Journal articles on the topic 'Gun control – Data processing'
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1
Xu, Zhen Hui, Li Xu, Shi Hai Zhou, and Zhen Jun Yang. "Research on Technical Conditions Testing Line of Certain Tank Gun Weapon System." Applied Mechanics and Materials 602-605 (August 2014): 1630–33. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1630.
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In certain tank gun technical conditions integrated testing line, we firstly realize on line test of tank gun under non disassembly situation by using data acquisition card ----4472B produced in American NI Company. It realizes multiple data concurrent acquisition and processing and ensures the integral correlation of each testing data. The system adopts PXI main control platform and actualizes the technical conditions integrated test system of breechblock and recoil system based on Virtual Instrument technology. Through practical use, the result proves that the system has prominent character of integrated testing, online testing, visual data and convenient operation.
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Ren, Zhong Wei, Zhen Yun Duan, Wen Hui Zhao, Guo Fu Tian, and Yuan Tao Wang. "Research on On-line Detection and Data Processing Technology of Welding Seam for Bellow." Applied Mechanics and Materials 66-68 (July 2011): 1714–17. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.1714.
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In order to improve the welding quality and welding speed of irregular bellows, according to the characteristic of welding seam of bellows, an automatic and on-line detection technology is proposed in welding technique. The infrared laser sensor is used for measuring with non-contact and real-time detection of welded joint center, the position of welding seam is transfered by the IEEE-1394(Fire Wire). After data preprocessing, the coordinates of feature point are extracted, be storaged and forecasted, then sent to the control system, the moving of welding gun is controlled along track scanning. According to the accuracy analysis of on-line detection system of bellows, the technology of tracking measurement is stable and reliable, and it is effective for improving measuring accuracy and efficiency.
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Kimmel, James, and Michael Rowe. "A Behavioral Addiction Model of Revenge, Violence, and Gun Abuse." Journal of Law, Medicine & Ethics 48, S4 (2020): 172–78. http://dx.doi.org/10.1177/1073110520979419.
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Data from multiple sources point to the desire for revenge in response to grievances or perceived injustices as a root cause of violence, including firearm violence. Neuroscience and behavioral studies are beginning to reveal that the desire for revenge in response to grievances activates the same neural reward-processing circuitry as that of substance addiction, suggesting that grievances trigger powerful cravings for revenge in anticipation of experiencing pleasure. Based on this evidence, the authors argue that a behavioral addiction framework may be appropriate for understanding and addressing violent behavior. Such an approach could yield significant benefits by leveraging scientific and public health-oriented drug abuse prevention and treatment strategies that target drug cravings to spur development of scientific and public-health-oriented “gun abuse” prevention and treatment strategies targeting the revenge cravings that lead to violence. An example of one such “motive control” strategy is discussed. Approaching revenge-seeking, violence, and gun abuse from the perspective of compulsion and addiction would have the added benefit of avoiding the stigmatization as violent of individuals with mental illness while also acknowledging the systemic, social, and cultural factors contributing to grievances that lead to violent acts.
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Lai, Xuhui, and Zhengying Wei. "Slicing Algorithm and Partition Scanning Strategy for 3D Printing Based on GPU Parallel Computing." Materials 14, no. 15 (July 31, 2021): 4297. http://dx.doi.org/10.3390/ma14154297.
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Aiming at the problems of over stacking, warping deformation and rapid adjustment of layer thickness in electron beam additive manufacturing, the 3D printing slicing algorithm and partition scanning strategy for numerical control systems are studied. The GPU (graphics processing unit) is used to slice the 3D model, and the STL (stereolithography) file is calculated in parallel according to the normal vector and the vertex coordinates. The voxel information of the specified layer is dynamically obtained by adjusting the projection matrix to the slice height. The MS (marching squares) algorithm is used to extract the coordinate sequence of the binary image, and the ordered contour coordinates are output. In order to avoid shaking of the electron gun when the numerical control system is forming the microsegment straight line, and reduce metal overcrowding in the continuous curve C0, the NURBS (non-uniform rational b-splines) basis function is used to perform curve interpolation on the contour data. Aiming at the deformation problem of large block components in the forming process, a hexagonal partition and parallel line variable angle scanning technology is adopted, and an effective temperature and deformation control strategy is formed according to the European-distance planning scan order of each partition. The results show that the NURBS segmentation fits closer to the original polysurface cut line, and the error is reduced by 34.2% compared with the STL file slice data. As the number of triangular patches increases, the algorithm exhibits higher efficiency, STL files with 1,483,132 facets can be cut into 4488 layers in 89 s. The slicing algorithm involved in this research can be used as a general data processing algorithm for additive manufacturing technology to reduce the waiting time of the contour extraction process. Combined with the partition strategy, it can provide new ideas for the dynamic adjustment of layer thickness and deformation control in the forming process of large parts.
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Piacentino, Esteban, Alvaro Guarner, and Cecilio Angulo. "Generating Synthetic ECGs Using GANs for Anonymizing Healthcare Data." Electronics 10, no. 4 (February 5, 2021): 389. http://dx.doi.org/10.3390/electronics10040389.
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In personalized healthcare, an ecosystem for the manipulation of reliable and safe private data should be orchestrated. This paper describes an approach for the generation of synthetic electrocardiograms (ECGs) based on Generative Adversarial Networks (GANs) with the objective of anonymizing users’ information for privacy issues. This is intended to create valuable data that can be used both in educational and research areas, while avoiding the risk of a sensitive data leakage. As GANs are mainly exploited on images and video frames, we are proposing general raw data processing after transformation into an image, so it can be managed through a GAN, then decoded back to the original data domain. The feasibility of our transformation and processing hypothesis is primarily demonstrated. Next, from the proposed procedure, main drawbacks for each step in the procedure are addressed for the particular case of ECGs. Hence, a novel research pathway on health data anonymization using GANs is opened and further straightforward developments are expected.
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Potdar, Swapnil, Aleksandr Ianevski, John-Patrick Mpindi, Dmitrii Bychkov, Clément Fiere, Philipp Ianevski, Bhagwan Yadav, et al. "Breeze: an integrated quality control and data analysis application for high-throughput drug screening." Bioinformatics 36, no. 11 (March 2, 2020): 3602–4. http://dx.doi.org/10.1093/bioinformatics/btaa138.
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Abstract Summary High-throughput screening (HTS) enables systematic testing of thousands of chemical compounds for potential use as investigational and therapeutic agents. HTS experiments are often conducted in multi-well plates that inherently bear technical and experimental sources of error. Thus, HTS data processing requires the use of robust quality control procedures before analysis and interpretation. Here, we have implemented an open-source analysis application, Breeze, an integrated quality control and data analysis application for HTS data. Furthermore, Breeze enables a reliable way to identify individual drug sensitivity and resistance patterns in cell lines or patient-derived samples for functional precision medicine applications. The Breeze application provides a complete solution for data quality assessment, dose–response curve fitting and quantification of the drug responses along with interactive visualization of the results. Availability and implementation The Breeze application with video tutorial and technical documentation is accessible at https://breeze.fimm.fi; the R source code is publicly available at https://github.com/potdarswapnil/Breeze under GNU General Public License v3.0. Contact swapnil.potdar@helsinki.fi Supplementary information Supplementary data are available at Bioinformatics online.
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Wang, Jinglu, Ying Zhang, Jianjun Du, Xiaodi Pan, Liming Ma, Meng Shao, and Xinyu Guo. "Combined analysis of genome-wide expression profiling of maize (Zea mays L.) leaves infected with Ustilago maydis." Genome 61, no. 7 (July 2018): 505–13. http://dx.doi.org/10.1139/gen-2017-0226.
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Although many gene expression profiling studies of maize leaves infected with Ustilago maydis have been published, heterogeneity of the results, caused by various data processing methods and pathogenic strains in different data sets, remains strong. Hence, we conducted a combined analysis of six genome-wide expression data sets of maize leaves infected with five different U. maydis strains by using the same pre-processing and quality control procedures. Six data sets were regrouped into five groups according to pathogenic strain used. Subsequently, each group of data set was processed by Multi-array Average for pre-processing and by pair-wise Pearson correlation for quality control. The differentially expressed genes were calculated by a standard linear mixed-effect model and then validated by various sensitivity analysis and multiple evidences. Finally, 44 unique differentially expressed genes were identified. Pathway enrichment analysis indicated that these genes related to response to fungus, oxidation-reduction, transferase activity, and several carbohydrate metabolic and catabolic processes. In addition, the hub genes within protein–protein interaction networks showed high relevance with the basic pathogenesis. We report a highly credible differentially expressed list, and the genes with multiple validations may denote a common signature of U. maydis in maize, which provides a new window for disease-resistant protection of maize plants.
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Poudevigne-Durance, Thomas, Owen Dafydd Jones, and Yipeng Qin. "MaWGAN: A Generative Adversarial Network to Create Synthetic Data from Datasets with Missing Data." Electronics 11, no. 6 (March 8, 2022): 837. http://dx.doi.org/10.3390/electronics11060837.
