bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adjust Planning Strategies to Accommodate Reinforcement Learning Agents
Authors: Chen Xuerun 
Published: February 2021

Abstract
In agent control issues, the idea of combining reinforcement learning and planning has attracted much attention. Two methods focus on micro and macro action respectively. Their advantages would show together if there is good cooperation between them. An essential for the cooperation is to find an appropriate boundary, assigning different functions to each method. Such a boundary could be represented by parameters in a planning algorithm. In this paper, we create an optimization strategy for planning parameters, through analysis of the connection of reaction and planning; we also create a non-gradient method for accelerating the optimization. The whole algorithm can find a satisfactory setting of planning parameters, making full use of the reaction capability of specific agents.
A Hybrid Approach for Signal Modulation Recognition Using Deep Learning Methods
Authors: Fan Changjun , Wang Yufeng , Liu Ming 
Published: February 2021

Abstract
In this study, a novel signal modulation recognition framework has been proposed for automatically classifying eleven different modulation types with various SNR values. The framework employs both the raw complex-valued I/Q signal and its time-frequency description to represent the radio signal. And, a hybrid deep neural network is presented to recognize different modulation types from the representation data by leveraging the appealing properties of a convolutional neural network (CNN) and a long short-term memory (LSTM) network. Extensive validation of our scheme is performed on a large public dataset by comparing it with three existing 。 methods from literature, and our scheme yields quite promising results in terms of recognition accuracy.
Research of Attention-Based Bi-GRU-CRF for Slot Filling
Authors: Zhou Luwang , Huang Zhimin , Nie Ying 
Published: February 2021

Abstract
Slot Filling (SF) is a critical part of spoken language understanding (SLU) which targets to capture semantic constituents from a specific utterance. It is considered as a sequence labeling issue. Currently, recurrent neural networks have shown promising effectiveness in this issue. Considering the effects of interrelated information within adjacent words and labels, we present that a novel approach consists of bi-directional gate recurrent unit (Bi-GRU), attention mechanism, and conditional random field (CRF). Our model can utilize interrelated information from words in the neighborhood, highlight key information, and exploit the dependencies within labels corresponding to surrounding words. The empirical experiments illustrate that our model significantly boosts the F1 score with around 1% and 5.1% relative enhancement on two public benchmark dataset ATIS and SNIPS separately.
Collaborative Filtering Algorithm Based on Improved Time Function and User Similarity
Authors: Zhang Weiguo , Zhou Xiran , Yuan Weixuan 
Published: February 2021

Abstract
As a typical representative of information filtering technology in the era of big data, a recommendation system is an important means to solve the problem of information overload. A collaborative filtering recommendation algorithm is one of the important technologies to realize the recommendation system, but the traditional collaborative filtering algorithm only considers the similarity of ratings between users. As the number of users and the number of items increases, it faces user interest drift and reduced recommendation Precision, and other issues. In this regard, a collaborative filtering algorithm based on improved time function and user similarity is proposed. First, considering that user interests will dynamically change over time, this paper introduces an improved time function in the traditional scoring similarity; secondly, considering the impact of the number of items evaluated by users on the calculation of similarity measurement, this paper takes the Pearson correlation coefficient weighted scoring into account; Finally, the fusion recommendation is based on the improved time function and the weighted Pearson correlation coefficient to improve the Precision of recommendation prediction. This paper conducts simulation experiments on MovieLens 100K and 1M data sets respectively. The results show that, compared with the traditional collaborative filtering recommendation algorithm, combining the improved time function and the weighted Pearson correlation coefficient can effectively improve the recommendation Precision.
Multilayer Convolution Sparse Coding for Expression Recognition
Authors: Chen Shuda , Wu Yan 
Published: February 2021

