in the paper that the track of ionic movement relates to the electrodes gap, potential and magnetic induction intensity and furthermore; under the given conditions, the movement also relate to the electrolyte. it can be inferred that there must be an optimum value in respect of the magnetic induction intensity influencing the efficiency of finishing machining, and at the same time, the rational matching among the interelectrodes voltage, gap sizes and magnetic induction intensity can raise the efficiency and quality as well as improve the surface roughness to the maximum. In short, the co-action of the Lorentz’ force and electric field force change the motion track of anions and make more uneven distribution of the electric current density on the anodes surface, thus the dissolving velocity and product diffusion velocity of the peak points or side faces of the anode are raised. All those and the forced agitation of magnetic field towards the electrode surface are the principal mechanism for surface finishing. This point has been proved from the experimental results in this paper.

Machine learning enables intelligent screening of interface materials towards minimizing voltage losses for p-i-n type perovskite solar cells

Interface engineering is proved to be the most important strategy to push the device performance of the perovskite solar cell(PSC) to its limit, and numerous works have been conducted to screen efficient materials. Here, on the basis of the previous studies, we employ machine learning to map the relationship between the interface material and the device performance, leading to intelligently screening interface materials towards minimizing voltage losses in p-i-n type PSCs. To enhance the explainability of the machine learning models, molecular descriptors are used to represent the materials. Furthermore,experimental analysis with different characterization methods and device simulation based on the drift-diffusion physical model are conducted to get physical insights and validate the machine learning models. Accordingly, 3-thiophene ethylamine hydrochloride(Th EACl) is screened as an example, which enables remarkable improvements in VOCand PCE of the PSCs. Our work reveals the critical role of datadriven analysis in the high throughput screening of interface materials, which will significantly accelerate the exploration of new materials for high-efficiency PSCs.

A Novel Machine Learning-Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

Machine learning is a technique for analyzing data that aids the construction of mathematical models.Because of the growth of the Internet of Things(IoT)and wearable sensor devices,gesture interfaces are becoming a more natural and expedient human-machine interaction method.This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns.The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition.Potential applications of hand gesture recognition research span from online gaming to surgical robotics.The location of the hands,the alignment of the fingers,and the hand-to-body posture are the fundamental components of hierarchical emotions in gestures.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.In this scenario,it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or trembling.When a user performs the same dynamic gesture,the hand shapes and speeds of each user,as well as those often generated by the same user,vary.A machine-learning-based Gesture Recognition Framework(ML-GRF)for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered generation.We have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions,and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture codes.The findings from the simulation support the accuracy,precision,gesture recognition,sensitivity,and efficiency rates.The Machine Learning-based Gesture Recognition Framework(ML-GRF)had an accuracy rate of 98.97%,a precision rate of 97.65%,a gesture recognition rate of 98.04%,a sensitivity rate of 96.99%,and an efficiency rate of 95.12%.

Machine Learning Driven Latency Optimization for Internet of Things Applications in Edge Computing

Emerging Internet of Things(IoT)applications require faster execution time and response time to achieve optimal performance.However,most IoT devices have limited or no computing capability to achieve such stringent application requirements.To this end,computation offloading in edge computing has been used for IoT systems to achieve the desired performance.Nevertheless,randomly offloading applications to any available edge without considering their resource demands,inter-application dependencies and edge resource availability may eventually result in execution delay and performance degradation.We introduce Edge-IoT,a machine learning-enabled orchestration framework in this paper,which utilizes the states of edge resources and application resource requirements to facilitate a resource-aware offloading scheme for minimizing the average latency.We further propose a variant bin-packing optimization model that co-locates applications firmly on edge resources to fully utilize available resources.Extensive experiments show the effectiveness and resource efficiency of the proposed approach.

Dimensional Complexity and Algorithmic Efficiency

This paper uses the concept of algorithmic efficiency to present a unified theory of intelligence. Intelligence is defined informally, formally, and computationally. We introduce the concept of dimensional complexity in algorithmic efficiency and deduce that an optimally efficient algorithm has zero time complexity, zero space complexity, and an infinite dimensional complexity. This algorithm is used to generate the number line.

Efficiency Enhancement of Four-Quadrant Permanent Magnet Direct Current Motor Control through Combination of Power Electronics Switches

This paper presents two four-quadrant topologies for Permanent Magnet Direct Current （PMDC） motor drives, built using combination of power electronics switches. The issue is the increased efficiency of the topology compared to conventional ones built using only one type of power electronic switches. The suggested combinations-MOSFET-IGBT and MOSFET-SCR improve the current bridge topologies by uniting the advantages of the different switches-the high switching capabilities of the MOSFET and the better antiparallel diodes of IGBTs and SCRs. The total efficiency of the motor control is improved by several percents. This reduces the overall consumption of the converter circuitry, which can be very beneficial-especially at high power motors, as the ones presented in the paper. Statistical and experimental data is presented proving the efficiency of the suggest topologies.

Machine learning inspired workflow to revise field development plan under uncertainty

We present an efficient and risk-informed closed-loop field development (CLFD) workflow for recurrently revising the field development plan (FDP) using the accrued information. To make the process practical, we integrated multiple concepts of machine learning, an intelligent selection process to discard the worst FDP options and a growing set of representative reservoir models. These concepts were combined and used with a cluster-based learning and evolution optimizer to efficiently explore the search space of decision variables. Unlike previous studies, we also added the execution time of the CLFD workflow and worked with more realistic timelines to confirm the utility of a CLFD workflow. To appreciate the importance of data assimilation and new well-logs in a CLFD workflow, we carried out researches at rigorous conditions without a reduction in uncertainty attributes. The proposed CLFD workflow was implemented on a benchmark analogous to a giant field with extensively time-consuming simulation models. The results underscore that an ensemble with as few as 100 scenarios was sufficient to gauge the geological uncertainty, despite working with a giant field with highly heterogeneous characteristics. It is demonstrated that the CLFD workflow can improve the efficiency by over 85% compared to the previously validated workflow. Finally, we present some acute insights and problems related to data assimilation for the practical application of a CLFD workflow.

An OP-TEE Energy-Efficient Task Scheduling Approach Based on Mobile Application Characteristics

Trusted Execution Environment(TEE)is an important part of the security architecture of modern mobile devices,but its secure interaction process brings extra computing burden to mobile devices.This paper takes open portable trusted execution environment(OP-TEE)as the research object and deploys it to Raspberry Pi 3B,designs and implements a benchmark for OP-TEE,and analyzes its program characteristics.Furthermore,the application execution time,energy consumption and energy-delay product(EDP)are taken as the optimization objectives,and the central processing unit(CPU)frequency scheduling strategy of mobile devices is dynamically adjusted according to the characteristics of different applications through the combined model.The experimental result shows that compared with the default strategy,the scheduling method proposed in this paper saves 21.18%on average with the Line Regression-Decision Tree scheduling model with the shortest delay as the optimization objective.The Decision Tree-Support Vector Regression(SVR)scheduling model,which takes the lowest energy consumption as the optimization goal,saves 22%energy on average.The Decision Tree-K-Nearest Neighbor(KNN)scheduling model with the lowest EDP as the optimization objective optimizes about 33.9%on average.