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The creation of synthetic data are important for a range of applications, for example, to anonymise sensitive datasets or to increase the volume of data in a dataset. When the target dataset has missing data, then it is common to just discard incomplete observations, even though this necessarily means some loss of information. However, when the proportion of missing data are large, discarding incomplete observations may not leave enough data to accurately estimate their joint distribution. Thus, there is a need for data synthesis methods capable of using datasets with missing data, to improve accuracy and, in more extreme cases, to make data synthesis possible. To achieve this, we propose a novel generative adversarial network (GAN) called MaWGAN (for masked Wasserstein GAN), which creates synthetic data directly from datasets with missing values. As with existing GAN approaches, the MaWGAN synthetic data generator generates samples from the full joint distribution. We introduce a novel methodology for comparing the generator output with the original data that does not require us to discard incomplete observations, based on a modification of the Wasserstein distance and easily implemented using masks generated from the pattern of missing data in the original dataset. Numerical experiments are used to demonstrate the superior performance of MaWGAN compared to (a) discarding incomplete observations before using a GAN, and (b) imputing missing values (using the GAIN algorithm) before using a GAN.
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Ong, Yong Zheng, and Haizhao Yang. "Generative imaging and image processing via generative encoder." Inverse Problems & Imaging 16, no. 3 (2022): 525. http://dx.doi.org/10.3934/ipi.2021060.
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<p style='text-indent:20px;'>This paper introduces a novel generative encoder (GE) framework for generative imaging and image processing tasks like image reconstruction, compression, denoising, inpainting, deblurring, and super-resolution. GE unifies the generative capacity of GANs and the stability of AEs in an optimization framework instead of stacking GANs and AEs into a single network or combining their loss functions as in existing literature. GE provides a novel approach to visualizing relationships between latent spaces and the data space. The GE framework is made up of a pre-training phase and a solving phase. In the former, a GAN with generator <inline-formula><tex-math id="M1">\begin{document}$ G $\end{document}</tex-math></inline-formula> capturing the data distribution of a given image set, and an AE network with encoder <inline-formula><tex-math id="M2">\begin{document}$ E $\end{document}</tex-math></inline-formula> that compresses images following the estimated distribution by <inline-formula><tex-math id="M3">\begin{document}$ G $\end{document}</tex-math></inline-formula> are trained separately, resulting in two latent representations of the data, denoted as the generative and encoding latent space respectively. In the solving phase, given noisy image <inline-formula><tex-math id="M4">\begin{document}$ x = \mathcal{P}(x^*) $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M5">\begin{document}$ x^* $\end{document}</tex-math></inline-formula> is the target unknown image, <inline-formula><tex-math id="M6">\begin{document}$ \mathcal{P} $\end{document}</tex-math></inline-formula> is an operator adding an addictive, or multiplicative, or convolutional noise, or equivalently given such an image <inline-formula><tex-math id="M7">\begin{document}$ x $\end{document}</tex-math></inline-formula> in the compressed domain, i.e., given <inline-formula><tex-math id="M8">\begin{document}$ m = E(x) $\end{document}</tex-math></inline-formula>, the two latent spaces are unified via solving the optimization problem</p><p style='text-indent:20px;'><disp-formula> <label/> <tex-math id="FE1"> \begin{document}$ z^* = \underset{z}{\mathrm{argmin}} \|E(G(z))-m\|_2^2+\lambda\|z\|_2^2 $\end{document} </tex-math></disp-formula></p><p style='text-indent:20px;'>and the image <inline-formula><tex-math id="M9">\begin{document}$ x^* $\end{document}</tex-math></inline-formula> is recovered in a generative way via <inline-formula><tex-math id="M10">\begin{document}$ \hat{x}: = G(z^*)\approx x^* $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M11">\begin{document}$ \lambda>0 $\end{document}</tex-math></inline-formula> is a hyperparameter. The unification of the two spaces allows improved performance against corresponding GAN and AE networks while visualizing interesting properties in each latent space.</p>
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Feigin, Leon, Amir Weinberg, and Ariel Nause. "Algorithm Verification of Single-Shot Relativistic Emittance Proposed Measuring Method." Electronics 11, no. 13 (July 4, 2022): 2092. http://dx.doi.org/10.3390/electronics11132092.
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A 6 MeV hybrid photo-cathode gun is driving a THz-FEL in Ariel University, as well as other applications. An electron bunch with small transverse emittance is extracted from a copper photo-cathode using a 1 ps UV laser pulse, and then accelerated to a kinetic energy of 6.5 MeV. The Hybrid term is due to the unique standing wave-traveling wave sections in a single RF cavity. Since low emittance is crucial for FEL operation, the characterization of the electron beam requires measuring the transverse emittance, which will be compared with the predicted design and the 3D simulation obtained values, in order to verify their correctness. In this paper, we confirm the use of the multi-slit technique to measure emittance in the Hybrid beam in a single shot and develop a simple and convenient algorithm to be used in the experimental measurements. The experimental analysis requires image processing of the measured data, combined with a custom LabVIEW and Matlab scripts to control the hardware, and analyze the obtained data. Prior to experimentally measuring emittance, we perform a simulated experiment, using a simulated beam from the General Particle Tracer (GPT) code to test these algorithms and scripts, and compare the emittance obtained using the algorithm with GPT’s estimated emittance. Once concluded, this method will allow for a simple, fast and accurate single shot emittance measurement for the Hybrid accelerator beam.
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Khaleghi, Nastaran, Tohid Yousefi Rezaii, Soosan Beheshti, Saeed Meshgini, Sobhan Sheykhivand, and Sebelan Danishvar. "Visual Saliency and Image Reconstruction from EEG Signals via an Effective Geometric Deep Network-Based Generative Adversarial Network." Electronics 11, no. 21 (November 7, 2022): 3637. http://dx.doi.org/10.3390/electronics11213637.
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Reaching out the function of the brain in perceiving input data from the outside world is one of the great targets of neuroscience. Neural decoding helps us to model the connection between brain activities and the visual stimulation. The reconstruction of images from brain activity can be achieved through this modelling. Recent studies have shown that brain activity is impressed by visual saliency, the important parts of an image stimuli. In this paper, a deep model is proposed to reconstruct the image stimuli from electroencephalogram (EEG) recordings via visual saliency. To this end, the proposed geometric deep network-based generative adversarial network (GDN-GAN) is trained to map the EEG signals to the visual saliency maps corresponding to each image. The first part of the proposed GDN-GAN consists of Chebyshev graph convolutional layers. The input of the GDN part of the proposed network is the functional connectivity-based graph representation of the EEG channels. The output of the GDN is imposed to the GAN part of the proposed network to reconstruct the image saliency. The proposed GDN-GAN is trained using the Google Colaboratory Pro platform. The saliency metrics validate the viability and efficiency of the proposed saliency reconstruction network. The weights of the trained network are used as initial weights to reconstruct the grayscale image stimuli. The proposed network realizes the image reconstruction from EEG signals.
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Wu, Sixue, Gerrit Blacquière, and Gert-Jan Adriaan van Groenestijn. "Shot repetition: An alternative seismic blending code in marine acquisition." GEOPHYSICS 83, no. 6 (November 1, 2018): P29—P37. http://dx.doi.org/10.1190/geo2017-0649.1.
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In blended seismic acquisition, or simultaneous source seismic acquisition, source encoding is essential at the acquisition stage to allow for separation of the blended sources at the processing stage. In land seismic surveys, the vibroseis sources may be encoded with near-orthogonal sweeps for blending. In marine seismic surveys, the sweep type of source encoding is difficult because the main source type in marine seismic exploration is the air-gun array, which has an impulsive character. Another issue in marine streamer seismic data acquisition is that the spatial source sampling is generally coarse. This hinders the deblending performance of algorithms based on the random time delay blending code that inherently requires a dense source sampling because they exploit the signal coherency in the common-receiver domain. We have developed an alternative source code called shot repetition that exploits the impulsive character of the marine seismic source in blending. This source code consists of repeated spikes of ones and can be realized physically by activating a broadband impulsive source more than once at (nearly) the same location. Optimization of the shot-repetition type of blending code was done to improve the deblending performance. As a result of using shot repetition, the deblending process can be carried out in individual shot gathers. Therefore, our method has no need for a regular dense source sampling: It can cope with irregular sparse source sampling; it can help with real-time data quality control. In addition, the use of shot repetition is beneficial for reducing the background noise in the deblended data. We determine the feasibility of our method on numerical examples.
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Kooverjee, Nishai, Steven James, and Terence van Zyl. "Investigating Transfer Learning in Graph Neural Networks." Electronics 11, no. 8 (April 9, 2022): 1202. http://dx.doi.org/10.3390/electronics11081202.
