Score Prediction of Sports Events Based on Parallel Self-Organizing Nonlinear Neural Network

This paper introduces the basic concepts and main characteristics of parallel self-organizing networks and analyzes and predicts parallel self-organizing networks through neural networks and their hybrid models. First, we train and describe the law and development trend of the parallel self-organizing network through historical data of the parallel self-organizing network and then use the discovered law to predict the performance of the new data and compare it with its true value. Second, this paper takes the prediction and application of chaotic parallel self-organizing networks as the main research line and neural networks as the main research method. Based on the summary and analysis of traditional neural networks, it jumps out of inertial thinking and first proposes phase space. Reconstruction parameters and neural network structure parameters are unified and optimized, and then, the idea of dividing the phase space into multiple subspaces is proposed. The multi-neural network method is adopted to track and predict the local trajectory of the chaotic attractor in the subspace with high precision to improve overall forecasting performance. During the experiment, short-term and longer-term prediction experiments were performed on the chaotic parallel self-organizing network. The results show that not only the accuracy of the simulation results is greatly improved but also the prediction performance of the real data observed in reality is also greatly improved. When predicting the parallel self-organizing network, the minimum error of the self-organizing difference model is 0.3691, and the minimum error of the self-organizing autoregressive neural network is 0.008, and neural network minimum error is 0.0081. In the parallel self-organizing network prediction of sports event scores, the errors of the above models are 0.0174, 0.0081, 0.0135, and 0.0381, respectively.

Residual Life Estimation of Humidity Sensor DHT11 Using Artificial Neural Networks

Electronic systems have become an integral part of our daily lives. From toy to radar, system is dependent on electronics. The health conditions of humidity sensor need to be monitored regularly. Temperature can be taken as a quality parameter for electronics systems, which work under variable conditions. Using various environmental testing techniques, the performance of DHT11 has been analysed. The failure of humidity sensor has been detected using accelerated life testing, and an expert system is modelled using various artificial intelligence techniques (i.e., Artificial Neural Network, Fuzzy Inference System, and Adaptive Neuro-Fuzzy Inference System). A comparison has been made between the response of actual and prediction techniques, which enable us to choose the best technique on the basis of minimum error and maximum accuracy. ANFIS is proven to be the best technique with minimum error for developing intelligent models.

Modeling and Experimental Research of One Kind of New Planar Vortex Actuator Based on Shape Memory Alloy

In order to alleviate the problems of complex structure and low reliability of traditional Shape Memory Alloy (SMA) rotary actuator, a planar vortex actuator (PVA) based on SMA material was proposed to directly output torque and angular displacement. Based on the calculation method of PVA and the constitutive model of the phase transition equation of SMA, the mechanical model is established, and the pre-tightening torque, temperature, output torque, and rotation angle are obtained. The relationship expression between the tests has verified the mechanical model. The results show that the relationship between the excitation temperature and the output torque, the coefficient of determination between the calculated value and the tested value, is 0.938, the minimum error is 0.46%, and the maximum error is 49.8%. In the relationship between angular displacement and torque, the coefficient of determination between the calculated value and the test value is 0.939, the maximum error is 58.5%, and the minimum error is 28.0%. The test results show that the calculated values of mechanical model and experimental data have similar representation form.

Optimal Unambiguous Discrimination And Quantum Nonlocality Without Entanglement: Locking And Unlocking By Post-Measurement Information

The phenomenon of nonlocality without entanglement(NLWE) arises in discriminating multi-party quantum separable states. Recently, it has been found that the post-measurement information about the prepared subensemble can lock or unlock NLWE in minimum-error discrimination of non-orthogonal separable states. Thus it is natrual to ask whether the availability of the post-measurement information can influence on the occurrence of NLWE even in other state-discrimination stratigies. Here, we show that the post-measurement information can be used to lock as well as unlock the occurence of NLWE in terms of optimal nambiguous discrimination. Our results can provide a useful application for hiding or sharing information based on non-orthogonal separable states.

