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Journal articles on the topic 'Input data matrix'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

Smith, Eric D., William T. Siefert, and David Drain. "Risk matrix input data biases." Systems Engineering 12, no. 4 (2009): 344–60. http://dx.doi.org/10.1002/sys.20126.

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Munaf, Alfatihah Reno MNSP, Amanda Pratama Putra, Wa Ode Zuhayeni Madjida, Ignatius Aditya Setyadi, and Amin Rois Sinung Nugroho. "Data Input Quality Metrics on Mobile Positioning Data (MPD)." Proceedings of The International Conference on Data Science and Official Statistics 2021, no. 1 (2022): 833–41. http://dx.doi.org/10.34123/icdsos.v2021i1.134.

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Statistics Indonesia (BPS) has been using Mobile Positioning Data (MPD) to support official statistics since 2016. As a source of big data, MPD also has veracity characteristics, indicating uncertainty in the data. Therefore, it is necessary to check that the data are good enough to allow further analysis and the quality assurance process. Currently, there is no established international standard for quality assurance of MPD. This paper describes the quality matrix used by BPS in examining data from mobile operators. BPS uses thirteen indicators in conducting quality assurance, where the inspe

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Ugryumova, Diana, Rik Pintelon, and Gerd Vandersteen. "Frequency Response Matrix Estimation From Missing Input–Output Data." IEEE Transactions on Instrumentation and Measurement 64, no. 11 (2015): 3124–36. http://dx.doi.org/10.1109/tim.2015.2433612.

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Kamaruddin, Kamaruddin, Ellis Mardiana Panggabean, and Irvan Irvan. "Analysis of Algebra Structure Implementation Input-Output Applications in the Economic Field." Journal Of Education And Teaching Learning (JETL) 5, no. 3 (2023): 63–71. http://dx.doi.org/10.51178/jetl.v5i3.1530.

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Mathematicsi isi ai sciencei thati is usefuli ini iall fields, and is also useful in many other sciences. Mathematical models are useful, for example, in ieconomics. One of them uses matrix theory and Gauss-Jordan ielimination in iinput-output ianalysis ito studyi the inter-sectoral iinterconnectedness iof the economy. This study describes the input-output ianalysis. This isi thei implementationi ofi matrixi materiali wherei each sector usesi as inputi the output produced by the output of other sectors, on the other hand the output produced is also the input of other sectors. This research is

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Homaeinezhad, Mohammad Reza, and Sadeq Yaqubi. "Two-sided linear matrix inequality solution of affine input matrix for feasible discrete finite-time sliding mode control of uncertain nonlinear mechanical machines." Journal of Vibration and Control 26, no. 23-24 (2020): 2243–60. http://dx.doi.org/10.1177/1077546320916637.

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In this article, a new control method is proposed based on finite-time discrete sliding mode control for uncertain multi-input multi-output systems which are affine to their inputs considering uncertain input multipliers in the case where signs of input gains remain constant over uncertainty spaces. In addition, a method for solving a set of convex control inequalities is introduced. The proposed control strategy is based on merging data obtained from investigation of common candidate Lyapunov functions assigned to various subsystems and their subsequent decoupling based on matrix elementary r

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Gedeon, Tamás D. "Data Mining of Inputs: Analysing Magnitude and Functional Measures." International Journal of Neural Systems 08, no. 02 (1997): 209–18. http://dx.doi.org/10.1142/s0129065797000227.

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The problem of data encoding and feature selection for training back-propagation neural networks is well known. The basic principles are to avoid encrypting the underlying structure of the data, and to avoid using irrelevant inputs. This is not easy in the real world, where we often receive data which has been processed by at least one previous user. The data may contain too many instances of some class, and too few instances of other classes. Real data sets often include many irrelevant or redundant input fields. This paper examines the use of weight matrix analysis techniques and functional

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Gao, Zhiqiang, Bruce Tabachnik, and Razvan V. Savescu. "Transfer function matrix identification from input—output frequency response data." Journal of the Franklin Institute 331, no. 4 (1994): 435–48. http://dx.doi.org/10.1016/0016-0032(94)90007-8.

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Sünderhauf, Christoph, Earl Campbell, and Joan Camps. "Block-encoding structured matrices for data input in quantum computing." Quantum 8 (January 11, 2024): 1226. http://dx.doi.org/10.22331/q-2024-01-11-1226.

