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Shine, Philip, and Michael D. Murphy. "Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study." Sensors 22, no. 1 (2021): 52. http://dx.doi.org/10.3390/s22010052.
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Machine learning applications are becoming more ubiquitous in dairy farming decision support applications in areas such as feeding, animal husbandry, healthcare, animal behavior, milking and resource management. Thus, the objective of this mapping study was to collate and assess studies published in journals and conference proceedings between 1999 and 2021, which applied machine learning algorithms to dairy farming-related problems to identify trends in the geographical origins of data, as well as the algorithms, features and evaluation metrics and methods used. This mapping study was carried out in line with PRISMA guidelines, with six pre-defined research questions (RQ) and a broad and unbiased search strategy that explored five databases. In total, 129 publications passed the pre-defined selection criteria, from which relevant data required to answer each RQ were extracted and analyzed. This study found that Europe (43% of studies) produced the largest number of publications (RQ1), while the largest number of articles were published in the Computers and Electronics in Agriculture journal (21%) (RQ2). The largest number of studies addressed problems related to the physiology and health of dairy cows (32%) (RQ3), while the most frequently employed feature data were derived from sensors (48%) (RQ4). The largest number of studies employed tree-based algorithms (54%) (RQ5), while RMSE (56%) (regression) and accuracy (77%) (classification) were the most frequently employed metrics used, and hold-out cross-validation (39%) was the most frequently employed evaluation method (RQ6). Since 2018, there has been more than a sevenfold increase in the number of studies that focused on the physiology and health of dairy cows, compared to almost a threefold increase in the overall number of publications, suggesting an increased focus on this subdomain. In addition, a fivefold increase in the number of publications that employed neural network algorithms was identified since 2018, in comparison to a threefold increase in the use of both tree-based algorithms and statistical regression algorithms, suggesting an increasing utilization of neural network-based algorithms.
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López, Óscar, Clara Murillo, and Alfonso González. "Systematic Literature Reviews in Kansei Engineering for Product Design—A Comparative Study from 1995 to 2020." Sensors 21, no. 19 (2021): 6532. http://dx.doi.org/10.3390/s21196532.
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Individual products and models on the market must be specifically differentiated from the rest to meet user demand. In terms of consumer purchasing behaviour, consumers increasingly base their decisions on subjective terms or the impression that the product leaves on them, both in terms of functionality, usability, safety, and price adequacy, and regarding the emotions and feelings that it triggers in them. This demand has lead both Asia and Europe to implement new methodologies to develop new products, such as “emotional design” or Kansei engineering. This paper presents a systematic literature review (SLR) on the most relevant methodologies based on Kansei engineering and their relevant results in the specific discipline of product design, addressing these five questions: (RQ1) How many studies on KE and emotional design are there in the Scopus and Web of Science (WoS) databases from 1995 to February 2021? (RQ2) Which research topics and types of KE are addressed? (RQ3) Who is leading the research on KE and emotional design? (RQ4) What are the benefits and drawbacks of using and applying the methodology? (RQ5) What are the limitations of the current research? We analysed 87 studies focusing on the Kansei methodology used for product design and device technologies (e.g., shape design, actuators, sensors, structure) and aesthetic aspects (e.g., Kansei words selection, the quantification of measured emotions of results, and detected shortcomings), and provided the database with all the collected information. One identified and highlighted sector in the results is the electronic–technological-device sector. Results confirm that this type of methodology has a majority and direct application in these sectors, and they are widely represented in the automotive and electronics industries. Lastly, this SLR provides researchers with a guide for comparative emotional-design work, and facilitates future designers who want to implement emotional design in their work by selecting the specific type according to the results of the SLR.
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Poslad, Stefan, Tayyaba Irum, Patricia Charlton, et al. "How IoT-Driven Citizen Science Coupled with Data Satisficing Can Promote Deep Citizen Science." Sensors 22, no. 9 (2022): 3196. http://dx.doi.org/10.3390/s22093196.
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To study and understand the importance of Internet of Things-driven citizen science (IoT-CS) combined with data satisficing, we set up and undertook a citizen science experiment for air quality (AQ) in four Pakistan cities using twenty-one volunteers. We used quantitative methods to analyse the AQ data. Three research questions (RQ) were posed as follows: Which factors affect CS IoT-CS AQ data quality (RQ1)? How can we make science more inclusive by dealing with the lack of scientists, training and high-quality equipment (RQ2)? Can a lack of calibrated data readings be overcome to yield otherwise useful results for IoT-CS AQ data analysis (RQ3)? To address RQ1, an analysis of related work revealed that multiple causal factors exist. Good practice guidelines were adopted to promote higher data quality in CS studies. Additionally, we also proposed a classification of CS instruments to help better understand the data quality challenges. To answer RQ2, user engagement workshops were undertaken as an effective method to make CS more inclusive and also to train users to operate IoT-CS AQ devices more understandably. To address RQ3, it was proposed that a more feasible objective is that citizens leverage data satisficing such that AQ measurements can detect relevant local variations. Additionally, we proposed several recommendations. Our top recommendations are that: a deep (citizen) science approach should be fostered to support a more inclusive, knowledgeable application of science en masse for the greater good; It may not be useful or feasible to cross-check measurements from cheaper versus more expensive calibrated instrument sensors in situ. Hence, data satisficing may be more feasible; additional cross-checks that go beyond checking if co-located low-cost and calibrated AQ measurements correlate under equivalent conditions should be leveraged.
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Hidayatulloh, Nurma M., and Tedjo Sukmono. "Determination of Production Instrumentation Equipment Maintenance Intervals In the Paper Industry." PROZIMA (Productivity, Optimization and Manufacturing System Engineering) 4, no. 1 (2021): 23–31. http://dx.doi.org/10.21070/prozima.v4i1.1275.
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PT. XYZ is a manufacturing industry engaged in paper processing with afval raw materials. The problem faced is machine failure that occurs suddenly without predictability, this is because there is no scheduled maintenance (preventive main-tenance). The object of this research is focused on production instrumentation equipment. This study uses the Failure Mode and Effect Analyzer (FMEA) method to identify the causes of failure and the effects of these failures by determining the critical value of the component, namely the Risk Priority Number (RPN) which is the largest, then the Reliability Centered Maintenance (RCM) II Decision Worsheet method for determine maintenance intervals of production instrumentation equipment. Based on the results of RPN calculations in the FMEA method to determine the critical components of the Instrumentation equipment, namely the Control Valve, it can be seen that the highest total RPN value is found in three components, namely Restrictor with an RPN value of 390, Power Supply with RPN of 297, and also a Pilot Positioner. with an RPN value of 240. And with optimum maintenance intervals, among others, the Restrictor every 40 hours, the Power Supply every 41 hours, and the Pilot Positioner every 47 hours.
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Vaezi-Nejad, SM, M. Cox, and N. Cooper. "Novel instrumentation for measurement of relative intensity noise." Transactions of the Institute of Measurement and Control 34, no. 4 (2011): 477–86. http://dx.doi.org/10.1177/0142331211399330.
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Laser diode relative intensity noise (RIN) metrology capabilities have been developed and demonstrated, providing significantly improved sensitivity and accuracy compared with existing methods. The novel use of the demonstrated reference noise source has shown significant advantages, achieving improved sensitivity, reducing measurement accuracy as low as ±1 dB and simplifying the system calibration methodology, thus improving flexibility. Laser RINs of between 10 and 14 dB below the shot RIN have been shown (typically −170 dBm/Hz), which is a direct result of the improved system sensitivity. A neodinium yag 1319-nm ring laser provided a ‘cold’ reference source, in a similar manner to that used in RF electrical metrology. Application of the ‘flat’ low noise optical RF noise source from 10 MHz to 20 GHz has been demonstrated for the first time in optical RF metrology, providing a calculable reference traceable via the incident optical power received. Because of the simplistic nature of this approach, system calibration can be performed for each RIN measurement that is carried out, reducing measurement uncertainty associated with RF mismatch, system linearity and loss. High specification components have been assessed individually and in the combined system indicating an overall system noise figure of 2–3 dB over the 10 MHz to 20 GHz frequency range (−171 to −172 dBm), some 4–5 dB better than previously reported.
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S, Raehan Adillah, Jufrizel Jufrizel, Putut Son Maria, and Hilman Zarory. "Analisa Keandalan Instrumentasi Boiler Feed Pump Menggunakan Metode Failure Mode and Effect Analysis (FMEA) di PT.PLN Nusantara Power UP Tenayan." JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI 9, no. 3 (2024): 276. http://dx.doi.org/10.36722/sst.v9i3.2882.
