Information analysis of single photon emission computed tomography with count losses

An analysis is presented of the information transfer from emitter-space to detector-space in single photon emission computed tomography (SPECT) systems. The analysis takes into account the fact that count loss side information is generally not available at the detector. Side information corresponds to the number gamma-rays lost deleted due to lack of interaction with the detector data. It is shown that the information transfer depends on the structure of the likelihood function of the emitter locations associated with the detector data. This likelihood function is the average of a set of ideal-detection likelihood functions, each matched to a particular set of possible deleted gamma-ray paths. A lower bound is derived for the information gain due to incorporating the count loss side information at the detector. This is shown to be significant when the mean emission rate is small or when the gamma-ray deletion probability is strongly dependent on emitter location. Numerical evaluations of the mutual information, with and without side information, associated with information-optimal apertures and uniform parallel-hole collimators are presented.     

Data stream classification with novel class detection: a review,comparison and challenges

Knowledge and Information Systems - Developing effective and efficient data stream classifiers is challenging for the machine learning community because of the dynamic nature of data streams. As a...     

在CodeWarrior编译环境下运行μC／OS—Ⅲ

μC／OS-Ⅱ是面向8／16位及低端32位单片机应用的RTOS,而新近推出的μC／OS-Ⅲ则面向高性能32位单片机,如ARMCortex等。以Cortex—M4为内核的Kinetis系列单片机,不仅用于全国大学生飞思卡尔杯智能车竞赛,也用于诸多大学的嵌入式系统教学,官方提供的开发环境是CodeWarrior。本文介绍如何利用CodeWarrior开发环境,将μC／OS-Ⅲ在Kinetis单片机上运行起来,以便将μC／OS-Ⅲ引入教学、科研与应用。     

漫射红外技术的智能家居无线局域网设计

分析了红外技术在短距离无线通信中的优势,并将其应用在智能家居局域网中;结合安华高科技公司的HSDL-3020红外收发模块搭建了硬件电路,构成网络的主从节点;介绍了网络组建的过程,包括IrDA协议栈结构、数据编码格式、链路连接、网络管理机制等。本设计实现了对家居设备的统一管理,具有较高的应用价值。     

A new admissibility condition of discrete-time singular systems with time-varying delays

This paper gives a novel delay-dependent admissibility condition of discrete-time singular systems with time-varying delays. For convenience, the time-varying delay is assumed to be the sum of delay lower bound and the integral multiples of a constant delay. Specially, if the constant delay is of unit length, the delay is an interval-like time-varying delay. The proposed admissibility condition is presented and expressed in terms of linear matrix inequality (LMI) by Lyapunov approach. Generally, the uncertainty of time-varying delay would lead to conservatism. In this paper, this critical issue is tackled by accurately estimating the time-varying delay. Consequently, the proposed admissibility condition is less conservative than the existing results, which is demonstrated by a numerical example.     

Data preprocessing using hybrid general regression neural networks and particle swarm optimization for remote terminal units

Data corruption in SCADA systems refers to errors that occur during acquisition, processing, or transmission, introducing unintended changes to the original data. In SCADA-based power systems, the data gathered by remote terminal units (RTUs) is subject to data corruption due to noise interference or lack of calibration. In this study, an effective approach based on the fusion of the general regression neural network (GRNN) and the particle swarm optimization (PSO) technique is employed to deal with errors in RTU data. The proposed hybrid model, denoted as GRNN-PSO, is able to handle noisy data in a fast speed, which makes it feasible for practical applications. Experimental results show the GRNN-PSO model has better performance in removing the unintended changes to the original data compared with existing methods.     

Study on heat conduction and adsorption/desorption characteristic of MIL-101/few layer graphene composite

Journal of Porous Materials - Aiming at the poor heat conduction performance of porous MIL-101 applied in adsorption cooling process, few layer graphene (FLG) was selected as a promising thermal...     

挤压变形对Mg95Zn4.3Y0.7合金组织和性能的影响

研究了在250℃、不同挤压比下挤压变形对Mg95 Zn4.3 Y0.7合金组织及性能的影响。结果表明，Mg95 Zn4.3 Y0.7经过挤压变形后，合金中晶界处的共晶相破碎，弥散分布至晶粒内部，并且晶粒显著细化。同时，随着挤压比的增大，晶粒细化程度增加，合金的力学性能单调增加。当挤压比为16时，合金晶粒尺寸为5-8μm，抗拉强度为288.9MPa，显微硬度HV值为117.8。     

长轴表面激光淬火变形与控制

应用激光淬火强化技术控制长轴的淬火变形，分析了激光淬火变形的原因和影响因素。研究结果表明，激光淬火不仅可使长轴振摆变形量控制在很小的范围内，而且可将已变形的长轴校直到很高的精度。     

Ensemble deep transfer learning driven by multisensor signals for the fault diagnosis of bevel-gear cross-operation conditions

The existing intelligent fault diagnosis techniques of bevel gear focus on single-sensor signal analysis under the steady operation condition. In this study, a new method is proposed based on ensemble deep transfer learning and multisensor signals to enhance the fault diagnosis adaptability and reliability of bevel gear under various operation conditions. First, a novel stacked autoencoder(NSAE) is constructed using a denoising autoencoder, batch normalization, and the Swish activation function. Second, a series of source-domain NSAEs with multisensor vibration signals is pretrained. Third, the good model parameters provided by the source-domain NSAEs are transferred to initialize the corresponding target-domain NSAEs. Finally, a modified voting fusion strategy is designed to obtain a comprehensive result. The multisensor signals collected under the different operation conditions of bevel gear are used to verify the proposed method. The comparison results show that the proposed method can diagnose different faults in an accurate and stable manner using only one target-domain sample, thereby outperforming the existing methods.