bluuebunny/crossref_metadata_2025_split huggingface dataset:
One scheme of stabilization and sample-data control for T-S fuzzy time-delay systems
Authors: Wang Wenhao , Zhu Hong , Zhong Shouming , Shi Kaibo 
Published: April 2021

Abstract
In this article, a refined Lyapunov functional is proposed to study the stabilization problem for Takagi-Sugeno (T-S) fuzzy systems with time delays. Considering both the time-varying delays of the system and the constant delays of the part of sample-data control loop, the refined Lyapunov function in this paper makes it more practical in actual sampling control pattern compared with some existing research. Some techniques of dealing the linear matrix inequalities (LMIs) are used to derive the less conservative criteria and an example is presented in the end to prove the availability of the work.
The forecast of household power load based on genetic algorithm optimizing BP neural network
Authors: Liu Hengchao 
Published: April 2021

Abstract
In view of the fact that power load forecasting is increasingly becoming an important part of power system planning and power dispatching, this paper proposed a forecasting method based on genetic algorithm optimizing BP neural network and applied it to household power load forecasting. First of all, the original data were pre-processed by factor analysis to eliminate the correlation between variables, and then the household load from January 1st to March 22nd was predicted by using BP and GA-BP models respectively. The author obtained the predicted results in the next 5 days summing to 120 hours and utilized MAPE and RMSE to compare their prediction errors quantitatively. The predicted results of the calculation example are in good agreement with actual values. Therefore it is demonstrated that the BP neural network optimized by genetic algorithm has better forecasting effects, which can provide reference for residential and municipal short-term load forecasting.
A Reusable Test Case Management System Based on Exploratory Testing
Authors: Yu Jiujiu , Zhang Jishan , Chen Yun , Wu Ning , Mei Yingying , Zhang Deqing , Zhu Canglu , Zhu Lili 
Published: April 2021

Abstract
The research is mainly based on the concept of technologies of software exploratory testing, Questing Patterns (Q-Patterns) and reusable test case, taking a reusable test case as an example, this study is devoted to the development of a feasible reusable test case management system based on exploratory testing in rapid prototype development method, which the process of design, storage, reuse and management of test cases could be implemented positively in the first system version. Furthermore, further work for technical improvement in the next version of this system is put forward.
A method based on cuckoo search for optimal network reconfiguration
Authors: Gao Zhengzhong , Xu Yujuan , Li Zongfu , Sun Yuchen , Liu Guangjie 
Published: April 2021

Abstract
The optimal reconfiguration of the distribution network doesn’t require any cost and becomes an effective technique for reducing power loss. So this paper proposes an efficient cuckoo search algorithm for this problem. First, the mapping relationship between the cuckoo search algorithm and the distribution network is established; secondly, the algorithm generates the initial population based on the set of feasible solutions; finally, algorithm updates the nest position by the Lévy flight and nest parasitic behavior. A variable domain search algorithm is introduced to improve for the shortcomings of the cuckoo search algorithm in the process of solving the distribution network reconfiguration problem. Finally, it is verified that the algorithm has the ability to quickly solve the optimal solution by the IEEE33 model simulation analysis.
Construction of Scene Tibetan Dataset Based on GAN
Authors: Zhang Guowei , Wang Weilan , Zhao Penghai , Li Jincheng 
Published: April 2021

Abstract
For the research of Tibetan scene text detection and recognition, it is time-consuming and laborious to collect and annotate natural scene data labels manually. Therefore, artificial synthetic data is of great significance for promoting relevant work. This paper focuses on the study of replacing other languages in the scene with Tibetan, while maintaining the style of the original text. We decompose the problem into three sub-networks: text style transfer network, background inpainting network and fusion network. Firstly, the text in the foreground image is replaced by the text style transfer network to generate the foreground image. Then the background inpainting network erases the original text in the style image, and uses the surrounding information around the text to fill the text area to generate the background image. Finally, the generated foreground image and background image are used to generate the target image by the fusion network. We experimented with conversions from English to Tibetan and English to English to verify the generalization and robustness of the network. Experimental results show that its accuracy (SSIM, PSNR) on some datasets (SVT, ICDAR 2013) has been improved to some extent.
Pedestrian detection based on one-stage YOLO algorithm
Authors: Zuo Xun , Li Jiaojun , Huang Jie , Yang Fan , Qiu Tian , Jiang Yang 
Published: April 2021

