Journal articles: 'Data-driven experiments'

1

Perng, Sung-Yueh, Rob Kitchin, and Leighton Evans. "Locative media and data-driven computing experiments." Big Data & Society 3, no. 1 (January 5, 2016): 205395171665216. http://dx.doi.org/10.1177/2053951716652161.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Cruz, Sérgio Manuel Serra da, and José Antonio Pires do Nascimento. "Towards integration of data-driven agronomic experiments with data provenance." Computers and Electronics in Agriculture 161 (June 2019): 14–28. http://dx.doi.org/10.1016/j.compag.2019.01.044.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

De Persis, Claudio, and Pietro Tesi. "Designing Experiments for Data-Driven Control of Nonlinear Systems." IFAC-PapersOnLine 54, no. 9 (2021): 285–90. http://dx.doi.org/10.1016/j.ifacol.2021.06.085.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Koning, A. J., J. P. Delaroche, and O. Bersillon. "Nuclear data for accelerator driven systems: Nuclear models, experiments and data libraries." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 414, no. 1 (September 1998): 49–67. http://dx.doi.org/10.1016/s0168-9002(98)00528-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Frederik, Joeri, Lars Kröger, Gerd Gülker, and Jan-Willem van Wingerden. "Data-driven repetitive control: Wind tunnel experiments under turbulent conditions." Control Engineering Practice 80 (November 2018): 105–15. http://dx.doi.org/10.1016/j.conengprac.2018.08.011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Lin, Shangjing, Jianguo Yu, and Ji Ma. "Big Data Driven Mobile Cellular Networks: Modelling, Experiments, and Applications." IOP Conference Series: Materials Science and Engineering 466 (December 28, 2018): 012074. http://dx.doi.org/10.1088/1757-899x/466/1/012074.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Van Ameijde, Jeroen. "Data-driven Urban Design." SPOOL 9, no. 1 (May 27, 2022): 35–48. http://dx.doi.org/10.47982/spool.2022.1.03.

Full text

Abstract:

Nicholas Negroponte and MIT’s Architecture Machine Group speculated in the 1970s about computational processes that were open to participation, incorporating end-user preferences and democratizing urban design. Today’s ‘smart city’ technologies, using the monitoring of people’s movement and activity patterns to offer more effective and responsive services, might seem like contemporary interpretations of Negroponte’s vision, yet many of the collectors of user information are disconnected from urban policy making. This article presents a series of theoretical and procedural experiments conducted through academic research and teaching, developing user-driven generative design processes in the spirit of ‘The Architecture Machine’. It explores how new computational tools for site analysis and monitoring can enable data-driven urban place studies, and how these can be connected to generative strategies for public spaces and environments at various scales. By breaking down these processes into separate components of gathering, analysing, translating and implementing data, and conceptualizing them in relation to urban theory, it is shown how data-driven urban design processes can be conceived as an open-ended toolkit to achieve various types of user-driven outcomes. It is argued that architects and urban designers are uniquely situated to reflect on the benefits and value systems that control data-driven processes, and should deploy these to deliver more resilient, liveable and participatory urban spaces.

APA, Harvard, Vancouver, ISO, and other styles

8

Fiordalis, Andrew, and Christos Georgakis. "Data-driven, using design of dynamic experiments, versus model-driven optimization of batch crystallization processes." Journal of Process Control 23, no. 2 (February 2013): 179–88. http://dx.doi.org/10.1016/j.jprocont.2012.08.011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Murari, A., E. Peluso, T. Craciunescu, S. Dormido-Canto, M. Lungaroni, R. Rossi, L. Spolladore, J. Vega, and M. Gelfusa. "Frontiers in data analysis methods: from causality detection to data driven experimental design." Plasma Physics and Controlled Fusion 64, no. 2 (December 31, 2021): 024002. http://dx.doi.org/10.1088/1361-6587/ac3ded.

Full text

Abstract:

Abstract On the route to the commercial reactor, the experiments in magnetical confinement nuclear fusion have become increasingly complex and they tend to produce huge amounts of data. New analysis tools have therefore become indispensable, to fully exploit the information generated by the most relevant devices, which are nowadays very expensive to both build and operate. The paper presents a series of innovative tools to cover the main aspects of any scientific investigation. Causality detection techniques can help identify the right causes of phenomena and can become very useful in the optimisation of synchronisation experiments, such as the pacing of sawteeth instabilities with ion cyclotron radiofrequency heating modulation. Data driven theory is meant to go beyond traditional machine learning tools, to provide interpretable and physically meaningful models. The application to very severe problems for the tokamak configuration, such as disruptions, could help not only in understanding the physics but also in extrapolating the solutions to the next generation of devices. A specific methodology has also been developed to support the design of new experiments, proving that the same progress in the derivation of empirical models could be achieved with a significantly reduced number of discharges.

APA, Harvard, Vancouver, ISO, and other styles

10

Knox, Joseph E., Kameron Decker Harris, Nile Graddis, Jennifer D. Whitesell, Hongkui Zeng, Julie A. Harris, Eric Shea-Brown, and Stefan Mihalas. "High-resolution data-driven model of the mouse connectome." Network Neuroscience 3, no. 1 (January 2019): 217–36. http://dx.doi.org/10.1162/netn_a_00066.

Full text

Abstract:

Knowledge of mesoscopic brain connectivity is important for understanding inter- and intraregion information processing. Models of structural connectivity are typically constructed and analyzed with the assumption that regions are homogeneous. We instead use the Allen Mouse Brain Connectivity Atlas to construct a model of whole-brain connectivity at the scale of 100 μm voxels. The data consist of 428 anterograde tracing experiments in wild type C57BL/6J mice, mapping fluorescently labeled neuronal projections brain-wide. Inferring spatial connectivity with this dataset is underdetermined, since the approximately 2 × 105 source voxels outnumber the number of experiments. To address this issue, we assume that connection patterns and strengths vary smoothly across major brain divisions. We model the connectivity at each voxel as a radial basis kernel-weighted average of the projection patterns of nearby injections. The voxel model outperforms a previous regional model in predicting held-out experiments and compared with a human-curated dataset. This voxel-scale model of the mouse connectome permits researchers to extend their previous analyses of structural connectivity to much higher levels of resolution, and it allows for comparison with functional imaging and other datasets.