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The cost of data input can dominate the run-time of quantum algorithms. Here, we consider data input of arithmetically structured matrices via block encoding circuits, the input model for the quantum singular value transform and related algorithms. We demonstrate how to construct block encoding circuits based on an arithmetic description of the sparsity and pattern of repeated values of a matrix. We present schemes yielding different subnormalisations of the block encoding; a comparison shows that the best choice depends on the specific matrix. The resulting circuits reduce flag qubit number a

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Szwabe, Andrzej, Pawel Misiorek, Michal Ciesielczyk, and Czeslaw Jedrzejek. "Collaborative Filtering Based on Bi-Relational Data Representation." Foundations of Computing and Decision Sciences 38, no. 1 (2013): 67–83. http://dx.doi.org/10.2478/v10209-011-0021-x.

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Abstract Widely-referenced approaches to collaborative filtering (CF) are based on the use of an input matrix that represents each user profile as a vector in a space of items and each item as a vector in a space of users. When the behavioral input data have the form of (userX, likes, itemY) and (userX, dislikes, itemY) triples one has to propose a representation of the user feedback data that is more suitable for the use of propositional data than the ordinary user-item ratings matrix. We propose to use an element-fact matrix, in which columns represent RDF-like behavioral data triples and ro

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Li, Shujie, Liang Li, Ruiying Geng, et al. "Unifying Structured Data as Graph for Data-to-Text Pre-Training." Transactions of the Association for Computational Linguistics 12 (2024): 210–28. http://dx.doi.org/10.1162/tacl_a_00641.

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Abstract Data-to-text (D2T) generation aims to transform structured data into natural language text. Data-to-text pre-training has proved to be powerful in enhancing D2T generation and yields impressive performance. However, previous pre-training methods either oversimplified structured data into a sequence without considering input structures or designed training objectives tailored for a specific data structure (e.g., table or knowledge graph). In this paper, we unify different types of structured data (i.e., table, key-value data, knowledge graph) into the graph format and cast different D2

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Lazar, M., and P. C. N. Verheijen. "Generalized Data–Driven Predictive Control: Merging Subspace and Hankel Predictors." Mathematics 11, no. 9 (2023): 2216. http://dx.doi.org/10.3390/math11092216.

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Data–driven predictive control (DPC) is becoming an attractive alternative to model predictive control as it requires less system knowledge for implementation and reliable data is increasingly available in smart engineering systems. Two main approaches exist within DPC: the subspace approach, which estimates prediction matrices (unbiased for large data) and the behavioral, data-enabled approach, which uses Hankel data matrices for prediction (allows for optimizing the bias/variance trade–off). In this paper we develop a novel, generalized DPC (GDPC) algorithm by merging subspace and Hankel pre

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Choi, Sang-Min, Dongwoo Lee, Kiyoung Jang, Chihyun Park, and Suwon Lee. "Improving Data Sparsity in Recommender Systems Using Matrix Regeneration with Item Features." Mathematics 11, no. 2 (2023): 292. http://dx.doi.org/10.3390/math11020292.

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With the development of the Web, users spend more time accessing information that they seek. As a result, recommendation systems have emerged to provide users with preferred contents by filtering abundant information, along with providing means of exposing search results to users more effectively. These recommendation systems operate based on the user reactions to items or on the various user or item features. It is known that recommendation results based on sparse datasets are less reliable because recommender systems operate according to user responses. Thus, we propose a method to improve t

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Kaliba, Aloyce R., Donald R. Andrews, Ghirmay S. Ghebreyesus, and Ashagre A. Yigletu. "Estimating the Impact of Transaction Cost and Taxes on Ghana's Economywide Technical Efficiency Using Social Accounting Matrix Data." Journal of Developing Areas 59, no. 1 (2025): 179–206. https://doi.org/10.1353/jda.2025.a952660.

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ABSTRACT: This study focuses on analyzing the technical efficiency of the Ghana economy within an input-output model framework using a biased-corrected data envelope analysis technique. The input-output data is from Ghana's 2019 Social Accounting Matrix, a detailed empirical representation of an economy's circular flow between production, income distribution, and expenditure, reflecting input allocation according to the existing production technology. The input-output table from the matrix represents the composition of product-sale destinations (outputs) in rows and the composition of raw mate

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Al-Battaineh, Omar, and Isam A. Kaysi. "Commodity-Based Truck Origin–Destination Matrix Estimation Using Input–Output Data and Genetic Algorithms." Transportation Research Record: Journal of the Transportation Research Board 1923, no. 1 (2005): 37–45. http://dx.doi.org/10.1177/0361198105192300105.

