Effects of human activities on benthic macroinvertebrate community composition and water quality in the upper catchment of the Mara River Basin,Kenya

Land‐use changes in the upper reaches of the Mara River Basin have modified their biophysical and hydrological processes, resulting in water quality degradation in streams. This study was conducted to investigate the effects of human activities on water quality and macroinvertebrates along the Nyangores River, one of the main tributaries of the Mara River, Kenya. Seven sampling sites were chosen to correspond to the loss of riparian cover, livestock watering and human activities (e.g. laundry washing, bathing, cultivation, wastewater inputs, dumping of solid wastes from urban areas and settlements along the river). Physical–chemical variables and water samples for nutrient analyses were collected monthly from February to July 2012. Benthic macroinvertebrates also were collected at the same sites as for the water quality samples. Two‐way analysis of variance tested the significant differences for each variable among the sites. Similarity percentages (SIMPER) analysis was used to identify the key taxa contributing to differences between minimally disturbed and most disturbed conditions in the study area. The results indicated increased nutrient concentrations in agricultural and settlement areas. Significant (P < 0.05) spatial–temporal variations in water quality variables were observed. A total of 42 macroinvertebrate genera were encountered, with Ephemeroptera, Plecoptera and Trichoptera orders dominating the minimally disturbed areas, and Diptera dominating the disturbed areas receiving point and no‐point solid and liquid wastes, including nutrients, from urban areas and settlements. Canonical correspondence analysis (CCA) revealed significant relationships between macroinvertebrate communities and measured physicochemical variables. The results of this study indicate the need for protection of riparian zones and treatment of sewerage wastes before their release into waterways. The dumping of solid wastes near streams and rivers also is discouraged, to maintain the quality of surface waters and aquatic organisms.     

Development of the remote-controlled platform truck for handling heavy object and the remote control human interface for the disaster response

General interest for robotic technology has been increased by the public and the media after Fukushima Daiichi Nuclear Power Plant (hereafter referred to as 1F) disaster. Especially, robots which can work under the very severe condition where personnel cannot access have demands for development. As to respond to such high demands, NEDO established ‘Disaster response unmanned systems development project’ in 2012.[1] This project is specialized in the development of various remote-controlled equipment, such as remote-controlled platform truck and remote control human interface under ‘Mobile Robot Development’. Remote-controlled platform truck is designed to safely and surely deliver robots and supplies, instead of using stairs and elevator, etc. in and out of building where it is too critical for personnel to work under. Remote control human interface for robots is designed to standardize the command and operation screen for operator, based on opinions from project members, and manned facility construction.[2] In this article, mechanical structure and development tasks for remote-controlled platform truck, and commonalization approach for operation and camera display of various remote-controlled equipment and robot for remote control human interface are stated. Background: due to hydrogen explosion triggered by The Great East Japan Earthquake on 11 March 2011, a reactor building at Fukushima Daiichi Nuclear Power Plant was severely damaged. It is required to reduce high radiation dose in the atmosphere of the reactor building to perform restoration. To pursue decontamination of the reactor building, equipment to lift and carry decontamination devices to upper floor are required.     

A Hybrid Color Quantization Algorithm Incorporating a Human Visual Perception Model

Color quantization is a common image processing technique where full color images are to be displayed using a limited palette of colors. The choice of a good palette is crucial as it directly determines the quality of the resulting image. Standard quantization approaches aim to minimize the mean squared error (MSE) between the original and the quantized image, which does not correspond well to how humans perceive the image differences. In this article, we introduce a color quantization algorithm that hybridizes an optimization scheme based with an image quality metric that mimics the human visual system. Rather than minimizing the MSE, its objective is to maximize the image fidelity as evaluated by S‐CIELAB, an image quality metric that has been shown to work well for various image processing tasks. In particular, we employ a variant of simulated annealing with the objective function describing the S‐CIELAB image quality of the quantized image compared with its original. Experimental results based on a set of standard images demonstrate the superiority of our approach in terms of achieved image quality.     

