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Predicting early signs of illness in older adults by utilizing a continuous, unobtrusive nursing home monitoring system has been shown to increase the quality of life and decrease the cost of care. Illness prediction is based on sensor data such as motion and bed and uses algorithms such as support vector machine (SVM) or k-nearest neighbor (kNN). One of the greatest challenges in developing prediction...
In this paper, a novel kernel independent component analysis method which is named improved DKICA is proposed for dynamic industry processes' fault detection and fault diagnosis. The primary idea of this method is how to obtain an augmented measurement matrix in the data kernel space, the independent component analysis is used, so the dynamic and nonlinear features can be extracted in non-linear non-Gaussian...
Pneumatic control valves are the most frequently used actuators in industrial processes. Its property definitely affects the performance of processes and therefore process monitoring of the pneumatic control valve is of great importance. Canonical Variate Analysis (CVA) is a multivariate data-driven method which considers time correlations and has been demonstrated to be superior to some methods in...
As cloud computing becomes popular, Platform as a Service (PaaS) plays an important role in a modern cloud system. The orchestration and efficiency attract more attention and demand a more intelligent solution. In this paper, we present Next Generation PaaS (NG-PaaS) which provides resource management, application management and performance monitoring. The NG-PaaS is built on docker to enable fine-grained...
With the rapid advances in IoT technologies, the role of IoT gateways becomes even more important. Therefore, improving the reliability, availability and serviceability (RAS) of IoT gateways is crucial. Nowadays, Linux is widely adopted for core enterprise systems not only because it is a free operating system but also because it offers advantages in regards to operational stability. With many Linux...
With the growing popularity of cloud-leveraged infrastructure and services, the creation and operation of multiple virtualized boxes over a single physical box is increasing rapidly. It is however becoming difficult to maintain transport performance and security with an increasing number of inter-connections among them. To address this issue, in this paper, we utilize the eBPF-based packet tracing...
In process monitoring of batch process, Fisher discriminant analysis is a very popular method and has be widely applied. In this paper, a new kernel local Fisher discriminant analysis (KLFDA) algorithm is proposed for fault diagnosis. The main contributions of the presented approach are as follows: 1) the proposed algorithm can simultaneously extract the global European distribution of data and local...
Nonintrusive monitoring is a powerful tool to increase the quality, reliability, and efficiency of power electronics systems. In recent years, the use of intelligent systems to perform this type of tasks has been increased due to its high capacity to correctly predict the values of the parameters under monitoring. Support Vector Machines (SVM) is a supervised learning method that maps input data into...
Monitoring of dynamic industrial process has been increasingly important due to more and more strict safety and reliability requirements. Popular methods like time lagged arrangement-based and subspace-based approaches exhibit good performance in fault detection, however, they suffer from difficulty in accurately isolating faulty variables and diagnosing fault types. To alleviate this difficulty,...
This paper evaluates a mechanism for applying machine learning (ML) to identify over-constrained IaaS virtual machines (VMs). Herein, over-constrained VMs are defined as those who are not given sufficient system resources to meet their workload specific objective functions. To validate our approach, a variety of workload-specific benchmarks inspired by common Infrastructure-as-a-Service (IaaS) cloud...
Cyber threats push the researchers towards developing detection frameworks for protecting Internet users. Remote administration tool (RAT) is one of the serious cyber tools used by the attackers to fully control the targeted victim machine. In this paper a host based detection framework is introduced for RAT detection. The proposed framework depends on fully analysis of the system behavior of the...
In this paper we present lo2s - a lightweight performance monitoring tool to sample applications as well as the executing system. It enables the user to analyze the performance of a parallel application without requiring the time-consuming and error-prone process of application instrumentation. The collected performance data is complemented with various metric data, i.e., perf counters, kernel tracepoints,...
The emergence of power efficiency as a primary constraint in processor and system designs poses new challenges concerning power and energy awareness for numerical libraries and scientific applications. Power consumption also plays a major role in the design of data centers in particular for peta- and exa-scale systems. Understanding and improving the energy efficiency of numerical simulation becomes...
Process monitoring of incipient faults, as opposed to abrupt faults, in an industrial process is increasingly becoming more important. These are slowly developing faults that may eventually lead to severe abnormal conditions, and ultimately, failure of a critical component. Data-driven multivariate statistical process monitoring (MSPM) methods are extensively studied and widely used for abrupt fault...
Excessive memory usage in software applications has become a frequent issue. A high degree of parallelism and the monitoring difficulty for the developer can quickly lead to memory shortage, or can increase the duration of garbage collection cycles. There are several solutions introduced to monitor memory usage in software. However they are neither efficient nor scalable. In this paper, we propose...
Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of the input video signals. This integrated solution defines an image descriptor that reflects the global motion information over...
Advances in data processing, electronics and wireless communications have made the vision of wireless sensor nodes an important reality. Wireless sensor nodes are cheap tiny sensor apparatus integrated with sensing, processing and short-range wireless communication abilities. Recent experimentations have been exploding in terms of usage and performance to improve the way of working in many contexts...
The increasing integration of distributed energy resources (DERs) calls for new monitoring and operational planning tools to ensure stability and sustainability in distribution grids. One idea is to use existing monitoring tools in transmission grids and some primary distribution grids. However, they usually depend on the knowledge of the system model, e.g., the topology and line parameters, which...
To satisfy quality of service requirements in a cost-efficient manner, cloud service providers would benefit from providing a means for quantifying the level of operational uncertainty within their systems. This uncertainty arises due to the dynamic nature of the cloud. Since tasks requiring various amounts of resources may enter and leave the system at any time, systems plagued by high volatility...
In this paper, we address the problem of fault detection (FD) of chemical processes using improved generalized likelihood ratio test. The improved GLRT is the method that combines the advantages of the exponentially weighted moving average (EWMA) filter with those of the GLRT method. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data...
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