Статті в журналах: "In situ computing"

1

Kamath, Goutham, Lei Shi, Edmond Chow, Wenzhan Song, and Junjie Yang. "Decentralized multigrid for in-situ big data computing." Tsinghua Science and Technology 20, no. 6 (December 2015): 545–59. http://dx.doi.org/10.1109/tst.2015.7349927.

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Mencagli, Gabriele, Felipe MG França, Cristiana Barbosa Bentes, Leandro Augusto Justen Marzulo, and Mauricio Lima Pilla. "Special issue on parallel applications for in-situ computing on the next-generation computing platforms." International Journal of High Performance Computing Applications 33, no. 3 (December 26, 2018): 429–30. http://dx.doi.org/10.1177/1094342018820155.

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3

Troxel, Ian, Eric Grobelny, and Alan D. George. "System Management Services for High-Performance In-situ Aerospace Computing." Journal of Aerospace Computing, Information, and Communication 4, no. 2 (February 2007): 636–56. http://dx.doi.org/10.2514/1.26832.

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4

Consolvo, Sunny, Beverly Harrison, Ian Smith, Mike Y. Chen, Katherine Everitt, Jon Froehlich, and James A. Landay. "Conducting In Situ Evaluations for and With Ubiquitous Computing Technologies." International Journal of Human-Computer Interaction 22, no. 1-2 (April 2007): 103–18. http://dx.doi.org/10.1080/10447310709336957.

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5

Spence, Allan D., D. Alan Sawula, James R. Stone, and Yu Pin Lin. "In-Situ Measurement and Distributed Computing for Adjustable CNC Machining." Computer-Aided Design and Applications 11, no. 6 (June 10, 2014): 659–69. http://dx.doi.org/10.1080/16864360.2014.914384.

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6

Dorier, Matthieu, Zhe Wang, Srinivasan Ramesh, Utkarsh Ayachit, Shane Snyder, Rob Ross, and Manish Parashar. "Towards elastic in situ analysis for high-performance computing simulations." Journal of Parallel and Distributed Computing 177 (July 2023): 106–16. http://dx.doi.org/10.1016/j.jpdc.2023.02.014.

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7

Zhu, Wenkang, Hui Li, Shengnan Shen, Yingjie Wang, Yuqing Hou, Yikai Zhang, and Liwei Chen. "In-situ monitoring additive manufacturing process with AI edge computing." Optics & Laser Technology 171 (April 2024): 110423. http://dx.doi.org/10.1016/j.optlastec.2023.110423.

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8

Zyarah, Abdullah M., and Dhireesha Kudithipudi. "Semi-Trained Memristive Crossbar Computing Engine with In Situ Learning Accelerator." ACM Journal on Emerging Technologies in Computing Systems 14, no. 4 (December 11, 2018): 1–16. http://dx.doi.org/10.1145/3233987.

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9

Alimi, Roger, Elad Fisher, and Kanna Nahir. "In Situ Underwater Localization of Magnetic Sensors Using Natural Computing Algorithms." Sensors 23, no. 4 (February 5, 2023): 1797. http://dx.doi.org/10.3390/s23041797.

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In the shallow water regime, several positioning methods for locating underwater magnetometers have been investigated. These studies are based on either computer simulations or downscaled laboratory experiments. The magnetic fields created at the sensors’ locations define an inverse problem in which the sensors’ precise coordinates are the unknown variables. This work addresses the issue through (1) a full-scale experimental setup that provides a thorough scientific perspective as well as real-world system validation and (2) a passive ferromagnetic source with (3) an unknown magnetic vector. The latter increases the numeric solution’s complexity. Eight magnetometers are arranged according to a 2.5 × 2.5 m grid. Six meters above, a ferromagnetic object moves according to a well-defined path and velocity. The magnetic field recorded by the network is then analyzed by two natural computing algorithms: the genetic algorithm (GA) and particle swarm optimizer (PSO). Single- and multi-objective versions are run and compared. All the methods performed very well and were able to determine the location of the sensors within a relative error of 1 to 3%. The absolute error lies between 20 and 35 cm for the close and far sensors, respectively. The multi-objective versions performed better.

10

Aupy, Guillaume, Brice Goglin, Valentin Honoré, and Bruno Raffin. "Modeling high-throughput applications for in situ analytics." International Journal of High Performance Computing Applications 33, no. 6 (May 22, 2019): 1185–200. http://dx.doi.org/10.1177/1094342019847263.

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With the goal of performing exascale computing, the importance of input/output (I/O) management becomes more and more critical to maintain system performance. While the computing capacities of machines are getting higher, the I/O capabilities of systems do not increase as fast. We are able to generate more data but unable to manage them efficiently due to variability of I/O performance. Limiting the requests to the parallel file system (PFS) becomes necessary. To address this issue, new strategies are being developed such as online in situ analysis. The idea is to overcome the limitations of basic postmortem data analysis where the data have to be stored on PFS first and processed later. There are several software solutions that allow users to specifically dedicate nodes for analysis of data and distribute the computation tasks over different sets of nodes. Thus far, they rely on a manual resource partitioning and allocation by the user of tasks (simulations, analysis). In this work, we propose a memory-constraint modelization for in situ analysis. We use this model to provide different scheduling policies to determine both the number of resources that should be dedicated to analysis functions and that schedule efficiently these functions. We evaluate them and show the importance of considering memory constraints in the model. Finally, we discuss the different challenges that have to be addressed to build automatic tools for in situ analytics.

11

Avila, Anderson, Renata Hax Sander Reiser, Maurício Lima Pilla, and Adenauer Correa Yamin. "Improving in situ GPU simulation of quantum computing in the D-GM environment." International Journal of High Performance Computing Applications 33, no. 3 (January 16, 2019): 462–72. http://dx.doi.org/10.1177/1094342018823251.

