智能卡车调度系统在紫金山露天矿的应用

紫金山露天矿采用多种开拓运输方式,设备大型化、机械化程度较高,但采矿生产过程存在机械设备作业范围大、作业面广、作业点多的特征,安全管理、生产管理形势严峻。本文立足紫金山生产模式,针对该矿智能卡车调度系统的建设,提出进一步完善卡调系统运行管理、提高露采数字化管控水平的措施,旨在充分发挥卡调系统对生产的智能辅助,提升露天采矿安全管理、生产管理、资源管理及成本管理水平,打造新时期智慧矿山建设新标杆。     

Potential application of metal-organic frameworks (MOFs) for hydrogen storage: Simulation by artificial intelligent techniques

Metal-organic frameworks are a new class of materials for hydrogen adsorption/storage applications. The hydrogen storage capacity of this structure is typically related to pressure, temperature, surface area, and adsorption enthalpy. Literature provides no reliable correlation for estimating the hydrogen uptake capacity of MOFs from these easy-measured variables. Therefore, this study introduces several straightforward and accurate artificial intelligence (AI) techniques to fill this gap, initially determining the appropriate topology of AI-based methods, then comparing their performances by statistical criteria, and introducing the most accurate. This study used artificial neural networks, hybrid neuro-fuzzy systems, and support vector machines as estimators. The general regression neural networks (GRNN) with a spread of 7.92 × 10⁻⁴ shows the highest correlation with the literature data and provides a relative absolute deviation of 5.34%, mean squared error of 0.059, and coefficient of determination of 0.9946.     

Generation expansion planning in electricity markets: A novel framework based on dynamic stochastic MPEC

This paper presents a novel framework for generation expansion planning (GEP) of restructured power systems under uncertainty in a multi-period horizon, which includes generation investment from a price maker perspective. The investment problem is modeled as a bi-level optimization problem. The first level problem includes decisions related to investment in order to maximize total profit in the planning horizon. The second level problem consists of maximizing social welfare where the power market is cleared. Rival uncertainties on offering and investment are modeled using sets of scenarios. The bi-level optimization problem is then converted to a dynamic stochastic MPEC and represented as a mixed integer linear program (MILP) after linearization. The proposed framework is examined on a typical six-bus power network, MAZANDARAN regional electric company (MREC) transmission network as an area of IRAN interconnected power system and IEEE RTS 24-bus network. Simulation results confirm that the proposed framework can be a useful tool for analyzing the behavior of investments in electricity markets.     

The PAR(p) Interconfigurations model used by the Brazilian Electric Sector

This study discusses the characteristics of the Periodic Autoregressive model, PAR(p), which is used to generate synthetic series of inflow energies that serve as entries for computer platforms that implement the planning and expansion of the operations of the BES – the Brazilian Electric Sector (SEB – Sistema Elétrico Brasileiro). The methodology for the design of a generating plant is presented in addition to the fundamentals of the “PAR(p) Interconfigurations” Model, which is referred to as the Inflow Energy Generation Model (IEGM) in this study. The major contribution of this study is to provide the first scientific discussion of the representation of multiple configurations using the PAR(p) model. For this purpose, several topics related to the time series are discussed, such as the definition of the model order, the matter of stationarity and the need to address possible outliers. Finally, a case study is presented, wherein the results of the estimation and generation of the described model’s scenarios are demonstrated.     

基于Android的智能家居控制系统的设计研究

随着科学技术的发展，各种电器和家具控制的智能化是目前市场流行起来。人们生活水平的不断提高使人们对生活的质量有了一定的要求。在人们的日常生活中，家用电器是不可缺少的生活用品，如何快捷方便的对这些电器进行管理，使我们需要进行研究的任务。智能家居控制系统的出现为人们的生活带来了便利，也逐渐的改变和影响着人们的生活方式。本文就基于Android的智能家居控制系统的设计进行分析和研究。     

Cooperative path planning with priority target assignment and collision avoidance guidance for rescue unmanned surface vehicles in a complex ocean environment

In this paper, a novel cooperative path planning scheme of unmanned surface vehicles (USVs) for rescuing targets in a complex ocean environment is proposed. The primary objective of the rescue USVs is to bring all targets back safely on the premise of first rescuing priority targets, while optimizing the path length, the navigation time and the angular energy. The main contributions of this paper are as follows: (1) The proposed K-means-division (KMD) algorithm is able to identify a complex ocean environment with collision-free zone and static-obstacles zone; (2) The proposed path planning method with fast-marching-method-based ellipse guidance range (E-FMM) is able to optimize the angular energy while ensuring safety; (3) The proposed cooperative management system (including priority-target-assignment (PTA) with reward-mechanism genetic-optimization (RM-GO) and collision-avoidance (CA) guidance law with Tangent-based surge-varying wave-disturbances-observer (Tangent-SV-WDO)) can accomplish the mission of the rescue USVs. Comparative studies with the state-of-the-art methods demonstrate that the proposed cooperative path planning scheme is superior in terms of priority-target-assignment (PTA) and collision-avoidance (CA) of the actual rescue work.     