APA, Harvard, Vancouver, ISO, and other styles

11

Liang, Jingwei, Jianwei Ma, and Xiaoqun Zhang. "Seismic data restoration via data-driven tight frame." GEOPHYSICS 79, no. 3 (May 1, 2014): V65—V74. http://dx.doi.org/10.1190/geo2013-0252.1.

Full text

Abstract:

Restoration/interpolation of missing traces plays a crucial role in the seismic data processing pipeline. Efficient restoration methods have been proposed based on sparse signal representation in a transform domain such as Fourier, wavelet, curvelet, and shearlet transforms. Most existing methods are based on transforms with a fixed basis. We considered an adaptive sparse transform for restoration of data with complex structures. In particular, we evaluated a data-driven tight-frame-based sparse regularization method for seismic data restoration. The main idea of the data-driven tight frame (TF) is to adaptively learn a set of framelet filters from the currently interpolated data, under which the data can be more sparsely represented; hence, the sparsity-promoting [Formula: see text]-norm (SPL1) minimization methods can produce better restoration results by using the learned filters. A split inexact Uzawa algorithm, which can be viewed as a generalization of the alternating direction of multiplier method (ADMM), was applied to solve the presented SPL1 model. Numerical tests were performed on synthetic and real seismic data for restoration of randomly missing traces over a regular data grid. Our experiments showed that our proposed method obtains the state-of-the-art restoration results in comparison with the traditional Fourier-based projection onto convex sets, the tight-frame-based method, and the recent shearlet regularization ADMM method.

APA, Harvard, Vancouver, ISO, and other styles

12

Haghani, Adel, Torsten Jeinsch, Mathias Roepke, Steven X. Ding, and Nick Weinhold. "Data-driven monitoring and validation of experiments on automotive engine test beds." Control Engineering Practice 54 (September 2016): 27–33. http://dx.doi.org/10.1016/j.conengprac.2016.05.011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Ashok, Sachin, Shubham Tiwari, Nagarajan Natarajan, Venkata N. Padmanabhan, and Sundararajan Sellamanickam. "Data-Driven Network Path Simulation with iBox." Proceedings of the ACM on Measurement and Analysis of Computing Systems 6, no. 1 (February 24, 2022): 1–26. http://dx.doi.org/10.1145/3508026.

Full text

Abstract:

While network simulation is widely used for evaluating network protocols and applications, ensuring realism remains a key challenge. There has been much work on simulating network mechanisms faithfully (e.g., links, buffers, etc.), but less attention on the critical task of configuring the simulator to reflect reality. We present iBox ("Internet in a Box"), which enables data-driven network path simulation, using input/output packet traces gathered at the sender/receiver in the target network to create a model of the end-to-end behaviour of a network path. Our work builds on recent work in this direction and makes three contributions: (1) estimation of a lightweight non reactive cross-traffic model, (2) estimation of a more powerful reactive cross-traffic model based on Bayesian optimization, and (3) evaluation of iBox in the context of congestion control variants in an Internet research testbed and also controlled experiments with known ground truth.

APA, Harvard, Vancouver, ISO, and other styles

14

Ashok, Sachin, Shubham Tiwari, Nagarajan Natarajan, Venkata N. Padmanabhan, and Sundararajan Sellamanickam. "Data-Driven Network Path Simulation with iBox." ACM SIGMETRICS Performance Evaluation Review 50, no. 1 (June 20, 2022): 47–48. http://dx.doi.org/10.1145/3547353.3522646.

Full text

Abstract:

While network simulation is widely used for evaluating network protocols and applications, ensuring realism remains a key challenge. There has been much work on simulating network mechanisms faithfully (e.g., links, buffers, etc.), but less attention on the critical task of configuring the simulator to reflect reality. We present iBox ("Internet in a Box"), which enables data-driven network path simulation, using input/output packet traces gathered at the sender/receiver in the target network to create a model of the end-to-end behaviour of a network path. Our work builds on recent work in this direction [2, 6] and makes three contributions: (1) estimation of a lightweight non-reactive cross-traffic model, (2) estimation of a more powerful reactive cross-traffic model based on Bayesian optimization, and (3) evaluation of iBox in the context of congestion control variants in an Internet research testbed and also controlled experiments with known ground truth. This paper represents an abridged version of [3].

APA, Harvard, Vancouver, ISO, and other styles

15

Hu, Kai, Lang Tian, Chenghang Weng, Liguo Weng, Qiang Zang, Min Xia, and Guodong Qin. "Data-Driven Control Algorithm for Snake Manipulator." Applied Sciences 11, no. 17 (September 2, 2021): 8146. http://dx.doi.org/10.3390/app11178146.

Full text

Abstract:

In some environments where manual work cannot be carried out, snake manipulators are instead used to improve the level of automatic work and ensure personal safety. However, the structure of the snake manipulator is diverse, which renders it difficult to establish an environmental model of the control system. It is difficult to obtain an ideal control effect by using the traditional manipulator control method. In view of this, this paper proposes a data-driven snake manipulator control algorithm. After collecting data, the algorithm uses the strong learning and decision-making ability of the deep deterministic strategy gradient to learn these system data. A data-driven controller based on the deep deterministic policy gradient was trained in order to solve the manipulator system control problem when the control system environment model is uncertain or even unknown. The data of simulation experiments show that the control algorithm has good stability and accuracy in the case of model uncertainty.

APA, Harvard, Vancouver, ISO, and other styles

16

Bassili, John N., Marilyn C. Smith, and Colin M. MacLeod. "Auditory and Visual Word-Stem Completion: Separating Data-Driven and Conceptually Driven Processes." Quarterly Journal of Experimental Psychology Section A 41, no. 3 (August 1989): 439–53. http://dx.doi.org/10.1080/14640748908402375.

