Reducing Waste in 3D Printing Using a Neural Network Based on an Own Elbow Exoskeleton

Traditional rehabilitation systems are evolving into advanced systems that enhance and improve rehabilitation techniques and physical exercise. The reliable assessment and robotic support of the upper limb joints provided by the presented elbow exoskeleton are important clinical goals in early rehabilitation after stroke and other neurological disorders. This allows for not only the support of activities of daily living, but also prevention of the progression neuromuscular pathology through proactive physiotherapy toward functional recovery. The prices of plastics are rising very quickly, as is their consumption, so it makes sense to optimize three dimensional (3D) printing procedures through, for example, improved artificial intelligence-based (AI-based) design or injection simulation, which reduces the use of filament, saves material, reduces waste, and reduces environmental impact. The time and cost savings will not reduce the high quality of the products and can provide a competitive advantage, especially in the case of thinly designed mass products. AI-based optimization allows for one free print after every 6.67 prints (i.e., from materials that were previously wasted).

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The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

Remote Sensing ◽

10.3390/rs13091701 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1701

Author(s):

Leonardo Bagaglini ◽

Paolo Sanò ◽

Daniele Casella ◽

Elsa Cattani ◽

Giulia Panegrossi

Keyword(s):

Neural Network ◽

Passive Microwave ◽

Retrieval Algorithm ◽

Climate Data ◽

High Quality ◽

Data Record ◽

Global Precipitation ◽

Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

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Experimental Investigation of Color Reproduction Quality of Color 3D Printing Based on Colored Layer Features

Molecules ◽

10.3390/molecules25122909 ◽

2020 ◽

Vol 25 (12) ◽

pp. 2909 ◽

Cited By ~ 2

Author(s):

Jiangping Yuan ◽

Jieni Tian ◽

Chen Chen ◽

Guangxue Chen

Keyword(s):

3D Printing ◽

Numerical Models ◽

Three Dimensional ◽

Chromatic Aberration ◽

Structural Similarity ◽

Color Reproduction ◽

Color Test ◽

Layer Sequence ◽

Colored Layer

Color three-dimensional (3D) printing is an advanced 3D printing technique for reproducing colorful 3D objects, but it still has color accuracy issues. Plastic-based color 3D printing is a common color 3D printing process, and most factors affecting its color reproduction quality have been studied from printing materials to parameters in the fixed consecutive layers. In this work, and combined with variable stair thickness, the colored layer sequence in sliced layers of a specific 3D color test chart is deliberately changed to test the effects of colored layer features on its final color reproduction quality. Meanwhile, the colorimetric measurement and image acquisition of printed 3D color test charts are both achieved under standard conditions. Results clearly show that the chromatic aberration values and mean structural similarity (MSSIM) values of color samples have a significant correlation with the colored stair thickness, but both did not display a linear relationship. The correlation trends between colored layer sequence and the above two indexes are more localized to the colored stair thickness. Combined with color structural similarity (SSIM) maps analysis, a comprehensive discussion between colored layer features and color reproduction quality of color 3D printing is presented, providing key insights for developing further accurate numerical models.

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Physical model of fluid flows in spacecraft tank

Mechanics and Advanced Technologies ◽

10.20535/2521-1943.2021.5.2.126106 ◽

2021 ◽

Vol 5 (2) ◽

pp. 212-217

Author(s):

Vasyl Kovalev ◽

Volodumur Kalyuzhny ◽

Vadum Gornostay

Keyword(s):

Experimental Data ◽

Liquid Fuel ◽

Axisymmetric Flow ◽

Three Dimensional ◽

Fluid Flows ◽

High Quality ◽

Spherical Tank ◽

Measurement Results ◽

Spherical Reservoir

The results of an experimental study of liquid fuel flows in the tanks of a spacecraft during its rotation are presented. The analysis of structure and character of flows development on time is carried out, variants of graphic dependences approximation are offered. Graphical diagrams are presented, according to which it is convenient to present a three-dimensional nonlinear picture of non-stationary axisymmetric flow in a spherical reservoir, as well as methods of influencing flows with the help of internal baffles. Estimation of the obtained experimental data probability testifies to the rather high quality of the measurement results and the constructed picture of the currents in the spherical tank with internal baffles.

