安卓平台上的医用图像信息采集系统的开发设计

为了探讨在安卓平台上构建医用图像采集系统的开发个案,分析通过以智能手机、平板电脑为核心安卓设备通过拍照获得化验单数据后进行文本识别并提交智慧医疗系统的解决方案。本文首先通过二值化算法形成低阈值图像数据,使用卷积神经元网络算法对文本进行逐一识别,使用K-means算法对识别后的单字文本进行字段记录值的整合并形成元数据库服务于其他智慧医疗系统模块。在使用9000组数据对神经元网络进行前期训练的前提下,该系统的识别准确率达到了99.5%以上。本系统具有一定的可行性,对未来智慧医疗的系统开发有实践意义。     

Looking into the TESSERACT: Time-drifts in event streams using series of evolving rolling averages of completion times

Business processes are dynamic and change due to diverse factors. While existing approaches aim to detect drifts in the process structure, Tesseract looks for temporal drifts in activity interim times. This orthogonal view on the process extends the traditional data cube of events – case id, activities and timestamps – by a fourth dimension and improves the operational support by a visualization of temporal drifts in real-time.Insights about temporal deviations lead to an augmented awareness of imminent failures or improved service times. The detection of related structural concept drifts can be improved by early warning, as operation times of critical parts often increase before they catastrophically fail.     

语音录入系统在针织成品检验中的应用

介绍纺织面料成品检验语音输入系统的方案设计,通过语音识别与合成技术构建语音录入系统,通过自适应学习提高识别效率。阐述通过成品检验环境噪音测试、验布工人声音采集建立语音系统,以及该系统在语音验布上的应用。经过应用测试显示,使用语音系统代替手写录入可使疵点检验率从57%提升到73%。在针织成品检验中采用语音录入系统能够快速录入疵点信息,提高检验效率,进一步简化验布工操作流程,降低漏验和堵布风险,提高面料整体的质量控制能力。     

ED-SWE: Event detection based on scoring and word embedding in online social networks for the internet of people

Online social media networks are gaining attention worldwide, with an increasing number of people relying on them to connect, communicate and share their daily pertinent event-related information. Event detection is now increasingly leveraging online social networks for highlighting events happening around the world via the Internet of People. In this paper, a novel Event Detection model based on Scoring and Word Embedding (ED-SWE) is proposed for discovering key events from a large volume of data streams of tweets and for generating an event summary using keywords and top-k tweets. The proposed ED-SWE model can distill high-quality tweets, reduce the negative impact of the advent of spam, and identify latent events in the data streams automatically. Moreover, a word embedding algorithm is used to learn a real-valued vector representation for a predefined fixed-sized vocabulary from a corpus of Twitter data. In order to further improve the performance of the Expectation-Maximization (EM) iteration algorithm, a novel initialization method based on the authority values of the tweets is also proposed in this paper to detect live events efficiently and precisely. Finally, a novel automatic identification method based on the cosine measure is used to automatically evaluate whether a given topic can form a live event. Experiments conducted on a real-world dataset demonstrate that the ED-SWE model exhibits better efficiency and accuracy than several state-of-art event detection models.     

Multimodal Attentive Fusion Network for audio-visual event recognition

Event classification is inherently sequential and multimodal. Therefore, deep neural models need to dynamically focus on the most relevant time window and/or modality of a video. In this study, we propose the Multimodal Attentive Fusion Network (MAFnet), an architecture that can dynamically fuse visual and audio information for event recognition. Inspired by prior studies in neuroscience, we couple both modalities at different levels of visual and audio paths. Furthermore, the network dynamically highlights a modality at a given time window relevant to classify events. Experimental results in AVE (Audio-Visual Event), UCF51, and Kinetics-Sounds datasets show that the approach can effectively improve the accuracy in audio-visual event classification. Code is available at: https://github.com/numediart/MAFnet     