Abstract
Facial expression recognition is widely used in various research fields. For facial expression recognition problems, deep neural network methods have a complex structure and poor interpretability, while traditional machine learning methods have less plentiful diverse features and low recognition rates. Therefore, a new Multilayer Convolution Sparse Coding (MCSC) method is proposed for facial expression recognition. The MCSC method deeply extracts the salient features of the human face through a convolutional neural network. Furthermore, it uses a multilayer sparse coding to learn layer by layer to recognize different facial expression features based on sparse coding, which improves the recognition accuracy of facial expressions. Finally, the MCSC method was validated on three public facial expression datasets, i.e. JAFFE, CK +, and Fer2013. We also compared and analyzed 5 feature extraction approaches. The results show that MCSC has the best facial expression recognition performance in the comparison algorithm. Its accuracies of the three data sets reach to 90.8%, 98.2%, and 72.4%, respectively.
A Novel Multi-Agent Deep Reinforcement Learning Approach
Authors: Yin Dong , Zhao Zhe , Dai Yinglong , Long Han 
Published: February 2021

Abstract
Borrowing the power of deep neural networks, deep reinforcement learning achieved big success in games, and it becomes a popular method to solve the sequential decision-making problems. However, the success is still restricted to single agent training environment. Multi-agent reinforcement learning still is a challenge problem. Although some multi-agent deep reinforcement learning methods have been proposed, they can only perform well when the number of agents is very limited. In this paper, by analyzing the dynamic changing observation space and action space of multi-agent environment, we propose a novel multi-agent deep RL method that compress the joint observation space and action space as the time goes on. The proposed method is potential for a large number of agents cooperative or competitive tasks
Application Analysis Of Smart Wearable Devices in Students’ Physical Activity Under the Background Of Big Data
Authors: Li Mingxia , Sun Minghao , Wang Liangliang 
Published: February 2021

Abstract
With the rapid development of information technology, big data is gradually being popularized and promoted in various industries. Smart wearable devices have attracted much attention for their objective, accurate and continuous measurement of the amount of exercise. With the continuous improvement of intelligent equipment performance, large data analysis and intelligent wearable device technology can real-time capture the movement of the students in class data: trajectory, heart rate, speed, cost, etc., through data analysis, to monitor and intervene student’s sports activities in the class at the same time, can help the teacher more scientific system of the training plan, a more comprehensive understanding of the students in the class level and performance, enhance the level of student’s physical stamina and physical health and will bring far-reaching influence to sports research.
An Effective Algorithm for Mining Indirect Association Rules
Authors: Qiaoling Duan 
Published: February 2021

Abstract
In order to solve the problem that it is necessary to scan the database many times and produce unnecessary frequent itemsets in the process of mining indirect association rules, a new algorithm FPI-mine based on FP-Tree is designed to mine the indirect association rules in transaction database. Firstly, FP-Tree is constructed, and then the indirect item pairs and intermediate support sets of all frequent items are found. Finally, all the indirect association rules are obtained by mining algorithm. It can directly mine indirect association rules without generating all frequent itemsets. Finally, the effectiveness of the algorithm is verified by experiments.
Finding Subgroups of UAV Swarms Using a Trajectory Clustering Method
Authors: Gu Kongjing , Mao Ziyang , Qi Mingze , Duan Xiaojun 
Published: February 2021

Abstract
Unmanned Aerial Vehicles(UAV) swarm is a rapidly developing field, and with it comes the need to identify the swarm based on observations. The problem of trajectory clustering is put forward in the identification of UAV swarms, especially modularized UAV swarms. We propose a new method of Network Integrated trajectory clustering(NIT) to solve the trajectory clustering problem in a fast-changing and chaotic environment which requires a quick response, fault tolerance, and accuracy. The experiment results prove the flexibility and adaptability of the NIT method towards various demands and multi-dimensional data. Moreover, the algorithm proposed based on the method shows priority over the other three trajectory clustering methods(DTW, Fréchet distance, GMM) on the accuracy, and fault tolerance in clustering swarm trajectories. The method raised in this paper is an innovation to both multi-agent systems identification and trajectory clustering methods.
Improved Mask Wearing Detection Algorithm for SSD
Authors: Deng Houkang , Zhang Jin , Chen Lingyu , Cai Meiling 
Published: February 2021