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Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces. Transfer learning has proven extremely successful for traditional deep learning problems, resulting in faster training and improved performance. Despite the increasing interest in GNNs and their use cases, there is little research on their transferability. This research demonstrates that transfer learning is effective with GNNs, and describes how source tasks and the choice of GNN impact the ability to learn generalisable knowledge. We perform experiments using real-world and synthetic data within the contexts of node classification and graph classification. To this end, we also provide a general methodology for transfer learning experimentation and present a novel algorithm for generating synthetic graph classification tasks. We compare the performance of GCN, GraphSAGE and GIN across both synthetic and real-world datasets. Our results demonstrate empirically that GNNs with inductive operations yield statistically significantly improved transfer. Further, we show that similarity in community structure between source and target tasks support statistically significant improvements in transfer over and above the use of only the node attributes.
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Reine, Agnese. "Ensuring Protection of Personal Data in Time of Remote Work during the Covid-19 Pandemic." SOCRATES. Rīgas Stradiņa universitātes Juridiskās fakultātes elektroniskais juridisko zinātnisko rakstu žurnāls / SOCRATES. Rīga Stradiņš University Faculty of Law Electronic Scientific Journal of Law 2, no. 17 (2020): 11–18. http://dx.doi.org/10.25143/socr.17.2020.2.011-018.
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The biggest changes and challenges in the employment relationship in the last six months, especially regarding the provision of personal data processing, are related to the spread of the Covid-19 virus and measures to limit its spread. Globally, the situation developed faster; at the national level, however, starting from the moment when the Cabinet of Ministers Order No 103 of 12 March 2020 On Declaring a State of Emergency was issued in Latvia [1]. At the national level, the necessary restrictive measures to limit the spread of the virus and reduce the risk of disease were analysed. Although the spread of this virus and the legal consequences of the restrictions can be linked to medical issues, the state of emergency also had a major impact on employment relations. When the state of emergency was declared, employers had to evaluate the nature of their business and the possibilities of its realisation when employees perform work remotely. During this assessment, employers were initially divided into two categories – employers whose business specifics do not allow employees to work remotely and those employers who are able to ensure business continuity for employees performing work partially or completely remotely. The purpose of this article is to analyse legality of personal data processing by performing work remotely, respectively studying both employer’s responsibilities for processing employees’ personal data to ensure control over employee’s performance and employees responsibilities for personal data processing. Pēdējā pusgadā lielākās izmaiņas un izaicinājumi darba tiesiskajās attiecībās tieši attiecībā uz personas datu apstrādes nodrošināšanu ir saistīti ar Covid-19 vīrusa izplatību un tā izplatības ierobežošanas pasākumiem. Globāli situācija attīstījās jau agrāk, bet nacionālajā līmenī – sākot ar brīdi, kad Latvijā tika izdots Ministru kabineta 2020. gada 12. marta rīkojums Nr. 103 “Par ārkārtējās situācijas izsludināšanu”. Valstiskā līmenī tika analizēti nepieciešamie ierobežojošie pasākumi, lai ierobežotu vīrusa izplatību un samazinātu saslimstības riskus. Lai arī pirmšķietami šī vīrusa izplatība un ierobežojumu tiesiskās sekas ir saistāmas ar medicīniska rakstura jautājumiem, valstī izsludinātā ārkārtējā situācija radīja lielu ietekmi arī uz darba tiesiskajām attiecībām. Līdzko tika izsludināta ārkārtējā situācija, darba devējiem bija jāizvērtē sava biznesa būtība un tā realizēšanas iespējas, ja darbinieki veic darbu attālināti. Veicot šādu izvērtējumu, sākotnēji darba devēji iedalījās divās kategorijās – bija darba devēji, kuru biznesa specifika nesniedz iespēju darbiniekiem veikt darbu attālināti, un darba devēji, kuri spēj nodrošināt biznesa nepārtrauktību, darbiniekiem veicot darbu daļēji vai pilnībā attālināti. Šī raksta mērķis ir analizēt personas datu apstrādes tiesiskuma nodrošināšanu gadījumā, ja darbinieks veic darbu attālināti, attiecīgi analizējot gan darba devēja pienākumus, apstrādājot darbinieku personas datus, lai nodrošinātu darbinieka veiktā darba izpildes kontroli, gan darbinieka pienākumus attiecībā uz darba pienākumu ietvaros veikto personas datu apstrādi.
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Chen, Lu, Hongjun Wang, and Xianghao Meng. "Remote Sensing Image Dataset Expansion Based on Generative Adversarial Networks with Modified Shuffle Attention." Sensors 21, no. 14 (July 16, 2021): 4867. http://dx.doi.org/10.3390/s21144867.
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With the development of science and technology, neural networks, as an effective tool in image processing, play an important role in gradual remote-sensing image-processing. However, the training of neural networks requires a large sample database. Therefore, expanding datasets with limited samples has gradually become a research hotspot. The emergence of the generative adversarial network (GAN) provides new ideas for data expansion. Traditional GANs either require a large number of input data, or lack detail in the pictures generated. In this paper, we modify a shuffle attention network and introduce it into GAN to generate higher quality pictures with limited inputs. In addition, we improved the existing resize method and proposed an equal stretch resize method to solve the problem of image distortion caused by different input sizes. In the experiment, we also embed the newly proposed coordinate attention (CA) module into the backbone network as a control test. Qualitative indexes and six quantitative evaluation indexes were used to evaluate the experimental results, which show that, compared with other GANs used for picture generation, the modified Shuffle Attention GAN proposed in this paper can generate more refined and high-quality diversified aircraft pictures with more detailed features of the object under limited datasets.
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Baronti, Luca, Aleksandra Michalek, Marco Castellani, Pavel Penchev, Tian Long See, and Stefan Dimov. "Artificial neural network tools for predicting the functional response of ultrafast laser textured/structured surfaces." International Journal of Advanced Manufacturing Technology 119, no. 5-6 (January 7, 2022): 3501–16. http://dx.doi.org/10.1007/s00170-021-08589-9.
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AbstractArtificial Neural Networks (ANNs) are well-established knowledge acquisition systems with proven capacity for learning and generalisation. Therefore, ANNs are widely applied to solve engineering problems and are often used in laser-based manufacturing applications. There are different pattern recognition and control problems where ANNs can be effectively applied, and one of them is laser structuring/texturing for surface functionalisation, e.g. in generating Laser-Induced Periodic Surface Structures (LIPSS). They are a particular type of sub-micron structures that are very sensitive to changes in laser processing conditions due to processing disturbances like varying Focal Offset Distance (FOD) and/or Beam Incident Angle (BIA) during the laser processing of 3D surfaces. As a result, the functional response of LIPSS-treated surfaces might be affected, too, and typically needs to be analysed with time-consuming experimental tests. Also, there is a lack of sufficient process monitoring and quality control tools available for LIPSS-treated surfaces that could identify processing patterns and interdependences. These tools are needed to determine whether the LIPSS generation process is in control and consequently whether the surface’s functional performance is still retained. In this research, an ANN-based approach is proposed for predicting the functional response of ultrafast laser structured/textured surfaces. It was demonstrated that the processing disturbances affecting the LIPSS treatments can be classified, and then, the surface response, namely wettability, of processed surfaces can be predicted with a very high accuracy using the developed ANN tools for pre- and post-processing of LIPSS topography data, i.e. their areal surface roughness parameters. A Generative Adversarial Network (GAN) was applied as a pre-processing tool to significantly reduce the number of required experimental data. The number of areal surface roughness parameters needed to fully characterise the functional response of a surface was minimised using a combination of feature selection methods. Based on statistical analysis and evolutionary optimisation, these methods narrowed down the initial set of 21 elements to a group of 10 and 6 elements, according to redundancy and relevance criteria, respectively. The validation of ANN tools, using the salient surface parameters, yielded accuracy close to 85% when applied for identification of processing disturbances, while the wettability was predicted within an r.m.s. error of 11 degrees, equivalent to the static water contact angle (CA) measurement uncertainty.
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Wu, Yue, Xidao Hu, Xiaolong Fan, Wenping Ma, and Qiuyue Gao. "Learning Data-Driven Propagation Mechanism for Graph Neural Network." Electronics 12, no. 1 (December 22, 2022): 46. http://dx.doi.org/10.3390/electronics12010046.
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A graph is a relational data structure suitable for representing non-Euclidean structured data. In recent years, graph neural networks (GNN) and their subsequent variants, which utilize deep neural networks to complete graph analysis and representation, have shown excellent performance in various application fields. However, the propagation mechanism of existing methods relies on hand-designed GNN layer connection architecture, which is prone to information redundancy and over-smoothing problems. To alleviate this problem, we propose a data-driven propagation mechanism to adaptively propagate information between layers. Specifically, we construct a bi-level optimization objective and use the gradient descent algorithm to learn the forward propagation architecture, which improves the efficiency of learning different layer combinations in multilayer networks. The experimental results of the model on seven benchmark datasets demonstrate the effectiveness of the proposed method. Furthermore, combining this data-driven propagation mechanism with models, such as Graph Attention Networks, can consistently improve the performance of these models.
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Cho, Eunnuri, Tai-Woo Chang, and Gyusun Hwang. "Data Preprocessing Combination to Improve the Performance of Quality Classification in the Manufacturing Process." Electronics 11, no. 3 (February 6, 2022): 477. http://dx.doi.org/10.3390/electronics11030477.
