bluuebunny/crossref_metadata_2025 huggingface dataset:
Analog Combining in Intelligent Reflecting Surface Assisted System with Low-Resolution ADCs
Authors: Xu Zhixin , Wang Rui , Liu Erwu
Published: December 2020

Intelligent Reflection Surface (IRS) and low-resolution analog-to-digital converters (ADCs) are proven to be efficient ways to release the burden of power consumption for future wireless systems. In this paper, the effect of IRS and quantization on the performance of multiple-input multiple-output (MDVIO) channels is investigated in terms of channel capacity. The reflection matrix of the IRS and analog combiner matrix are optimized jointly. To this end, the expression of capacity with low SNR is derived, and the optimization problem of the reflection matrix is converted to semidefinite programming (SDP). A gradient-based approach is proposed to optimize the analog combiner matrix and the reflection matrix in a general case. Simulation results are provided to assess the performance of proposed designs, it is shown that the IRS can improve the performance of the system where there is no line-of-sight channel to some extent, and a larger capacity could be obtained in low SNR expression than general expression under same conditions.
Multi-Modal Biometrics based on Data Fusion
Authors: Yang Hongxun , Sun Eason , Cheng Cheng , Ding Anthony H
Published: December 2020

With the development of intelligent application, biometrics recognition technology has been widely concerned and applied in many fields of the real world, such as access control and payment. The traditional biometrics are usually based on single modality data of the subjects, but they are limited by the feature information capacity and the bottleneck in recognition accuracy. In this paper, a multi-modal biometric recognition framework is presented, which utilizes a multi-kernel learning algorithm to fuse heterogeneous information of different modal data. In order to extract complementary information from them, we combine the kernel matrix to form the mixed kernel matrix, and then give the final classification results. The experimental results on multiple biometric datasets show that our method can obtain higher recognition accuracy compared with the existing single mode and multi-mode fusion methods.
Visual Analysis of World Earthquakes based on Data Science and Statistical Methods
Authors: Xiangrong Xing
Published: December 2020

Earthquake is a very serious geological disaster, often accompanied by serious casualties. Therefore, it is necessary to explore the law of earthquake occurrence and take corresponding measures in time to reduce losses and casualties. However, it is difficult to predict the mechanism and principle of earthquakes. In this study, we utilize the methods and tools of data science to explore the regularity of earthquake occurrence. This paper is a practice of enforcing the workflow of data science, which uses computer algorithm to make fast statistics of data and conduct visualization processing to study the law of earthquake occurrence. Compared with the traditional research methods, it has important significance.
Intelligent Steward The Definition of Intelligent Machine Assistant and the Chronological Method of Intelligent Era Machine
Authors: Qi Kailong
Published: December 2020

This paper proposes the definition of intelligent steward, a personal intelligent technology assistant who can improve business management and quality of life by using AI technology for the first time. Intelligent steward mainly serves employers to complete business steward business by using comprehensive tools such as artificial intelligence, intelligent machine, voice recognition, image recognition, machine translation, in-depth learning, human-computer interaction, mobile Internet. From the perspective of practical activities of business steward mode innovation, its main modes are classified into four types: independent professional business steward company, business steward in subdivided industries, generalized business steward crowd sourcing organization and AI intelligent steward with the intelligent machine as the main carrier. Intelligent steward is an intelligent partner in the business work and daily life of users, which will realize the extension and agency of human brain, and provide a new economic development form for the era of economic decision-making and consumer sovereignty. In this paper, we use Fibonacci series of mathematics and computer model program to get the corresponding relationship between the intelligent era and the common era, which to a certain extent promotes the evolution of human civilization. Taking 2020 as the first year of intelligent era era, the intelligent era chronology is the product of the era of machine intelligent civilization. It will be gradually recognized by the machine network world as the computing power and thinking of the machine evolve, completing unified evolution of human civilization, machine civilization, human and machines mixed civilization.
Research and analysis on the development of intelligent toilet
Authors: Gong Ming , Li Kejing , Tian Tao , Mao Xianbian , Wang Chen
Published: December 2020