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A commodity-based model to estimate a truck origin–destination (O-D) matrix is presented. The model takes advantage of the genetic algorithm global search method to find the best O-D matrix that when assigned to the network gives the minimum deviation between observed and estimated data. The model is flexible with respect to the type of data used in estimating the O-D matrix; however, the case study presented in this paper takes into consideration only two sets of information: commodity flow on specific links and column and row sums of the O-D matrix. Flows are treated as commodity dollar valu

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GOVORKOV, Denis A., Viktor P. NOVIKOV, Il'ya G. SOLOV'EV, and Vladimir R. TSIBUL'SKII. "A computational scheme to estimate the Leontief model matrix coefficients according to input-output table data for the southern regions of the Tyumen Oblast." Regional Economics: Theory and Practice 19, no. 12 (2021): 2360–83. http://dx.doi.org/10.24891/re.19.12.2360.

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Subject. This article deals with the control and management aspects of regional development on the basis of Leontief’s balance model. Objectives. The article aims to develop schemes for stable estimation of aggregate parameters of region balance models based on a shortened sample of input-output statistical data and rules for their subsequent regularization. Methods. For the study, we used multiple forms of regional economic balance model transformation based on the aggregation of data of the selected regional subsystems. Results. The primary estimates of aggregate input-output matrix for the

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Oday, Kamil Hamid, Bassil Abduljabbar Riyadh, and Jabbar Alhyani Nazar. "Fast and robust approach for data security in communication channel using pascal matrix." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 19, no. 1 (2020): 248–56. https://doi.org/10.11591/ijeecs.v19.i1.pp248-256.

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This paper described fast and robust approach of text encryption and decryption based on Pascal matrix. The technique of encryption can be applied on both Arabic and English text. The results shows that the ciphered text unintelligible and rubush for the interuder or hukers. The encryption scheme usually uses a pseudo-random encryption key generated by an algorithm. All this done by using Pascal matrix. Encryption and decryption simulated using MATLAB version 10 and notepad ++to write the input text.

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Schwabe, Markus, and Michael Heizmann. "Influence of input data representations for time-dependent instrument recognition." tm - Technisches Messen 88, no. 5 (2021): 274–81. http://dx.doi.org/10.1515/teme-2020-0100.

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Abstract An important preprocessing step for several music signal processing algorithms is the estimation of playing instruments in music recordings. To this aim, time-dependent instrument recognition is realized by a neural network with residual blocks in this approach. Since music signal processing tasks use diverse time-frequency representations as input matrices, the influence of different input representations for instrument recognition is analyzed in this work. Three-dimensional inputs of short-time Fourier transform (STFT) magnitudes and an additional time-frequency representation based

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Morozov, Alexey Anatolievich, Yuri Pavlovich Galachyants, and Yelena Valentinovna Likhoshway. "Inferring Phylogenetic Networks from Gene Order Data." BioMed Research International 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/503193.

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Existing algorithms allow us to infer phylogenetic networks from sequences (DNA, protein or binary), sets of trees, and distance matrices, but there are no methods to build them using the gene order data as an input. Here we describe several methods to build split networks from the gene order data, perform simulation studies, and use our methods for analyzing and interpreting different real gene order datasets. All proposed methods are based on intermediate data, which can be generated from genome structures under study and used as an input for network construction algorithms. Three intermedia

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Kosaka, Manabu, Hiroshi Uda, Eiichi Bamba, and Hiroshi Shibata. "State-space Model Identification Using Input and Output Data With Steady State Values Zeroing Multiple Integrals of Output Error." Journal of Dynamic Systems, Measurement, and Control 128, no. 3 (2005): 746–49. http://dx.doi.org/10.1115/1.2238872.