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The recent introduction of smart manufacturing, also called the ‘smart factory’, has made it possible to collect a significant number of multi-variate data from Internet of Things devices or sensors. Quality control using these data in the manufacturing process can play a major role in preventing unexpected time and economic losses. However, the extraction of information about the manufacturing process is limited when there are missing values in the data and a data imbalance set. In this study, we improve the quality classification performance by solving the problem of missing values and data imbalances that can occur in the manufacturing process. This study proceeds with data cleansing, data substitution, data scaling, a data balancing model methodology, and evaluation. Five data balancing methods and a generative adversarial network (GAN) were used to proceed with data imbalance processing. The proposed schemes achieved an F1 score that was 0.5 higher than the F1 score of previous studies that used the same data. The data preprocessing combination proposed in this study is intended to be used to solve the problem of missing values and imbalances that occur in the manufacturing process.
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Long, Ting, Yutong Xie, Xianyu Chen, Weinan Zhang, Qinxiang Cao, and Yong Yu. "Multi-View Graph Representation for Programming Language Processing: An Investigation into Algorithm Detection." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (June 28, 2022): 5792–99. http://dx.doi.org/10.1609/aaai.v36i5.20522.
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Program representation, which aims at converting program source code into vectors with automatically extracted features, is a fundamental problem in programming language processing (PLP). Recent work tries to represent programs with neural networks based on source code structures. However, such methods often focus on the syntax and consider only one single perspective of programs, limiting the representation power of models. This paper proposes a multi-view graph (MVG) program representation method. MVG pays more attention to code semantics and simultaneously includes both data flow and control flow as multiple views. These views are then combined and processed by a graph neural network (GNN) to obtain a comprehensive program representation that covers various aspects. We thoroughly evaluate our proposed MVG approach in the context of algorithm detection, an important and challenging subfield of PLP. Specifically, we use a public dataset POJ-104 and also construct a new challenging dataset ALG-109 to test our method. In experiments, MVG outperforms previous methods significantly, demonstrating our model's strong capability of representing source code.
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Liu, Bokun, Shaojing Su, and Junyu Wei. "The Effect of Data Augmentation Methods on Pedestrian Object Detection." Electronics 11, no. 19 (October 4, 2022): 3185. http://dx.doi.org/10.3390/electronics11193185.
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Night landscapes are a key area of monitoring and security as information in pictures caught on camera is not comprehensive. Data augmentation gives these limited datasets the most value. Considering night driving and dangerous events, it is important to achieve the better detection of people at night. This paper studies the impact of different data augmentation methods on target detection. For the image data collected at night under limited conditions, three different types of enhancement methods are used to verify whether they can promote pedestrian detection. This paper mainly explores supervised and unsupervised data augmentation methods with certain improvements, including multi-sample augmentation, unsupervised Generative Adversarial Network (GAN) augmentation and single-sample augmentation. It is concluded that the dataset obtained by the heterogeneous multi-sample augmentation method can optimize the target detection model, which can allow the mean average precision (mAP) of a night image to reach 0.76, and the improved Residual Convolutional GAN network, the unsupervised training model, can generate new samples with the same style, thus greatly expanding the dataset, so that the mean average precision reaches 0.854, and the single-sample enhancement of the deillumination can greatly improve the image clarity, helping improve the precision value by 0.116.
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Owen, Medge D., Can B. Unal, Michael F. Callahan, Kavita Trivedi, Catherine York, and William R. Millington. "Glycyl-glutamine inhibits the respiratory depression, but not the antinociception, produced by morphine." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 279, no. 5 (November 1, 2000): R1944—R1948. http://dx.doi.org/10.1152/ajpregu.2000.279.5.r1944.
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Glycyl-glutamine (Gly-Gln; β-endorphin30–31) is an endogenous dipeptide that is synthesized through the posttranslational processing of β-endorphin in brain stem regions that control respiration and autonomic function. This study tested the hypothesis that Gly-Gln administration to conscious rats will prevent the respiratory depression caused by morphine without affecting morphine antinociception. Rats were administered Gly-Gln (1–100 nmol) or saline (10 μl) intracerebroventricularly followed, 5 min later, by morphine (40 nmol icv). Arterial blood gases and pH were measured immediately before Gly-Gln and 30 min after morphine injection. Gly-Gln pretreatment inhibited morphine-induced hypercapnia, hypoxia, and acidosis significantly. The response was dose dependent and significant at Gly-Gln doses as low as 1 nmol. In contrast, Gly-Gln (1–300 nmol) had no effect on morphine-evoked antinociception in the paw withdrawal test. When given alone to otherwise untreated animals, Gly-Gln did not affect nociceptive latencies or blood gas values. These data indicate that Gly-Gln inhibits morphine-induced respiratory depression without compromising morphine antinociception.
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Tam, Prohim, Inseok Song, Seungwoo Kang, Seyha Ros, and Seokhoon Kim. "Graph Neural Networks for Intelligent Modelling in Network Management and Orchestration: A Survey on Communications." Electronics 11, no. 20 (October 19, 2022): 3371. http://dx.doi.org/10.3390/electronics11203371.
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The advancing applications based on machine learning and deep learning in communication networks have been exponentially increasing in the system architectures of enabled software-defined networking, network functions virtualization, and other wired/wireless networks. With data exposure capabilities of graph-structured network topologies and underlying data plane information, the state-of-the-art deep learning approach, graph neural networks (GNN), has been applied to understand multi-scale deep correlations, offer generalization capability, improve the accuracy metrics of prediction modelling, and empower state representation for deep reinforcement learning (DRL) agents in future intelligent network management and orchestration. This paper contributes a taxonomy of recent studies using GNN-based approaches to optimize the control policies, including offloading strategies, routing optimization, virtual network function orchestration, and resource allocation. The algorithm designs of converged DRL and GNN are reviewed throughout the selected studies by presenting the state generalization, GNN-assisted action selection, and reward valuation cooperating with GNN outputs. We also survey the GNN-empowered application deployment in the autonomous control of optical networks, Internet of Healthcare Things, Internet of Vehicles, Industrial Internet of Things, and other smart city applications. Finally, we provide a potential discussion on research challenges and future directions.
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Iliadou, Vasiliki Vivian, Doris-Eva Bamiou, Christos Sidiras, Nikolaos P. Moschopoulos, Magda Tsolaki, Ioannis Nimatoudis, and Gail D. Chermak. "The Use of the Gaps-In-Noise Test as an Index of the Enhanced Left Temporal Cortical Thinning Associated with the Transition between Mild Cognitive Impairment and Alzheimer’s Disease." Journal of the American Academy of Audiology 28, no. 05 (May 2017): 463–71. http://dx.doi.org/10.3766/jaaa.16075.
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Background: The known link between auditory perception and cognition is often overlooked when testing for cognition. Purpose: To evaluate auditory perception in a group of older adults diagnosed with mild cognitive impairment (MCI). Research Design: A cross-sectional study of auditory perception. Study Sample: Adults with MCI and adults with no documented cognitive issues and matched hearing sensitivity and age. Data collection: Auditory perception was evaluated in both groups, assessing for hearing sensitivity, speech in babble (SinB), and temporal resolution. Results: Mann–Whitney test revealed significantly poorer scores for SinB and temporal resolution abilities of MCIs versus normal controls for both ears. The right-ear gap detection thresholds on the Gaps-In-Noise (GIN) Test clearly differentiated between the two groups (p < 0.001), with no overlap of values. The left ear results also differentiated the two groups (p < 0.01); however, there was a small degree of overlap #x02DC;8-msec threshold values. With the exception of the left-ear inattentiveness index, which showed a similar distribution between groups, both impulsivity and inattentiveness indexes were higher for the MCIs compared to the control group. Conclusions: The results support central auditory processing evaluation in the elderly population as a promising tool to achieve earlier diagnosis of dementia, while identifying central auditory processing deficits that can contribute to communication deficits in the MCI patient population. A measure of temporal resolution (GIN) may offer an early, albeit indirect, measure reflecting left temporal cortical thinning associated with the transition between MCI and Alzheimer’s disease.
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Zou, Fumin, Qiang Ren, Junshan Tian, Feng Guo, Shibin Huang, Lyuchao Liao, and Jinshan Wu. "Expressway Speed Prediction Based on Electronic Toll Collection Data." Electronics 11, no. 10 (May 18, 2022): 1613. http://dx.doi.org/10.3390/electronics11101613.
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Expressway section speed can visually reflect the section operation condition, and accurate short time section speed prediction has a wide range of applications in path planning and traffic guidance. However, existing expressway speed prediction data have defects, such as sparse density and incomplete object challenges. Thus, this paper proposes a framework for a combined expressway traffic speed prediction model based on wavelet transform and spatial-temporal graph convolutional network (WSTGCN) of the Electronic Toll Collection (ETC) gantry transaction data. First, the framework pre-processes the ETC gantry transaction data to construct the section speeds. Then wavelet decomposition and single-branch reconstruction are performed on the section speed sequences, and the spatial features are captured by graph convolutional network (GCN) for each reconstructed single-branch sequence, and the temporal features are extracted by connecting the gated recurrent unit (GRU). The experiments use the ETC gantry transaction data of the expressway from Quanzhou to Xiamen. The results indicate that the WSTGCN model makes notable improvements compared to the model of the baseline for different prediction ranges.
