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1
Boukadida, Souha, Soufien Gdaim, and Abdellatif Mtiba. "Sensor Fault Detection and Isolation Based on Artificial Neural Networks and Fuzzy Logic Applicated on Induction Motor for Electrical Vehicle." International Journal of Power Electronics and Drive Systems (IJPEDS) 8, no. 2 (June 1, 2017): 601. http://dx.doi.org/10.11591/ijpeds.v8.i2.pp601-611.
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<p>Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical vehicle. Like failures of a position sensor, a voltage sensor, and current sensors. Three-phase induction motors are the “workhorses” of industry and are the most widely used electrical machines. This paper presents a scheme for Fault Detection and Isolation (FDI). The proposed approach is a sensor-based technique using the mains current measurement. Current sensors are widespread in power converters control and in electrical drives. Thus, to ensure continuous operation with reconfiguration control, a fast sensor fault detection and isolation is required. In this paper, a new and fast faulty current sensor detection and isolation is presented. It is derived from intelligent techniques. The main interest of field programmable gate array is the extremely fast computation capabilities. That allows a fast residual generation when a sensor fault occurs. Using of Xilinx System Generator in Matlab / Simulink allows the real-time simulation and implemented on a field programmable gate array chip without any VHSIC Hardware Description Language coding. The sensor fault detection and isolation algorithm was implemented targeting a Virtex5. Simulation results are given to demonstrate the efficiency of this FDI approach.</p>
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Tran, Duc M., Kyungah Kim, and Joon-Young Choi. "CLB-Based Development of BiSS-C Interface Master for Motor Encoders." Electronics 12, no. 4 (February 9, 2023): 886. http://dx.doi.org/10.3390/electronics12040886.
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Encoder interfaces should be operated in real time with high precision and fast processing for industrial motor control systems. The continuous bidirectional serial synchronous (BiSS-C) interface is an open-source serial communication protocol designed for motor encoders and is suitable for industrial purposes because of its fast serial communication speed. In this study, we propose a method for developing a BiSS-C interface master for a motor encoder slave, using only the configurable logic block (CLB) peripheral integrated into TI microcontroller units. By analyzing the detailed operation protocol of the BiSS-C interface, we create the truth and state tables for logic circuits and finite state machines, which are required for the BiSS-C interface master. Then, by programming the CLB based on the created truth and state tables, we implement the master clock, serial peripheral interface (SPI) clock, and operational process for the master. This approach is cost-efficient because additional hardware components, such as a field-programmable gate array or a complex programmable logic device, are not required for the master implementations. The developed method can be immediately applied to developing the masters for other BiSS-C encoders with different specifications, which is certainly necessary for a motor drive development and test. By building an AC motor control system with the developed master and performing various experiments, we verify the performance and practical usefulness of the developed BiSS-C interface master. The maximum master clock frequency without any CRC errors is achieved by 6.25 MHz, which can cope with more than 20 kHz motor control cycle frequency. The usefulness is demonstrated by showing the motor speed and position control performance that are acceptable in real applications.
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KIROLOS, SAMI, and YEHIA MASSOUD. "DYNAMIC VOLTAGE SCALING CONTINUOUS ADAPTIVE-SIZE CELL DESIGN TECHNIQUE." Journal of Circuits, Systems and Computers 17, no. 05 (October 2008): 871–83. http://dx.doi.org/10.1142/s0218126608004630.
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In this paper, we present an adaptive circuit design that is capable of increasing the effective size-ratio of combinational logic gates to extend the balanced operation in the subthreshold region as well as to maintain high performance at the nominal VDD. We optimize the sizes of the PMOS transistors in the pull-up network for minimum power dissipation and propagation delay over a wide range of supply voltage. In addition to the minimized energy operation, the dynamically adjustable gate size-ratio allows the gate to preserve a symmetric voltage transfer characteristic at both normal supply and subthreshold operation, which translates to maximized noise margins. Simulation results show that up to 70.9% reduction in the energy can be achieved for a ring oscillator, as compared to the fixed size design capable of operating under supply voltage in the range of 75 mV to 1.2 V. For designs working under dynamic voltage scaling schemes, our technique presents a very effective and efficient solution for balanced minimum energy operation in the subthreshold region while preserving high performance at the nominal supply voltage.
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Plyusnin, Nikolay. "Tunable logic of complex variables and quantum networks on its basis." Robotics and Technical Cybernetics 10, no. 4 (December 2022): 267–74. http://dx.doi.org/10.31776/rtcj.10404.
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Continuous - additive-multiplicative (AM) logic is considered, in which logical operations are replaced by algebraic operations («×» and «+») or operations with vectors, and binary variables «0» and «1» are replaced by continuous scalar ones («0- 1») or complex variables. To build this logic, a continuous analogue of the canonical form of Boolean logic is used in the form of a perfect disjunctive or conjunctive normal form (KAM logic). A feature of KAM logic is a continuous dependence on input variables and a potential variety of continuous logic functions. Based on the previously proposed «fuzzy» (distributed) continuous function, in the form of a superposition of «clear» functions, and the tunable QAM element circuit that implements it, this element is generalized to a network QAM element with several tunable outputs. The multiplication functions in this QAM element can be performed using a known memristor, which can be replaced by a memtransistor based on a field effect transistor. In quantum QAM networks, these elements are, respectively: «k-memristor» and «k-memtransistor». One of the options for a k-memtransistor is a composite hybrid spin-field-effect transistor based on a planar spin valve with magnetic memory and a field-effect transistor with a ferroelectric memory. It is noted that the main technological problem of quantum QAM networks based on such a hybrid spin transistor is the creation of planar conducting and ferromagnetic elements based on ultrathin metallic and ferromagnetic films on silicon.
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Li, Zhitao, Yuqian Guo, and Weihua Gui. "Asymptotical feedback controllability of continuous-time probabilistic logic control networks." Nonlinear Analysis: Hybrid Systems 47 (February 2023): 101265. http://dx.doi.org/10.1016/j.nahs.2022.101265.
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Yadav, Neetika, Neeta Pandey, and Deva Nand. "Leakage reduction in dual mode logic through gated leakage transistors." Microprocessors and Microsystems 84 (July 2021): 104269. http://dx.doi.org/10.1016/j.micpro.2021.104269.
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Chen, Lanlan. "Secure Data Sequence Recognition of All-Optical High-Speed Network Using Semiconductor Optical Amplifier." Journal of Nanoelectronics and Optoelectronics 16, no. 10 (October 1, 2021): 1667–74. http://dx.doi.org/10.1166/jno.2021.3124.
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Semiconductor optical amplifier (SOA) has nonlinear optical effect and integration advantages, and is widely used in all-optical logic gates. Hence, it is used in the identification of all-optical high-speed network security data sequences. First, the SOA model is established and then simplified based on the existing model. On this basis, the identification model of all-optical high-speed network security data sequence is established, and the concept of integrated turbo-switch is introduced. The structure of the turbo-switch is analyzed. Then, an integrated turbo-switch architecture based on SOA-Mach Zehnder interferometer (MZI) is proposed, and its performance is verified by simulation. A data sequence recognition method for all-optical high-speed network security is proposed based on the integrated acceleration switch, and the difficulty of result recognition is analyzed. The simulation results of integrated turbo-switch show that when 1550 nm light passes through MZI, the interference can be almost completely cancelled, and the corresponding phase difference of 1538 nm light is less than 3 radians, that is, some light can pass through MZI. After the bias current of SOA of upper and lower arms of SOA-MZI is properly adjusted, MZI can play the function of tunable filter. Adjusting the bias current of SOA1 and SOA2 in the turbo-switch can control the “overshoot” program in the gain recovery curve, make SOA2 in a saturated working state, optimize the gain recovery curve, and improve the SOArelated mode effect, so that the turbo-switch can also output a more stable waveform under continuous “1” input. The recognition difficulty test shows that the target sequence after the cycle and the data sequence to be recognized are used for the “Exclusive NOR” operation, and the “AND gate” is added to realize the data sequence recognition and reduce the recognition difficulty.