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<p><em> </em><strong>PT. PLN Nusantara Power UP Tenayan is a company operating in the power generation sector and focuses on operating PLTUs Tenayan, Indonesia. One part of the machine that influences the operation of the generator is the boiler feed pump. Lack of optimal boiler feed pump instrumentation often results in failure in operating activities. This research uses the Failure Mode and Effect Analysis (FMEA) method with the research objective of finding out the causes of failures that occur, identifying the type of failure, and determining the RPN value and the impact that occurs. The analysis results, it shows that the Boiler Feed Pump instrumentation components still meet operating standards because the Risk Priority Number (RPN) value is below 200, even though the Speed Sensor has a fairly high RPN value but is still in the reliable category, the result of identifying the type of failure that occurs is that the indicator reading is not actual, the component is not functioning, the highest RPN value for the Boiler Feed Pump component is the Speed Sensor component with an RPN value of 160 and the lowest RPN value is for the pressure indicator component with an RPN value of 30. The most important recommended action for the Speed Sensor component is to carry out maintenance for 1 month very.</strong></p><p><strong><em>Keywords</em></strong> – <em>Boiler Feed Pump, FMEA, Keandalan, Pembangkit, RPN.</em></p>
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Andriyan, Septa, Jufrizel Jufrizel, Aulia Ulah, and Ahmad Faizal. "Analisa Keandalan Instrumentasi Pada Lime Kiln Unit Menggunakan Metode Reliability Centered Maintenance (RCM) di PT. Indah Kiat Pulp and Paper Perawang." JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI 9, no. 2 (2024): 205. http://dx.doi.org/10.36722/sst.v9i2.2785.
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<p><strong>PT. Indah Kiat Pulp and Paper Perawang is a company that prioritizes paper production. There is a crucial unit known as the lime kiln within the company. Issues occurring within this unit often disrupt the overall production process. Therefore, conducting reliability analysis on the instrumentation of this unit is highly important. This research utilizes the Reliability Centered Maintenance (RCM) method. The objective of this study is to determine which devices within the lime kiln are most vulnerable to problems by calculating the Risk Priority Number (RPN) for each component, evaluating the reliability level of these devices, and providing optimal maintenance schedule recommendations. The research findings indicate the sequence of RPN values for lime kiln instrumentation from highest to lowest as follows: temperature sensor 336, on-off valve 288, flow transmitter 252, control valve 245, proximity sensor 240, pressure gauge 216, sensor indication of motorized damper 210. Furthermore, the reliability values of each instrumentation did not meet the threshold set by the Indonesian Industry Standard (SII), which is 0.7. Therefore, it can be concluded that maintenance action is required for these instrumentation devices. Maintenance schedule recommendations for temperature sensor are 252 days, on-off valve 294 days, flow transmitter 293 days, control valve 251 days, proximity sensor 293 days, pressure gauge 352 days, and sensor indication of motorized damper 353 days.</strong><strong></strong></p><p><strong><em>Keywords</em></strong> – <em>Keandalan, Instrumentasi, Maintenance, Lime kiln, RCM.</em></p>
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Torres Cedillo, Sergio G., and Philip Bonello. "Empirical identification of the inverse model of a squeeze-film damper bearing using neural networks and its application to a nonlinear inverse problem." Journal of Vibration and Control 24, no. 2 (2016): 357–78. http://dx.doi.org/10.1177/1077546316640985.
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The identification of nonlinear squeeze-film damper (SFD) bearings, typically used in aero-engines, has so far focused on their forward model (i.e. displacement input/force output). The contributions of this paper are the non-parametric identification of the inverse model of the SFD bearing (force input/displacement output) from empirical data, and its application to a nonlinear inverse rotor-bearing problem. This work is motivated by the need for a reliable substitute for internal instrumentation, to enable the identification of rotor unbalance using vibration data from externally mounted sensors, in applications where the rotor is inaccessible under operating conditions and there is no adequate linear connection between rotor and casing. The identification of the inverse model is fundamentally different from that of the forward model due to the need to account for system memory. A suitably trained Recurrent Neural network (RNN) is shown to be capable of identifying the inverse model of an actual SFD through two validation studies. In the first study, the RNN model satisfactorily predicted the SFD journal’s displacement time histories for given periodic time histories of the Cartesian SFD forces, although it could not predict the user-applied static offset in the SFD since it was not trained to do so. This was no limitation for the second study where, for both centred and non-centred SFD conditions, the RNN proved to be a reliable substitute for actual instrumentation as part of the inverse problem solution process for identifying the amplitudes and phases of the external excitation forces on a simple test rig.
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K.H.K. Prasad, B.T. Krishna. "RNN Based Deep Learning Approach for ECG Beat Classification." Tuijin Jishu/Journal of Propulsion Technology 44, no. 5 (2023): 200–210. http://dx.doi.org/10.52783/tjjpt.v44.i5.2451.
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The electrical patterns of the heart, captured through an electrocardiogram (ECG/EKG), serve as a diagnostic tool to identify potential issues such as heart attacks, irregular heart rhythms, heart failure, and arrhythmia, which manifests as irregularities in the heartbeat's rhythm. Deep Learning (DL) architectures have been successfully employed for arrhythmia detection and classification and offered superior performance to traditional shallow Machine Learning (ML) approaches. This paper introduces a novel approach utilizing deep learning techniques, specifically a Bidirectional Gated Recurrent Unit (Bi-GRU) model a variant of RNN, to classify ECG arrhythmia beats into distinct categories. The Bi-GRU model is employed in this work due to its capability to capture temporal dependencies in ECG signals, enabling a nuanced understanding of beat sequences for precise classification. Leveraging the MIT-BIH arrhythmia database, a comprehensive dataset containing annotated ECG signals, this study explores the efficacy of deep learning in accurately categorizing beats in to five super classes as per the standard of Association for the Advancement of Medical Instrumentation (AAMI). Evaluation metrics encompassing accuracy (Acc), specificity (Spe), sensitivity (Sen), positive predictive value (Ppv), and F1-score are utilized to assess the model performance in distinguishing between diverse arrhythmia classes.
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Zaluzec, Nestor J. "Innovative Instrumentation for Analysis of Nanoparticles: The π Steradian Detector". Microscopy Today 17, № 4 (2009): 56–59. http://dx.doi.org/10.1017/s1551929509000224.
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The analysis was finally completed, the data graphed, and I had to admit to myself, it was looking like an auspicious moment. Only a few weeks earlier, Charlie Lyman and I had been discussing a project that I had been working on for some time, one in which I was admittedly being deliberately vague concerning the details. However, I had promised to keep him updated when it succeeded. Given the date (April 1st) and the data in hand, I couldn't resist sharing with him (and a few colleagues) the first results from that experiment—and my thinly veiled attempt at a quasi-April Fools Day joke. After we exchanged a number of emails that day, Charlie concluded his last message with the line that many of us have heard from both him and Ron Anderson: “A little more text and you can have an article in the July issue of MT.” Have I gotten you curious? Well then read on!
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Ellwood, Stephen. "A Retrospective Review of Spinal Radiofrequency Neurotomy Procedures in Patients with Metallic Posterior Spinal Instrumentation – Is it Safe?" January 2018 1, no. 21;1 (2018): E477—E482. http://dx.doi.org/10.36076/ppj.2018.5.e477.
