Thank you very much for downloading modern applied statistics with s. As you may know, people have search hundreds times for their favorite readings like this modern applied statistics with s, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. modern applied statistics with s is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the modern applied statistics with s is universally compatible with any devices to read.

Modern Applied Statistics With S

A transformation is underway in electric power and energy systems (EPESs) to provide clean distributed energy for sustainable global economic growth. Internet of Things (IoT) is at the forefront of this transformation imparting capabilities, such as real-time monitoring, situational awareness and intelligence, control, and cyber security to transform the existing EPES into intelligent cyber-enabled EPES, which is more efficient, secure, reliable, resilient, and sustainable. Additionally, digitizing the electric power ecosystem using IoT improves asset visibility, optimal management of distributed generation, eliminates energy wastage, and create savings. IoT has a significant impact on EPESs and offers several opportunities for growth and development. There are several challenges with the deployment of IoT for EPESs. Viable solutions need to be developed to overcome these challenges to ensure continued growth of IoT for EPESs. The advancements in computational intelligence capabilities can evolve an intelligent IoT system by emulating biological nervous systems with cognitive computation, streaming and distributed analytics including at the edge and device levels. This review paper provides an assessment of the role, impact and challenges of IoT in transforming EPESs.

Review of Internet of Things (IoT) in Electric Power and Energy Systems

As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.

/pdf/digital-twin-based-sustainable-intelligent-manufacturing-a-32gjodfp5s.pdf

Digital twin-based sustainable intelligent manufacturing: a review

The use of containers in cloud architectures has become widespread, owing to advantages such as limited overheads, easier and faster deployment, and higher portability. Moreover, they present a suitable architectural solution for the deployment of applications created using a microservice development pattern. Despite the large number of solutions and implementations, there remain open issues that have not been completely addressed in container automation and management. Container resource allocation influences system performance and resource consumption, and so it is a key factor for cloud providers. We propose a genetic algorithm approach, using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), to optimize container allocation and elasticity management, motivated by the good results obtained with this algorithm in other resource management optimization problems in cloud architectures. Our optimization algorithm enhances system provisioning, system performance, system failure, and network overhead. A model for cloud clusters, containers, microservices, and four optimization objectives is presented. Experimental results demonstrate that our approach is a suitable solution for addressing the problem of container allocation and elasticity, and it obtains better objective values than the container management policies implemented in Kubernetes.

Genetic Algorithm for Multi-Objective Optimization of Container Allocation in Cloud Architecture

Unlike a traditional virtual machine (VM), a container is an emerging lightweight virtualization technology that operates at the operating system level to encapsulate a task and its library dependencies for execution. The Container as a Service (CaaS) strategy is gaining in popularity and is likely to become a prominent type of cloud service model. Placing container instances on virtual machine instances is a classical scheduling problem. Previous research has focused separately on either virtual machine placement on physical machines (PMs) or container, or only tasks without containerization, placement on virtual machines. However, this approach leads to underutilized or overutilized PMs as well as underutilized or overutilized VMs. Thus, there is a growing research interest in developing a container placement algorithm that considers the utilization of both instantiated VMs and used PMs simultaneously. The goal of this study is to improve resource utilization, in terms of number of CPU cores and memory size for both VMs and PMs, and to minimize the number of instantiated VMs and active PMs in a cloud environment. The proposed placement architecture employs scheduling heuristics, namely, Best Fit (BF) and Max Fit (MF), based on a fitness function that simultaneously evaluates the remaining resource waste of both PMs and VMs. In addition, another meta-heuristic placement algorithm is proposed that uses Ant Colony Optimization based on Best Fit (ACO-BF) with the proposed fitness function. Experimental results show that the proposed ACO-BF placement algorithm outperforms the BF and MF heuristics and maintains significant improvement of the resource utilization of both VMs and PMs.

/pdf/a-placement-architecture-for-a-container-as-a-service-caas-4e83074n18.pdf

A placement architecture for a container as a service (CaaS) in a cloud environment

Cloud computing and virtualization technologies play important roles in modern service-oriented computing paradigm. More conventional services are being migrated to virtualized computing environments to achieve flexible deployment and high availability. We introduce a schedule algorithm based on fuzzy inference system (FIS), for global container resource allocation by evaluating nodes' statuses using FIS. We present the approaches to build containerized test environment and validates the effectiveness of the resource allocation policies by running sample use cases. Experiment results show that the presented infrastructure and schema derive optimal resource configurations and significantly improves the performance of the cluster.

Dynamic Resource Allocation Algorithm for Container-Based Service Computing

Thanks to the ubiquitous computing, the scale of data collection in various fields has been growing rapidly. Medicine is one of the fields that can benefit from the big data. However, it is faced with a big challenge because medical datasets are normally in high dimensions. Therefore, reducing dimensionality and finding the optimal set of features or attributes is of great importance. This paper presents a hybrid approach that combines the ReliefF algorithm (RA) and Random Forest for modeling and analysis of complex disease, with improved speed and stability. The method is also tested with eleven datasets from real medical records. Experimental results prove that the proposed method improves the accuracy of medical data classification for illness diagnosis.

An Improved Random Forest Method Based on RELIEFF for Medical Diagnosis

The traditional DV-HOP algorithm for three-dimensional localization precision is not good enough to use. Its positioning deviation is too large to find out the unknown nodes, and the scope of coverage is too small to find out all the nodes. Because of these shortcomings, a novel three-dimensional localization DV-Hop algorithm (NTLDV-HOP) is proposed. The proposed algorithm revises per jump distance to enhance the accuracy of distance calculation between unknown nodes and anchor nodes, and uses the Total Least Squares (TLS) method to modify the positioning deviation of the anchor node itself. Simulation shows that the NTLDV-HOP improves the location accuracy.

A novel three-dimensional localization algorithm based on DV-HOP

Activity recognition is mainly used in health care, authentication and many other fields. Activity recognition based on a smart watch usually performs much better than any other means of activity recognition in these areas. With its small size, high integration and multi-function, smart watch holds more advantages over the devices used in image based activity recognition, for example, a monitor, which is usually location fixed and view limited. In this paper, we collected data of five behaviors, i.e. stand, walk, run, upstairs, downstairs from the accelerometer on a smart watch. These data are classified with CART decision tree. Experimental results prove that the proposed method improves the accuracy of activity recognition.

An Improved Activity Recognition Method Based on Smart Watch Data

Based on a practical application, this paper proposes the design and implementation to build an embedded-Linux downloading server and network proxy, which is mainly applied to download resources such as movies and music from the Internet via various protocols (http, ftp, bittorrent, emule, etc.) as well as to be a network proxy. As a downloading server and network proxy to instead of computers (PC), this prototype is proved more available and effective to protect the environment and save the energy.

Xiaodong Wang

Papers

Dynamic Resource Allocation Algorithm for Container-Based Service Computing

An Improved Random Forest Method Based on RELIEFF for Medical Diagnosis

A novel three-dimensional localization algorithm based on DV-HOP

An Improved Activity Recognition Method Based on Smart Watch Data

Design and Implementation of An Embedded-Linux Downloading Server and Network Proxy