Search

This article uses the nonlinear digital chaos theory algorithm to generate the corresponding encryption system initial parameters, by analysing the correlation degree of image elements from the angles of horizontal, vertical, and diagonal direction, in order to study computer three-dimensional (3D) image encryption processing. The correlation degree of the cypher text obtained by the nonlinear algorithm is weak in the image's adjacent pixels, and the adjacent pixels are not related at all, horizontal angle: 0.915989, vertical angle: 0.968184, diagonal angle: 0.913533. The nonlinear algorithm distributes the image's statistical features into the random cypher text. By applying permutations and replacements in 3D space, the proposed approach improves performance parameters and widens key space in comparison to previous image cryptography investigations. The important qualities of such a secure system are its simplicity and efficacy. Simulations and analysis show that the proposed method can produce a large key space while also surviving standard cipher attacks. Because of its powerful cryptographic properties, it is suited for image applications. The nonlinear algorithm has very high sensitivity to the secret key and plaintext, as well as better statistical performance, higher security, and higher efficiency in the operation of the algorithm.

Wearable and movable lodged health monitoring gadgets, micro-sensors, human system locating gadgets, and other gadgets started to appear as low-power communication mechanisms and microelectronics mechanisms grew in popularity. More people are interested in energy capture technology, which turns the energy created by motion technology into electric energy. To understand the difference in motor skill levels, a nonlinear feature-oriented method was proposed. A bi-stable magnetic-coupled piezoelectric cantilever was designed to detect the horizontal difference of motion technology. The horizontal difference was increased by the acceleration generated by the oscillation of the leg and the impression betwixt the leg and the ground during the movement. Based on the Hamiltonian principle and motion technique signal, a nonlinear dynamic model for energy capture in motion technique is established. According to the shaking features of human leg motion, a moveable nonlinear shaking energy-gaining system was the layout, which realized the dynamic characteristics of straight, nonlinear, mono-stable, and bi-stable. The experimental outcome shows that nonlinearity can effectively detect the difference of motion techniques. The experimental results of different human movement states confirm the benefits of the uncertain bi-stable human power capture mechanism and the effectiveness of the electromechanical combining design established. The nonlinear mono-stable beam moves in the same way as the straight mono-stable beam in the assessment, but owing to its higher stiffness, its frequency concentration range (13.85 Hz) is moved to the right compared to the linear mono-stable beam, and the displacement of the cantilever beam is reduced. If the velocity is 8 km/h, the mean energy of the bi-stable method extends to the utmost value of 23.2 μW. It is proved that the nonlinear method can understand the difference in the level of motion technique effectively.

To study the static software defect detection system, based on the traditional static software defect detection system design, a new static software defect detection system design based on big data technology is proposed. The proposed method can optimize the distribution of test resources and improve the quality of software products by predicting the potential defect program modules and design the software and hardware of the static software defect detection system of big data technology. It is found that the traditional static software defect detection system design based on code source data takes a long time, averaging 65 h /day. However, the traditional static software defect detection system based on deep learning has a short detection time, averaging 35 h/day. In this article, the detection time of the static software defect detection system based on big data is shorter than that of the other two traditional system designs, with an average of 15 h/day. Because the system design adjusts the operating state of the system, it improves the accuracy of data operation. On the premise of data collection, the system inspection research is completed, which ensures the operational safety of software data, alleviates the contradiction between system and data to a high degree, improves the efficiency of system operation, reduces unnecessary operations, further shortens the time required for inspection, improves the system performance, and has higher research and operation value.

In the present study, nonlinear dynamic process data are mapped into the kernel state space by kernel gauge variable analysis method to obtain decorrelated state data. The time-lapse covariance matrix of the state data is weighted and summed to obtain the time-lapse structure matrix of the state data, and then supervised kernel independent component analysis (SKICA) is established, the independent component feature data is extracted from the status data and the monitoring statistics are constructed to detect the process faults. The data show that kernel independent component analysis (ICA) method (KICA) method can detect slow fault faster than the ICA method, except that the statistical detection ability of F3 and FS is reduced, and the KICA method can significantly improve the detection performance of other faults and statistics. By analyzing the detection results of SKICA method, it is obvious that in the detection process of all five kinds of slow faults, the fault detection capability of SKICA is better than that of ICA and KICA. The results of continuous stirred reactor simulation system show that, compared with the basic linear process, the slow fault detection has a good monitoring performance, it can detect the small deviation in the process sensitively and give alarm information to the slow fault in time, to improve the fault detection rate.