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Cardoso, Renato, Dejan Golubovic, Ignacio Peluaga Lozada, Ricardo Rocha, João Fernandes, and Sofia Vallecorsa. "Accelerating GAN training using highly parallel hardware on public cloud." EPJ Web of Conferences 251 (2021): 02073. http://dx.doi.org/10.1051/epjconf/202125102073.
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With the increasing number of Machine and Deep Learning applications in High Energy Physics, easy access to dedicated infrastructure represents a requirement for fast and efficient R&D. This work explores different types of cloud services to train a Generative Adversarial Network (GAN) in a parallel environment, using Tensorflow data parallel strategy. More specifically, we parallelize the training process on multiple GPUs and Google Tensor Processing Units (TPU) and we compare two algorithms: the TensorFlow built-in logic and a custom loop, optimised to have higher control of the elements assigned to each GPU worker or TPU core. The quality of the generated data is compared to Monte Carlo simulation. Linear speed-up of the training process is obtained, while retaining most of the performance in terms of physics results. Additionally, we benchmark the aforementioned approaches, at scale, over multiple GPU nodes, deploying the training process on different public cloud providers, seeking for overall efficiency and cost-effectiveness. The combination of data science, cloud deployment options and associated economics allows to burst out heterogeneously, exploring the full potential of cloud-based services.
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Wang, Zhong, Liwen Liu, Chenyu Wang, Jianjing Deng, Kui Zhang, Yunchuan Yang, and Jianbo Zhou. "Data Enhancement of Underwater High-Speed Vehicle Echo Signals Based on Improved Generative Adversarial Networks." Electronics 11, no. 15 (July 25, 2022): 2310. http://dx.doi.org/10.3390/electronics11152310.
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Underwater target recognition is currently one of the hottest topics in computational intelligence research. However, underwater target recognition tasks based on deep learning techniques are difficult to conduct due to the shortage of acoustic echo signal samples, which results in poor training performance for existing deep learning models. Generative adversarial networks (GANs) have been widely used in data enhancement and image generation, providing a novel strategy for dealing with challenges in the research field mentioned above. To address the insufficiency of echo signal data for underwater high−speed vehicles, this paper proposes an underwater echo signal data enhancement method that uses an improved GAN based on convolution units for small sample sizes. First, we take pool test data as the training sample input and carry out data standardization, data interception, and copy−related processing work. Secondly, this paper proposes an improved generative adversarial network underwater (IGAN−UW) model to generate underwater echo signals. Finally, a CNN model combines the generated data with the original data to conduct classification training for underwater targets. Experimental results show that the IGAN−UW model is suitable for the generation of highly realistic original echo signals in cases with small sample sizes, providing a new approach to the active detection and recognition of underwater targets.
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Hu, Wenyu, and Zhizhong Mao. "Forecasting for Chaotic Time Series Based on GRP-lstmGAN Model: Application to Temperature Series of Rotary Kiln." Entropy 25, no. 1 (December 27, 2022): 52. http://dx.doi.org/10.3390/e25010052.
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Rotary kiln temperature forecasting plays a significant part of the automatic control of the sintering process. However, accurate forecasts are difficult owing to the complex nonlinear characteristics of rotary kiln temperature time series. With the development of chaos theory, the prediction accuracy is improved by analyzing the essential characteristics of time series. However, the existing prediction methods of chaotic time series cannot fully consider the local and global characteristics of time series at the same time. Therefore, in this study, the global recurrence plot (GRP)-based generative adversarial network (GAN) and the long short-term memory (LSTM) combination method, named GRP-lstmGAN, are proposed, which can effectively display important information about time scales. First, the data is subjected to a series of pre-processing operations, including data smoothing. Then, transforming one-dimensional time series into two-dimensional images by GRP makes full use of the global and local information of time series. Finally, the combination of LSTM and improves GAN models for temperature time series prediction. The experimental results show that our model is better than comparison models.
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Jilani, Umair, Muhammad Asif, Munaf Rashid, Ali Akbar Siddique, Syed Muhammad Umar Talha, and Muhammad Aamir. "Traffic Congestion Classification Using GAN-Based Synthetic Data Augmentation and a Novel 5-Layer Convolutional Neural Network Model." Electronics 11, no. 15 (July 22, 2022): 2290. http://dx.doi.org/10.3390/electronics11152290.
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Private automobiles are still a widely prevalent mode of transportation. Subsequently, traffic congestion on the roads has been more frequent and severe with the continuous rise in the numbers of cars on the road. The estimation of traffic flow, or conversely, traffic congestion identification, is of critical importance in a wide variety of applications, including intelligent transportation systems (ITS). Recently, artificial intelligence (AI) has been in the limelight for sophisticated ITS solutions. However, AI-based schemes are typically heavily dependent on the quantity and quality of data. Typical traffic data have been found to be insufficient and less efficient in AI-based ITS solutions. Advanced data cleaning and preprocessing methods offer a solution for this problem. Such techniques enable quality improvement and augmenting additional information in the traffic congestion dataset. One such efficient technique is the generative adversarial network (GAN), which has attracted much interest from the research community. This research work reports on the generation of a traffic congestion dataset with enhancement through GAN-based augmentation. The GAN-enhanced traffic congestion dataset is then used for training artificial intelligence (AI)-based models. In this research work, a five-layered convolutional neural network (CNN) deep learning model is proposed for traffic congestion classification. The performance of the proposed model is compared with that of a number of other well-known pretrained models, including ResNet-50 and DenseNet-121. Promising results present the efficacy of the proposed scheme using GAN-based data augmentation in a five-layered convolutional neural network (CNN) model for traffic congestion classification. The proposed technique attains accuracy of 98.63% compared with the accuracies of ResNet-50 and DenseNet-121, 90.59% and 93.15%, respectively. The proposed technique can be used for urban traffic planning and maintenance managers and stakeholders for the efficient deployment of intelligent transportation system (ITS).
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Jia, Chang Zhi, Guang Sheng Liu, Yao Xin He, and Jing Bo Zhang. "Study on Processing Quality Monitoring Technology for Gun Barrel Caliber." Advanced Materials Research 490-495 (March 2012): 1574–78. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1574.
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At present, the production and processing of the gun barrel caliber are mostly small batch. If the processing quality is controlled using the traditional SPC method directly, it is difficult to ensure the required sample size. For this situation, the small batch quality control theory and international basic ideas were introduced. The control model of the gun barrel caliber processing quality was established based on X—Rs control chart. Finally, taking the processing of a certain type of gun barrel caliber for example was analyzed.
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Yang, Yu, Lei Sun, Xiuqing Mao, and Min Zhao. "Data Augmentation Based on Generative Adversarial Network with Mixed Attention Mechanism." Electronics 11, no. 11 (May 27, 2022): 1718. http://dx.doi.org/10.3390/electronics11111718.
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Some downstream tasks often require enough data for training in deep learning, but it is formidable to acquire data in some particular fields. Generative Adversarial Network has been extensively used in data augmentation. However, it still has problems of unstable training and low quality of generated images. This paper proposed Data Augmentation Based on Generative Adversarial Network with Mixed Attention Mechanism (MA-GAN) to solve those problems. This method can generate consistent objects or scenes by correlating the remote features in the image, thus improving the ability to create details. Firstly, the channel-attention and the self-attention mechanism are added into the generator and discriminator. Then, the spectral normalization is introduced into the generator and discriminator so that the parameter matrix satisfies the Lipschitz constraint, thus improving the stability of the model training process. By qualitative and quantitative evaluations on small-scale benchmarks (CelebA, MNIST, and CIFAR-10), the experimental results show that the proposed method performs better than other methods. Compared with WGAN-GP (Improved Training of Wasserstein GANs) and SAGAN (Self-Attention Generative Adversarial Networks), the proposed method contributes to higher classification accuracy, indicating that this method can effectively augment the data of small samples.
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Chamran, Mohammad Kazem, Kok-Lim Alvin Yau, Rafidah M. D. Noor, and Richard Wong. "A Distributed Testbed for 5G Scenarios: An Experimental Study." Sensors 20, no. 1 (December 19, 2019): 18. http://dx.doi.org/10.3390/s20010018.