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DEVELOPMENT OF ALGORITHMIC SUPPORT AND A DEDICATED DEVICE FOR IMAGING THE INTERNAL ORGANS OF THE PATIENT ACCORDING TO MRI AND CT SCANNERS

Proceedings of Southwest State University ◽

10.21869/2223-1560-2018-22-6-189-197 ◽

2019 ◽

Vol 22 (6) ◽

pp. 189-197

Author(s):

E. S. Sirota ◽

M. I. Truphanov

Keyword(s):

Neural Network ◽

Three Dimensional ◽

Color Difference ◽

Learning Criterion ◽

Average Value ◽

Advantages And Disadvantages ◽

The Neural Network ◽

Training Stage ◽

Algorithmic Support

In work the algorithm of restoration of the images damaged as a result of influence of noise of various nature is considered. The advantages and disadvantages of the existing approaches, as well as the prospects of using artificial neural networks, are noted. A double-layer neural network is used as an image restoration tool, and it is assumed that the location of the damaged pixels is known. A neuron is represented as a 3x3 array, where each element of the array has a pixel color value that corresponds to the value of that color in the palette. The neural network is trained on intact images, while the color difference of pixels acts as a learning criterion. For a more accurate restoration, it is recommended at the training stage to select images similar in color to damaged ones. At the recovery stage, neurons (3x3) are formed around the damaged pixels, so that the damaged pixel is located in the middle of the neuron data array. The damaged pixel is assigned a neuron value depending on the average value of the weights matrix. An algorithm for the restoration of pixels, as well as its software implementation. The simulation was carried out in the RGB palette separately for each channel. To assess the quality of the recovery were selected groups of images with varying degrees of damage. Unlike existing solutions, the algorithm has the simplicity of implementation. The research results show that regardless of the degree of damage (within 50%), about 70% of damaged pixels are restored. Further studies suggest a modification of the algorithm to restore images with enlarged areas of damage, as well as adapting it to restore three-dimensional images.

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Successful occupational therapy at end of life for a patient with prostate sarcoma

BMJ Case Reports ◽

10.1136/bcr-2021-242056 ◽

2021 ◽

Vol 14 (6) ◽

pp. e242056

Author(s):

Takayoshi Yamaga ◽

Katsutoshi Asano ◽

Masanao Ikeya ◽

Kosuke Nakanishi

Keyword(s):

Quality Of Life ◽

Occupational Therapy ◽

Activities Of Daily Living ◽

Daily Living ◽

Poor Prognosis ◽

Clinical Report ◽

Limited Time ◽

High Quality ◽

Time Period

Most previous studies on palliative rehabilitation highlight medical care, with occupational therapy (OT) primarily focused on normal and instrumental activities of daily living. This clinical report describes the case of a 35-year-old patient with prostate sarcoma with a poor prognosis who received OT to help him focus on participating in a meaningful occupation. Initially, the patient was depressed and withdrawn, but with OT, he was able to participate in an occupation, and his life became more active. Although the patient was at the end of his life, he was able to maintain a high quality of life during the limited time period.

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Hull Plate Bending Springback Prediction Based on Artificial Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.988.309 ◽

2014 ◽

Vol 988 ◽

pp. 309-312

Author(s):

Shao Juan Su ◽

Yong Hu ◽

Cheng Fang Wang ◽

Bin Liu

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Three Dimensional ◽

Plate Bending ◽

Forming Process ◽

Cold Bending ◽

Matlab Programming ◽

Springback Prediction ◽

Surface Plate

In the process of three-dimensional curved hull plate forming, springback caused serious influence on the forming accuracy, in order to ensure the forming quality of the asymmetric multiple pressure heads CNC bending machine of ship hull 3D surface plate, to achieve the automatic processing, it is necessary to solve the problem of springback in the hull plate forming process. It is rarely to see the research on the cold bending springback problem of middle-thickness hull plate now. To established nonlinear model of plate parameters and springback amount based on BP neural network, accurately analyzing the prediction of springback, and getting the sptringback prediction model based on the BP neural network in the Matlab programming.

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Three-dimensional Printing in Spine Surgery: Protocol of a Systematic Review and Meta-analysis

10.21203/rs.3.rs-93116/v1 ◽

2020 ◽

Author(s):

Guanli Xie ◽

Tao Wang ◽

Bo Jiang ◽

Chunyan Yang ◽

Deguang Li ◽

...

Keyword(s):

