scholarly journals Building Intelligence in Automated Traffic Signal Performance Measures with Advanced Data Analytics

2018 ◽  
Vol 2672 (18) ◽  
pp. 154-166 ◽  
Author(s):  
Tingting Huang ◽  
Subhadipto Poddar ◽  
Cristopher Aguilar ◽  
Anuj Sharma ◽  
Edward Smaglik ◽  
...  
Keyword(s):  
Performance Measures ◽  
Data Analytics ◽  
Traffic Signal ◽  
Future Research ◽  
Data Quality Control ◽  
Performance Measurements ◽  
Data Logging ◽  
Efficient Manner ◽  
High Resolution Data ◽  
Traffic Flow Theory

Automated traffic signal performance measures (ATSPMs) are designed to equip traffic signal controllers with high-resolution data-logging capabilities which may be used to generate performance measures. These measures allow practitioners to improve operations as well as to maintain and operate their systems in a safe and efficient manner. While they have changed the way that operators manage their systems, several shortcomings of ATSPMs, as identified by signal operators, include a lack of data quality control and the extent of resources required to use the tool properly for system-wide management. To address these shortcomings, intelligent traffic signal performance measurements (ITSPMs) are presented in this paper, using the concepts of machine learning, traffic flow theory, and data visualization to reduce the operator resources needed for overseeing data-driven ATSPMs. In applying these concepts, ITSPMs provide graphical tools to identify and remove logging errors and data from bad sensors, to determine trends in demand intelligently, and to address the question of whether or not coordination may be needed at an intersection. The focus of ATSPMs and ITSPMs on performance measures for multimodal users is identified as a pressing need for future research.

scholarly journals Portable Take-Home System Enables Proportional Control and High-Resolution Data Logging with a Multi-Degree-of-Freedom Bionic Arm

2020 ◽  
Author(s):  
Mark Brinton ◽  
Elliott Barcikowski ◽  
Tyler Davis ◽  
Michael Paskett ◽  
Jacob George ◽  
...  
Keyword(s):  
Force Sensor ◽  
Degree Of Freedom ◽  
Future Research ◽  
High Temporal Resolution ◽  
Data Logging ◽  
Essential Information ◽  
Proportional Control ◽  
High Resolution Data ◽  
Manual Tasks ◽  
At Home

AbstractThis paper describes a portable, prosthetic control system for at-home use of an advanced bionic arm. The system uses a modified Kalman filter to provide 6 degree-of-freedom, real-time, proportional control. We describe (a) how the system trains motor control algorithms for use with an advanced bionic arm, and (b) the system’s ability to record an unprecedented and comprehensive dataset of EMG, hand positions and force sensor values. Intact participants and a transradial amputee used the system to perform activities-of-daily-living, including bi-manual tasks, in the lab and at home. This technology enables at-home dexterous bionic arm use, and provides a high-temporal resolution description of daily use—essential information to determine clinical relevance and improve future research for advanced bionic arms.

Data-Driven Ranking of Coordinated Traffic Signal Systems for Maintenance and Retiming

2018 ◽  
Vol 2672 (18) ◽  
pp. 167-178 ◽  
Author(s):  
Christopher M. Day ◽  
Howell Li ◽  
James R. Sturdevant ◽  
Darcy M. Bullock
Keyword(s):  
Performance Measures ◽  
Capacity Allocation ◽  
Traffic Signal ◽  
System Level ◽  
High Resolution Data ◽  
Individual Agency ◽  
Signal Systems ◽  
Analysis Process ◽  
Quantitative Performance ◽  
Further Development

Automated traffic signal performance measures (ATSPMs) have been deployed with increasing frequency. At present, the existing ATSPMs are focused on the performance of individual movements or intersections. As the number of ATSPM users has increased, a need for system-level metrics has emerged. This paper proposes a method of evaluating corridor performance at the system level using high-resolution data. The method is demonstrated for eight signalized corridors in Indiana, including 87 intersections. This method develops five subscores for the areas of communication, detection, safety, capacity allocation, and progression; these five interrelated aspects of performance are each given a category subscore based on quantitative performance measures, with scales appropriate to the context of the operation. An overall score for each corridor is determined from the lowest subscore of each of the five areas. This approach simplifies the analysis process, as opposed to examining several hundred individual movements as currently would be required using ATSPM tools that are commonly available at present. The methodology is presented as a prototype for further development and adaptation to individual agency objectives and data sources.