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Exponential increase and global access to read/write memory states in quantum computing (QC) simulation limit both the number of qubits and quantum transformations which can be currently simulated. Although QC simulation is parallel by nature, spatial and temporal complexity are major performance hazards, making this a nontrivial application for high performance computing. A new methodology employing reduction and decomposition optimizations has shown relevant results, but its GPU implementation could be further improved. In this work, we develop a new kernel for in situ GPU simulation that better explores its resources without requiring further hardware. Shor’s and Grover’s algorithms are simulated up to 25 and 21 qubits respectively and compared to our previous version, to [Formula: see text] simulator and to ProjectQ framework, showing better results with relative speedups up to 4.38×, 3357.76× and 333× respectively.

12

Wong, Trevor, Karn N. Watcharasupat, Bhan Lam, Kenneth Ooi, Zhen Ting Ong, Furi Andi Karnapi, Woon Seng Gan, Samuel Yeong, and Irene Lee. "Deployment of an IoT System for Adaptive In-Situ Soundscape Augmentation." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 5 (February 1, 2023): 2013–21. http://dx.doi.org/10.3397/in_2022_0290.

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Soundscape augmentation is an emerging approach for noise mitigation by introducing additionalsounds known as "maskers" to increase indices like acoustic comfort. Traditionally, the choice of maskers is oftenpredicated on expert guidance or post-hoc analysis which can be time-consuming and sometimesarbitrary. Moreover, this often results in a static set of maskers that are inflexible to the dynamicnature of real-world acoustic environments. Overcoming the inflexibility of traditional soundscapeaugmentation is two-fold. First, given a snapshot of a soundscape, the system must be able to select anoptimal masker without human supervision. Second, the system must also be able to react to changesin the acoustic environment with near real-time latency. In this work, we harness the combinedprowess of cloud computing and internet-of-things (IoT) to allow in-situ listening and playback usingmicrocontrollers while delegating the computationally expensive inference tasks to the cloud. Inparticular, a serverless cloud architecture was used for inference, ensuring near real-time latencyand scalability without the need to provision computing resources. A working prototype of the systemis currently being deployed in a public area experiencing high traffic noise, as well as undergoingpublic evaluation for future improvements.

13

Niu, Xuezhong, Bobo Tian, Qiuxiang Zhu, Brahim Dkhil, and Chungang Duan. "Ferroelectric polymers for neuromorphic computing." Applied Physics Reviews 9, no. 2 (June 2022): 021309. http://dx.doi.org/10.1063/5.0073085.

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The last few decades have witnessed the rapid development of electronic computers relying on von Neumann architecture. However, due to the spatial separation of the memory unit from the computing processor, continuous data movements between them result in intensive time and energy consumptions, which unfortunately hinder the further development of modern computers. Inspired by biological brain, the in situ computing of memristor architectures, which has long been considered to hold unprecedented potential to solve the von Neumann bottleneck, provides an alternative network paradigm for the next-generation electronics. Among the materials for designing memristors, i.e., nonvolatile memories with multistate tunable resistances, ferroelectric polymers have drawn much research interest due to intrinsic analog switching property and excellent flexibility. In this review, recent advances on artificial synapses based on solution-processed ferroelectric polymers are discussed. The relationship between materials' properties, structural design, switching mechanisms, and systematic applications is revealed. We first introduce the commonly used ferroelectric polymers. Afterward, device structures and the switching mechanisms underlying ferroelectric synapse are discussed. The current applications of organic ferroelectric synapses in advanced neuromorphic systems are also summarized. Eventually, the remaining challenges and some strategies to eliminate non-ideality of synaptic devices are analyzed.

14

Yang, Le, Han Wang, Jiajian Zheng, Xin Duan, and Qishuo Cheng. "Research and Application of Visual Object Recognition System Based on Deep Learning and Neural Morphological Computation." International Journal of Computer Science and Information Technology 2, no. 1 (March 4, 2024): 10–17. http://dx.doi.org/10.62051/ijcsit.v2n1.02.

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The development of advanced optoelectronic vision sensors for high-level image recognition and data preprocessing is poised to accelerate the progress of machine vision and mobile electronic technology. Compared to traditional sensory computing methods, such as analog-to-digital signal conversion and digital logic computation tasks (i.e., Von Neumann computing), neural morphological vision computing can significantly improve energy efficiency and data processing speed by minimizing unnecessary raw data transmission between front-end photosensitive sensors and back-end processors. Neural morphological vision sensors are typically designed for tasks such as denoising, edge enhancement, spectral filtering, and visual information recognition. These methods can be categorized into approaches using near-sensor and sensor-internal computing processors based on whether preprocessing can be performed in situ. In near-sensor computing approaches, the image sensor for capturing visual information and the memory computing processor for preprocessing captured images are separate. A memory computing processor can simultaneously perform memory and computing tasks based on analog memory functions. Neural morphological vision sensors for in-sensor computing can be constructed using single-element image sensors, enabling both the reception of visual information and the execution of memory computing processes to be achieved in the same device. This represents an ideal scenario for future artificial intelligence machines and mobile electronic devices in visual computing systems.

15

KIM, Yonghun, Jung-Dae KWON, and Jongwon YOON. "2D Materials-based Neuromorphic Computing Electronic Device." Physics and High Technology 32, no. 11 (November 30, 2023): 10–16. http://dx.doi.org/10.3938/phit.32.029.

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Nowadays, with the rapid information explosion connected to all devices, there is a huge demand for effectively processing big data. In particular, conventional von Neumann computing system with physically separated processing and memory units face significant problems in dealing with massive unstructured data such as sound, images, and video because of a von Neumann bottleneck. As a key feature of parallel operations, neuromorphic computing systems can analyze massive unstructured data in a time and energy efficient manner. However, critical issues related to reliability and variability of nonlinearity and asymmetric weight update, have been great challenges in the implementation of artificial synaptic device in practical neuromorphic hardware system. Also, hardware systems enabling artificial neural networks in-situ personal data are essential for adaptive wearable neuromorphic edge computing.

16

Peterka, Tom, Deborah Bard, Janine C. Bennett, E. Wes Bethel, Ron A. Oldfield, Line Pouchard, Christine Sweeney, and Matthew Wolf. "Priority research directions for in situ data management: Enabling scientific discovery from diverse data sources." International Journal of High Performance Computing Applications 34, no. 4 (March 27, 2020): 409–27. http://dx.doi.org/10.1177/1094342020913628.