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Buckley, J. J., and Yoichi Hayashi. "Numerical relationships between neural networks, continuous functions, and fuzzy systems." Fuzzy Sets and Systems 60, no. 1 (November 1993): 1–8. http://dx.doi.org/10.1016/0165-0114(93)90283-n.
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Csiszár, Orsolya, Gábor Csiszár, and József Dombi. "Interpretable neural networks based on continuous-valued logic and multicriteria decision operators." Knowledge-Based Systems 199 (July 2020): 105972. http://dx.doi.org/10.1016/j.knosys.2020.105972.
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Sturlaugson, Liessman, Logan Perreault, and John W. Sheppard. "Factored performance functions and decision making in continuous time Bayesian networks." Journal of Applied Logic 22 (July 2017): 28–45. http://dx.doi.org/10.1016/j.jal.2016.11.030.
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Levin, V. I. "Continuous logic and combinatorial problems decision." Journal of Computer and Systems Sciences International 47, no. 3 (June 2008): 413–21. http://dx.doi.org/10.1134/s1064230708030118.
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Tan, Shoutao, Zhanfeng Fang, Yanyi Liu, Zhe Wu, Hang Du, Renjie Xu, and Yunfei Liu. "An SSD-MobileNet Acceleration Strategy for FPGAs Based on Network Compression and Subgraph Fusion." Forests 14, no. 1 (December 27, 2022): 53. http://dx.doi.org/10.3390/f14010053.
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Over the last decade, various deep neural network models have achieved great success in image recognition and classification tasks. The vast majority of high-performing deep neural network models have a huge number of parameters and often require sacrificing performance and accuracy when they are deployed on mobile devices with limited area and power consumption. To address this problem, we present an SSD-MobileNet-v1 acceleration method based on network compression and subgraph fusion for Field-Programmable Gate Arrays (FPGAs). Firstly, a regularized pruning algorithm based on sensitivity analysis and Filter Pruning via Geometric Median (FPGM) was proposed. Secondly, the Quantize Aware Training (QAT)-based network full quantization algorithm was designed. Finally, a strategy for computing subgraph fusion is proposed for FPGAs to achieve continuous scheduling of Programmable Logic (PL) operators. The experimental results show that using the proposed acceleration strategy can reduce the number of model parameters by a factor of 11 and increase the inference speed on the FPGA platform by a factor of 9–10. The acceleration algorithm is applicable to various mobile edge devices and can be applied to the real-time monitoring of forest fires to improve the intelligence of forest fire detection.
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ISLAM, MUHAMMAD ASIFUL, C. R. RAMAKRISHNAN, and I. V. RAMAKRISHNAN. "Inference in probabilistic logic programs with continuous random variables." Theory and Practice of Logic Programming 12, no. 4-5 (July 2012): 505–23. http://dx.doi.org/10.1017/s1471068412000154.
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AbstractProbabilistic Logic Programming (PLP), exemplified by Sato and Kameya's PRISM, Poole's ICL, Raedt et al.'s ProbLog and Vennekens et al.'s LPAD, is aimed at combining statistical and logical knowledge representation and inference. However, the inference techniques used in these works rely on enumerating sets of explanations for a query answer. Consequently, these languages permit very limited use of random variables with continuous distributions. In this paper, we present a symbolic inference procedure that uses constraints and represents sets of explanations without enumeration. This permits us to reason over PLPs with Gaussian or Gamma-distributed random variables (in addition to discrete-valued random variables) and linear equality constraints over reals. We develop the inference procedure in the context of PRISM; however the procedure's core ideas can be easily applied to other PLP languages as well. An interesting aspect of our inference procedure is that PRISM's query evaluation process becomes a special case in the absence of any continuous random variables in the program. The symbolic inference procedure enables us to reason over complex probabilistic models such as Kalman filters and a large subclass of Hybrid Bayesian networks that were hitherto not possible in PLP frameworks.
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PAN, HEPING, and LIN LIU. "FUZZY BAYESIAN NETWORKS — A GENERAL FORMALISM FOR REPRESENTATION, INFERENCE AND LEARNING WITH HYBRID BAYESIAN NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 07 (November 2000): 941–62. http://dx.doi.org/10.1142/s021800140000060x.
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This paper proposes a general formalism for representation, inference and learning with general hybrid Bayesian networks in which continuous and discrete variables may appear anywhere in a directed acyclic graph. The formalism fuzzifies a hybrid Bayesian network into two alternative forms: the first form replaces each continuous variable in the given directed acyclic graph (DAG) by a partner discrete variable and adds a directed link from the partner discrete variable to the continuous one. The mapping between two variables is not crisp quantization but is approximated (fuzzified) by a conditional Gaussian (CG) distribution. The CG model is equivalent to a fuzzy set but no fuzzy logic formalism is employed. The conditional distribution of a discrete variable given its discrete parents is still assumed to be multinomial as in discrete Bayesian networks. The second form only replaces each continuous variable whose descendants include discrete variables by a partner discrete variable and adds a directed link from that partner discrete variable to the continuous one. The dependence between the partner discrete variable and the original continuous variable is approximated by a CG distribution, but the dependence between a continuous variable and its continuous and discrete parents is approximated by a conditional Gaussian regression (CGR) distribution. Obviously, the second form is a finer approximation, but restricted to CGR models, and requires more complicated inference and learning algorithms. This results in two general approximate representations of a general hybrid Bayesian networks, which are called here the fuzzy Bayesian network (FBN) form-I and form-II. For the two forms of FBN, general exact inference algorithms exists, which are extensions of the junction tree inference algorithm for discrete Bayesian networks. Learning fuzzy Bayesian networks from data is different from learning purely discrete Bayesian networks because not only all the newly converted discrete variables are latent in the data, but also the number of discrete states for each of these variables and the CG or CGR distribution of each continuous variable given its partner discrete parents or both continuous and discrete parents have to be determined.
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Mojica-Nava, Eduardo, Jimmy Salgado, Duvan Tellez, and Alvaro Lopez. "Optimal Control of Switching Topology Networks." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/268541.
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We present an extension of a previously proposed approach based on the method of moments for solving the optimal control problem for a switching system considering now a continuous external input. This method is based on the transformation of a nonlinear, nonconvex optimal control problem, into an equivalent optimal control problem with linear and convex structure, which allows us to obtain an equivalent convex formulation more appropriate to be solved by high-performance numerical computing. Finally, the design of optimal logic-based controllers for networked systems with a dynamic topology is presented as an application of this work.
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Meng, Xiangyu, and Tongwen Chen. "Event-Based Stabilization over Networks with Transmission Delays." Journal of Control Science and Engineering 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/212035.