Abstract
Pedestrian detection is mostly used in autonomous driving scenarios, which require high real-time detection. Most of the existing algorithms are based on Two-Stage detection, with poor real-time performance. This paper proposes a pedestrian detection system based on One-Stage, and uses One-Stage-based YOLO-Tiny, YOLO and YOLO-SPP algorithms to test and analyze the system in the scene of detecting pedestrians of different sizes. The results show that YOLO and YOLO-SPP have high average confidence, and YOLO-Tiny has a fast detection speed, which is suitable for fast detection scenarios.
A Dynamic Load Balancing Strategy Based on Improved Ant Colony Algorithm
Authors: Shi Hang Qi , Hao ZiLi , Shi Hang Qi 
Published: April 2021

Abstract
Load balancing technique plays a fundamental role in distribution, while facing numerous challenges. It is difficult for ordinary load algorithms to cope with complex scenarios, and it is likely that uneven load will lead to downtime of a single server, thus causing an avalanche effect and eventually breaking down the whole cluster. This paper proposes a load balancing strategy based on improved ant colony algorithm, considers the influence of task length and processing efficiency in the algorithm based on the original ant colony algorithm, and takes the load rate of nodes as an important index for ants to select path. In addition, the load coefficient includes CPU, memory, IO, and network bandwidth into the calculation range, which makes the obtained server load condition more accurate. Finally, test data of different algorithms are obtained through experiments, and the advantages and disadvantages of this load balancing strategy are verified after horizontal comparison.
Research on the Application of Computer Music Making Technology in New Media Environment
Authors: Zhang Meili , Hou Kewen 
Published: April 2021

Abstract
The rapid development of science and technology makes the use of new media technology more and more common. In the 21st century, the communication mode of new media is becoming more and more diversified, and its convenient and efficient communication mode can meet the needs of people’s modern office and life. Digital audio has a variety of production technology, and the production accuracy and controllability are very high, so it occupies the main position in the information composition of new media. As a sensory media, sound is mainly manifested in voice and music in daily life. At the same time, computer music production technology mainly includes digital processing technology and MIDI production technology. It can be seen that computer music production technology has become the core technology of sound editing, production and processing under the background of new media. This paper takes the application of computer music production technology as the research object, analyzes the internal relationship between new media and music production technology, the application and development of music production technology and so on, so as to provide the development direction for computer music production technology.
A Brief Analysis of the 5G+4K/8K+AI Strategic Layout of Central Radio and Television Station —— Taking the 2019 National Day Campaign Publicity Report as an Example
Authors: Wang W H , Du C X 
Published: April 2021

Abstract
On August 20th, 2019, at the Beijing International Radio, TV & Film Exhibition, the Central Radio and Television General Station announced the 5G+4K/8K+AI strategy, including four main parts: integrated broadcasting, ultra-high-definition television, mobile new media and artificial intelligence. CCTV is now comprehensively abandoning standard production system to achieve comprehensive high-definition, comprehensively promoting the development of 4K/8K, building the first national 5G new media platform, combing artificial intelligence with media, and building a new strategic layout. On the occasion of the 70th anniversary of the Chinese National Day, the Central Radio and Television Station integrated various television channels, broadcast frequency, and website platforms, carried out panoramic and three-dimensional live and broadcast, realized the whole process of 5G+4K ultra-high-definition and 5.1 stereo live broadcast, and provided a new audio-visual experience for end users. This article will take 2019 National Day 70th Anniversary publicity report as an example, excavate the characteristics and advantages of 5G+4K/8K+AI strategic layout, and discuss the development trends and predicament of the initial stage of strategic layout.
Image Inpainting of Multi-Spectral Image with Laser Lines Based on Generative Adversarial Network
Authors: Lu L , Zhong G , Dong J 
Published: April 2021

Abstract
This paper presents a Generative Adversarial Network based on image in-painting, which can reconstruct the shape using a multi-spectral image with a laser line. One of the difficulties in multi-spectral photometric stereo is to extract the laser line, because the required illumination for multi-spectral photometric stereo, e.g. the red, green, and blue lights, may pollute the colour of the laser line. In this paper, we presents a method, which uses the Generative Adversarial Network based on image in-painting, to separate a multi-spectral image with a laser line into a clean laser image and an uncorrupted multi-spectral image without the laser line, to reconstruct the shape using a multi-spectral image with a laser line. To make the proposed method applicable to real-world objects, a rendered image dataset obtained using the rendering models in ShapeNet has been used for training the network, and the evaluation shows the superiority of the proposed approach over several previous methods, on both rendered images and real-world images.
Pyramid and Similarity Based Feature Enhancement Network for Person Re-identification
Authors: Chu Chengguo , Qi Meibin , Jiang Jianguo , Chen Cuiqun , Wu Jingjing 
Published: April 2021

