Data Location Mechanism in Agent-Based Autonomic Storage Systems

This paper introduces agent-based methodology to build a distributed autonomic storage system infrastructure, and an effectively negotiation mechanism based on agent is applied for data location. We present Availability-based Data Allocation （ADA） algorithm as a data placement strategy to achieve high efficient utilization of storage resources by employing multiple distributed storage resources. We use Bloom filter in each storage device to track the location of data. We present the data lookup strategy that small size of read request is handled directly, and large size of read request is handled by cooperation with storage devices.The performance evaluation shows that the data location mechanism is high available and can work well for heterogeneous autonomic storage systems.

Bus transit travel time reliability evaluation based on automatic vehicle location data

In order to provide important parameters for schedule designing, decision-making bases for transit operation management and references for passengers traveling by bus, bus transit travel time reliability is analyzed and evaluated based on automatic vehicle location （AVL） data. Based on the statistical analysis of the bus transit travel time, six indices including the coefficient of variance, the width of travel time distribution, the mean commercial speed, the congestion frequency, the planning time index and the buffer time index are proposed. Moreover, a framework for evaluating bus transit travel time reliability is constructed. Finally, a case study on a certain bus route in Suzhou is conducted. Results show that the proposed evaluation index system is simple and intuitive, and it can effectively reflect the efficiency and stability of bus operations. And a distinguishing feature of bus transit travel time reliability is the temporal pattern. It varies across different time periods.

MapReduce in the Cloud: Data-Location-Aware VM Scheduling

We have witnessed the fast-growing deployment of Hadoop,an open-source implementation of the MapReduce programming model,for purpose of data-intensive computing in the cloud.However,Hadoop was not originally designed to run transient jobs in which us ers need to move data back and forth between storage and computing facilities.As a result,Hadoop is inefficient and wastes resources when operating in the cloud.This paper discusses the inefficiency of MapReduce in the cloud.We study the causes of this inefficiency and propose a solution.Inefficiency mainly occurs during data movement.Transferring large data to computing nodes is very time-con suming and also violates the rationale of Hadoop,which is to move computation to the data.To address this issue,we developed a dis tributed cache system and virtual machine scheduler.We show that our prototype can improve performance significantly when run ning different applications.

Lightning Nowcasting with an Algorithm of Thunderstorm Tracking Based on Lightning Location Data over the Beijing Area

A thunderstorm tracking algorithm is proposed to nowcast the possibility of lightning activity over an area of concern by using the total lightning data and neighborhood technique.The lightning radiation sources observed from the Beijing Lightning Network(BLNET)were used to obtain information about the thunderstorm cells,which are significantly valuable in real-time.The boundaries of thunderstorm cells were obtained through the neighborhood technique.After smoothing,these boundaries were used to track the movement of thunderstorms and then extrapolated to nowcast the lightning approaching in an area of concern.The algorithm can deliver creditable results prior to a thunderstorm arriving at the area of concern,with accuracies of 63%,80%,and 91%for lead times of 30,15,and 5 minutes,respectively.The real-time observations of total lightning appear to be significant for thunderstorm tracking and lightning nowcasting,as total lightning tracking could help to fill the observational gaps in radar reflectivity due to the attenuation by hills or other obstacles.The lightning data used in the algorithm performs well in tracking the active thunderstorm cells associated with lightning activities.

Use of community mobile phone big location data to recognize unusual patterns close to a pipeline which may indicate unauthorized activities and possible risk of damage

Damage caused by people and organizations unconnected with the pipeline management is a major risk faced by pipelines,and its consequences can have a huge impact.However,the present measures to monitor this have major problems such as time delays,overlooking threats,and false alarms.To overcome the disadvantages of these methods,analysis of big location data from mobile phone systems was applied to prevent third-party damage to pipelines,and a third-party damage prevention system was developed for pipelines including encryption mobile phone data,data preprocessing,and extraction of characteristic patterns.By applying this to natural gas pipelines,a large amount of location data was collected for data feature recognition and model analysis.Third-party illegal construction and occupation activities were discovered in a timely manner.This is important for preventing third-party damage to pipelines.

SHT-based public auditing protocol with error tolerance in FDL-empowered IoVs

With the intelligentization of the Internet of Vehicles(lovs),Artificial Intelligence(Al)technology is becoming more and more essential,especially deep learning.Federated Deep Learning(FDL)is a novel distributed machine learning technology and is able to address the challenges like data security,privacy risks,and huge communication overheads from big raw data sets.However,FDL can only guarantee data security and privacy among multiple clients during data training.If the data sets stored locally in clients are corrupted,including being tampered with and lost,the training results of the FDL in intelligent IoVs must be negatively affected.In this paper,we are the first to design a secure data auditing protocol to guarantee the integrity and availability of data sets in FDL-empowered IoVs.Specifically,the cuckoo filter and Reed-Solomon codes are utilized to guarantee error tolerance,including efficient corrupted data locating and recovery.In addition,a novel data structure,Skip Hash Table(SHT)is designed to optimize data dynamics.Finally,we illustrate the security of the scheme with the Computational Diffie-Hellman(CDH)assumption on bilinear groups.Sufficient theoretical analyses and performance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.

