Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.     

Prediction of risk factors of cyberbullying-related words in Korea: Application of data mining using social big data

The study examined a decision tree analysis using social big data to conduct the prediction model on types of risk factors related to cyberbullying in Korea. The study conducted an analysis of 103,212 buzzes that had noted causes of cyberbullying and data were collected from 227 online channels, such as news websites, blogs, online groups, social network services, and online bulletin boards. Using opinion-mining method and decision tree analysis, the types of cyberbullying were sorted using SPSS 25.0. The results indicated that the total rate of types of cyberbullying in Korea was 44%, which consisted of 32.3% victims, 6.4% perpetrators, and 5.3% bystanders. According to the results, the impulse factor was also the greatest influence on the prediction of the risk factors and the propensity for dominance factor was the second greatest factor predicting the types of risk factors. In particular, the impulse factor had the most significant effect on bystanders, and the propensity for dominance factor was also significant in influencing online perpetrators. It is necessary to develop a program to diminish the impulses that were initiated by bystanders as well as victims and perpetrators because many of those bystanders have tended to aggravate impulsive cyberbullying behaviors.     

Teff (Eragrostis tef) processing,utilization and future opportunities: a review

The demand for food production has been constantly increasing due to rising population. In developed countries, for example, the emergence of regional production of old grains that are rarely utilized, along with the production of commonly consumed grains, has gained importance in recent years. These grains, known collectively as ancient or heirloom grains, have offered both farmers and consumers novel ways of cultivation and products with interesting taste, characteristics and nutritional value. Among the 30 000 plant species known, only five cereals currently provide more than 50% of the world's energy intake – bread wheat (Triticum aestivum), rice (Oryza sativa), sorghum (Sorghum bicolor), millets (Panicum sp.) and maize (Zea mays). The excessive utilization of these selected species has a great potential to cause genetic losses and difficulty in bridging future agricultural demands. Teff (Eragrostis tef), an ancient grain extensively cultivated in countries like Eritrea and Ethiopia, provides promising alternatives for new food uses since its nutritional value is significantly higher than most others cereal grains. The absence of gluten allows flexibility in food utilization since it can be directly substituted to gluten-containing products. The grain also offers an excellent balance of essential amino acids and minerals, which can fulfil the recommended daily intake and eliminates the need for fortification and enrichment. This review provides a general overview of the physical properties and nutritional composition of teff grains related to processing and applications in the food and feed industries. The current status of teff utilization, as well as the challenges in production and commercialization, and future opportunities is presented and discussed.     

Engineering grain boundary anisotropy to elucidate grain growth behavior in alumina

Current grain growth models have evolved to account for the relationship between grain boundary energy/mobility anisotropy and the five degrees of grain boundary character. However, the role of grain boundary networks on overall growth kinetics remains poorly understood. To experimentally investigate this problem, a highly textured Al₂O₃ was fabricated by colloidal casting in a strong magnetic field to engineer a unique spatial distribution of grain boundary character. Microstructural evolution was quantified and compared to an untextured sample. From this comparison, a prevalence of (0001)/(0001) terminated grain boundaries with anisotropic networks were identified in the textured sample. These boundaries and their networks were found to be driving grain growth at a faster rate than predicted by models. These findings will allow better modelling of grain growth in real systems by experimentally exploring the impact thereon of grain boundary plane anisotropy and relative energy/mobility differences between neighboring boundaries.     

Maximum Data Generation Rate Routing Protocol Based on Data Flow Controlling Technology for Rechargeable Wireless Sensor Networks

For rechargeable wireless sensor networks, limited energy storage capacity, dynamic energy supply, low and dynamic duty cycles cause that it is unpractical to maintain a fixed routing path for packets delivery permanently from a source to destination in a distributed scenario. Therefore, before data delivery, a sensor has to update its waking schedule continuously and share them to its neighbors, which lead to high energy expenditure for reestablishing path links frequently and low efficiency of energy utilization for collecting packets. In this work, we propose the maximum data generation rate routing protocol based on data flow controlling technology. For a sensor, it does not share its waking schedule to its neighbors and cache any waking schedules of other sensors. Hence, the energy consumption for time synchronization, location information and waking schedule shared will be reduced significantly. The saving energy can be used for improving data collection rate. Simulation shows our scheme is efficient to improve packets generation rate in rechargeable wireless sensor networks.     

Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

A single-phase algorithm for mining high utility itemsets using compressed tree structures

Mining high utility itemsets (HUIs) from transaction databases considers such factors as the unit profit and quantity of purchased items. Two-phase tree-based algorithms transform a database into compressed tree structures and generate candidate patterns through a recursive pattern-growth procedure. This procedure requires a lot of memory and time to construct conditional pattern trees. To address this issue, this study employs two compressed tree structures, namely, Utility Count Tree and String Utility Tree, to enumerate valid patterns and thus promote fast utility computation. Furthermore, the study presents an algorithm called single-phase utility computation (SPUC) that leverages these two tree structures to mine HUIs in a single phase by incorporating novel pruning strategies. Experiments conducted on both real and synthetic datasets demonstrate the superior performance of SPUC compared with IHUP, UP-Growth, and UP-Growth+ algorithms.     

[Zr0.73(Cu0.59Ni0.41)0.27]87Al13合金过冷液相区热塑性成形研究

为探究[Zr_(0.73)(Cu_(0.59)Ni_(0.41))_(0.27)]_(87)Al_(13)非晶合金的热塑性成形性能以及绘制其对应的热加工图谱,用Gleeble3500型热模拟压缩实验机对该非晶合金进行不同参数下的热模拟压缩实验。结果表明,合金在压缩过程中变形行为由牛顿流变演变为非牛顿流变;同时,过高或过低的热加工温度均能导致合金晶化;进一步对数据分析得到该合金在不同热塑性成形参数下的功率耗散图与流变失稳图,并绘制出相应的热加工谱图,谱图分析结果表明,该合金在温度为420与430℃、应变速率为10~(-3) s~(-1)时具有较高功率耗散系数且没有失稳区域,因此,合金可选的热塑性加工参数为温度420～430℃,应变速率10~(-3) s~(-1)。     

机载光电雷达它机试飞技术研究

机载光电雷达受平台及大气环境影响较大,而它机试飞能有效规避研制风险,是产品研制的必经阶段。本文介绍了机载光电雷达它机试飞试验系统组成、试验流程和数据处理等内容。经工程验证,实施过程满足任务需求,对其他光电系统的它机及本机试飞具有参考价值。