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Lack of safety and efficacy are the two major reasons for the failures of drug candidates in drug discovery and development. Reliable prediction of blood-brain barrier permeation even before chemical synthesis still remains as one of the major challenges in drug discovery. New approaches and models that are reliable and can reduce experimental evaluations of pre-clinical candidates are in urgent need...
Current methods to compare measurement series are based on the comparison of multiple measurement points or fit parameters. These approaches depend on the quality of the fit and are prone to type I error accumulation, when standard statistical tests are used for evaluation. In this work, we present an approach based on a two-sample Kolmogorov-Smirnov test of residuals of data series to a fit in order...
Usage-based statistical testing employs knowledge about the actual or anticipated usage profile of the system under test for estimating system reliability. For many systems, usage-based statistical testing involves generating synthetic test data. Such data must possess the same statistical characteristics as the actual data that the system will process during operation. Synthetic test data must further...
This paper presents a rapidly and lower neural networks to treat those waste water index that is difficult to be measured. Model called soft sensor is composited two parts: one is used to estimate the principal linear output, the other one is used to adjust estimated error to obtain better accuracy. Selection of features that effects greatly computation scale and predict accuracy is discussed also...
The paper presents a novel Telepower (−48V DC) reliability method, directly utilising site asset data in simulation software, to determine the effectiveness of battery replacement programs. Reliability is often estimated by a simple count of the number of outages per annum. This approach has many shortcomings because a count of outages can change dramatically even when the reliability of the physical...
This paper proposes a mathematical maintenance model that analyses the effect of maintenance on the survival probability of medical equipment based on maintenance history and age of the equipment. The proposed model is simulated in Scilab using real data extracted from maintenance history of Anaesthesia Machine from Draeger. The analysis using survival approach reveals that conducting preventive maintenance...
The complexity of the mechanism brings great difficulty to the calculation of the mechanism motion reliability, and the computer simulation algorithm based on Monte Carlo method needs a great number of simulation. The further-development of LMS Virtual. Lab was carried out, and the least square support vector machine algorithm was used to construct the response surface proxy model. The PSO-GA algorithm...
Machine learning has become one of the go-to methods for solving problems in the field of networking. This development is driven by data availability in large-scale networks and the commodification of machine learning frameworks. While this makes it easier for researchers to implement and deploy machine learning solutions on networks quickly, there are a number of vital factors to account for when...
The article considers the problem of complex estimation of the product state, associated with the need to help decision-makers in managing the life cycle of space facilities. A system analysis of the subject area was conducted, which showed the presence of limitations in the existing information system of the technical state and reliability of space facilities. The article presents a new intellectual...
Fault detection method using k nearest neighbor rule has shown its advantages in dealing with nonlinear, multi-mode, and nonGaussian distributed data. Once a fault is detected in industrial processes, recognizing fault variables becomes the crucial task subsequently. Recently, the method of fault variables recognition using k nearest neighbor reconstruction (FVR-kNN) has been reported. However, the...
Semi-supervised learning (SSL) is an import paradigm to make full use of a large amount of unlabeled data in machine learning. A bottleneck of SSL is the overfitting problem when training over the limited labeled data, especially on a complex model like a deep neural network. To get around this bottleneck, we propose a bio-inspired SSL framework on deep neural network, namely Deep Growing Learning...
The paper deals with the problem of synthesis and analysis for procedures of reliability parameters estimation in case of radioelectronic devices technical state deterioration. The synthesis problem was solved on the basis of the maximum likelihood method. The analysis problem was solved using analytical calculations and based on statistical simulation.
In this paper, we introduce the main concepts of a new maximum livelihood evidential reasoning (MAKER) framework for data-driven inferential modelling and decision making under different types of uncertainty. It consists of two types of model: state space model (SSM) and evidence space model (ESM), driven by the data that reflects the relationships between system inputs and output. SSM is constructed...
New model of multi-layer service network is discussed in the paper. Each server or any group of servers of a given layer of an hierarchical queuing network may be a customer in queuing systems of next layer. Conception of hypernet uses for representing structure of such multi-layer network, which we name as Q-hypernet. Some typical cases of Q-hypernet with possible applications are discussed along...
Click Trough Rate (CTR) estimation is a crucial measure (or procedure) in online digital advertising (Ad). It defines the probability of a displayed Ad being clicked by viewers, and can serve as a performance metric to validate the effectiveness of Ad campaigns with respect to pages, sites, or media types etc. Due to real-time response nature of the online digital advertising eco-systems, it is vital...
The purpose of this study was to explore the important factors that influence users’ continuance usage behavior toward mobile online group-buying. Based on the characteristics of the mobile Internet, an extension of the UTAUT model is introduced to verify the proposed hypotheses. The structural equation modeling method is used to analyze the empirical data which come from universities in China. The...
This paper proposes a combination of data mining and natural language processing technology, try to analyze students' learning behavior and content in MOOCs interactive part, to dig their learning interest, difficulty, tendencies, to evaluate their homework effect, through the interaction between teachers and students, students posting, homework or answer content, preventing of cheating behavior,...
Infrastructure monitoring is an important class of wireless sensor network (WSN) applications. When the topology of a WSN designed for infrastructure monitoring is linear in nature, such as bridges, highways and pipelines, this kind of WSN is known to be linear WSN (LWSN). To achieve the high-reliable infrastructure monitoring services, hybrid LWSN has been designed where a limited number of support...
Online professional social networks such as LinkedIn have enhanced the ability of job seekers to discover and assess career opportunities, and the ability of job providers to discover and assess potential candidates. For most job seekers, salary (or broadly compensation) is a crucial consideration in choosing a new job. At the same time, job seekers face challenges in learning the compensation associated...
In this paper, a new and simple method named Weibull criterion is proposed to identify whether metastable states occur in single random telegraph noise (RTN), which has been verified by both simulation and experiment results. It is helpful for comprehensive understanding of trap properties and providing a direct evidence of oxide traps with multiple states.
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