Articles de revues : « Binary output »

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Anashin, Vladimir. "Discreteness causes waves." Facta universitatis - series: Physics, Chemistry and Technology 14, no. 3 (2016): 143–96. http://dx.doi.org/10.2298/fupct1603143a.

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In the paper, we show that matter waves can be derived from discreteness and causality. Namely we show that matter waves can naturally be ascribed to finite discrete causal systems, the Mealy automata having binary input/output which are bit sequences. If assign real numerical values (?measured quantities?) to bit sequences, the waves arise as a correspondence between the numerical values of input sequences (?impacts?) and output sequences (?system-evoked responses?). We show that among all discrete causal systems with arbitrary (not necessarily binary) inputs/outputs, only the ones with binary input/output can be ascribed to matter waves ?(x,t) = ei(kx??t).

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Moreno, E. G., B. Iniguez, M. Roca, J. Segura, and S. Sureda. "CMOS radiation sensor with binary output." IEEE Transactions on Nuclear Science 42, no. 3 (1995): 174–78. http://dx.doi.org/10.1109/23.387358.

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Yum, Bong-Jin, and Seong-Jun Kim. "On parameter design of binary-input-and-binary-output dynamic systems." Quality and Reliability Engineering International 9, no. 6 (1993): 471–76. http://dx.doi.org/10.1002/qre.4680090603.

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Yang, Sibo, Shusheng Wang, Lanyin Sun, Zhongxuan Luo, and Yuan Bao. "Output Layer Structure Optimization for Weighted Regularized Extreme Learning Machine Based on Binary Method." Symmetry 15, no. 1 (2023): 244. http://dx.doi.org/10.3390/sym15010244.

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In this paper, we focus on the redesign of the output layer for the weighted regularized extreme learning machine (WRELM). For multi-classification problems, the conventional method of the output layer setting, named “one-hot method”, is as follows: Let the class of samples be r; then, the output layer node number is r and the ideal output of s-th class is denoted by the s-th unit vector in Rr (1≤s≤r). Here, in this article, we propose a “binarymethod” to optimize the output layer structure: Let 2p−1<r≤2p, where p≥2, and p output nodes are utilized and, simultaneously, the ideal outputs are encoded in binary numbers. In this paper, the binary method is employed in WRELM. The weights are updated through iterative calculation, which is the most important process in general neural networks. While in the extreme learning machine, the weight matrix is calculated in least square method. That is, the coefficient matrix of the linear equations we solved is symmetric. For WRELM, we continue this idea. And the main part of the weight-solving process is a symmetry matrix. Compared with the one-hot method, the binary method requires fewer output layer nodes, especially when the number of sample categories is high. Thus, some memory space can be saved when storing data. In addition, the number of weights connecting the hidden and the output layer will also be greatly reduced, which will directly reduce the calculation time in the process of training the network. Numerical experiments are conducted to prove that compared with the one-hot method, the binary method can reduce the output nodes and hidden-output weights without damaging the learning precision.

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Gurevich, Yuri, and Saharon Shelah. "Time polynomial in input or output." Journal of Symbolic Logic 54, no. 3 (1989): 1083–88. http://dx.doi.org/10.2307/2274767.

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AbstractWe introduce the class PIO of functions computable in time that is polynomial in max {the length of input, the length of output}, observe that there is no notation system for total PIO functions but there are notation systems for partial PIO functions, and give an algebra of partial PIO functions from binary strings to binary strings.

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Xue, Aijun, and Xiaodan Wang. "Cost-sensitive design of error correcting output codes." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 10 (2017): 1871–81. http://dx.doi.org/10.1177/0954406217709303.

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Many real world applications involve multiclass cost-sensitive learning problems. However, some well-worked binary cost-sensitive learning algorithms cannot be extended into multiclass cost-sensitive learning directly. It is meaningful to decompose the complex multiclass cost-sensitive classification problem into a series of binary cost-sensitive classification problems. So, in this paper we propose an alternative and efficient decomposition framework, using the original error correcting output codes. The main problem in our framework is how to evaluate the binary costs for each binary cost-sensitive base classifier. To solve this problem, we proposed to compute the expected misclassification costs starting from the given multiclass cost matrix. Furthermore, the general formulations to compute the binary costs are given. Experimental results on several synthetic and UCI datasets show that our method can obtain comparable performance in comparison with the state-of-the-art methods.

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Ren, Xiao Guang, Yu Hua Tang, and Xin Hai Xu. "Format Optimization for Periodical Snapshot Output in CFD Simulation." Applied Mechanics and Materials 457-458 (October 2013): 910–18. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.910.

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Computational Fluid Dynamics (CFD) simulation often needs to periodically output the intermediate results to files in the form of snapshots for visualization or restart, which seriously impacts the performance. Traditional CFD simulation takes ASCII format with typesetting in the snapshot output process, which results in precision loss of the data in the snapshot array and more storage space occupancy than the original binary format. In this paper, we propose the binary non-typesetting format optimization for the snapshot output in CFD simulation, aiming at eliminating the impact of ASCII format. In our optimization method, the snapshot array is output in binary format without typesetting. On one hand, it reserves the original binary precision in the output files, while the ASCII format always has precision loss, which seriously impacts the accuracy of CFD simulation; On the other hand, non-typesetting provide the opportunity of output the snapshot as a continuous memory block, which will signally improve the efficiency of the output. We design the multiply output mode framework for CFD applications through the combination of ASCII typesetting format and binary non-typesetting format. We implement this multiply output mode framework in the open source CFD software OpenFOAM. Experimental results on Tianhe-2 supercomputer demonstrate that our binary non-typesetting format optimization technique can achieve good optimization effect for the periodical snapshot output in CFD application.

