Manual Data Gathering Research Articles

From Traffic Analysis to Real-Time Management: A Hazard-Based Modeling for Incident Durations Extracted Through Traffic Detector Data Anomaly Detection

The time and location of occurrence, among other characteristics of incidents, are needed to distribute traveler information and dispatch emergency service for traffic congestion and safety mitigation purposes. However, the corresponding clearance/recovery time is an important, yet under-studied, subject of research. Existing studies on incident duration modeling mainly utilize duration data gathered and provided by authorities through the efforts of individual agents. This type of manual data gathering may suffer coverage and consistency issues. In addition, the explanatory variables considered in these studies are static, that is, observed at the incident formation stage and assumed to be consistent over the incident periods. This setup, however, overlooks traffic flow dynamics; time-varying traffic variables during the incident episodes affect incidents clearance and disruption characteristics. In line with the above limitations, the objective of this study is to utilize hazard-based modeling to explain incident durations mined from traffic detector data while factoring in traffic flow dynamics. The 2014–2016 Virginia statewide traffic detector data were utilized to extract incident durations by implementing a machine-learning-based incident detection algorithm followed by a semi-parametric proportional hazard function. Such a function accounts for the time-varying traffic-descriptive covariates for incident duration modeling. The resulting monotonically decreasing baseline hazard reveals the snowball effect and the inertia effect of disruptions induced by incidents, which leads to the advice that incidents should be detected and mitigated within a 30 min duration. The normalized flow, density, and speed measures, and their temporal differences are all shown to be significant covariates scaling up or down the hazard.

Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network

The voltage supply of induction motors of various sizes is typically provided by a shared power bus in an industrial production powerline network. A single motor’s dynamic behavior produces a signal that travels along the powerline. Powerline networks are efficient at transmitting and receiving signals. This could be an indication that there is a problem with the motor down immediately from its location. It is possible for the consolidated network signal to become confusing. A mathematical model is used to measure and determine the possible known routing of various signals in an electricity network based on attenuation and estimate the relationship between sensor signals and known fault patterns. A laboratory WSN based induction motors testbed setup was developed using Xbee devices and microcontroller along with the variety of different-sized motors to verify the progression of faulty signals and identify the type of fault. These motors were connected in parallel to the main powerline through this architecture, which provided an excellent concept for an industrial multi-motor network modeling lab setup. A method for the extraction of Xbee node-level features has been developed, and it can be applied to a variety of datasets. The accuracy of the real-time data capture is demonstrated to be very close data analyses between simulation and testbed measurements. Experimental results show a comparison between manual data gathering and capturing Xbee sensor nodes to validate the methodology’s applicability and accuracy in locating the faulty motor within the power network.

Utilization of Industry 4.0 Related Equipment in Assembly Line Balancing Procedure

In recent decades, production in high-volume/low-variety batches is replaced with low-volume/high-variety production type. This type of production demands excessive flows of both material and information. Recent advances in information and communication technologies (ICT), together with the concept of cyber-psychical system (CPS) enable the concept of Industry 4.0 (I4.0). In this paper, the performance of I4.0 related equipment implementation is presented in iterative assembly line balancing (ALB) process of a gearbox assembly line. Largest candidate rule method through spreadsheet simulation was used for tasks reallocations, with the objective to minimize the cycle time when the number of stations is fixed. Utilization of human analysts using snap back method for manual data gathering process still shown advantage over I4.0 equipment utilization in manual ALB. The assembly process is performed in the learning factory environment, and it is considered as very close to real industry process. The major conclusion is that I4.0 is excellent in process data monitoring and product tracking, but activities to be performed to effectively exploit I4.0 is demanding for task reallocations during the balancing procedure. Nevertheless, future enhancements of I4.0 system are listed to bridge this gap and to increase I4.0 system usefulness in the manual assembly line balancing process.