Abstract
Person re-identifification (Re-ID) has becomes an increasingly important task due to its wide applications in the field of intelligent video surveillance. However, Because of the camera shooting angle, the different proportions and pose changes of pedestrians in images are also different, such problem substantially hinders the model’s capability on further improving feature extraction. In this paper, we propose a new end-to-end architecture named Pyramid and Similarity Based Feature Enhancement Network (PS-Net) which includes Pyramid Joint Attention(PJA) and Similarity Feature Fusion Branch(SFF) to enhance the feature maps performance. Among them, PJA contains spatial attention at different receptive fields and channel attention to enhances the discriminative area of pedestrian features by combining the advantages of two different attentions. SFF fuses the features which have different levels of information but have congruent relationships, enriches the information contained in pedestrian features and generates more robust feature. Extensive experiments have been conducted to validate the superiority of our PS-Net for Re-ID over a wide variety of state-of-the-art methods on two large-scale datasets: Market-1501 and DukeMTMC-ReID.
Research on Vehicle Appearance Damage Recognition Based on Deep Learning
Authors: Zhu Qianqian , Hu Wei , Liu Yingnan , Zhao Zihao 
Published: April 2021

Abstract
Economic development has promoted the booming of the auto industry. With the increase of the number of cars, car insurance has become the largest type of insurance in the insurance industry with more than half of the market share. After the emergence of traditional vehicles, professional loss assessment personnel need to go to the scene to investigate the accident and complete the loss assessment. In recent years, With the rapid development of science and technology, the insurance industry has been changing from artificial and information to automation and intelligence. This paper presents a vehicle appearance damage recognition algorithm based on deep learning and its model evaluation method, which can accurately judge the vehicle damage in the image. The research shows that the Mask R-CNN model based on KL-loss performs well in vehicle damage detection and has good robustness; at the same time, the accuracy of the evaluation model results is greatly improved by replacing the traditional IOU calculation accuracy method with the component position.
A Context-Aware Gated Recurrent Units with Self-Attention for Emotion Recognition
Authors: Li Lin , Yang Miaomiao 
Published: April 2021

Abstract
Text-oriented emotion recognition research has made significant research progress, however, very few works pay attention to learn the highquality long-distance contextual information for the utterance emotion recognition. In this paper, we proposed a context-aware gated recurrent units with self-attention for emotion recognition. The two bidirectional gated recurent units can obtain the sequence ralationship between words and utterances. Compared with the self-attention mechanism that cannot capture long-distance contextual information, three contexts are used in the self-attention mechanism, namely global, deep and deep-global context, which can capture the highquality long-distance contextual information. And the connection mechanism can enhance the embedding information of words and utterances. Experimental results on public Friends and EmotionPush datasets demonstrate that the three contexts in the self-attention mechanism outperforms several baselines on some emotion types recognition and indicate the effectiveness of the designed model, especially the deep-global context increased by 1% and 0.2% on UWA compared with all models.
A Multi-Channel LSTM Model for Sea Surface Temperature Prediction
Authors: Lin Yidi , Zhong Guoqiang 
Published: April 2021

Abstract
Sea Surface Temperature (SST) plays an important role in marine ecology. SST prediction raises considerable attention in ocean-related fields. Recently, deep learning models are widely used in SST prediction, but it is not easy to obtain optimal prediction results using historical observation data directly. More than temporal information, SST data also contains other features, such as trend, periodicity and disturbance. In this paper, we proposed a Multi-Channel LSTM (MC-LSTM) model to improve SST prediction. Firstly, a wavelet transform is used to decompose time-sequence data into multiple sequences representing trend, period and disturbance respectively. Secondly, we use multiple LSTM channels to train these data in parallel, and then obtain the combined prediction results. MC-LSTM can predict Sea surface temperature only by historical SST data, without the help of other spatial and climatic information, so it is easy to get the data. Compared with the direct use of LSTM for prediction, MC-LSTM can improve the prediction accuracy by 26%.
Research on Replica Placement Strategy for New Building Intelligent Platform
Authors: Yang Y L , Zhu J C , Zhang R , Zhu X L , Wang M Y , Wang C 
Published: April 2021

Abstract
The new building intelligent platform adopts single point storage in the data storage mode, which does not reflect the distributed characteristics of the platform. Once the node is damaged, the data will be lost. To solve this problem, this paper improved the data storage mode of the new building intelligent platform, combined Hadoop distributed file system (HDFS) with the platform, and realized the reliable backup of the platform data. For the deficiency of HDFS default replica placement strategy, the paper advanced a algorithm of replica placement for the new building intelligent platform based on PSO, and obtained the optimized replica placement strategy by considering the storage space residual rate of nodes, bandwidth residual rate and network distance between nodes. Simulation was on the CloudSim platform. The results of experiment show that the proposed scheme was superior to the default replica placement strategy in load balancing, which can reduce the load pressure of single node to a certain extent and improve the load balancing of the whole platform.
Design and Realization of Modern Smart Home System Based On Multimedia Network Technology
Authors: Yuan Hongmu 
Published: April 2021