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PÉREZ ARCILA, MAURICIO, and MARTIN ALONSO TAMAYO VELEZ. "IMPLEMENTATION OF LEBESGUE SAMPLING METHOD AND DIGITAL SENSORS FOR CONTROLLING THE LEVEL VARIABLE IN A CONTINUOUS SYSTEM." DYNA NEW TECHNOLOGIES 8, no. 1 (2021): [14 P.]. http://dx.doi.org/10.6036/nt10248.

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This study aims to show that the continuous control from a level system can be efficiently measured and controlled using capacitive digital binary sensors, which in this case, replace the measurement signal from an analog differential pressure transmitter in a level control system. The binary sensors low cost and the digital output they process allow the reproduction of a correct signal and the estimation of a variable for controlling the water level inside the process tank through a proportional pneumatic level control valve, which receives the control signal from the Lebesgue sampling estimation algorithm applied herein for processing digital measurements. In this particular case, the Lebesgue algorithm is applied to reproduce the estimation of values obtained from the continuous signal in the real level process for the measurement and control. Also, are compared both, simulated and real outputs obtained using the Lebesgue algorithm and digital sensors, which were applied to a state observer controller that relates digital signals for controlling the real level system output. The application of the Lebesgue algorithm in the real level process concludes that the analog level signal can be efficiently reproduced using this method. In addition, the controller enables the system to smoothly conduct digital output processing using digital sensors to control the system output correctly, validating that not only analog sensors should be applied for controlling the output of proportional actuators, because it is shown that digital binary signals can be used for controlling and emulating continuous signals, which were processed and applied to the pneumatic valve. Keywords: Lebesgue sampling, estimation, binary sensor, observer controller, finite state machine, continuous system, control, LTI systems, identification, state variable, estimated output, proportional actuator

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Silva, Carla, Ana Aguiar, and Inês Dutra. "Quantum Binary Classification (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 15889–90. http://dx.doi.org/10.1609/aaai.v35i18.17941.

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We implement a quantum binary classifier where given a dataset of pairs of training inputs and target outputs our goal is to predict the output of a new input. The script is based in a hybrid scheme inspired in an existing PennyLane's variational classifier and to encode the classical data we resort to PennyLane's amplitude encoding embedding template. We use the quantum binary classifier applied to the well known Iris dataset and to a car traffic dataset. Our results show that the quantum approach is capable of performing the task using as few as 2 qubits. Accuracies are similar to other quantum machine learning research studies, and as good as the ones produced by classical classifiers.

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Rahmadhany, Sri. "IDENTIFIKASI POLA KARAKTER ANAK DENGAN ALGORITMA PERCEPTRON." JURNAL TEKNOLOGI INFORMASI 3, no. 1 (2019): 86. http://dx.doi.org/10.36294/jurti.v3i1.695.

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Abstract - Artificial Neural Network is a computational method that works like a human brain. The Perceptron algorithm is one method that exists in Artificial Neural Networks. The research carried out was the identification of children's character patterns using the Perceptron algorithm. The Perceptron algorithm is very reliable in recognizing patterns, one of which is the child's character pattern as was done in this study. The Perceptron algorithm identifies the character patterns of children through three inputs and two outputs. The three outputs are taken from nature variables, attitude variables and behavioral variables. The output is four human temperaments according to Hipocrates, namely sanguin, melancholy, choleric and plegamatic. All inputs and outputs will be converted into binary numbers to be trained with Matlab software.Keywords - Artificial Neural Networks, Perceptron Algorithms, child character patterns, input, output, binary numbers. Abstrak - Jaringan Syaraf Tiruan merupakan salah satu metode komputasi yang dapat bekerja seperti layaknya otak manusia. Algortima Perceptron merupakan salah satu metode yang ada pada Jaringan Syaraf Tiruan. Penelitian yang dilakukan adalah identifikasi pola karakter anak dengan menggunakan algoritma Perceptron. Algoritma Perceptron sangat handal dalam mengenali pola salah satunya yaitu pola karakter anak seperti yang dilakukan dalam penelitian ini. Algoritma Perceptron mengidentifikasi pola karakter anak melalui tiga input dan dua output. Tiga output tersebut diambil dari variabel sifat, variabel sikap dan variabel tingkah laku. Adapun output merupakan empat temperamen manusia menurut Hipocrates yaitu sanguin, melankolis, koleris dan plegamatis. Seluruh input dan output akan diubah menjadi bilangan biner untuk dilatih dengan software Matlab.Kata Kunci - Jaringan Syaraf Tiruan, Algoritma Perceptron, pola karakter anak, input, output, bilangan biner.

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Gómez, Sergio, David Sánchez, Joan Mauricio, et al. "Multiple Use SiPM Integrated Circuit (MUSIC) for Large Area and High Performance Sensors." Electronics 10, no. 8 (2021): 961. http://dx.doi.org/10.3390/electronics10080961.