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This paper investigates asymptotic stabilization for linear systems over networks based on event-driven communication. A new communication logic is proposed to reduce the feedback effort, which has some advantages over traditional ones with continuous feedback. Considering the effect of time-varying transmission delays, the criteria for the design of both the feedback gain and the event-triggering mechanism are derived to guarantee the stability and performance requirements. Finally, the proposed techniques are illustrated by an inverted pendulum system and a numerical example.
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Alkhayyat, Ahmed, Firas Abedi, Ashish Bagwari, Pooja Joshi, Haider Mahmood Jawad, Sarmad Nozad Mahmood, and Yousif K. Yousif. "Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review." International Journal of Distributed Sensor Networks 18, no. 7 (July 2022): 155013292211135. http://dx.doi.org/10.1177/15501329221113508.
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Cognitive radios are expected to play an important role in capturing the constantly growing traffic interest on remote networks. To improve the usage of the radio range, a cognitive radio hub detects the weather, evaluates the open-air qualities, and then makes certain decisions and distributes the executives’ space assets. The cognitive radio works in tandem with artificial intelligence and artificial intelligence methodologies to provide a flexible and intelligent allocation for continuous production cycles. The purpose is to provide a single source of information in the form of a survey research to enable academics better understand how artificial intelligence methodologies, such as fuzzy logics, genetic algorithms, and artificial neural networks, are used to various cognitive radio systems. The various artificial intelligence approaches used in cognitive radio engines to improve cognition capabilities in cognitive radio networks are examined in this study. Computerized reasoning approaches, such as fuzzy logic, evolutionary algorithms, and artificial neural networks, are used in the writing audit. This topic also covers cognitive radio network implementation and the typical learning challenges that arise in cognitive radio systems.
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Tirian, Gelu-Ovidiu, Ioan Filip, and Gabriela Proştean. "Adaptive control system for continuous steel casting based on neural networks and fuzzy logic." Neurocomputing 125 (February 2014): 236–45. http://dx.doi.org/10.1016/j.neucom.2012.11.052.
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Jin, Guangyin, Lingbo Liu, Fuxian Li, and Jincai Huang. "Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (June 26, 2023): 14268–76. http://dx.doi.org/10.1609/aaai.v37i12.26669.
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Traffic congestion event prediction is an important yet challenging task in intelligent transportation systems. Many existing works about traffic prediction integrate various temporal encoders and graph convolution networks (GCNs), called spatio-temporal graph-based neural networks, which focus on predicting dense variables such as flow, speed and demand in time snapshots, but they can hardly forecast the traffic congestion events that are sparsely distributed on the continuous time axis. In recent years, neural point process (NPP) has emerged as an appropriate framework for event prediction in continuous time scenarios. However, most conventional works about NPP cannot model the complex spatio-temporal dependencies and congestion evolution patterns. To address these limitations, we propose a spatio-temporal graph neural point process framework, named STGNPP for traffic congestion event prediction. Specifically, we first design the spatio-temporal graph learning module to fully capture the long-range spatio-temporal dependencies from the historical traffic state data along with the road network. The extracted spatio-temporal hidden representation and congestion event information are then fed into a continuous gated recurrent unit to model the congestion evolution patterns. In particular, to fully exploit the periodic information, we also improve the intensity function calculation of the point process with a periodic gated mechanism. Finally, our model simultaneously predicts the occurrence time and duration of the next congestion. Extensive experiments on two real-world datasets demonstrate that our method achieves superior performance in comparison to existing state-of-the-art approaches.
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Qiao, Litao, Weijia Wang, and Bill Lin. "Alternative Formulations of Decision Rule Learning from Neural Networks." Machine Learning and Knowledge Extraction 5, no. 3 (August 3, 2023): 937–56. http://dx.doi.org/10.3390/make5030049.
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This paper extends recent work on decision rule learning from neural networks for tabular data classification. We propose alternative formulations to trainable Boolean logic operators as neurons with continuous weights, including trainable NAND neurons. These alternative formulations provide uniform treatments to different trainable logic neurons so that they can be uniformly trained, which enables, for example, the direct application of existing sparsity-promoting neural net training techniques like reweighted L1 regularization to derive sparse networks that translate to simpler rules. In addition, we present an alternative network architecture based on trainable NAND neurons by applying De Morgan’s law to realize a NAND-NAND network instead of an AND-OR network, both of which can be readily mapped to decision rule sets. Our experimental results show that these alternative formulations can also generate accurate decision rule sets that achieve state-of-the-art performance in terms of accuracy in tabular learning applications.
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Levin, V. I. "Continuous logic in problems of finite automata dynamics." Journal of Computer and Systems Sciences International 49, no. 1 (February 2010): 59–64. http://dx.doi.org/10.1134/s1064230710010077.
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Azizan, Farah Liyana, Saratha Sathasivam, Majid Khan Majahar Ali, Nurshazneem Roslan, and Caicai Feng. "Hybridised Network of Fuzzy Logic and a Genetic Algorithm in Solving 3-Satisfiability Hopfield Neural Networks." Axioms 12, no. 3 (March 1, 2023): 250. http://dx.doi.org/10.3390/axioms12030250.
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This work proposed a new hybridised network of 3-Satisfiability structures that widens the search space and improves the effectiveness of the Hopfield network by utilising fuzzy logic and a metaheuristic algorithm. The proposed method effectively overcomes the downside of the current 3-Satisfiability structure, which uses Boolean logic by creating diversity in the search space. First, we included fuzzy logic into the system to make the bipolar structure change to continuous while keeping its logic structure. Then, a Genetic Algorithm is employed to optimise the solution. Finally, we return the answer to its initial bipolar form by casting it into the framework of the hybrid function between the two procedures. The suggested network’s performance was trained and validated using Matlab 2020b. The hybrid techniques significantly obtain better results in terms of error analysis, efficiency evaluation, energy analysis, similarity index, and computational time. The outcomes validate the significance of the results, and this comes from the fact that the proposed model has a positive impact. The information and concepts will be used to develop an efficient method of information gathering for the subsequent investigation. This new development of the Hopfield network with the 3-Satisfiability logic presents a viable strategy for logic mining applications in future.
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Bavkar, Dnyaneshwar Madhukar, Ramgopal Kashyap, and Vaishali Khairnar. "Multimodal Sarcasm Detection via Hybrid Classifier with Optimistic Logic." Journal of Telecommunications and Information Technology 3, no. 2022 (September 29, 2022): 97–114. http://dx.doi.org/10.26636/jtit.2022.161622.
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This work aims to provide a novel multimodal sarcasm detection model that includes four stages: pre-processing, feature extraction, feature level fusion, and classification. The pre-processing uses multimodal data that includes text, video, and audio. Here, text is pre-processed using tokenization and stemming, video is pre-processed during the face detection phase, and audio is pre-processed using the filtering technique. During the feature extraction stage, such text features as TF-IDF, improved bag of visual words, n-gram, and emojis as well on the video features using improved SLBT, and constraint local model (CLM) are extraction. Similarly the audio features like MFCC, chroma, spectral features, and jitter are extracted. Then, the extracted features are transferred to the feature level fusion stage, wherein an improved multilevel canonical correlation analysis (CCA) fusion technique is performed. The classification is performer using a hybrid classifier (HC), e.g. bidirectional gated recurrent unit (Bi-GRU) and LSTM. The outcomes of Bi-GRU and LSTM are averaged to obtain an effective output. To make the detection results more accurate, the weight of LSTM will be optimally tuned by the proposed opposition learning-based aquila optimization (OLAO) model. The MUStARD dataset is a multimodal video corpus used for automated sarcasm Discovery studies. Finally, the effectiveness of the proposed approach is proved based on various metrics.
