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Genomic selection (GS) is a marker-assisted selection approach to enhance quantitative traits in breeding population in which whole genome single-nucleotide polymorphisms (SNPs) markers can be used to predict breeding values (BV). GS has been proved to increase breeding efficiency in both plant and animal breeding, such as dairy cattle, pig, rice, soybean and loblolly pine. Here, we propose a deep-learning...
Robust state estimation of a discrete time-varying uncertain system is investigated in this paper when there are not only process and measurement noise, but also parameter uncertainties which affect a state-space model arbitrarily. The expectation minimization based robust state estimation is generalized to uncertain linear systems with a known deterministic input signal. The derived robust estimator...
With the increase of the operation speed and the miniaturization, the power consumption assessment of multilayer printed circuit board (PCB) becomes a crucial task during the design phase. More accurate power integrity (PI) analysis is necessary for the complex multilayer PCB. An unfamiliar modelling of static and dynamic power distributed through a four-layer interconnect structure is introduced...
This paper investigates the missile attitude control problem. The model used in this paper is with respect to the Euler attitude angles and their derivatives which can be measured and computed easily. A robust practical finite-time control algorithm is proposed based on the terminal sliding mode approach. It is proved that all of the signals in the closed-loop system are bounded and the tracking error...
Sparse coding (SC) is a unsupervised learning scheme that has received an increasing amount of interests in recent years. However, conventional SC vectorizes the input images, which destructs the intrinsic spatial structures of the images. In this paper, we propose a novel graph regularized tensor sparse coding (GTSC) for image representation. GTSC preserves the local proximity of elementary structures...
Analyzing performance and understanding the potential best-case, worst-case and distribution of program execution times are very important software engineering tasks. There have been model-based and program analysis-based approaches for performance analysis. Model-based approaches rely on analytical or design models derived from mathematical theories or software architecture abstraction, which are...
The environment in a Martian dust storm is critical to the operation of Mars airplanes and rovers due to high speed dust which may cause surface abrasion and equipment failure. In this work, a Martian dust storm simulation wind tunnel (MDSSWT) is designed and numerically investigated. This facility would be capable of performing simulation of the environment inside a Martian dust storm for ground-based...
Prospective memory (PM) failure, which is the failure to recall future events or intentions, can lead to serious consequences. Although many PM aid systems have been developed, there are three critical challenges which are yet to be addressed by any existing systems: (1) determining appropriate number of reminders, (2) arranging effective reminder schedule and (3) selecting appropriate reminding method...
Malware detection is one central topic in cybersecurity, which ideally requires an accurate, efficient and robust (to malware variants) solution. In this work, we propose a hardwareassisted architecture to perform online malware detection with two phases. In the offline phase, we learn the attack model of malware in the form of Deterministic Finite Automaton (DFA). During the runtime phase, we implement...
This paper presents a modular designed dynamic positioning (DP) experiment system. Three modules are designed for the DP experiment system, which are onboard computer module, position sensing module and onshore control computer module. The hardware system and software are introduced. A PC/104 computer is used as the main controller of the onboard computer system, and an ARM microcontroller is used...
Deep learning technologies have been successfully applied to acoustic emotion recognition lately. In this work, we propose to apply multi-task learning for acoustic emotion recognition based on the Deep Belief Network (DBN) framework. We treat the categorical emotion recognition task as the major task. For the secondary task, we leverage two continuous labels, valence and activation. Two strategies...
In this paper we study the problem of how to detect and extract a particular type of propagation structure that arises in phishing activities. One of the most interesting phenomena induced by phishing is fast-flux, whereby a single malicious domain is mapped to a constantly changing IP address in order to evade capture and shut-down. This leads to malicious activities observed to be propagating through...
This paper introduces a massively parallel electromagnetic field computation program named JEMS-FDTD (J ElectroMagnetic Solver-Finite Difference Time Domain). JEMS-FDTD is designed to simulate the radiation, propagation, coupling of electromagnetic waves using the FDTD method with MPI and OPENMP library, oriented to being executed on tens of thousands of processors with high efficiency. JEMS-FDTD...
Latent Dirichlet Allocation (LDA) has been widely applied to text mining. LDA is a probabilistic topic model which processes documents as the probability distribution of topics. One challenging issue in application of LDA is to select the optimal number of topics in LDA model. This paper presents a topic selection method which considers the density of each topic and computes the most unstable topic...
The processing and mining information in large scale graph data have proven to be challenging. The bulk synchronous parallel (BSP) computing model is suitable for this task. In this paper, we implement the multi-level step-wise partitioning (MSP) algorithm in BSP programming model, and replace the original graph partition method. The results on both experimental data and real world data proved this...
Stochastic model checking is using the verification method of model checking to quantitative verification system model with stochastic behaviours. In recent years, stochastic model checking make a great advancement. In this paper, the high level system model PPN is extended with label, and is used to as the formal model for system with stochastic behaviours; PCTL∗ is selected to as the property specification,...
Hadoop is a popular open source implementation of MapReduce, that has a number of prominent users including Yahoo!, Facebook, and Twitter. Though several works have focused on deploying algorithms on Hadoop MapReduce, research efforts into applying formal methods to prove the correctness of hadoop systems are limited. In this paper we propose a holistic approach to verify the correctness of hadoop...
Model checking provides a way to automatically verify hardware and software systems, whereas the goal of planning is to produce a sequence of actions that leads from the initial state to the desired goal states. Recent research indicates that there is a strong connection between model checking and planning problem solving. In this paper, we investigate the feasibility of using different model checking...
This paper proposes an effective forecasting framework for the Oilfield Class-A materials, which take a large scale of proportion in the total purchasing cost. Based on the ARIMA model in time series analysis method, the dynamic forecasting framework is constructed to make short-time price prediction for Class-A materials in Oilfield, which only needs a small sample set to obtain high prediction accuracy...
Model checking provides a way to automatically verify hardware and software systems, whereas the goal of planning is to produce a sequence of actions that leads from the initial state to the desired goal state. Recently research indicates that there is a strong connection between model checking and planning problem solving. In this paper, we investigate the feasibility of using a newly developed model...
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