Abstract
Coronavirus disease is seriously affecting the world in 2019. Wearing a mask in public places is a major way to protect people. However, there are few studies on mask detection based on image analysis. In this paper, an improved mask wearing inspection algorithm based on the SSD algorithm is proposed. The SSD algorithm is improved to add a face mask wearing detection task. Based on the original SSD model, the algorithm improves the mask wearing detection capability by introducing inverse convolution and feature fusion in combination with an attention mechanism to filter out the information to be retained. A dataset containing 3656 tensor images was created and manually labeled for network training. Experiments on this dataset show that the algorithm has good accuracy for mask wearing inspection.
Research on Load Forecasting of Charging Station Based on XGBoost and LSTM Model
Authors: Xue Mingfeng , Wu Lin , Zhang Qi Pei , Lu Ji Xiang , Mao Xiaobo , Pan Yongtao 
Published: February 2021

Abstract
At this stage, due to the increasing use of electric vehicles, the position of electric vehicle load scheduling in grid power scheduling is becoming more and more important. Effective electric vehicle power dispatching can balance the peak-valley difference of power dispatching, increase the power supply utilization rate of power grid dispatching, and reduce the power supply pressure of line transformer. The load forecast can describe the user’s electricity consumption habits in the next period of time, and can provide important data basis for power dispatching. This paper summarizes the research status of electric vehicle charging load, analyzes traditional charging load research methods, propose a charging load forecasting method combining XGBoost(Extreme Gradient Boosting) and LSTM (Long Short Term Memory Network), And use the data of a charging station in Jiangsu to verify the calculation example. The proposed method is based on the prediction results of the XGBoost model for feature engineering, extracting data features using phase space reconstruction techniques and statistical methods. In addition, training the LSTM model for load prediction. Based on the charging record data of domestic charging stations, this paper applies the artificial intelligence method to the charging load forecast of domestic charging stations for the first time. The charging station load forecasting method studied in this paper can support the regional load forecasting research of electric vehicles with high permeability, and further optimize power dispatching.
The research of Random Forest Intrusion Detection Model based on Optimization in Internet of Vehicles
Authors: Li Yalin , Li Fei , Song Jiaqi 
Published: February 2021

Abstract
Security model is the main means to protect the network information security of vehicle. Due to the rapid development of artificial intelligence in recent years, machine learning technology is also emerging in the field of Internet of vehicles security. The random forest model is a strong classifier and can prevent overfitting better than the decision tree model. However, only using the traditional random forest invasion detection model has some problems, such as: the model detection time is long, the false alarm rate is high, the ability of using platform transplantation is poor, etc. In this paper, it is optimized in a lightweight way to reduce the time consumption and improve the accuracy of intrusion detection in the vehicle networking intrusion detection model.
The Early-Warning and Inspection System For Intelligent Greenhouse Based on Internet of Things
Authors: Wang Liangliang , Jia Lianqin , Wang Jiamin , Zhou Qian 
Published: February 2021

Abstract
The early-warning and inspection system for the intelligent greenhouse is an inspection robot based on the Internet of things and equipped with various sensors and intelligent cameras with 4G or 5G communication functions, such as temperature and humidity, light, gas, and flame. Which uses the wireless network transmission to the cloud computing platform for data analysis, sending warning information through smartphones to enable farmers to real-time monitoring in agricultural greenhouse crop growth status, temperature and humidity, illumination, CO 2concentration, plant diseases, and insect pests, etc., to achieve automatic control, scientific planting and raising the yield and increase their income. This paper mainly analyzes the hardware and software structure of this early-warning and inspection system, data transmission and processing, and Android application, so as to promote the application of intelligent planting.
Research of Intelligent Temperature Measuring Robot System
Authors: Tian Jianying , Jia Lianqin , Mu Shile 
Published: February 2021