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This paper demonstrates the use of Universal Software Radio Peripheral (USRP), together with Raspberry Pi3 B+ (RP3) as the brain (or the decision making engine), to develop a distributed wireless network in which nodes can communicate with other nodes independently and make decision autonomously. In other words, each USRP node (i.e., sensor) is embedded with separate processing units (i.e., RP3), which has not been investigated in the literature, so that each node can make independent decisions in a distributed manner. The proposed testbed in this paper is compared with the traditional distributed testbed, which has been widely used in the literature. In the traditional distributed testbed, there is a single processing unit (i.e., a personal computer) that makes decisions in a centralized manner, and each node (i.e., USRP) is connected to the processing unit via a switch. The single processing unit exchanges control messages with nodes via the switch, while the nodes exchange data packets among themselves using a wireless medium in a distributed manner. The main disadvantage of the traditional testbed is that, despite the network being distributed in nature, decisions are made in a centralized manner. Hence, the response delay of the control message exchange is always neglected. The use of such testbed is mainly due to the limited hardware and monetary cost to acquire a separate processing unit for each node. The experiment in our testbed has shown the increase of end-to-end delay and decrease of packet delivery ratio due to software and hardware delays. The observed multihop transmission is performed using device-to-device (D2D) communication, which has been enabled in 5G. Therefore, nodes can either communicate with other nodes via: (a) a direct communication with the base station at the macrocell, which helps to improve network performance; or (b) D2D that improve spectrum efficiency, whereby traffic is offloaded from macrocell to small cells. Our testbed is the first of its kind in this scale, and it uses RP3 as the distributed decision-making engine incorporated into the USRP/GNU radio platform. This work provides an insight to the development of a 5G network.
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Sheybani, Ehsan, and Giti Javidi. "Integrating Software Defined Radio with USRP." International Journal of Interdisciplinary Telecommunications and Networking 9, no. 3 (July 2017): 1–9. http://dx.doi.org/10.4018/ijitn.2017070101.
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The USRP1 is the original Universal Software Radio Peripheral hardware (USRP) that provides entry-level RF processing capability. Its primary purpose is to provide flexible software defined radio development capability at a low price. You can control the frequency you receive and transmit by installing different daughter-boards. The authors' USRP model had been configured to receive a signal from local radio stations in the DC, Maryland metropolitan area with the BasicRX model daughterboard. The programmable USRP was running python block code implemented in the GNU Radio Companion (GRC) on Ubuntu OS. With proper parameters and sinks the authors were able to tune into the radio signal, record the signal and extract the in-phase (I) and quadrature phase (Q) data and plot the phase and magnitude of the signal. Using the terminal along with proper MATLAB and Octave code, they were able to read the I/Q data and look at the Fast Fourier Transform (FFT) plot along with the I/Q data. With the proper equations, you could determine not only the direction of arrival, but one would also be able to calculate the distance from the receiver to the exact location where the signal is being transmitted. The purpose of doing this experiment was to gain experience in signal processing and receive hands on experience with the USRP and potentially add a tracking system to the authors' model for further experiments.
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Biskaborn, B. K., J. P. Lanckman, H. Lantuit, K. Elger, D. A. Streletskiy, W. L. Cable, and V. E. Romanovsky. "The Global Terrestrial Network for Permafrost Database: metadata statistics and prospective analysis on future permafrost temperature and active layer depth monitoring site distribution." Earth System Science Data Discussions 8, no. 1 (March 9, 2015): 279–315. http://dx.doi.org/10.5194/essdd-8-279-2015.
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Abstract. The Global Terrestrial Network for Permafrost (GTN-P) provides the first dynamic database associated with the Thermal State of Permafrost (TSP) and the Circumpolar Active Layer Monitoring (CALM) programs, which extensively collect permafrost temperature and active layer thickness data from Arctic, Antarctic and Mountain permafrost regions. The purpose of the database is to establish an "early warning system" for the consequences of climate change in permafrost regions and to provide standardized thermal permafrost data to global models. In this paper we perform statistical analysis of the GTN-P metadata aiming to identify the spatial gaps in the GTN-P site distribution in relation to climate-effective environmental parameters. We describe the concept and structure of the Data Management System in regard to user operability, data transfer and data policy. We outline data sources and data processing including quality control strategies. Assessment of the metadata and data quality reveals 63% metadata completeness at active layer sites and 50% metadata completeness for boreholes. Voronoi Tessellation Analysis on the spatial sample distribution of boreholes and active layer measurement sites quantifies the distribution inhomogeneity and provides potential locations of additional permafrost research sites to improve the representativeness of thermal monitoring across areas underlain by permafrost. The depth distribution of the boreholes reveals that 73% are shallower than 25 m and 27% are deeper, reaching a maximum of 1 km depth. Comparison of the GTN-P site distribution with permafrost zones, soil organic carbon contents and vegetation types exhibits different local to regional monitoring situations on maps. Preferential slope orientation at the sites most likely causes a bias in the temperature monitoring and should be taken into account when using the data for global models. The distribution of GTN-P sites within zones of projected temperature change show a high representation of areas with smaller expected temperature rise but a lower number of sites within arctic areas were climate models project extreme temperature increase. This paper offers a scientific basis for planning future permafrost research sites on large scales.
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Zhang, Xiaolong, Xiaoguang Wei, Lin Zheng, Chenghao Wang, and Huafeng Wang. "Research on Vibration Data-Driven Fault Diagnosis for Iron Core Looseness of Saturable Reactor in UHVDC Thyristor Valve Based on CVAE-GAN and Multimodal Feature Integrated CNN." Energies 15, no. 24 (December 15, 2022): 9508. http://dx.doi.org/10.3390/en15249508.
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The imbalance of data samples and fluctuating operating conditions are the two main challenges faced by vibration data-driven fault diagnosis for the iron core looseness of saturable reactors in UHVDC thyristor valves. This paper proposes a vibration data-driven saturable reactor iron core looseness fault diagnosis strategy named CVG-MFICNN based on CVAE-GAN and MFICNN to overcome the two challenges. This strategy uses a novel 1-D CVAE-GAN model to produce generated samples and expand the training set based on imbalanced training samples. An MFICNN model structure is designed to allow the simultaneous processing of multimodal features such as the SST time-frequency spectrum, time-domain vibration sequence, frequency-domain power spectrum sequence, and time-domain statistics. Using these multimodal features and the MFICNN model, the hidden fault information in vibration data can be effectively mined. An experiment is conducted to collect vibration data of saturable reactors with different faults. Models based on the proposed strategy and other methods are trained and tested using the collected data. The comparison results show that the performance of the proposed CVG-MFICNN approach is significantly superior to that of single-feature CNNs, traditional machine learning methods, and classical image classification CNNs in the application of UHVDC thyristor valve saturable reactor iron core looseness fault diagnosis.
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Bazarbaev, Manas, Tserenpurev Chuluunsaikhan, Hyoseok Oh, Ga-Ae Ryu, Aziz Nasridinov, and Kwan-Hee Yoo. "Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network." Sensors 22, no. 1 (December 22, 2021): 29. http://dx.doi.org/10.3390/s22010029.
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Product quality is a major concern in manufacturing. In the metal processing industry, low-quality products must be remanufactured, which requires additional labor, money, and time. Therefore, user-controllable variables for machines and raw material compositions are key factors for ensuring product quality. In this study, we propose a method for generating the time-series working patterns of the control variables for metal-melting induction furnaces and continuous casting machines, thus improving product quality by aiding machine operators. We used an auxiliary classifier generative adversarial network (AC-GAN) model to generate time-series working patterns of two processes depending on product type and additional material data. To check accuracy, the difference between the generated time-series data of the model and the ground truth data was calculated. Specifically, the proposed model results were compared with those of other deep learning models: multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). It was demonstrated that the proposed model outperformed the other deep learning models. Moreover, the proposed method generated different time-series data for different inputs, whereas the other deep learning models generated the same time-series data.
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Biskaborn, B. K., J. P. Lanckman, H. Lantuit, K. Elger, D. A. Streletskiy, W. L. Cable, and V. E. Romanovsky. "The new database of the Global Terrestrial Network for Permafrost (GTN-P)." Earth System Science Data 7, no. 2 (September 14, 2015): 245–59. http://dx.doi.org/10.5194/essd-7-245-2015.
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Abstract. The Global Terrestrial Network for Permafrost (GTN-P) provides the first dynamic database associated with the Thermal State of Permafrost (TSP) and the Circumpolar Active Layer Monitoring (CALM) programs, which extensively collect permafrost temperature and active layer thickness (ALT) data from Arctic, Antarctic and mountain permafrost regions. The purpose of GTN-P is to establish an early warning system for the consequences of climate change in permafrost regions and to provide standardized thermal permafrost data to global models. In this paper we introduce the GTN-P database and perform statistical analysis of the GTN-P metadata to identify and quantify the spatial gaps in the site distribution in relation to climate-effective environmental parameters. We describe the concept and structure of the data management system in regard to user operability, data transfer and data policy. We outline data sources and data processing including quality control strategies based on national correspondents. Assessment of the metadata and data quality reveals 63 % metadata completeness at active layer sites and 50 % metadata completeness for boreholes. Voronoi tessellation analysis on the spatial sample distribution of boreholes and active layer measurement sites quantifies the distribution inhomogeneity and provides a potential method to locate additional permafrost research sites by improving the representativeness of thermal monitoring across areas underlain by permafrost. The depth distribution of the boreholes reveals that 73 % are shallower than 25 m and 27 % are deeper, reaching a maximum of 1 km depth. Comparison of the GTN-P site distribution with permafrost zones, soil organic carbon contents and vegetation types exhibits different local to regional monitoring situations, which are illustrated with maps. Preferential slope orientation at the sites most likely causes a bias in the temperature monitoring and should be taken into account when using the data for global models. The distribution of GTN-P sites within zones of projected temperature change show a high representation of areas with smaller expected temperature rise but a lower number of sites within Arctic areas where climate models project extreme temperature increase. GTN-P metadata used in this paper are available at doi:10.1594/PANGAEA.842821.