Distributed travel-time seismic tomography in large-scale sensor networks

Current geophysical techniques for visualizing seismic activity employ image reconstruction methods that rely on a centralized approach for processing the raw data captured by seismic sensors. The data is either gathered manually, or relayed by expensive broadband stations, and then processed at a base station. This approach is time-consuming (weeks to months) and hazardous as the task involves manual data gathering in extreme conditions. Also, raw seismic samples are typically in the range of 16–24 bit, sampled at 50–200 Hz and transferring this high fidelity sample from large number of sensors to a centralized station results in a bottleneck due to bandwidth limitations. To avoid these issues, a new distributed method is required which processes raw seismic samples inside each node and obtains a high-resolution seismic tomography in real time. In this paper, we present a component-averaged distributed multi-resolution evolving tomography algorithm for processing data and inverting volcano tomography in the network while avoiding centralized computation and costly data collection. The algorithm is first evaluated for the correctness using a synthetic model in a CORE emulator. Later, our proposed algorithm runs using the real data obtained from Mt. St. Helens, WA, USA. The results validate that our distributed algorithm is able to obtain a satisfactory image similar to centralized computation under constraints of network resources, while distributing the computational burden to sensor nodes.

Aggregation of Production Data for the Strategic Planning of Global Production Networks

In the last two decades a large amount of companies decided to establish productions sites all over the world in order to profit from cost benefits and to gain access to new markets and know-how. This internationalisation trend leads to more and more complex production networks. According to this development managers are confronted with growing intransparencies within their companies due to increasing information asymmetries, growing amount of interfaces and a growing effort for coordination. Nowadays a lot of strategic decisions, e.g. location decisions are made according to someone’s instinct. The usage of a valid data base within decision making processes can improve the quality of taken decisions. There are two ways to establish a sound data base: Manual data gathering, which is always associated with great effort or the usage of “big data”. Technologies from the era “Industrie 4.0” enable companies to gather and store a large amount of data. This data amount can establish transparency to support an objective assessment. Therefore the aim of this paper is the development of a method to aggregate production data systematically to support the strategic planning of global production networks.

SOSYAL MEDYA GAZETELERİ NASIL ETKİLİYOR? HABERLERİN TWITTER’DA YAYILMASI ÜZERİNE BİR ARAŞTIRMA

Social media has been an important outlet for newspapers. Social media have advantages in terms of reaching wider audience and sharing comments about news. Being a digital platform, modularity and automation make social media suitable for newspapers to disseminate their news. In social media there are very large amounts of data. Some of them are about news. To collect those data and analyze them to get meaningful information is a difficult task. Beside the software designed to be used for this purpose, it is possible to collect data with simple search tools like Twitter’s search function. Although automation very similar to manual data gathering is possible, data gathering is limited with this function. On the other hand, it is easy to use such functions and gives an idea on how complicated monitoring software work. In this work, by looking at the dispersion of news from different sections of newspapers on Twitter, the tweeting ratio of news were calculated. The relation of dissemination of news between newspapers and social media was examined. It is found that the higher the comment ratio on newspaper Website, the higher the dissemination of news on Twitter.

Human Role in Industrial Installed Base Information Gathering

Manual data gathering and data quality in industrial context have been, to some extent, discussed in the existing literature. The focus has been on companies’ own staff as data collectors. In the case of industrial companies, own staff might often be the only resource that can be harnessed to data gathering. However, this does not always hold true, and hence exploring also other alternatives is worthwhile. We have identified four data collector groups that can possibly be involved in the manual data gathering process, namely own staff, customers, equipment users and other parties. This paper explores the characteristics of the identified groups as potential data gatherers and provides our insights on how manual installed base information gathering utilizing multiple data collector groups could be approached.

Electronic Patient Record System for psychiatry units

Electronic Patient Records can improve the quality of patient care in psychiatry units. Many patients have long term illnesses and have multiple patient encounters with psychiatric services. Although manual data gathering is available at the moment, data retrieval is time consuming and as files have to be manually searched. Storage of past data is hampered by lack of space. We describe the first electronic patient record system developed for use in a psychiatry unit in Sri Lanka. The system facilitates an electronic storage of data and easy retrieval of information. The system identifies a patient by a unique number and allows different episodes of in- patient and out- patient care to be linked together. The systems’ value is enhanced by the generation of reports to assist and improve health administration. Clinical care is enhanced by linking different episodes of care of a patient over time. Discharge reports follow up reports of out- patient attendance and reports of analysis of data are generated by the system. The follow up report identifies outpatients who default treatment enabling community follow up. This would help improve compliance and reduce relapses. This is not currently done as it is not feasible to identify all those who default using a manual record keeping system. Analysis of patients based on different criteria such as diagnosis and treatment will assist in identifying trends of illnesses and also provide a database for research. The system is developed using mysql database and is hosted on apache server. As this uses only open source software this can be deployed in both Linux and windows environment allowing easy, and low cost deployment in other such wards. The system is web based and can be connected to a network expanding the access capabilities.

Electronic Patient Record System for a Psychiatry Unit in Sri Lanka

Electronic patient records improve the quality of patient care in psychiatry units. Many patients with long term illnesses have multiple patient encounters with psychiatric services. Although manual data gathering is available at present, data retrieval is time consuming as files have to be manually searched. Storage of past data is hampered by lack of space. We describe the first electronic patient record system developed for use in a psychiatry unit in Sri Lanka. The system facilitates an electronic storage of data and easy retrieval of information. The system identifies a patient by a unique number and allows different episodes of in-patient and out-patient care to be linked together. The systems' value is enhanced by the generation of reports to assist and improve health administration. Clinical care is enhanced by the ability to view the longitudinal history of a patient. Discharge reports, reports of out-patient attendance and reports of analysis of data are generated by the system. The follow up report identifies outpatients who default treatment enabling community follow up. This would help improve compliance and reduce relapses. This is not currently done as it is not feasible to identify all those who default using a manual record keeping system. Analysis of patients based on different criteria such as diagnosis and treatment will assist in identifying trends and provide a database for research. The system was developed using MySQL database and is hosted on Apache server. As this uses only open source software this can be deployed in both Linux and Windows environment allowing easy and low cost deployment. The system is web based and can be connected to a network expanding the access capabilities. Key Words: Psychiatry; web based system; open source; report generation; electronic patient records; Sri Lanka. Sri Lanka Journal of Bio-Medical Informatics 2010;1(4):214-22 DOI: 10.4038/sljbmi.v1i4.2250

Computer‐aided collection of demographic data within day‐night‐level contours around airports

In recent years computer models have been developed that make it possible to calculate the day‐night levels around airports at a reasonable cost. Determining the number of people exposed to various noise levels has always been a time‐consuming and expensive operation because it required extensive manual data gathering. This paper discusses how the output from an airport noise prediction model (NOISEMAP) has been modified so that the output is suitable as input into a commercially available computer program that automatically collects demographic data within an arbitrary polygon. A discussion of how the information produced can then be used in environmental impact reporting is given. This includes using the fractional impact method for defining impact.

Cholinesterase inhibition studies by stopped-flow instrumentation and automated data processing.

Abstract An automated system encompassing stopped-flow techniques and automated data acquisition and processing has been developed to conduct studies on cholinesterase kinetics, including inhibition experiments for the evaluation of the binding constant, K d , the first-order inhibition constant, k 2 , and the overall second-order inhibition constant, k i . The instrumentation, hardware, and software have been developed and combined such that no training in computer languages and only a limited knowledge of computer systems interaction is necessary for its use. Application of the system to kinetic investigations requires no manual data gathering and manipulation. The automated system has been utilized in determining the inhibition constants for a combination of three inhibitors, paraoxon, amiton, and carbaryl, and two acetylcholinesterases, bovine erythrocyte and eel, obtaining six sets of inhibition constants.