Abstract
With the outbreak of novel coronavirus pneumonia worldwide, a kind of epidemic prevention robot is urgently needed in society. The intelligent temperature measurement robot system mainly includes the robot’s high stability multi-attitude omnidirectional movement module, robot path planning and autonomous navigation module, robot environment information acquisition and recognition module, robot remote control module, and robot system integration. According to the design principle of the wheeled robot, high stability and multi attitude omni-directional mobile platform of temperature measuring robot are constructed; path planning and autonomous navigation of robot are realized based on sensor data fusion; temperature detection and remote care function are realized by thermal imaging sensor system by capturing the thermal radiation emitted by the human body, and based on open modular control system architecture. Based on the existing research foundation, the intelligent temperature measurement robot system is established by integrating the robot multi attitude omni-directional mobile system, robot path planning, and autonomous navigation system, robot environment information acquisition and recognition system, and robot remote control system.
Design of Climbing Robot for Power Transmission Tower Maintenance
Authors: Tian Jianying , Liu Qin , Yuan Kexin 
Published: February 2021

Abstract
Periodic maintenance of transmission lines is a necessary guarantee for stable power transmission and avoiding serious power accidents. In order to reduce the safety risk of the first person climbing the transmission tower and the last person going down the tower when climbing the transmission tower manually, this paper develops an auxiliary climbing robot for the maintenance of transmission tower based on the bionic climbing principle. It includes the modular mechanical structure of the main body lifting, obstacle crossing telescopic, terminal clamping, and body jacking, as well as airborne motion control system and hierarchical control system of upper computer monitoring terminal. The comprehensive test results of indoor and outdoor simulated transmission tower environment show that the robot system can effectively complete the high-altitude climbing of power tower, and ensure the follow-up tasks of hanging, dismantling, and maintenance of safety anti-falling device.
An Adaptive Double Area Page Replacement Algorithm for NAND Flash
Authors: Li Jiatong , Hu Huaixiang 
Published: February 2021

Abstract
In order to improve the hit rate of memory pages and prolong the service life of flash memo ry, an adaptive double area page replacement algorithm is proposed. The buffer is divided into page area and page cluster area. The initial size o f each area is set. The size of the area is dynamically changed according to the number of page hits in the two areas. The replacement unit of the page area is a single page. The CCF-LRU algorithm is used in page area replacement policy. When the page in the page area needs to be replaced, the rep laced page is put into the page cluster area. The replacement unit of page cluster area is the whole page cluster. When the page area cluster needs to be replaced, the dirty memory pages in the page cluster are divided into small memo ry pages with the same size as the flash memory pages, and only the dirty small memory pages are written back. According to the analysis of experimental results, compared with CF-LRU, CCF-LRU and FAB, the hit rate is increased by 9%, 8% and 11% respectively
Research on the new Method of Real Estate Integration Confirmation and Registration Based on small Program App
Authors: Feng Yougui , Xing Zhurong , Han Ze , Xie Jiying , Wei Yequn , Xu Yanming , Wang Lei 
Published: February 2021

Abstract
In order to solve the problem of repeated collection of ownership information in the process of real estate integration confirmation and registration, a collection system applet was specially developed, which changed from the original technical staff from door to door collecting ownership information to government-led and farmer cooperation. According to the simple and clear system interface, the farmers enter the ownership information, upload the photos of the right source materials, the homestead and the house photos, which solves the big problem that the ownership investigation must be repeated and the ownership information must be entered one by one, which has changed to a certain extent. The operation mode of traditional ownership investigation has greatly reduced the pressure of field work, reduced the cost of manpower and material resources, improved work efficiency, and protected the legitimate rights and interests of farmers.
Graph Node Strength Histogram Publication Method with Node Differential Privacy
Authors: Liu Wenfen , Liu Bixia , Xu Qiang , Lei Hui 
Published: February 2021