Full text

Abstract:

Two experiments investigated the contributions of data-driven and conceptually driven processing on an implicit word-stem completion task. In Experiment 1, individual words were studied either visually or auditorily and were tested using either visual or auditory word-stems. Keeping modality the same from study to test led to more priming than did changing modality, but there was reliable cross-modal priming. In Experiment 2, subjects read sentences like The boat travelled underwater and inferred the subject noun (i.e. “submarine”) or sentences like The submarine travelled underwater and categorized the subject noun (i.e. “boat”). At test, there was reliable priming for both actually read nouns and inferred nouns. In addition, a modality effect was evident for the actually read nouns but not for the inferred nouns. Taken together, these results imply that there is a basic conceptually driven contribution to priming plus an additional contribution of data-driven processing when surface form is the same at study and test.

APA, Harvard, Vancouver, ISO, and other styles

17

Fisk, G. A., G. A. Mastin, and S. A. Sheffield. "Digital image processing of velocity‐interferometer data obtained from laser‐driven shock experiments." Journal of Applied Physics 60, no. 7 (October 1986): 2266–71. http://dx.doi.org/10.1063/1.337187.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Catlett, Charlie, Eugenio Cesario, Domenico Talia, and Andrea Vinci. "Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments." Pervasive and Mobile Computing 53 (February 2019): 62–74. http://dx.doi.org/10.1016/j.pmcj.2019.01.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Beirnaert, Charlie, Laura Peeters, Pieter Meysman, Wout Bittremieux, Kenn Foubert, Deborah Custers, Anastasia Van der Auwera, et al. "Using Expert Driven Machine Learning to Enhance Dynamic Metabolomics Data Analysis." Metabolites 9, no. 3 (March 20, 2019): 54. http://dx.doi.org/10.3390/metabo9030054.

Full text

Abstract:

Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. As untargeted metabolomics datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficiently sophisticated. In addition, the ground truth for untargeted metabolomics experiments is intrinsically unknown and the performance of tools is difficult to evaluate. Here, the problem of dynamic multi-class metabolomics experiments was investigated using a simulated dataset with a known ground truth. This simulated dataset was used to evaluate the performance of tinderesting, a new and intuitive tool based on gathering expert knowledge to be used in machine learning. The results were compared to EDGE, a statistical method for time series data. This paper presents three novel outcomes. The first is a way to simulate dynamic metabolomics data with a known ground truth based on ordinary differential equations. This method is made available through the MetaboLouise R package. Second, the EDGE tool, originally developed for genomics data analysis, is highly performant in analyzing dynamic case vs. control metabolomics data. Third, the tinderesting method is introduced to analyse more complex dynamic metabolomics experiments. This tool consists of a Shiny app for collecting expert knowledge, which in turn is used to train a machine learning model to emulate the decision process of the expert. This approach does not replace traditional data analysis workflows for metabolomics, but can provide additional information, improved performance or easier interpretation of results. The advantage is that the tool is agnostic to the complexity of the experiment, and thus is easier to use in advanced setups. All code for the presented analysis, MetaboLouise and tinderesting are freely available.

APA, Harvard, Vancouver, ISO, and other styles

20

He, Yongxiang, Hongwu Guo, and Yang Han. "A Novel Hybrid Data-Driven Modeling Method for Missiles." Symmetry 12, no. 1 (December 22, 2019): 30. http://dx.doi.org/10.3390/sym12010030.

Full text

Abstract:

This paper proposes a novel hybrid data-driven modeling method for missiles. Based on actual flight test data, the missile hybrid model is established by combining neural networks and the mechanism modeling method, considering the uncertainties and nonlinear factors in missiles. This method can avoid the problems in missile mechanism modeling and traditional data-driven modeling, and can also provide a solution for nonlinear dynamic system modeling problems in offline usage scenarios. Finally, the feasibility of the proposed method and the credibility of the established model are verified by simulation experiments and statistical analysis.

APA, Harvard, Vancouver, ISO, and other styles

21

Kyriacou, Costas, Paraskevas Evripidou, and Pedro Trancoso. "CacheFlow: Cache Optimizations for Data Driven Multithreading." Parallel Processing Letters 16, no. 02 (June 2006): 229–44. http://dx.doi.org/10.1142/s0129626406002599.

Full text

Abstract:

Data-Driven Multithreading is a non-blocking multithreading model of execution that provides effective latency tolerance by allowing the computation processor do useful work, while a long latency event is in progress. With the Data-Driven Multithreading model, a thread is scheduled for execution only if all of its inputs have been produced and placed in the processor's local memory. Data-driven sequencing leads to irregular memory access patterns that could affect negatively cache performance. Nevertheless, it enables the implementation of short-term optimal cache management policies. This paper presents the implementation of CacheFlow, an optimized cache management policy which eliminates the side effects due to the loss of locality caused by the data-driven sequencing, and reduces further cache misses. CacheFlow employs thread-based prefetching to preload data blocks of threads deemed executable. Simulation results, for nine scientific applications, on a 32-node Data-Driven Multithreaded machine show an average speedup improvement from 19.8 to 22.6. Two techniques to further improve the performance of CacheFlow, conflict avoidance and thread reordering, are proposed and tested. Simulation experiments have shown a speedup improvement of 24% and 32%, respectively. The average speedup for all applications on a 32-node machine with both optimizations is 26.1.

APA, Harvard, Vancouver, ISO, and other styles

22

Toleu, Alymzhan, Gulmira Tolegen, Rustam Mussabayev, Alexander Krassovitskiy, and Irina Ualiyeva. "Data-Driven Approach for Spellchecking and Autocorrection." Symmetry 14, no. 11 (October 27, 2022): 2261. http://dx.doi.org/10.3390/sym14112261.

Full text

Abstract:

This article presents an approach for spellchecking and autocorrection using web data for morphologically complex languages (in the case of Kazakh language), which can be considered an end-to-end approach that does not require any manually annotated word–error pairs. A sizable web of noisy data is crawled and used as a base to infer the knowledge of misspellings with their correct forms. Using the extracted corpus, a sub-string error model with a context model for morphologically complex languages are trained separately, then these two models are integrated with a regularization parameter. A sub-string alignment model is applied to extract symmetric and non-symmetric patterns in two sequences of word–error pairs. The model calculates the probability for symmetric and non-symmetric patterns of a given misspelling and its candidates to obtain a suggestion list. Based on the proposed method, a Kazakh Spellchecking and Autocorrection system is developed, which we refer to as QazSpell. Several experiments are conducted to evaluate the proposed approach from different angles. The results show that the proposed approach achieves a good outcome when only using the error model, and the performance is boosted after integrating the context model. In addition, the developed system, QazSpell, outperforms the commercial analogs in terms of overall accuracy.