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Timchenko, Lubov T., Polina Iakova, Alana L. Welm, Z. J. Cai, and Nikolai A. Timchenko. "Calreticulin Interacts with C/EBPα and C/EBPβ mRNAs and Represses Translation of C/EBP Proteins." Molecular and Cellular Biology 22, no. 20 (October 15, 2002): 7242–57. http://dx.doi.org/10.1128/mcb.22.20.7242-7257.2002.
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ABSTRACT We previously identified an RNA binding protein, CUGBP1, which binds to GCN repeats located within the 5′ region of C/EBPβ mRNAs and regulates translation of C/EBPβ isoforms. To further investigate the role of RNA binding proteins in the posttranscriptional control of C/EBP proteins, we purified additional RNA binding proteins that interact with GC-rich RNAs and that may regulate RNA processing. In HeLa cells, the majority of GC-rich RNA binding proteins are associated with endogenous RNA transcripts. The separation of these proteins from endogenous RNA identified several proteins in addition to CUGBP1 that specifically interact with the GC-rich 5′ region of C/EBPβ mRNA. One of these proteins was purified to homogeneity and was identified as calreticulin (CRT). CRT is a multifunctional protein involved in several biological processes, including interaction with and regulation of rubella virus RNA processing. Our data demonstrate that both CUGBP1 and CRT interact with GCU repeats within myotonin protein kinase and with GCN repeats within C/EBPα and C/EBPβ mRNAs. GCN repeats within these mRNAs form stable SL structures. The interaction of CRT with SL structures of C/EBPβ and C/EBPα mRNAs leads to inhibition of translation of C/EBP proteins in vitro and in vivo. Deletions or mutations abolishing the formation of SL structures within C/EBPα and C/EBPβ mRNAs lead to a failure of CRT to inhibit translation of C/EBP proteins. CRT-dependent inhibition of C/EBPα is sufficient to block the growth-inhibitory activity of C/EBPα. This finding further defines the molecular mechanism for posttranscriptional regulation of the C/EBPα and C/EBPβ proteins.
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Dong, Guan-Sen, Hua-Ping Wan, Yaozhi Luo, and Michael D. Todd. "A fast sparsity-free compressive sensing approach for vibration data reconstruction using deep convolutional GAN." Mechanical Systems and Signal Processing 188 (April 2023): 109937. http://dx.doi.org/10.1016/j.ymssp.2022.109937.
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Gonzalez-Abril, Luis, Cecilio Angulo, Juan Antonio Ortega, and José-Luis Lopez-Guerra. "Statistical Validation of Synthetic Data for Lung Cancer Patients Generated by Using Generative Adversarial Networks." Electronics 11, no. 20 (October 12, 2022): 3277. http://dx.doi.org/10.3390/electronics11203277.
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The development of healthcare patient digital twins in combination with machine learning technologies helps doctors in therapeutic prescription and in minimally invasive intervention procedures. The confidentiality of medical records or limited data availability in many health domains are drawbacks that can be overcome with the generation of synthetic data conformed to real data. The use of generative adversarial networks (GAN) for the generation of synthetic data of lung cancer patients has been previously introduced as a tool to solve this problem in the form of anonymized synthetic patients. However, generated synthetic data are mainly validated from the machine learning domain (loss functions) or expert domain (oncologists). In this paper, we propose statistical decision making as a validation tool: Is the model good enough to be used? Does the model pass rigorous hypothesis testing criteria? We show for the case at hand how loss functions and hypothesis validation are not always well aligned.
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Dahal, Kamana, and Mohd Hasan Ali. "A Hybrid GAN-Based DL Approach for the Automatic Detection of Shockable Rhythms in AED for Solving Imbalanced Data Problems." Electronics 12, no. 1 (December 20, 2022): 13. http://dx.doi.org/10.3390/electronics12010013.
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Sudden cardiac arrest (SCA) is one of the global health issues causing high mortality. Hence, timely and agile detection of such arrests and immediate defibrillation support to SCA victims is of the utmost importance. An automated external defibrillator (AED) is a medical device used to treat patients suffering from SCA by delivering an electric shock. An AED implements the machine learning (ML)- or deep learning (DL)-based approach to detect whether the patient needs an electric shock and then automates the shock if needed. However, the effectiveness of these models has relied on the availability of well-balanced data in class distribution. Due to privacy concerns, collecting sufficient data is more challenging in the medical domain. Generative adversarial networks (GAN) have been successfully used to create synthetic data and are far better than standard oversampling techniques in maintaining the original data’s probability distribution. We, therefore, proposed a GAN-based DL approach, external classifier–Wasserstein conditional generative adversarial network (EC–WCGAN), to detect the shockable rhythms in an AED on an imbalanced ECG dataset. Our experiments demonstrate that the classifier trained with real and generated data via the EC–WCGAN significantly improves the performance metrics on the imbalanced dataset. Additionally, the WCGAN for generating synthetic data outperformed the standard oversampling technique, such as adaptive synthetic (ADASYN). In addition, our model achieved a high sensitivity, specificity, and F1-score (more than 99%) and a low balanced error rate (0.005) on the balanced 4-s segmented public Holter databases, meeting the American Health Association criteria for AEDs.
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Kirlangic, Mehmet Eylem, Safwan Al-Qadhi, Christian Hauptmann, and Hans-Joachim Freund. "A Matlab toolbox for analyzing repetitive movements: application in gait and tapping experiments." Biomedical Engineering / Biomedizinische Technik 65, no. 4 (August 27, 2020): 447–59. http://dx.doi.org/10.1515/bmt-2018-0189.
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AbstractCoordination and timing in repetitive movements have been intensively investigated in diverse experimental settings for understanding the underlying basic mechanisms in healthy controls. On this basic research side, there are mainly two theoretical models: the Wing-Kristofferson (WK) model and the Haken-Kelso-Bunz (HKB) model. On the clinical side of the research, several efforts have been spent on quantitatively assessing gait and other repetitive movements such as tapping, especially as an outcome measure of clinical trials in diverse neurological disorders. Nevertheless, Parkinson’s disease (PD) remains the predominant disorder in the clinical literature in this context, as the tremor activity and the changes in the gait are both common symptoms in PD. Although there are motion recording systems for data acquisition in clinical settings, the tools for analysis and quantification of the extracted time-series offered by these systems are severely restricted. Therefore, we introduce a toolbox which enables the analysis of repetitive movements within the framework of the two main theoretical models of motor coordination, which explicitly focuses on varying clinical and experimental settings such as self-paced vs. cued or uni-manual vs. bi-manual measurements. The toolbox contains particular pipelines for digital signal processing. Licensed under the GNU General Public License (GNU-GPL), the open source toolbox is freely available and can be downloaded from the Github link: https://github.com/MehmetEylemKirlangic/RepetitiveMovementAnalysis. We illustrate the application of the toolbox on sample experiments of gait and tapping with a control subject, as well as with a Parkinson’s patient. The patient has gone through a brain surgery for deep brain stimulation (DBS); hence, we present the results for both stimulation ON and stimulation OFF modes. Sample data are freely accessible at: https://github.com/MehmetEylemKirlangic/DATA.
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Chen, Wei Yi, Qi Xie, and Yun Luo. "Research on the Test Technology of Fire Control System of Tank." Advanced Materials Research 694-697 (May 2013): 2040–43. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2040.
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Based on the test scheme and principle of armoured vehicle fire control system, proposes electrical measurement method unite gyros method to test armoured vehicle fire control system; Established the processing model of gun drifting, stability accuracy, gun transfer speed, intergrade quality; according to the gyros drifting problem, proposes drift trend arithmetic to enhancing the accuracy of measuring system.
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Wang, Tianshu. "Application of 5G Internet of Things Multisensor Information Fusion Model in Piano Performance." Journal of Sensors 2022 (June 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/9407713.
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In this paper, we use melodic multisensor information fusion combined with 5G IoT to conduct an in-depth study and analysis of the experience model of piano performance. In the form of multimodal data, two main storage forms, audio and MIDI, are chosen. First, audio signal processing technology and deep learning technology are used to extract shallow and high-level feature sequences in turn, and then, the alignment of the two modal data is completed with the help of sequence alignment algorithm. For the problem that encrypted data cannot be queried by uploading blockchain, this paper proposes an IoT encrypted data query mechanism based on blockchain and Bloom’s filter. The blockchain stores IoT encrypted indexes by temporal attributes to ensure data consistency, tamper-evident, and traceability. A new loss function training multimodal model is designed for piano performance signals. The piano performance generated by this model differs from the traditional piano performance generation in that it does not need to add complex piano performance rules manually but generates piano performance directly with piano performance theory rules by training the initial piano performance dataset and improves the stability of the generated piano performance by chord constraints and enhances the note dependence on time. In the analysis of the experimental results, the generated melodies were invited 50 for evaluation and analysis. The overall style-based GAN network piano performance generation model proposed in the study makes the generated piano performance melodies more pleasing to the ear through chord constraints and the content of autonomous learning moments, which has important theoretical and practical implications for the creation and realization of mass and batch piano performances.