Abstract
The node differential privacy (node-DP) can be used to protect the private information of nodes and edges in the graph. In this paper, we propose an algorithm of publishing node strength histogram under node-DP, which could improve the accuracy of publishing the node strength histogram. In this algorithm, we use the Sequence Edge removal to reduce the sensitivity of query function and restrict the weight of edges to make the distribution of node strength denser. Furthermore, we use the histogram grouping algorithm Hierarchical Cluster Grouping to group the buckets to merge buckets with close values into one group. The experiments show that our algorithm maintains higher data utility than those traditional histogram publishing algorithms under the same privacy budget.
The Stability Study and Simulation of Soil Landslide Based on Bishop Method
Authors: Yang Min , Zhao Hua 
Published: February 2021

Abstract
Landslide is one of the most common disasters in nature and is a kind of slope failure. Many countries in the world suffered, because of its wide distribution, frequent occurrence and great harm. In China, the national economy and people’s lives and property suffer heavy losses due to the landslide disaster. In this paper, first of all, we used the Bishop method to theoretically analyze the stability of a landslide in Sichuan Province, and then we established a simulation model in Flac3D, the rock and soil mechanics calculation software developed by ITASCA company in the United States, to analyze the stability of the landslide under complex conditions. Finally, the results of theoretical analysis and simulation are consistent, which can provide ideas and methods for geological disaster prevention in the future.
Electrical conductivity of MoS2 thin films and graphite under continuous injection to protons
Authors: Burkhanov G S , Lachenkov S A , Kononov M A , Bashlakov A U , Prosvirnin D V 
Published: February 2021

Abstract
The electrical conductivity of thin films of MoS2 and graphite has been studied under conditions of continuous injection of low-energy protons (∼ 1-4 keV). The effect of a sharp increase (by 3-4 orders of magnitude) in the electrical conductivity of substances with a layered structure (MoS2 and graphite) at T ∼ 293 K has been established. The observed effect increases (from 2 to 10 times) as the temperature decreases from room temperature to liquid nitrogen temperature (T ∼ 77 K) and with increasing number of protons injected into the sample. The established temperature variation of electrical resistance is typical for materials with a metallic type of conductivity. It is shown that a thin surface layer, onto which protons penetrate, is responsible for the change in the electrical conductivity of MoS2 and graphite films.
Investigation of the preparation of W-Cr system powders from oxide compounds by magnesium vapors reduction
Authors: Kolosov V N , Miroshnichenko M N , Yu Prokhorova T 
Published: February 2021

Abstract
The process of producing W-Cr powders by the reduction of the oxide compounds mixture Cr2O3-WO3 and CaCrO4-CaWO4 with magnesium vapor in the temperature range 700– 800 °C at rarefaction of 5-15 kPa was studied. W-Cr powders with a specific surface area at the level of 34 m2·g−1 were obtained during the reduction of mixture of CaCrO4-CaWO4 compounds. The resulting W-Cr powders are characterized by a mesoporous structure.
Tungsten-rich Ni-W coatings, electrodeposited from concentrated electrolyte for complex geometry parts protection
Authors: Krasikov A.V. , Merkulova M.V. , Markov M.A. , Bykova A.D. 
Published: February 2021

Annotation
The process of electrodeposition of nickel-tungsten coatings from a citrate electrolyte with an increased concentration of salts of the deposited metals has been investigated. Based on the results of studying the effect of pH, the ratio of the concentrations of nickel and tungsten salts in the electrolyte, the temperature of the electrolyte on the composition and current efficiency of the Ni-W alloy, optimal conditions have been determined that make it possible to obtain Ni-50% W coatings at a rate acceptable for industrial use. Evaluation of the properties of the coating showed that it is non-porous and has a microhardness of 600-640 HV.