A Stochastic Hybrid Framework for Driver Behavior Modeling Based on Hierarchical Dirichlet Process

Scalability is one of the major issues for real-world Vehicle-to-Vehicle network realization. To tackle this challenge, a stochastic hybrid modeling framework based on a non-parametric Bayesian inference method, i.e., hierarchical Dirichlet process (HDP), is investigated in this paper. This framework is able to jointly model driver/vehicle behavior through forecasting the vehicle dynamical time-series. This modeling framework could be merged with the notion of model-based information networking, which is recently proposed in the vehicular literature, to overcome the scalability challenges in dense vehicular networks via broadcasting the behavioral models instead of raw information dissemination. This modeling approach has been applied on several scenarios from the realistic Safety Pilot Model Deployment (SPMD) driving data set and the results show a higher performance of this model in comparison with the zero-hold method as the baseline.Comment: This is the accepted version of the paper in 2018 IEEE 88th Vehicular Technology Conference (VTC2018-Fall) (references added, title and abstract modified

A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks

Situational awareness in vehicular networks could be substantially improved utilizing reliable trajectory prediction methods. More precise situational awareness, in turn, results in notably better performance of critical safety applications, such as Forward Collision Warning (FCW), as well as comfort applications like Cooperative Adaptive Cruise Control (CACC). Therefore, vehicle trajectory prediction problem needs to be deeply investigated in order to come up with an end to end framework with enough precision required by the safety applications' controllers. This problem has been tackled in the literature using different methods. However, machine learning, which is a promising and emerging field with remarkable potential for time series prediction, has not been explored enough for this purpose. In this paper, a two-layer neural network-based system is developed which predicts the future values of vehicle parameters, such as velocity, acceleration, and yaw rate, in the first layer and then predicts the two-dimensional, i.e. longitudinal and lateral, trajectory points based on the first layer's outputs. The performance of the proposed framework has been evaluated in realistic cut-in scenarios from Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable improvement in the prediction accuracy in comparison with the kinematics model which is the dominant employed model by the automotive industry. Both ideal and nonideal communication circumstances have been investigated for our system evaluation. For non-ideal case, an estimation step is included in the framework before the parameter prediction block to handle the drawbacks of packet drops or sensor failures and reconstruct the time series of vehicle parameters at a desirable frequency

A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles

Vehicle to Vehicle (V2V) communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative Adaptive Cruise Control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented and its performance is evaluated using realistic driving scenarios from Safety Pilot Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I

Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application

While the development of Vehicle-to-Vehicle (V2V) safety applications based on Dedicated Short-Range Communications (DSRC) has been extensively undergoing standardization for more than a decade, such applications are extremely missing for Vulnerable Road Users (VRUs). Nonexistence of collaborative systems between VRUs and vehicles was the main reason for this lack of attention. Recent developments in Wi-Fi Direct and DSRC-enabled smartphones are changing this perspective. Leveraging the existing V2V platforms, we propose a new framework using a DSRC-enabled smartphone to extend safety benefits to VRUs. The interoperability of applications between vehicles and portable DSRC enabled devices is achieved through the SAE J2735 Personal Safety Message (PSM). However, considering the fact that VRU movement dynamics, response times, and crash scenarios are fundamentally different from vehicles, a specific framework should be designed for VRU safety applications to study their performance. In this article, we first propose an end-to-end Vehicle-to-Pedestrian (V2P) framework to provide situational awareness and hazard detection based on the most common and injury-prone crash scenarios. The details of our VRU safety module, including target classification and collision detection algorithms, are explained next. Furthermore, we propose and evaluate a mitigating solution for congestion and power consumption issues in such systems. Finally, the whole system is implemented and analyzed for realistic crash scenarios

Predictors of malnutrition among Zahedan& rsquo s children age ranging from 2-5 years old in 2007-2008

