双侧采空不规则煤柱稳定性分析

为确定某矿3303工作面不规则煤柱处于两侧采空状态时的稳定程度,通过数学模型对不规则煤柱最小安全尺寸及煤柱稳定性系数进行计算,在此基础上,以数值模拟对不规则煤柱两侧采空状态下的应力变化规律展开分析。结果表明:煤柱最小安全尺寸为31.2 m,大于3303工作面推进36.5 m范围内不规则煤柱尺寸;煤柱稳定性系数为1.14,根据煤柱稳定性判别指标判定煤柱为稳定状态;不规则煤柱应力随工作面推进距离增大呈上升趋势,最大应力值与理论计算煤柱承载强度最小值基本一致;综合评定双侧采空状态下,不规则煤柱能够保持稳定。     

面向自动化精密装配的视觉定位引导系统

针对总装生产线精密装配现状,项目设计开发了一种机器视觉定位引导系统,主要实现工业机器人在运动过程中完成在线采集不规则工件图像、视觉定位、角度补偿及精密装配作业。该系统首先采集实时工件图像,利用改进优化的九点标定算法对机器人进行视觉标定可以提高算法的精度;通过预处理操作获取质量较好的图像,按顺序创建卡尺工具,采用加权最小二乘法拟合工件亚像素边缘,再根据相关二维测量算法获取工件中心坐标。最终将得到的位姿数据传送给6轴机器人进行精密装配任务。实验结果表明,该视觉定位引导系统具有现场环境下的高精度定位检测功能,自动化装配定位精度在X方向最大定位误差为0.0281mm、Y方向最大定位误差为0.026mm,足以达到自动化装配生产线的技术要求,具有较好的应用前景和参考价值。     

Development of HL-2M data integration and acquisition system for magnetic diagnostics

Magnetic measurement and diagnostics are critical for the operation of magnetic confinement experimental facilities and plasma analysis,while differential signals are mostly detected by a detector.For this,we have developed and designed a stable and reliable data integration system for HL-2 M magnetic measurement and magnetic diagnostics.The system will be used for realtime control of HL-2 M after the construction of HL-2 M is completed.The system is built based on the PXI platform,and the software system is based on the LABVIEW platform.Key technologies realized by the system primarily include drift compensation,pulse data acquisition technology,multi-threading processing technology and transmission control communication protocol.Trials of the system were successfully carried out on HL-2 A,and the results showed that the system could fully meet the construction needs of HL-2 M.     

pFFT方法在波浪与结构作用中的不规则频率消除技术

对于求解波浪与大型结构物作用的pFFT方法,即预修正快速傅里叶变换方法(Pre-corrected Fast Fourier Transform),该文提出了消除"不规则频率"影响,从而保证在各频率下均可得到正确解的求解技术。该方法通过在内水面上配置源点的方法得到了唯一解的积分方程,并通过叠加物体内部积分方程而得到了便于求解的方形矩阵。算例表明:该方法能够消除"不规则频率"的影响,在整个频率内均可得到精确的计算结果;对于大型计算,在计算效率和内存空间的使用上,均较传统的高阶边界元方法有很大优势。     

Cryptography during the French and American Wars in Vietnam

After Vietnam’s Declaration of Independence on 2 September 1945, the country had to suffer through two long, brutal wars, first against the French and then against the Americans, before finally becoming a unified country free of colonial domination in 1975. The authors’ purpose is to examine the role of cryptography in those two wars. Despite the far greater technological resources of their opponents, the communications intelligence specialists of the Vi?t Minh, the National Liberation Front, and the Democratic Republic of Vietnam had considerable success in both protecting Vietnamese communications and acquiring tactical and strategic secrets from the enemy. Perhaps surprisingly, in both wars there was a balance between the sides. Generally speaking, cryptographic knowledge and protocol design were at a high level at the central commands, but deployment for tactical communications in the field was difficult, and there were many failures on all sides.     

