Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed

In order to detect the cerebral microbleed (CMB) voxels within brain, we used susceptibility weighted imaging to scan the subjects. Then, we used undersampling to solve the accuracy paradox caused from the imbalanced data between CMB voxels and non-CMB voxels. we developed a seven-layer deep neural network (DNN), which includes one input layer, four sparse autoencoder layers, one softmax layer, and one output layer. Our simulation showed this method achieved a sensitivity of 95.13%, a specificity of 93.33%, and an accuracy of 94.23%. The result is better than three state-of-the-art approaches.     

Fruit category classification via an eight-layer convolutional neural network with parametric rectified linear unit and dropout technique

Multimedia Tools and Applications - In this paper, we apply an improved deep convolutional neural network (CNN) in fruit category classification, which is a hotspot in computer vision field. We...     

Deep learning for sleep stages classification: modified rectified linear unit activation function and modified orthogonal weight initialisation

Multimedia Tools and Applications - Each stage of sleep can affect human health, and not getting enough sleep at any stage may lead to sleep disorder like parasomnia, apnea, insomnia, etc....     

Actuator fault detection and isolation in linear systems

A scheme for the detection and isolation of actuator faults in linear systems is proposed. A bank of unknown input observers is constructed to generate residual signals which will deviate in characteristic ways in the presence of actuator faults. Residual signals are unaffected by the unknown inputs acting on the system and this decreases the false alarm and miss probabilities. The results are illustrated through a simulation study of actuator fault detection and isolation in a pilot plant doubleeffect evaporator.     

On-line fault detection in discrete event systems by Petri nets and integer linear programming

The paper addresses the fault detection problem for discrete event systems in a Petri Net (PN) framework. Assuming that the structure of the PN model and the initial marking are known, faults are modelled by unobservable transitions. Moreover, we assume that there may be additional unobservable transitions associated with the system legal behaviour and that the marking reached after the firing of any transition is unknown. The proposed diagnoser works on-line: it waits for the firing of an observable transition and employs an algorithm based on the definition and solution of some integer linear programming problems to decide whether the system behaviour is normal or exhibits some possible faults. The results characterize the properties that the PN modelling the system fault behaviour has to fulfill in order to reduce the on-line computational effort.     

Amalgamation of iterative double automated thresholding and morphological filtering: a new proposition in the early detection of cerebral aneurysm

Multimedia Tools and Applications - Cerebral aneurysm (CA) has been emerging as one of the life threatening diseases in adults which results due to the pathological distension of cerebral arteries....     

Impact of action unit detection in automatic emotion recognition

In this paper, we investigate the interest of action unit (AU) detection for automatic emotion recognition. We propose and compare two emotion detectors: the first works directly on a high-dimensional feature space and the second projects facial image in the low-dimensional space of AU intensities before recognizing emotion. In both approaches, facial images are coded by local Gabor binary pattern (LGBP) histogram differences. These features reduce the sensitivity to subject identity by computing differences between two LGBP histograms: one computed on an expressive image and the other synthesized and approaching the one we would compute on a neutral face of the same subject. As classifiers, we test support vector machines with different kernels. A new kernel is proposed, the histogram difference intersection kernel that increases classification performances. This kernel is well suited when exploiting the proposed histogram differences. Thorough experiments on three challenging databases (respectively, the Cohn-Kanade, MMI and Bosphorus databases) show the accuracy of our AU and emotion detectors. They lead to significant conclusions on three critical issues: (1) the interest of combining different training databases labeled by different AU coders, (2) the influence of each AU according to its type and detection accuracy on emotion recognition and (3) the sensitivity to identity variations.     

Fault detection in linear discrete dynamic systems by a reduced order generalized-likelihood-ratio method

The detectability by conventional step-hypothesized generalized-likelihood-ratio (SHGLR) method for detection of a parameter change (fault detection) in a linear discrete dynamic system is analysed and it is shown that a weakly-diagnosable-space (WDS) exists for dynamics and sensor faults. Based on the fault detectability, a reduced order SHGLR method is then developed which highly improves the detection rate and speed. In the same framework of the GLR method, another reduced order detection scheme is given, which makes the most use of the information about the input and the state of the system to raise the detectability for faults for the case where the step hypothesis cannot be applied effectively.     

电池管理系统均衡控制电路设计

随着全球经济的持续发展,工业设备排放的废弃物对环境的污染日趋严重。大力发展节能新技术,积极开发新型动力能源来减轻工业设备对常规化石燃料的依赖已经变得刻不容缓。以锂离子电池作为工业设备的动力能源装置能够很好地解决这个问题。电池管理系统(BMS)作为锂电池动力系统中的关键部件,是电池与整个动力系统的连接纽带。电池管理系统主要通过实时监测电池参数(电压、电流、温度等),判断出电池当前的工作状态,若存在不平衡则通过均衡控制单元使整个锂电池组回归到基本平衡态。     

Direct range measurement by linear stereovision for real-time obstacle detection in road traffic

This paper describes an obstacle detection method based on a passive 3D sensor using two line-scan cameras instead of standard video cameras. This approach permits good precision as well as low computational requirements, which allows a real-time implementation on a small, low cost computer, as required for obstacle detection on a car. Real-world examples are presented, that show the efficiency of the system.     