Intelligent toilet, also known as “electronic toilet” or “smart toilet”, was first invented by the Americans in 1964, and then introduced to Japan by Japanese businessmen. After the product is optimized in design, it began to be popularized in Japan in the 1980s and 1990s.In the early 1990s, Japanese businessmen has introduced intelligent toilet into China, so far, opened the domestic. The development of domestic intelligent toilet can be roughly divided into three stages: the first stage of domestic intelligent toilet development is 1990-1995, also known as the birth period of Chinese intelligent toilet, domestic intelligent toilet experienced the first stage of the cradle period, began to enter its growth period 1995-2015;2015 is a special and unusual year in the history of China’s intelligent toilet. After 2015, domestic intelligent toilet enters its third stage of development.
The Application of LightGBM in Microsoft Malware Detection
Authors: Pan Qiangjian , Tang Weiliang , Yao Siyue
Published: December 2020

The development of new technologies has caused computers one of the most popular electronic products. However, there is always a number of people who intend to take advantages of others through attacking others’ computers. To avoid property damage as much as possible, a precise and efficient detection is essential. This work uses the dataset which was generated by combining heartbeat and threat reports collected by Microsoft’ s endpoint protection solution to find out an effective solution. Since the dataset is large and has many categorical variables, reduction of memory and label encoding are used in data cleaning. Further, to handle the dimension problem and improve training efficiency, Chi-square testing is applied, and the top 42 fields are selected. Then, three algorithms (Logistic Regression, KNN and LightGBM) are chosen to build models and results are got respectively. The results show that LightGBM model achieves the best accuracy that AUC reaches 0.720687, and it is the most time-saving way. To the end, according to the feature importance from LightGBM algorithm, this work pick top-three important variables to analyze the underlying causes in the malware attack. One of the results reveals that the computer which has anti-virus software with bugs or pitfalls will suffer more attacks.
A comparative analysis of multi-machine learning algorithms for data-driven RANS turbulence modelling
Authors: Wu Junjie , Li Jiahua , Qiu Xiang , Liu Yulu
Published: December 2020

To improve the speed or accuracy of Reynolds-averaged Navier-Stokes in numerical simulations of turbulence, five machine learning algorithms including Random Forests, eXtreme Gradient Boosting, K-Nearest Neighbours, Support Vector Machine and Artificial Neural Network are introduced in this paper. We establish the nonlinear mapping relationship between the average flow field and the steady-state eddy viscosity. The machine learning surrogate models for the Spalart-Allmaras turbulence model are constructed and used to solve the backward facing step problem. It is demonstrated that the machine learning surrogate models constructed by the five machine learning algorithms can effectively express the complex mapping relationship between the average turbulent flow field and the steady-state eddy viscosity. They can not only obtain reasonable simulation accuracy but also significantly accelerate the solution time of the flow field. The algorithms of eXtreme Gradient Boosting, K-Nearest Neighbours and Artificial Neural Network have better performance when considering the calculation accuracy, time cost and memory cost. It shows the great potential of applying machine learning algorithms to RANS turbulence model and also provides a new idea for industrial simulations of turbulent flows.
Short-term load forecasting model based on EEMD-LSTS-ARIMA
Authors: Guantong Huang , Lanxin Hu
Published: December 2020

To improve the accuracy of the short-term power load forecasting, this paper designed a model, namely, the first to use based on empirical mode decomposition (EMD) method based on the collection of ensemble empirical mode decomposition (EEMD) in order to improve signal decomposition in the process of modal aliasing phenomenon and false component, and using the decomposition method of power load data decomposition, modal components with different characteristics, and according to its characteristics can be divided into high frequency components, intermediate frequency and low frequency components, Then the Long Short-Term Memory network (LSTM) is used for load forecasting. Using the autoregressive Integrated moving average model (Arima) to correct the predicted residual error, the load forecasting model based on EEMD-LSTM-ARIMA is established. Finally, the data of Queensland, Australia is used as the research sample. On the above prediction model, the data of this region is input into the model for power load prediction. At the end of the prediction, the final prediction results are compared with the EEMD-LSTM model through root mean square error (RMSE) and mean absolute percentage error (MAPE). Through comparison, it is found that the method used in this paper has a high prediction accuracy.
Lump solutions to the (2+1)-dimensional potential Kadomtsev-Petviashvili equation
Authors: Fang Shuo , Jiao Ailing , Li Wenting , Jiang Kun
Published: December 2020