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Samkov, L. M. "The Logic of mental models in evidence-based medicine." Vrach i informacionnye tehnologii, no. 4 (2020): 78–85. http://dx.doi.org/10.37690/1811-0193-2020-4-78-85.
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The actual task of supplementing intelligent systems of evidence-based medicine with technically implemented mental models is set. Using these models, the user understands the results of digital models in Big Data systems. Clarified concepts related to this issue. The electronic components of neural networks for the implementation of mental models is defined. A variant of continuous logic of mental models is proposed. Functional expressions of convolutions of input signals of artificial neurons are constructed. The basic operations for use in the computational architecture of a neural network are defined. Prospects for the development of this issue are outlined.
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Jiang, Haifeng, Guangzhi Han, He Wang, Xinping Li, and Guopeng Zhang. "Fuzzy-logic-based data-differentiated service supported routing protocol for emergency communication networks in underground mines." International Journal of Distributed Sensor Networks 15, no. 7 (July 2019): 155014771986476. http://dx.doi.org/10.1177/1550147719864762.
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Hybrid wireless mesh networks are suitable to construct emergency communication networks after disasters in underground mines. The routing decision in emergency scene is more difficult to give an accurate mathematical description due to the constraints of various data types, different data transmission requirements, and multi-parameters. Based on the fuzzy decision theory, this article has proposed a fuzzy-logic-based data-differentiated service supported routing protocol. Through the use of the adaptive fuzzy decision system, fuzzy-logic-based data-differentiated service supported routing protocol can provide data-differentiated services and make optimized routing decisions to satisfy the transmission requirements of different data types. In addition, a path soft handoff strategy has been proposed to maintain continuous data transmission when the path quality deteriorates. Based on NS2, we set three transmission scenarios (transmitting emergency data, regular data, or mixed data) to test the performances of fuzzy-logic-based data-differentiated service supported routing protocol, ad hoc on-demand distance vector, FUZZY-ad hoc on-demand distance vector, and multi-criteria routing metric. The results show that the fuzzy-logic-based data-differentiated service supported routing protocol has a higher delivery ratio and lower end-to-end delay when transmitting emergency data. When transmitting regular data, fuzzy-logic-based data-differentiated service supported routing protocol has achieved higher throughput and longer network lifetime than that of similar algorithms.
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Jiang, Yaozhi. "Dialectical Logic K-Model: A Mathematical Model for Machine." Journal of Mathematics Research 9, no. 6 (October 27, 2017): 82. http://dx.doi.org/10.5539/jmr.v9n6p82.
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An axiom system for dialectical logic K-model which based on Kirchhoff energy-method is established by author in the paper. The author describes that subjective-laws is the mirror imagine reflected from objective-laws and defines that the three-step which named by sensation, abstraction and thinking in artificial intelligence. At same time, describes that axiom system for dialectical logic K-model, in which contains such as logic-variable energy conservation law, Mozi’s principle( mini-max principle) and forbidden law, etc. In the axiom system also contain such as a continuous true-value-function system valued on interval and the K-graph for logic-variable. And describes the giving value method by matrix based on K-graph satisfied Kirchhoff laws to the logic variable. The author describes simply the linear and nonlinear logic variable system. And describes simply the logic variable involved three-dimension Euclidean space and topology networks space separately. Dialectical logic K-model would supply an computation algorithm idea for machine so that the machine is able to think by dialectical logic method, thus an important information-treated method maybe the dialectical logic.
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Castillo, Oscar, Juan R. Castro, Patricia Melin, and Antonio Rodriguez-Diaz. "Universal Approximation of a Class of Interval Type-2 Fuzzy Neural Networks in Nonlinear Identification." Advances in Fuzzy Systems 2013 (2013): 1–16. http://dx.doi.org/10.1155/2013/136214.
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Neural networks (NNs), type-1 fuzzy logic systems (T1FLSs), and interval type-2 fuzzy logic systems (IT2FLSs) have been shown to be universal approximators, which means that they can approximate any nonlinear continuous function. Recent research shows that embedding an IT2FLS on an NN can be very effective for a wide number of nonlinear complex systems, especially when handling imperfect or incomplete information. In this paper we show, based on the Stone-Weierstrass theorem, that an interval type-2 fuzzy neural network (IT2FNN) is a universal approximator, which uses a set of rules and interval type-2 membership functions (IT2MFs) for this purpose. Simulation results of nonlinear function identification using the IT2FNN for one and three variables and for the Mackey-Glass chaotic time series prediction are presented to illustrate the concept of universal approximation.
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Lu, Weijun, Ning Bao, Tangren Zheng, Xiaorui Zhang, and Yutong Song. "Memristor-Based Read/Write Circuit with Stable Continuous Read Operation." Electronics 11, no. 13 (June 27, 2022): 2018. http://dx.doi.org/10.3390/electronics11132018.
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In recent years, computation-intensive applications, such as artificial intelligence, video processing and encryption, have been developing rapidly. On the other hand, the problems of “storage wall” and “power consumption wall” for the traditional storage and computing separated architectures limit the computing performance. The computational circuits and memory cells based on nonvolatile memristors are unified and become a competitive solution to this problem. However, there are various problems that prevent memristor-based circuits from entering practical applications, one of which is the memristor state deviation problem caused by continuous reading. In this paper, we study some circuits studied by predecessors on read/write circuit, compare the experimental results, analyze the reason for the resistance state deviation of memristor, and put forward a new parallel structure of memristor based on opposite polarity. The logic “1” and logic “0” are represented by the positive and negative voltage difference of two memristors with opposite polarity, which can effectively alleviate the problem of the resistance state deviation caused by continuous reading. A reading voltage of 2 V is applied to the four circuits at the same time, and continuous reading is carried out until the output voltage becomes stable. The voltage offset of the optimized circuit when reading logic “0” is reduced to 78 mV, which is significantly smaller than that of other circuits. In addition, when reading logic “1”, it has the effect of enhancing the information stored in the memristor.
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Forti, Stefano. "Trending Topics in Software Engineering (1)." ACM SIGSOFT Software Engineering Notes 47, no. 4 (September 27, 2022): 6. http://dx.doi.org/10.1145/3561846.3561847.
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The continuous evolution of Software Engineering (SE) comes with a series of methodological and technical challenges to be faced, modelled and suitably tackled. Particularly, we observed that modern software systems are more and more deployed onto pervasive Cloud-IoT networks and composed of heterogeneous distributed components that interact to achieve a common business logic. Such phenomena naturally lead to the continuous development of newly trending topics worth exploring and discussing within our community, spanning the whole lifecycle of software applications (i.e. from design to development, testing, deployment, and management).
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Jones, Gareth, Chris Lovell, Hywel Morgan, and Klaus-Peter Zauner. "Organising Chemical Reaction Networks in Space and Time with Microfluidics." International Journal of Nanotechnology and Molecular Computation 3, no. 1 (January 2011): 35–56. http://dx.doi.org/10.4018/jnmc.2011010104.