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The 8-channel Multiple Use Silicon Photo-multiplier (SiPM) Integrated Circuit (MUSIC) Application specific integrated circuit (ASIC) for SiPM anode readout has been designed for applications where large photo-detection areas are required. MUSIC offers three main features: (1) Sum of the eight input channels using a differential output driver, (2) eight individual single ended (SE) analog outputs, and (3) eight individual SE binary outputs using a time over threshold technique. Each functionality, summation and individual readout includes a selectable dual-gain configuration. Moreover, the signal sum implements a dual-gain output providing a 15-bit dynamic range. The circuit contains a tunable pole zero cancellation of the SiPM recovery time constant to deal with most of the available SiPM devices in the market. Experimental tests show how MUSIC can linearly sum signals from different SiPMs and distinguish even a few photons. Additionally, it provides a single photon output pulse width at half maximum (FWHM) between 5–10 ns for the analog output and a single-photon time resolution (SPTR) around 118 ps sigma using a Hamamatsu SiPM S13360-3075CS for the binary output. Lastly, the summation mode has a power consumption of ≈200 mW, whereas the individual readout consumes ≈30 mW/ch.

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Li, Guo He, Xiang Yue, Wei Jiang Wu, and Jiang Hui Zhao. "Method of Mathematical Modeling Based on PSO Algorithms." Applied Mechanics and Materials 347-350 (August 2013): 2447–51. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2447.

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In order to set up universal and non-linear map of variables, a full binary tree is constructed as mathematical model. Leaf nodes of the full binary tree are linear combination of input variables, and used as inputs of next nodes. On the basis of weighting two inputs by selector for inner node, the inputs are again linearly combined and used as output for next node. The inputs and outputs of all the inner nodes are constructed in turn as the same, and the output of root node is the output of mathematical model, implementing segment-linear approximation. With the means of machine learning of particle swarm optimization for data from some areas, all the coefficients of mathematical model are achieved for the special. The mathematical model is applied to seismic inversion to interpret stratum by seismic data, approving it very practical.

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Zhao, Yujie, Zhanyong Tang, Guixin Ye, Xiaoqing Gong, and Dingyi Fang. "Input-Output Example-Guided Data Deobfuscation on Binary." Security and Communication Networks 2021 (December 13, 2021): 1–16. http://dx.doi.org/10.1155/2021/4646048.

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Data obfuscation is usually used by malicious software to avoid detection and reverse analysis. When analyzing the malware, such obfuscations have to be removed to restore the program into an easier understandable form (deobfuscation). The deobfuscation based on program synthesis provides a good solution for treating the target program as a black box. Thus, deobfuscation becomes a problem of finding the shortest instruction sequence to synthesize a program with the same input-output behavior as the target program. Existing work has two limitations: assuming that obfuscated code snippets in the target program are known and using a stochastic search algorithm resulting in low efficiency. In this paper, we propose fine-grained obfuscation detection for locating obfuscated code snippets by machine learning. Besides, we also combine the program synthesis and a heuristic search algorithm of Nested Monte Carlo Search. We have applied a prototype implementation of our ideas to data obfuscation in different tools, including OLLVM and Tigress. Our experimental results suggest that this approach is highly effective in locating and deobfuscating the binaries with data obfuscation, with an accuracy of at least 90.34%. Compared with the state-of-the-art deobfuscation technique, our approach’s efficiency has increased by 75%, with the success rate increasing by 5%.

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Pouliquen, Mathieu, Eric Pigeon, Olivier Gehan, Abdelhak Goudjil, and Romain Auber. "Impulse response identification from input/output binary measurements." Automatica 123 (January 2021): 109307. http://dx.doi.org/10.1016/j.automatica.2020.109307.

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Koggersbøl, A., T. R. Andersen, J. Bagterp, and S. B. Jørgensen. "An output multiplicity in binary distillation: Experimental verification." Computers & Chemical Engineering 20 (January 1996): S835—S840. http://dx.doi.org/10.1016/0098-1354(96)00147-0.

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Bansal, Abhishek, and Vinay Kumar. "Steganography Technique Inspired by Rook." International Journal of Information Security and Privacy 15, no. 2 (2021): 53–67. http://dx.doi.org/10.4018/ijisp.2021040103.

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A steganographic technique inspired by rook is presented in this paper to ensure privacy and secrecy. In this approach, the cover image is partitioned into n × 1 pixel blocks and converted equivalent n × 8 binary bit planes. Then the functional output of each block is calculated on the basis of the number of rook positions, which are attacked by opponent rooks. The rook is a chess piece that moves only forward and backward in a straight line. In binary bit plane, 0 and 1 are considered as a black and white opponent rook, respectively. The secret information is considered as stream of binary bits. The binary bits of secret information are compared with the functional output of the corresponding block. If it is equal to the functional output of the corresponding block, then nothing needs to be done. In case of inequality, the small number of bits needs to be flipped in such a way that the functional output of the corresponding block becomes equal to the corresponding secret binary bits and the distortion of the cover is minimized.

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Hyde, Brett. "The odd-parity input problem in metrical stress theory." Phonology 29, no. 3 (2012): 383–431. http://dx.doi.org/10.1017/s0952675712000218.