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This study proposes a new deterministic off-line identification method that obtains a state-space model using input and output data with steady state values. This method comprises of two methods: Zeroing the 0∼N-tuple integral values of the output error of single-input single-output transfer function model (Kosaka et al., 2004) and Ho-Kalman’s method (Zeiger and McEwen, 1974). Herein, we present a new method to derive a matrix similar to the Hankel matrix using multi-input and multi-output data with steady state values. State space matrices A, B, C, and D are derived from the matrix by the met

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Kurniawan, Budi, and Susiawati Kristiarini. "Komparasi Penggunaan Matriks Kebalikan Leontief & Ghosian Untuk Peramalan Dalam Model Input Output." Diophantine Journal of Mathematics and Its Applications 1, no. 1 (2022): 1–8. http://dx.doi.org/10.33369/diophantine.v1i1.25703.

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This study is an empirical study that compares the use of two types of inverse matrices in the input output model. The Input Output (IO) model is based on a system of mathematical equations that applies general equilibrium phenomena. The matrix operating system in the equation derived from the IO model allows the Output value (X) to be calculated as an effect of the final demand induction (F) with the formulation X=(I-A)-1F where A is the technical coefficient matrix. This equation model uses the Leontief Inverse Matrix to calculate the impact of output with final demand (F) as a stimulant. Ca

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Kong and Xu. "A MIMO-SAR Tomography Algorithm Based on Fully-Polarimetric Data." Sensors 19, no. 22 (2019): 4839. http://dx.doi.org/10.3390/s19224839.

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A fully-polarimetric unitary multiple signal classification (UMUSIC) tomography algorithm is proposed, which can be used for acquiring high-resolution three-dimensional (3D) imagery, in a polarimetric multiple-input multiple-output synthetic aperture radar (MIMO-SAR) with a small number of baselines. In terms of the elevation resolution, UMUSIC provides an improvement over standard MUSIC by utilizing the conjugate of the complex sample data and converting the complex covariance matrix into a real matrix. The combination of UMUSIC and fully-polarimetric data permits a further reduction of the n

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Cariow, Aleksandr, and Marina Polyakova. "The Fast Discrete Tchebichef Transform Algorithms for Short-Length Input Sequences." Signals 6, no. 2 (2025): 23. https://doi.org/10.3390/signals6020023.

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In this article, the fast algorithms for the discrete Tchebichef transform (DTT) are proposed for input sequences of lengths in the range from 3 to 8. At present, DTT is widely applied in signal processing, image compression, and video coding. The review of the articles related to fast DTT algorithms has shown that such algorithms are mainly developed for input signal lengths 4 and 8. However, several problems exist for which signal and image processing with different apertures is required. To avoid this shortcoming, the structural approach and a sparse matrix factorization are applied in this

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De Vader, Christy L., and Allan G. Bateson. "Matrixboot: A Fortran Program to Bootstrap Pearson Correlations Computed from Data Matrix Input." Educational and Psychological Measurement 51, no. 3 (1991): 673–77. http://dx.doi.org/10.1177/0013164491513018.

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Fan, Li Ying. "Exponentially Stable Sampled-Data Control for Uncertain Systems." Advanced Materials Research 981 (July 2014): 551–54. http://dx.doi.org/10.4028/www.scientific.net/amr.981.551.

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In this paper, the problem of the exponentially stable sampled-data control was investigated for a class of uncertain systems. Based on the input delay approach, the system was modeled as a continuous-time system with the delayed control input. Attention was focused on the design of a state feedback sampled-data controller which guarantees the exponential stability of the closed-loop system for all admissible parametric uncertainties. Using linear matrix inequality (LMI) approach, sufficient conditions are obtained. Simulation example was given to demonstrate the effectiveness and correctness

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Wang, Jianhong, Yunfeng Zhang, Ricardo A. Ramirez-Mendoza, et al. "Subspace Data-Driven Control for Linear Parameter Varying Systems." International Journal of Service Science, Management, Engineering, and Technology 14, no. 1 (2023): 1–25. http://dx.doi.org/10.4018/ijssmet.321198.

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In this research, a unique subspace data driven control for linear parameter changing system with scheduling parameters is presented. This control paves the way for investigating the nonlinear system based on the results regarding the linear system that are already known. Only the data matrix is utilized to represent the output prediction value in the future various time instants, while the input-output observation data matrix is used to identify Markov parameters in the form of state space forms. The cost function in data-driven control is then adjusted using the output prediction value. The

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Hochstuhl, Sylvia, Niklas Pfeffer, Antje Thiele, Horst Hammer, and Stefan Hinz. "Your Input Matters—Comparing Real-Valued PolSAR Data Representations for CNN-Based Segmentation." Remote Sensing 15, no. 24 (2023): 5738. http://dx.doi.org/10.3390/rs15245738.