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Background: Recent studies have shown that medial branch radiofrequency neurotomy (RFN) procedures done at the level of a pedicle screw can increase pedicle screw temperature, and it has been speculated that pedicle screw heating may cause thermal injury. There has been a limited amount of investigation into the real-world safety profile of RFN procedures in patients with pedicle screws. Objectives: We aim to demonstrate that the occurrence of serious adverse events is rare for a medial branch RFN procedure completed at a level with metallic spinal hardware when performed according to the Spine Intervention Society practice standards. Study Design: This study involved retrospective chart reviews of every patient who received an RFN procedure for spinal facet joint pain during the 5-year time period from 2012-2016. Setting: The research took place within a single university-based interventional pain management center. Methods: The study sample included 507 patient charts. Data collection included patient demographics, RF denervation sites at a level with metallic hardware, and all serious RF-related complications that could be attributable to heated metallic hardware. The research team developed medical-chart abstraction criteria for each of the following categorized complications: a) superficial burns, b) deep burns, c) denervation of dorsal ramus, d) denervation of ventral ramus, and e) coagulation of a spinal vascular structure. Results: Of the 36 patients who met the inclusion criteria for this study, 43.6% were men and 56.4% were women. The mean age was 59.5 years old, with an age range of 25 to 87 years. There were a total of 56 ablations performed at a level with metallic spinal hardware, of which 11 were cervical, 44 were lumbar, and 1 was thoracic . There were zero documented complications found among our patient population in any of the 5 categories of serious complications. Limitations: As a retrospective chart review, this study was dependent on the availability and accuracy of medical records. Chart abstraction criteria for each outcome measure were developed by the research team without scientific testing. Conclusions: There have been no reported complications attributable to hardware temperature increases when performing medial branch RFNs at the level of a pedicle screw. For safety, it is important to use multiplanar fluoroscopic imaging techniques to ensure that the RFN cannula is not in contact with the pedicle screw. Key Words: Radiofrequency neurotomy, medial branch nerve ablation, safety, thermal injuries, metallic spinal hardware, pedicle screws, lateral mass screws, cervical facet joints, severe complications, adverse events
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Peña, Carlos, David Romero, and Julieta Noguez. "Workforce Learning Curves for Human-Based Assembly Operations: A State-of-the-Art Review." Applied Sciences 12, no. 19 (2022): 9608. http://dx.doi.org/10.3390/app12199608.
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In this state-of-the-art review, the authors explore the recent advancements in the topics of learning curve models and their estimation methods for manual operations and processes as well as the data collection and monitoring technologies used for supporting these. This objective is achieved by answering the following three research questions: (RQ1) What calculation methods for estimating the learning curve of a worker exist in the recent scientific literature? (RQ2) What other usages are manufacturing enterprises giving to the modern learning curve prediction models according to the recent scientific literature? and (RQ3) What data collection and monitoring technologies exist to automatically acquire the data needed to create and continuously update the learning curve of an assembly operator? To do so, the PRISMA methodology for literature reviews was used, only including journal articles and conference papers referencing the topic of manual operations and processes, and to fulfil the criteria of a state-of-the-art review, only the literary corpus generated in the last five years (from 2017 to 2022) was reviewed. The scientific databases where the explorative research was carried out were Scopus and Web of Science. Such research resulted in 11 relevant journal articles and international conference papers, which were first reviewed, synthesized, and then compared. Four estimating methods were found for learning curves, and one recently developed learning curve model was found. As for the data collection and monitoring technologies, six frameworks were found and reviewed. Lastly, in the discussion, different areas of opportunity were found in the current state-of-the-art, mainly by combining the existing learning curve models and their estimation methods and feeding these with modern real-time data collection and monitoring frameworks.
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Kriech, Matthew A., and John C. Conboy. "Using the Intrinsic Chirality of a Molecule as a Label-Free Probe to Detect Molecular Adsorption to a Surface by Second Harmonic Generation." Applied Spectroscopy 59, no. 6 (2005): 746–53. http://dx.doi.org/10.1366/0003702054280711.
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Chiral second harmonic generation (C-SHG) has been used for the label-free detection of (R)-(+)-1,1′-bi-2-naphthol (RBN) and (S)-(+)-1,1′-bi-2-naphthol (SBN) binding to planar-supported lipid bilayers of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphotidylcholine (POPC) based on the intrinsic chirality of the molecules. C-SHG adsorption isotherms of RBN and SBN reveal Langmuir adsorption behavior with binding constants of 2.7 ± 0.2 × 105 M−1 and 3.0 ± 0.1 × 105 M−1, respectively. The kinetics of RBN binding to a POPC bilayer was also measured. It was determined that the adsorption rate for RBN was 5.7 ± 0.4 × 103 s−1M−1 and the desorption rate was 2.1 ± 0.8 × 10−2 s−1. From the kinetic data a binding constant of 2.7 ± 1.0 × 105 M−1 was calculated, which agrees well with the thermodynamic measurement. The C-SHG technique was correlated with surface tension measurements in order to determine the RBN surface excess within the POPC membrane. The maximum surface excess of RBN in a monolayer of POPC was 4.3 ± 0.5 × 10−11 mol cm2. Using the maximum surface excess in conjunction with the C-SHG binding data a lower limit of detection of 1.5 ±0.1 × 10−13 mols cm−2 was calculated. The results of these studies show that C-SHG is a powerful tool for the study of chiral molecular interactions at surfaces.
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Sun, Shiming, Xin Shan, Xueyun Wei, Chunliang Tai, and Chao Liu. "Learning-based Similarity Join for Power Data." Journal of Physics: Conference Series 2425, no. 1 (2023): 012002. http://dx.doi.org/10.1088/1742-6596/2425/1/012002.
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Abstract The increasing instrumentation of physical and computing processes has given us unprecedented capabilities to collect massive volumes of time series. Power data is a typical kind of time series. Considering that the original time series data has ineluctable limitations such as uneven distribution, non-uniform length, poor sampling rate and noisy, we propose a learning=based similarity join for power data consisting of RNN encoder and matrix model. In addition, we develop the partition techniques by grouping process nodes following the matrix join model, ensuring the accuracy and efficiency of similarity join for data series. We conduct experiments on real data-set to evaluate the performance of our approach, demonstrating the effectiveness and scalability of our method.
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Elbadawi, I., M. A. Ashmawy, W. A. Yusmawiza, I. A. Chaudhry, N. B. Ali, and A. Ahmad. "Application of Failure Mode Effect and Criticality Analysis (FMECA) to a Computer Integrated Manufacturing (CIM) Conveyor Belt." Engineering, Technology & Applied Science Research 8, no. 3 (2018): 3023–27. http://dx.doi.org/10.48084/etasr.2043.
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Fault finding and failure predicting techniques in manufacturing and production systems often involve forecasting failures, their effects, and occurrences. The majority of these techniques predict failures that may appear during the regular system production time. However, they do not estimate the failure modes and they require extensive source code instrumentation. In this study, we suggest an approach for predicting failure occurrences and modes during system production time intervals at the University of Hail (UoH). The aim of this project is to implement failure mode effect and criticality analysis (FMECA) on computer integrated manufacturing (CIM) conveyors to determine the effect of various failures on the CIM conveyor belt by ranking and prioritizing each failure according to its risk priority number (RPN). We incorporated the results of FMECA in the development of formal specifications of fail-safe CIM conveyor belt systems. The results show that the highest RPN values are for motor over current failure (450), conveyor chase of vibration (400), belt run off at the head pulley (200), accumulated dirt (180), and Bowed belt (150). The study concludes that performing FMECA is highly effective in improving CIM conveyor belt reliability and safety in the mechanical engineering workshop at UoH.
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Jellish, W. Scott, Randy L. Jensen, Douglas E. Anderson, and John F. Shea. "Intraoperative electromyographic assessment of recurrent laryngeal nerve stress and pharyngeal injury during anterior cervical spine surgery with Caspar instrumentation." Journal of Neurosurgery: Spine 91, no. 2 (1999): 170–74. http://dx.doi.org/10.3171/spi.1999.91.2.0170.
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Object. Recurrent laryngeal nerve (RLN) injury occurs after anterior cervical spine procedures. In this study the authors used intraoperative electromyographic (EMG) monitoring of the posterior pharynx as a surrogate for RLN function and monitored endotracheal tube (ET) cuff pressure to determine if there was an association between these variables and clinical outcome. Methods. Sixty patients in whom anterior cervical spine procedures were to be performed comprised the study population. After intubation, the ET cuff was adjusted to a just-seal volume and attached to a pressure monitor. A laryngeal surface electrode was placed in the posterior pharynx, and spontaneous EMG activity was monitored throughout the procedure. Cuff pressures and EMG activity were recorded during neck retraction and when EMG activity increased 20% above baseline. Patients were divided into two groups: those with sore throat/dysphonia and those without symptoms. Cuff pressures and EMG values were compared between groups, and the differences were correlated with clinical outcome. Conclusions. Hoarseness immediately after surgery was reported in 38% of patients whereas 15% exhibited severe symptoms. In symptomatic patients the period of intubation had been longer, and the ET cuff pressures had been elevated. In most patients EMG activity increased during insertion of the retractor and decreased after its removal. In these patients a greater number of episodes of elevated EMG activity during surgery were also noted. Two patients experienced prolonged hoarseness, and one required teflon injections of the vocal fold. This patient's EMG activity increased (15–18 times baseline) during surgery. In the few patients who were symptomatic with increased EMG activity, neither the timing nor direction of change could be associated with symptoms. Intubation time and elevated ET cuff pressure were the most important contributors to dysphonia and sore throat after anterior cervical spine surgery.