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In January 2019, the US Department of Energy, Office of Science program in Advanced Scientific Computing Research, convened a workshop to identify priority research directions (PRDs) for in situ data management (ISDM). A fundamental finding of this workshop is that the methodologies used to manage data among a variety of tasks in situ can be used to facilitate scientific discovery from many different data sources—simulation, experiment, and sensors, for example—and that being able to do so at numerous computing scales will benefit real-time decision-making, design optimization, and data-driven scientific discovery. This article describes six PRDs identified by the workshop, which highlight the components and capabilities needed for ISDM to be successful for a wide variety of applications—making ISDM capabilities more pervasive, controllable, composable, and transparent, with a focus on greater coordination with the software stack and a diversity of fundamentally new data algorithms.

17

Yue, Xiao Lei, Yong Li, and Han Peng Wang. "Inversion of Initial In Situ Stress Field by the Method of Multivariate Analysis and Engineering Application." Applied Mechanics and Materials 44-47 (December 2010): 1203–6. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.1203.

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Based on engineering geological conditions and measured data of the in-situ stress at a hydropower station, 3D geological models under each affecting factor are calculated by means of finite element computing tools ABAQUS and MATLAB. Then, a multivariate regression model is created between the measured and calculated value of in-situ stress at measurement points, and the optimal regression coefficient of the model is found. Consequently, the distributions of initial in-situ stress of this area are obtained. It is the first time to take into account the independence and internal relations among each stress components. Thus, the more reasonable distributions of initial in-situ stress of this area are obtained. The results indicate that the 3D calculating in-situ stress field is reasonable.

18

Li, Fei, and Ningdong Chang. "The Optimization Algorithm for Large-Scale In Situ Stress Field." Advances in Civil Engineering 2021 (March 1, 2021): 1–10. http://dx.doi.org/10.1155/2021/5539898.

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In situ stress state is a predominant factor for the design and safe construction of geotechnical engineering. For a real construction site, the amount of calculation using a finite element method for in situ stress field increases dramatically with the increase of the calculation freedom due to large-scale uncertainties. In order to reduce the computing cost without losing the accuracy of the calculation, an optimization algorithm combined with a reduced order model, which is realized by the proper orthogonal decomposition algorithm (POD) for large-scale in situ stress field, is put forward in this paper. The POD algorithm produces a set of orthogonal bases through the extraction of the field variables, combining with the Galerkin finite element method to create a reduced order numerical model. The reduced order model is then calculated with a global optimization algorithm to inversely find the solution for the actual in situ stress field. In order to verify the accuracy and efficiency of the method, two examples are presented to simulate the inverse calculation of the in situ stress field. They showed that the computation time of the POD method could reach 1/10 of the ordinary computation time. Also, the results showed good accuracy with a minimum computational expense, which can provide a reference for inverse calculation of large-scale in situ stress field.

19

Chen, ZhiQiang, and Jianfei Chen. "Mobile Imaging and Computing for Intelligent Structural Damage Inspection." Advances in Civil Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/483729.

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Optical imaging is a commonly used technique in civil engineering for aiding the archival of damage scenes and more recently for image analysis-based damage quantification. However, the limitations are evident when applying optical imaging in the field. The most significant one is the lacking of computing and processing capability in the real time. The advancement of mobile imaging and computing technologies provides a promising opportunity to change this norm. This paper first provides a timely introduction of the state-of-the-art mobile imaging and computing technologies for the purpose of engineering application development. Further we propose a mobile imaging and computing (MIC) framework for conducting intelligent condition assessment for constructed objects, which features in situ imaging and real-time damage analysis. This framework synthesizes advanced mobile technologies with three innovative features: (i) context-enabled image collection, (ii) interactive image preprocessing, and (iii) real-time image analysis and analytics. Through performance evaluation and field experiments, this paper demonstrates the feasibility and efficiency of the proposed framework.

20

Cheng, Shaobo, Min-Han Lee, Richard Tran, Yin Shi, Xing Li, Henry Navarro, Coline Adda, et al. "Inherent stochasticity during insulator–metal transition in VO2." Proceedings of the National Academy of Sciences 118, no. 37 (September 7, 2021): e2105895118. http://dx.doi.org/10.1073/pnas.2105895118.

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Vanadium dioxide (VO2), which exhibits a near-room-temperature insulator–metal transition, has great potential in applications of neuromorphic computing devices. Although its volatile switching property, which could emulate neuron spiking, has been studied widely, nanoscale studies of the structural stochasticity across the phase transition are still lacking. In this study, using in situ transmission electron microscopy and ex situ resistive switching measurement, we successfully characterized the structural phase transition between monoclinic and rutile VO2 at local areas in planar VO2/TiO2 device configuration under external biasing. After each resistive switching, different VO2 monoclinic crystal orientations are observed, forming different equilibrium states. We have evaluated a statistical cycle-to-cycle variation, demonstrated a stochastic nature of the volatile resistive switching, and presented an approach to study in-plane structural anisotropy. Our microscopic studies move a big step forward toward understanding the volatile switching mechanisms and the related applications of VO2 as the key material of neuromorphic computing.

21

Fang, Xu Dong, Yu Hua Tang, and Jun Jie Wu. "A Review on Memristive Stateful Logic." Advanced Materials Research 791-793 (September 2013): 1845–49. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1845.

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With the realization of physical memristors, using memristors to perform stateful logic operations has been demonstrated feasible. In such operations, memristors simultaneously serve as latches and logic gates, thus enabling the in-situ computing which may open a new computing paradigm for computer architecture. In this paper, we first analyze two types of typical memristive stateful logic gates to reveal the working mechanism of the stateful logic, and then review the recent researches on the memristive stateful logic, and finally discuss the pros and cons of the stateful logic. We reach the conclusion that the stateful logic promises a novel computing paradigm which may revolutionize the conventional computer architecture, while its development is currently subjected to the state drift problem and is constrained by the lack of a general design methodology and physically verification.

