Multi-resource collaborative optimization for adaptive virtual machine placement

The unbalanced resource utilization of physical machines (PMs) in cloud data centers could cause resource wasting, workload imbalance and even negatively impact quality of service (QoS). To address this problem, this paper proposes a multi-resource collaborative optimization control (MCOC) mechanism for virtual machine (VM) migration. It uses Gaussian model to adaptively estimate the probability that the running PMs are in the multi-resource utilization balance status. Given the estimated probability of the multi-resource utilization balance state, we propose effective selection algorithms for live VM migration between the source hosts and destination hosts, including adaptive Gaussian model-based VMs placement (AGM-VMP) algorithm and VMs consolidation (AGM-VMC) method. Experimental results show that the AGM-VMC method can effectively achieve load balance and significantly improve resource utilization, reduce data center energy consumption while guaranteeing QoS.

Traffic Lights Detection and Recognition Method Based on the Improved YOLOv4 Algorithm

For facing of the problems caused by the YOLOv4 algorithm’s insensitivity to small objects and low detection precision in traffic light detection and recognition, the Improved YOLOv4 algorithm is investigated in the paper using the shallow feature enhancement mechanism and the bounding box uncertainty prediction mechanism. The shallow feature enhancement mechanism is used to extract features from the network and improve the network’s ability to locate small objects and color resolution by merging two shallow features at different stages with the high-level semantic features obtained after two rounds of upsampling. Uncertainty is introduced in the bounding box prediction mechanism to improve the reliability of the prediction of the bounding box by modeling the output coordinates of the prediction bounding box and adding the Gaussian model to calculate the uncertainty of the coordinate information. The LISA traffic light data set is used to perform detection and recognition experiments separately. The Improved YOLOv4 algorithm is shown to have a high effectiveness in enhancing the detection and recognition precision of traffic lights. In the detection experiment, the area under the PR curve value of the Improved YOLOv4 algorithm is found to be 97.58%, which represents an increase of 7.09% in comparison to the 90.49% score gained in the Vision for Intelligent Vehicles and Applications Challenge Competition. In the recognition experiment, the mean average precision of the Improved YOLOv4 algorithm is 82.15%, which is 2.86% higher than that of the original YOLOv4 algorithm. The Improved YOLOv4 algorithm shows remarkable advantages as a robust and practical method for use in the real-time detection and recognition of traffic signal lights.

Variance of Voltages in a Lattice Coulomb Gas

We study the behavior of the variance of the difference of energies for putting an additional electric unit charge at two different locations in the two-dimensional lattice Coulomb gas in the high-temperature regime. For this, we exploit the duality between this model and a discrete Gaussian model. Our estimates follow from a spontaneous symmetry breaking in the latter model.

Retrieval of Phytoplankton Pigment Composition from Their In Vivo Absorption Spectra

Algal pigment composition is an indicator of phytoplankton community structure that can be estimated from optical observations. Assessing the potential capability to retrieve different types of pigments from phytoplankton absorption is critical for further applications. This study investigated the performance of three models and the utility of hyperspectral in vivo phytoplankton absorption spectra for retrieving pigment composition using a large database (n = 1392). Models based on chlorophyll-a (Chl-a model), Gaussian decomposition (Gaussian model), and partial least squares (PLS) regression (PLS model) were compared. Both the Gaussian model and the PLS model were applied to hyperspectral phytoplankton absorption data. Statistical analysis revealed the advantages and limitations of each model. The Chl-a model performed well for chlorophyll-c (Chl-c), diadinoxanthin, fucoxanthin, photosynthetic carotenoids (PSC), and photoprotective carotenoids (PPC), with a median absolute percent difference for cross-validation (MAPDCV) < 58%. The Gaussian model yielded good results for predicting Chl-a, Chl-c, PSC, and PPC (MAPDCV < 43%). The performance of the PLS model was comparable to that of the Chl-a model, and it exhibited improved retrievals of chlorophyll-b, alloxanthin, peridinin, and zeaxanthin. Additional work undertaken with the PLS model revealed the prospects of hyperspectral-resolution data and spectral derivative analyses for retrieving marker pigment concentrations. This study demonstrated the applicability of in situ hyperspectral phytoplankton absorption data for retrieving pigment composition and provided useful insights regarding the development of bio-optical algorithms from hyperspectral and satellite-based ocean-colour observations.

Calculation of crosswind integrated concentration by using different dispersion schemes

The Gaussian model is the most extensively used model for local dispersion. The Gaussian formula for a continuous release from a point source (GPM) is integrated to get crosswind integrated concentration. Different schemes such as Irwin, power law, Briggs, Standard method, and split sigma theta method can be used to obtain integrated concentration. Also downwind speed in power law, plume rise and Statistical measures are used in the model to know which is the best scheme agrees with the observed concentration data obtained from Copenhagen, Denmark.

Flexible Power-Normal Models with Applications

The main object of this paper is to propose a new asymmetric model more flexible than the generalized Gaussian model. The probability density function of the new model can assume bimodal or unimodal shapes, and one of the parameters controls the skewness of the model. Three simulation studies are reported and two real data applications illustrate the flexibility of the model compared with traditional proposals in the literature.

Three-Dimensional LiDAR Wake Measurements in an Offshore Wind Farm and Comparison with Gaussian and AL Wake Models

Huaneng Rudong 300 MW offshore wind farm project is located in eastern China. The wake effect is one of the major concerns for wind farm operators, as all 70 units are plotted in ranks, and the sea surface roughness is low. This paper investigated the wake intensity by combining a field test and a numerical simulation. To carry out further yaw optimization, a Gaussian wake model was adopted. Firstly, a 3D Light Detection and Ranging device (LiDAR) was used to capture the features in both horizontal and vertical directions of the wake. It indicated that Gaussian wake model can precisely predict the characteristics under time average and steady state in the wind farm. The predicted annual energy production (AEP) of the whole wind farm by the Gaussian model is compared with the calculation result of the actuator line (AL)-based LES method, and the difference between the two methods is mostly under 10%.

MODELS DESCRIBING THE ENVIRONMENTAL EFFECTS OF POLLUTANT EMISSIONS BY ROAD TRANSPORT

Air pollution is the most serious environmental problem facing most industrial cities in the world and in China. The World Health Organization measured the concentration of sulfur dioxide, nitrogen dioxide and total suspended particulate matter in 272 cities in 53 countries around the world, listing the ten most severely polluted cities in the world. The spatial and temporal distribu-tion of air pollutants depends on various factors such as the meteorological field, the source of emissions, the complex bottom surface of the site, the interplay of physical and chemical processes, and has strong non-linear characteristics [5]. Air quality forecasting is commonly used in the field of statistical forecasting methods, according to long-term monitoring data, the creation of a statisti-cal forecasting model, the model is simple, easy to operate business, but no solid physical founda-tion, and another numerical forecasting model based on atmospheric physics and material transfer model although the physical foundation is solid, comprehensive forecast results, but the forecast results are not reliable. Already in the 1950s, the system of meteorology of air pollution was gradu-ally formed, the box model, the Gaussian model, the Lagrange model, the Euler model, the dense gas model and other five types of models appeared. The first Gaussian model allows one to obtain a diffusion model of a local small-scale space and make predictions, then, based on the Gaussian model of the study, a modified model is obtained for other reliefs and weather conditions. There-fore, the modeling accuracy and applicable conditions are difficult to cope with the needs of large-scale complex meteorological conditions of air quality models.