Data Mining Strategies for Real-Time Control in New York City
استراتژی داده کاوی برای کنترل زمان واقعی در شهر نیویورک-2105
The Data Mining System (DMS) at New York City Department of Transportation (NYCDOT) mainly consists of four database systems for traffic and pedestrian/bicycle volumes, crash data, and signal timing plans as well as the Midtown in Motion (MIM) systems which are used as part of the NYCDOT Intelligent Transportation System (ITS) infrastructure. These database and control systems are operated by different units at NYCDOT as an independent database or operation system. New York City experiences heavy traffic volumes, pedestrians and cyclists in each Central Business District (CBD) area and along key arterial systems. There are consistent and urgent needs in New York City for real-time control to improve mobility and safety for all users of the street networks, and to provide a timely response and management of random incidents. Therefore, it is necessary to develop an integrated DMS for effective real-time control and active transportation management (ATM) in New York City. This paper will present new strategies for New York City suggesting the development of efficient and cost-effective DMS, involving: 1) use of new technology applications such as tablets and smartphone with Global Positioning System (GPS) and wireless communication features for data collection and reduction; 2) interface development among existing database and control systems; and 3) integrated DMS deployment with macroscopic and mesoscopic simulation models in Manhattan. This study paper also suggests a complete data mining process for real-time control with traditional static data, current real timing data from loop detectors, microwave sensors, and video cameras, and new real-time data using the GPS data. GPS data, including using taxi and bus GPS information, and smartphone applications can be obtained in all weather conditions and during anytime of the day. GPS data and smartphone application in NYCDOT DMS is discussed herein as a new concept. © 2014 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of Elhadi M. Shakshu Keywords: Data Mining System (DMS), New York City, real-time control, active transportation management (ATM), GPS data
Quantile regression in big data: A divide and conquer based strategy
رگرسیون کمی در داده های بزرگ: یک استراتژی مبتنی بر تقسیم و غلبه-2020
Quantile regression, which analyzes the conditional distribution of outcomes given a set of covariates, has been widely used in many fields. However, the volume and velocity of big data make the estimation of quantile regression model extremely difficult due to the intensive computation and the limited storage. Based on divide and conquer strategy, a simple and efficient method is proposed to address this problem. The proposed approach only keeps summary statistics of each data block and then can use them to reconstruct the estimator of the entire data with asymptotically negligible approximation error. This property makes the proposed method particularly appealing when data blocks are retained in multiple servers or come in the form of data stream. Furthermore, the proposed estimator is shown to be consistent and asymptotically as efficient as the estimating equation estimator calculated using the entire data together when certain conditions hold. The merits of the proposed method are illustrated using both simulation studies and real data analysis
Keywords: Data stream | Divide and conquer | Estimating equation | Massive data sets | Quantile regression
Differential shedding: A study of the fiber transfer mechanisms of blended cotton and polyester textiles
ریختن دیفرانسیل: مطالعه مکانیسم های انتقال فیبر پارچه های مخلوط پنبه و پلی استر-2020
One of the primary interests of forensic sciences is the study of traces, better conceived as silent witnesses to criminal activity whose existence is attributable to Locard’s principle. Thus, textile fibers are commonly exploited as they are easily transferred during contact which can vary in intensity depending upon the type of activity that occurred. Regardless, current knowledge pertaining to fiber transfer mechanisms, particularly in regards to blended textiles, is limited. It is recognized that the intensity of the contact, the type of textile as well as the size and type of fibers composing it have a significant influence on the amount of fibers transferred. However, when the donor textile is blended (eg. 50% cotton, 50% polyester), it often happens that one of the two types of fibers is transferred in greater proportion to the receiving surface (eg. 80% cotton and 20% polyester). The percentages indicated on the manufactured label are however not representative of the respective proportions (based on the number of fibers) of each type of fiber composing the fabric, but rather the weight of each respective type of fiber used to fabricate the garment. Therefore, the amount of collected fibers (traces) cannot be easily correlated to the proportions indicated on the manufactured label used to describe the textile. The objective of this study was to test the transfer capacities of blended textiles of different cotton and polyester proportions by performing several simulations under controlled conditions (i.e. contact between two textiles with a constant force and speed). The results were then correlated to the fiber type, morphology, and size. Overall, the project contributes to improving the comprehension of fiber transfer mechanisms, and provides insight on the quantity and the proportions of fibers capable of being transferred between the donor and the recipient textiles following a specific type of action and contact (legitimate or otherwise).