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Inspired by the success of Convolutional Neural Network (CNN)-based deep learning methods for optical image segmentation, there is a growing interest in applying these methods to Polarimetric Synthetic Aperture Radar (PolSAR) data. However, effectively utilizing well-established real-valued CNNs for PolSAR image segmentation requires converting complex-valued data into real-valued representations. This paper presents a systematic comparison of 14 different real-valued representations used as CNN input in the literature. These representations encompass various approaches, including the use of c

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Fedorchuk, Olga A., and Natalia N. Goncharova. "Comparability of results from canonical discriminant analysis based on different input data." Moscow University Anthropology Bulletin (Vestnik Moskovskogo Universiteta. Seria XXIII. Antropologia) 15, no. 1 (2023): 62–77. http://dx.doi.org/10.32521/2074-8132.2023.1.062-077.

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Canonical discriminant analysis, based on the mean values of the traits, is widely used by anthropologists. These analyses use standard deviation means, as well as standard correlation coefficients. The question of the comparability of the results of such analysis with the results based on individual values remains open. Moreover, the existing inter-group variability in correlation coefficients can lead to altered analysis results when applying the correlation matrix calculated for the specific under analysis groups. This study compares the results of three variants of the canonical discrimina

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Ivanov, Sergey, and Mykola Ivanov. "Marketing forecasting based on Big Data information." SHS Web of Conferences 107 (2021): 05002. http://dx.doi.org/10.1051/shsconf/202110705002.

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In the paper discusses the use of big data as a tool to increase data transfer speed while providing access to multidimensional data in the process of forecasting product sales in the market. In this paper discusses modern big data tools that use the MapReduce model. The big data presented in this article is a single, centralized source of information across your entire domain. In the paper also proposes the structure of a marketing analytics system that includes many databases in which transactions are processed in real time. For marketing forecasting of multidimensional data in Matlab, a neu

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Al-Battaineh, Omar, and Isam A. Kaysi. "Genetically-optimized origin-destination estimation (GOODE) model: application to regional commodity movements in Ontario." Canadian Journal of Civil Engineering 34, no. 2 (2007): 228–38. http://dx.doi.org/10.1139/l06-127.

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The problem of origin-destination (O-D) matrix estimation has attracted significant research attention in the past few decades. This paper proposes a novel approach to estimate a regional freight O-D matrix using different data sources. The genetically optimized origin-destination estimation (GOODE) model takes advantage of the genetic algorithm's (GA) global search procedure to find the O-D matrix that is associated with the minimum deviation between estimated and observed data values. The GOODE-commodity model, an extension of the GOODE model, estimates the freight O-D matrix by interfacing

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Cheng, Yun, and Huan-Li Gao. "Matrix-Type Network DEA Model with Its Application Based on Input-Output Tables." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/505941.

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The matrix-type network data envelopment analysis (DEA) model is established for evaluating the relative performance of the matrix-type structure. The existence of solution and property of the new model is given. The equivalence of DEA efficiency and Pareto solutions of corresponding objective programming problem is proved. Using data in input-output tables, the new model is tested and the results show that the new model can be feasible in evaluating the relative performance of the matrix-type structure.

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Lee, Jeonghyeok, Sangwook Han, Seungwon Choi, and Jungwook Choi. "Power-Efficient Deep Neural Network Accelerator Minimizing Global Buffer Access without Data Transfer between Neighboring Multiplier—Accumulator Units." Electronics 11, no. 13 (2022): 1996. http://dx.doi.org/10.3390/electronics11131996.

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This paper presents a novel method for minimizing the power consumption of weight data movements required by a convolutional operation performed on a two-dimensional multiplier–accumulator (MAC) array of a deep neural-network accelerator. The proposed technique employs a local register file (LRF) at each MAC unit in a manner such that once weight pixels are read from the global buffer into the LRF, they are reused from the LRF as many times as desired instead of being repeatedly fetched from the global buffer in each convolutional operation. One of the most evident merits of the proposed metho

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Sayfulin, Marsel Zh. "Correlation data processing for determining the averaged differential chromatogram in the case of multiple sample injections." Vestnik of Samara State Technical University. Technical Sciences Series 32, no. 4 (2024): 69–81. https://doi.org/10.14498/tech.2024.4.5.