Based on the Hirota bilinear method, we obtained the lump solutions to the (2+1)-dimensional potential Kadomtsev-Petviashvili equation by aid of symbolic computation with Maple. Examples are given to illustrate the different appearances and movements of the solutions when different value of the involved parameters are chosen in this paper.
A Byzantine consensus based on proof-of-work of nodes’ behaviors
Authors: Dingyu Wang , Dingmin Wang , Lupin Qin , MinLong Xu
Published: December 2020

Blockchain is a decentralized distributed peer-to-peer system, and decision-making in the system relies on a reliable consensus protocol. Under the current consensus protocol in the blockchain system, the behavior of nodes are not regulated. Based on the existing consensus protocol, this paper analyzes the problems encountered in application scenarios, introduces a credit calculation method to regulate the behavior of system nodes, and with a simplified consistency protocol, it can promote the alliance chain system to enter a virtuous cycle, and improve the efficiency of consensus in scenarios with many wrong nodes.
Research on Query Term Expansion based on RankSVM and LDA Model
Authors: Peng Lin , Lu Guo-quan
Published: December 2020

The lack of semantic association will lead to poor performance of retrieval system. This paper proposed a query terms extension method based on RankSVM and LDA model. Firstly, with the aid of RankSVM’s excellent performance of sorting, high-quality initial retrieved results will be obtained. Then utilizing the LDA model to select the documents related to query term and to generate topic model for sequenced initial retrieval results.in the post-processing step, using threshold to select the highest probability among each topic and regards them as query terms set. Experiments show that the query terms expansion method proposed in this paper is superior to the current query expansion methods based on pseudo-relevance feedback in the five evaluation index, therefore this is a feasible method.
Program Semantics-based Task Decomposition
Authors: Wang Zhiming
Published: December 2020

Recently, automatic code generation technology has been extensively studied and gradually becomes an important part of software development. However, when facing coarsegrained, highly general task description, the current automatic code generation technology still cannot achieve good results. Therefore, we propose program semantics-based task decomposition technology. For a task description given by the user, natural language processing and deep learning technology are used to learn and understand it. Combined with the program semantics contained therein, we decompose the task into ordered subtask sequence. We evaluate our proposed model on constructed data set and achieve a BLEU-4 score of 18.13.
A Routing Algorithm for Delay Tolerant Networks
Authors: Bin Liu , Zhi-lin Wang , Ren-jie Zhu
Published: December 2020

Based on the limitations of traditional submission ratio of Prophet caused by the performance difference and state difference of nodes in the new service environment, and the idea of utility quantization, a Prophet-BSAS algorithm based on node state was designed. The utility parameters and utility functions of the improved algorithm were analyzed in detail, and the performance of Prophet-BSAS and other classical routing algorithms was simulated and analyzed under the typical mobility model.
A Clothing Retrieval Model Based on DenseNet and Multi-Similarity Loss
Authors: Feifei Xu , Yu Tian , Shuting Zheng
Published: December 2020

With the rapid development of deep learning in recent years, Deep Neural Network structure has been applied to all aspects of image algorithm, including image classification and recognition, target detection, image retrieval. Image retrieval is suffering from some challenges, such as crease, cover, background. Based on these challenges, using the local information of the image to replace the image is proposed which learns embedding vectors by local information to reduce noise interference. Inspired by the above discussion, a new deep learning model named IAPM (Integrated and Partial Model) is proposed in this paper. According to global information and local information, Joint training is carried out. For feature extraction, DenseNet is used to extract the feature vector. And the idea of transfer learning is applied to transform classification problems to retrieval problems. Meanwhile, the multi-task model is adopted to construct a framework, and Multi-Similarity Loss is used to pull the position of the embedded vector in feature space. Experimental results show its effectiveness.
A Visual transformation method of SysML model to Modelica model
Authors: Fu Chao , Liu Jihong , Yu HongYan , Xu WenTing
Published: December 2020