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Shen, Shun, Yunfei Sha, Chunhui Deng, Daxi Fu, Jiakuan Chen, and Xiangmin Zhang. "Comparison of Solid-Phase Microextraction, Supercritical Fluid Extraction, Steam Distillation, and Solvent Extraction Techniques for Analysis of Volatile Consituents in Fructus Amomi." Journal of AOAC INTERNATIONAL 88, no. 2 (2005): 418–23. http://dx.doi.org/10.1093/jaoac/88.2.418.
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Abstract Four sampling techniques, solid-phase microextraction (SPME), supercritical fluid extraction (SFE), steam distillation (SD), and solvent extraction (SE), were compared for the analysis of volatile constituents from a traditional Chinese medicine (TCM) of the dried ripe fruit of Fructus Amomi (Sha Ren). A total of 38 compounds were identified by gas chromatography/mass spectrometry. Different SFE and SPME parameters (modifier content, extraction pressure, and temperature for SFE and fibers, extraction temperature, and time for SPME) were studied. The results by SFE and SPME were compared with those obtained by conventional SD and SE methods. The results showed that SFE and SPME are better sample preparation techniques than SD and SE. Due to SFE's requirement for expensive specialized instrumentation, the simplicity, low cost, and speed of SPME make it a more appropriate technique for extraction of volatile constituents in TCMs.
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Wang, Junyi, Qinggang Meng, Peng Shang, and Mohamad Saada. "Road surface real-time detection based on Raspberry Pi and recurrent neural networks." Transactions of the Institute of Measurement and Control 43, no. 11 (2021): 2540–50. http://dx.doi.org/10.1177/01423312211003372.
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This paper focuses on road surface real-time detection by using tripod dolly equipped with Raspberry Pi 3 B+, MPU 9250, which is convenient to collect road surface data and realize real-time road surface detection. Firstly, six kinds of road surfaces data are collected by utilizing Raspberry Pi 3 B+ and MPU 9250. Secondly, the classifiers can be obtained by adopting several machine learning algorithms, recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks. Among the machine learning classifiers, gradient boosting decision tree has the highest accuracy rate of 97.92%, which improves by 29.52% compared with KNN with the lowest accuracy rate of 75.60%. The accuracy rate of LSTM neural networks is 95.31%, which improves by 2.79% compared with RNN with the accuracy rate of 92.52%. Finally, the classifiers are embedded into the Raspberry Pi to detect the road surface in real time, and the detection time is about one second. This road surface detection system could be used in wheeled robot-car and guiding the robot-car to move smoothly.
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Fatemimoghadam, Armita, Hamid Toshani, and Mohammad Manthouri. "Control of magnetic levitation system using recurrent neural network-based adaptive optimal backstepping strategy." Transactions of the Institute of Measurement and Control 42, no. 13 (2020): 2382–95. http://dx.doi.org/10.1177/0142331220911821.
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In this paper, a novel approach is proposed for adjusting the position of a magnetic levitation system using projection recurrent neural network-based adaptive backstepping control (PRNN-ABC). The principles of designing magnetic levitation systems have widespread applications in the industry, including in the production of magnetic bearings and in maglev trains. Levitating a ball in space is carried out via the surrounding attracting or repelling magnetic forces. In such systems, the permissible range of the actuator is significant, especially in practical applications. In the proposed scheme, the procedure of designing the backstepping control laws based on the nonlinear state-space model is carried out first. Then, a constrained optimization problem is formed by defining a performance index and taking into account the control limits. To formulate the recurrent neural network (RNN), the optimization problem is first converted into a constrained quadratic programming (QP). Then, the dynamic model of the RNN is derived based on the Karush-Kuhn-Tucker (KKT) optimization conditions and the variational inequality theory. The convergence analysis of the neural network and the stability analysis of the closed-loop system are performed using the Lyapunov stability theory. The performance of the closed-loop system is assessed with respect to tracking error and control feasibility.
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de Bakker, Christopher J., and Peter M. Fredericks. "Determination of Petroleum Properties by Fiber-Optic Fourier Transform Raman Spectrometry and Partial Least-Squares Analysis." Applied Spectroscopy 49, no. 12 (1995): 1766–71. http://dx.doi.org/10.1366/0003702953965975.
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The petroleum products unleaded gasoline and reformate have been analyzed by Fourier transform (FT) Raman spectrometry with the use of a fiber optic cable that allows a measurement to be carried out remotely from the spectrometer. Samples were contained in glass cuvettes, and collection of high-quality spectra was simple. Fuel properties such as research octane number (RON), motor octane number (MON), density, benzene content, and flexible volatility index (FVI) were determined by application of the partial least-squares multivariate statistical approach. Analytically useful calibrations ( R2 > 0.97) were obtained for all of the significant fuel properties studied. Cross-validation results were, as expected, worse than the calibration results but still indicated the usefulness of the method. Standard errors of prediction with the use of cross validation for models that contained 14–28 samples include: MON = 0.12, RON = 0.16, benzene % = 0.09, and density = 0.0018. These results demonstrate that it is feasible to undertake on-line analyses of petroleum fuels by fiber-optic FT-Raman spectrometry.
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Keller, L. M., K. A. Baker, M. A. Lazzara, and J. Gallagher. "A Comparison of Meteorological Observations from South Pole Station before and after Installation of a New Instrument Suite." Journal of Atmospheric and Oceanic Technology 26, no. 8 (2009): 1605–13. http://dx.doi.org/10.1175/2009jtecha1220.1.
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Abstract The Amundsen–Scott South Pole surface meteorological instrument suite was upgraded in 2004. To ensure that the new and old instruments were recording similar information, the two suites of instruments ran simultaneously for a year. Statistical analysis of the time series of temperature, pressure, and wind was performed to determine if there were any significant differences in the observations. Significant differences were found in some of the winter months for temperature and wind speed. No differences were found for the wind direction distribution. There are also noticeable differences in wind speed between the Clean Air platform near the Clean Air facility and the platform at the approach end of the skiway. Wind speeds are lower at the skiway tower when the wind is from the northeast quadrant and at the Clean Air tower when the wind is from the southwest quadrant, reflecting the effect of increased surface roughness and flow distortion over and around the station structures. Because of a change in elevation of the pressure sensor, the pressure data were recalculated at a common station elevation (2836 m). Although the resulting differences are small (around 0.1 hPa), there is a systematic sign change between summer and winter. The results of this analysis, while revealing some significant differences, show that the new instrumentation at South Pole station is generally reporting observations that are similar to those of the old instrumentation, and most of the differences are within the accuracy of the instruments. However, the instrument placement and construction of official aviation routine weather reports (METARs) do have an impact on the usefulness of the data for research.
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Napoli, Christian, Francesco Bonanno, and Giacomo Capizzi. "An hybrid neuro-wavelet approach for long-term prediction of solar wind." Proceedings of the International Astronomical Union 6, S274 (2010): 153–55. http://dx.doi.org/10.1017/s174392131100679x.
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AbstractNowadays the interest for space weather and solar wind forecasting is increasing to become a main relevance problem especially for telecommunication industry, military, and for scientific research. At present the goal for weather forecasting reach the ultimate high ground of the cosmos where the environment can affect the technological instrumentation. Some interests then rise about the correct prediction of space events, like ionized turbulence in the ionosphere or impacts from the energetic particles in the Van Allen belts, then of the intensity and features of the solar wind and magnetospheric response. The problem of data prediction can be faced using hybrid computation methods so as wavelet decomposition and recurrent neural networks (RNNs). Wavelet analysis was used in order to reduce the data redundancies so obtaining representation which can express their intrinsic structure. The main advantage of the wavelet use is the ability to pack the energy of a signal, and in turn the relevant carried informations, in few significant uncoupled coefficients. Neural networks (NNs) are a promising technique to exploit the complexity of non-linear data correlation. To obtain a correct prediction of solar wind an RNN was designed starting on the data series. As reported in literature, because of the temporal memory of the data an Adaptative Amplitude Real Time Recurrent Learning algorithm was used for a full connected RNN with temporal delays. The inputs for the RNN were given by the set of coefficients coming from the biorthogonal wavelet decomposition of the solar wind velocity time series. The experimental data were collected during the NASA mission WIND. It is a spin stabilized spacecraft launched in 1994 in a halo orbit around the L1 point. The data are provided by the SWE, a subsystem of the main craft designed to measure the flux of thermal protons and positive ions.