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The relationship between the input and output signals of a chromatograph is demonstrated. The expediency of improving the signal-to-noise ratio and using correlation data processing for this purpose is noted. An algorithm for calculating a chromatogram averaged over multiple injections of a sample from a specific individual specimen is presented. This algorithm is then modified for the case of calculating a differential averaged chromatogram, which is required to determine the difference in the component composition of two different specimens. A Legendre pseudorandom sequence is used to genera

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Zhao, Jianrong, and Wei Liu. "Sampled-data control for switched neutral systems with quantization input under asynchronous switching." Transactions of the Institute of Measurement and Control 40, no. 16 (2018): 4287–97. http://dx.doi.org/10.1177/0142331217745353.

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This paper is concerned with sampled-data control for switched neutral systems with quantization input under asynchronous switching. By combining the multiple function method and the average dwell time technique, a lemma gives input-to-state stability conditions for switched nonlinear neutral systems. Considering the relationship between the sampling periods and minimal dwell time of two switching instants, sampled-data control is extended into the input-to-state stability of switched neutral systems under asynchronous switching. The sector bound approach is used to analyse quantization phenom

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Jia, Deepa, Saima siraj Soomro, Shamshad Lakho, Waqas Ali, Baqir Ali, and Akhtar Hussain. "Bit Pattern based Sindhi Character Recognition using Neural Network." VAWKUM Transactions on Computer Sciences 12, no. 2 (2024): 253–63. https://doi.org/10.21015/vtcs.v12i2.1957.

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In this paper, bit pattern based character recognition for Sindhi language has been presented. The characters of sindhi language are very much complexed to recognize for particular domain. Although there are many studies that have already been done in this recognition but all those are based on image recognition, to give novelty in the idea our system uses bit patterns for characters and provide outcome on the basis of that input pattern. A data set with nine no. of inputs and six outputs for each character is created. We have used patterns due to the computational complexity constant that are

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Verlan, A., and Jo Sterten. "Digital Correction Filter in Problems of Recovery of Input Signals and Observing Systems’ Data in Energy Objects." Mathematical and computer modelling. Series: Technical sciences, no. 22 (November 26, 2021): 31–38. http://dx.doi.org/10.32626/2308-5916.2021-22.31-38.

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The task of signal recovery is one of the most important for auto-mated diagnostics and control systems of an energy object. When solv-ing the inverse problems of recovering signals, images and other types of data, spectral distortions and losses occur (in some cases, very sig-nificant ones). They are primarily stipulated due to ill-posedness of these problems, which is the result of loss of information about the original signal due to strong (and even complete) suppression in the observed signal of a part of spectral components, which become indis-tinguishable against the background of errors

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Kolar, Davor, Dragutin Lisjak, Michał Pająk, and Danijel Pavković. "Fault Diagnosis of Rotary Machines Using Deep Convolutional Neural Network with Wide Three Axis Vibration Signal Input." Sensors 20, no. 14 (2020): 4017. http://dx.doi.org/10.3390/s20144017.

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Fault diagnosis is considered as an essential task in rotary machinery as possibility of an early detection and diagnosis of the faulty condition can save both time and money. This work presents developed and novel technique for deep-learning-based data-driven fault diagnosis for rotary machinery. The proposed technique input raw three axes accelerometer signal as high definition 1D image into deep learning layers which automatically extract signal features, enabling high classification accuracy. Unlike the researches carried out by other researchers, accelerometer data matrix with dimensions

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Tsibulsky, Vladimir R., Larisa V. Vazhenina, Ilya G. Solovyov, Denis A. Govorkov, and Viktor P. Novikov. "Assessment of the Leontiev productive matrix of the economic development model for Tyumen region (Russia)." R-Economy 8, no. 2 (2022): 135–47. http://dx.doi.org/10.15826/recon.2022.8.2.011.

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Relevance. Monitoring has always been an important part of the management of regional development. Recently, this task has been gaining currency due to the development of modern information technologies of data collection and processing, citizen-government interaction and digital modelling. The task of goal evaluation, that is, comparison of the actual results with the intended ones in different spheres, among other things, requires us to build and adjust the macro-economic model of regional development and in particular to update the input-output matrix based on the region’s statistical data.