Framework for Quantitative Annual Evaluation of Traffic Signal Systems

2017 ◽  
Vol 2619 (1) ◽  
pp. 20-35 ◽  
Author(s):  
Danilo Radivojevic ◽  
Aleksandar Stevanovic
Keyword(s):  
Qualitative Evaluation ◽  
Evaluation Process ◽  
Traffic Monitoring ◽  
Traffic Signal ◽  
Evaluation Methodology ◽  
Data Logging ◽  
Data Types ◽  
Confidence Measure ◽  
High Resolution Data ◽  
Signal Systems

The evaluation of traffic signal systems on an agency level can be of great importance for identifying problems, self-assessing, budgeting, creating a strategy for future steps, and so on. The most famous similar effort of this type is the National Traffic Signal Report Card, which is used as an evaluation methodology for agencies countrywide. The main difference in the proposed methodology is that it steps away from qualitative evaluation and grading and presents a new set of procedures for implementation of quantitative—and therefore more unbiased—evaluation methodology. The proposed methodology should enable self-evaluation and comparison between agencies in relation to agency management, traffic signal operations, signal timing practices, traffic monitoring, data collection, and maintenance. For two agencies, the numerical and logical values of the answers are used in the evaluation process to obtain preliminary results that are displayed with a confidence measure to explain that process. The proposed methodology shows potential, especially if the number of the available data types increases with the introduction of high-resolution data-logging controllers into regular operations. With those additional performance measures, the methodology could be used for tracking the results of operating traffic signals by government institutions or private companies.

scholarly journals Edge Computing – “An Enabling Technology for Industrial IoT (IIoT) Devices” – Exploring Its Challenges and Security Issues

2020 ◽  
Vol 8 (6S4) ◽  
pp. 6-8
Keyword(s):  
Data Storage ◽  
Data Analytics ◽  
High Volume ◽  
Edge Computing ◽  
Security And Privacy ◽  
Future Research ◽  
Efficient Manner ◽  
Smart Healthcare ◽  
Industrial Iot ◽  
Iot Devices

Internet of Things (IoT) is latest technology these days which generates high volume of data. Efficient use of data analytics techniques on discrete data using Cloud Computing provides significant and precise information. In view of the previously used applications, an application that is IoT enabled such as environmental monitoring, application for navigation and smart healthcare systems being developed with different requirements such as portability, fast and real-time response etc. However, the typical architecture of cloud system cannot fulfill these requirements as the processing of the data being distributed across the world remotely from physical location of installed IoT devices. Hence, the concept of edge computing emerged to perform data storage and processing at the extreme end devices that is nearer to data collection sources than the cloud storage. This makes applications computationally intelligent and location notified. But edge computing suffers from many challenges related to security and privacy when it is been applied to data analytics in association with IoT devices. The literature collected till date still deficient in detail review on the advancements in security and safe data analytics techniques used in edge computing. This paper, first introduce the various concepts and characteristics related to edge computing, and then we try to propose solutions for performing data analytics in a secured and efficient manner, thereafter reviewing the underlying some security attacks in the field of edge computing. Based on our literature survey, we have highlighted current open issues and some future research areas in this field

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajesh Kumar Singh ◽  
Saurabh Agrawal ◽  
Abhishek Sahu ◽  
Yigit Kazancoglu
Keyword(s):  
Health Care ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Sector ◽  
Future Research ◽  
Research Gaps ◽  
Content Type

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

An experimental investigation of the impact of using big data analytics on customers’ performance measurement

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marwa Rabe Mohamed Elkmash ◽  
Magdy Gamal Abdel-Kader ◽  
Bassant Badr El Din
Keyword(s):  
Big Data ◽  
Performance Measurement ◽  
Data Analytics ◽  
Positive Impact ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Future Research ◽  
Web Based ◽  
Content Type ◽  
The Impact

Purpose This study aims to investigate and explore the impact of big data analytics (BDA) as a mechanism that could develop the ability to measure customers’ performance. To accomplish the research aim, the theoretical discussion was developed through the combination of the diffusion of innovation theory with the technology acceptance model (TAM) that is less developed for the research field of this study. Design/methodology/approach Empirical data was obtained using Web-based quasi-experiments with 104 Egyptian accounting professionals. Further, the Wilcoxon signed-rank test and the chi-square goodness-of-fit test were used to analyze data. Findings The empirical results indicate that measuring customers’ performance based on BDA increase the organizations’ ability to analyze the customers’ unstructured data, decrease the cost of customers’ unstructured data analysis, increase the ability to handle the customers’ problems quickly, minimize the time spent to analyze the customers’ data and obtaining the customers’ performance reports and control managers’ bias when they measure customer satisfaction. The study findings supported the accounting professionals’ acceptance of BDA through the TAM elements: the intention to use (R), perceived usefulness (U) and the perceived ease of use (E). Research limitations/implications This study has several limitations that could be addressed in future research. First, this study focuses on customers’ performance measurement (CPM) only and ignores other performance measurements such as employees’ performance measurement and financial performance measurement. Future research can examine these areas. Second, this study conducts a Web-based experiment with Master of Business Administration students as a study’s participants, researchers could conduct a laboratory experiment and report if there are differences. Third, owing to the novelty of the topic, there was a lack of theoretical evidence in developing the study’s hypotheses. Practical implications This study succeeds to provide the much-needed empirical evidence for BDA positive impact in improving CPM efficiency through the proposed framework (i.e. CPM and BDA framework). Furthermore, this study contributes to the improvement of the performance measurement process, thus, the decision-making process with meaningful and proper insights through the capability of collecting and analyzing the customers’ unstructured data. On a practical level, the company could eventually use this study’s results and the new insights to make better decisions and develop its policies. Originality/value This study holds significance as it provides the much-needed empirical evidence for BDA positive impact in improving CPM efficiency. The study findings will contribute to the enhancement of the performance measurement process through the ability of gathering and analyzing the customers’ unstructured data.