Nonconcurrent error detection and correction in fault-tolerant linear finite-state machines

In this paper, we construct fault-tolerant linear finite-state machines (LFSMs) in which error detection and correction can be performed nonconcurrently (e.g., periodically). More specifically, by jointly choosing the state encoding constraints and the redundant dynamics of the fault-tolerant LFSM, we enable an external checker to detect and identify errors due to past faults based on the current, possibly corrupted state of the LFSM. The paper presents systematic constructions of fault-tolerant LFSMs based on a characterization of nonconcurrent error detection/correction in terms of state encoding constraints and redundant dynamics. In particular, we develop a scheme that uses Bose-Chaudhuri-Hocquenghem (BCH) coding and obtains fault-tolerant LFSMs that require 2D additional state variables and have the ability to correct up to D errors in any state variable at any time step in the time interval consisting of the latest N time steps of operation. The construction uses the minimum possible number of additional state variables and requires an error detecting/correcting mechanism with computational complexity that is only linear in N.     

Statistical detection and isolation of additive faults in linear time-varying systems

This paper describes a statistical approach to fault detection and isolation for linear time-varying (LTV) systems subject to additive faults with time-varying profiles. The proposed approach combines a generalized likelihood ratio (GLR) test with a recursive filter that cancels out the dynamics of the monitored fault effects. To our knowledge, the proposed recursive filter is new for the considered faults. The resulting algorithm handles fault isolation with weaker assumptions than usual, in particular regarding the requirements on the number of sensors and on the stability of the monitored system. Numerical results for leakage detection in a gas transportation network illustrate the effectiveness of the proposed method.     

Disturbance decoupling in fault detection of linear periodic systems

This paper studies fault detection problems of linear discrete-time periodic systems. The aim is to design residual generators, which deliver a residual signal fully decoupled from unknown disturbances. First, a periodic parity relation based full decoupling residual generator with a periodically varying parity vector is established. Then, the relation between periodic parity vectors and periodic observer-based residual generators is investigated. It is shown that a periodic observer-based full decoupling residual generator can be obtained from a periodic full decoupling parity vector. Finally, the condition of disturbance decoupling is discussed and an example is given to illustrate the proposed approaches.     

抽油机检测系统的设计与应用分析

针对当前中国大部分油田主要凭借人工经验判断抽油机运行状况而无法及时发现抽油机故障的问题,从系统功能、硬件设计及软件设计等方面介绍了一种全新的抽油机检测系统设计方案。该系统采用集散式控制结构及GPRS无线通信方式,实现了对抽油机电动机电流及电压、荷载、位移等参数的监测。     

阶梯法在线阵CCD位置检测中的应用

线阵CCD在经纬仪准直系统上已大量采用,但目前常采用二值化法和重心法的处理方法,其处理准确度不能完全满足需要。通过分析二值化法和重心法的处理过程,对影响准确度的因素进行了分析,比较了2种方法的优缺点,基于对波形的稳定性处理,提出了阶梯法的处理思想,克服了二值化法和重心法的缺陷,并分析了阶梯法处理的基本原理及其处理方法,使其准确度得到了提高,基本满足了需要。     

Significance of tectonics in linear feature detection and interpretation on satellite images

There are two main concepts of linear feature evaluation based on satellite images. One puts emphasis on finding equivalence between the image and the field data, in the other, the basic aim is to correlate them after their more or less separate interpretation. A comparative study of the two methods has been carried out on a test field in Southwest Hungary using several kinds of geological and geophysical data as reference. Combining the statistical and deterministic approach a block-faulting mechanism can be recognized and described with ENE-WSW main axis. Correlation of satellite and reference data proved the tectonic difference of the northern and southern parts of the test field. This led to the conclusion that one can derive textural information on tectonics from a Landsat image concerning especially short lines. This implies that not only the present surface expression of faults and tectonics is detectable but the determination of the latest tectonically active surface is also possible.     

Genetic algorithms for outlier detection and variable selection in linear regression models

This article addresses some problems in outlier detection and variable selection in linear regression models. First, in outlier detection there are problems known as smearing and masking. Smearing means that one outlier makes another, non-outlier observation appear as an outlier, and masking that one outlier prevents another one from being detected. Detecting outliers one by one may therefore give misleading results. In this article a genetic algorithm is presented which considers different possible groupings of the data into outlier and non-outlier observations. In this way all outliers are detected at the same time. Second, it is known that outlier detection and variable selection can influence each other, and that different results may be obtained, depending on the order in which these two tasks are performed. It may therefore be useful to consider these tasks simultaneously, and a genetic algorithm for a simultaneous outlier detection and variable selection is suggested. Two real data sets are used to illustrate the algorithms, which are shown to work well. In addition, the scalability of the algorithms is considered with an experiment using generated data.I would like to thank Dr Tero Aittokallio and an anonymous referee for useful comments.     

Error detection and correction in switched linear controllers via periodic and non-concurrent checks

Control systems that utilize switched linear controllers have proven to be useful (and, in some cases, essential) for accomplishing certain control objectives in particular classes of plants. These controllers are often digital in nature and, as such, are subject to internal hardware malfunctions (faults). In this paper, we present a systematic methodology for constructing embeddings to protect switched linear controllers against hardware faults that corrupt their internal state. Our methodology is based on replacing the original controller with a redundant (higher dimensional) controller that preserves the functionality of the original controller while enabling error detection and correction. More importantly, this methodology allows an external mechanism to detect and identify transient state-transition faults through non-concurrent (e.g. periodic) parity checks. The resulting error detection and correction procedures can then be performed periodically, thereby relaxing the reliability requirements and overhead associated with the checking mechanism.