VMD-WSLSTM Load Prediction Model Based on Shapley Values

Accurate short-term load forecasting can ensure the safe operation of the grid. Decomposing load data into smooth components by decomposition algorithms is a common approach to address data volatility. However, each component of the decomposition must be modeled separately for prediction, which leads to overly complex models. To solve this problem, a VMD-WSLSTM load prediction model based on Shapley values is proposed in this paper. First, the Shapley value is used to select the optimal set of special features, and then the VMD decomposition method is used to decompose the original load into several smooth components. Finally, WSLSTM is used to predict each component. Unlike the traditional LSTM model, WSLSTM can simplify the prediction model and extract common features among the components by sharing the parameters among the components. In order to verify the effectiveness of the proposed model, several control groups were used for experiments. The results show that the proposed method has higher prediction accuracy and training speed compared with traditional prediction methods.

A new combination of Hankel and sparsity-promoting dynamic mode decompositions and its application to the prediction of plasma turbulence

A new combined use of dynamic mode decomposition algorithms is proposed, which is suitable for the analysis of spatiotemporal data from experiments with few observation points, unlike computational fluid dynamics with many observation points. The method was applied to our data from a plasma turbulence experiment. As a result, we succeeded in constructing a quite accurate model for our training data and it made progress in predictive performance as well. In addition, modal patterns from the longer-term analysis help to understand the underlying mechanism more clearly, which is demonstrated in the case of plasma streamer structure. This method is expected to be a powerful tool for the data-driven construction of a reduced-order model and a predictor in plasma turbulence research and also any nonlinear dynamics researches of other applied physics fields.

Recommendation system using the k-nearest neighbors and singular value decomposition algorithms

<span>Nowadays, recommendation systems are used successfully to provide items (example: movies, music, books, news, images) tailored to user preferences. Amongst the approaches existing to recommend adequate content, we use the collaborative filtering approach of finding the information that satisfies the user by using the reviews of other users. These reviews are stored in matrices that their sizes increase exponentially to predict whether an item is relevant or not. The evaluation shows that these systems provide unsatisfactory recommendations because of what we call the cold start factor. Our objective is to apply a hybrid approach to improve the quality of our recommendation system. The benefit of this approach is the fact that it does not require a new algorithm for calculating the predictions. We are going to apply two algorithms: k-nearest neighbours (KNN) and the matrix factorization algorithm of collaborative filtering which are based on the method of (singular-value-decomposition). Our combined model has a very high precision and the experiments show that our method can achieve better results.</span>

Influence of Sampling Frequency Ratio on Mode Mixing Alleviation Performance: A Comparative Study of Four Noise-Assisted Empirical Mode Decomposition Algorithms

Four noise-assisted empirical mode decomposition (EMD) algorithms, i.e., ensemble EMD (EEMD), complementary ensemble EMD (CEEMD), complete ensemble EMD with adaptive noise (CEEMDAN), and improved complete ensemble EMD with adaptive noise (ICEEMDAN), are noticeable improvements to EMD, aimed at alleviating mode mixing. However, the sampling frequency ratio (SFR), i.e., the ratio between the sampling frequency and the maximum signal frequency, may significantly impact their mode mixing alleviation performance. Aimed at this issue, we investigated and compared the influence of the SFR on the mode mixing alleviation performance of these four noise-assisted EMD algorithms. The results show that for a given signal, (1) SFR has an aperiodic influence on the mode mixing alleviation performance of four noise-assisted EMD algorithms, (2) a careful selection of SFRs can significantly improve the mode mixing alleviation performance and avoid decomposition instability, and (3) ICEEMDAN has the best mode mixing alleviation performance at the optimal SFR among the four noise-assisted EMD algorithms. The applications include, for instance, tool wear monitoring in machining as well as fault diagnosis and prognosis of complex systems that rely on signal decomposition to extract the components corresponding to specific behaviors.

Gradient Decomposition Methods for Training Neural Networks With Non-ideal Synaptic Devices

While promising for high-capacity machine learning accelerators, memristor devices have non-idealities that prevent software-equivalent accuracies when used for online training. This work uses a combination of Mini-Batch Gradient Descent (MBGD) to average gradients, stochastic rounding to avoid vanishing weight updates, and decomposition methods to keep the memory overhead low during mini-batch training. Since the weight update has to be transferred to the memristor matrices efficiently, we also investigate the impact of reconstructing the gradient matrixes both internally (rank-seq) and externally (rank-sum) to the memristor array. Our results show that streaming batch principal component analysis (streaming batch PCA) and non-negative matrix factorization (NMF) decomposition algorithms can achieve near MBGD accuracy in a memristor-based multi-layer perceptron trained on the MNIST (Modified National Institute of Standards and Technology) database with only 3 to 10 ranks at significant memory savings. Moreover, NMF rank-seq outperforms streaming batch PCA rank-seq at low-ranks making it more suitable for hardware implementation in future memristor-based accelerators.

