SOINN-Based Abnormal Trajectory Detection for Efficient Video Condensation

With the evolution of video surveillance systems, the requirement of video storage grows rapidly; in addition, safe guards and forensic officers spend a great deal of time observing surveillance videos to find abnormal events. As most of the scene in the surveillance video are redundant and contains no information needs attention, we propose a video condensation method to summarize the abnormal events in the video by rearranging the moving trajectory and sort them by the degree of anomaly. Our goal is to improve the condensation rate to reduce more storage size, and increase the accuracy in abnormal detection. As the trajectory feature is the key to both goals, in this paper, a new method for feature extraction of moving object trajectory is proposed, and we use the SOINN (Self-Organizing Incremental Neural Network) method to accomplish a high accuracy abnormal detection. In the results, our method is able to shirk the video size to 10% storage size of the original video, and achieves 95% accuracy of abnormal event detection, which shows our method is useful and applicable to the surveillance industry.     

高泉背斜地层压力测井多参数综合解释与异常高压成因

目前国内外地层压力测井解释方法较多,但大多数方法基于欠压实沉积理论并局限于单一的地层岩性,尤其是针对盆地边缘山前深层构造,单一测井参数确定地层压力误差较大,且无法解释沉积压实机制以外的异常高压。为此,将多种异常高压机制相关的测井参数进行综合解释,建立适合山前复杂构造的地层压力测井多参数计算模型,评价准噶尔盆地南缘高泉背斜高探1井的地层压力剖面,其计算结果与钻井实测数据吻合,相对误差小于3.00%.同时,基于原始沉积加载—卸载过程力学原理,利用地层压力测井多参数分析判别出高泉背斜深层各层系异常高压的形成机制。     

覆岩结构类型与开采覆岩损害特征分析

煤层开采打破了上覆岩层原有的平衡,必然引起岩层的移动、破断,导致应力场和裂隙场的改变,工作面出现矿压显现。上覆岩层的岩性和组合结构对工作面开采覆岩损害特征分布影响显著,一般岩性越坚硬,上覆岩层应力集中程度越高,采动裂隙越发育,矿压显现也越明显;坚硬岩层尤其是关键层对覆岩的破断运动起控制作用,覆岩破断发展到一定层位后软弱岩层往往是抑制覆岩破断进一步向上发展的关键。当工作面上覆岩层同时存在远近场关键层时,近场关键层的周期性破断引起裂隙带范围内岩层的运动和工作面周期来压;而远场关键层的周期性破断导致地表裂缝或台阶下沉,工作面压力急剧上升,有时甚至导致煤矿动压灾害,尤其是远近场关键层同步破断时动力现象更明显。通过地面钻孔使远场关键层提前破断是防止此类动力灾害的关键。     

Performance assessment of gas crossover phenomenon and water transport mechanism in high pressure PEM electrolyzer

To improve proton exchange membrane (PEM) electrolyzes’ performance the voltage loss through them should be avoided. In this work, it is intended to analyze losses including of diffusion loss, ohmic loss due to electrode, bipolar plate (BP), and membrane resistances, and gas crossover associated with the water transferring mechanisms. All of the losses are associated with water transferring mechanisms, which is created due to electro-osmoic drag, pressure differential between the anode and cathode sides, and diffusion. Furthermore, the effect of membrane thickness, cathode pressure, and operating temperature on the hydrogen crossover is examined. In addition, the contribution of ohmic loss due to electrode bipolar plate (BP), and membrane resistances is studied and, the contribution of different losses on the cell performance is discussed. Results show that raising cathode pressure from 1 to 40 bar lead to the increment of anodic hydrogen content from 1.038% to 21% at the specific current density of 10,000 A/m2. Enhancing the thickness of membrane has considerable impact on decrementing anodic hydrogen content, but the mass transfer loss rises from 0.022 to 0.027 V with enhancing membrane thickness from 50 to 300 μm, respectively. Furthermore, the contribution of voltage losses, assigned to each of losses are equal to 85%, 3%, and 12% for activation, diffusion and ohmic losses, respectively. It is found that, from the reported contribution for ohmic loss, the contribution of electrode BP, and membrane resistances are 31% and 69%, respectively.     