APA, Harvard, Vancouver, ISO, and other styles

23

Ruf, Alexander, Pauline Poinot, Claude Geffroy, Louis Le Sergeant d’Hendecourt, and Gregoire Danger. "Data-Driven UPLC-Orbitrap MS Analysis in Astrochemistry." Life 9, no. 2 (May 2, 2019): 35. http://dx.doi.org/10.3390/life9020035.

Full text

Abstract:

Meteorites have been found to be rich and highly diverse in organic compounds. Next to previous direct infusion high resolution mass spectrometry experiments (DI-HR-MS), we present here data-driven strategies to evaluate UPLC-Orbitrap MS analyses. This allows a comprehensive mining of structural isomers extending the level of information on the molecular diversity in astrochemical materials. As a proof-of-concept study, Murchison and Allende meteorites were analyzed. Both, global organic fingerprint and specific isomer analyses are discussed. Up to 31 different isomers per molecular composition are present in Murchison suggesting the presence of ≈440,000 different compounds detected therein. By means of this time-resolving high resolution mass spectrometric method, we go one step further toward the characterization of chemical structures within complex extraterrestrial mixtures, enabling a better understanding of organic chemical evolution, from interstellar ices toward small bodies in the Solar System.

APA, Harvard, Vancouver, ISO, and other styles

24

Yang, Yaming, Xingtong Xia, Xingyao Yin, Kun Li, Jianli Wang, and Hoagie Liu. "Data-driven fast prestack structurally constrained inversion." GEOPHYSICS 87, no. 3 (April 4, 2022): N31—N43. http://dx.doi.org/10.1190/geo2021-0145.1.

Full text

Abstract:

Classic multitrace inversion, such as structurally constrained inversion (SCI), tends to arrange seismic data trace by trace to introduce constraints that are more in line with the structure or reflection features. However, this strategy of rearranging seismic traces extensively increases the number of calculations, thereby limiting the wide application of SCI in prestack inversion. We have developed two improved prestack SCI algorithms that can simultaneously invert all seismic traces with structural constraints in a short time and substantially reduce the dependence on seismic data quality. The key to the proposed techniques lies in achieving structural constraints by introducing a Hadamard product operator without rearranging the seismic traces because it avoids the generation of large-scale and memory-intensive convolution matrices and structural constraint operators. We have deduced a corresponding fast algorithm and named it fast structurally constrained inversion. In addition because the quality of seismic data plays a decisive role in inversion, we further have developed a data-driven fast structurally constrained inversion (DFSCI) algorithm wherein a local crosscorrelation coefficient reflects the reliability of the local seismic data to a certain extent. Therefore, applying DFSCI to control the contribution of seismic data at each sampling point in the inversion can reduce the dependence of seismic data quality. The noise resistance capability and the spatial continuity of the proposed methods have been verified through numerical experiments and a field data example.

APA, Harvard, Vancouver, ISO, and other styles

25

Yang, Heng, Ziliang Jin, Jianhua Wang, Yong Zhao, Hejia Wang, and Weihua Xiao. "Data-Driven Stochastic Scheduling for Energy Integrated Systems." Energies 12, no. 12 (June 17, 2019): 2317. http://dx.doi.org/10.3390/en12122317.

Full text

Abstract:

As the penetration of intermittent renewable energy increases and unexpected market behaviors continue to occur, new challenges arise for system operators to ensure cost effectiveness while maintaining system reliability under uncertainties. To systematically address these uncertainties and challenges, innovative advanced methods and approaches are needed. Motivated by these, in this paper, we consider an energy integrated system with renewable energy and pumped-storage units involved. In addition, we propose a data-driven risk-averse two-stage stochastic model that considers the features of forbidden zones and dynamic ramping rate limits. This model minimizes the total cost against the worst-case distribution in the confidence set built for an unknown distribution and constructed based on data. Our numerical experiments show how pumped-storage units contribute to the system, how inclusions of the aforementioned two features improve the reliability of the system, and how our proposed data-driven model converges to a risk-neutral model with historical data.

APA, Harvard, Vancouver, ISO, and other styles

26

Kim, Hyunsoo. "Wearable Sensor Data-Driven Walkability Assessment for Elderly People." Sustainability 12, no. 10 (May 14, 2020): 4041. http://dx.doi.org/10.3390/su12104041.

Full text

Abstract:

Active living improves the lives and social networks of the elderly. In terms of active living, walkability is an essential element in the daily life of the elderly. To support active living, it is important to create an age-friendly environment. Considering that the elderly carry out a large part of their activities by walking, a good walkable environment is one of the most important elements of an age-friendly environment. Existing studies have involved surveys of experts, audit tools, and questionnaires. However, despite their merits, current methods of measuring walkability remain limited as they do not include the actual walking activity of the elderly. Therefore, the purpose of this study is to investigate the possibility of using a wearable sensor to measure the walking of the elderly quantitatively, and to compare different walking environments based on data collected from their actual walking. To accomplish this, experiments were conducted in four types of environments with 30 elderly subjects. During the experiments, the subjects were asked to attach a smartphone that includes an inertial measurement unit (IMU). The IMU sensor collected the body movement using tri-axial accelerations. The collected data were used to calculate walkability by investigating how constant a subject’s walking pattern is. The consistency of pattern can be regarded as gait stability that can be quantitatively measured via the maximum Lyapunov exponent (MaxLE—a metric used for measuring the stability of human body during locomotion. As a result of the experiment, it was found that the stability of walking of elderly people differs according to the walking environment, which means that by investigating the stability the current conditions of a specific walking environment can be inferred. This result helps improve the active life of the elderly by providing opportunities for continuous diagnosis of the walking environment.