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Information processing is essential for any lifeform to maintain its organisation despite continuous entropic disturbance. Macromolecules provide the ubiquitous underlying substrate on which nature implements information processing and have also come into focus for technical applications. There are two distinct approaches to the use of molecules for computing. Molecules can be employed to mimic the logic switches of conventional computers or they can be used in a way that exploits the complex functionality offered by a molecular computing substrate. Prerequisite to the latter is a mapping of input-output transform provided by the substrate. This paper reviews microfluidic technology as a versatile means to achieve this, show how it can be used, and provide proven recipes for its application.
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Dembitsky, Nikolay. "Continuum Logic of Control Signals in Analog Cyber–Physical Nets." Inventions 8, no. 4 (August 11, 2023): 101. http://dx.doi.org/10.3390/inventions8040101.
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The use of embedded processors is the most promising direction in the development of automatic control systems. The article is devoted to analog models and technical solutions that allow continuous analysis of information in a technical system in order to synthesize control signals. Technical solutions are obtained on the basis of continuum logic methods, which aim to increase the speed of embedded computing networks, reduce power consumption, and unify the element base of analog processors. The effect of high speed is achieved due to the transition from sequential digital calculations to parallel synthesis of analog control signals. Examples of the implementation of schemes for the synthesis of control commands using the developed models of logical operations are given.
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Bazazzadeh, Mehrdad, and Ali Shahriari. "Enhancing the Performance of Jet Engine Fuel Controller Using Neural Networks." Applied Mechanics and Materials 390 (August 2013): 393–97. http://dx.doi.org/10.4028/www.scientific.net/amm.390.393.
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This paper proposes a fuzzy logic controller for a specific turbojet engine. The turbine engines require control systems to achieve the appropriate performance. The control systems typically featured loops to prevent engine flame out, over speeds, compressor surge, and check turbine inlet temperature limit, either by scheduling the fuel flow during accelerations and decelerations or by controlling the acceleration and deceleration rates of engine spool. This paper presents a successful approach in designing a Fuzzy Logic Controller for a specific Jet Engine. At first a suitable mathematical model for the jet engine is presented by the aid of SIMULINK simulation software. Then by applying different reasonable fuel flow functions via the engine model, some important engine continuous time operation parameters (such as: thrust, compressor surge margin, turbine inlet temperature and engine spool speed...) are obtained. These parameters provide a precious database which can be used by a neural network. At the second step, by designing and training a feedforward multilayer perceptron neural network according to this available database; a number of different reasonable fuel flow functions for various engine acceleration operations are determined. These functions are used to define the desired fuzzy fuel functions. Indeed, the neural networks are used as an effective method to define the optimum fuzzy fuel functions. At the next step we design a fuzzy logic controller by using the engine simulation model and the neural network results. The proposed control scheme is proved by computer simulation using the designed engine model. The simulation results of engine model with fuzzy controller in comparison with the engine testing operation illustrate that the proposed controller achieves the desired performance.
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Oudah, Manal Kadhim, Mohammed Qasim Sulttan, and Salam Waley Shneen. "Fuzzy type 1 PID controllers design for TCP/AQM wireless networks." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (January 1, 2021): 118. http://dx.doi.org/10.11591/ijeecs.v21.i1.pp118-127.
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<p><span>The search of FLC_PID controller for TCP/AQM Wireless Networks, to deal with congestion for Internet users and to get good performance and capabilities of TCP / IP networks. Neglect of controlling the network delay and the number of continuous users that leads to a problem in the transmission process. Recently, automatic control units are adapted to solve this problem with the difficulty of controlling congestion in the presence of wireless links. This modest research presents one of the traditional PID controller methods with fuzzy logic so that wireless networks and congestion can be controlled by various configurations. The proposed methods were simulated with the required comparisons in the adoption of nonlinear systems to determine the best performance and it was found that the use of the Fuzzy logic control can achieve the best performance (reducing the delay time of delivering packets and packets loss). The simulation of the current work shows through its results the possibility of controlling the behavior of the system and through testing the balance when changed with time delay through the impact of communication time and its relationship with the stability of the work of the system.</span></p>
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Divyanshu, Divyanshu, Rajat Kumar, Danial Khan, Selma Amara, and Yehia Massoud. "Design of VGSOT-MTJ-Based Logic Locking for High-Speed Digital Circuits." Electronics 11, no. 21 (October 30, 2022): 3537. http://dx.doi.org/10.3390/electronics11213537.
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Emerging spintronics devices in recent research have received much interest in various fields. Their unique physical aspects are being explored to keep Moore’s law alive. Therefore, the hardware security aspects of system-on-a-chip (SoC) designs using spintronics devices becomes important. Magnetic tunnel junctions (MTJ) are a potential candidate in spintronics-based devices for beyond-CMOS applications. This work uses voltage-gated spin-orbit torque-assisted magnetic tunnel junction (VGSOT-MTJ) based on the Verilog-A behavioral model to design a possible logic-locking system for hardware security. Compared with the SOT MTJ, which uses a heavy metal strip below the MTJ stack, VGSOT-MTJ has an antiferromagnetic (AFM) strip that utilizes the voltage-controlled magnetic anisotropy (VCMA) effect to significantly reduce the JSOT,critical. To design the logic-locking block, we performed a Monte Carlo analysis to account for the effect of process variation (PV) on critical MTJ parameters. Eye diagram tests and mask designing were performed, which included the effect of thermal noise and PV for high-speed digital circuit operations. Finally, transient performance was analyzed to demonstrate the VGSOT-MTJ’s ability to design logic-locking blocks from the circuit operation perspective.
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Cheng, Xianrui, Mengyang Sun, and Joshua E. S. Socolar. "Autonomous Boolean modelling of developmental gene regulatory networks." Journal of The Royal Society Interface 10, no. 78 (January 6, 2013): 20120574. http://dx.doi.org/10.1098/rsif.2012.0574.
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During early embryonic development, a network of regulatory interactions among genes dynamically determines a pattern of differentiated tissues. We show that important timing information associated with the interactions can be faithfully represented in autonomous Boolean models in which binary variables representing expression levels are updated in continuous time, and that such models can provide a direct insight into features that are difficult to extract from ordinary differential equation (ODE) models. As an application, we model the experimentally well-studied network controlling fly body segmentation. The Boolean model successfully generates the patterns formed in normal and genetically perturbed fly embryos, permits the derivation of constraints on the time delay parameters, clarifies the logic associated with different ODE parameter sets and provides a platform for studying connectivity and robustness in parameter space. By elucidating the role of regulatory time delays in pattern formation, the results suggest new types of experimental measurements in early embryonic development.
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Sanober, Sumaya, Deepak Kumar Goyal, Sudhir Keshari, Farrukh Arslan, and Betty Nokobi Dugbakie. "Secure Wireless Networks Based on Fuzzy Logic for Smart HVAC Systems in Small-Scale Industries." Security and Communication Networks 2022 (May 11, 2022): 1–10. http://dx.doi.org/10.1155/2022/3659961.
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The traditional heating, ventilation, and air conditioning (HVAC) system depends on wired mechanical thermostats and regulators deployed at various points in the industrial environment for temperature monitoring. Due to ineffective dynamic changes according to the environment, the traditional HVAC system consumes more electrical energy. Next-generation wireless network (NGWN) systems play a vital role in improving the overall efficiency of the system by continuous monitoring and analysis. A small-scale industry using HVAC systems needs an active smart energy saver technique for maintaining an economical budget. This work uses MATLAB software to simulate and analyze the utilization of fuzzy-based NGWN to reduce the energy consumption requirements of small-scale manufacturing. In the model, a fuzzy rule is designed and applied to a small-scale enterprise divided into five sectors, one of which is temperature sensitive, with the goal of lowering the energy bill. The model employs fuzzy rules to enhance the NGWN, which minimizes the energy usage cost by 30% as compared with the traditional existing HVAC systems.