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Under the weak layering approach to prosodic structure (Itô & Mester 1992), the requirement that output forms be exhaustively parsed into binary feet, even when the input contains an odd-number of syllables, results in the odd-parity input problem, which consists of two sub-problems. The odd heavy problem is a pathological type of quantity-sensitivity where a single odd-numbered heavy syllable in an odd-parity output is parsed as a monosyllabic foot. The even output problem is the systematic conversion of odd-parity inputs to even-parity outputs. The article examines the typology of binary stress patterns predicted by two approaches, symmetrical alignment (McCarthy & Prince 1993) and iterative foot optimisation (Pruitt 2008, 2010), to demonstrate that the odd-parity input problem is pervasive in weak layering accounts. It then demonstrates that the odd-parity input problem can be avoided altogether under the alternative structural assumptions of weak bracketing (Hyde 2002).

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Andrew, L. L. H. "Binary output of cellular neural networks with smooth activation." IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 44, no. 9 (1997): 821–24. http://dx.doi.org/10.1109/81.622985.

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Binshtok, N., and S. Shamai. "Integer metrics for binary input symmetric output memoryless channels." IEEE Transactions on Communications 47, no. 11 (1999): 1636–45. http://dx.doi.org/10.1109/26.803498.

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Mostari, Latifa, Abdelmalik Taleb-Ahmed, and Abdennacer Bounoua. "Simplified soft output demapper for non-binary LDPC codes." Optik 126, no. 24 (2015): 5074–76. http://dx.doi.org/10.1016/j.ijleo.2015.09.210.

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Mun, Ye-Ji, and Je-Won Kang. "Ensemble of Random Binary Output Encoding for Adversarial Robustness." IEEE Access 7 (2019): 124632–40. http://dx.doi.org/10.1109/access.2019.2937604.

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Badade, Meena, and T. V. Ramanathan. "Probabilistic frontier regression models for binary type output data." Journal of Applied Statistics 46, no. 13 (2019): 2460–80. http://dx.doi.org/10.1080/02664763.2019.1597838.

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Zhao, Yanlong, Le Yi Wang, G. George Yin, and Ji-Feng Zhang. "Identification of Wiener systems with binary-valued output observations." Automatica 43, no. 10 (2007): 1752–65. http://dx.doi.org/10.1016/j.automatica.2007.03.006.

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Loskot, Pavel, and Norman C. Beaulieu. "The input–output weight enumeration of binary Hamming codes." European Transactions on Telecommunications 17, no. 4 (2006): 483–88. http://dx.doi.org/10.1002/ett.1133.

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Opanasenko, V. M., and S. L. Kryvyi. "Synthesis multilevel structure with multiple output." PROBLEMS IN PROGRAMMING, no. 2-3 (June 2016): 048–62. http://dx.doi.org/10.15407/pp2016.02-03.048.

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The method for solution of adaptation problem of the logical network with many outputs for the restoration of the input set of binary vectors when given only the lower values of this set and the values of the outputs is considered. The algorithm synthesis of the logical network is based on the description of its polynomial Zhegalkin.

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Mandal, Dhoumendra, Sumana Mandal, Mrinal Kanti Mandal, and Sisir Kumar Garai. "Alternative Approach of Developing Optical Binary Adder Using Reversible Peres Gates." International Journal of Optics 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/8541371.

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All-optical devices will play a very significant and crucial role in the modern all-optical network by eliminating the bottleneck of opto-electro-opto- (O-E-O-) conversion. Unfortunately, the conventional logic gates lose information at the output, and the states of the outputs cannot give any credible impressions of the states of the inputs. In this article, at first, the authors have proposed a method of designing an optical three-input-three-output reversible Peres gate. Authors have deployed polarization switching characteristic of Semiconductor Optical Amplifier (SOA) for designing this circuit. The authors have also proposed a method of designing an optical reversible full adder, using two such Peres gates and subsequently a data recovery circuit which can recover the input data of the adder. The authors have chosen frequency encoded data for processing the operation. The proposed scheme has been verified by simulation results.

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Fateh, Rachid, Hicham Oualla, Es-said Azougaghe, et al. "Machine Learning Based System Identification with Binary Output Data Using Kernel Methods." Journal of Telecommunications and Information Technology, no. 1 (February 12, 2024): 17–25. http://dx.doi.org/10.26636/jtit.2024.1.1430.

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Within the realm of machine learning, kernel methods stand out as a prominent class of algorithms with widespread applications, including but not limited to classification, regression, and identification tasks. Our paper addresses the challenging problem of identifying the finite impulse response (FIR) of single-input single-output nonlinear systems under the influence of perturbations and binary-valued measurements. To overcome this challenge, we exploit two algorithms that leverage the framework of reproducing kernel Hilbert spaces (RKHS) to accurately identify the impulse response of the Proakis C channel. Additionally, we introduce the application of these kernel methods for estimating binary output data of nonlinear systems. We showcase the effectiveness of kernel adaptive filters in identifying nonlinear systems with binary output measurements, as demonstrated through the experimental results presented in this study.

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Vieira, M. A., M. Vieira, V. Silva, P. Louro, and M. Barata. "Optoelectronic logic functions using optical bias controlled SiC multilayer devices." MRS Proceedings 1536 (2013): 91–96. http://dx.doi.org/10.1557/opl.2013.703.