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Bai, Siqi, Mingjiang Yan, Qun Wan, Long He, Xinrui Wang, and Junlin Li. "DL-RNN: An Accurate Indoor Localization Method via Double RNNs." IEEE Sensors Journal 20, no. 1 (2020): 286–95. http://dx.doi.org/10.1109/jsen.2019.2936412.
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Jijin, Jofina, Boon-Chong Seet, and Peter Han Joo Chong. "Smart-Contract-Based Automation for OF-RAN Processes: A Federated Learning Use-Case." Journal of Sensor and Actuator Networks 11, no. 3 (2022): 53. http://dx.doi.org/10.3390/jsan11030053.
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The opportunistic fog radio access network (OF-RAN) expands its offloading computation capacity on-demand by establishing virtual fog access points (v-FAPs), comprising user devices with idle resources recruited opportunistically to execute the offloaded tasks in a distributed manner. OF-RAN is attractive for providing computation offloading services to resource-limited Internet-of-Things (IoT) devices from vertical industrial applications such as smart transportation, tourism, mobile healthcare, and public safety. However, the current OF-RAN design is lacking a trusted and distributed mechanism for automating its processes such as v-FAP formation and service execution. Motivated by the recent emergence of blockchain, with smart contracts as an enabler of trusted and distributed systems, we propose an automated mechanism for OF-RAN processes using smart contracts. To demonstrate how our smart-contract-based automation for OF-RAN could apply in real life, a federated deep learning (DL) use-case where a resource-limited client offloads the resource-intensive training of its DL model to a v-FAP is implemented and evaluated. The results validate the DL and blockchain performances of the proposed smart-contract-enabled OF-RAN. The appropriate setting of process parameters to meet the often competing requirements is also demonstrated.
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Tang, Mincheng, Rezki Becheker, Pierre-Henry Hanzard, et al. "Low Noise High-Energy Dissipative Soliton Erbium Fiber Laser for Fiber Optical Parametric Oscillator Pumping." Applied Sciences 8, no. 11 (2018): 2161. http://dx.doi.org/10.3390/app8112161.
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We report on a mode-locked erbium-doped fiber laser delivering highly-chirped pulses with several tens of nanojoules of energy around 1560 nm and its exploitation to efficiently pump a fiber optical parametric oscillator (FOPO), thus enabling picosecond pulse generation around 1700 nm. The laser cavity features a high normal dispersion and mode-locking is sustained using tailored spectral filtering combined with nonlinear polarization evolution and a semiconductor saturable absorber. Numerical simulations show that the laser dynamics is governed by a strong mode-locking mechanism compensating for the large spectral and temporal pulse evolution along the cavity. In the frame of high energy picosecond pulse generation around 1700 nm, we then demonstrate that using highly-chirped pulses as pump pulses allows for the efficient tuning of the FOPO idler wavelength between 1620 and 1870 nm. In addition, satisfying noise characteristics have been achieved both for the Er-laser and the FOPO, with respective relative intensity noises (RIN) of −154 and −140 dBc/Hz, thus paving the way for the use of such sources in ultrafast instrumentation.
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Azariah, Wilfrid, Fransiscus Asisi Bimo, Chih-Wei Lin, Ray-Guang Cheng, Navid Nikaein, and Rittwik Jana. "A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches." Sensors 24, no. 3 (2024): 1038. http://dx.doi.org/10.3390/s24031038.
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The open radio access network (RAN) aims to bring openness and intelligence to the traditional closed and proprietary RAN technology and offer flexibility, performance improvement, and cost-efficiency in the RAN’s deployment and operation. This paper provides a comprehensive survey of the Open RAN development. We briefly summarize the RAN evolution history and the state-of-the-art technologies applied to Open RAN. The Open RAN-related projects, activities, and standardization is then discussed. We then summarize the challenges and future research directions required to support the Open RAN. Finally, we discuss some solutions to tackle these issues from the open source perspective.
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Abhold, Mark E., and Michael C. Baker. "MCNP–REN: a Monte Carlo tool for neutron detector design." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 485, no. 3 (2002): 576–84. http://dx.doi.org/10.1016/s0168-9002(01)02106-4.
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Zhao, Qiulong, Chunxue Wang, Jiaxin Cheng, et al. "Pharmacokinetic Study of Coadministration with Cefuroxime Sodium for Injection Influencing ReDuNing Injection-Derived Seven Phytochemicals and Nine Metabolites in Rats." Journal of Analytical Methods in Chemistry 2022 (July 2, 2022): 1–17. http://dx.doi.org/10.1155/2022/2565494.
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According to the sixth edition of China’s “New Coronavirus Diagnosis and Treatment Plan (NCDTP),” ReDuNing injection (RDN) was firstly introduced to treat severe and critical COVID-19, whereas its combination with broad-spectrum antibiotics was suggested to take with extreme caution and full reasons. Therefore, we aim to describe the pharmacokinetics of seven active phytochemicals and semiquantification of nine relevant metabolites in ReDuNing injection (RDN) after combining with cefuroxime sodium (CNa) for injection in rat plasma. Male Sprague–Dawley rats were randomly assigned to six groups, and they were intravenously administered, respectively, with different prescriptions of RDN (2 mL/kg) and CNa (225 mg/kg). At different time points (0.03, 0.08, 0.17, 0.24, 0.33, 0.50, 0.67, 1, and 6 h) after administration, the drug concentrations of iridoids glycosides, organic acids, and metabolites in rat plasma were determined using ultrahigh-pressure liquid chromatography coupled with linear ion rap-orbitrap tandem mass spectrometry (UHPLC–LTQ–Orbitrap–MS), and main pharmacokinetic parameters were estimated by noncompartment model. The results showed that there were differences in pharmacokinetic parameters, AUC(0-t), T1/2, Cmax, CL of iridoids glycosides, and organic acids, after the intravenous administration of the different combinations of RDN and CNa. Moreover, different combinations of the injections also resulted in different curves of relative changes of each metabolite. The obtained results suggested that RDN and CNa existed pharmacokinetic drug–herb interactions in rats. The findings not only lay the foundation for evaluating the safety of RDN injection combined with CNa but also make contributions to clinically applying RDN injection combined with CNa, which works potentially against severe forms of COVID-19.
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Sepúlveda, Samuel, and Ania Cravero. "Reasoning Algorithms on Feature Modeling—A Systematic Mapping Study." Applied Sciences 12, no. 11 (2022): 5563. http://dx.doi.org/10.3390/app12115563.
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Context: Software product lines (SPLs) have reached a considerable level of adoption in the software industry. The most commonly used models for managing the variability of SPLs are feature models (FMs). The analysis of FMs is an error-prone, tedious task, and it is not feasible to accomplish this task manually with large-scale FMs. In recent years, much effort has been devoted to developing reasoning algorithms for FMs. Aim: To synthesize the evidence on the use of reasoning algorithms for feature modeling. Method: We conducted a systematic mapping study, including six research questions. This study included 66 papers published from 2010 to 2020. Results: We found that most algorithms were used in the domain stage (70%). The most commonly used technologies were transformations (18%). As for the origins of the proposals, they were mainly rooted in academia (76%). The FODA model continued to be the most frequently used representation for feature modeling (70%). A large majority of the papers presented some empirical validation process (90%). Conclusion: We were able to respond to the RQs. The FODA model is consolidated as a reference within SPLs to manage variability. Responses to RQ2 and RQ6 require further review.
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Iturria-Rivera, Pedro Enrique, Han Zhang, Hao Zhou, Shahram Mollahasani, and Melike Erol-Kantarci. "Multi-Agent Team Learning in Virtualized Open Radio Access Networks (O-RAN)." Sensors 22, no. 14 (2022): 5375. http://dx.doi.org/10.3390/s22145375.