Highly Stretchable and Sensitive SBS/Graphene Composite Fiber for Strain Sensors

Flexible strain sensors have attracted tremendous interests due to the emergence of intelligent wearable technology. Electrically conductive fibers are desirable candidates for flexible strain sensors, but up til now, there still exist enormous challenges to obtain conductive fibers exhibiting simultaneously high stretchability and high strain sensitivity. This paper introduces a poly (styrene‐butadiene‐styrene) (SBS)/graphene (Gr) composite fiber‐based flexible strain sensor fabricated by a facile and highly scalable wet spinning method. The results demonstrate that the graphene content has significant influence on the morphology, mechanical properties, and electromechanical properties of the composite fibers. The fibers with 5 wt% graphene have a wide response range of up to 100% strain, a high electrical sensitivity with the gauge factor of 10083.98 at 100% strain, and meanwhile, a high level of stability for 2100 stretching–releasing cycles under an applied strain of 20%. Furthermore, the SBS‐5%Gr composite fibers display excellent sensing performance in detecting human upper limb movements at different joints including hand joints, wrist joints, elbow joints, and shoulder joints.     

基于相位补偿的矿井超宽带雷达压缩感知成像算法

基于UWB信号的井下探测成像系统中,根据Nyquist采样定理,提高发射信号的带宽以获取高分辨率,将导致大量的采样数据产生,过大的采样率将会在成像系统的硬件实现上带来很大困难。另一方面,在实际成像过程中,成像数据的完整性决定了最后成像结果的准确性,但由于存在不可避免的误差和噪声影响,以及塌方体阻碍下目标的隐蔽性,这些因素都将使得成像目标的回波数据无法满足成像要求。因此,在对目标回波进行成像处理之前,必须对回波数据进行处理,通过相关算法使回波数据满足理想的成像模型,最后聚焦方位向回波信息得到目标成像结果。为了提高穿透塌方体遮蔽目标成像的分辨率,并解决超宽带信号穿透塌方体成像得到方位向回波数据不足的问题,通过对传统的压缩感知成像算法进行分析介绍,结合井下具体成像环境,提出一种基于相位误差估计的CS成像算法对回波数据进行补偿。该算法的核心思想就是通过对回波数据计算得到相位误差,首先对回波信号的稀疏性进行验证,并构建目标信号的测量矩阵,利用CS成像算法对回波数据重构的同时对数据进行补偿,通过反复迭代误差估计,逐渐提高回波数据量和成像的质量。最后通过不同场景的仿真试验,验证所提算法在回波数据补偿问题上的有效性。     

东峡煤矿主煤流运输系统智能化集控设计

通过对东峡煤矿主煤流运输系统的改造,设计了一套智能化集控系统,采用变频驱动,并结合料流传感器,增设无线基站、本安网络摄像仪和语音扩播终端等设备,实现了带式输送机载荷分布的自动调节,提高了系统的可靠性和运行效率,实现了减员、节能、增效的目的。     

State of charge estimation for lithium-ion battery based on an intelligent adaptive unscented Kalman filter

Adaptive unscented Kalman filter (AUKF) has been widely used for state of charge (SOC) estimation of lithium-ion battery. The noise covariance of the conventional AUKF method is updated based on the innovation covariance matrix (ICM), which is estimated using the error innovation sequence (EIS). However, the distribution of EIS changes due to the time-varying noise, load current dynamics and modelling error, which will lead to inaccurate ICM estimation. Therefore, an intelligent adaptive unscented Kalman filter (IAUKF) method is proposed to detect the distribution change of EIS. Then, the ICM is estimated based on the EIS after the distribution change. Results show that the IAUKF method can improve SOC estimation accuracy significantly. Compared with that of the AUKF method, the root mean squared error and the mean absolute error of SOC based on the IAUKF method decrease by 43.70% and 72.37% under random walk discharge condition, respectively. In addition, the computation time of the IAUKF method slightly increases by 6.27% compared with that of AUKF method. Finally, the effect of initial parameters on the SOC estimation accuracy was analysed. The results indicate that proper algorithm tuning, such as initial window length of EIS for ICM update and the threshold value, can further improve the SOC accuracy based on the proposed IAUKF method. The proposed IAUKF method also shows high robustness against initial measurement noise covariance.