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Alam, Naushad, Puja Sareen, and Aftab Alam. "The influence of input matrix variations on corporate performance : Exploring leadership dynamics." Journal of Information and Optimization Sciences 45, no. 7 (2024): 1881–97. http://dx.doi.org/10.47974/jios-1740.

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This study examines the complex dynamics of changes in input matrix within the banking industry, with a special focus on how it affects organizational decision-making processes and performance indicators. A relevant questionnaire was prepared and solicitated many Senior management respondents from different banks, utilizing snowball sampling approaches. By utilizing component analysis, the research revealed crucial elements that contribute to the variation in the input matrix. These factors account for a significant 72.1% of the overall variance. Later, Partial Least Squares Structural Equatio

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Ganian, Robert, Iyad Kanj, Sebastian Ordyniak, and Stefan Szeider. "On the Parameterized Complexity of Clustering Incomplete Data into Subspaces of Small Rank." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3906–13. http://dx.doi.org/10.1609/aaai.v34i04.5804.

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We consider a fundamental matrix completion problem where we are given an incomplete matrix and a set of constraints modeled as a CSP instance. The goal is to complete the matrix subject to the input constraints and in such a way that the complete matrix can be clustered into few subspaces with low rank. This problem generalizes several problems in data mining and machine learning, including the problem of completing a matrix into one with minimum rank. In addition to its ubiquitous applications in machine learning, the problem has strong connections to information theory, related to binary li

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Sakai, S., S. Kohda, C. Minagawa, and K. Masuda. "Defect-tolerant active matrix circuit with duplicated data input routes for large liquid crystal display." Electronics Letters 21, no. 22 (1985): 1051. http://dx.doi.org/10.1049/el:19850746.

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Priel, Marc, and Michèle Désenfant. "Implementation of the calibration’s VIM3 definition using the matrix of variance–covariance of input data." Accreditation and Quality Assurance 20, no. 2 (2015): 107–14. http://dx.doi.org/10.1007/s00769-015-1107-6.

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Holoubek, Josef, and Petr Zach. "Using Excel to reduce a Square Matrix." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 60, no. 4 (2012): 109–14. http://dx.doi.org/10.11118/actaun201260040109.

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When solving operations research problems, one can use either specialised computer programs such as Lingo, Lindo, Storm or more universal programs such Excel, Matlab, and R. To obtain the input data, one can use either a program’s own editor or other programs commonly available such as Excel. While the problem-solving methods, being part of various programs, are the subjects of numerous publications (such as Gros, 2003; Jablonský, 2002; Plevný – Žižka, 2007; Stevenson – Ozgur, 2009), the way the input data are obtained, recorded, and processed receives far less attention although this part of

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Singh, Avinash Kumar, and Kampan Mukherjee. "PROJECT PORTFOLIO DECISION POLICY ALIGNED WITH ORGANIZATIONAL OBJECTIVES." International Journal of Business & Economics (IJBE) 6, no. 1 (2021): 9–21. http://dx.doi.org/10.58885/ijbe.v06i1.009.as.

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A Markov Decision Process based decision model is proposed in this paper for computation of optimal decision policy for the project portfolios. As projects are one time activity, often sufficient past data is not available for estimation of the input values required for the decision model. This model requires two matrices the state transition matrix and rewards matrix as input. In this paper, Analytic Hierarchy Process is used for estimation of these input matrices through the use of collective wisdom of decision makers. Markov Decision Process is used for computation of the optimal policy. Th

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Sam, D. S. Shylu, P. Sam Paul, Jennifer ,. Elizah, et al. "A Novel low power 2-D to 3-D Array Priority Encoder using Split-Logic Technique for Data Path Applications." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 17 (January 7, 2022): 42–49. http://dx.doi.org/10.37394/23203.2022.17.5.

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In this work, an ascendable low power 64-bit priority encoder is designed using a two-directional array to three-directional array conversion, and Split-logic technique and 6-bit is obtained as the output. By using this method, the high performance priority encoder can be achieved. In the conventional priority encoder, a single bit is set as an input, but for a priority encoder with 3-Darray, every input are specified in the matrix form. The I-bit input file is split hooked on M × N bits, similar to 2-D Matrix. In priority encoder with 3-Darray, three directional output comes out, unlike tradi

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KURYSHEV, Nikolai I. "The problem of measuring the quantity of output in the input–output model by W. Leontief in modeling the trends in economic reproduction of nations and regions." Regional Economics: Theory and Practice 19, no. 8 (2021): 1568–92. http://dx.doi.org/10.24891/re.19.8.1568.