Prediction of the stock market can play a vital role in everyone's life to attain sustainable growth. This can also lead to an attractive profit by making the proper choices in the financial stock market. The stock market prediction is a big challenge which requires extensive advanced tools and techniques to analyze the future and present data. The modern stock market institutions provide better self-trading and investment options, enabling regular people and traders to enter the market through numerous online applications and websites that are available via smart devices. Financial institutions are investing more and more in talent development so that investors may make the maximum money. There are a multitude of reasons for this, including volatility of market and a collection of other interrelated and independent parameters that control the market value of a different stock. Because of these factors, it is excessively difficult for any specialist in financial markets to accurately predict the market's rise and collapse. This research paper proposed an artificial neural network (ANN) technique which combines with fuzzy logic to predict the stock market for short duration with high accuracy. First, historical and real-time data are used to classify and gather information for prediction. Then the short-duration prediction is done with a high accuracy of 96% and the error margin for a small instance is 2.3%. Through training, testing and validation it is clear that for sustainable short duration prediction, ANN integrated with fuzzy is a good choice. [ABSTRACT FROM AUTHOR]

The application of wireless sensor networks in mechanical equipment condition monitoring is proposed to make up for the limitations of wired mechanical equipment condition monitoring systems. To record vibration signals from mechanical equipment, it is essential to design a dual-processor wireless sensor network node. Researching the self-organizing network, wireless transmission protocol, and on-chip processing function is also important to develop a full wireless sensor network mechanical vibration monitoring system. The clustering network topology, which is advantageous for applying distributed algorithms, has good scalability, can quickly adapt to system changes, and is appropriate for large-scale networks. Through the design of high-performance vibration measurement nodes, the problem of limited acquisition and storage capacity when a wireless sensor network is applied to mechanical vibration signal acquisition is preliminarily solved. The experimental results show that the time domain signal collected by the node is small, and the corresponding amplitude will be small, but the on-chip processing results of the node correspond to the processing results of the host computer, and the amplitude difference is small, and the maximum error is 8.18%, indicating that the node performance can meet the requirements of on-chip processing. The feasibility of the design scheme is verified by experiments. [ABSTRACT FROM AUTHOR]

Microgrids can benefit from multi-objective optimization dispatch in several ways, including reduced operation costs and improved service dependability. While using the traditional power network optimization method to solve power network planning, the algorithm mostly falls into the local optimal solution, rendering the global optimal solution intractable. In this research, multi-objective power grid planning is applied to an eight-bus system using a multi-objective optimization technique, such as reducing distribution network building costs and losses, planning distribution network growth and fixed capacity, and distributed generation (DG) addressing planning. Improvements are made to the quantum particle swarm optimization algorithm so that it can be applied to solving discrete problems. This report also employs a binary-coded quantum particle swarm optimization technique to design the distribution network without DG and runs the debugged program in MATLAB to compare the final optimization results. Finally, MATLAB software is used to simulate the example, and the corresponding planning results are obtained. From the model verification results, it can be observed that the quantum particle swarm optimization algorithm applied in this research can complete the task of power grid planning well under the premise of ensuring the calculation speed in the multi-objective design of a smart grid. [ABSTRACT FROM AUTHOR]

To detect computer communication network failures, a computer communication network fault detection based on an improved neural network algorithm is proposed. A network fault diagnosis example is used to verify the effectiveness of the method. There are many network failure phenomena. Here, the author selected 13 network fault information parameters for a comprehensive diagnosis of network failures. The author designed a three-layer backpropagation (BP) neural network. There are 13 nodes in the input layer, corresponding to the above 13 network fault parameter information. The output layer has three nodes, which output the fault code sequence. The training of the network uses the trainlm() function. The performance function uses the mean square error performance function mse() and set e = 0.001; the network learning rate is set to a = 0.05. The author selects 100 failure data as the training set for network training and selects 10 sets of samples as the test set. The experimental data shows that after the network has been trained 25 times, the output error reaches the set precision e. After training the BP network using this algorithm 140 times, the output error reaches the set precision e. This method effectively improves the effectiveness and accuracy of S network fault diagnosis. [ABSTRACT FROM AUTHOR]

To solve the problem of difficult identification of the internal overvoltage category of the distribution network, enhance the mastery of the internal voltage identification technology of the distribution network, a method of identifying the internal overvoltage of the distribution network based on the convolutional neural network is proposed. The effectiveness and superiority of the proposed evaluation method are illustrated by an example analysis of a medium-voltage distribution network in East China demonstrates the effectiveness and superiority of the proposed evaluation method. The results show that under ten tests, ACC1 and ACC2 of each type of overvoltage are tested. In the cross-validation process, only two cases of misjudgments occur when switching capacitor banks, while the recognition rates of the remaining six types of overvoltage are all at 100%. The total recognition rate of the test samples is 99.57%, indicating that the features extracted by convolutional neural network (CNN) have covered all the seven types of overvoltage. The CNN network constructed by the proposed method recognizes the 110 overvoltage samples obtained, and its recognition accuracy reaches 100%. It shows that the proposed recognition method has a high recognition rate and verifies the effectiveness and superiority of CNN. [ABSTRACT FROM AUTHOR]

For an integer k ≥ 2, a kth-order slant Hankel operator DΦ with symbol Φ in L∞ is an operator on L² whose representing matrix is Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed., where at's are the Fourier coefficients of Φ. In this paper, we discuss some spectral properties of the operator DΦ and identify its spectrum and essential spectrum. [ABSTRACT FROM AUTHOR]