APA, Harvard, Vancouver, ISO, and other styles

27

Du, Tao, Shouning Qu, and Qin Wang. "A Data-Driven Parameter Adaptive Clustering Algorithm Based on Density Peak." Complexity 2018 (October 21, 2018): 1–14. http://dx.doi.org/10.1155/2018/5232543.

Full text

Abstract:

Clustering is an important unsupervised machine learning method which can efficiently partition points without training data set. However, most of the existing clustering algorithms need to set parameters artificially, and the results of clustering are much influenced by these parameters, so optimizing clustering parameters is a key factor of improving clustering performance. In this paper, we propose a parameter adaptive clustering algorithm DDPA-DP which is based on density-peak algorithm. In DDPA-DP, all parameters can be adaptively adjusted based on the data-driven thought, and then the accuracy of clustering is highly improved, and the time complexity is not increased obviously. To prove the performance of DDPA-DP, a series of experiments are designed with some artificial data sets and a real application data set, and the clustering results of DDPA-DP are compared with some typical algorithms by these experiments. Based on these results, the accuracy of DDPA-DP has obvious advantage of all, and its time complexity is close to classical DP-Clust.

APA, Harvard, Vancouver, ISO, and other styles

28

Chen, Weizhe, Weinan Zhang, Duo Liu, Weiping Li, Xiaojun Shi, and Fei Fang. "Data-Driven Multimodal Patrol Planning for Anti-poaching." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (May 18, 2021): 15270–77. http://dx.doi.org/10.1609/aaai.v35i17.17792.

Full text

Abstract:

Wildlife poaching is threatening key species that play important roles in the ecosystem. With historical ranger patrol records, it is possible to provide data-driven predictions of poaching threats and plan patrols to combat poaching. However, the patrollers often patrol in a multimodal way, which combines driving and walking. It is a tedious task for the domain experts to manually plan such a patrol and as a result, the planned patrol routes are often far from optimal. In this paper, we propose a data-driven approach for multimodal patrol planning. We first use machine learning models to predict the poaching threats and then use a novel mixed-integer linear programming-based algorithm to plan the patrol route. In a field test focusing on the machine learning prediction result at Jilin Huangnihe National Nature Reserve (HNHR) in December 2019, the rangers found 42 snares, which is significantly higher than the historical record. Our offline experiments show that the resulting multimodal patrol routes can improve the efficiency of patrol and thus they can serve as the basis for future deployment in the field.

APA, Harvard, Vancouver, ISO, and other styles

29

Paredes, Jose, Gerardo Simari, Maria Martinez, and Marcelo Falappa. "First Steps towards Data-Driven Adversarial Deduplication." Information 9, no. 8 (July 27, 2018): 189. http://dx.doi.org/10.3390/info9080189.

Full text

Abstract:

In traditional databases, the entity resolution problem (which is also known as deduplication) refers to the task of mapping multiple manifestations of virtual objects to their corresponding real-world entities. When addressing this problem, in both theory and practice, it is widely assumed that such sets of virtual objects appear as the result of clerical errors, transliterations, missing or updated attributes, abbreviations, and so forth. In this paper, we address this problem under the assumption that this situation is caused by malicious actors operating in domains in which they do not wish to be identified, such as hacker forums and markets in which the participants are motivated to remain semi-anonymous (though they wish to keep their true identities secret, they find it useful for customers to identify their products and services). We are therefore in the presence of a different, and even more challenging, problem that we refer to as adversarial deduplication. In this paper, we study this problem via examples that arise from real-world data on malicious hacker forums and markets arising from collaborations with a cyber threat intelligence company focusing on understanding this kind of behavior. We argue that it is very difficult—if not impossible—to find ground truth data on which to build solutions to this problem, and develop a set of preliminary experiments based on training machine learning classifiers that leverage text analysis to detect potential cases of duplicate entities. Our results are encouraging as a first step towards building tools that human analysts can use to enhance their capabilities towards fighting cyber threats.

APA, Harvard, Vancouver, ISO, and other styles

30

Choudhury, Sanjiban, Mohak Bhardwaj, Sankalp Arora, Ashish Kapoor, Gireeja Ranade, Sebastian Scherer, and Debadeepta Dey. "Data-driven planning via imitation learning." International Journal of Robotics Research 37, no. 13-14 (July 12, 2018): 1632–72. http://dx.doi.org/10.1177/0278364918781001.

Full text

Abstract:

Robot planning is the process of selecting a sequence of actions that optimize for a task=specific objective. For instance, the objective for a navigation task would be to find collision-free paths, whereas the objective for an exploration task would be to map unknown areas. The optimal solutions to such tasks are heavily influenced by the implicit structure in the environment, i.e. the configuration of objects in the world. State-of-the-art planning approaches, however, do not exploit this structure, thereby expending valuable effort searching the action space instead of focusing on potentially good actions. In this paper, we address the problem of enabling planners to adapt their search strategies by inferring such good actions in an efficient manner using only the information uncovered by the search up until that time. We formulate this as a problem of sequential decision making under uncertainty where at a given iteration a planning policy must map the state of the search to a planning action. Unfortunately, the training process for such partial-information-based policies is slow to converge and susceptible to poor local minima. Our key insight is that if we could fully observe the underlying world map, we would easily be able to disambiguate between good and bad actions. We hence present a novel data-driven imitation learning framework to efficiently train planning policies by imitating a clairvoyant oracle: an oracle that at train time has full knowledge about the world map and can compute optimal decisions. We leverage the fact that for planning problems, such oracles can be efficiently computed and derive performance guarantees for the learnt policy. We examine two important domains that rely on partial-information-based policies: informative path planning and search-based motion planning. We validate the approach on a spectrum of environments for both problem domains, including experiments on a real UAV, and show that the learnt policy consistently outperforms state-of-the-art algorithms. Our framework is able to train policies that achieve up to [Formula: see text] more reward than state-of-the art information-gathering heuristics and a [Formula: see text] speedup as compared with A* on search-based planning problems. Our approach paves the way forward for applying data-driven techniques to other such problem domains under the umbrella of robot planning.