Deriving cryptographic keys from physiological signals

Biosensors aim at providing pervasive healthcare by collecting and communicating highly sensitive medical information. Due to their extreme limitations, lightweight and secure key management infrastructures are required. For this reason, biosensors use physiological parameters that are generated from different vital signals (i.e., electrocardiogram, photoplethysmogram, blood pressure) to protect the exchanged private health information. In this paper, we define two novel physiological parameter generation techniques and analyze both the performance and the quality of the outcomes. Our results show that we generate good candidates of physiological parameters that can be used as cryptographic keys to secure the communication among the biosensors.     

Generalized neural network and wavelet transform based approach for fault location estimation of a transmission line

To maintain the efficient and reliable operation of power systems, it is extremely important that the transmission line faults need to be detected and located in a reliable and accurate manner. A number of mathematical and intelligent techniques are available in the literature for estimating the fault location. However, the results are not satisfactory due to the wide variation in operating conditions such as system loading level, fault inception instance, fault resistance and dc offset and harmonics contents in the transient signal of the faulted transmission line. Keeping in view of aforesaid, a new approach based on generalized neural network (GNN) with wavelet transform is presented for fault location estimation. Wavelet transform is used to extract the features of faulty current signals in terms of standard deviation. Obtained features are used as an input to the GNN model for estimating the location of fault in a given transmission systems. Results obtained from GNN model are compared with ANN and well established mathematical models and found more accurate.     

Reduction of quantization noise via periodic code for oversampled input signals and the corresponding optimal code design

This paper proposes to reduce the quantization noise using a periodic code, derives a condition for achieving an improvement on the signal to noise ratio (SNR) performance, and proposes an optimal design for the periodic code. To reduce the quantization noise, oversampled input signals are first multiplied by the periodic code and then quantized via a quantizer. The signals are reconstructed via multiplying the quantized signals by the same periodic code and then passing through an ideal lowpass filter. To derive the condition for achieving an improvement on the SNR performance, first the quantization operator is modeled by a deterministic polynomial function. The coefficients in the polynomial function are defined in such a way that the total energy difference between the quantization function and the polynomial function is minimized subject to a specification on the upper bound of the absolute difference. This problem is actually a semi-infinite programming problem and our recently proposed dual parameterization method is employed for finding the globally optimal solution. Second, the condition for improving the SNR performance is derived via a frequency domain formulation. To optimally design the periodic code such that the SNR performance is maximized, a modified gradient descent method that can avoid the obtained solution to be trapped in a locally optimal point and guarantee its convergence is proposed. Computer numerical simulation results show that the proposed system could achieve a significant improvement compared to existing systems such as the conventional system without multiplying to the periodic code, the system with an additive dithering and a first order sigma delta modulator.     

Classification of brain hemodynamic signals arising from visual action observation tasks for brain–computer interfaces: A functional near-infrared spectroscopy study

A brain–computer interface (BCI) is a way of translating an individuals’ thoughts to control a computer or an external mechanical device. Studying brain activities in a reproducible manner, this study explores the possibility of using real-time functional-near infrared spectroscopy (fNIRS) to detect brain hemodynamic features for BCI commands. Sixteen channel brain activities associated with two distinct mental tasks were measured from seven healthy subjects. The tasks represented neural activities arising from a visual observation of a motor action related to hand movements of the subjects. Sensitive signatures of task relevant neural activities were further extracted from hemodynamic signals in the prefrontal cortex of the brain, and subsequently were translated into pre-determined computer commands using a set of algorithms. The decoded commands allowed volunteer subjects to control an external device in real-time through their mental intentions. The obtained results demonstrate the potential of the current study as an alternative fNIRS-BCI paradigm.     

Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals

In analysing ECG data, the main aim is to differentiate between the signal patterns of healthy subjects and those of individuals with specific heart conditions. We propose an approach for classifying multivariate ECG signals based on discriminant and wavelet analyses. For this purpose we use multiple-scale wavelet variances and wavelet correlations to distinguish between the patterns of multivariate ECG signals based on the variability of the individual components of each ECG signal and on the relationships between every pair of these components. Using the results of other ECG classification studies in the literature as references, we demonstrate that our approach applied to 12-lead ECG signals from a particular database compares favourably. We also demonstrate with real and synthetic ECG data that our approach to classifying multivariate time series out-performs other well-known approaches for classifying multivariate time series.