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KRASILENKO, VLADIMIR, YURCHUK NATALIYA, and ALEXANDER LAZAREV. "THE NEW BASIC REALIZATIONS OF OPERATIONS “EQUIVALENCE” OF NEURO-FUZZY AND BIOINSPIRED NEURO-LOGICS TO CREATE HARDWARE ACCELERATORS OF ADVANCED EQUIVALENTAL MODELS OF NEURAL STRUCTURES AND MACHINE VISION SYSTEMS." Herald of Khmelnytskyi National University 303, no. 6 (December 2021): 153–66. http://dx.doi.org/10.31891/2307-5732-2021-303-6-153-166.
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The perspective of neural networks equivalental models (EM) base on vector-matrix procedure with basic operations of continuous and neuro-fuzzy logic (equivalence, absolute difference) are shown. Capacity on base EMs exceeded the amount of neurons in 4-10 times. This is larger than others neural networks paradigms. Amount neurons of this neural networks on base EMs may be 10 – 100 thousand. The base operations in EMs are normalized equivalence operations. The family of new operations “equivalence” and “non-equivalence” of neuro-fuzzy logic’s, which we have elaborated on the based of such generalized operations of fuzzy-logic’s as fuzzy negation, t-norm and s-norm are shown. Generalized rules of construction of new functions (operations) “equivalence” which uses operations of t-norm and s-norm to fuzzy negation are proposed. Despite the wide variety of types of operations on fuzzy sets and fuzzy relations and the related variety of new synthesized equivalence operations based on them, it is possible and necessary to select basic operations, taking into account their functional completeness in the corresponding algebras of continuous logic, as well as their most effective circuitry implementations. Among these elements the following should be underlined: 1) the element which fulfills the operation of limited difference; 2) the element which algebraic product (intensifier with controlled coefficient of transmission or multiplier of analog signals); 3) the element which fulfills a sample summarizing (uniting) of signals (including the one during normalizing). The basic element of pixel cells for the construction of hardware accelerators EM NM is a node on the current-reflecting mirrors (CM), which implements the operation of a limited difference (LD) of continuous logic (CL). Synthesized structures which realize on the basic of these elements the whole spectrum of required operations: t-norm, s-norm and new operations – “equivalence” are shown. These realizations on the basic of CMOS transistors current mirror represent the circuit with analog and time-pulse optical input signals. Possibilities of “equivalence” circuits synthesis by such functions limited difference cells are shown. Such circuits consist of several dozen CMOS transistors, have low power supply voltage (1.8…3.3V), the range of an input photocurrent is 0.1…24 μA, the transformation time is less than 1 μs, low power consumption (microwatts). The circuits and the simulation results of their design with OrCAD are shown.
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Coqueiro, Thiago, José Jailton, Tássio Carvalho, and Renato Francês. "A Fuzzy Logic System for Vertical Handover and Maximizing Battery Lifetime in Heterogeneous Wireless Multimedia Networks." Wireless Communications and Mobile Computing 2019 (January 10, 2019): 1–13. http://dx.doi.org/10.1155/2019/1213724.
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Bandwidth and power hungry applications are proliferating in mobile networks at a rapid pace. However, mobile devices have been suffering from a lack of sufficient battery capacity for the intensive/continuous use of these applications. In addition, the mobile ecosystem is currently heterogeneous and comprises a plethora of networks with different technologies such as LTE, Wi-Fi, and WiMaX. Hence, an issue must be addressed to ensure that quality of experience (QoE) is provided for the users in this scenario: an energy-efficient strategy that is designed to extend the battery lifetime of mobile devices. This paper proposes an architecture which provides an intelligent decision-making support system based on Fuzzy Logic for saving the energy of mobile devices within an integrated LTE and Wi-Fi network. The simulated experiments show the benefits of the solution this architecture can provide by using QoE metrics.
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Gomilanovic, Miljan, Milos Tanasijevic, Sasa Stepanovic, and Filip Miletic. "A Model for Determining Fuzzy Evaluations of Partial Indicators of Availability for High-Capacity Continuous Systems at Coal Open Pits Using a Neuro-Fuzzy Inference System." Energies 16, no. 7 (March 23, 2023): 2958. http://dx.doi.org/10.3390/en16072958.
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This paper presents a model for determining fuzzy evaluations of partial indicators of the availability of continuous systems at coal open pits using a neuro-fuzzy inference system. The system itself is a combination of fuzzy logic and artificial neural networks. The system availability is divided into partial indicators. By combining the fuzzy logic and artificial neural networks, a model is obtained that has the ability to learn and uses expert judgment for that learning. This paper deals with the ECC system (bucket wheel excavator-conveyor-crushing plant) of the open pit Drmno-Kostolac, which operates within the Electric Power Company of Serbia. The advantage of a model of this type is that it does not rely on the historical experiences of experts and usual predicted values for the fuzzy evaluation of partial indicators, which are based on the assumption that similar systems affect availability in a similar way. The fuzzy evaluation of partial indicators is based on historical data for the specific system for which the model was created. As such, it can more accurately predict continuous systems availability on the basis of expert evaluations in the appropriate time period. Another advantage of this model is that the availability is estimated on a quarterly basis, which gives a more accurate view because it uses a smaller time period with more similar characteristics and, thus, includes certain external influences which are related to the quarterly meteorological conditions.
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Al-Azzam, Saad, and Ahmad Sharieh. "A data estimation for failing nodes using fuzzy logic with integrated microcontroller in wireless sensor networks." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (August 1, 2020): 3623. http://dx.doi.org/10.11591/ijece.v10i4.pp3623-3634.
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Continuous data transmission in wireless sensor networks (WSNs) is one of the most important characteristics which makes sensors prone to failure. a backup strategy needs to co-exist with the infrastructure of the network to assure that no data is missing. The proposed system relies on a backup strategy of building a history file that stores all collected data from these nodes. This file is used later on by fuzzy logic to estimate missing data in case of failure. An easily programmable microcontroller unit is equipped with a data storage mechanism used as cost worthy storage media for these data. An error in estimation is calculated constantly and used for updating a reference “optimal table” that is used in the estimation of missing data. The error values also assure that the system doesn’t go into an incremental error state. This paper presents a system integrated of optimal data table, microcontroller, and fuzzy logic to estimate missing data of failing sensors. The adapted approach is guided by the minimum error calculated from previously collected data. Experimental findings show that the system has great potentials of continuing to function with a failing node, with very low processing capabilities and storage requirements.
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KATSIRI, ELEFTHERIA, JEAN BACON, and ALAN MYCROFT. "LINKING TEMPORAL FIRST ORDER LOGIC AND HIDDEN MARKOV MODELS WITH ABSTRACT EVENTS." International Journal on Artificial Intelligence Tools 19, no. 06 (December 2010): 857–93. http://dx.doi.org/10.1142/s0218213010000443.