基于动作识别的情绪提取方法研究

基于运动数据的动作识别技术是当今计算机视觉和模式识别研究领域的热点问题,然而根据动作识别结果进行人体情绪提取的研究却较少。为此,在现有人体行为学研究的基础上,结合计算机图形学、人工智能和机器学习等技术,得出一套关于人体情绪的数据集合,包括开心、愤怒、悲伤、恐惧4种基本情绪和惊喜、厌恶2种衍生情绪。从动作的角度分析这6种情绪得到动作,即情绪关系,按照动作识别的划分基准,将Period作为情绪提取的最小单位,确定单个Period的情绪列表,再结合动作和速率的参数,提取出在执行该动作时人体的情绪。实验结果表明,该方法能够有效地从3D运动数据中提取情绪信息。     

WANO人误研究平台

针对传统WANO人误研究工作效率低下,流程混乱的特征,研究从WANO人误研究工作实际需求出发,采用面向对象技术设计,实现了一个基于信息系统的WANO人误研究平台.从平台构建的角度阐述了WANO人误研究平台的功能结构、平台框架、WANO人误数据库构建等技术要点,并介绍了平台在WANO人误数据ETL方面的应用效果.     

Rendering of HDR images to improve brightness discrimination

The impression of quality of images can be enhanced on a high dynamic range (HDR) displays. Generally, a conventional 8‐bit image can be processed to an HDR image by inverse tone mapping operators. Among the operators, brightness discrimination mapping by applying brightness adaptation model attempted to mimic the human visual system. In this paper, we use a brightness adaptation model to derive a brightness discrimination mapping algorithm for HDR displays. The proposed algorithm maximizes a function, which represents the local and global brightness discrimination range by exploiting characteristics of the human visual system. Enhancement of details is verified by visualizing HDR images from dark to bright regions. Improvement of dynamic range is quantified by measuring increased discrimination ratio.     

人机交互中的声源定位与增强方法

基于人机交互的实际应用场景,提出了一种交互目标声源三维定位与语音增强算法。该算法首先在广义相关法的基础上提出一种声达时延差的估计方法,通过由6个麦克风构成的平行均匀线阵接收模型,实现对目标声源的三维定位;然后在交互目标声源定位的基础上,通过调整时延波束叠加的权值,实现对目标语音的增强。仿真实验结果表明,文中提出的算法能够准确定位目标声源并对目标语音进行有效增强。在信噪比大于1.5 dB 的环境中,该算法可使目标声源达到98%以上定位精度,5 dB 以上信噪比改善,同时算法运算量小且易于硬件实现。     

融合梯度信息与HVS滤波器的无参考清晰度评价

目的 目前无参考图像质量评价算法的性能存在较大的提升空间,为了提高清晰度评价技术,提出了一种基于梯度信息与HVS滤波器的无参考清晰度评价算法(GI-F)。方法 该算法首先利用梯度算子计算各像素点的梯度信息,再通过HVS滤波器得到加权和作为图像的清晰度指标。结果 在公开数据库LIVE、TID2008和CSIQ上进行的实验,GI-F与S3(Spectral and Spatial Sharpness)、CPBD(Cumulative Probability of Blur Detection)和LPC-SI(Local Phase Coherence-based Sharpness Index)相比,性能指标RMSE(Root Mean Squared Error)、PLCC(Pearson Linear Correlation Coefficient)和SROCC(Spearman Rank-Order Correlation Coefficient)分别提升了20.66%、4.61%和3.33%;同时GI-F还具有更低的计算复杂度,即使与目前最好的BRISQUE(Blind/Referenceless Image Spatial QUality Evaluator)算法相比,耗时也降低了79.72%。结论 该算法只需耗费更少的时间即可计算出与人眼感知更加接近的客观清晰度指标,可广泛用于无参考图像情况下的清晰度指标计算场合,同时还可以通过并行计算进一步降低其计算时间。     

供电企业绩效管理对策研究

绩效管理是人力资源管理中的重要举措之一,对现代企业增强对员工的掌控力和核心竞争力具有重要作用。随着我国供电体制改革的不断深入,供需平衡已成为电力能源供应的发展趋势。基于当前成功企业的绩效管理方法和供电企业在实施绩效管理工作中存在的突出问题,从绩效管理的计划、指标、流程和应用等环节,深入研究了供电企业建立符合自身特点的全员绩效管理体系的方法。     

微波传感技术在智能马桶上的应用

介绍了一种应用于智能马桶,可以穿透陶瓷和塑料来检测人体运动的微波传感技术。该技术利用多普勒原理实现对人体运动的检测。当微波传感器检测到人体移动后,会输出多普勒信号,该信号经过滤波、放大后送给PIC单片机进行处理。实际应用证明,该设计具有很好的检测效果。