扫描地图的计算机自动识别

扫描地图的计算机自动识别是当今地图制图学科的前沿课题，也是其重要的研究方向。为此，本文首先将模式识别技术引入到地图制图领域中，提出了四种地图符号的计算机描述方法，然后又讨论了扫描地图的信息获取与处理，并具体地分析了扫描地图的计算机识别方法及其识别系统．最后还给出了相应的符号识别实验。     

Influence of ozone-limonene reactions on perceived air quality

This study conducted short-term assessments of perceived air quality (PAQ) for six different realistic concentrations of ozone and limonene, separately or together, in room air. The impact of filtration and the influence of the ozone generation method were also examined. The evaluations were made in four identical 40 m3 low-polluting test offices ventilated at 1.4 h(-1) or in two identical 30 m3 stainless-steel chambers ventilated at 1.9 h(-1). Concentrations of ozone, total volatile organic compounds and size-fractionated particles were continuously monitored in each experiment. The results indicate that, for each of the six conditions, the PAQ was poorer when ozone and limonene were present together compared with when only ozone or only limonene was present. In the test offices a correlation was observed between the number of secondary organic aerosols produced by a given ozone/limonene condition and the sensory pollution load for that condition. The particles themselves do not appear to be the primary causative agents, but instead are co-varying surrogates for sensory offending gas-phase species. PRACTICAL IMPLICATIONS: Although the health consequences of long-term exposures to the products of ozone-initiated indoor chemistry remain to be determined, we judge that the sensory offending nature of selected products provides an additional reason to limit indoor ozone levels. Devices that emit ozone at significant rates should not be used indoors. Ozone-filtration of make-up air should also be beneficial in mechanically ventilated buildings located in regions that repeatedly violate outdoor ozone standards. Additionally, the use of limonene containing products should be curtailed during periods when indoor ozone levels are elevated.     

汽车牌照自动识别系统在公路收费口的应用

最近及未来的几年 ,上海市境内将建成多条高速公路 ,如何提高高速公路的服务质量 ,出入口的通行能力是一条比较重要的衡量标准。汽车牌照自动识别系统利用先进的摄像技术、图像识别技术和计算机技术 ,对抓拍图像进行正确分割和快速处理 ,自动识别出汽车的牌照号码 ,提升了高速公路的服务水平     

注册建筑师制度在中国的建立及管理统计

文章重点从政策层面比较系统地回顾了中国建设行业注册建筑师执业管理制度从建立到发展演变的过程以及注册资格国际互认方面的情况。     

Advancing assessment and design of stormwater monitoring programs using a self-organizing map: characterization of trace metal concentration profiles in stormwater runoff

Stormwater runoff poses a great challenge to the scientific assessment of the effects of diffuse pollution sources on receiving waters. In this study, a self-organizing map (SOM), a research tool for analyzing specific patterns in a large array of data, was applied to the monitoring data obtained from a stormwater monitoring survey to acquire new insights into stream water quality profiles under different rainfall conditions. The components of the input data vectors used by the SOM included concentrations of 10 metal elements, river discharge, and rainfall amount which were collected at the inlet and endpoint of an urban segment of the Yeongsan River, Korea. From the study, it was found that the SOM displayed significant variability in trace metal concentrations for different monitoring sites and rainfall events, with a greater impact of stormwater runoff on stream water quality at the upstream site than at the downstream site, except under low rainfall conditions (≤4 mm). In addition, the SOM clearly determined the water quality characteristics for “non-storm” and “storm” data, where the parameters nickel and arsenic and the parameters chromium, cadmium, and lead played an important role in reflecting the spatial and temporal water quality, respectively. When the SOM was used to examine the efficacy of stormwater quality monitoring programs, between 34 and 64% of the sample size in the current data set was shown to be sufficient for estimating the stormwater pollutant loads. The observed errors were small, generally being below 10, 6, and 20% for load estimation, map resolution, and clustering accuracy, respectively. Thus, the method recommended may be used to minimize monitoring costs if both the efficiency and accuracy are further determined by examining a large existing data set.