22

Mutis, Ivan, and Abhijeet Ambekar. "Challenges and enablers of augmented reality technology for in situ walkthrough applications." Journal of Information Technology in Construction 25 (January 29, 2020): 55–71. http://dx.doi.org/10.36680/j.itcon.2020.003.

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The successful implementation of situational-awareness enhancement using next-generation augmented reality in construction project walkthroughs has significant associated challenges. In this research, an augmented reality (AR) approach was developed to evaluate and showcase the trade-offs between capacity, latency, and reliability for the technology. The approach, named “i-Tracker”, overlays visualizations of designs by creating virtual content to be superimposed into physical contexts. i-Tracker uses the latest generation of mobile computing technology to effectively locate design information from existing parametric engineering designs (architectural, structural, and mechanical objects) and create a fully animated scene in situ. The i-Tracker technology uses a combination of depth-sensing cameras and inertial measurement unit (IMU) sensors. The evaluation of this technology demonstrates the requirements and limitations applicable to the implementation of this technology in job sites. In the example use case, the device’s relative position and orientation with respect to the user are estimated. Performance features such as motion tracking, localization, error dispersion with respect to luminance, system processing speed, and ambiguity in the feature tracking are evaluated. i-Tracker contributes to the body of literature and current work on the use of positioning and tracking systems in real construction sites within the AR context. This project advances understanding of rapid implementations and the use of AR visualizations on job sites, utilizing significant progress in mobile and ubiquitous computing with faster central processing units (CPUs) and graphical processing units (GPUs).

23

Carvalho, Caio B. G., Victor C. Ferreira, Felipe M. G. França, Cristiana B. Bentes, Gabriele Mencagli, Tiago A. O. Alves, Alexandre C. Sena, and Leandro A. J. Marzulo. "A dataflow runtime environment and static scheduler for edge, fog and in-situ computing." International Journal of Grid and Utility Computing 10, no. 3 (2019): 235. http://dx.doi.org/10.1504/ijguc.2019.099685.

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24

Marzulo, Leandro A. J., Tiago A. O. Alves, Alexandre C. Sena, Gabriele Mencagli, Caio B. G. Carvalho, Cristiana B. Bentes, Felipe M. G. França, and Victor C. Ferreira. "A dataflow runtime environment and static scheduler for edge, fog and in-situ computing." International Journal of Grid and Utility Computing 10, no. 3 (2019): 235. http://dx.doi.org/10.1504/ijguc.2019.10021333.

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25

Kim, Choongmin, Jacob A. Abraham, Woochul Kang, and Jaeyong Chung. "A Neural Network Decomposition Algorithm for Mapping on Crossbar-Based Computing Systems." Electronics 9, no. 9 (September 18, 2020): 1526. http://dx.doi.org/10.3390/electronics9091526.

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Crossbar-based neuromorphic computing to accelerate neural networks is a popular alternative to conventional von Neumann computing systems. It is also referred as processing-in-memory and in-situ analog computing. The crossbars have a fixed number of synapses per neuron and it is necessary to decompose neurons to map networks onto the crossbars. This paper proposes the k-spare decomposition algorithm that can trade off the predictive performance against the neuron usage during the mapping. The proposed algorithm performs a two-level hierarchical decomposition. In the first global decomposition, it decomposes the neural network such that each crossbar has k spare neurons. These neurons are used to improve the accuracy of the partially mapped network in the subsequent local decomposition. Our experimental results using modern convolutional neural networks show that the proposed method can improve the accuracy substantially within about 10% extra neurons.

26

Liu, Wei Qun, Ting Song, Yu Shou Li, Shu Fei Zheng, and Jing Yang. "Inverse Analysis of Regional In Situ Stress Prediction Based on Multi-Points Measurement and BP Neural Network." Applied Mechanics and Materials 510 (February 2014): 226–31. http://dx.doi.org/10.4028/www.scientific.net/amm.510.226.

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Based on the measurement of in-situ stress and engineering-geological conditions, we built computing models with pre-exerting boundary loads and simulated the regional stress field involved. Boundary loads can be approximately determined by use of the multiple linear regressions, and be further optimized with the artificial neural network. By calculation, the corresponding initial in-situ stress field can reach ideal accuracy. As an example, we inversely analyzed an engineering problem in Chinese Wo-bei mine. The results shows that the simulation can meet the point measurement very well, and the regional-stress estimation may play an important role in engineering.

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An, Nguyen Van, Nguyen Thanh Tuong, Le Ngoc Hanh, and Tran Thi An. "Monitoring Droughts in the Vu Gia-Thu Bon River Basin Using the Cloud-Based Google Earth Engine." IOP Conference Series: Earth and Environmental Science 1170, no. 1 (April 1, 2023): 012005. http://dx.doi.org/10.1088/1755-1315/1170/1/012005.

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Abstract This study implemented an index-based approach to monitor drought in the Vu Gia – Thu Bon river basin using remote sensing data and Google Earth Engine (GEE) cloud computing services. Landsat’s time-series remote sensing data are effectively used to calculate various drought indices. In this investigation, we evaluated the performance of various remote sensing-based drought indices (RSDI) utilizing the cloud-based Google Earth Engine (GEE) computing platform. Results indicated that there was a significant correlation between RSDI and the in-situ Potential Evapotranspiration (PET) and the soil temperature. The empirical results of this study demonstrated the possible utility of remote sensing data in drought monitoring for data-scarce regions.

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CHEOK, ADRIAN DAVID, MAN FUNG HO, EVA YUSTINA, and SHANG PING LEE. "MOBILE COMPUTING WITH PERSONAL AREA NETWORK AND HUMAN POWER GENERATION." International Journal of Software Engineering and Knowledge Engineering 15, no. 02 (April 2005): 169–75. http://dx.doi.org/10.1142/s0218194005002348.