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ABSTRACTThe purpose of this paper is the design of simple combinational optoelectronic circuit based on SiC technology, able to act simultaneously as a 4-bit binary encoder or a binary decoder in a 4-to-16 line configurations. The 4-bit binary encoder takes all the data inputs, one by one, and converts them to a single encoded output. The binary decoder decodes a binary input pattern to a decimal output code.The optoelectronic circuit is realized using a a-SiC:H double pin/pin photodetector with two front and back optical gates activated trough steady state violet background. Four red, green, blue and violet input channels impinge on the device at different bit sequences allowing 16 possible inputs. The device selects, through the violet background, one of the sixteen possible input logic signals and sends it to the output.Results show that the device acts as a reconfigurable active filter and allows optical switching and optoelectronic logic functions development. A relationship between the optical inputs and the corresponding digital output levels is established. A binary color weighted code that takes into account the specific weights assigned to each bit position establish the optoelectronic functions. A truth table of an encoder that performs 16-to-1 multiplexer (MUX) function is presented.

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Griot, Miguel, Andres I. Vila Casado, Wen-Yen Weng, Herwin Chan, Jiadong Wang, and Richard D. Wesel. "Nonlinear Trellis Codes for Binary-Input Binary-Output Multiple-Access Channels with Single-User Decoding." IEEE Transactions on Communications 60, no. 2 (2012): 364–74. http://dx.doi.org/10.1109/tcomm.2012.010512.070116.

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SOKOLOV, ARTEM, and ASA BEN-HUR. "HIERARCHICAL CLASSIFICATION OF GENE ONTOLOGY TERMS USING THE GOstruct METHOD." Journal of Bioinformatics and Computational Biology 08, no. 02 (2010): 357–76. http://dx.doi.org/10.1142/s0219720010004744.

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Protein function prediction is an active area of research in bioinformatics. Yet, the transfer of annotation on the basis of sequence or structural similarity remains widely used as an annotation method. Most of today's machine learning approaches reduce the problem to a collection of binary classification problems: whether a protein performs a particular function, sometimes with a post-processing step to combine the binary outputs. We propose a method that directly predicts a full functional annotation of a protein by modeling the structure of the Gene Ontology hierarchy in the framework of kernel methods for structured-output spaces. Our empirical results show improved performance over a BLAST nearest-neighbor method, and over algorithms that employ a collection of binary classifiers as measured on the Mousefunc benchmark dataset.

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Irshid, Mansour I. "A Simple Binary Run-Length Compression Technique for Non-Binary Sources Based on Source Mapping." Active and Passive Electronic Components 24, no. 4 (2001): 211–21. http://dx.doi.org/10.1155/2001/23505.

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In this paper, we propose a very simple and efficient binary run-length compression technique for non-binary sources. The technique is based on mapping the non-binary information source into an equivalent binary source using a new fixed-length code instead of the ASCII code. The codes are chosen such that the probability of one of the two binary symbols; say zero, at the output of the mapper is made as small as possible. Moreover, the “all ones” code is excluded from the code assignments table to ensure the presence of at least one “zero” in each of the output codewords. Compression is achieved by encoding the number of “ones” between two consecutive “zeros” using either a fixed-length code or a variable-length code. When applying this simple encoding technique to English text files, we achieve a compression of 5.44bits/character and 4.6bits/character for the fixed-length code and the variablelength (Huffman) code, respectively.

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Akkaya, Ramazan, and Fatih Alpaslan Kazan. "A New Method for Angular Speed Measurement with Absolute Encoder." Elektronika ir Elektrotechnika 26, no. 1 (2020): 18–22. http://dx.doi.org/10.5755/j01.eie.26.1.25307.

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The encoders are electromechanical devices that give information about the angular position and the number of turns of the shaft, which they are connected. These devices are divided into digital or analogue according to output types. On digital outputs, the output can be binary coded, gray coded or pulsed. The frequency (M) or period (T) method produces accurate results in pulse-output encoders. However, these methods alone cannot give accurate results in some encoder types, which are saw tooth type encoders that produce analogue outputs in different shapes. In this study, a new method was proposed, which reduces the relative error ratio too much below 1 % in an absolute encoder that produces an analogue output ranging from 0 V to 5 V according to the angular position of the shaft. Unlike in the studies in the literature, M method and Analog Digital Converter (ADC) were used together. Thanks to this proposed method (M + ADC), it is possible to increase the measurement accuracy of the encoders with analogue output in all speed regions.

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Alderman, Phillip D. "Parallel gridded simulation framework for DSSAT-CSM (version 4.7.5.21) using MPI and NetCDF." Geoscientific Model Development 14, no. 10 (2021): 6541–69. http://dx.doi.org/10.5194/gmd-14-6541-2021.

