Memristive Cluster Based Compact High-Density Nonvolatile Memory Design and Application for Image Storage

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)As a new type of nonvolatile device, the memristor has become one of the most promising technologies for designing a new generation of high-density memory. In this paper, a 4-bit high-density nonvolatile memory based on a memristor is designed and applied to image storage. Firstly, a memristor cluster structure consisting of a transistor and four memristors is designed. Furthermore, the memristor cluster is used as a memory cell in the crossbar array structure to realize the memory design. In addition, when the designed non-volatile memory is applied to gray scale image storage, only two memory cells are needed for the storage of one pixel. Through the Pspice circuit simulation, the results show that compared with the state-of-the-art technology, the memory designed in this paper has better storage density and read–write speed. When it is applied to image storage, it achieves the effect of no distortion and fast storage.Peer reviewe

Structural Deep Clustering Network

Clustering is a fundamental task in data analysis. Recently, deep clustering, which derives inspiration primarily from deep learning approaches, achieves state-of-the-art performance and has attracted considerable attention. Current deep clustering methods usually boost the clustering results by means of the powerful representation ability of deep learning, e.g., autoencoder, suggesting that learning an effective representation for clustering is a crucial requirement. The strength of deep clustering methods is to extract the useful representations from the data itself, rather than the structure of data, which receives scarce attention in representation learning. Motivated by the great success of Graph Convolutional Network (GCN) in encoding the graph structure, we propose a Structural Deep Clustering Network (SDCN) to integrate the structural information into deep clustering. Specifically, we design a delivery operator to transfer the representations learned by autoencoder to the corresponding GCN layer, and a dual self-supervised mechanism to unify these two different deep neural architectures and guide the update of the whole model. In this way, the multiple structures of data, from low-order to high-order, are naturally combined with the multiple representations learned by autoencoder. Furthermore, we theoretically analyze the delivery operator, i.e., with the delivery operator, GCN improves the autoencoder-specific representation as a high-order graph regularization constraint and autoencoder helps alleviate the over-smoothing problem in GCN. Through comprehensive experiments, we demonstrate that our propose model can consistently perform better over the state-of-the-art techniques.Comment: Published at The Web Conference (WWW) 2020, full pape

Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection

Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority. It has gained substantial attention in various domains of information security, including network intrusion, financial fraud, and malicious comments, et al. Existing methods are primarily developed in an unsupervised manner due to the challenge in obtaining labeled data. For lack of guidance from prior knowledge in unsupervised manner, the identified anomalies may prove to be data noise or individual data instances. In real-world scenarios, a limited batch of labeled anomalies can be captured, making it crucial to investigate the few-shot problem in graph anomaly detection. Taking advantage of this potential, we propose a novel few-shot Graph Anomaly Detection model called FMGAD (Few-shot Message-Enhanced Contrastive-based Graph Anomaly Detector). FMGAD leverages a self-supervised contrastive learning strategy within and across views to capture intrinsic and transferable structural representations. Furthermore, we propose the Deep-GNN message-enhanced reconstruction module, which extensively exploits the few-shot label information and enables long-range propagation to disseminate supervision signals to deeper unlabeled nodes. This module in turn assists in the training of self-supervised contrastive learning. Comprehensive experimental results on six real-world datasets demonstrate that FMGAD can achieve better performance than other state-of-the-art methods, regardless of artificially injected anomalies or domain-organic anomalies

Research on the Protection and Utilisation Design Strategy of Hangzhou Traditional Villages on “Three Rivers and Two Banks” from the Perspective of Clusters

At the beginning of 2023, Hangzhou launched the "Poetry Road Culture - Three Rivers and Two Banks" Water Golden Tourism Line Project, aimed at revitalizing the countryside in the western part of Hangzhou and creating a model area of common wealth. This article integrates natural and cultural resources on "Three Rivers and Two Banks" in Hangzhou, using kernel density analysis, proximity analysis, and weighted spatial superposition analysis of ArcGIS 10.8 software to classify the traditional villages on "Three Rivers and Two Banks" in Hangzhou into high-resource characteristic villages, intermediate resource characteristic villages, and general resource characteristic villages based on the distribution of resource characteristics. Finally, the traditional villages are examined within a systematic geographical framework to explore the establishment of traditional village cluster protection and development modes and methods. Strategies such as historical excavation, cluster construction, culture and tourism integration are proposed to achieve the goal of coordinating the rational allocation of regional resources, facilitating facility sharing, enhancing the endogenous dynamics of traditional villages, and forming distinctive village brand and cultural groups. This research aims to provide valuable insights for the protection of traditional village clusters

Research on fast solid state DC breaker based on a natural current zero-crossing point

The DC fault characteristics of voltage source converter based high voltage direct current (VSC-HVDC) systems are analyzed in this paper. The phenomenon whereby the capacitor on DC side discharges quickly during a DC fault contributes to a large short-circuit fault current. Neither traditional DC breakers nor DC switches can cut off the fault current under this condition. A fast solid state DC breaker design method is proposed in this paper. This method is based on the fault current characteristics of the inverter in multi-terminal HVDC systems (MTDC), where a fault current appears at the natural zero-crossing point near the inverter. At this point, by coordinating the AC breakers near the rectifier, the DC breaker could reliably cut off the DC fault current and protect the system. A detailed model for this fast solid state DC breaker and its operation sequence are studied, based on this design method. Simulations modeling a five-terminal meshed DC grid and a fast DC breaker were carried out with PSCAD/EMTDC using this design method. The results from the simulations confirmed the validity of the design method

Not All Negatives Are Worth Attending to: Meta-Bootstrapping Negative Sampling Framework for Link Prediction

The rapid development of graph neural networks (GNNs) encourages the rising of link prediction, achieving promising performance with various applications. Unfortunately, through a comprehensive analysis, we surprisingly find that current link predictors with dynamic negative samplers (DNSs) suffer from the migration phenomenon between "easy" and "hard" samples, which goes against the preference of DNS of choosing "hard" negatives, thus severely hindering capability. Towards this end, we propose the MeBNS framework, serving as a general plugin that can potentially improve current negative sampling based link predictors. In particular, we elaborately devise a Meta-learning Supported Teacher-student GNN (MST-GNN) that is not only built upon teacher-student architecture for alleviating the migration between "easy" and "hard" samples but also equipped with a meta learning based sample re-weighting module for helping the student GNN distinguish "hard" samples in a fine-grained manner. To effectively guide the learning of MST-GNN, we prepare a Structure enhanced Training Data Generator (STD-Generator) and an Uncertainty based Meta Data Collector (UMD-Collector) for supporting the teacher and student GNN, respectively. Extensive experiments show that the MeBNS achieves remarkable performance across six link prediction benchmark datasets