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For mobile computing system such as wearable computer, one of the most critical hardware issues is the provision of electric power. Various different sources of power for wearable computer have been investigated; however, we are interested only in power that can be generated in-situ, such as human power. This paper describes a novel mean of multiple source human power generation for small wearable electronic devices, and then demonstrates the digital information transfer between wearable computing devices by using human skin as "antenna". There are a wide range of peripheral devices in a mobile computing system, such as sensors and identification memory tags. As the amount of such devices increases in a mobile computing design, there is a need for these devices to communicate efficiently with the central processing unit. Also, it is highly desirable that these devices could be conveniently connected to the wearable computer without many dangling wires. We developed a personal area network (PAN) system which attempts to interconnect such devices, and at the same time uses human body as communication channel. This system is in a way novel because it is totally powered by human motion.

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Baumgartl, Hermann, Josef Tomas, Ricardo Buettner, and Markus Merkel. "A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring." Progress in Additive Manufacturing 5, no. 3 (February 18, 2020): 277–85. http://dx.doi.org/10.1007/s40964-019-00108-3.

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Abstract Additive manufacturing of metal components with laser-powder bed fusion is a very complex process, since powder has to be melted and cooled in each layer to produce a part. Many parameters influence the printing process; however, defects resulting from suboptimal parameter settings are usually detected after the process. To detect these defects during the printing, different process monitoring techniques such as melt pool monitoring or off-axis infrared monitoring have been proposed. In this work, we used a combination of thermographic off-axis imaging as data source and deep learning-based neural network architectures, to detect printing defects. For the network training, a k-fold cross validation and a hold-out cross validation were used. With these techniques, defects such as delamination and splatter can be recognized with an accuracy of 96.80%. In addition, the model was evaluated with computing class activation heatmaps. The architecture is very small and has low computing costs, which means that it is suitable to operate in real time even on less powerful hardware.

30

Chen, Shi Kuo, Tian Hong Yang, Hong Lei Liu, and Wan Cheng Zhu. "Water Inrush Monitoring of Zhangmatun Mine Grout Curtain and Seepage-Stress-Damage Research." Materials Science Forum 704-705 (December 2011): 558–62. http://dx.doi.org/10.4028/www.scientific.net/msf.704-705.558.

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Based on extensive literature review, the state of the art of coupled hydromechanical models and in-situ monitoring for groundwater inrush predictions are summarized in detail, based on which, it is proposed that the key issues for describing the seepage characteristics during groundwater inrush are to calibrate the equations for damage-induced evolution of permeability and of effective stress. Depending on in-situ experiments and numerical simulations, a new academic idea, i.e.“the rock micro seismicity induced by mining processes and water pressure disturbance is in essence the index of groundwater inrush” is put forward based on case studies, coupled hydro-mechanical theory, high-performance computing technology and microseismic monitoring. The authors propose that the tendency for analyzing and predicting the groundwater inrush is to synthetically inverse the inrush pathway formation, strata microseismic precursor and high performance computing results. And relying on the microseismic monitoring events, the groundwater inrush models are calibrated, which could be used to clarify the precursory characteristics and to locate the inrush pathway. This study will lay theoretical basis for establishing the models to predict the groundwater inrush in underground mining. Key words：rock mechanics, groundwater inrush models, calibrating, numerical simulation, microseismic monitoring

31

Shinya, Atsushi, Hirofumi Koh, Toshiaki Kumata, Takuo Matsunobe, Toshiki Yamaoka, Kazuo Shimosako, Kazuki Nakamura, and Harumi Takeda. "In-Situ Ergonomics: A Proposal of Product Operation Information Gathering Method for Ubiquitous Computing Environment." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 48, no. 21 (September 2004): 2489–93. http://dx.doi.org/10.1177/154193120404802110.

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32

Chen, Yong, Jianbiao Bai, Shuai Yan, Shengpeng Hao, and Viet Doan Dao. "A method for computing unsupported roof distance in roadway advancement and its in-situ application." International Journal of Mining Science and Technology 26, no. 4 (July 2016): 551–56. http://dx.doi.org/10.1016/j.ijmst.2016.05.003.

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33

Wong, Voon-Kean, Sarbudeen Mohamed Rabeek, Szu Cheng Lai, Marilyne Philibert, David Boon Kiang Lim, Shuting Chen, Muthusamy Kumarasamy Raja, and Kui Yao. "Active Ultrasonic Structural Health Monitoring Enabled by Piezoelectric Direct-Write Transducers and Edge Computing Process." Sensors 22, no. 15 (July 30, 2022): 5724. http://dx.doi.org/10.3390/s22155724.

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While the active ultrasonic method is an attractive structural health monitoring (SHM) technology, many practical issues such as weight of transducers and cables, energy consumption, reliability and cost of implementation are restraining its application. To overcome these challenges, an active ultrasonic SHM technology enabled by a direct-write transducer (DWT) array and edge computing process is proposed in this work. The operation feasibility of the monitoring function is demonstrated with Lamb wave excited and detected by a linear DWT array fabricated in situ from piezoelectric P(VDF-TrFE) polymer coating on an aluminum alloy plate with a simulated defect. The DWT array features lightweight, small profile, high conformability, and implementation scalability, whilst the edge-computing circuit dedicatedly designed for the active ultrasonic SHM is able to perform signal processing at the sensor nodes before wirelessly transmitting the data to a remote host device. The successful implementation of edge-computing processes is able to greatly decrease the amount of data to be transferred by 331 times and decrease the total energy consumption for the wireless module by 224 times. The results and analyses show that the combination of the piezoelectric DWT and edge-computing process provides a promising technical solution for realizing practical wireless active ultrasonic SHM system.

34

K, Padmaja. "Design and Simulation of Op-Amp Based Neuron Circuit." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 4204–8. http://dx.doi.org/10.22214/ijraset.2022.45943.

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Abstract: Combining CMOS analog spike neurons with memory synapses, neuromorphic chips can provide massively parallel processing and density of neural networks, providing a promising solution for brain inspired computing. This work demonstrates a leaky integral firing neuron design that implements current integral and synaptic driven dualmode operation, and crossbar resistance synaptic enabled in situ learning with a single op amp. The proposed design was implemented with 0.18 μm CMOS technology.

