Power enterprise risk identification model based on convolutional neural network and adaptive comparison algorithm

Combined with the actual characteristics of risk identification in electric power enterprises, a convolutional neural network model suitable for load sequence data prediction is determined. Particle Swarm Optimization (PSO) algorithm is used to transform the convolutional neural network (convolutional neural network) to improve the global Optimization ability and convergence speed. Simulation results show that CNN can effectively extract sample information through its convolutional layer and pool layer. After particle swarm optimization, it also achieves good results in prediction accuracy and prediction speed. Secondly, classical interpretation combination model (ISM) is used to analyze the structure of the risk system of electric power enterprises, and the link relationship model of the risk of electric power enterprises is constructed. Through the structural analysis of risk and risk factors, the paper finds out the mutual influence relationship between risk and risk factors, and further finds out the risk chain and risk source. The classical explanatory structure model is extended to the fuzzy set, and then the influence intensity model of power enterprise risk is built. This model considers the influence of risk intensity when analyzing the risk relationship of electric power enterprises, and gives different risk link relations based on different impact intensity. Through comparative analysis, the relationship between the link relationship model and the influence intensity model of the risk of electric power enterprises is obtained. Put forward the sequence similarity matching algorithm based on adaptive search window (ADTW), average algorithm using Piecewise gathered (Piecewise Aggregate Approximation, PAA) strategy for sequence sampling sequence, low precision and low calculation precision sequence alignment of paths, and according to the change of gradient on the low precision of distance matrix forecast path deviation, expand the scope of limiting path search window; Then, the algorithm gradually improves the sequence accuracy, corrects the path in the search window, calculates the new search window, and finally realizes the fast solution of DTW distance and similarity alignment path.

Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images

The purpose of this study is to evaluate the various control parameters of a modeled fast non-local means (FNLM) noise reduction algorithm which can separate color channels in light microscopy (LM) images. To achieve this objective, the tendency of image characteristics with changes in parameters, such as smoothing factors and kernel and search window sizes for the FNLM algorithm, was analyzed. To quantitatively assess image characteristics, the coefficient of variation (COV), blind/referenceless image spatial quality evaluator (BRISQUE), and natural image quality evaluator (NIQE) were employed. When high smoothing factors and large search window sizes were applied, excellent COV and unsatisfactory BRISQUE and NIQE results were obtained. In addition, all three evaluation parameters improved as the kernel size increased. However, the kernel and search window sizes of the FNLM algorithm were shown to be dependent on the image processing time (time resolution). In conclusion, this work has demonstrated that the FNLM algorithm can effectively reduce noise in LM images, and parameter optimization is important to achieve the algorithm’s appropriate application.

Organizing thematic search in open archive system

Problems of search organization in open archive system are specified and analyzed. The experience of thematic search in digital resources is generalized. The approaches to organizing open archive search interface oriented toward untrained and trained users are characterized. The pros and cons of single search window are discussed. Vectors to improve search instruments and performance are defined. The rubricator version to support thematic search adaptation is proposed. The choice of UDC as the rubricator foundation is substantiated provided that its correspondence with the State Rubricator of Scitech information and Library Bibliographic Classification is set up. The fragment of the suggested rubricator is given as an example. The main purpose of the analysis is to summarize on the problems related to thematic search and to propose approaches to design optimum close-knit rubricator, and to identify its application possibilities.

Fast motion tracking based on moth-flame optimization and kernel correlation filter

Kernel Correlation Filter (KCF) tracker has shown great potential on precision, robustness and efficiency. However, the candidate region used to train the correlation filter is fixed, so tracking is difficult when the target escapes from the search window due to fast motion. In this paper, an improved KCF is put forward for long-term tracking. At first, the moth-flame optimization (MFO) algorithm is introduced into tracking to search for lost target. Then, the candidate sample strategy of KCF tracking method is adjusted by MFO algorithm to make it has the capability of fast motion tracking. Finally, we use the conservative learning correlation filter to judge the moving state of the target, and combine the improved KCF tracker to form a unified tracking framework. The proposed algorithm is tested on a self-made dataset benchmark. Moreover, our method obtains scores for both the distance precision plot (0.891 and 0.842) and overlap success plots (0.631 and 0.601) on the OTB-2013 and OTB-2015 data sets, respectively. The results demonstrate the feasibility and effectiveness compared with the state-of-the-art methods, especially in dealing with fast or uncertain motion.