Performance of microheaters for tunable on-chip interferometer

Mach-Zehnder interferometer (MZI) is a valuable practical tool in many optical science areas. In particular, high-contrast MZI are required for experimental realization of displacement-based quantum receivers that can discriminate weak coherent states of light with the minimum error rate. In this work we study phase modulators of tunable on-chip interferometer on silicon nitride (Si3N4) platform for telecom wavelength (1550 nm) consisting of several MZI. Phase modulators on one of the arms of MZI consists of microheaters and waveguide. Microheaters heat waveguides changing its refractive index due to thermo-optical effect providing a phase shift. We measure the bandwidth of phase modulators and study their operation in pulse mode.

Implementing Self Organising Map to Organise the Unstructured Data

Surface reconstruction is significant in reverse engineering because it should present the correct surface with minimum error using the data available. It has become a challenging process when the data are in the unstructured type and the existing methods are still suffering from accuracy issues. The unstructured data will produce an incorrect surface because there is no connectivity information among the data. So, the unstructured data should undergo the organising process to obtain the correct shape. The Self Organising Map (SOM) has been extensively applied in previous works to solve surface reconstruction problems. However, the performance of the SOM models has remained uncertain. It can be evaluated and tested using different types of data sets. The objectives of this research are to examine the performance and to determine the weaknesses of SOM models. 2D SOM, 3D SOM, and Cube Kohonen (CK) SOM models are investigated and tested using three data sets in this research. As shown in the experimental results, the CKSOM model has proved to perform better because it can represent the correct closed surface with the lowest minimum error.

Quantum Contextual Advantage Depending on Nonzero Prior Probabilities in State Discrimination of Mixed Qubit States

Recently, Schmid and Spekkens studied the quantum contextuality in terms of state discrimination. By dealing with the minimum error discrimination of two quantum states with identical prior probabilities, they reported that quantum contextual advantage exists. Meanwhile, if one notes a striking observation that the selection of prior probability can affect the quantum properties of the system, it is necessary to verify whether the quantum contextual advantage depends on the prior probabilities of the given states. In this paper, we consider the minimum error discrimination of two states with arbitrary prior probabilities, in which both states are pure or mixed. We show that the quantum contextual advantage in state discrimination may depend on the prior probabilities of the given states. In particular, even though the quantum contextual advantage always exists in the state discrimination of two nonorthogonal pure states with nonzero prior probabilities, the quantum contextual advantage depends on prior probabilities in the state discrimination of two mixed states.

Leaf Area Calculation Models for Vines Based on Foliar Descriptors

In the case of foliar area studies on vines, with a large number of determinations, a simple, fast, sufficiently accurate and low-cost method is very useful. The typology of leaves on the vine is complex, characterized by several descriptive parameters: median rib; secondary venations of the first and second order; angles between the median rib and the secondary venations; sinuses; length and width of the leaf. The present study aimed to evaluate models for calculating the leaf area based on descriptive parameters and KA (KA as the surface constant used to calculate the leaf area) for six vine cultivars, ‘Cabernet Sauvignon’ (CS), ‘Muscat Iantarnîi’ (MI), ‘Muscat Ottonel’ (MO), ‘Chasselas’ (Ch), ‘Victoria’ (Vi) and ‘Muscat Hamburg’ (MH). The determined KA surface constants had subunit values (0.91 to 0.97), except for the cultivars ‘Muscat Iantarnîi’ and ‘Muscat Ottonel’ where the surface constant KA2 (in relation to the second-order secondary venations) had supraunitary values (1.07 and 1.08, respectively). The determination of the leaf area was possible under different conditions of statistical accuracy (R2 = 0.477, p = 0.0119, up to R2 = 0.988, p < 0.001) in relation to the variety and parametric descriptors considered. The models obtained from the regression analysis facilitated a more reliable prediction of the leaf area based on the elements on the left side of the leaf, in relation to the median rib, compared to those on the right. The accuracy of the results was checked on the basis of minimum error (ME) and confirmed by parameters R2, p and RMSE.