Three-Phase Unbalance Prediction of Electric Power Based on Hierarchical Temporal Memory

The difference in electricity and power usage time leads to an unbalanced current among the three phases in the power grid.The three-phase unbalanced is closely related to power planning and load distribution.When the unbalance occurs,the safe operation of the electrical equipment will be seriously jeopardized.This paper proposes a Hierarchical Temporal Memory(HTM)-based three-phase unbalance prediction model consisted by the encoder for binary coding,the spatial pooler for frequency pattern learning,the temporal pooler for pattern sequence learning,and the sparse distributed representations classifier for unbalance prediction.Following the feasibility of spatial-temporal streaming data analysis,we adopted this brain-liked neural network to a real-time prediction for power load.We applied the model in five cities(Tangshan,Langfang,Qinhuangdao,Chengde,Zhangjiakou)of north China.We experimented with the proposed model and Long Short-term Memory(LSTM)model and analyzed the predict results and real currents.The results show that the predictions conform to the reality;compared to LSTM,the HTM-based prediction model shows enhanced accuracy and stability.The prediction model could serve for the overload warning and the load planning to provide high-quality power grid operation.

Investigation on the Chinese Text Sentiment Analysis Based on Convolutional Neural Networks in Deep Learning

Nowadays,the amount of wed data is increasing at a rapid speed,which presents a serious challenge to the web monitoring.Text sentiment analysis,an important research topic in the area of natural language processing,is a crucial task in the web monitoring area.The accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass data.Deep learning is a hot research topic of the artificial intelligence in the recent years.By now,several research groups have studied the sentiment analysis of English texts using deep learning methods.In contrary,relatively few works have so far considered the Chinese text sentiment analysis toward this direction.In this paper,a method for analyzing the Chinese text sentiment is proposed based on the convolutional neural network(CNN)in deep learning in order to improve the analysis accuracy.The feature values of the CNN after the training process are nonuniformly distributed.In order to overcome this problem,a method for normalizing the feature values is proposed.Moreover,the dimensions of the text features are optimized through simulations.Finally,a method for updating the learning rate in the training process of the CNN is presented in order to achieve better performances.Experiment results on the typical datasets indicate that the accuracy of the proposed method can be improved compared with that of the traditional supervised machine learning methods,e.g.,the support vector machine method.

Ore Image Segmentation Method Based on U-Net and Watershed

Ore image segmentation is a key step in an ore grain size analysis based on image processing.The traditional segmentation methods do not deal with ore textures and shadows in ore images well Those methods often suffer from under-segmentation and over-segmentation.In this article,in order to solve the problem,an ore image segmentation method based on U-Net is proposed.We adjust the structure of U-Net to speed up the processing,and we modify the loss function to enhance the generalization of the model.After the collection of the ore image,we design the annotation standard and train the network with the annotated image.Finally,the marked watershed algorithm is used to segment the adhesion area.The experimental results show that the proposed method has the characteristics of fast speed,strong robustness and high precision.It has great practical value to the actual ore grain statistical task.

A Novel GLS Consensus Algorithm for Alliance Chain in Edge Computing Environment

Edge computing devices are widely deployed.An important issue that arises is in that these devices suffer from security attacks.To deal with it,we turn to the blockchain technologies.The note in the alliance chain need rules to limit write permissions.Alliance chain can provide security management functions,using these functions to meet the management between the members,certification,authorization,monitoring and auditing.This article mainly analyzes some requirements realization which applies to the alliance chain,and introduces a new consensus algorithm,generalized Legendre sequence(GLS)consensus algorithm,for alliance chain.GLS algorithms inherit the recognition and verification efficiency of binary sequence ciphers in computer communication and can solve a large number of nodes verification of key distribution issues.In the alliance chain,GLS consensus algorithm can complete node address hiding,automatic task sorting,task automatic grouping,task node scope confirmation,task address binding and stamp timestamp.Moreover,the GLS consensus algorithm increases the difficulty of network malicious attack.

A Heterogeneous Virtual Machines Resource Allocation Scheme in Slices Architecture of 5G Edge Datacenter

In the paper,we investigate the heterogeneous resource allocation scheme for virtual machines with slicing technology in the 5G/B5G edge computing environment.In general,the different slices for different task scenarios exist in the same edge layer synchronously.A lot of researches reveal that the virtual machines of different slices indicate strong heterogeneity with different reserved resource granularity.In the condition,the allocation process is a NP hard problem and difficult for the actual demand of the tasks in the strongly heterogeneous environment.Based on the slicing and container concept,we propose the resource allocation scheme named Two-Dimension allocation and correlation placement Scheme(TDACP).The scheme divides the resource allocation and management work into three stages in this paper:In the first stage,it designs reasonably strategy to allocate resources to different task slices according to demand.In the second stage,it establishes an equivalent relationship between the virtual machine reserved resource capacity and the Service-Level Agreement(SLA)of the virtual machine in different slices.In the third stage,it designs a placement optimization strategy to schedule the equivalent virtual machines in the physical servers.Thus,it is able to establish a virtual machine placement strategy with high resource utilization efficiency and low time cost.The simulation results indicate that the proposed scheme is able to suppress the problem of uneven resource allocation which is caused by the pure preemptive scheduling strategy.It adjusts the number of equivalent virtual machines based on the SLA range of system parameter,and reduces the SLA probability of physical servers effectively based on resource utilization time sampling series linear.The scheme is able to guarantee resource allocation and management work orderly and efficiently in the edge datacenter slices.