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Jones, Christopher M., James Price, Bin Dai, Jian Li, David L. Perkins, and Michael L. Myrick. "In Situ Methane Determination in Petroleum at High Temperatures and High Pressures with Multivariate Optical Computing." Analytical Chemistry 91, no. 24 (October 29, 2019): 15617–24. http://dx.doi.org/10.1021/acs.analchem.9b03715.

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36

Tang, Wenjun, Jialong Liu, Hongtian Li, Deyun Chen, Chen Jiang, Xueqing Li, and Huazhong Yang. "Computing-in-memory with thin-filmtransistors: challenges and opportunities." Flexible and Printed Electronics 7, no. 2 (April 20, 2022): 024001. http://dx.doi.org/10.1088/2058-8585/ac541d.

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Abstract Thin-film transistors (TFTs) have attracted significant interest recently fortheir great potential in a wide range of edge computing applications, due to their advantages such as large-area low-cost flexible fabrications, and well integration with sensors and displays. With the support of in situ processing of sensor data, TFT-based edge systems show their advantages in large-scale dense sensing with real-time energy-efficient processing and interaction, and more excitingly, they provide the opportunity to eliminate the massive data transfer to the cloud servers. However, the design of high-performance processing modules based on TFT is difficult, due to large device variation, poor stability, and low mobility. Computing-in-memory (CiM), which has been proposed recently as a high-efficiency high-parallelism computing approach, is expected to improve the capacity of TFT-based edge computing systems. In thispaper, various recent works on TFT-based CiM have been summarized, showing the superiority to conventional processing flow by efficient in-memory analog computation with mitigation of data transfer, and reduced analog-to-digital converter usage for sensor data. With both opportunities and challenges, the design space and trend of TFT-based CiM to be explored are then described. Finally, further development and co-optimization from device to system are discussed for the flourishing of the next-generation intelligent TFT-based edge system.

37

Corona-Romero, Pedro, and Pete Riley. "Development of a formalism for computing in situ transits of Earth-directed CMEs – Part 2: Towards a forecasting tool." Annales Geophysicae 38, no. 3 (June 5, 2020): 657–81. http://dx.doi.org/10.5194/angeo-38-657-2020.

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Abstract. Earth-directed coronal mass ejections (CMEs) are of particular interest for space weather purposes, because they are precursors of major geomagnetic storms. The geoeffectiveness of a CME mostly relies on its physical properties like magnetic field and speed. There are multiple efforts in the literature to estimate in situ transit profiles of CMEs, most of them based on numerical codes. In this work we present a semi-empirical formalism to compute in situ transit profiles of Earth-directed fast halo CMEs. Our formalism combines analytic models and empirical relations to approximate CME properties as would be seen by a spacecraft near Earth's orbit. We use our formalism to calculate synthetic transit profiles for 10 events, including the Bastille Day event and 3 varSITI Campaign events. Our results show qualitative agreement with in situ measurements. Synthetic profiles of speed, magnetic intensity, density, and temperature of protons have average errors of 10 %, 27 %, 46 %, and 83 %, respectively. Additionally, we also computed the travel time of CME centers, with an average error of 9 %. We found that compression of CMEs by the surrounding solar wind significantly increased our uncertainties. We also outline a possible path to apply this formalism in a space weather forecasting tool.

38

Zeng, Jianmin, Xinhui Chen, Shuzhi Liu, Qilai Chen, and Gang Liu. "Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications." Nanomaterials 13, no. 5 (February 22, 2023): 803. http://dx.doi.org/10.3390/nano13050803.

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Memristors have been considered to be more efficient than traditional Complementary Metal Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are fundamental yet very critical components of neurons as well as neural networks. Compared with inorganic counterparts, organic memristors have many advantages, including low-cost, easy manufacture, high mechanical flexibility, and biocompatibility, making them applicable in more scenarios. Here, we present an organic memristor based on an ethyl viologen diperchlorate [EV(ClO4)]2/triphenylamine-containing polymer (BTPA-F) redox system. The device with bilayer structure organic materials as the resistive switching layer (RSL) exhibits memristive behaviors and excellent long-term synaptic plasticity. Additionally, the device’s conductance states can be precisely modulated by consecutively applying voltage pulses between the top and bottom electrodes. A three-layer perception neural network with in situ computing enabled was then constructed utilizing the proposed memristor and trained on the basis of the device’s synaptic plasticity characteristics and conductance modulation rules. Recognition accuracies of 97.3% and 90% were achieved, respectively, for the raw and 20% noisy handwritten digits images from the Modified National Institute of Standards and Technology (MNIST) dataset, demonstrating the feasibility and applicability of implementing neuromorphic computing applications utilizing the proposed organic memristor.

39

Liu, Chao, and Jiashu Sun. "AI in Measurement Science." Annual Review of Analytical Chemistry 14, no. 1 (June 5, 2021): 1–19. http://dx.doi.org/10.1146/annurev-anchem-091520-091450.

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Measurement of biological systems containing biomolecules and bioparticles is a key task in the fields of analytical chemistry, biology, and medicine. Driven by the complex nature of biological systems and unprecedented amounts of measurement data, artificial intelligence (AI) in measurement science has rapidly advanced from the use of silicon-based machine learning (ML) for data mining to the development of molecular computing with improved sensitivity and accuracy. This review presents an overview of fundamental ML methodologies and discusses their applications in disease diagnostics, biomarker discovery, and imaging analysis. We next provide the working principles of molecular computing using logic gates and arithmetical devices, which can be employed for in situ detection, computation, and signal transduction for biological systems. This review concludes by summarizing the strengths and limitations of AI-involved biological measurement in fundamental and applied research.

40

Cheng, Shaobo, Min-Han Lee, Xing Li, Lorenzo Fratino, Federico Tesler, Myung-Geun Han, Javier del Valle, et al. "Operando characterization of conductive filaments during resistive switching in Mott VO2." Proceedings of the National Academy of Sciences 118, no. 9 (February 23, 2021): e2013676118. http://dx.doi.org/10.1073/pnas.2013676118.