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Weatherburn, Don. "Theoretical Note: Gun Control and Homicide." Australian & New Zealand Journal of Criminology 28, no. 1 (March 1995): 116–20. http://dx.doi.org/10.1177/000486589502800107.
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A Japanese tourist in the US was recently shot dead by a gun owner who mistakenly thought he was being attacked by a tourist. The circumstances surrounding the episode suggest the possibility that the risk of a fatal gun attack by a gun owner may not be independent of the general level of gun ownership. The possible consequences of this are explored using New South Wales data on homicide and gun ownership rates.
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Buccioni, Francesco, Chiara Purgatorio, Francesca Maggio, Stefania Garzoli, Chiara Rossi, Luca Valbonetti, Antonello Paparella, and Annalisa Serio. "Unraveling the Antimicrobial Effectiveness of Coridothymus capitatus Hydrolate against Listeria monocytogenes in Environmental Conditions Encountered in Foods: An In Vitro Study." Microorganisms 10, no. 5 (April 27, 2022): 920. http://dx.doi.org/10.3390/microorganisms10050920.
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The increased resistance of bacteria to antimicrobials, as well as the growing interest in innovative and sustainable alternatives to traditional food additives, are driving research towards the use of natural food preservatives. Among these, hydrolates (HYs) have gained attention as “mild” alternatives to conventional antimicrobial compounds. In this study, the response of L. monocytogenes ATCC 7644 exposed to increasing concentrations of Coridothymus capitatus HY (CHY) for 1 h at 37 °C was evaluated by means of Phenotype Microarray, modelling the kinetic data obtained by inoculating control and treated cells into GEN III microplates, after CHY removal. The results revealed differences concerning the growth dynamics in environmental conditions commonly encountered in food processing environments (different carbon sources, pH 6.0, pH 5.0, 1–8% NaCl). More specifically, for treated cells, the lag phase was extended, the growth rate was slowed down and, in most cases, the maximum concentration was diminished, suggesting the persistence of stress even after CHY removal. Confocal Laser Scanner Microscopy evidenced a diffuse aggregation and suffering of the treated cells, as a response to the stress encountered. In conclusion, the treatment with HY caused a stressing effect that persisted after its removal. The results suggest the potential of CHY application to control L. monocytogenes in food environments.
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Najda, Stephen P., Piotr Perlin, Tadek Suski, Lucja Marona, Mike Leszczyński, Przemek Wisniewski, Szymon Stanczyk, et al. "GaN Laser Diode Technology for Visible-Light Communications." Electronics 11, no. 9 (April 29, 2022): 1430. http://dx.doi.org/10.3390/electronics11091430.
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Gallium nitride (GaN) laser diodes (LDs) are considered for visible light communications (VLC) in free space, underwater, and in plastic optical fibers (POFs). A review of recent results is presented, showing high-frequency operation of AlGaInN laser diodes with data transmission rates up to 2.5 Gbit/s in free space and underwater and high bandwidths of up to 1.38 GHz through 10 m of plastic optical fiber. Distributed feedback (DFB) GaN LDs are fabricated to achieve single-frequency operation. We report on single-wavelength emissions of GaN DFB LDs with a side-mode suppression ratio (SMSR) in excess of 35 dB.
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Liakos, Konstantinos G., Georgios K. Georgakilas, Fotis C. Plessas, and Paris Kitsos. "GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS." Electronics 11, no. 2 (January 13, 2022): 245. http://dx.doi.org/10.3390/electronics11020245.
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A significant problem in the field of hardware security consists of hardware trojan (HT) viruses. The insertion of HTs into a circuit can be applied for each phase of the circuit chain of production. HTs degrade the infected circuit, destroy it or leak encrypted data. Nowadays, efforts are being made to address HTs through machine learning (ML) techniques, mainly for the gate-level netlist (GLN) phase, but there are some restrictions. Specifically, the number and variety of normal and infected circuits that exist through the free public libraries, such as Trust-HUB, are based on the few samples of benchmarks that have been created from circuits large in size. Thus, it is difficult, based on these data, to develop robust ML-based models against HTs. In this paper, we propose a new deep learning (DL) tool named Generative Artificial Intelligence Netlists SynthesIS (GAINESIS). GAINESIS is based on the Wasserstein Conditional Generative Adversarial Network (WCGAN) algorithm and area–power analysis features from the GLN phase and synthesizes new normal and infected circuit samples for this phase. Based on our GAINESIS tool, we synthesized new data sets, different in size, and developed and compared seven ML classifiers. The results demonstrate that our new generated data sets significantly enhance the performance of ML classifiers compared with the initial data set of Trust-HUB.
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Dzharov, Volodya. "CONTROL OF THE ELECTRON BEAM DEFLECTION SYSTEM OF AN ELECTRON BEAM INSTALLATION." EurasianUnionScientists 2, no. 7(76) (August 20, 2020): 21–26. http://dx.doi.org/10.31618/esu.2413-9335.2020.2.76.900.
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This paper explores patterns of electronic beam movement by controlling the transverse axis of the bundle of the uniform magnetic field generated by the coils of the electronic gun. For electron beam processes, the type of process, the technological mode, the design dimensions of the electronic gun, and the shape of the machined parts determines beam motion. The free and precise movement on random trajectories determines the possible applications of the electron beam process in performing various scientific experiments on material processing.
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Kleck, Gary, Tomislav Kovandzic, and Jon Bellows. "Does Gun Control Reduce Violent Crime?" Criminal Justice Review 41, no. 4 (October 4, 2016): 488–513. http://dx.doi.org/10.1177/0734016816670457.
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Do gun control laws reduce violence? To answer this question, a city-level cross-sectional analysis was performed on data pertaining to every U.S. city with a population of at least 25,000 in 1990 ( n = 1,078), assessing the impact of 19 major types of gun control laws, and controlling for gun ownership levels and numerous other possible confounders. Models were estimated using instrumental variables (IVs) regression to address endogeneity of gun levels due to reverse causality. Results indicate that gun control laws generally show no evidence of effects on crime rates, possibly because gun levels do not have a net positive effect on violence rates. Although a minority of laws seem to show effects, they are as likely to imply violence-increasing effects as violence-decreasing effects. There were, however, a few noteworthy exceptions: requiring a license to possess a gun and bans on purchases of guns by alcoholics appear to reduce rates of both homicide and robbery. Weaker evidence suggests that bans on gun purchases by criminals and on possession by mentally ill persons may reduce assault rates, and that bans on gun purchase by criminals may also reduce robbery rates.
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Memon, Mujahid Hussain. "Design of Centralized Intelligent Expert System and Contamination Detection of Tissue Cultured Sugarcane Crop." Sukkur IBA Journal of Emerging Technologies 4, no. 2 (December 20, 2021): 47–63. http://dx.doi.org/10.30537/sjet.v4i2.845.
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This paper presents the design a cloud based IoT enabled smart agriculture application for Hi-Tech tissue cultured sugarcane crop entitled “Design of Centralized Intelligent Expert System and Contamination Detection of Tissue Cultured Sugarcane Crop”. This expert system comprises of Raspberry Pi-4 (RPi), Arduino-Mega, GSM-Modem (Sim900) and sensor-modules for monitoring and control of essential parameters of laboratory for monitoring the physical parameters. The parameters monitored are temperature, humidity and light intensity of the tissue culture growth rooms with artificial day light timing and control, however, AI-based health prediction suggests the image processing for detection of culture contamination of sugarcane crop inside the growth-room. In addition, fire-smoke sensor and methane gas sensor are incorporated for fire protection and to avoid any disastrous situation. Three numbers of webcams are attached to the RPi for monitoring growth and health of explants. An AI-Model / weight was developed for detection of contamination that predicts the for health of Tissue Cultured Sugarcane Crop. Moreover, image enhancement was covered applying Generative Adversarial Networks (GAN)”. In this system, the RPi reads sensor's data through Arduino and convert it to data-frame with timestamp and geo-tag. The data along with the captured images are sent to a centralize cloud application for applying data mining and Artificial Intelligence; however, the model of contamination detection has been applied at edge device. This is to get meaningful insights of data for future decision making in maximizing crop yield and quality. Due to the great need of sugarcane crop in Pakistan, the Plant Tissue Culture (PTC) technology has been incorporated with Artificial Intelligence, the proposed system is aimed to be installed at established PTC-growth-rooms for sugarcane crop so the experts of field can be connected to the cloud application for its monitoring, control and data analytics. In addition, the use of telepresence through cloud application will enable PTC-experts to provide assistance to the remote user and resolve their issues timely, thus extending PTC technology all over the country which will eventually lead to increased crop yield with quality products in affordable price.