3D Printing ◽

Spinal Surgery ◽

Three Dimensional ◽

Clinical Heterogeneity ◽

Three Dimensional Printing ◽

3D Printing Technology ◽

Randomized Controlled ◽

Randomized Controlled Studies ◽

Dimensional Printing

Abstract BackgroundThe complexity and diversity of spine pathology lead to the complexity and diversity of spinal surgery. The emergence and application of three-dimensional printing (3DP) technology has brought good news to surgeons and patients. However, the use of 3DP in spinal surgery remains controversial. Therefore, this study was designed to investigate whether 3D printing technology is beneficial for spinal surgery.MethodsThree English online databases including EMBASE (via embase.com), Medline (via PubMed), and Cochrane Central Register of Controlled Trials (CENTRAL) will be searched from inception until August 31, 2020. Document records retrieved according to the pre-defined search strategy will be managed by EndNote X7. The MINORST (methodological index for non-randomized studies) item recommended for non-randomized controlled interventional studies in surgery will be used to assess the quality of non-randomized controlled studies. The “Risk of bias” (ROB) table will be used to assess the quality of randomized controlled studies. The data extraction will be completed by two authors independently, one of whom extracts and the other checks. If there is any missing data, original author will be contacted to obtain the data required. Any inconsistencies were agreed upon by discussion with a third investigator. If the collected data can be synthesized, Review Manager (RevMan5.3) will be used to estimate the overall effect of 3DP for Spinal surgery. Otherwise, only the qualitative analysis will be carried out. According to the results of clinical heterogeneity test, random effects model or fixed effects model will be used for data synthesis. The sources of clinical heterogeneity will be explored by meta-regression and subgroup analysis. If more than 10 studies are included, funnel plots will be used to assess the publication bias. This review will be carried out in strict accordance with Cochrane Handbook for Systematic Reviews of Interventions.ConclusionThis study will can provide surgeons and patients with evidence-based evidence for the use of 3D printing technology in spinal surgery.Systematic review registrationPROSPERO/ID = CRD42020204053.

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Development of fall detection and activity recognition using threshold based method and neural network

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v17.i3.pp1338-1347 ◽

2020 ◽

Vol 17 (3) ◽

pp. 1338

Author(s):

Sai Siong Jun ◽

Hafiz Rashidi Ramli ◽

Azura Che Soh ◽

Noor Ain Kamsani ◽

Raja Kamil Raja Ahmad ◽

...

Keyword(s):

Neural Network ◽

Activity Recognition ◽

Human Activity ◽

Daily Living ◽

Fall Detection ◽

The Elderly ◽

Recognition System ◽

Severe Injuries ◽

Series Of Experiments

Falls are dangerous and contribute to over 80% of injury-related hospitalization especially amongst the elderly. Hence, fall detection is important for preventing severe injuries and accidental deaths. Meanwhile, recognizing human activity is important for monitoring health status and quality of life as it can be applied in geriatric care and healthcare in general. This research presents the development of a fall detection and human activity recognition system using Threshold Based Method (TBM) and Neural Network (NN). Intentional forward fall and six other activities of daily living (ADLs), which include running, jumping, walking, sitting, lying, and standing are performed by 15 healthy volunteers in a series of experiments. There are four important stages involved in fall detection and ADL recognition, which are signal filtering, segmentation, features extraction and classification. For classification, TBM achieved an accuracy of 98.41% and 95.40% for fall detection and activity recognition respectively whereas NN achieved an accuracy of 97.78% and 96.77% for fall detection and activity recognition respectively.

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Natural three-dimensional display with high quality of reconstructed images based on dense sampling

Optik ◽

10.1016/j.ijleo.2015.08.048 ◽

2015 ◽

Vol 126 (23) ◽

pp. 4605-4607 ◽

Cited By ~ 4

Author(s):

Xunbo Yu ◽

Xinzhu Sang ◽

Duo Chen ◽

Peng Wang ◽

Xin Gao ◽

...

Keyword(s):

Three Dimensional ◽

High Quality ◽

Three Dimensional Display ◽

Dense Sampling

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3D Reconstruction Role in Surgical Treatment of Sinonasal Tumours

Revista de Chimie ◽

10.37358/rc.18.6.6345 ◽

2018 ◽

Vol 69 (6) ◽

pp. 1455-1457

Author(s):

Dragos Octavian Palade ◽

Bogdan Mihail Cobzeanu ◽

Petronela Zaharia ◽

Marius Dabija

Keyword(s):

3D Printing ◽

Three Dimensional ◽

Patient Specific ◽

Three Dimensional Printing ◽

Great Progress ◽

3D Printed ◽

Medical Modeling ◽

Dimensional Printing ◽

Tissue Defects

Three-dimensional printing has numerous applications and has gained much interest in the medical world. The constantly improving quality of 3D-printing applications has contributed to their increased use on patients. Nowadays, 3D printing is very well integrated in the surgical practice and research. Also, the field of head and neck reconstructive surgery is constantly evolving because of the three-dimensional printing, a technology which can be widely used in a variety of situations such as reconstruction of tissue defects, surgical planning, medical modeling and prosthesis. By using 3D printing into tissue engineering and materials, it may be possible for otolaryngologists to implant 3D printed functional grafts into patients and will also provide a rapid production of personalized patient-specific devices. Advances in 3D printed implants and future tissue-engineered constructs will bring great progress to the field of otorhinolaryngology.

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