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Vanadium dioxide (VO2) has attracted much attention owing to its metal–insulator transition near room temperature and the ability to induce volatile resistive switching, a key feature for developing novel hardware for neuromorphic computing. Despite this interest, the mechanisms for nonvolatile switching functioning as synapse in this oxide remain not understood. In this work, we use in situ transmission electron microscopy, electrical transport measurements, and numerical simulations on Au/VO2/Ge vertical devices to study the electroforming process. We have observed the formation of V5O9 conductive filaments with a pronounced metal–insulator transition and that vacancy diffusion can erase the filament, allowing for the system to “forget.” Thus, both volatile and nonvolatile switching can be achieved in VO2, useful to emulate neuronal and synaptic behaviors, respectively. Our systematic operando study of the filament provides a more comprehensive understanding of resistive switching, key in the development of resistive switching-based neuromorphic computing.

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Chen, Qilai, Tingting Han, Jianmin Zeng, Zhilong He, Yulin Liu, Jinglin Sun, Minghua Tang, Zhang Zhang, Pingqi Gao, and Gang Liu. "Perovskite-Based Memristor with 50-Fold Switchable Photosensitivity for In-Sensor Computing Neural Network." Nanomaterials 12, no. 13 (June 28, 2022): 2217. http://dx.doi.org/10.3390/nano12132217.

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In-sensor computing can simultaneously output image information and recognition results through in-situ visual signal processing, which can greatly improve the efficiency of machine vision. However, in-sensor computing is challenging due to the requirement to controllably adjust the sensor’s photosensitivity. Herein, it is demonstrated a ternary cationic halide Cs0.05FA0.81MA0.14 Pb(I0.85Br0.15)3 (CsFAMA) perovskite, whose External quantum efficiency (EQE) value is above 80% in the entire visible region (400–750 nm), and peak responsibility value at 750 nm reaches 0.45 A/W. In addition, the device can achieve a 50-fold enhancement of the photoresponsibility under the same illumination by adjusting the internal ion migration and readout voltage. A proof-of-concept visually enhanced neural network system is demonstrated through the switchable photosensitivity of the perovskite sensor array, which can simultaneously optimize imaging and recognition results and improve object recognition accuracy by 17% in low-light environments.

42

DENIEL, Jean-Marc. "Computing spatial distribution of tube and louvre luminaires efficiency from their description." International Journal of Sustainable Lighting 22, no. 1 (June 15, 2020): 12–27. http://dx.doi.org/10.26607/ijsl.v22i1.94.

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Lighting computation requires photometry data that are not always available. Lacking photometry data limits lighting study to in situ measurement, luminaire measurement or use of similar luminaire photometry. This is not satisfactory, neither for convenience nor cost and accuracy reasons. Fitting the spatial distribution of luminaire efficiency to their description would allow lighting computations in this kind of situation. An efficiency spatial distribution model is proposed for grid and louvre tube luminaires, taking optic width, louvre between-axis and gloss as parameters. It is constructed over 12 measured efficiency spatial distributions and the corresponding luminaire descriptors. Even if optic and louvre gloss cannot be differentiated, this model fits to measurements and allows for computed irradiance close to experiments within −5% to +19%. In addition, luminaire descriptors can freely vary inside their experimental range and even be extrapolated.

43

Areemit, Natthapong, Michael Montgomery, Constantin Christopoulos, and Agha Hasan. "Identification of the dynamic properties of a reinforced concrete coupled shear wall residential high-rise building." Canadian Journal of Civil Engineering 39, no. 6 (June 2012): 631–42. http://dx.doi.org/10.1139/l2012-047.

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As high-rise buildings increase with height and slenderness, they become increasingly sensitive to dynamic vibrations, and therefore the natural frequency of vibration and damping ratio are very important design parameters, as they directly impact the design wind forces. Recent advances in sensing and computing technology have made it possible to monitor the dynamic behaviour of full-scale structures, which was not possible in the past. Full-scale validation of the dynamic properties is useful for high-rise designers to verify design assumptions, especially since recent measurements have shown that damping decreases as the height of the building increases, and in situ damping measurements have been lower than many currently assumed design values, potentially leading to unconservative designs. A 50-storey residential building in downtown Toronto, with a reinforced concrete coupled shear wall lateral load resisting system with outriggers was monitored using current state-of-the-art sensing technologies and techniques to determine, in situ, the dynamic properties under real wind loads. The in situ measurements were then compared with results obtained using current state-of-the-art computer modelling techniques.

44

Lee, Hyunsoo, Soowon Kang, and Uichin Lee. "Understanding Privacy Risks and Perceived Benefits in Open Dataset Collection for Mobile Affective Computing." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 2 (July 4, 2022): 1–26. http://dx.doi.org/10.1145/3534623.

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Collecting large-scale mobile and wearable sensor datasets from daily contexts is essential in developing machine learning models for enabling everyday affective computing applications. However, there is a lack of knowledge on data contributors' perceived benefits and risks in participating in open dataset collection projects. To bridge this gap, we conducted an in-situ study on building an open dataset with mobile and wearable devices for affective computing research (N = 100, 4 weeks). Our study results showed that a mixture of financial and altruistic benefits was important in eliciting data contribution. Sensor-specific risks were largely associated with the revelation of personal traits and social behaviors. However, most of the participants were less concerned with open dataset collection and their perceived sensitivity of each sensor data did not change over time. We further discuss alternative approaches to promote data contributors' motivations and suggest design guidelines to alleviate potential privacy concerns in mobile open dataset collection.

45

Kaloop, Mosbeh R., Jong Wan Hu, and Emad Elbeltagi. "Predicting the Pullout Capacity of Small Ground Anchors Using Nonlinear Integrated Computing Techniques." Shock and Vibration 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/2601063.

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This study investigates predicting the pullout capacity of small ground anchors using nonlinear computing techniques. The input-output prediction model for the nonlinear Hammerstein-Wiener (NHW) and delay inputs for the adaptive neurofuzzy inference system (DANFIS) are developed and utilized to predict the pullout capacity. The results of the developed models are compared with previous studies that used artificial neural networks and least square support vector machine techniques for the same case study. The in situ data collection and statistical performances are used to evaluate the models performance. Results show that the developed models enhance the precision of predicting the pullout capacity when compared with previous studies. Also, the DANFIS model performance is proven to be better than other models used to detect the pullout capacity of ground anchors.