Keywords: Blended textiles | Textile characteristics | Shedding capacity | Primary transfer simulation | Fiber proportions
Bias reduction in the population size estimation of large data sets
کاهش تمایل در برآورد اندازه جمعیت مجموعه داده های بزرگ-2020
Estimation of the population size of large data sets and hard to reach populations can be a significant problem. For example, in the military, manpower is limited and the manual processing of large data sets can be time consuming. In addition, accessing the full population of data may be restricted by factors such as cost, time, and safety. Four new population size estimators are proposed, as extensions of existing methods, and their performances are compared in terms of bias with two existing methods in the big data literature. These would be particularly beneficial in the context of time-critical decisions or actions. The comparison is based on a simulation study and the application to five real network data sets (Twitter, LiveJournal, Pokec, Youtube, Wikipedia Talk). Whilst no single estimator (out of the four proposed) generates the most accurate estimates overall, the proposed estimators are shown to produce more accurate population size estimates for small sample sizes, but in some cases show more variability than existing estimators in the literature.
Keywords: Relative bias | Twitter | Size estimator | Youtube | Random walk sampling
Performance assessment of coupled green-grey-blue systems for Sponge City construction
ارزیابی عملکرد سیستم های سبز و خاکستری-آبی همراه برای ساخت و ساز شهر اسفنجی-2020
In recent years, Sponge City has gained significant interests as a way of urban water management. The kernel of Sponge City is to develop a coupled green-grey-blue system which consists of green infrastructure at the source, grey infrastructure (i.e. drainage system) at the midway and receiving water bodies as the blue part at the terminal. However, the current approaches for assessing the performance of Sponge City construction are confined to green-grey systems and do not adequately reflect the effectiveness in runoff reduction and the impacts on receiving water bodies. This paper proposes an integrated assessment framework of coupled green-grey-blue systems on compliance of water quantity and quality control targets in Sponge City construction. Rainfall runoff and river system models are coupled to provide quantitative simulation evaluations of a number of indicators of landbased and river quality. A multi-criteria decision-making method, i.e., Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is adopted to rank design alternatives and identify the optimal alternative for Sponge City construction. The effectiveness of this framework is demonstrated in a typical plain river network area of Suzhou, China. The results demonstrate that the performance of Sponge City strategies increases with large scale deployment under smaller rainfall events. In addition, though surface runoff has a dilution effect on the river water quality, the control of surface pollutants can play a significant role in the river water quality improvement. This framework can be applied to Sponge City projects to achieve the enhancement of urban water management.
Keywords: Low impact development | Sponge City | Green-grey-blue system | Performance assessment | TOPSIS
A robust co-state predictive model for energy management of plug-in hybrid electric bus
یک مدل پیش بینی شده مشترک قدرتمند برای مدیریت انرژی اتوبوس برقی هیبریدی پلاگین-2020
This paper proposes a robust co-state predictive model for Pontryagin’s Minimum Principle (PMP)-based energy management of plug-in hybrid electric bus (PHEB). The main innovation is that the robust costate predictive model is only expressed by a simplified formula. Moreover, it is exclusively designed by the Design For Six Sigma (DFSS) method in consideration of noises of driving cycles and stochastic vehicle mass. Because the DFSS strives to minimize the weighted sum of mean and standard deviation of fuel consumption, the proposed strategy can simultaneously improve the fuel economy of the PHEB and its robustness. The DFSS results show that the coefficients of the robust co-state predictive model can be found; the simulation results demonstrate that the proposed strategy has similar fuel economy to dynamic programming (DP); the hardware-in-loop (HIL) results demonstrate that the proposed strategy has good real-time control performance, and can averagely improve the fuel economy by 35.19% compared to a rule-based control strategy.
Keywords: Plug-in hybrid electric bus | Energy management | PMP | Co-state predictive model | Design for six sigma
Recovery & identification of human Y-STR DNA from immatures of chrysomya albiceps (Diptera: Calliphoridae). Simulation of sexual crime investigation involving victim corpse in state of decay
ترمیم و شناسایی DNA Y-STR انسانی از ناخالصی های کریسومیا آلبیسپس (Diptera: Calliphoridae). شبیه سازی تحقیقات مربوط به جرم و جنایات مربوط به جسد قربانی در حالت پوسیدگی-2020
The number of sexual crimes in Brazil, as in several other countries, is very high. In many of these crimes the women raped are murdered and their bodies are found days later, in an advanced state of decomposition, with intense cadaverous fauna. Forensic Entomology studies insects and other arthropods that can be used in the expert analysis of various types of crimes. Diptera, the order of insects that comprises the two-winged or true flies, represents one of the largest known groups of insects and is the principal source of cadaveric entomofauna. Members of its Calliphoridae family are observed in cadavers in all phases of decomposition. The retrieval and identification of human Y-STR DNA from the gastrointestinal tract of Calliphoridae species Chrysomya albiceps maggots and pupae can provide a good tool for the gathering of evidence in sexual crime investigations involving rape and death, in which the abandoned victims body is found in a putrefied state. In this study, the animal model used was a female pig, Sus scrofa, which was sacrificed in a forested area with three shots from a 0.40 calibre Taurus pistol, and inoculated with semen to its anal and vaginal regions, simulating rape and homicide. During decomposition, 20–80 maggots were collected every 24 h and preserved in 70 % alcohol, totalling 289 maggots and 157 pupae (446 immatures) over a period of 14 days (336 h) of decomposition. Each maggot was then dissected for removal of the digestive tract, which was placed in extraction buffer. The molecular phase proceeded with extraction, quantification, amplification and capillary electrophoresis of samples, testing 16 STR loci of the Y chromosome. It was possible to establish a partial Y-STR DNA profile, with the amplification of up to eight sites, by considering a combination of the samples taken at hours 144 h, 168 h, 192 h, 216 h, 240 h, 288 h, 312 h and 336 h..