Abstract
With the development of science and technology and the progress of multimedia network technology, various intelligent electronic products are more and more widely used in people’s lives. High-tech and intelligent technology have moved from intelligent buildings to residential home users, and home intelligent technology has become a cutting-edge technology topic. In this regard, the purpose of this article is to study the design and implementation of modern smart home systems based on multimedia network technology. This article first studies the development status of domestic and foreign multimedia network technology and smart home technology, and conducts a questionnaire survey on some residents to understand the current status of smart home. Finally, based on this, this article uses high-level programming language to code the system. According to the method and technology of test theory, the function and performance of the software system are tested completely, and compared with the traditional smart home system. The experimental results show that the modern smart home system using multimedia network technology is more recognized and loved by residents, and the residents’ preference for smart home has increased by about 20%, which has played a significant role in increasing the utilization rate of smart home systems and is a multimedia network the future development of smart home systems provides an important reference.
Big Data Analysis of Threat Intelligence Based on IP
Authors: Xu Fajian , Chen Shuzhen 
Published: April 2021

Abstract
This paper first discusses the concept of network threat intelligence and related resource integration, and proposes a three-tier architecture model of threat intelligence analysis system based on IP. Then, it discusses the diversified data acquisition technology, information index extraction technology, data mining and data modeling analysis technology, the key technology of threat intelligence credible intelligence analysis model based on machine learning and privacy protection. Finally, this paper considers and summarizes the legal standardization of threat intelligence analysis.
Analysis of Difference for Gully Density and the Relationship between Terrain Factors and Gully Density in the Losses Plateau Based on Google Earth and GIS
Authors: Zhu Jiangshan , Hou Chenglei 
Published: April 2021

Abstract
Based on ASTER GDEM V2 (30 meter) and the corresponding Google Earth high-resolution images data, small-catchments artificial interaction and random points sample method were used to estimate gully density of five selected catchments in the Losses Plateau and analysis the relationship between the difference for gully density from two methods and the areas of catchments. Besides the gully density, terrain factors such as slope, flow accumulation as well as the distribution probability of gully were calculated by using spatial analysis method. The statistical analysis method was used to analyze the correlation between gully density and terrain factors and obtain gully characteristic distribution prediction model. The results of the study indicate the following: (1) the average difference of gully density extracted by random point method and artificial interactive method is 7.86%, and the difference appears to increase first and then decrease with the watershed area. When the area of catchment is 76 km2, the difference is closed to 0; when the area is 131 km2, the difference is closed to maximum value, which is 0.182. The time-saving and labor-saving random point method can replace the tedious artificial interaction method. (2) The gully slope on Loess Plateau is mainly distributed at 10°∼30° (74%), and the flow accumulation of gully distribution is mainly distributed at 0∼0.07 km2 (95%); 10°∼30° and 0∼0.07 km2 can be used as critical reference values for, respectively. (3) The gully distribution probability showed a when slope gradient was lower than 30 degree, while a negative correlation of gully density and the flow accumulation were found in this study.
Garment Textile Correction System Based on Artificial Intelligence under Computer Parameter Optimization Design
Authors: Yan Lifeng , zhang Qianyun , Liu Wei 
Published: April 2021

Abstract
As artificial intelligence technology is widely used in more and more fields, it has become the frontier technology of social innovation. Artificial intelligence based on the development of computer parameters has been widely used in the clothing textile industry, and the technology of the clothing textile correction system has appeared. This article will discuss in detail the development trend of my country’s artificial intelligence technology and the establishment of computer parameters for clothing textiles in combination with the development, model status, and development status of domestic and foreign clothing textiles. Trend and production models will also be developed and analyzed for apparel textile correction system models. The clothing system’s artificial intelligence and production integration, computer parameters, product specifications and other aspects are analyzed. The purpose of this article is to create a clothing textile correction system, which can provide a useful reference for the development of artificial intelligence in the clothing textile industry in our country. Based on relevant knowledge and modern theories in practice, this article is based on a clothing textile correction system using artificial intelligence in computer parameter optimization design. In the course of the experiment, the implementation of the garment textile correction system summarized the technical value and function, as well as the technical purpose and development prospects, through rigorous experiments in theory and practice, to innovate and develop the traditional garment textile production mode. Experimental data shows that under the optimized design of computer parameters, the creation of artificial intelligence-based clothing textile correction system can provide a more convenient way for the clothing textile industry, integrate artificial intelligence into clothing textile production, and improve management efficiency and production efficiency.