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In order to extract the information from the massive unstructured recipe text, a named entity recognition method based on BERT pre-trained language model is proposed by this paper. Firstly, after pre-processing the public recipe texts obtained from the web, the entities were classified into three categories of main ingredients, auxiliary ingredients and cooking methods by combining the domain expert knowledge and BIO annotation method to build a large-scale Chinese cuisine domain named entity recognition dataset, and then the BERT-Attention-BiLSTM-CRF(BABC) model proposed in this paper is used to train on this dataset. Finally, we compared the results of other named entity recognition models on this dataset, and the experimental results showed that BABC could identify the entities in the recipe text more accurately.

With the development of deep learning, it has achieved impressive results in feature extraction field. This paper drives research in feature extraction based on deep learning. First, this paper gives a brief introduction on the world's research status on deep learning and principle of Restricted Boltzmann machine (RBM). Then this paper conducts reducing experiment based on RBM for handwritten digits. According to the analysis based on the results of the experiments, this paper tries to get a proper dimension which handwritten digits reduced to achieve better performance. Finally, this paper finds that it reach the goal when handwritten digits is reduced to half dimensional raw digits. This is an important foundation of deep learning layering and offers help to researchers in feature extraction based on deep learning.

Ant Colony Optimization ( ACO ) is a novel bionic evolutionary algorithm for solving complex combinatorial optimization problems. This research approach lies at initial stage at present, and a new adaptive ant algorithm is proposed for the traditional ant algorithm easily appears precocious and stagnation behavior phenomenon in this paper. And the traditional parameter of pheromone of ant colony algorithm is self-adaptive adjusted. Selecting a number of typical TSP problems to experiment, the results are indicated that the new adaptive ant colony algorithm has a better ability to search the global optimal solution and have better stability and astringency.

Aimed at the controlled object’s character of long delay-time, this paper presents a strategy that the long delay-time object is remodeled into short delay-time object through decreasing time-delay control, after the adoption of BP neural network PID controller to control the transformed system. In this paper, the three-layer BP neural network is designed and BP neural network PID controller algorithm is analyzed. The simulation results showed that the PID controller based on BP neural network has good control effect in the performance of following, anti-interference, robust.

Wireless Sensor Networks (WSN) are composed of a large number of sensor nodes which are connected by wireless channels. Some of these sensor nodes are regarded as critical nodes which are responsible for the management of the other nodes in their sphere. Therefore, update of node information must be implemented through these critical nodes’ multicast operation. In this paper, we present a novel Node Information Update Multicast (NIUM) algorithm to optimize the transmission performance among critical nodes. After applying with our algorithm, each critical node could explore a reasonable transportation way according to its distance apart from other critical nodes. Despite of the ideal assumption, our algorithm is also tested under different circumstances and the results prove that it behaves well for prompt node information update in wireless sensor networks. Both velocity and efficiency of the network can be improved tremendously.

With the swift development of hardware simulation technology, virtual laboratory environment has appealed more and more attention of educators. In this paper, we develop an interactive virtual laboratory environment for hardware chips application experiment. It provides the students with real expression, convenient operation and powerful simulation capability. Furthermore, interactivity and expansibility make this system distinctive under different circumstances. Till now, our virtual laboratory environment has been implemented and successfully applied in a Computer Interface Technology course in Shanghai University and has gained positive appraisal from teachers and students.

The purpose of an intellectual answering system is that the system can answer questions automatically. To construct this kind of system, it must collect and store questions and answers organically. The intelligent Q&A system will contain a Q&A library, which is built through the semantic understanding of natural language technology and it will be growing automatically during students question and getting answer with the system.

Select the close-to-optimal collective algorithm based on the parameters of the collective call at the run time is an important step in achieving good performance of collective operation in base station wireless network. In this paper, we present our Reversed Multicast Tag Collective (RMTC) scheme to optimize the collective routing in base station wireless network. We verify our mechanism in a simulation platform which is implemented according to the message passing mechanism of base station wireless network and analyze the result and expect run time performance penalty. The results indicate that our Reversed Multicast Tag Collective scheme is flexible and feasible in base station wireless network with its high efficiency and wide applicability.

Base station multicast is an important enabling service for the current and future Internet. With the explosive growth of the Internet, a challenging issue facing Base Station multicast is scalability, in particular, the problem of multicast forwarding state and control explosion. In this paper, we propose a new methodology to address the multicast scalability problem for backbone domains - distributed message copying multicast (DMCM). This multicast scheme is designed for the wireless network, and is independent of any underlying intra-domain multicast protocols. The feasibility and performance of our algorithm is demonstrated through analysis and simulations.

A mathematical model for sideways overturns of wheel-tractor and trailer combinations, by the conventional method of disassembly and assembly of system components supplemented with a method of energy symmetry, devised for the present purpose to take care of the singularity range of the roll angle, was developed with a corresponding digital computer program and verified with tests on a 1/4.5 scaled, self-powered remote-controlled test model with agreeable results.

This paper proposes a graph feature selection method for dementia diagnosis, by adding the information inherent in the observations into a sparse multi-task learning framework. Specifically, this paper first defines two relations (i.e., the feature-feature relation and the sample-sample relation, respectively) based on the prior knowledge in the data. The feature-feature selection enforces the similarity relationship between features to be preserved in the coefficient matrix while the sample-sample relation is designed to preserve the relation between samples invariant in the predicted space. Then we embed these two kinds of relations into a multi-task learning framework (i.e., a least square loss function plus an l2-norm regularization term) to conduct feature selection. Furthermore, we feed the reduced data into Support Vector Machine (SVM) for conducting the identification of Alzheimer's Disease (AD). Finally, the experimental results on a subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset showed the effectiveness of the proposed method in terms of classification accuracy, by comparing with the state-of-the-art methods, including k Nearest Neighbor (kNN), ridge regression, SVM, and so on. Refereed/Peer-reviewed

since amount of unlabeled and high-dimensional datasets need to be preprocessed, unsupervised learning plays a more and more important role in machine learning field. This paper proposed a novel unsupervised feature selection algorithm that can select informative features from dataset without label, by mixing two sparse representation and self-representation loss function into a unified framework. That is, we use self-representation loss function to represent every feature with remainder features and achieve minimum reconstruction mirror, and then utilize l 2 , 1-norm regularization term and l 1-norm regularization term simultaneously to enforce coefficient matrix to be sparse, such that filter redundant and irrelative features in order to conduct feature selection, where l 2 , 1-norm regularization can enforce group sparsity while l 1-norm regularization enforce element sparsity. By this way that utilize both of sparse representation terms, we can choose representative features more accurately. At final, we feed reduced data into support vector machine (SVM) to conduct classification accuracy, which is main assessment criteria to validate performance of algorithm. Extensive experiments on synthetic datasets and real-world datasets have exhibited that our proposed method outperform most of common-used methods, such as PCA, LPP and so on. Refereed/Peer-reviewed