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Starting from the concept of the Cloud Radio Access Network (C-RAN), continuing with the virtual Radio Access Network (vRAN) and most recently with the Open RAN (O-RAN) initiative, Radio Access Network (RAN) architectures have significantly evolved in the past decade. In the last few years, the wireless industry has witnessed a strong trend towards disaggregated, virtualized and open RANs, with numerous tests and deployments worldwide. One unique aspect that motivates this paper is the availability of new opportunities that arise from using machine learning, more specifically multi-agent team learning (MATL), to optimize the RAN in a closed-loop where the complexity of disaggregation and virtualization makes well-known Self-Organized Networking (SON) solutions inadequate. In our view, Multi-Agent Systems (MASs) with MATL can play an essential role in the orchestration of O-RAN controllers, i.e., near-real-time and non-real-time RAN Intelligent Controllers (RIC). In this article, we first provide an overview of the landscape in RAN disaggregation, virtualization and O-RAN, then we present the state-of-the-art research in multi-agent systems and team learning as well as their application to O-RAN. We present a case study for team learning where agents are two distinct xApps: power allocation and radio resource allocation. We demonstrate how team learning can enhance network performance when team learning is used instead of individual learning agents. Finally, we identify challenges and open issues to provide a roadmap for researchers in the area of MATL based O-RAN optimization.
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Hamdan, Mutasem Q., Haeyoung Lee, Dionysia Triantafyllopoulou, et al. "Recent Advances in Machine Learning for Network Automation in the O-RAN." Sensors 23, no. 21 (2023): 8792. http://dx.doi.org/10.3390/s23218792.
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The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.
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Zhang, Jingjing, Yuanyuan Li, Teng Li, Lina Xun, and Caifeng Shan. "License Plate Localization in Unconstrained Scenes Using a Two-Stage CNN-RNN." IEEE Sensors Journal 19, no. 13 (2019): 5256–65. http://dx.doi.org/10.1109/jsen.2019.2900257.
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Xiaoli, Chu, Yuan Hongfu, and Lu Wanzhen. "In-Line Monitoring of Several Pilot Scale Catalytic Reforming Units Using a Short-Wavelength near Infrared Analyser." Journal of Near Infrared Spectroscopy 13, no. 1 (2005): 37–45. http://dx.doi.org/10.1255/jnirs.455.
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This paper reports a novel application of in-line, short wavelength, near infrared (SW-NIR) spectroscopy to monitor eight pilot scale reforming units, for real-time determination of research octane number (RON) and aromatic compositions (benzene, toluene, xylene and total aromatics) of reforming gasoline, by an on-line SW-NIR system constructed by ourselves. Robust partial least squares calibration models for sample composition and measurement conditions were built using a global calibration set including off-line and in-line samples. Prediction results of the global models, over a six month period, for the eight units show excellent correlation with the corresponding reference data. The spectral pretreatment method of mean sample residual spectrum correction was used to remove spectral difference among multi-channels caused by small coupling differences among fibres and the multiplexer in the analyser. The prediction statistics showed consistency among the eight channels without any systematic errors. The applications of the NIR analyser can rapidly reflect the state of the process at a given time without any operator intervention. Moreover, the high stability and ruggedness of the CCD array-based on-line instrumentation guarantees easy and reliable operation.
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Firouzi, Ramin, and Rahim Rahmani. "5G-Enabled Distributed Intelligence Based on O-RAN for Distributed IoT Systems." Sensors 23, no. 1 (2022): 133. http://dx.doi.org/10.3390/s23010133.
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Edge-based distributed intelligence techniques, such as federated learning (FL), have recently been used in many research fields thanks, in part, to their decentralized model training process and privacy-preserving features. However, because of the absence of effective deployment models for the radio access network (RAN), only a tiny number of FL apps have been created for the latest generation of public mobile networks (e.g., 5G and 6G). There is an attempt, in new RAN paradigms, to move toward disaggregation, hierarchical, and distributed network function processing designs. Open RAN (O-RAN), as a cutting-edge RAN technology, claims to meet 5G services with high quality. It includes integrated, intelligent controllers to provide RAN with the power to make smart decisions. This paper proposes a methodology for deploying and optimizing FL tasks in O-RAN to deliver distributed intelligence for 5G applications. To accomplish model training in each round, we first present reinforcement learning (RL) for client selection for each FL task and resource allocation using RAN intelligence controllers (RIC). Then, a slice is allotted for training depending on the clients chosen for the task. Our simulation results show that the proposed method outperforms state-of-art FL methods, such as the federated averaging algorithm (FedAvg), in terms of convergence and number of communication rounds.
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Thornton, John, Benedicta D. Arhatari, Mitchell Sesso, et al. "Failure Evaluation of a SiC/SiC Ceramic Matrix Composite During In-Situ Loading Using Micro X-ray Computed Tomography." Microscopy and Microanalysis 25, no. 3 (2019): 583–91. http://dx.doi.org/10.1017/s1431927619000187.
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AbstractIn this study, we have examined ceramic matrix composites with silicon carbide fibers in a melt-infiltrated silicon carbide matrix (SiC/SiC). We subjected samples to tensile loads while collecting micro X-ray computed tomography images. The results showed the expected crack slowing mechanisms and lower resistance to crack propagation where the fibers ran parallel and perpendicular to the applied load respectively. Cracking was shown to initiate not only from the surface but also from silicon inclusions. Post heat-treated samples showed longer fiber pull-out than the pristine samples, which was incompatible with previously proposed mechanisms. Evidence for oxidation was identified and new mechanisms based on oxidation or an oxidation assisted boron nitride phase transformation was therefore proposed to explain the long pull-out. The role of oxidation emphasizes the necessity of applying oxidation resistant coatings on SiC/SiC.
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Nguyen, Anh-Hang, and Hyuk-Kee Sung. "Improving the Performance of Optical Phased Array by Reducing Relative Intensity Noise of Optically Injection-Locked Laser Array." Photonics 9, no. 11 (2022): 868. http://dx.doi.org/10.3390/photonics9110868.
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Relative intensity noise (RIN) is an important factor that determines the performance of optical phased arrays (OPA) that are configured using semiconductor lasers as light emission sources. This study proposes a method of improving the optical signal-to-noise ratio (OSNR) of an OPA by reducing the RIN and using high coherence of optically injection-locked (OIL) laser arrays. We numerically demonstrated a laser RIN reduction of 22.7 dB by the OIL laser compared to a free-running laser. We achieved an OPA RIN reduction of 13.2 dB by combining the coherent outputs with the uncorrelated noise of 21 OIL lasers, compared to a single OIL laser RIN. Consequently, we demonstrated an OPA OSNR increase of approximately 13.8 dB based on the OIL-based OPA compared to that of the conventional noise-correlated OPA configuration. Additionally, we confirmed the maintenance of OPA OSNR improvement during OPA operations.
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Linden, Gabriel S., Semhal Ghessese, Danielle Cook, and Daniel J. Hedequist. "Pedicle Screw Placement in Adolescent Idiopathic Scoliosis: A Comparison between Robotics Coupled with Navigation versus the Freehand Technique." Sensors 22, no. 14 (2022): 5204. http://dx.doi.org/10.3390/s22145204.
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(1) Background: Robotics coupled with navigation (RAN) is a modern surgical platform shown to increase screw placement accuracy during pediatric scoliosis surgery. Our institution uses a technique which combines the RAN platform for apical pedicle screw placement and the freehand (FH) technique for terminal pedicle screw placement during scoliosis surgery (termed hybrid technique). We question if the complementary use of the RAN technology affects intraoperative outcomes, relative to the FH-only approach. (2) Methods: 60 adolescent idiopathic scoliosis (AIS) patients, ages 11–19 at surgery, who were operated on from 2019 through 2020 by a single surgeon, were retrospectively reviewed. Patients were separated by surgery type (hybrid RAN or FH), matched on demographic and surgical factors, and their intraoperative outcomes were compared statistically. (3) Results: Hybrid RAN patients had more screws placed (p = 0.01) and were of a higher BMI percentile (p = 0.005). Controlling for the number of screws placed, BMI%, and initial curve magnitude, there were no statistical differences in estimated blood loss per screw (p = 0.51), curve correction (p = 0.69), complications (p = 0.52), or fluoroscopy time (p = 0.88), between groups. However, operative time was two minutes longer per screw for hybrid RAN patients (p < 0.001). (4) Conclusions: Hybrid RAN surgeries took longer than FH, but yielded comparable effectiveness and safety as the FH technique during the initial RAN adoption phase.
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Dryjański, Marcin, Łukasz Kułacz, and Adrian Kliks. "Toward Modular and Flexible Open RAN Implementations in 6G Networks: Traffic Steering Use Case and O-RAN xApps." Sensors 21, no. 24 (2021): 8173. http://dx.doi.org/10.3390/s21248173.