The core idea of Model based systems engineering (MBSE) is to model all the information related to the system design starting from the beginning of the system development in the integrated form, and serve as a central system model throughout the product system life cycle. In view of the lack of executable capability of the standard system modeling language (SysML) within MBSE, the system design-simulation model transformation method was studied. Visualizing the transformation process based on SysML could contribute to the low complexity of the transformation. Finally, an example of 3D printer is used to verify the proposed method.
Interaction solutions to the (2+1)-dimensional generalized Bogoyavlensky-Konopelchenko-like equation
Authors: Jiao Ailing , Jiang Kun , Li Wenting , Fang Shuo
Published: December 2020

By using the generalized bilinear operations with a prime number p=3, a (2+1)-dimensional gBK-like equation is introduced. classes of interaction solutions of the (2+1)-dimensional gBK-like equation are generated through symbolic computation with maple. Some images were plotted to illustrate the dynamical movement of the solutions with specific values of the involved parameters.
A new red wine prediction framework using machine learning
Authors: Ye Chao , Li Ke , Jia Guo-zhu
Published: December 2020

Red wine has become an integral part of people’s lives and culture. Modeling the red wine quality is crucial. We propose a new framework to predict the red wine quality ratings. MF-DCCA was utilized to quantitatively investigate the cross-correlation between quality and physicochemical data. The long-range correlations importance was ranked. We compared two machine learning algorithms with other common algorithms implemented on the red wine data set, which was taken from UC Irvine Machine Learning Repository to ensure the reliability and performance. These data sets contain 1599 instances for red wine with 11 features of physicochemical data. Our model has better performance than other results.
Analysis and Evaluation on the Network Security Defense in Power Marketing Industrial Control System
Authors: Li Jiawei , Wu Kehe , Zhang Bo
Published: December 2020

With the implementation of the energy Internet, ‘Internet+power’, power IOT, and digital transformation and upgrading strategy, the power marketing system has carried out the massive metering acquisition and load control terminals, which are interactive and open accessed. Also, it has enhanced the coordinated operation and control with core power control systems such as power dispatching, transportation inspection. The current protection is mainly based on traditional passive defense concepts and technologies such as isolation protection, which cannot meet the demand of high reliability, high availability, safe and stable operation in the energy Internet environment. Therefore, it is urgent to establish a new generation of industrial control system for power marketing security protection system, which breaks through technologies in control terminal widely safe access, network safe and reliable interaction, system safe and trusted operation. The proactive defense technology brings new inspiration to the research on the security protection theory of the industrial control system for power marketing, including ontology security immune enhancement and pre-warning detection. Moreover, applying the theory of proactive defense to the field of power marketing security can provide a new way to improve the security protection of power marketing system..
Cross- Scenario Person Re-identification
Authors: Jia Ruoran , Liu Shuguang
Published: December 2020

Person Reid is a challenging task for two factors, first one is background interference, such as changes in light, weather, posture, and camera position. Second is domain adaptive capacity, such as model train by market1501 achieve the same performance on Duke dataset. To solve the above problems, we come up with adopt human semantic to remove clutter from unwanted background information, is naturally a better alternative compare with bounding box, we adopt Local Regions Representation to extra the image features, which can preeminently improve the representation of local feature and global feature. Our proposed CSReID integrates human semantic and Local Regions Representation in person re-identification and not need to train on the evaluation dataset can achieve state of the art cross-modal performance.
Using molecular fingerprints and unsupervised learning algorithms to find simulants of chemical warfare agents
Authors: Gu Leilei , Zhang Xinxin , Li Ke , Jia Guozhu
Published: December 2020

The emergence of novel coronavirus highlights the importance of research and development of biological protective materials and functional protective equipment. As an important experimental material, the direct application of chemical warfare agents (CWAs) will cause great pollution to the environment. The effective search for simulants determines the process of CWAs experiments. This paper combines molecular fingerprint and unsupervised learning algorithm to develop a simulants selection framework. A selection strategy is developed based on the silhouette coefficient. The closest simulants are found (GA (TEP/DEEP), GB (DFP), GD (DEHP), HD (CEES), VX (Amiton)) under a threshold (Silhouette coefficient: 0.2). This study can effectively help researchers to find the best approximate simulant to a certain extent.
Research on the construction of information fusion oriented social work information system
Authors: Liu Pengfei , Huang Xiaolian , Zhang Weifeng , He Xiaxu
Published: December 2020

