Social licence: power imbalances and levels of consciousness – two case studies

ABSTRACT

Social licence to operate (SLO) is a term that is finding increasing acceptance in a number of industries. Like all new terms, its precise meaning and implications are still being investigated. Using data from previous studies, this paper offers an analysis of the SLO of two case studies with each study being viewed separately through the grid of a distinct theoretical framework. Case study 1 looks at the development of differential social licence negotiated in the Hamlet of Cambridge Bay, Nunavut, as a result of an impact and benefit agreement negotiated between Tahera Diamond Corp. and the Kitikmeot Inuit Organisation in 2004. The study demonstrates that general acceptance of resource development can be uneven and reflect an unequal distribution of decision-making power. In case study 2, stakeholders of a failed mineral development project were queried across time about the specifics of the proposed mine development and were queried about resource development across different levels of consciousness. Perhaps, SLO is variable across different levels of consciousness. The paper concludes with observations about the variable nature of SLO acceptance across populations and across levels of consciousness within individuals. Perhaps, the concept of SLO is, in fact, complex, difficult to define and measure and, at this point, of limited utility as a measure of resource development acceptance.     

Voltage reference architectures for low-supply-voltage low-power applications

This paper presents a voltage reference generator architecture and two different realizations of it that have been fabricated within a standard 0.18 μm CMOS technology. The architecture takes the advantage of utilizing a sampled-data amplifier (SDA) to optimize the power consumption. The circuits achieve output voltages on the order of 190 mV with temperature coefficients of 43 ppm/°C and 52.5 ppm/°C over the temperature range of 0 to 120°C without any trimming with a 0.8 V single supply. The power consumptions of the circuits are less then 500 nW while occupying an area of 0.2 mm² and 0.08 mm², respectively.     

不同制样方法对直流辉光放电质谱法测定氮化硼中27种杂质元素的影响

讨论了3种制样方法对直流辉光放电质谱法(dc-GDMS)检测氮化硼中的Na、Mg、Al、Si等27种杂质元素的影响。3种制样方法分别如下所示:方法1,直接把氮化硼压在铟薄片上;方法2,把氮化硼压在铟薄片上后,再盖上一层铟罩;方法3,把压碎后的氮化硼放在针状钽勺上。在优化的辉光放电参数下对比了3种不同制样方法对基体信号强度的影响。试验表明:在方法1中,当氮化硼尺寸约为3mm×3mm,厚度小于1mm时,基体¹¹B的信号可达1.8×10⁷ cps;在方法2中,选择铟孔大小合适的铟罩,基体¹¹B的信号可达1.0×10⁷ cps;方法3获得基体信号强度比方法1、方法2高一个数量级。大部分元素在中分辨率下可获得较好的结果,而对于在高分辨率下也较难分离的元素,可选择丰度较低的同位素在中分辨率下进行测定,如Ge选择⁷⁰Ge⁺,Se选择⁸²Se⁺,Cd选择¹¹¹Cd⁺,Sn选择¹¹⁹Sn⁺,Ag选择¹⁰⁹Ag⁺,Pt选择¹⁹⁴Pt⁺。氮化硼中的杂质元素含量可通过样品片中待测元素含量减去来自于铟薄片或钽勺中该元素贡献的含量来计算获得。将样品平行测定5次,相对标准偏差均在20%以内。对于Al、Si、Ti等元素的测定,3种制样方法的测定结果基本一致;方法1、方法2中检测到的In含量较大,使得铟中的Ni、Cu对氮化硼的测定值影响较大;方法3由于钽中Fe、Cu的贡献导致氮化硼中Fe、Cu的检测值较大,但方法3获得的基体信号强度大,可降低部分元素的检出限,如Cr、Mn、Ga、Ge等。综上所述,方法3为优选方法。     

低觉醒脑电识别与唤醒的可穿戴系统研究

为智能化地识别警戒作业人员出现的低觉醒、注意力下降的生理状态，本文介绍了一种基于FPGA和脑电信号处理的低觉醒状态检测与唤醒系统，系统通过传感器从大脑头皮采集脑电信号，转换为数字信号，经傅里叶变换获取了脑电信号的θ相对能量、α相对能量、重心频率、谱熵等4个特征量，由4个特征量表征低觉醒状态并运用支持向量机对低警戒状态进行识别，当识别出低觉醒状态时采用声音报警模块发出声音，唤醒警戒作业人员。设计系统能够较好地识别出低觉醒状态，识别率达90.8%，可为提高警戒作业工作绩效提供一种可穿戴的智能装备。     