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The development of cellular wireless systems has entered the phase when 5G networks are being deployed and the foundations of 6G solutions are being identified. However, in parallel to this, another technological breakthrough is observed, as the concept of open radio access networks is coming into play. Together with advancing network virtualization and programmability, this may reshape the way the functionalities and services related to radio access are designed, leading to modular and flexible implementations. This paper overviews the idea of open radio access networks and presents ongoing O-RAN Alliance standardization activities in this context. The whole analysis is supported by a study of the traffic steering use case implemented in a modular way, following the open networking approach.
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Hull, R. "Direct Transmission Electron Microscope Observations of Doping Variations in InP-Based Semiconductor Laser Diodes." Microscopy and Microanalysis 4, S2 (1998): 648–49. http://dx.doi.org/10.1017/s1431927600023369.
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The ability to map semiconductor doping distributions with high spatial resolution (∽10 nm) and high compositional sensitivity (of order 1 ppm) is of enormous importance to the microelectronics and optoelectronics industry. Although several methods (e.g. scanning capacitance microscopy, masked secondary ion mass spectroscopy, secondary electron microscopy, electron holography) are under development, no technique currently offers the combination of sufficient resolution, sensitivity and reproducibility to fully address characterization requirements. In this paper, we describe development of a technique which utilizes focused ion beam (FIB) sputtering and transmission electron microscopy (TEM) to enable direct imaging of dopant distributions in InP-based semiconductor devices, with spatial resolution of order 10 - 30 ran, and compositional sensitivity of order 1017 cm-3 (i.e. 5 ppm).Laser diode samples are prepared for TEM imaging using a FEI 200 30 kV Ga+ focused ion beam system. A schematic of the relevant structure is shown in Figure 1(a).
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Winey, Karen I., Jonathan H. Laurer, and Brian P. Kirkmeyer. "Ionic Nano-Aggregates in Polyethylene-Based Ionomers: Comparison of Stem and Saxs Results." Microscopy and Microanalysis 6, S2 (2000): 1110–11. http://dx.doi.org/10.1017/s1431927600038046.
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Semicrystalline ionomers are industrially important materials in adhesives, packaging, engineering thermoplastics, coatings, and more. The most common of these are based on poly(ethylene-ran-methacrylic acid), E/MAA random copolymers partially neutralized with various cations and produced by DuPont under the tradename Surlyn®. In 1990, Register and Cooper described the morphology of semicrystalline ionomers as crystalline lamellae separated by an amorphous polymeric matrix that contains isolated cation-rich domains. Their anomalous small angle X-ray scattering experiments are consistent with the relative electron densities associated with this “three-phase” morphological model. Despite this agreement and the commercial importance of semicrystalline ionomers, a number of fundamental issues have remained unresolved regarding both the morphology of the ionic nanoaggregates and the efficiency of aggregation.Recently, we have imaged the ionic aggregates using a scanning transmission electron microscope equipped with a field emission electron gun. This microscopy method offers the advantages of reduced phase contrast, high spatial resolution, and enhanced atomic number contrast relative to transmission electron microscopy.
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Pniov, Alexey, Andrey Zhirnov, Dmitriy Shelestov, et al. "Yb,Er:glass Microlaser at 1.5 µm for optical fiber sensing: development, characterization and noise reduction." ACTA IMEKO 5, no. 4 (2016): 24. http://dx.doi.org/10.21014/acta_imeko.v5i4.423.
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A fiber-pumped single-frequency microchip erbium laser was developed and characterized with the aim of using it in coherent Optical Time Domain Reflectometry (OTDR) measurements and sensing. The laser is pumped by a fiber-coupled 976 nm laser diode and provides 8 mW TEM00 single frequency output power at 1.54 µm wavelength, suitable for efficient coupling to optical fibers. The amplitude and phase noise of this 200 THz oscillator were experimentally investigated and a Relative Intensity Noise (RIN) control loop was developed providing 27 dB RIN peak reduction at the relaxation oscillation frequency of 800 kHz
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Shen, Wei, Weida Ni, Rui Yong, et al. "Estimating RQD for Rock Masses Based on a Comprehensive Approach." Applied Sciences 13, no. 23 (2023): 12855. http://dx.doi.org/10.3390/app132312855.
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Rock Quality Designation (RQD) is among the widely used measures of the quality of rock masses and can be derived through Monte Carlo stochastic process-based fracture network simulations. However, repeated simulations can yield variable RQD results. Here, we introduce a four-step approach that incorporates class ratio analysis to estimate the representative RQD, which includes (1) extracting the mean and confidence interval of the RQD sample, in terms of the Confidence Neutrosophic Number Cubic Value (CNNCV), (2) employing class ratio analysis to determine the thresholds of the number of virtual boreholes and that of the number of models for a given size D, beyond which the CNNCV remains substantially unchanged, (3) accepting the CNNCV at the thresholds of the number of models as the representative RQD for the model of size D (RQD(D)) and (4) determining the representative RQD (rRQD), defined as the specific value which, once D exceeds, the RQD(D) does not change significantly. The introduced approach is illustrated with a case study of an open-pit slope in China, and it was tested for its performance. The RQD calculation results of the proposed method and the traditional single-model approach exhibit differences, which diminish with increasing model sizes. At the 95% confidence level, the stable size of the RQD determined by the proposed method is 13 m, compared to 25 m for the single-model approach. This method enhances the accuracy of representative elementary volume predictions by accounting for the diversity in the simulation results of RQDs for the same size. Overall, the introduced approach offers a reliable method for obtaining RQD estimates.
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O’Reilly, Jamie A., Jordan Wehrman, and Paul F. Sowman. "A Guided Tutorial on Modelling Human Event-Related Potentials with Recurrent Neural Networks." Sensors 22, no. 23 (2022): 9243. http://dx.doi.org/10.3390/s22239243.
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In cognitive neuroscience research, computational models of event-related potentials (ERP) can provide a means of developing explanatory hypotheses for the observed waveforms. However, researchers trained in cognitive neurosciences may face technical challenges in implementing these models. This paper provides a tutorial on developing recurrent neural network (RNN) models of ERP waveforms in order to facilitate broader use of computational models in ERP research. To exemplify the RNN model usage, the P3 component evoked by target and non-target visual events, measured at channel Pz, is examined. Input representations of experimental events and corresponding ERP labels are used to optimize the RNN in a supervised learning paradigm. Linking one input representation with multiple ERP waveform labels, then optimizing the RNN to minimize mean-squared-error loss, causes the RNN output to approximate the grand-average ERP waveform. Behavior of the RNN can then be evaluated as a model of the computational principles underlying ERP generation. Aside from fitting such a model, the current tutorial will also demonstrate how to classify hidden units of the RNN by their temporal responses and characterize them using principal component analysis. Statistical hypothesis testing can also be applied to these data. This paper focuses on presenting the modelling approach and subsequent analysis of model outputs in a how-to format, using publicly available data and shared code. While relatively less emphasis is placed on specific interpretations of P3 response generation, the results initiate some interesting discussion points.
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Vilà, Irene, Oriol Sallent, and Jordi Pérez-Romero. "On the Design of a Network Digital Twin for the Radio Access Network in 5G and Beyond." Sensors 23, no. 3 (2023): 1197. http://dx.doi.org/10.3390/s23031197.
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A Network Digital Twin (NDT) is a high-fidelity digital mirror of a real network. Given the increasing complexity of 5G and beyond networks, the use of an NDT becomes useful as a platform for testing configurations and algorithms prior to their application in the real network, as well as for predicting the performance of such algorithms under different conditions. While an NDT can be defined for the different subsystems of the network, this paper proposes an NDT architecture focusing on the Radio Access Network (RAN), describing the components to represent and model the operation of the different RAN elements, and to perform emulations. Different application use cases are identified, and among them, the paper puts the focus on the training of Reinforcement Learning (RL) solutions for the RAN. For this use case, the paper introduces a framework aligned with O-RAN specifications and discusses the functionalities needed to integrate the NDT. This use case is illustrated with the description of a RAN NDT implementation used for training an RL-based capacity-sharing solution for network slicing. Presented results demonstrate that the implemented RAN NDT is a suitable platform to successfully train the RL solution, achieving service-level agreement satisfaction values above 85%.
