四螺旋创新集群:研究型大学人工智能发展生态重构与路向探究——以加拿大多伦多大学为例

以人工智能为代表的知识迭代发展,促使知识生产模式发生重大转变,催生了“大学—产业—政府—公众”四螺旋动力结构的建立和研究型大学知识生产新生态重构。以多伦多大学人工智能发展为例,考察其四螺旋利益相关主体及实践。从内在机理看,4个主体在互动运作中不仅重新确定了各自角色,而且还建立了大学(知识)—产业(产品)—政府(治理)—公众(公益)的新型逻辑链条,平衡公私利益格局,把公益指向作为人工智能四螺旋运作的中心目标;从外在特征看,4个主体形成了以大学为中心的区域创新网络,并牵引其他主体形成环高校创新集群。当前,我国在人工智能发展中不断发力,已进入世界第一方阵,但仍需要在国际比较学习中不断提升自身竞争力。基于案例分析,我国研究型大学发展人工智能有必要把握4个方面:一是走进中心,塑造大学在人工智能发展中的领导地位;二是以专促通,创新研究型大学人工智能专业培养模式;三是引企入研,提升校企合作人工智能创新的转化升级;四是人本导向,突出大学在人工智能发展中的公共价值。总体来看,我国研究型大学发展人工智能不仅要面临技术上的挑战,更要面临来自治理的挑战。     

基于热中子探测评价裂缝的示踪剂特性研究

针对裂缝热中子示踪探测方法,从中子双组扩散理论入手结合蒙特卡洛数值模拟方法,分析压裂前后地层热中子分布影响因素,并模拟不同类型示踪剂及含量的热中子裂缝响应规律,筛选最佳示踪剂并给出其在支撑剂中含量的上、下限。结果表明:压裂前后热中子计数变化量主要受裂缝宽度和标记支撑剂中示踪剂含量的影响;钆对裂缝宽度变化反应最灵敏,钆元素是作为标记支撑剂的最佳示踪元素;随支撑剂中Gd 2O 3含量的增加,热中子计数率降低,热中子计数变化量D增加;示踪剂中氧化钆的上、下限值随裂缝宽度呈指数降低,且当裂缝宽度为1.0 cm时,Gd 2O 3的质量分数上限为0.68%,下限为0.03%。     

天然自然与人工自然的张力--信息论视域中的SARS事件形而上学探源

自然界在人类出现以前以天然自然的状态存在着，遵循自己固有的演化规律。人类创造的人工自然中所蕴含的人工信息具有直接和间接效应，其中间接效应尤其具有不可预测、难以控制、不可逆、强度大等特点。随着科学技术的发展，人工信息及其给生态系统和人类社会带来的效应都飞速增加。人类社会对人工信息的正面效应和负面效应的承受力是不对称的，大量增加的负面效应(例如非典病毒)如果超过临界值，可以将人类社会摧毁。因此它预示眷一个新时代的来临。     

Accurate forecasting of the undecided population in a public opinion poll

The problem of pollsters is addressed which is to forecast accurately the final answers of the undecided respondents to the primary question in a public opinion poll. The task is viewed as a pattern‐recognition problem of correlating the answers of the respondents to the peripheral questions in the survey with their primary answers. The underlying pattern is determined with a supervised artificial neural network that is trained using the peripheral answers of the decided respondents whose primary answers are also known. With peripheral answers as inputs, the trained network outputs the most probable primary response of an undecided respondent. For a poll conducted to determine the approval rating of the (former) Philippine president, J. E. Estrada in December 1999 and March 2000, the trained network predicted with a 95% success rate the direct responses of a test population that consists of 24.57% of the decided population who were excluded in the network training set. For the undecided population (22.67% of December respondents; 23.67% of March respondents), the network predicted a final response distribution that is consistent with the approval/disapproval ratio of the decided population. Copyright © 2002 John Wiley & Sons, Ltd.     

专家系统技术在注塑模故障诊断中的应用

论述了用人工智能技术对模具故障进行诊断和确定对策的原理和方法。对模具在工作中常遇到的与模具制造、工艺条件及塑料材料有关的问题及相应对策进行了归纳整理，组成了模具诊断与维修知识库。收集了常用工程塑料的各种有关成型工艺条件和主要性能，建立了塑料材料数据库。采用正向推理控制策略搜索知识库，并将推理结果与理论计算结果进行对比校验，最后确定出模具工作中出现故障的原因，并输出相应的解决办法和建议．     

浅谈人工挖孔桩

分析了人工挖孔桩施工中遇到的问题(如水、溶洞、淤泥层、流砂),并提出了相应的处理措施。     

论辅助生殖技术引发的伦理问题及其应对机制

简单介绍了辅助生殖技术,分析了辅助生殖技术引发的伦理问题,提出了应对这些伦理问题的机制。     

Samples Selection for Artificial Neural Network Training in Preliminary Structural Design

An artificial neural network (ANN) is applied in the preliminary structural design of reticulated shells. Major efforts are made to enhance the generalization ability of networks through well-selected training samples. Number-theoretic methods (NTMs) are adopted to generate samples with low discrepancy, i.e.uniformly scattered in the domain, where discrepancy is a quantitative measurement of the uniformity. The discrepancy of the NTM-based sample set is 1/6-1/7 that of samples with equal spacing. In a case study,networks trained by NTM-based samples are compared with those trained by equal-spaced samples in generalizing performance. The results show that both the computational precision and stability of the former ANNs are more satisfactory than those of the latter. It is concluded that the flexibility of ANNs in generalizing can be effectively increased by use of uniformly distributed training samples rather than simply piling data.More reliable uniformity should be obtained, however, through NTMs instead of equal-spaced samples.