Temporally Grounding Language Queries in Videos by Contextual Boundary-Aware Prediction

The task of temporally grounding language queries in videos is to temporally localize the best matched video segment corresponding to a given language (sentence). It requires certain models to simultaneously perform visual and linguistic understandings. Previous work predominantly ignores the precision of segment localization. Sliding window based methods use predefined search window sizes, which suffer from redundant computation, while existing anchor-based approaches fail to yield precise localization. We address this issue by proposing an end-to-end boundary-aware model, which uses a lightweight branch to predict semantic boundaries corresponding to the given linguistic information. To better detect semantic boundaries, we propose to aggregate contextual information by explicitly modeling the relationship between the current element and its neighbors. The most confident segments are subsequently selected based on both anchor and boundary predictions at the testing stage. The proposed model, dubbed Contextual Boundary-aware Prediction (CBP), outperforms its competitors with a clear margin on three public datasets.

An enhanced adaptive non-local means algorithm for Rician noise reduction in magnetic resonance brain images

Background The Rician noise formed in magnetic resonance (MR) imaging greatly reduced the accuracy and reliability of subsequent analysis, and most of the existing denoising methods are suitable for Gaussian noise rather than Rician noise. Aiming to solve this problem, we proposed fuzzy c-means and adaptive non-local means (FANLM), which combined the adaptive non-local means (NLM) with fuzzy c-means (FCM), as a novel method to reduce noise in the study. Method The algorithm chose the optimal size of search window automatically based on the noise variance which was estimated by the improved estimator of the median absolute deviation (MAD) for Rician noise. Meanwhile, it solved the problem that the traditional NLM algorithm had to use a fixed size of search window. Considering the distribution characteristics for each pixel, we designed three types of search window sizes as large, medium and small instead of using a fixed size. In addition, the combination with the FCM algorithm helped to achieve better denoising effect since the improved the FCM algorithm divided the membership degrees of images and introduced the morphological reconstruction to preserve the image details. Results The experimental results showed that the proposed algorithm (FANLM) can effectively remove the noise. Moreover, it had the highest peak signal-noise ratio (PSNR) and structural similarity (SSIM), compared with other three methods: non-local means (NLM), linear minimum mean square error (LMMSE) and undecimated wavelet transform (UWT). Using the FANLM method, the image details can be well preserved with the noise being mostly removed. Conclusion Compared with the traditional denoising methods, the experimental results showed that the proposed approach effectively suppressed the noise and the edge details were well retained. However, the FANLM method took an average of 13 s throughout the experiment, and its computational cost was not the shortest. Addressing these can be part of our future research.

Cartographer SLAM Method for Optimization with an Adaptive Multi-Distance Scan Scheduler

This paper presents the use of Google’s simultaneous localization and mapping (SLAM) technique, namely Cartographer, and adaptive multistage distance scheduler (AMDS) to improve the processing speed. This approach optimizes the processing speed of SLAM which is known to have performance degradation as the map grows due to a larger scan matcher. In this proposed work, the adaptive method was successfully tested in an actual vehicle to map roads in real time. The AMDS performs a local pose correction by controlling the LiDAR sensor scan range and scan matcher search window with the help of scheduling algorithms. The scheduling algorithms manage the SLAM that swaps between short and long distances during map data collection. As a result, the algorithms efficiently improved performance speed similar to short distance LiDAR scans while maintaining the accuracy of the full distance of LiDAR. By swapping the scan distance of the sensor, and adaptively limiting the search size of the scan matcher to handle difference scan sizes, the pose’s generation performance time is improved by approximately 16% as compared with a fixed scan distance, while maintaining similar accuracy.