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Xiong, Yongcheng, Wenhong Jia, Limin Zhang, Ying Zhao, and Lifang Zheng. "Feedforward Control of Piezoelectric Ceramic Actuators Based on PEA-RNN." Sensors 22, no. 14 (2022): 5387. http://dx.doi.org/10.3390/s22145387.
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Multilayer perceptron (MLP) has been demonstrated to implement feedforward control of the piezoelectric actuator (PEA). To further improve the control accuracy of the neural network, reduce the training time, and explore the possibility of online model updating, a novel recurrent neural network named PEA-RNN is established in this paper. PEA-RNN is a three-input, one-output neural network, including one gated recurrent unit (GRU) layer, seven linear layers, and one residual connection in the linear layers. The experimental results show that the displacement linearity error of piezoelectric ceramics reaches 8.96 μm in the open-loop condition. After using PEA-RNN compensation, the maximum displacement error of piezoelectric ceramics is reduced to 0.465 μm at the operating frequency of 10 Hz, which proves that PEA-RNN can accurately compensate piezoelectric ceramics’ dynamic hysteresis nonlinearity. At the same time, the training epochs of PEA-RNN are only 5% of the MLP, and fewer training epochs provide the possibility to realize online updates of the model in the future.
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Shin, Moonsun, Seonmin Hwang, Byungcheol Kim, Sungbo Seo, and Junghwan Kim. "IoT-Based Intelligent Monitoring System Applying RNN." Applied Sciences 12, no. 20 (2022): 10421. http://dx.doi.org/10.3390/app122010421.
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In this paper, we propose an intelligent monitoring framework based on the Internet of Things (IoT) by applying a Recurrent Neural Network (RNN) for the predictive maintenance of a biobanking system. RNN, which is one of the deep learning models, is used for time series data. It is called a sequence model because it processes inputs and outputs in sequence units. The proposed framework measures the internal temperature of the cryogenic freezer and the temperature of each component simultaneously, monitors the internal temperatures of internal and middle layers in real time, sends the sensing temperature data to the server, and performs predictive learning. Thus, it is possible to support the intelligent predictive maintenance of the biobank by performing a time series data analysis of the temperature sensor using RNN. Among RNN methods, a simple RNN has a longer-term dependency problem; therefore, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), which have higher learning performance, are selected. To support the intelligent predictive maintenance of the biobank, both the LSTM and GRU models were constructed, and comparative experiments were performed. The proposed system can ensure the safety of bio-resources by performing predictive maintenance using RNN and provide an accurate status of the biobank in real-time. In addition, before an abnormal situation occurs, it is possible to respond immediately to emergencies that may damage biological resources.
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Wang, Yung-Chung, Yi-Chun Houng, Han-Xuan Chen, and Shu-Ming Tseng. "Network Anomaly Intrusion Detection Based on Deep Learning Approach." Sensors 23, no. 4 (2023): 2171. http://dx.doi.org/10.3390/s23042171.
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The prevalence of internet usage leads to diverse internet traffic, which may contain information about various types of internet attacks. In recent years, many researchers have applied deep learning technology to intrusion detection systems and obtained fairly strong recognition results. However, most experiments have used old datasets, so they could not reflect the latest attack information. In this paper, a current state of the CSE-CIC-IDS2018 dataset and standard evaluation metrics has been employed to evaluate the proposed mechanism. After preprocessing the dataset, six models—deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), CNN + RNN and CNN + LSTM—were constructed to judge whether network traffic comprised a malicious attack. In addition, multi-classification experiments were conducted to sort traffic into benign traffic and six categories of malicious attacks: BruteForce, Denial-of-service (DoS), Web Attacks, Infiltration, Botnet, and Distributed denial-of-service (DDoS). Each model showed a high accuracy in various experiments, and their multi-class classification accuracy were above 98%. Compared with the intrusion detection system (IDS) of other papers, the proposed model effectively improves the detection performance. Moreover, the inference time for the combinations of CNN + RNN and CNN + LSTM is longer than that of the individual DNN, RNN and CNN. Therefore, the DNN, RNN and CNN are better than CNN + RNN and CNN + LSTM for considering the implementation of the algorithm in the IDS device.
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Rota-Rodrigo, Sergio, Daniel Leandro, Giorgio Santarelli, Manuel Lopez-Amo, and Juan Diego Ania-Castañón. "Effect of Linewidth on the Relative Intensity Noise in Random Distributed Feedback Raman Fiber Lasers." Sensors 22, no. 21 (2022): 8381. http://dx.doi.org/10.3390/s22218381.
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We experimentally explore the relation between spectral linewidth and RIN transfer in half-open cavity random distributed feedback Raman lasers, demonstrating for the first time the possibility of adjusting the pump-to-signal RIN transfer intensity and cut-off frequency by using spectral filtering in the reflector section. We apply this approach to a 50-km laser system, operating in the C-Band, reliant on a standard single-mode fiber. We obtained a minimum bandwidth of 13 pm, which translates into a visible RIN cut-off at 800 MHz.
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Cheng, Tianqing, Fangfang Jiang, Qing Li, Jitao Zeng, and Biyong Zhang. "Quantitative Analysis Using Consecutive Time Window for Unobtrusive Atrial Fibrillation Detection Based on Ballistocardiogram Signal." Sensors 22, no. 15 (2022): 5516. http://dx.doi.org/10.3390/s22155516.
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Atrial fibrillation (AF) is the most common clinically significant arrhythmia; therefore, AF detection is crucial. Here, we propose a novel feature extraction method to improve AF detection performance using a ballistocardiogram (BCG), which is a weak vibration signal on the body surface transmitted by the cardiogenic force. In this paper, continuous time windows (CTWs) are added to each BCG segment and recurrence quantification analysis (RQA) features are extracted from each time window. Then, the number of CTWs is discussed and the combined features from multiple time windows are ranked, which finally constitute the CTW–RQA features. As validation, the CTW–RQA features are extracted from 4000 BCG segments of 59 subjects, which are compared with classical time and time-frequency features and up-to-date energy features. The accuracy of the proposed feature is superior, and three types of features are fused to obtain the highest accuracy of 95.63%. To evaluate the importance of the proposed feature, the fusion features are ranked using a chi-square test. CTW–RQA features account for 60% of the first 10 fusion features and 65% of the first 17 fusion features. It follows that the proposed CTW–RQA features effectively supplement the existing BCG features for AF detection.
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Koufos, Konstantinos, Karim EI Haloui, Mehrdad Dianati, et al. "Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps." Journal of Sensor and Actuator Networks 10, no. 4 (2021): 60. http://dx.doi.org/10.3390/jsan10040060.
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The increasing complexity of communication systems, following the advent of heterogeneous technologies, services and use cases with diverse technical requirements, provide a strong case for the use of artificial intelligence (AI) and data-driven machine learning (ML) techniques in studying, designing and operating emerging communication networks. At the same time, the access and ability to process large volumes of network data can unleash the full potential of a network orchestrated by AI/ML to optimise the usage of available resources while keeping both CapEx and OpEx low. Driven by these new opportunities, the ongoing standardisation activities indicate strong interest to reap the benefits of incorporating AI and ML techniques in communication networks. For instance, 3GPP has introduced the network data analytics function (NWDAF) at the 5G core network for the control and management of network slices, and for providing predictive analytics, or statistics, about past events to other network functions, leveraging AI/ML and big data analytics. Likewise, at the radio access network (RAN), the O-RAN Alliance has already defined an architecture to infuse intelligence into the RAN, where closed-loop control models are classified based on their operational timescale, i.e., real-time, near real-time, and non-real-time RAN intelligent control (RIC). Different from the existing related surveys, in this review article, we group the major research studies in the design of model-aided ML-based transceivers following the breakdown suggested by the O-RAN Alliance. At the core and the edge networks, we review the ongoing standardisation activities in intelligent networking and the existing works cognisant of the architecture recommended by 3GPP and ETSI. We also review the existing trends in ML algorithms running on low-power micro-controller units, known as TinyML. We conclude with a summary of recent and currently funded projects on intelligent communications and networking. This review reveals that the telecommunication industry and standardisation bodies have been mostly focused on non-real-time RIC, data analytics at the core and the edge, AI-based network slicing, and vendor inter-operability issues, whereas most recent academic research has focused on real-time RIC. In addition, intelligent radio resource management and aspects of intelligent control of the propagation channel using reflecting intelligent surfaces have captured the attention of ongoing research projects.