A 6-DOF humanoid wall-climbing robot with flexible adsorption feet based on negative pressure suction

A wide range of dangerous and special tasks have witnessed the applications of wall-climbing robots, but they still cannot adapt well torough or sloping walls. This paper proposes a 6-DOF (degree of freedom) humanoid wall-climbing robot (HWCR) based on the principle of negative pressure suction. HWCR has the advantages of flexible adsorption feet, strong adaptability, strong anti-subversion performance, and high friction to the wall. We deduce mechanics formulas and carry out a parametric design of the foot structure so that it can meet the requirement of robot wall climbing. We use Fluent to analyze the flow field of the adsorption foot and determine the motor speed that can provide a reliable adsorption force. Using the D-H matrix to plan gait, we also design a compound cycloid-based foot trajectory to reduce the impact between the HWCR and the wall. Experiments on the uneven wall and sloping wall show that the vehicle can walk with an ideal gait, and the resistance value of the servo on each joint is much lower than the critical value, which ensures the smooth movement of the HWCR.     

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.     

Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing

Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers, providers and the workers. Requisition for Edge Computing based items have been increasing tremendously. Apart from the advantages it holds, there remain lots of objections and restrictions, which hinders it from accomplishing the need of consumers all around the world. Some of the limitations are constraints on computing and hardware, functions and accessibility, remote administration and connectivity. There is also a backlog in security due to its inability to create a trust between devices involved in encryption and decryption. This is because security of data greatly depends upon faster encryption and decryption in order to transfer it. In addition, its devices are considerably exposed to side channel attacks, including Power Analysis attacks that are capable of overturning the process. Constrained space and the ability of it is one of the most challenging tasks. To prevail over from this issue we are proposing a Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing. The t-Distributed Stochastic Neighbor Embedding is one of the efficient dimensionality reduction technique that greatly decreases the size of the non-linear data. The three dimensional image data obtained from the system, which are connected with it, are dimensionally reduced, and then lightweight encryption algorithm is employed. Hence, the security backlog can be solved effectively using this method.     

基于主成分分析法的熟化菠萝蜜种子品质评价

以菠萝蜜种子为原料,研究水煮、汽蒸、焙烤3种熟化方式对其感官品质、色泽、质构、糊化度、基本营养含量、脂肪酶抑制率、质量损失率和微观结构的影响,并对其品质指标进行主成分分析。结果表明:水煮、汽蒸、焙烤3种熟化方式对不同指标有较大影响,水煮方式糊化度最高且质量损失率最大;焙烤显著加深菠萝蜜种子色泽且脂肪酶抑制率最大;汽蒸熟化后菠萝蜜种子感官评分最高;通过主成分分析可知,焙烤方式获得的品质评价综合得分最高,可作为菠萝蜜种子预熟化的较优选择。     

米糠的功能组分及其在化妆品中的应用

为促进我国米糠资源的多样化利用和开发,介绍了米糠中的功能组分及其在化妆品领域的应用。米糠的功能组分主要有米糠油、米糠蛋白、植物甾醇、米糠多糖、维生素等。米糠在化妆品中的应用主要有两种途径,一是直接使用,二是提取的功效成分的应用。米糠中提取的功效成分如米糠油、谷维素、植物甾醇、二十八烷醇、维生素、植酸等,可赋与化妆品保湿、防晒、抗衰老、抗氧化、抗皱等功效。     

计及RSC控制的DFIG短路电流直流分量解析表达

现有文献针对计及转子变流器(RSC)控制的双馈感应风电机组(DFIG)定子短路电流解析表达,将定子磁链当作一阶直流衰减分量或忽略功率外环控制。基于DFIG电压、磁链和RSC控制方程,得到定子电流关于定子电压和定子功率的传递函数,提出定子电流的精确解析表达式。基于RSC内、外环PI参数关系,推导直流分量衰减时间常数和角频率关于PI参数的表达式。分析了RSC内外环PI参数对定子电流直流衰减分量的影响。仿真结果验证了解析表达式的准确性,为PI参数选取和保护装置测量、整定提供依据。     

蒸制时间对粉蒸肉挥发性风味成分的影响

采用氮气吹扫/捕集-热脱附-气相色谱-质谱-嗅闻仪联用法对一种粉蒸肉产品加工过程中挥发性风味物质进行测定以确定最佳蒸制时间。结果显示:未加热和分别蒸制30、60、90 min 4个粉蒸肉样品中共检测到77种挥发性风味物质,各阶段分别为69、43、50种和56种,共有物质33种。在加工过程中,挥发性物质的总含量呈现逐渐增加的趋势,由加热前的1 646.39μg/kg增加至2 657.10μg/kg,且3个加热时间样品中挥发性物质种类也逐渐增多。但是通过气味活度值(odor activity value,OAV)分析,对风味具有贡献的化合物(OAV0.1)含量均呈现先增加后降低的趋势,在加热60 min时OAV最大。其中壬醛、癸醛和肉桂酸甲酯对粉蒸肉产品特征风味的形成贡献最大,构成了粉蒸肉的特征风味。主成分分析显示加热60 min样品与其他样品区分明显并且在第1、2主成分上贡献较高。总体来说蒸制60 min能使产品获得较好的风味。     

远志助眠功效成分提取及分析

通过直接观察睡眠法、高效液相色谱法(HPLC)测定小鼠海马区多巴胺(DA)含量,研究远志助眠的功效成分;通过理化检测、液质联用(HPLC-MS)、核磁共振(NMR)推断助眠有效成分的化学结构。结果表明:远志醇提物乙酸乙酯萃取物MCI柱70%乙醇洗脱物是助眠的有效部分,该部分分离得化合物A,检测得该化合物为2个互为同分异构体的皂苷类物质,该物质含有氮元素和1个由3个糖组成的糖链。     

Stability of electronic nose (e-nose) as determined by considering date-pits heated at different temperatures

Variability and sensitivity of a portable electronic nose (32 sensors) was assessed by considering different variables for measurement (i.e., reference, standard, first and second purges, sample draw time, waiting time for the volatiles released in the headspace, and mass of sample or headspace volume of the jar containing sample). In this study, dried date-pits were used a model sample. The highest stability was achieved when both reference and standard (i.e., dried date-pits, no heating treatment) were used before test sample measurement. Higher sample draw time more than 10 s significantly decreased the stability, whereas optimum second purge was observed at 50 s. Optimum time to generate volatile was observed as 24 h. A sample of 100 g increased the signal intensity compared to the 50 g sample for the 60°C and 100°C treated samples, while an opposite trend was observed for the 150°C treated sample. Finally, the responses of volatile components in date-pits heated at different temperatures (60°C, 100°C, and 150°C) were measured using the optimum operating conditions. Principal component analysis explored the relationships between the volatile features and classified date-pits heated at different temperatures. The results showed that an electronic nose was able to classify date-pits based on their volatile components generated by different degrees of heating (93.3% accuracy).