46

Pedretti, Giacomo, and Daniele Ielmini. "In-Memory Computing with Resistive Memory Circuits: Status and Outlook." Electronics 10, no. 9 (April 30, 2021): 1063. http://dx.doi.org/10.3390/electronics10091063.

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In-memory computing (IMC) refers to non-von Neumann architectures where data are processed in situ within the memory by taking advantage of physical laws. Among the memory devices that have been considered for IMC, the resistive switching memory (RRAM), also known as memristor, is one of the most promising technologies due to its relatively easy integration and scaling. RRAM devices have been explored for both memory and IMC applications, such as neural network accelerators and neuromorphic processors. This work presents the status and outlook on the RRAM for analog computing, where the precision of the encoded coefficients, such as the synaptic weights of a neural network, is one of the key requirements. We show the experimental study of the cycle-to-cycle variation of set and reset processes for HfO2-based RRAM, which indicate that gate-controlled pulses present the least variation in conductance. Assuming a constant variation of conductance σG, we then evaluate and compare various mapping schemes, including multilevel, binary, unary, redundant and slicing techniques. We present analytical formulas for the standard deviation of the conductance and the maximum number of bits that still satisfies a given maximum error. Finally, we discuss RRAM performance for various analog computing tasks compared to other computational memory devices. RRAM appears as one of the most promising devices in terms of scaling, accuracy and low-current operation.

47

Zhu, Wei Shen, Shu Cai Li, Q. Zhang, and X. Qiu. "Model of Damage Fracture and Damage Rheology for Jointed Rock Mass and Its Engineering Application." Key Engineering Materials 306-308 (March 2006): 1385–90. http://dx.doi.org/10.4028/www.scientific.net/kem.306-308.1385.

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This paper puts forward a damage-fracture mechanical model and a damage-rheology mechanical model for the jointed rock mass of the high slopes in the ship lock area of Three Gorges Project. These two models are used to analyze the slopes’ stability. A comparison of the computed displacements at numbers of points on the slope surfaces with the results from 3-D analysis is also made. In addition, some computing results are compared with the in-situ measured ones, showing that the model proposed is basically reliable.

48

Ehala, Johannes, Jaanus Kaugerand, Raido Pahtma, Sergei Astapov, Andri Riid, Timo Tomson, Jürgo-Sören Preden, and Leo Mõtus. "Situation awareness via Internet of things and in-network data processing." International Journal of Distributed Sensor Networks 13, no. 1 (January 2017): 155014771668657. http://dx.doi.org/10.1177/1550147716686578.

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Computing on the edge of the Internet of things comprises among other tasks in-sensor signal processing and performing distributed data fusion and aggregation at network nodes. This poses a challenge to distributed sensor networks of low computing power devices that have to do complex fusion, aggregation and signal processing in situ. One of the difficulties lies in ensuring validity of data collected from heterogeneous sources. Ensuring data validity, for example, the temporal and spatial correctness of data, is crucial for correct in-network data fusion and aggregation. The article considers wireless sensor technology in military domain with the aim of improving situation awareness for military operations. Requirements for contemporary intelligence, surveillance and reconnaissance applications are explored and an experimental wireless sensor network, designed to enhance situation awareness to both in-the-field units and remote intelligence operatives, is described. The sensor nodes have the capability to perform in-sensor signal processing and distributed in-network data aggregation and fusion complying with edge computing paradigm. In-network data processing is supported by service-oriented middleware which facilitates run-time sensor discovery and tasking and ad hoc (re)configuration of the network links. The article describes two experiments demonstrating the ability of the wireless sensor network to meet intelligence, surveillance and reconnaissance requirements. The efficiency of distributed data fusion is evaluated and the importance and effect of establishing data validity is shown.

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KUMAR, GAJENDRA, SURESH CHAND, R. R. MALI, S. K. KUNDU, and A. K. BAXLA. "In-situ observational network for extreme weather events in India." MAUSAM 67, no. 1 (December 8, 2021): 67–76. http://dx.doi.org/10.54302/mausam.v67i1.1145.

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Extreme weather events, interacting with vulnerable human and natural systems, can lead to disasters, especially in absence of responsive social system. Accurate and timely monitoring and forecast of heavy rains, tropical cyclones, thunderstorms, hailstorms, cloudburst, drought, heat and cold waves, etc. are required to respond effectively to such events. Due to extreme weather events, crops over large parts of the country are adversely affected reducing production of total food grains, fodder, cash crops, vegetables and fruits which in turn affect the earnings and livelihood of individual farmers as well as the economy of the country. In situ observational network are the vital component for skilful prediction of extreme weather events. Current observational requirements for extreme weather prediction are met, to varying degrees by a range of in-situ observing systems and space-based systems. The augmentation of in-situ observational network is continuously progressing. IMD now has a network of Doppler Weather Radars (DWRs), Automatic Weather Stations (AWSs), Agro AWSs, Automatic Rain Gauges (ARGs), GPS upper air systems etc. These observations along with non-conventional (satellite) data are now being used to run its global and regional numerical prediction models on High Performance Computing Systems (HPCS). This has improved monitoring and forecasting capabilities for extreme weather events like cyclones, severe thunderstorm, heavy rainfall and floods in a significant manner. This paper provides an overview of the role of in-situ observational network for extreme weather events in India, framework for further augmentation to the network and other requirements to further enhance capabilities for high impact & extreme weather events and natural hazards.

50

Chen, Xiaochi, Yijie Huo, Seongjae Cho, Byung-Gook Park, and James S. Jr Harris. "Surface Treatment of Ge Grown Epitaxially on Si by Ex-Situ Annealing for Optical Computing by Ge Technology." IEIE Transactions on Smart Processing and Computing 3, no. 5 (October 31, 2014): 331–37. http://dx.doi.org/10.5573/ieiespc.2014.3.5.331.

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