Spatio-temporal characteristics of human activities using location big data in Qilian Mountain National Park

Human activities significantly impact the environment.Understanding the patterns and distribution of these activities is crucial for ecological protection.With location-based technology advancement,big data such as location and trajectory data can be used to analyze human activities on finer temporal and spatial scales than traditional remote sensing data.In this study,Qilian Mountain National Park(QMNP)was chosen as the research area,and Tencent location data were used to construct time series data.Time series clustering and decomposition were performed,and the spatio-temporal distribution characteristics of human activities in the study area were analyzed in conjunction with GPS trajectory data and land use data.The study found two distinct human activity patterns,Pattern A and Pattern B,in QMNP.Compared to Pattern B,Pattern A had a higher volume of location data and clear nighttime peaks.By incorporating land use and trajectory data,we conclude that Pattern A and Pattern B represent the activity patterns of the resident and tourist populations,respectively.Moreover,the study identified seasonal variations in human activities,with human activity in summer being approximately two hours longer than in winter.We also conducted an analysis of human activities in different counties within the study area.

Development of an automatic polishing system for aluminum wheel-hub surface

To improve polishing quality and cope with the shortage of skilled workers for aluminum wheel-hub surface polishing, an automatic surface polishing system with hierarchical control based on the teaching-playback method was presented. Multi-axis cutter location data (CL data) were generated with the teaching method. First, a helical tool path and a flexible polishing tool were adopted to achieve high quality and high efficiency; next, the initial irregular data were processed into continuous polishing CL data. The important factor affecting polishing quality, namely the interpolation cycle in the multi-axis CL data was calculated based on a constant removal rate. Results from polishing experiments show that the quality of automatic machine polishing is better and stabler than manual polishing.

Quantitative estimates of collective geo-tagged human activities in response to typhoon Hato using location-aware big data

Location-aware big data from social media have been widely used to quantitatively characterize natural disasters and disaster-induced losses.It is not clear how human activities collectively respond to a disaster.In this study,we examined the collective human activities in response to Typhoon Hato at multi spatial scales using aggregated location request data.We proposed a Multilevel Abrupt Changes Detection(MACD)methodological framework to detect and characterize the abrupt changes in location requests in response to Typhoon Hato.Results show that,at the grid level,most anomaly grids were located within a radius of 53 km around the typhoon trajectory.At the city level,there are significant spatial difference in terms of the human activity recovery duration(230 h on average).At the subnational level,the absolute magnitude of abrupt location request changes is strongly correlated with the typhoon-induced economic losses and the population affected.

Machining Robot with Vibrational Motion and 3D Printer-like Data Interface

In this paper, a vibration motion control is proposed and implemented on a foamed polystyrene machining robot to suppress the generation of undesirable cusp marks, and the basic performance of the controller is verified through machining experiments of foamed polystyrene. Then, a 3 dimensional （3D） printer-like data interface is proposed for the machining robot. The 3D data inter- face enables to control the machining robot directly using stereolithography （STL） data without conducting any computer-aided man- ufacturing （CAM） process. This is done by developing a robotic preprocessor that helps to remove the need for the conventional CAM process by directly converting the STL data into cutter location source data called cutter location （CL） or cutter location source （CLS） data. The STL is a file format proposed by 3D systems, and recently is supported by many computer aided design （CAD）/CAM soft- waxes. The STL is widely used for rapid prototyping with a 3D printer which is a typical additive manufacturing system. The STL deals with a triangular representation of a curved surface geometry. The developed 3D printer-like data interface allows to directly control the machining robot through a zigzag path, rectangular spiral path and circular spiral path generated according to the information included in STL data. The effectiveness and usefulness of the developed system are demonstrated through actual machining experiments.

Visualization and analysis of local and distant population flows on the Qinghai-Tibet Plateau using crowd-sourced data

Human migration between cities is one important aspect of spatial interaction that not only reflects urban attractiveness but also denotes interactions amongst agglomerations.We therefore implemented a web-based visualization system to analyze and interactively explore local and distant population flow patterns between cities on the Qinghai-Tibet Plateau(QTP).We utilized 2017 Tencent population flow data from which we initially constructed inbound and outbound vectors for cities on the QTP.We then used multidimensional scaling to examine and visualize migration patterns and similarities between cities.Results reveal the presence of six local and three distant human mobility patterns on the QTP as well as average summer monthly migrations more than twice the level of those in the winter.