Optimized Content Adaptive Approach for Image Detailed Enhancement to Reduce Color Distortion

Digital imaging has significantly influenced the outcome of research in various disciplines. For example, artificial intelligence and robotics, biometric security, multimedia and image processing, etc. Technically, image processing and the Human Visual System (HVS) relies heavily on image enhancement to improve the content of the image. One of the biggest challenges in image processing is detail enhancement due to halo artefacts and gradient inversion artefacts at edges. It has been used to enhance the visual quality of an image. Most algorithms that used to enhance the detail of an image essentially depend on edge-preserving decomposition techniques. in general, the image consist of two major elements are a base layer and a detail layer, which extracted by edge-preserving decomposition algorithms. The detail layer is enhanced to improve the details of the generated image. we propose in this paper, a new model to preserve the sharp edges and achieve better visual quality than the existing norm-based algorithm to enhance the details of the image. Experiments show that the proposed method reduces the distortion at the edges. It improves the details of the generated image significantly.

Truss Decomposition using Triangle Graphs

Recent studies have shown that social networks exhibit interesting characteristics such as community structures, i.e., vertexes can be clustered into communities that are densely connected together and loosely connected to other vertices. In order to identify communities, several definitions have been proposed that can characterize the density of connections among vertices in the networks. Dense triangle cores, also known as $k$-trusses, are subgraphs in which every edge participates at least $k-2$ triangles (a clique of size 3), exhibiting a high degree of cohesiveness among vertices. There are a number of research works that propose $k$-truss decomposition algorithms. However, existing in-memory algorithms for computing $k$-truss are inefficient for handling today’s massive networks. In this paper, we propose an efficient, yet scalable algorithm for finding $k$-trusses in a large-scale network. To this end, we propose a new structure, called triangle graph to speed up the process of finding the $k$-trusses and prove the correctness and efficiency of our method. We also evaluate the performance of the proposed algorithms through extensive experiments using real-world networks. The results of comprehensive experiments show that the proposed algorithms outperform the state-of-the-art methods by several orders of magnitudes in running time.

A Perspective on Muscle Synergies and Different Theories Related to Their Adaptation

The human motor system is a complex neuro-musculo sensory system that needs further investigations of neuro-muscular commands and sensory-motor coupling to decode movement execution. Some researchers suggest that the central nervous system (CNS) activates a small set of modules termed muscle synergies to simplify motor control. Further, these modules form functional building blocks of movement as they can explain the neurophysiological characteristics of movements. We can identify and extract these muscle synergies from electromyographic signals (EMG) recorded in the laboratory by using linear decomposition algorithms, such as principal component analysis (PCA) and non-Negative Matrix Factorization Algorithm (NNMF). For the past three decades, the hypothesis of muscle synergies has received considerable attention as we attempt to understand and apply the concept of muscle synergies in clinical settings and rehabilitation. In this article, we first explore the concept of muscle synergies. We then present different strategies of adaptation in these synergies that the CNS employs to accomplish a movement goal.

The Design of MIMO Precoders Based on Decomposition Algorithms

The Wireless Communication over Multiple Input and Multiple Output (MIMO) channel increases transmission rate by splitting the input data stream into a plethora of parallel data streams that are transmitted in simultaneously. The goal of precoding at the transmitter is to divide the channel into numerous unconnected subchannels so that many data streams may be sent out at the same time. This article examines a MIMO precoder's performance utilising different channel decomposition method, as well as its computational complexity in terms of the number of floating-point operations (FLOPs). Singular Value Decomposition (SVD), Geometric Mean Decomposition (GMD), LDLH, LU, Schur, QR, and Jordan decomposition are among the techniques discussed. According to simulation findings, precoding for MIMO based on QR decomposition beats all other precoding techniques based on channel decomposition in terms of BER performance and requires less FLOPs.

The Design of MIMO Precoders Based on Decomposition Algorithms

The Wireless Communication over Multiple Input and Multiple Output (MIMO) channel increases transmission rate by splitting the input data stream into a plethora of parallel data streams that are transmitted in simultaneously. The goal of precoding at the transmitter is to divide the channel into numerous unconnected subchannels so that many data streams may be sent out at the same time. This article examines a MIMO precoder's performance utilising different channel decomposition method, as well as its computational complexity in terms of the number of floating-point operations (FLOPs). Singular Value Decomposition (SVD), Geometric Mean Decomposition (GMD), LDLH, LU, Schur, QR, and Jordan decomposition are among the techniques discussed. According to simulation findings, precoding for MIMO based on QR decomposition beats all other precoding techniques based on channel decomposition in terms of BER performance and requires less FLOPs.