Cooperative Relay Selection Mechanism in Multi-Hop Networks

In this paper, we consider a three-hop relay system based on interference cancellation technique in Underlay cognitive radio (CR) network. Although underlay CR has been shown as a promising technique to better utilize the source of primary users (PUs), its secondary performance will be severely degraded. On one hand, by adapting the Underlay spectrum sharing pattern, secondary users (SUs) would observe the strict power constraints and be interfered by primary users. On the other hand, limited transmit power results in limited transmission range, which greatly degrade the secondary transmission capacity. To solve the problems above, we propose an interference cancellation protocol for multi-hop wireless communication networks in underlay CR, which could develop the long-distance transmission performance and improve the transmission efficiency significantly. As simulation results shows, proposed scheme significantly reduce the secondary outage probability and increase the secondary diversity than the traditional cases.

Scalable Skin Lesion Multi-Classification Recognition System

Skin lesion recognition is an important challenge in the medical field.In this paper,we have implemented an intelligent classification system based on convolutional neural network.First of all,this system can classify whether the input image is a dermascopic image with an accuracy of 99%.And then diagnose the dermoscopic image and the non-skin mirror image separately.Due to the limitation of the data,we can only realize the recognition of vitiligo by non-skin mirror.We propose a vitiligo recognition based on the probability average of three structurally identical CNN models.The method is more efficient and robust than the traditional RGB color space-based image recognition method.For the dermoscopic classification model,we were able to classify 7 skin lesions,use weighted optimization to overcome the unbalanced data set,and greatly improve the sensitivity of the model by means of model fusion.The optimization and expansion of the system depend on the increase of database.

Classification and Research of Skin Lesions Based on Machine Learning

Classification of skin lesions is a complex identification challenge.Due to the wide variety of skin lesions,doctors need to spend a lot of time and effort to judge the lesion image which zoomed through the dermatoscopy.The diagnosis which the algorithm of identifying pathological images assists doctors gets more and more attention.With the development of deep learning,the field of image recognition has made long-term progress.The effect of recognizing images through convolutional neural network models is better than traditional image recognition technology.In this work,we try to classify seven kinds of lesion images by various models and methods of deep learning,common models of convolutional neural network in the field of image classification include ResNet,DenseNet and SENet,etc.We use a fine-tuning model with a multi-layer perceptron,by training the skin lesion model,in the validation set and test set we use data expansion based on multiple cropping,and use five models’ensemble as the final results.The experimental results show that the program has good results in improving the sensitivity of skin lesion diagnosis.

Neural Dialogue Model with Retrieval Attention for Personalized Response Generation

With the success of new speech-based human-computer interfaces,there is a great need for effective and friendly dialogue agents that can communicate with people naturally and continuously.However,the lack of personality and consistency is one of critical problems in neural dialogue systems.In this paper,we aim to generate consistent response with fixed profile and background information for building a realistic dialogue system.Based on the encoder-decoder model,we propose a retrieval mechanism to deliver natural and fluent response with proper information from a profile database.Moreover,in order to improve the efficiency of training the dataset related to profile information,we adopt a method of pre-training and adjustment for general dataset and profile dataset.Our model is trained by social dialogue data from Weibo.According to both automatic and human evaluation metrics,the proposed model significantly outperforms standard encoder-decoder model and other improved models on providing the correct profile and high-quality responses.

Analysis of Underlay Cognitive Radio Networks Based on Interference Cancellation Mechanism

In this paper,we investigate the performance of secondary transmission scheme based on Markov ON-OFF state of primary users in Underlay cognitive radio networks.We propose flexible secondary cooperative transmission schemewith interference cancellation technique according to the ON-OFF status of primary transmitter.For maximal ratio combining(MRC)at destination,we have derived exact closed-form expressions of the outage probability in different situations.The numerical simulation results also reveal that the proposed scheme improve the secondary transmission performance compared with traditional mechanism in terms of secondary outage probability and energy efficiency.

Analysis of Underlay Cognitive Radio Networks Based on Interference Cancellation Mechanism

In this paper,we study the state-dependent interference channel,where the Rayleigh channel is non-causally known at cognitive network.We propose an active secondary transmission mechanism with interference cancellation technique according to the ON-OFF status of primary network.the secondary transmission mechanism is divided into four cases according to the active state of the primary user in the two time slots.For these interference cases,numerical results are provided to show that active interference cancellation mechanism significantly reduces the secondary transmission performance in terms of secondary outage probability and energy efficiency.