Computer aided automated detection and classification of brain tumors using CANFIS classification method

The development of abnormal cells in human brain leads to the formation of tumors. This article proposes an efficient approach for brain tumor detection and segmentation using image fusion and co-active adaptive neuro fuzzy inference system (CANFIS) classification method. The brain MRI images are fused and the dual tree complex wavelet transform is applied on the fused image. Then, the statistical features, local ternary pattern features and gray level co-occurrence matrix features. These extracted features are classified using CANFIS classification approach for the classification of source brain MRI image into either normal or abnormal. Further, morphological operations are applied on the abnormal brain MRI image for segmenting the tumor regions. The proposed methodology is evaluated with respect to the performance metrics sensitivity, specificity, positive predictive value, negative predictive value, tumor segmentation accuracy with detection rate. The proposed image fusion based brain tumor detection and classification methodology stated in this article achieves 96.5% of average sensitivity, 97.7% of average specificity, 87.6% of positive predictive value, 96.6% of negative predictive value, and 98.8% of average accuracy.     

Motion trajectory prediction model of hydrogen leak and diffusion in a stable thermally-stratified environment

The motion trajectory of hydrogen leakage is an essential safe issue for the application of hydrogen energy. A dimensionless fast-running motion trajectory prediction model is proposed to predict the dispersion characteristics of the buoyant jet of hydrogen leakage for the accident. The impact of different leakage angles, leakage velocity and thermal stratification of ambient air on hydrogen leakage behavior was analyzed. The new developed model was verified by experimental results in literatures. Leakage hydrogen can flow upwards freely in a uniform environment. However, it shows an oscillating trajectory at a certain height in a thermally stratified environment, which is so called “locking phenomenon”. The trajectory of hydrogen leakage is upward and hydrogen gathers at the top of the space to form stratification in a uniform environment, while the hydrogen leakage shows an oscillating trajectory at a certain height in a thermal stratification environment. With the increase of Froude number Fr, it shows that the stable height and maximum height of the leakage airflow have a trend of rising first and then falling in a thermally stratified environment. The findings are expected to give guidance in real-world situations, for example, a larger Fr value and a larger temperature gradient can lead to a decrease in the stable height in the thermally stratified environment. It is found that the fitting of the stable height with different temperature gradients satisfies the power function relationship. This work is expected to be helpful for reducing hydrogen leakage accumulation and explosion risk.     

原位合成WC-6Co复合粉末制备超细YG6硬质合金

将原位合成WC-6Co复合粉末采用干袋式冷等静压压制成型(压制压力1×10~8 Pa、保压时间15 s),将压制好的坯料采用低压烧结炉烧结(烧结温度1360℃、烧结时间40 min、加压5 MPa、保温保压时间20 min),烧结制备超细YG6硬质合金,对合金的形貌、金相组织及物理力学性能进行分析。结果表明:原位合成WC-6Co复合粉末制备的超细YG6硬质合金,晶粒异常长大,WC平均晶粒尺寸为0.8μm,硬度HV_(30)为(21500±100) MPa,较传统超细YG6X硬度高。再将WC-6Co复合粉末采用滚动湿磨、压力式喷雾干燥、掺成型剂、挤压成型、低压烧结等工序制备超细YG6硬质合金,研究不同晶粒长大抑制剂配比、球磨时间、挤压压力、烧结温度对合金性能的影响。结果表明:添加0.3%VC、0.8%Cr_3C_2(质量分数),湿磨48 h,挤压压力24 MPa,烧结温度1340℃,制备的超细YG6硬质合金WC晶粒均匀,无异常长大的WC晶粒,WC平均晶粒度尺寸0.4μm,呈多边形,外形较圆。强度、硬度最高,抗弯强度TRS为(2250±20) MPa、硬度HV30为(22600±100) MPa。断口形貌为沿晶断裂,沿WC与WC晶界断裂或WC与Co晶界断裂。     

基于流数据聚类算法的电力大数据异常检测

针对电力大数据流的异常检测问题,该文将流数据聚类算法与电力大数据相结合,针对现有流数据聚类算法不易存储全部数据、断电数据易丢失等问题,以及流数据聚类算法对于离线阶段聚类算法实时应答的要求,从数据的完整性、安全性以及流数据聚类算法的低时间复杂度的角度出发,对CluStream流数据聚类算法进行改进,提出流式K-means聚类算法。对在线阶段,使用Redis集群进行流数据的缓冲,并设计节点时间衰减策略,增大心跳消息中有效消息所占比例;对离线阶段聚类算法进行优化,使用最佳距离法确定初始聚类中心,减少迭代次数;最后,使用所提出的流式K-means聚类算法进行用户用电异常行为检测,实验结果表明,该算法能够很好的发现用户用电异常行为。     

振动筛运行异常的分析与研究

分析了振动筛在运行过程中的异常情况,并提出了相应的解决办法.