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Abstract. The Decision Support System for Agrotechnology Transfer Cropping Systems Model (DSSAT-CSM) is a widely used crop modeling system that has been integrated into large-scale modeling frameworks. Existing frameworks generate spatially explicit simulated outputs at grid points through an inefficient process of translation from binary spatially referenced inputs to point-specific text input files, followed by translation and aggregation back from point-specific text output files to binary spatially referenced outputs. The main objective of this paper was to document the design and implementation of a parallel gridded simulation framework for DSSAT-CSM. A secondary objective was to provide preliminary analysis of execution time and scaling of the new parallel gridded framework. The parallel gridded framework includes improved code for model-internal data transfer, gridded input–output with the Network Common Data Form (NetCDF) library, and parallelization of simulations using the Message Passing Interface (MPI). Validation simulations with the DSSAT-CSM-CROPSIM-CERES-Wheat model revealed subtle discrepancies in simulated yield due to the rounding of soil parameters in the input routines of the standard DSSAT-CSM. Utilizing NetCDF for direct input–output produced a 3.7- to 4-fold reduction in execution time compared to R- and text-based input–output. Parallelization improved execution time for both versions with between 12.2- (standard version) and 13.4-fold (parallel gridded version) speed-up when comparing 1 to 16 compute cores. Estimates of parallelization of computation ranged between 99.2 % (standard version) and 97.3 % (parallel gridded version), indicating potential for scaling to higher numbers of compute cores.

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Ismail Pellu, Deny, Hardi Maifra Kurniadi, Valentyn Paul Bodywein Hattu, Suryo Prakoso, and Usman Pasarai. "Study on Binary Cycle Power Plant Optimization of PLTP Tulehu Ambon with Aspen HYSYS." International Journal of Application on Sciences, Technology and Engineering 1, no. 1 (2023): 260–68. http://dx.doi.org/10.24912/ijaste.v1.i1.260-268.

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Ambon Island has potential geothermal energy resources, one of them is geothermal energy in Tulehu where it is managed by PT. PLN has carried out exploration drilling, but the results of the exploration well test results obtained are low temperature and low pressure, so that cannot use conventional power plants. One of the technologies that can be used in this case is the binary cycle. The binary cycle can be used for low temperature geothermal fluid by using the working fluid to drive a turbine. The purpose of this research is to optimize power output by varying the working fluid, turbine inlet pressure and condenser temperature. Working fluids used are isopentane, n-butane and isobutane. The research method uses the Aspen HYSYS and uses REFTROP as a support analysis. Research shows that the power outputs of each working fluid are 2,471.30 kW, 2,052.13 kW and 1,743.09 kW. Working fluid selected used for PLTP Tulehu is isopentane whit net power output is 2,232.55 kW.

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Zhao, Shuai, Yuan Liu, Xiaokang Zhang, Rui Qin, Bin Wang, and Qiang Zhang. "Mapping Temporally Ordered Inputs to Binary Message Outputs with a DNA Temporal Logic Circuit." Nanomaterials 13, no. 5 (2023): 903. http://dx.doi.org/10.3390/nano13050903.

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Molecular circuits and devices with temporal signal processing capability are of great significance for the analysis of complex biological processes. Mapping temporal inputs to binary messages is a process of history-dependent signal responses, which can help understand the signal-processing behavior of organisms. Here, we propose a DNA temporal logic circuit based on DNA strand displacement reactions, which can map temporally ordered inputs to corresponding binary message outputs. The presence or absence of the output signal is determined by the type of substrate reaction with the input so that different orders of inputs correspond to different binary outputs. We demonstrate that a circuit can be generalized to more complex temporal logic circuits by increasing or decreasing the number of substrates or inputs. We also show that our circuit had excellent responsiveness to temporally ordered inputs, flexibility, and expansibility in the case of symmetrically encrypted communications. We envision that our scheme can provide some new ideas for future molecular encryption, information processing, and neural networks.

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Windridge, David, Riccardo Mengoni, and Rajagopal Nagarajan. "Quantum error-correcting output codes." International Journal of Quantum Information 16, no. 08 (2018): 1840003. http://dx.doi.org/10.1142/s0219749918400038.

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Quantum machine learning is the aspect of quantum computing concerned with the design of algorithms capable of generalized learning from labeled training data by effectively exploiting quantum effects. Error-correcting output codes (ECOC) are a standard setting in machine learning for efficiently rendering the collective outputs of a binary classifier, such as the support vector machine, as a multi-class decision procedure. Appropriate choice of error-correcting codes further enables incorrect individual classification decisions to be effectively corrected in the composite output. In this paper, we propose an appropriate quantization of the ECOC process, based on the quantum support vector machine. We will show that, in addition to the usual benefits of quantizing machine learning, this technique leads to an exponential reduction in the number of logic gates required for effective correction of classification error.

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BLATT, MARCELO, EYTAN DOMANY, and IDO KANTER. "ON THE EQUIVALENCE OF TWO-LAYERED PERCEPTRONS WITH BINARY NEURONS." International Journal of Neural Systems 06, no. 03 (1995): 225–31. http://dx.doi.org/10.1142/s0129065795000160.

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We consider two-layered perceptrons consisting of N binary input units, K binary hidden units and one binary output unit, in the limit N≫K≥1. We prove that the weights of a regular irreducible network are uniquely determined by its input-output map up to some obvious global symmetries. A network is regular if its K weight vectors from the input layer to the K hidden units are linearly independent. A (single layered) perceptron is said to be irreducible if its output depends on every one of its input units; and a two-layered perceptron is irreducible if the K+1 perceptrons that constitute such network are irreducible. By global symmetries we mean, for instance, permuting the labels of the hidden units. Hence, two irreducible regular two-layered perceptrons that implement the same Boolean function must have the same number of hidden units, and must be composed of equivalent perceptrons.