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In previous work, we introduced a novel concept of a generalised event, an abstract event, which we define as a change of state of abstract predicates that represent knowledge about the surrounding world. Abstract predicates are defined by formulae in temporal first-order logic (Abstract Event Specification Language (AESL)) whose leaf predicates represent low-level sensor-derived knowledge. Abstract events are detected by Rete Networks structured as a deductive knowledge-base. Current Abstract Event detectors cannot express sufficiently well certain high-level situations, such activity derived from user trajectories. In this work we introduce a novel type of abstract event detector, a hidden Markov Model detector (hMM-detector). hMM-detectors are implemented as pattern recognition engines that use several stochastic models, hidden Markov Models (hMMs), in order to classify observed activities to the most likely activity class. We link hMM-detectors with AESL by specifying a new AESL operator for defining hMM-based Abstract Events, thus increasing AESL's expressive power. We describe the experimental evaluation of the above work that was carried out at the University of Cambridge. hMM-Detectors were trained and tested with real data from the Active BAT location system. We evaluate the expressiveness of the enhanced AESL by discussing three case studies in healthcare that relate to continuous monitoring of elderly or injured patients. We demonstrate that AESL can be used in order to improve the dependability of continuous monitoring of patients and the provision of high-quality healthcare.
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Hu, Haidai, Kenichi Ataka, Anaïs Menny, Zaineb Fourati, Ludovic Sauguet, Pierre-Jean Corringer, Patrice Koehl, Joachim Heberle, and Marc Delarue. "Electrostatics, proton sensor, and networks governing the gating transition in GLIC, a proton-gated pentameric ion channel." Proceedings of the National Academy of Sciences 115, no. 52 (December 12, 2018): E12172—E12181. http://dx.doi.org/10.1073/pnas.1813378116.
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The pentameric ligand-gated ion channel (pLGIC) from Gloeobacter violaceus (GLIC) has provided insightful structure–function views on the permeation process and the allosteric regulation of the pLGICs family. However, GLIC is activated by pH instead of a neurotransmitter and a clear picture for the gating transition driven by protons is still lacking. We used an electrostatics-based (finite difference Poisson–Boltzmann/Debye–Hückel) method to predict the acidities of all aspartic and glutamic residues in GLIC, both in its active and closed-channel states. Those residues with a predicted pKa close to the experimental pH50 were individually replaced by alanine and the resulting variant receptors were titrated by ATR/FTIR spectroscopy. E35, located in front of loop F far away from the orthosteric site, appears as the key proton sensor with a measured individual pKa at 5.8. In the GLIC open conformation, E35 is connected through a water-mediated hydrogen-bond network first to the highly conserved electrostatic triad R192-D122-D32 and then to Y197-Y119-K248, both located at the extracellular domain–transmembrane domain interface. The second triad controls a cluster of hydrophobic side chains from the M2-M3 loop that is remodeled during the gating transition. We solved 12 crystal structures of GLIC mutants, 6 of them being trapped in an agonist-bound but nonconductive conformation. Combined with previous data, this reveals two branches of a continuous network originating from E35 that reach, independently, the middle transmembrane region of two adjacent subunits. We conclude that GLIC’s gating proceeds by making use of loop F, already known as an allosteric site in other pLGICs, instead of the classic orthosteric site.
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Lombardo, Elia, Moritz Rabe, Yuqing Xiong, Lukas Nierer, Davide Cusumano, Lorenzo Placidi, Luca Boldrini, et al. "Offline and online LSTM networks for respiratory motion prediction in MR-guided radiotherapy." Physics in Medicine & Biology 67, no. 9 (April 19, 2022): 095006. http://dx.doi.org/10.1088/1361-6560/ac60b7.
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Abstract Objective. Gated beam delivery is the current clinical practice for respiratory motion compensation in MR-guided radiotherapy, and further research is ongoing to implement tracking. To manage intra-fractional motion using multileaf collimator tracking the total system latency needs to be accounted for in real-time. In this study, long short-term memory (LSTM) networks were optimized for the prediction of superior–inferior tumor centroid positions extracted from clinically acquired 2D cine MRIs. Approach. We used 88 patients treated at the University Hospital of the LMU Munich for training and validation (70 patients, 13.1 h), and for testing (18 patients, 3.0 h). Three patients treated at Fondazione Policlinico Universitario Agostino Gemelli were used as a second testing set (1.5 h). The performance of the LSTMs in terms of root mean square error (RMSE) was compared to baseline linear regression (LR) models for forecasted time spans of 250 ms, 500 ms and 750 ms. Both the LSTM and the LR were trained with offline (offline LSTM and offline LR) and online schemes (offline+online LSTM and online LR), the latter to allow for continuous adaptation to recent respiratory patterns. Main results. We found the offline+online LSTM to perform best for all investigated forecasts. Specifically, when predicting 500 ms ahead it achieved a mean RMSE of 1.20 mm and 1.00 mm, while the best performing LR model achieved a mean RMSE of 1.42 mm and 1.22 mm for the LMU and Gemelli testing set, respectively. Significance. This indicates that LSTM networks have potential as respiratory motion predictors and that continuous online re-optimization can enhance their performance.
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Trottet, Cécile, Thijs Vogels, Kristina Keitel, Alexandra V. Kulinkina, Rainer Tan, Ludovico Cobuccio, Martin Jaggi, and Mary-Anne Hartley. "Modular Clinical Decision Support Networks (MoDN)—Updatable, interpretable, and portable predictions for evolving clinical environments." PLOS Digital Health 2, no. 7 (July 17, 2023): e0000108. http://dx.doi.org/10.1371/journal.pdig.0000108.
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Clinical Decision Support Systems (CDSS) have the potential to improve and standardise care with probabilistic guidance. However, many CDSS deploy static, generic rule-based logic, resulting in inequitably distributed accuracy and inconsistent performance in evolving clinical environments. Data-driven models could resolve this issue by updating predictions according to the data collected. However, the size of data required necessitates collaborative learning from analogous CDSS’s, which are often imperfectly interoperable (IIO) or unshareable. We propose Modular Clinical Decision Support Networks (MoDN) which allow flexible, privacy-preserving learning across IIO datasets, as well as being robust to the systematic missingness common to CDSS-derived data, while providing interpretable, continuous predictive feedback to the clinician. MoDN is a novel decision tree composed of feature-specific neural network modules that can be combined in any number or combination to make any number or combination of diagnostic predictions, updatable at each step of a consultation. The model is validated on a real-world CDSS-derived dataset, comprising 3,192 paediatric outpatients in Tanzania. MoDN significantly outperforms ‘monolithic’ baseline models (which take all features at once at the end of a consultation) with a mean macro F1 score across all diagnoses of 0.749 vs 0.651 for logistic regression and 0.620 for multilayer perceptron (p < 0.001). To test collaborative learning between IIO datasets, we create subsets with various percentages of feature overlap and port a MoDN model trained on one subset to another. Even with only 60% common features, fine-tuning a MoDN model on the new dataset or just making a composite model with MoDN modules matched the ideal scenario of sharing data in a perfectly interoperable setting. MoDN integrates into consultation logic by providing interpretable continuous feedback on the predictive potential of each question in a CDSS questionnaire. The modular design allows it to compartmentalise training updates to specific features and collaboratively learn between IIO datasets without sharing any data.
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Wang, Chengqiang, Xiangqing Zhao, Can Wang, and Zhiwei Lv. "Synchronization of Takagi–Sugeno Fuzzy Time-Delayed Stochastic Bidirectional Associative Memory Neural Networks Driven by Brownian Motion in Pre-Assigned Settling Time." Mathematics 11, no. 17 (August 28, 2023): 3697. http://dx.doi.org/10.3390/math11173697.