Enabling supercapacitors to compete for ancillary services: An important step towards 100 % renewable energy

While wind and solar generation has increased dramatically over the past decade, there has been a much larger increase in gas generation (eia, 2019). This is driven in part by low gas prices but also in part by how electricity markets are organized. The intermittent nature of wind and solar generation increases the need for more flexible and reliable generation; a role gas plants fill well. However, current market structures and rules unfairly tip the balance in their favor compared to energy storage systems. They have created market barriers restrictive enough to prevent market participation of a technology key to a 100 % renewable grid: supercapacitors. Adjusting markets to remove the market barriers to supercapacitor and other energy storage systems will allow for increased renewable penetration while simultaneously improving grid performance and reducing costs.     

新工科理念下“扩频通信技术与应用” 课程教学实践

本文以“扩频通信技术与应用”课程为例，探讨新工科理念下工程学科实践教学改革，具体措施包括：开展虚拟仿真实验，充分发挥双师型教师的主观能动性，开展MATLAB仿真训练、工程技术实训及相关的创新创业训练，结合企业实习以及开展国际交流合作等。     

苏通GIL运输专用机具动力电池类型探讨

GIL运输专用机具是为安全、可靠、高效地完成苏通GIL综合管廊工程GIL单元的运输任务而研制的专用机具,其中动力电池系统设计是整机设计的重点之一。通过对各种常见动力电池优缺点的分析,并结合苏通GIL运输专用机具对动力电池的需求分析,确定了动力电池系统的技术路线,为苏通GIL运输专用机具的研制提供了坚实的基础。     

基于最小维修的石化安全设备区间截尾寿命数据分析

在石化安全关键设备寿命截尾数据的可靠性评估过程中，为避免传统可靠性评估方法未考虑维修对故障间隔时间的影响，以及中值填充法误差较大的缺点，本文提出了一种逆变换区间截尾数据填充方法。首先，在可修系统基于最小维修的假设下，通过非齐次泊松过程描述系统的故障趋势，利用逆变换填充得到监测区间的缺失数据；随后根据蒙特卡罗期望最大化算法，获得填充数据的参数估计值。通过蜡油加氢装置紧急泄压阀的案例分析，该算法能够很好地融合已有数据，得到的形状参数估计值偏差与中值填充法相比缩小4%以上，从而验证了该算法的有效性；这有助于安全关键设备的定量化完整性管理，保障石化装置平稳运行。     

Estimation of power density in IMPATT using different materials

ABSTRACT

The RF output power dissipated per unit area is calculated using Runge-Kutta method for the high-moderate-moderate-high (n⁺-n-p-p⁺) doping profile of double drift region (DDR)-based impact avalanche transit time (IMPATT) diode by taking different substrate at Ka band. Those substrates are silicon, gallium arsenide, germanium, wurtzite gallium nitride, indium phosphide and 4H-silicon carbide. A comparative study regarding power dissipation ability by the IMPATT using different material is being presented thereby modelling the DDR IMPATT diode in a one-dimensional structure. The IMPATT based on 4H-SiC element has highest power density in the order of 10¹⁰ Wm^?2 and the Si-based counterpart has lowest power density of order 10⁶ Wm^?2 throughout the Ka band. So, 4H-SiC-based IMPATT should be preferable over others for the power density preference based application. This result will be helpful to estimate the power density of the IMPATT for any doping profile and to select the proper element for the optimum design of the IMPATT as far as power density is concerned in the Ka band. Also, we have focused on variation of power density with different junction temperatures and modelled the heat sink with analysis of thermal resistances.     

基于降噪时序深度学习网络的风电功率短期预测方法

利用风电场历史功率数据预测未来一段时间内的风功率,对保障电网安全稳定运行具有重要的意义。本文提出一种基于奇异谱分析SSA(singular spectrum analysis)和长短时记忆LSTM(long-short term memory net⁃work)网络的时序特征预测框架用于短期风功率的预测。首先通过SSA对历史风功率原始数据进行降噪处理,然后经过数据转换之后,以LSTM网络为基础进行预测模型的训练,最后通过某风电场提供的两个风机的历史功率数据进行验证。实验结果表明,奇异谱分析对风电场的历史数据具有良好的降噪性,SSA+LSTM模型在测试数据上取得了较好的预测性能,能够有效进行短期风功率的预测。