Keywords: Forensic entomology | Forensic genetics | Sex crimes | Rape | Murder | Criminal profiling
Cooperative control strategy for plug-in hybrid electric vehicles based on a hierarchical framework with fast calculation
استراتژی کنترل تعاونی برای وسایل نقلیه برقی هیبریدی پلاگین بر اساس یک چارچوب سلسله مراتبی با محاسبه سریع-2020
Developing optimal control strategies with capability of real-time implementation for plug-in hybrid electric vehicles (PHEVs) has drawn explosive attention. In this study, a novel hierarchical control framework is proposed for PHEVs to achieve the instantaneous vehicle-environment cooperative control. The mobile edge computation units (MECUs) and the on-board vehicle control units (VCUs) are included as the distributed controllers, which enable vehicle-environment cooperative control and reduce the computation intensity on the vehicle by transferring partial work from VCUs to MECUs. On this basis, a novel cooperative control strategy is designed to successively achieve the energy management planned by the iterative dynamic programming (IDP) in MECUs and the energy utilization management achieved by the model predictive control (MPC) algorithm in the VCU. The performance of raised control strategy is validated by simulation analysis, highlighting that the cooperative control strategy can achieve superior performance in real-time application that is close to the global optimization results solved offline.
Keywords: Cooperative control strategy | Hierarchical framework | Iterative dynamic programming (IDP) | Model predictive control (MPC) | Plug-in hybrid electric vehicles (PHEVs)
A real-time blended energy management strategy of plug-in hybrid electric vehicles considering driving conditions
یک استراتژی مدیریت انرژی ترکیبی از زمان واقعی خودروهای برقی پلاگین با توجه به شرایط رانندگی-2020
In this study, a blended energy management strategy considering influences of driving conditions is proposed to improve the fuel economy of plug-in hybrid electric vehicles. To attain it, dynamic programming is firstly applied to solve and quantify influences of different driving conditions and driving distances. Then, the driving condition is identified by the K-means clustering algorithm in real time with the help of Global Positioning System and Geographical Information System. A blended energy management strategy is proposed to achieve the real-time energy allocation of the powertrain with incorporation of the identified driving conditions and the extracted rules, which includes the engine starting scheme, gear shifting schedule and torque distribution strategy. Simulation results reveal that the proposed strategy can effectively adapt to different driving conditions with the dramatic improvement of fuel economy and the decrement of calculation intensity and highlight the feasibility of real-time implementation
Keywords: Plug-in hybrid electric vehicles | Energy management strategy | Global optimization | Driving condition | Equivalent driving distance coefficient
To Hop or not to Hop: Exceptions in the FCS Diffusion Law
به هاپ آری یا نه : استثنائات در قانون پخش FCS-2020
Diffusion obstacles in membranes have not been directly visualized because of fast membrane dynamics and the occurrence of subresolution molecular complexes. To understand the obstacle characteristics, mobility-based methods are often used as an indirect way of assessing the membrane structure. Molecular movement in biological plasma membranes is often characterized by anomalous diffusion, but the exact underlying mechanisms are still elusive. Imaging total internal reflection fluorescence correlation spectroscopy (ITIR-FCS) is a well-established mobility-based method that provides spatially resolved diffusion coefficient maps and is combined with FCS diffusion law analysis to examine subresolution membrane organization. In recent years, although FCS diffusion law analysis has been instrumental in providing new insights into the membrane structure below the optical diffraction limit, there are certain exceptions and anomalies that require further clarification. To this end, we correlate the membrane structural features imaged by atomic force microscopy (AFM) with the dynamics measured using ITIR-FCS. We perform ITIR-FCS measurements on supported lipid bilayers (SLBs) of various lipid compositions to characterize the anomalous diffusion of lipid molecules in distinct obstacle configurations, along with the high-resolution imaging of the membrane structures with AFM. Furthermore, we validate our experimental results by performing simulations on image grids with experimentally determined obstacle configurations. This study demonstrates that FCS diffusion law analysis is a powerful tool to determine membrane heterogeneities implied from dynamics measurements. Our results corroborate the commonly accepted interpretations of imaging FCS diffusion law analysis, and we show that exceptions happen when domains reach the percolation threshold in a biphasic membrane and a network of domains behaves rather like a meshwork, resulting in hop diffusion.