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We are devoted, in this paper, to the study of the pre-assigned-time drive-response synchronization problem for a class of Takagi–Sugeno fuzzy logic-based stochastic bidirectional associative memory neural networks, driven by Brownian motion, with continuous-time delay and (finitely and infinitely) distributed time delay. To achieve the drive-response synchronization between the neural network systems, concerned in this paper, and the corresponding response neural network systems (identical to our concerned neural network systems), we bring forward, based on the structural properties, a class of control strategies. By meticulously coining an elaborate Lyapunov–Krasovskii functional, we prove a criterion guaranteeing the desired pre-assigned-time drive-response synchronizability: For any given positive time instant, some of our designed controls make sure that our concerned neural network systems and the corresponding response neural network systems achieve synchronization, with the settling times not exceeding the pre-assigned positive time instant. In addition, we equip our theoretical studies with a numerical example, to illustrate that the synchronization controls designed in this paper are indeed effective. Our concerned neural network systems incorporate several types of time delays simultaneously, in particular, they have a continuous-time delay in their leakage terms, are based on Takagi–Sugeno fuzzy logic, and can be synchronized before any pre-given finite-time instant by the suggested control; therefore, our theoretical results in this paper have wide potential applications in the real world. The conservatism is reduced by introducing parameters in our designed Lyapunov–Krasovskii functional and synchronization control.
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Liu, Chunyang, Jiping Liu, Jian Wang, Shenghua Xu, Houzeng Han, and Yang Chen. "An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation." ISPRS International Journal of Geo-Information 8, no. 8 (August 13, 2019): 355. http://dx.doi.org/10.3390/ijgi8080355.
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Point-of-interest (POI) recommendation is one of the fundamental tasks for location-based social networks (LBSNs). Some existing methods are mostly based on collaborative filtering (CF), Markov chain (MC) and recurrent neural network (RNN). However, it is difficult to capture dynamic user’s preferences using CF based methods. MC based methods suffer from strong independence assumptions. RNN based methods are still in the early stage of incorporating spatiotemporal context information, and the user’s main behavioral intention in the current sequence is not emphasized. To solve these problems, we proposed an attention-based spatiotemporal gated recurrent unit (ATST-GRU) network model for POI recommendation in this paper. We first designed a novel variant of GRU, which acquired the user’s sequential preference and spatiotemporal preference by feeding the continuous geographical distance and time interval information into the GRU network in each time step. Then, we integrated an attention model into our network, which is a personalized process and can capture the user’s main behavioral intention in the user’s check-in history. Moreover, we conducted an extensive performance evaluation on two real-world datasets: Foursquare and Gowalla. The experimental results demonstrated that the proposed ATST-GRU network outperforms the existing state-of-the-art POI recommendation methods significantly regarding two commonly-used evaluation metrics.
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Yotov, Kostadin, Emil Hadzhikolev, Stanka Hadzhikoleva, and Stoyan Cheresharov. "A Method for Extrapolating Continuous Functions by Generating New Training Samples for Feedforward Artificial Neural Networks." Axioms 12, no. 8 (August 1, 2023): 759. http://dx.doi.org/10.3390/axioms12080759.
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The goal of the present study is to find a method for improving the predictive capabilities of feedforward neural networks in cases where values distant from the input–output sample interval are predicted. This paper proposes an iterative prediction algorithm based on two assumptions. One is that predictions near the statistical sample have much lower error than those distant from the sample. The second is that a neural network can generate additional training samples and use them to train itself in order to get closer to a distant prediction point. This paper presents the results of multiple experiments with different univariate and multivariate functions and compares the predictions made by neural networks before and after their training with the proposed iterative algorithm. The results show that, having passed through the stages of the algorithm, artificial neural networks significantly improve their interpolation performance in long-term forecasting. The present study demonstrates that neural networks are capable of creating additional samples for their own training, thus increasing their approximating efficiency.
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Demeester, Thomas. "System Identification with Time-Aware Neural Sequence Models." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3757–64. http://dx.doi.org/10.1609/aaai.v34i04.5786.
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Established recurrent neural networks are well-suited to solve a wide variety of prediction tasks involving discrete sequences. However, they do not perform as well in the task of dynamical system identification, when dealing with observations from continuous variables that are unevenly sampled in time, for example due to missing observations. We show how such neural sequence models can be adapted to deal with variable step sizes in a natural way. In particular, we introduce a ‘time-aware’ and stationary extension of existing models (including the Gated Recurrent Unit) that allows them to deal with unevenly sampled system observations by adapting to the observation times, while facilitating higher-order temporal behavior. We discuss the properties and demonstrate the validity of the proposed approach, based on samples from two industrial input/output processes.
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Kang, Cheng, Xujing Yao, and Daniel Novak. "Fuzzy Windows with Gaussian Processed Labels for Ordinal Image Scoring Tasks." Applied Sciences 13, no. 6 (March 22, 2023): 4019. http://dx.doi.org/10.3390/app13064019.
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In this paper, we propose a Fuzzy Window with the Gaussian Processed Label (FW-GPL) method to mitigate the overlap problem in the neighboring ordinal category when scoring images. Many published conventional methods treat this challenge as a traditional regression problem and make a strong assumption that each ordinal category owns an adequate intrinsic rank to outline its distribution. Our FW-GPL method aims to refine the ordinal label pattern by using two novel techniques: (1) assembling fuzzy logic to the fully connected layer of convolution neural networks and (2) transforming the ordinal labels with a Gaussian process. Specifically, it incorporates a heuristic fuzzy logic from the ordinal characteristic and simultaneously plugs in ordinal distribution shapes that penalize the difference between the targeted label and its neighbors to ensure a concentrated regional distribution. Accordingly, the function of these proposed windows is leveraged to minimize the influence of majority classes that mislead the prediction of minority samples. Our model is specifically designed to carefully avoid partially missing continuous facial-age segments. It can perform competitively when using the whole continuous facial-age dataset. Extensive experimental results on three facial-aging datasets and one ambiguous medical dataset demonstrate that our FW-GPL can achieve compelling performance results compared to the State-Of-The-Art (SOTA).
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CAO, YINGJUN, PAUL P. WANG, and GERMANO RESCONI. "REVERSE ENGINEERING OF THE NK BOOLEAN NETWORK AND ITS EXTENSION — FUZZY LOGIC NETWORK." New Mathematics and Natural Computation 03, no. 01 (March 2007): 69–87. http://dx.doi.org/10.1142/s179300570700063x.
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In this paper, we first propose a reverse engineering solution to decode the NK Boolean dynamic network given the input and output of the system. This theory utilizes basis functions and dynamic matrices, through which the network's evolutionary behavior can be simulated in a rigorous mathematical manner. Then, a general network model, fuzzy logic network (FLN), is presented as the extension of the Boolean network. This novel network model assigns variable values in a continuous domain, and it can accommodate internal conflicts among variables. In addition, the FLN uses fuzzy logical functions instead of Boolean logical functions, which enables it to model highly non-linear relationships and periodicity. Using the approach of annealed approximations, we proved important theorems concerning the dynamic property of the FLN. Combined with the Zipf's law, the criteria of applying FLN to the modeling of gene regulatory networks were achieved.