The centers of gravity and dynamic types of body shape of terrestrial invertebrates

In the framework of dynamic morphology theory, the centers of gravity and geometric centers of projections have been determined for the bodies of more than 182 forms of invertebrates, and these data have been used to distinguish four types of dynamic body shapes. It has been demonstrated that specific dynamic types of body shape are characteristic of individual taxa. In addition, there are a group of “transformers,” which may assume any dynamic type of shape, and a group of invertebrates with one predominant dynamic type.     

化学沉淀组合电催化氧化处理脱硫废液技术研究

真空碳酸钾脱硫工艺碱洗段废液因含有高浓度硫化物、COD等有毒成分而影响焦化废水后续生物处理。以焦化企业实际碱洗段废液为研究对象,选用化学沉淀和电催化氧化的组合方式对其进行预处理。结果表明,室温下当氧化铜的投加量为65 g/L、反应时间60 min时废液中S2-去除率可达93%,且经过高温灼烧可实现Cu O沉淀剂的再生使用。沉淀后出水经电催化氧化处理,在电流10 A、电压4.9 V、电解时间120 min的条件下,出水COD可降至2 400 mg/L,满足常规生化系统进水的要求。     

基于FTA-SVM的车辆发动机故障识别方法

发动机结构日益复杂,其故障具有多样性和频发性的特点,收集大量故障样本存在很多实施障碍。为了提高车辆发动机的故障识别的效率和准确性,提出了一种新的结合故障树(FTA)和支持向量机(SVM)各自特点,从故障模式分析到故障类型识别的FTA-SVM故障识别方法。首先利用故障树在复杂系统故障模式分析中的优势,找出系统的故障模式,建立故障树模型,通过对故障树模型中各故障事件的分析,采集与故障事件状态相关的数据,建立数据与故障树底事件的映射模型,最后利用支持向量机在小样本数据处理中的优势,进行故障类型的识别。以发动机的失火故障为例建立了发动机失火故障树模型及故障数据与故障模式映射模型,验证了FTA-SVM方法的有效性和适用性。     

基于矿井通风三维仿真系统软件的矿用空气幕选型方法

根据矿井风流调节的具体要求,合理选择矿用空气幕的型号是应用矿用空气幕技术的关键。在分析矿用空气幕选型依据及原理的基础上,应用矿井通风三维仿真系统软件模拟矿用空气幕运行的效果,来研究确定矿用空气幕的选型参数及影响因素。结果表明,应用矿井通风三维仿真系统软件所选择的矿用空气幕与理论推导的计算结果一致,此方法能够最大限度地反映矿山实际情况,具有直观、高效等特点。     

Using Sound to Reduce Visual Distraction from In-vehicle Human–Machine Interfaces

Objective: Driver distraction and inattention are the main causes of accidents. The fact that devices such as navigation displays and media players are part of the distraction problem has led to the formulation of guidelines advocating various means for minimizing the visual distraction from such interfaces. However, although design guidelines and recommendations are followed, certain interface interactions, such as menu browsing, still require off-road visual attention that increases crash risk. In this article, we investigate whether adding sound to an in-vehicle user interface can provide the support necessary to create a significant reduction in glances toward a visual display when browsing menus.

Methods: Two sound concepts were developed and studied; spearcons (time-compressed speech sounds) and earcons (musical sounds). A simulator study was conducted in which 14 participants between the ages of 36 and 59 took part. Participants performed 6 different interface tasks while driving along a highway route. A 3 × 6 within-group factorial design was employed with sound (no sound /earcons/spearcons) and task (6 different task types) as factors. Eye glances and corresponding measures were recorded using a head-mounted eye tracker. Participants’ self-assessed driving performance was also collected after each task with a 10-point scale ranging from 1 = very bad to 10 = very good. Separate analyses of variance (ANOVAs) were conducted for different eye glance measures and self-rated driving performance.

Results: It was found that the added spearcon sounds significantly reduced total glance time as well as number of glances while retaining task time as compared to the baseline (= no sound) condition (total glance time M = 4.15 for spearcons vs. M = 7.56 for baseline, p =.03). The earcon sounds did not result in such distraction-reducing effects. Furthermore, participants ratings of their driving performance were statistically significantly higher in the spearcon conditions compared to the baseline and earcon conditions (M = 7.08 vs. M = 6.05 and M = 5.99 respectively, p =.035 and p =.002).

Conclusions: The spearcon sounds seem to efficiently reduce visual distraction, whereas the earcon sounds did not reduce distraction measures or increase subjective driving performance. An aspect that must be further investigated is how well spearcons and other types of auditory displays are accepted by drivers in general and how they work in real traffic.     

岩溶塌陷倾向性等级的KPCA-SVM预测模型

为了快速、有效地预测岩溶塌陷倾向性等级,在统计分析大量观测实例的基础上,选取岩性系数、岩体结构系数、地下水系数、覆盖层系数、地形地貌系数和环境条件系数作为特征指标。利用核主成分分析(KPCA)方法在高维空间提取岩溶塌陷影响因子的主成分,将获取的主成分作为支持向量机(SVM)的特征向量,建立基于KPCA的岩溶塌陷倾向性等级的SVM预测模型。将12组观测数据作为学习样本对模型进行训练。采用回代估计法进行回检,误判率为0。利用训练好的模型对2组待判样本进行预测。结果表明:经KPCA后指标个数减少,相关性降低,SVM运算的复杂度降低。用该模型所得预测结果的准确率为100%。     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine

Diesel engines are being increasingly adopted by many car manufacturers today, yet no exact mathematical diesel engine model exists due to its highly nonlinear nature. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size, and over-fitting risk. To overcome these drawbacks, this article proposes to apply an emerging machine learning technique, relevance vector machine (RVM), to model and predict the diesel engine performance. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are engine speed, load, and cooling water temperature, while the output parameters are the brake-specific fuel consumption and the amount of exhaust emissions like nitrogen oxides and carbon dioxide. Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.     

基于Matlab分析的台阶式场地平整方案优化设计

在某市餐厨垃圾处理厂场地平整设计中,利用Matlab软件对台阶式场地平整方案进行优化设计,即在Auto CAD环境下用等高线法计算各级台阶的土方量,用Matlab软件拟合出台阶设计高程和土方量的函数关系式,在约束条件下求解,得到了最优设计高程和基于土方挖填平衡条件下场地平整的最小土方工程量,为工程总平面布置提供优化方案。其分析设计方法可为工业企业台阶式场地的总平面布置设计提供借鉴。     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.