APA, Harvard, Vancouver, ISO, and other styles

31

Zhang, Minghu, Jianwen Guo, Xin Li, and Rui Jin. "Data-Driven Anomaly Detection Approach for Time-Series Streaming Data." Sensors 20, no. 19 (October 2, 2020): 5646. http://dx.doi.org/10.3390/s20195646.

Full text

Abstract:

Recently, wireless sensor networks (WSNs) have been extensively deployed to monitor environments. Sensor nodes are susceptible to fault generation due to hardware and software failures in harsh environments. Anomaly detection for the time-series streaming data of sensor nodes is a challenging but critical fault diagnosis task, particularly in large-scale WSNs. The data-driven approach is becoming essential for the goal of improving the reliability and stability of WSNs. We propose a data-driven anomaly detection approach in this paper, named median filter (MF)-stacked long short-term memory-exponentially weighted moving average (LSTM-EWMA), for time-series status data, including the operating voltage and panel temperature recorded by a sensor node deployed in the field. These status data can be used to diagnose device anomalies. First, a median filter (MF) is introduced as a preprocessor to preprocess obvious anomalies in input data. Then, stacked long short-term memory (LSTM) is employed for prediction. Finally, the exponentially weighted moving average (EWMA) control chart is employed as a detector for recognizing anomalies. We evaluate the proposed approach for the panel temperature and operating voltage of time-series streaming data recorded by wireless node devices deployed in harsh field conditions for environmental monitoring. Extensive experiments were conducted on real time-series status data. The results demonstrate that compared to other approaches, the MF-stacked LSTM-EWMA approach can significantly improve the detection rate (DR) and false rate (FR). The average DR and FR values with the proposed approach are 95.46% and 4.42%, respectively. MF-stacked LSTM-EWMA anomaly detection also achieves a better F2 score than that achieved by other methods. The proposed approach provides valuable insights for anomaly detection in WSNs by detecting anomalies in the time-series status data recorded by wireless sensor nodes.

APA, Harvard, Vancouver, ISO, and other styles

32

Belosloudtsev, D., P. Bertin, R. K. Bock, P. Boucard, V. Dörsing, P. Kammel, S. Khabarov, et al. "Programmable active memories in real-time tasks: implementing data-driven triggers for LHC experiments." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 356, no. 2-3 (March 1995): 457–67. http://dx.doi.org/10.1016/0168-9002(94)01397-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Weiß, Andreas, and Dimka Karastoyanova. "Enabling coupled multi-scale, multi-field experiments through choreographies of data-driven scientific simulations." Computing 98, no. 4 (October 18, 2014): 439–67. http://dx.doi.org/10.1007/s00607-014-0432-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Kallmeyer, Laura, and Wolfgang Maier. "Data-Driven Parsing using Probabilistic Linear Context-Free Rewriting Systems." Computational Linguistics 39, no. 1 (March 2013): 87–119. http://dx.doi.org/10.1162/coli_a_00136.

Full text

Abstract:

This paper presents the first efficient implementation of a weighted deductive CYK parser for Probabilistic Linear Context-Free Rewriting Systems (PLCFRSs). LCFRS, an extension of CFG, can describe discontinuities in a straightforward way and is therefore a natural candidate to be used for data-driven parsing. To speed up parsing, we use different context-summary estimates of parse items, some of them allowing for A* parsing. We evaluate our parser with grammars extracted from the German NeGra treebank. Our experiments show that data-driven LCFRS parsing is feasible and yields output of competitive quality.

APA, Harvard, Vancouver, ISO, and other styles

35

TUBBS, D. L., C. W. BARNES, J. B. BECK, N. M. HOFFMAN, J. A. OERTEL, R. G. WATT, T. BOEHLY, D. BRADLEY, and J. KNAUER. "Direct-drive cylindrical implosion experiments: Simulations and data." Laser and Particle Beams 17, no. 3 (July 1999): 437–49. http://dx.doi.org/10.1017/s0263034699173117.

Full text

Abstract:

We have studied a suite of cylindrical, polystyrene targets, both unperturbed and with mode-28, 1.5 μm initial-amplitude sinusoidal perturbations imposed azimuthally along the target length. All targets are driven by direct laser illumination at 3ω using the University of Rochester OMEGA laser facility. Our numerical simulation and experimental data demonstrate the proof of principle for direct-drive studies of complex hydrodynamic phenomena in convergent geometry. We obtain high-quality, time-dependent data, that demonstrate good implosion symmetry and against which numerical simulations show promising agreement within currently assessed error bars. Our results identify operational space for continuing experiments.

APA, Harvard, Vancouver, ISO, and other styles

36

Kirchsteiger, Harald, and Nayrana Daborer-Prado. "Data-driven Modelling of Thermal Solid Sorption Storage Systems." Renewable Energy and Environmental Sustainability 6 (2021): 27. http://dx.doi.org/10.1051/rees/2021026.

Full text

Abstract:

An approach fofsr data-driven modelling of open sorption storage systems using zeolite as storage material is presented. The overall dynamic simulation model has the inflow air stream mass flow and absolute humidity as inputs and computes the outflow air temperature. The model is sub-divided into several components, where dynamic state space and process model identification techniques are applied. A comparison of the proposed modelling technique with simulated data from a validated model based on first principles shows that a reasonable accuracy − for a model application in temperature control systems design − can be obtained. It was found that using the proposed strategy, only a limited number of experiments are required, thus saving experimental time. Moreover, the computational requirements for a simulation using the proposed model are greatly reduced compared to a simulation model where differential equations discretised in time and space must be solved.

APA, Harvard, Vancouver, ISO, and other styles

37

Yang, Jian, Jaewook Jung, Samira Ghorbanpour, and Sekyung Han. "Data–Driven Fault Diagnosis and Cause Analysis of Battery Pack with Real Data." Energies 15, no. 5 (February 23, 2022): 1647. http://dx.doi.org/10.3390/en15051647.

