A Multi-Mode Reasoning For Predicting Tobacco Baking Quality

In order to reduce the number of defective products caused by the unreasonable baking time during the tobacco production process, this paper proposes a method for establishing a multi-model reasoning tobacco baking quality prediction model. Conduct data mining and analysis on the data of various indicators of the original tobacco, and screen out the data that have an impact on the quality of tobacco baking. In order to reduce the complexity of the model and eliminate the influence between different dimensions, the data are carried out and standardized processing. Next, the normalized data is explored for the multi-input and multi-output mapping relationship. Finally, a mapping matrix is given for the multi-input and multi-output mapping relationship so as to establish a tobacco baking quality prediction model. The test results show that the predicted value of this model is basically the actual value, and the prediction accuracy rate is more than 90%. It has a high prediction accuracy rate. The cured tobacco leaves are basically the same as the actual cured yellow expected value. This model provides a practical guide method for tobacco baking, which has certain practical value in actual tobacco baking.

Explicit and Implicit Non-convex Sweeping Processes in the Space of Absolutely Continuous Functions

We show that sweeping processes with possibly non-convex prox-regular constraints generate a strongly continuous input-output mapping in the space of absolutely continuous functions. Under additional smoothness assumptions on the constraint we prove the local Lipschitz continuity of the input-output mapping. Using the Banach contraction principle, we subsequently prove that also the solution mapping associated with the state-dependent problem is locally Lipschitz continuous.

Evolution-Strategies-Driven Optimization on Secure and Reconfigurable Interconnection PUF Networks

Physical Unclonable Functions (PUFs) are known for their unclonability and light-weight design. However, several known issues with state-of-the-art PUF designs exist including vulnerability against machine learning attacks, low output randomness, and low reliability. To address these problems, we present a reconfigurable interconnected PUF network (IPN) design that significantly strengthens the security and unclonability of strong PUFs. While the IPN structure itself significantly increases the system complexity and nonlinearity, the reconfiguration mechanism remaps the input–output mapping before an attacker could collect sufficient challenge-response pairs (CRPs). We also propose using an evolution strategies (ES) algorithm to efficiently search for a network configuration that is capable of producing random and stable responses. The experimental results show that applying state-of-the-art machine learning attacks result in less than 53.19% accuracy for single-bit output prediction on a reconfigurable IPN with random configurations. We also show that, when applying configurations explored by our proposed ES method instead of random configurations, the output randomness is significantly improved by 220.8% and output stability by at least 22.62% in different variations of IPN.

Learning poly-synaptic paths with traveling waves

Traveling waves are commonly observed across the brain. While previous studies have suggested the role of traveling waves in learning, the mechanism remains unclear. We adopted a computational approach to investigate the effect of traveling waves on synaptic plasticity. Our results indicate that traveling waves facilitate the learning of poly-synaptic network paths when combined with a reward-dependent local synaptic plasticity rule. We also demonstrate that traveling waves expedite finding the shortest paths and learning nonlinear input/output mapping, such as exclusive or (XOR) function.

R. KALMAN `S PROBLEM ABOUT FIBONACCI `S NUMBERS

In this paper authors are considered the R. Kalman`s problem about of Fibonacci numbers. An overview of research methods for control theory systems in two concepts “state space” and the “input-output” mapping is presented. In this paper, we consider the problem of R. Kalman on Fibonacci numbers, which consists in the following. R. Kalman's problem on Fibonacci numbers is considered, which is as follows. Fibonacci numbers form a minimal Realization. The authors of the article formulated a theorem, which was given the name of the outstanding American Scientist R. Kalman. The proof of the theorem is very cumbersome, therefore, authors proved it using an example when the Fibonacci numbers are obtained on the basis of the application of the B. Ho`s algorithm. B. Ho is a purple of R. Kalman. In this paper, the algorithm of B. Ho is given, which allows one to find the parameters of the initial linear deterministic system. Based on these parameters, we find the initial Fibonacci numbers. Thus, Fibonacci numbers are closely related to the problem of linear deterministic implementation and to B. Ho's algorithm.

Precise determination of input-output mapping for multimodal gene circuits using data from transient transfection

The mapping of molecular inputs to their molecular outputs (input/output, I/O mapping) is an important characteristic of gene circuits, both natural and synthetic. Experimental determination of such mappings for synthetic circuits is best performed using stably integrated genetic constructs. In mammalian cells, stable integration of complex circuits is a time-consuming process that hampers rapid characterization of multiple circuit variants. On the other hand, transient transfection is quick. However, it is an extremely noisy process and it is unclear whether the obtained data have any relevance to the input/output mapping of a circuit obtained in the case of a stable integration. Here we describe a data processing workflow, Peakfinder algorithm for flow cytometry data (PFAFF), that allows extracting precise input/output mapping from single-cell protein expression data gathered by flow cytometry after a transient transfection. The workflow builds on the numerically-proven observation that the multivariate modes of input and output expression of multi-channel flow cytometry datasets, pre-binned by the expression level of an independent transfection reporter gene, harbor cells with circuit gene copy numbers distributions that depend deterministically on the properties of a bin. We validate our method by simulating flow cytometry data for seven multi-node circuit architectures, including a complex bi-modal circuit, under stable integration and transient transfection scenarios. The workflow applied to the simulated transient transfection data results in similar conclusions to those reached with simulated stable integration data. This indicates that the input/output mapping derived from transient transfection data using our method is an excellent approximation of the ground truth. Thus, the method allows to determine input/output mapping of complex gene network using noisy transient transfection data.

Analysis and Forecast of Urban Economic Vitality in Northeast China

Taking into account the passage of time, the original economic vitality index will vary with changes in social development, we use the BP neural network nearly a decade as the original GDP data for the next 30 years the GDP forecast. BP neural network in 1985, proposed by Rumelhart, the algorithm solves the system of learning problems multilayer neural network connection weights hidden layer [1].It consists of an input layer, a hidden layer, and an output layer. The principle is to continuously adjust the network weights and thresholds by transmitting errors backward and then correcting the errors to achieve the desired input-output mapping.

Designing of a reversible fault tolerant booth multiplier

Conventional logic dissipates more power by losing bits of information whereas reversibility recovers bit loss from the unique input-output mapping. This paper presents the design of a reversible fault tolerant booth multiplier which can multiply both signed and unsigned numbers. The proposed circuit tolerant designed using only fault tolerant reversible gates. Thus the entire scheme inherently becomes fault tolerant. Several theorems on the numbers of gates, garbage outputs, quantum cost of the proposed design have been presented to show the efficiency of the design. The result analysis shows that the proposed design is optimized in terms of all cost parameters. The simulation of the proposed circuit verifies the correctness of the circuit.Bangladesh J. Sci. Ind. Res.53(3), 199-204, 2018

On the adaptive control of spherical actuators

This paper addresses the control problem of multi-degree-of-freedom actuators with uncertain torque models and unmodelled system parameters, such as the rotor’s moments of inertia. The control scheme relies on (i) the parameterization of the system nonlinearity in terms of two regression functions and the unknown parameters, and (ii) a control structure with filtered input torque and projection-based adaptation laws. The input–output mapping analysis rigorously shows that the proposed controller maintains the bounded deviation of the control system from a stable, non-adaptive reference system. The size of the deviation is inversely proportional to the filter bandwidth. Simulations using a standard model of spherical motors illustrate the tracking performance of the controlled rotor’s orientations.