scholarly journals Workshop on Emerging Technology and Data Analytics for Behavioral Health (Preprint)

2017 ◽  
Author(s):  
David Kotz ◽  
Sarah E Lord ◽  
A James O'Malley ◽  
Luke Stark ◽  
Lisa A. Marsch
Keyword(s):  
Behavioral Health ◽  
Data Analytics ◽  
Health Behavior Change ◽  
Emerging Technology ◽  
Mobile Technologies ◽  
Dartmouth College ◽  
Future Research ◽  
Diverse Range ◽  
User Experience Design ◽  
Health Technologies

UNSTRUCTURED Wearable and portable digital devices can support self-monitoring for patients with chronic medical conditions, individuals seeking to reduce stress, and people seeking to modify health-related behaviors such as substance use or overeating. The resulting data may be used directly by a consumer, or shared with a clinician for treatment, a caregiver for assistance, or a health coach for support. The data can also be used by researchers to develop and evaluate just-in-time interventions that leverage mobile technology to help individuals manage their symptoms and behavior in real time and as needed. Such wearable systems have huge potential for promoting delivery of anywhere-anytime health care, improving public health, and enhancing the quality of life for many people. The Center for Technology and Behavioral Health at Dartmouth College, a P30 “Center of Excellence” supported by the National Institute on Drug Abuse at the National Institutes of Health, conducted a workshop in February 2017 on innovations in emerging technology, user-centered design, and data analytics for behavioral health, with presentations by a diverse range of experts in the field. The workshop focused on wearable and mobile technologies being used in clinical and research contexts, with an emphasis on applications in mental health, addiction, and health behavior change. In this paper, we summarize the workshop panels on mobile sensing, user experience design, statistics and machine learning, and privacy and security, and conclude with suggested research directions for this important and emerging field of applying digital approaches to behavioral health. Workshop insights yielded four key directions for future research: (1) a need for behavioral health researchers to work iteratively with experts in emerging technology and data analytics, (2) a need for research into optimal user-interface design for behavioral health technologies, (3) a need for privacy-oriented design from the beginning of a novel technology, and (4) the need to develop new analytical methods that can scale to thousands of individuals and billions of data points.

scholarly journals From Single Cells to Flowers – Biological Complexity Driving Plant Reproductive Development

2021 ◽  
Author(s):  
Isabel Schwarz ◽  
Manuel Neumann ◽  
Rosario Vega ◽  
Xiaocai Xu ◽  
Letizia Cornaro ◽  
...  
Keyword(s):  
Science Communication ◽  
Data Science ◽  
Single Cells ◽  
Reproductive Development ◽  
Floral Organ ◽  
Future Research ◽  
High Resolution Data ◽  
Interdisciplinary Approaches ◽  
Share Data ◽  
Plant Reproductive Development

The rise of data science in biology stimulates interdisciplinary collaborations to address fundamental questions. Here, we report the outcome of the first SINFONIA symposium focused on revealing the mechanisms governing plant reproductive development across biological scales. The intricate and dynamic target networks of known regulators of flower development remain poorly understood. To analyze development from the genome to the final floral organ morphology, high-resolution data that capture spatiotemporal regulatory activities are necessary and require advanced computational methods for analysis and modeling. Moreover, frameworks to share data, practices and approaches that facilitate the combination of varied expertise to advance the field are called for. Training young researchers in interdisciplinary approaches and science communication offers the opportunity to establish a collaborative mindset to shape future research.

scholarly journals The Application of Downhole Vibration Factor in Drilling Tool Reliability Big Data Analytics—A Review

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Yali Ren ◽  
Ning Wang ◽  
Jinwei Jiang ◽  
Junxiao Zhu ◽  
Gangbing Song ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Maintenance Planning ◽  
Drilling Tool ◽  
Operation Conditions ◽  
Drilling Tools ◽  
Drilling Operations ◽  
Field Reliability

In the challenging downhole environment, drilling tools are normally subject to high temperature, severe vibration, and other harsh operation conditions. The drilling activities generate massive field data, namely field reliability big data (FRBD), which includes downhole operation, environment, failure, degradation, and dynamic data. Field reliability big data has large size, high variety, and extreme complexity. FRBD presents abundant opportunities and great challenges for drilling tool reliability analytics. Consequently, as one of the key factors to affect drilling tool reliability, the downhole vibration factor plays an essential role in the reliability analytics based on FRBD. This paper reviews the important parameters of downhole drilling operations, examines the mode, physical and reliability impact of downhole vibration, and presents the features of reliability big data analytics. Specifically, this paper explores the application of vibration factor in reliability big data analytics covering tool lifetime/failure prediction, prognostics/diagnostics, condition monitoring (CM), and maintenance planning and optimization. Furthermore, the authors highlight the future research about how to better apply the downhole vibration factor in reliability big data analytics to further improve tool reliability and optimize maintenance planning.

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