Full text

Abstract:

Owing to the increasing use of electric vehicles (EVs), the demand for lithium-ion (Li-ion) batteries is rising. In this light, an essential factor governing the safety and efficiency of electric vehicles is the proper diagnosis of battery errors. In this article, we address the detection of battery problems by using the intraclass correlation coefficient (ICC) method and the order of cell voltages to enhance EV performance. Furthermore, we propose a framework for diagnosing problems with battery packs, which could be used to detect abnormal behavior. The proposed method calculates ICC values based on the terminal voltages extracted from a caravan battery pack. These ICC values are then used to determine whether the battery has a defect. In addition, the order of cell voltages is used to analyze the causes of faults. Furthermore, we conducted experiments to investigate and evaluate battery cell faults in EVs. The experimental results indicate that the proposed approach can be used to detect battery cell faults accurately.

APA, Harvard, Vancouver, ISO, and other styles

38

Freestone, Dean R., Kelvin J. Layton, Levin Kuhlmann, and Mark J. Cook. "Statistical Performance Analysis of Data-Driven Neural Models." International Journal of Neural Systems 27, no. 01 (November 8, 2016): 1650045. http://dx.doi.org/10.1142/s0129065716500453.

Full text

Abstract:

Data-driven model-based analysis of electrophysiological data is an emerging technique for understanding the mechanisms of seizures. Model-based analysis enables tracking of hidden brain states that are represented by the dynamics of neural mass models. Neural mass models describe the mean firing rates and mean membrane potentials of populations of neurons. Various neural mass models exist with different levels of complexity and realism. An ideal data-driven model-based analysis framework will incorporate the most realistic model possible, enabling accurate imaging of the physiological variables. However, models must be sufficiently parsimonious to enable tracking of important variables using data. This paper provides tools to inform the realism versus parsimony trade-off, the Bayesian Cramer-Rao (lower) Bound (BCRB). We demonstrate how the BCRB can be used to assess the feasibility of using various popular neural mass models to track epilepsy-related dynamics via stochastic filtering methods. A series of simulations show how optimal state estimates relate to measurement noise, model error and initial state uncertainty. We also demonstrate that state estimation accuracy will vary between seizure-like and normal rhythms. The performance of the extended Kalman filter (EKF) is assessed against the BCRB. This work lays a foundation for assessing feasibility of model-based analysis. We discuss how the framework can be used to design experiments to better understand epilepsy.

APA, Harvard, Vancouver, ISO, and other styles

39

Meng, Zhenzhu, Yating Hu, and Christophe Ancey. "Using a Data Driven Approach to Predict Waves Generated by Gravity Driven Mass Flows." Water 12, no. 2 (February 22, 2020): 600. http://dx.doi.org/10.3390/w12020600.

Full text

Abstract:

When colossal gravity-driven mass flows enter a body of water, they may generate waves which can have destructive consequences on coastal areas. A number of empirical equations in the form of power functions of several dimensionless groups have been developed to predict wave characteristics. However, in some complex cases (for instance, when the mass striking the water is made up of varied slide materials), fitting an empirical equation with a fixed form to the experimental data may be problematic. In contrast to previous empirical equations that specified the mathematical operators in advance, we developed a purely data-driven approach which relies on datasets and does not need any assumptions about functional form or physical constraints. Experiments were carried out using Carbopol Ultrez 10 (a viscoplastic polymeric gel) and polymer–water balls. We selected an artificial neural network model as an example of a data-driven approach to predicting wave characteristics. We first validated the model by comparing it with best-fit empirical equations. Then, we applied the proposed model to two scenarios which run into difficulty when modeled using those empirical equations: (i) predicting wave features from subaerial landslide parameters at their initial stage (with the mass beginning to move down the slope) rather than from the parameters at impact; and (ii) predicting waves generated by different slide materials, specifically, viscoplastic slides, granular slides, and viscoplastic–granular mixtures. The method proposed here can easily be updated when new parameters or constraints are introduced into the model.

APA, Harvard, Vancouver, ISO, and other styles

40

Zuo, Huahong, Sike Yang, Hailong Wu, Wei Guo, Lina Wang, Xiao Chen, and Yingqiang Su. "A Data-Driven Customer Profiling Method for Offline Retailers." Computational Intelligence and Neuroscience 2022 (June 16, 2022): 1–11. http://dx.doi.org/10.1155/2022/8069007.

Full text

Abstract:

In order to accelerate the transformation of offline retailers and improve sales by using big data technology, this paper proposes a data-driven customer profile modeling method based on the collected historical purchase records of offline consumers. This method is mainly divided into three aspects: (1) an incremental RFM model is designed to classify the value of historical consumers and support the dynamic update of the model, which is more efficient than the traditional RFM model; (2) the commodity preference of different types of customers is analyzed by the TGI model, so as to guide the retail terminal to optimize the marketing strategy; (3) a commodity purchase behavior prediction model based on LSTM is proposed, which can predict the commodity that each customer may purchase in the future, so as to optimize the retail strategy. According to extensive experiments based on a true tobacco dataset, the incremental RFM model can save 80% more time than the traditional method, and our proposed prediction model can achieve 59.32% accuracy, which is better than other baselines.

APA, Harvard, Vancouver, ISO, and other styles

41

Dösinger, Christoph, Tobias Spitaler, Alexander Reichmann, Daniel Scheiber, and Lorenz Romaner. "Applications of Data Driven Methods in Computational Materials Design." BHM Berg- und Hüttenmännische Monatshefte 167, no. 1 (December 21, 2021): 29–35. http://dx.doi.org/10.1007/s00501-021-01182-3.

Full text

Abstract:

AbstractIn today’s digitized world, large amounts of data are becoming available at rates never seen before. This holds true also for materials science where high-throughput simulations and experiments continuously produce new data. Data driven methods are required which can make best use of the information stored in large data repositories. In the present article, two of such data driven methods are presented. First, we apply machine learning to generalize and extend the results obtained from computationally intense density functional theory (DFT) simulations. We show how grain boundary segregation energies can be trained with gradient boosting regression and extended to many more positions in the grain boundary for a complete description. The second method relies on Bayesian inference, which can be used to calibrate models to give data and quantification of the model uncertainty. The method is applied to calibrate parameters in thermodynamic models of the Gibbs energy of Ti-W alloys. The uncertainty of the model parameters is quantified and propagated to the phase boundaries of the Ti-W system.