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Klimo, Martin, Peter Lukáč, and Peter Tarábek. "Deep Neural Networks Classification via Binary Error-Detecting Output Codes." Applied Sciences 11, no. 8 (2021): 3563. http://dx.doi.org/10.3390/app11083563.

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One-hot encoding is the prevalent method used in neural networks to represent multi-class categorical data. Its success stems from its ease of use and interpretability as a probability distribution when accompanied by a softmax activation function. However, one-hot encoding leads to very high dimensional vector representations when the categorical data’s cardinality is high. The Hamming distance in one-hot encoding is equal to two from the coding theory perspective, which does not allow detection or error-correcting capabilities. Binary coding provides more possibilities for encoding categorical data into the output codes, which mitigates the limitations of the one-hot encoding mentioned above. We propose a novel method based on Zadeh fuzzy logic to train binary output codes holistically. We study linear block codes for their possibility of separating class information from the checksum part of the codeword, showing their ability not only to detect recognition errors by calculating non-zero syndrome, but also to evaluate the truth-value of the decision. Experimental results show that the proposed approach achieves similar results as one-hot encoding with a softmax function in terms of accuracy, reliability, and out-of-distribution performance. It suggests a good foundation for future applications, mainly classification tasks with a high number of classes.

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Oualla, Hicham, Mathieu Pouliquen, Miloud Frikel, and Said Safi. "Recursive identification of IIR system using binary input/output measurements." International Journal of Modelling, Identification and Control 40, no. 4 (2022): 327. http://dx.doi.org/10.1504/ijmic.2022.125549.

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Safi, Said, Miloud Frikel, Hicham Oualla, and Mathieu Pouliquen. "Recursive identification of IIR system using binary input/output measurements." International Journal of Modelling, Identification and Control 40, no. 4 (2022): 327. http://dx.doi.org/10.1504/ijmic.2022.10050542.

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Diamantaras, Konstantinos, Gabriela Vranou, and Theophilos Papadimitriou. "Multi-Input Single-Output Nonlinear Blind Separation of Binary Sources." IEEE Transactions on Signal Processing 61, no. 11 (2013): 2866–73. http://dx.doi.org/10.1109/tsp.2013.2255046.

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Zlateva, P. "Output stabilization of fermentation processes: A binary control system approach." Bioprocess Engineering 23, no. 1 (2000): 81–87. http://dx.doi.org/10.1007/s004499900127.

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Yan, Lingqing, Thomas F. Edgar, and Michael Baldea. "Dynamic process intensification of binary distillation based on output multiplicity." AIChE Journal 65, no. 4 (2019): 1162–72. http://dx.doi.org/10.1002/aic.16506.

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She, Xiwei, Theodore W. Berger, and Dong Song. "A Double-Layer Multi-Resolution Classification Model for Decoding Spatiotemporal Patterns of Spikes With Small Sample Size." Neural Computation 34, no. 1 (2022): 219–54. http://dx.doi.org/10.1162/neco_a_01459.

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Abstract We build a double-layer, multiple temporal-resolution classification model for decoding single-trial spatiotemporal patterns of spikes. The model takes spiking activities as input signals and binary behavioral or cognitive variables as output signals and represents the input-output mapping with a double-layer ensemble classifier. In the first layer, to solve the underdetermined problem caused by the small sample size and the very high dimensionality of input signals, B-spline functional expansion and L1-regularized logistic classifiers are used to reduce dimensionality and yield sparse model estimations. A wide range of temporal resolutions of neural features is included by using a large number of classifiers with different numbers of B-spline knots. Each classifier serves as a base learner to classify spatiotemporal patterns into the probability of the output label with a single temporal resolution. A bootstrap aggregating strategy is used to reduce the estimation variances of these classifiers. In the second layer, another L1-regularized logistic classifier takes outputs of first-layer classifiers as inputs to generate the final output predictions. This classifier serves as a meta-learner that fuses multiple temporal resolutions to classify spatiotemporal patterns of spikes into binary output labels. We test this decoding model with both synthetic and experimental data recorded from rats and human subjects performing memory-dependent behavioral tasks. Results show that this method can effectively avoid overfitting and yield accurate prediction of output labels with small sample size. The double-layer, multi-resolution classifier consistently outperforms the best single-layer, single-resolution classifier by extracting and utilizing multi-resolution spatiotemporal features of spike patterns in the classification.

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Kajdanowicz, Tomasz, and Przemysław Kazienko. "Multi-label classification using error correcting output codes." International Journal of Applied Mathematics and Computer Science 22, no. 4 (2012): 829–40. http://dx.doi.org/10.2478/v10006-012-0061-2.

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A framework for multi-label classification extended by Error Correcting Output Codes (ECOCs) is introduced and empirically examined in the article. The solution assumes the base multi-label classifiers to be a noisy channel and applies ECOCs in order to recover the classification errors made by individual classifiers. The framework was examined through exhaustive studies over combinations of three distinct classification algorithms and four ECOC methods employed in the multi-label classification problem. The experimental results revealed that (i) the Bode-Chaudhuri-Hocquenghem (BCH) code matched with any multi-label classifier results in better classification quality; (ii) the accuracy of the binary relevance classification method strongly depends on the coding scheme; (iii) the label power-set and the RAkEL classifier consume the same time for computation irrespective of the coding utilized; (iv) in general, they are not suitable for ECOCs because they are not capable to benefit from ECOC correcting abilities; (v) the all-pairs code combined with binary relevance is not suitable for datasets with larger label sets.