چکیده: زمینه و هدف: ارزیابی دوره‌ای شاخص‌های تن سنجی یکی از مناسب ترین ابزارهای پایش وضعیت تغذیه کودکان در یک منطقه بوده و منبع اطلاعات خوبی به عنوان مرجع برای نظام مدیریت بخش سلامت کشور و شاخصی از عدالت در جامعه می‌باشد. هدف این مطالعه تعیین میزان شیوع هر کدام از شاخص های سوء تغذیه در کودکان 5-2 سال و پیشگویی کننده های آن می باشد. روش بررسی: در این مطالعه توصیفی- تحلیلی تعداد 1245 کودک 5-2 سال از 5 منطقه شهری شهر زاهدان به صورت چند مرحله ای انتخاب و بررسی شدند. اطلاعات با استفاده از پرونده ها و مصاحبه با مادر کودک جمع آوری شد. در این مطالعه سه شاخص وزن برای قد، قد برای سن و وزن برای سن به ترتیب به عنوان نمایه وضع تغذیه زمان حال یا لاغری، زمان گذشته یا کوتاه قدی و زمان حال و گذشته یا کم وزنی بر اساس 5/2-Z= مورد بررسی قرار گرفت. داده ها با استفاده از آزمون های t مستقل و مجذور کای و رگرسیون خطی مورد تجزیه و تحلیل قرار گرفت. یافته‌ها: میزان شیوع لاغری، کوتاه قدی وکم وزنی به ترتیب 2/4، 6/7 و 4/3 برآورد شد. در تحلیل تک متغیره، لاغری با وزن هنگام تولد و فاصله تولد، کوتاه قدی با وزن هنگام تولد، تحصیلات مادر، شغل پدر، نوع تغذیه زیر یکسال، سابقه عفونت، فاصله تولد و سن مادر و کم وزنی نیز با جنس، وزن هنگام تولد، نوع تغذیه زیر یکسال، سابقه عفونت، فاصله تولد و سن مادر رابطه معنی داری نشان دادند (05/0

Soda-Anthraquinone pulp from Malaysian cultivated Kenaf for linerboard production

The goal of this study was to prepare soda- anthraquinone pulp from kenaf whole stem and to compare the resultant core and bast pulps for linerboard production. Pulping was done under mild cooking conditions (active alkali 12-15%) with a cooking time of 30-90 min and a temperature of 160ºC. During the pulping process, kappa numbers ranged from 56.0 to 20.6, while total yields varied from 58.4 to 54.2% with a rejection rate of 2.3 to 0.1%. Based on the quality of pulp produced, kappa numbers 49.4 and 25.4 was selected as symbolic of high and low pulps respectively. The results of the study revealed significant difference between the properties of core, whole stem (KHK and KLK), and bast pulps. Core pulps with low freeness and high drainage time the study found produced sheets with greater density, tensile index, burst index and RCT, with lower light scattering coefficient and tear index than bast pulp. Whole stem pulps showed properties between those of core and bast pulps. Moreover, KLK with high drainage time produced papers with significantly higher strength properties than KHK

Detection of acrA, acrB, aac(6′)-Ib-cr, and qepA genes among clinical isolates of Escherichia coli and Klebsiella pneumoniae

Background: The distribution of drug resistance among clinical isolates of Escherichia coli and Klebsiella pneumoniae has limited the therapeutic options. The aim of this study was to report the prevalence of quinolone resistance genes among E. coli and K. pneumoniae clinical strains isolated from three educational hospitals of Tehran, Iran. Materials and methods: A total of 100 strains of E. coli from Labbafinejad and Taleghani Hospitals and 100 strains of K. pneumoniae from Mofid Children and Taleghani Hospitals were collected between January 2013 and May 2014. Antimicrobial susceptibility tests were done by disk diffusion method based on Clinical and Laboratory Standards Institute guidelines. Detection of qepA, aac(6′)-Ib-cr, acrA, and acrB genes was done by polymerase chain reaction (PCR). Results: In this study, fosfomycin and imipenem against E. coli and fosfomycin and tigecycline against K. pneumoniae had the best effect in antimicrobial susceptibility tests. PCR assay using specific primers demonstrated that the prevalence of qepA, aac(6′)-Ib-cr, acrA, and acrB genes among the 100 E. coli isolates was 0 (0%), 87 (87%), 92 (92%), and 84 (84%), respectively. The prevalence of qepA, aac(6′)-Ib-cr, acrA, and acrB genes among the 100 K. pneumoniae isolates was 4 (4%), 85 (85%), 94 (94%), and 87 (87%), respectively. Conclusion: The distribution of qepA, aac(6′)-Ib-cr, acrA, and acrB resistance determinants in E. coli and K. pneumoniae is a great concern. Therefore, infection control and prevention of spread of drug-resistant bacteria need careful management of medication and identification of resistant isolates