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Subject. This article deals with the problem of constructing a Leontief's input–output matrix. Objectives. The article aims to determine the rules for constructing a Leontief's input–output matrix on the basis of data on production time and quantity of product output. Methods. For the study, I used the methods of logical and mathematical analyses. Results. The article formulates the rules for constructing a Leontief's input–output matrix, taking into account differences in the time of production, quantity of output, as well as the conditions for the reproduction of the resources expended. It s

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Arslan, Bahar, Samuel D. Relton, and Marcel Schweitzer. "Structured level-2 condition numbers of matrix functions." Electronic Journal of Linear Algebra 40 (January 12, 2024): 28–47. http://dx.doi.org/10.13001/ela.2024.7905.

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Matrix functions play an increasingly important role in many areas of scientific computing and engineering disciplines. In such real-world applications, algorithms working in floating-point arithmetic are used for computing matrix functions and additionally input data might be unreliable, e.g., due to measurement errors. Therefore, it is crucial to understand the sensitivity of matrix functions to perturbations, which is measured by condition numbers. However, the condition number itself might not be computed exactly as well due to round-off and errors in the input. The sensitivity of the cond

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Pak, H. Ali, and G. Q. Li. "Zero Phase Error Tracking Control for Square Sampled-Data Systems." Journal of Dynamic Systems, Measurement, and Control 113, no. 3 (1991): 506–9. http://dx.doi.org/10.1115/1.2896439.

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A multivariable version of the zero phase error tracking control algorithm is presented for sampled-data systems. The feedforward controller is based on the minimal-order inverse of a square system’s transfer function matrix. It is shown that, apart from phase cancellation, complete input/output decoupling will result from the use of the controller. Using a simulation study, the control algorithm’s performance is demonstrated for a multivariable positioning system.

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Al-Hadeethi, Hanan, and Ikhlas Al-Hadeethi. "Using Mathematical Techniques to Analyse Biomedical Data: A K-complexes EEG Signal Classification Study." Journal Port Science Research 7, issue (2024): 208–18. http://dx.doi.org/10.36371/port.2024.special.19.

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This paper endeavored to characterize the design, elaboration, and investigation of the execution of the K-complexes classification method in Electroencephalogram (EEG) signals. To solve many-aims optimization issues for the high dimensionality of every database, a mechanism for feature extraction that depends on merging the Discrete Fourier Transform (Discrete-FT) with Covariance Matrix (Cov-matrix) has been suggested. An EEG signal was split into comparatively little intervals and segments as the first step of the model design. For every EEG segment, Discrete-FT was applied. The Cov-matrix w

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Jimoh, Mohammed, and Ado Dan’Isa. "State space model realization using step response data of MIMO system with input delays for model predictive control." Acta Periodica Technologica, no. 50 (2019): 93–104. http://dx.doi.org/10.2298/apt1950093j.

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This paper discusses state space realization algorithm from general multiple input multiple output (MIMO) step response data with or without input delays. It uses the factored form of the block Hankel matrix formed from the Markov parameters of the equivalent impulse response of the step response model to obtain an equivalent state space model. Though the state space model that best approximates the step response model has number of states equal to the rank of the block Hankel matrix, much lower rank state space model, adequate for use as internal model for model predictive control (MPC), can

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Dong, Yu Hua, and Hai Chun Ning. "Exterior Ballistic Data Processing by SVD and Wavelet Transform." Advanced Materials Research 562-564 (August 2012): 1394–97. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.1394.

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This paper proposes a method of wavelet transform combined with SVD (Singular Value Extracting), and the abnormal data elimination in its trajectory measurement is studied. After the wavelet decomposition of the observed data, combining the approximate component and the detail component, the phase space is reconstructed. The increment criterion of singular entropy is used for the input observed matrix of SVD, and the singular value is selected. Then the original signal is reconstructed by SVD inverse transform. This method overcomes the distortion problem of data end in phase space reconstruct

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