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Gottlieb, Daniel, and Humberto Moreira. "Simple contracts with adverse selection and moral hazard." Theoretical Economics 17, no. 3 (2022): 1357–401. http://dx.doi.org/10.3982/te2992.

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We study a principal–agent model with moral hazard and adverse selection. Risk‐neutral agents with limited liability have arbitrary private information about the distribution of outputs and the cost of effort. We show that under a multiplicative separability condition, the optimal mechanism offers a single contract. This condition holds, for example, when output is binary. If the principal's payoff must also satisfy free disposal and the distribution of outputs has the monotone likelihood ratio property, the mechanism offers a single debt contract. Our results generalize if the output distribution is “close” to multiplicatively separable. Our model suggests that offering a single contract may be optimal in environments with adverse selection and moral hazard when agents are risk‐neutral and have limited liability.

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Song, Yang, Qiyu Kang, and Wee Peng Tay. "Error-Correcting Output Codes with Ensemble Diversity for Robust Learning in Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (2021): 9722–29. http://dx.doi.org/10.1609/aaai.v35i11.17169.

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Though deep learning has been applied successfully in many scenarios, malicious inputs with human-imperceptible perturbations can make it vulnerable in real applications. This paper proposes an error-correcting neural network (ECNN) that combines a set of binary classifiers to combat adversarial examples in the multi-class classification problem. To build an ECNN, we propose to design a code matrix so that the minimum Hamming distance between any two rows (i.e., two codewords) and the minimum shared information distance between any two columns (i.e., two partitions of class labels) are simultaneously maximized. Maximizing row distances can increase the system fault tolerance while maximizing column distances helps increase the diversity between binary classifiers. We propose an end-to-end training method for our ECNN, which allows further improvement of the diversity between binary classifiers. The end-to-end training renders our proposed ECNN different from the traditional error-correcting output code (ECOC) based methods that train binary classifiers independently. ECNN is complementary to other existing defense approaches such as adversarial training and can be applied in conjunction with them. We empirically demonstrate that our proposed ECNN is effective against the state-of-the-art white-box and black-box attacks on several datasets while maintaining good classification accuracy on normal examples.

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Luo, Chao, Jun Zhao, Yulie Gong, Yongzhen Wang, and Weibin Ma. "Energy efficiency comparison between geothermal power systems." Thermal Science 21, no. 6 Part A (2017): 2633–42. http://dx.doi.org/10.2298/tsci151225074l.

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The geothermal water which can be considered for generating electricity with the temperature ranging from 80? to 150? in China because of shortage of electricity and fossil energy. There are four basic types of geothermal power systems: single flash, double flash, binary cycle, and flash-binary system, which can be adapted to geothermal energy utilization in China. The paper discussed the performance indices and applicable conditions of different power system. Based on physical and mathematical models, simulation result shows that, when geofluid temperature ranges from 100? to 130?, the net power output of double flash power is bigger than flash-binary system. When the geothermal resource temperature is between 130? and 150?, the net power output of flash-binary geothermal power system is higher than double flash system by the maximum value 5.5%. However, the sum water steam amount of double flash power system is 2 to 3 times larger than flash-binary power system, which will cause the bigger volume of equipment of power system. Based on the economy and power capacity, it is better to use flash-binary power system when the geofluid temperature is between 100? and 150?.

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Eldridge, J. J., E. R. Stanway, L. Xiao, et al. "Binary Population and Spectral Synthesis." Proceedings of the International Astronomical Union 12, S329 (2016): 396. http://dx.doi.org/10.1017/s1743921317003192.

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AbstractWe have recently released version 2.0 of the Binary Population and Spectral Synthesis (BPASS) population synthesis code. This is designed to construct the spectra and related properties of stellar populations built from ~200,000 detailed, individual stellar models of known age and metallicity. The output products enable a broad range of theoretical predictions for individual stars, binaries, resolved and unresolved stellar populations, supernovae and their progenitors, and compact remnant mergers. Here we summarise key applications that demonstrate that binary populations typically reproduce observations better than single star models.

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Beigi, Salman. "A lower bound on the value of entangled binary games." Quantum Information and Computation 10, no. 11&12 (2010): 911–24. http://dx.doi.org/10.26421/qic10.11-12-2.

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A two-player one-round binary game consists of two cooperative players who each replies by one bit to a message that he receives privately; they win the game if both questions and answers satisfy some predetermined property. A game is called entangled if the players are allowed to share a priori entanglement. It is well-known that the maximum winning probability (value) of entangled XOR-games (binary games in which the predetermined property depends only on the XOR of the two output bits) can be computed by a semidefinite program. In this paper we extend this result in the following sense; if a binary game is uniform, meaning that in an optimal strategy the marginal distributions of the output of each player are uniform, then its entangled value can be efficiently computed by a semidefinite program. We also introduce a lower bound on the entangled value of a general two-player one-round game; this bound depends on the size of the output set of each player and can be computed by a semidefinite program. In particular, we show that if the game is binary, $\omega_q$ is its entangled value, and $\omega_{sdp}$ is the optimum value of the corresponding semidefinite program, then $0.68\,\omega_{sdp} < \omega_q \leq \omega_{sdp}$.

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