APA, Harvard, Vancouver, ISO, and other styles

42

Linaza, Maria Teresa, Jorge Posada, Jürgen Bund, Peter Eisert, Marco Quartulli, Jürgen Döllner, Alain Pagani, et al. "Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture." Agronomy 11, no. 6 (June 17, 2021): 1227. http://dx.doi.org/10.3390/agronomy11061227.

Full text

Abstract:

One of the main challenges for the implementation of artificial intelligence (AI) in agriculture includes the low replicability and the corresponding difficulty in systematic data gathering, as no two fields are exactly alike. Therefore, the comparison of several pilot experiments in different fields, weather conditions and farming techniques enhances the collective knowledge. Thus, this work provides a summary of the most recent research activities in the form of research projects implemented and validated by the authors in several European countries, with the objective of presenting the already achieved results, the current investigations and the still open technical challenges. As an overall conclusion, it can be mentioned that even though in their primary stages in some cases, AI technologies improve decision support at farm level, monitoring conditions and optimizing production to allow farmers to apply the optimal number of inputs for each crop, thereby boosting yields and reducing water use and greenhouse gas emissions. Future extensions of this work will include new concepts based on autonomous and intelligent robots for plant and soil sample retrieval, and effective livestock management.

APA, Harvard, Vancouver, ISO, and other styles

43

THORNTON, MITCHELL. "Performance Evaluation of a Parallel Decoupled Data Driven Multiprocessor." Parallel Processing Letters 13, no. 03 (September 2003): 497–507. http://dx.doi.org/10.1142/s0129626403001458.

Full text

Abstract:

The Decoupled Data-Driven (D3) architecture has shown promising results from performance evaluations based upon deterministic simulations. This paper provides performance evaluations of the D3 architecture through the formulation and analysis of a stochastic model. The D3 architecture is a hybrid control/dataflow approach that takes advantage of inherent parallelism present in a program by dynamically scheduling program threads based on data availability and it also takes advantage of locality through the use of conventional processing elements that execute the program threads. The model is validated by comparing the deterministic and stochastic model responses. After model validation, various input parameters are varied such as the number of available processing elements and average threadlength, then the performance of the architecture is evaluated. The stochastic model is based upon a closed queueing network and utilizes the concepts of available parallelism and virtual queues in order to be reduced to a Markovian system. Experiments with varying computation engine threadlengths and communication latencies indicate a high degree of tolerance with respect to exploited parallelism.

APA, Harvard, Vancouver, ISO, and other styles

44

Jiang, Kai, Jianghao Su, and Juan Zhang. "A Data-Driven Parameter Prediction Method for HSS-Type Methods." Mathematics 10, no. 20 (October 14, 2022): 3789. http://dx.doi.org/10.3390/math10203789.

Full text

Abstract:

Some matrix-splitting iterative methods for solving systems of linear equations contain parameters that need to be specified in advance, and the choice of these parameters directly affects the efficiency of the corresponding iterative methods. This paper uses a Bayesian inference-based Gaussian process regression (GPR) method to predict the relatively optimal parameters of some HSS-type iteration methods and provide extensive numerical experiments to compare the prediction performance of the GPR method with other existing methods. Numerical results show that using GPR to predict the parameters of the matrix-splitting iterative methods has the advantage of smaller computational effort, predicting more optimal parameters and universality compared to the currently available methods for finding the parameters of the HSS-type iteration methods.

APA, Harvard, Vancouver, ISO, and other styles

45

Hu, Chuxiong, Zhipeng Hu, Yu Zhu, Ze Wang, and Suqin He. "Model-Data Driven Learning Adaptive Robust Control of Precision Mechatronic Motion Systems With Comparative Experiments." IEEE Access 6 (2018): 78286–96. http://dx.doi.org/10.1109/access.2018.2884947.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Georgakis, Christos. "Design of Dynamic Experiments: A Data-Driven Methodology for the Optimization of Time-Varying Processes." Industrial & Engineering Chemistry Research 52, no. 35 (May 22, 2013): 12369–82. http://dx.doi.org/10.1021/ie3035114.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Walsh, Geraldine M., Shujun Lin, Daniel M. Evans, Arash Khosrovi-Eghbal, Ronald C. Beavis, and Juergen Kast. "Implementation of a data repository-driven approach for targeted proteomics experiments by multiple reaction monitoring." Journal of Proteomics 72, no. 5 (July 2009): 838–52. http://dx.doi.org/10.1016/j.jprot.2008.11.015.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Glaws, Andrew, Paul G. Constantine, and R. Dennis Cook. "Inverse regression for ridge recovery: a data-driven approach for parameter reduction in computer experiments." Statistics and Computing 30, no. 2 (May 31, 2019): 237–53. http://dx.doi.org/10.1007/s11222-019-09876-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Long, Qingqi. "A framework for data-driven computational experiments of inter-organizational collaborations in supply chain networks." Information Sciences 399 (August 2017): 43–63. http://dx.doi.org/10.1016/j.ins.2017.03.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Liu, Jing, Yong Feng Dong, Yan Li, Si Yuan Lei, and Shu Qun He. "Composite Fault Diagnosis and Intelligent Maintenance Based on Data Driven." Key Engineering Materials 693 (May 2016): 1357–60. http://dx.doi.org/10.4028/www.scientific.net/kem.693.1357.

Full text

Abstract:

For composite fault is difficult to diagnose, the characteristics of the large amount of data. This paper presents a method of The Prediction method of Composite Fault Based on data driven to establish intelligence unit Based on a collection of virtual individuals associated with the virtual failure associated collection and virtual behavior associated collection. Composite fault warning engine modeling is proposed, and give the warning value of composite fault finally. This method is fully assessing the future "dominant state" on the basis of the fully aware of current "hidden state". The impact of factors such as disturbance of hidden failures on composite fault prediction are fully considered, to some extent, the long-span composite failure prediction problem is solved, and the experiments show that the method effectively increases the accuracy of composite fault prediction.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Data-driven experiments'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles