scholarly journals Development and evaluation of the coaxial cable casing imager: a cost-effective solution to real-time downhole monitoring for CO2sequestration wellbore integrity

2017 ◽  
Vol 7 (5) ◽  
pp. 927-941 ◽  
Author(s):  
Yurong Li ◽  
Baokai Cheng ◽  
Wenge Zhu ◽  
Hai Xiao ◽  
Runar Nygaard
Keyword(s):  
Real Time ◽  
Cost Effective ◽  
Coaxial Cable ◽  
Effective Solution ◽  
Wellbore Integrity ◽  
Downhole Monitoring

A Cost-Effective Solution for Real Time Remote Monitoring of Vital Signs in Patients

2021 ◽  
Author(s):  
Syed Razwanul Haque ◽  
Shovon Sudan Saha ◽  
Hasib Ahmed Chowdhury ◽  
Talukdar Raian Ferdous ◽  
Abrar Shams Chowdhury ◽  
...  
Keyword(s):  
Real Time ◽  
Remote Monitoring ◽  
Vital Signs ◽  
Cost Effective ◽  
Effective Solution

Utilization of Real-Time Data and Advanced Correlation/Interpretation Tool for Picking the Formation Tops and Casing Points Remotely: A Cost Effective Solution by RCC

2017 ◽  
Author(s):  
Dana AlAbdulkarim ◽  
Mohammed Saklou ◽  
Musab Al Khudiri ◽  
Yasser Ghamdi ◽  
Mohammad Barayyan ◽  
...  
Keyword(s):  
Real Time ◽  
Cost Effective ◽  
Effective Solution ◽  
Time Data ◽  
Real Time Data

Towards an efficient indoor navigation system: a near field communication approach

2017 ◽  
Vol 15 (4) ◽  
pp. 505-527 ◽  
Author(s):  
Wilson E. Sakpere ◽  
Nhlanhla Boyfriend Wilton Mlitwa ◽  
Michael Adeyeye Oshin
Keyword(s):  
Real Time ◽  
Navigation System ◽  
Social Impact ◽  
Near Field ◽  
Cost Effective ◽  
Indoor Navigation ◽  
Near Field Communication ◽  
Valuable Insight ◽  
Effective Solution ◽  
Content Type

Purpose This research aims to focus on providing interventions to alleviate usability challenges to strengthen the overall accuracy and the navigation effectiveness in indoor and stringent environments through the experiential manipulation of technical attributes of the positioning and navigation system. Design/methodology/approach The study followed a quantitative and experimental method of empirical enquiry and software engineering and synthesis research methods. The study further entails three implementation processes, namely, map generation, positioning framework and navigation service using a prototype mobile navigation application that uses the near field communication (NFC) technology. Findings The approach and findings revealed that the capability of NFC in leveraging its low-cost infrastructure of passive tags, its availability in mobile devices and the ubiquity of the mobile device provided a cost-effective solution with impressive accuracy and usability. The positioning accuracy achieved was less than 9 cm. The usability improved from 44 to 96 per cent based on feedbacks given by respondents who tested the application in an indoor environment. These showed that NFC is a viable alternative to resolve the challenges identified in previous solutions and technologies. Research limitations/implications The major limitation of the navigation application was that there is no real-time update of user position. This can be investigated and extended further by using NFC in a hybrid make-up with WLAN, radio-frequency identification (RFID) or Bluetooth as a cost-effective solution for real-time indoor positioning because of their coverage and existing infrastructures. The hybrid positioning model, which merges two or more techniques or technologies, is becoming more popular and will improve its accuracy, robustness and usability. In addition, it will balance complexity, compensate for the limitations in the technologies and achieve real-time mobile indoor navigation. Although the presence of WLAN, RFID and Bluetooth technologies are likely to result in system complexity and high cost, NFC will reduce the system’s complexity and balance the trade-off. Practical implications Whilst limitations in existing indoor navigation technologies meant putting up with poor signal and poor communication capabilities, outcomes of the NFC framework will offer valuable insight. It presents new possibilities on how to overcome signal quality limitations at improved turn-around time in constrained indoor spaces. Social implications The innovations have a direct positive social impact in that it will offer new solutions to mobile communications in the previously impossible terrains such as underground platforms and densely covered spaces. With the ability to operate mobile applications without signal inhibitions, the quality of communication – and ultimately, life opportunities – are enhanced. Originality/value While navigating, users face several challenges, such as infrastructure complexity, high-cost solution, inaccuracy and usability. Hence, as a contribution, this paper presents a symbolic map and path architecture of a floor of the test-bed building that was uploaded to OpenStreetMap. Furthermore, the implementation of the RFID and the NFC architectures produced new insight on how to redress the limitations in challenged spaces. In addition, a prototype mobile indoor navigation application was developed and implemented, offering novel solution to the practical problems inhibiting navigation in indoor challenged spaces – a practical contribution to the community of practice.

A Novel Handheld Electrochemical Analyzer System Interfaced to a Smartphone

2013 ◽  
Vol 543 ◽  
pp. 47-50 ◽  
Author(s):  
Vijayalakshmi Velusamy ◽  
Khalil Arshak ◽  
Olga Korostynska ◽  
Ahmed Al-Shamma'a
Keyword(s):  
Real Time ◽  
Electrochemical Cell ◽  
Specific Binding ◽  
Smart Phone ◽  
Cost Effective ◽  
Dna Biosensor ◽  
Effective Solution ◽  
Minimum Level ◽  
Sensing Applications ◽  
Electrochemical Analyzer

Detailed in this paper is the design of a novel handheld electrochemical analyzer system interfaced to a smart phone, which provides versatile and cost-effective solution for real-time sensing applications. It was characterised for electron transfer events associated with chemical and biological samples. The presented design is implemented based on the Arduino nanoopen source electronics prototyping platform. The versatility of the instrument is further demonstrated by employing the electrochemical analyser to a modified electrochemical cell which formed the basis of a DNA biosensor. Cyclic voltammetry technique was used to impose a triangular waveform on an electrochemical cell and the resulting current through the cell was then monitored. The DNA biosensor generated unique electrical signals in real-time between complementary and non-complementary oligonucleotides sequences of the Bacillus cereus DNA. The effects of hybridization and non-specific binding were compared when the probe DNA molecules were immobilized on a conducting polymer matrix. The results showed that the probe DNA immobilized using electrochemical adsorption yielded better hybridization signals compared to other immobilization methods. The performance of the DNA sensor proved to be effective in terms of selectivity, sensitivity and reproducibility of hybridization events. Analysis of these DNA probes showed that the minimum level of detection was 33.3 pg/ml.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

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