This paper studies the information storage and fusion need in the procedure of social work. Information management is a common problem to social work. According to the theory and method of information system development, this paper discusses the significance and design of social work information system development, and finally gives the overall implementation framework and some common function modules of information system.
Research on modulation recognition of Communication Radar Based on spread spectrum technology
Authors: Bao Dujiya , Guo Hua , Zhang Wenxia , Bao Xinxin
Published: December 2020

Aiming at the problem that the traditional radar signal recognition method needs to be completed under the noiseless condition, which leads to the low recognition accuracy of low SNR signal, the modulation recognition method of communication radar signal based on spread spectrum technology is studied. The phase difference and instantaneous frequency characteristics of communication radar signal are extracted after the spread spectrum processing of communication radar signal with direct sequence spread spectrum. The training sample is used to train PNN neural network, and the modulation recognition of communication radar signal is completed. Simulation experiments under different parameters show that the recognition method based on spread spectrum technology has an effective recognition rate higher than 90% for low SNR signal, which is superior to the traditional method and can be applied in practice.
Research on mapping technology of laser navigation robot without reflector
Authors: Haiying Zhang , Shengming Liu , Zhiying Jiang
Published: December 2020

In view of the problems existing in the construction of large sparse environment map for laser navigation robot without reflector, an incremental SLAM algorithm based on multistage ICP matching is proposed. This method constructs a new image according to the pose of the robot, and develops a regional closed-loop algorithm combined with incremental optimization method based on the graph optimization algorithm. Experimental results show that due to the low amount of environmental information, the algorithm can effectively avoid falling into local minima, eliminate the cumulative error of mapping, reduce the computational complexity, and realize accurate mapping and real-time application.
The design and implementation of book lending system based on RFID technology
Authors: Sunmin
Published: December 2020

With the continuous enrichment of library collection resources and the expansion of service connotation and extension, the traditional integrated management system based on paper resources management can no longer meet the needs of the library’s continuous development. Therefore, the research and development of a new generation of library management system urgently needs to be put on the agenda. the library management and lending system have been already reformed. Finding the relevant literature, comparing and analyzing the previous research results. This paper introduce the design and implementation of RFID technology, including the advantages of the library lending system which established by RFID technology, and at the same time it puts forward the deficiency and the application prospect of the future RFID technology.
Design and modeling of satellite network structure for business scenarios
Authors: Ren Lintao , Pei Zhongmin , Xiong Wei , Luo Zhangkai
Published: December 2020

In view of the actual decentralized development of the satellite service constellation and the trend of space-ground integrated information network, based on the idea of clustering, methodology of complex network community structure, a business scenario-oriented satellite network structure is designed. On the one hand, it reflects the different separation and integration of satellite constellations in business scenarios. On the other hand, the “head node” mechanism is introduced to be responsible for the interaction between constellation in different business scenarios. The satellite network structure is modeled using the growth and priority connection mechanism, and through computational derivation and simulation validation, the scale-free characteristics of the satellite network are revealed. Finally, through the simulation experiments on different satellite network parameters, the effect of different parameters on the static geometry, network efficiency, etc., are obtained. The influence of the law of the network will provide theoretical and methodological reference for the design of satellite network structure.
Design and Implementation of Cloud Platform Knowledge Service Mode for High-end Equipment Manufacturing
Authors: Li Shuang , Wang Jian
Published: December 2020

With the advancement of intelligent high-end equipment manufacturing, knowledge in the field of high-end equipment manufacturing provides decision support and knowledge innovation services for all aspects of the product life cycle, and plays an increasingly important role in design and production. However, the multi-source, heterogeneous and fragmented nature of domain knowledge brings difficulties to the organization and management of knowledge. To solve this problem, this paper proposes a cloud platform knowledge service mode for high-end equipment manufacturing throughout the product life cycle, including three-dimensional knowledge classification standard, ontology-based knowledge representation and integration, knowledge sharing through knowledge navigation, knowledge retrieval and knowledge reasoning. The application of aerospace manufacturing related fields shows that this mode can provide effective knowledge support for the product life cycle, strengthen the production collaboration between upstream and downstream enterprises in high-end equipment manufacturing, and provide a comprehensive guarantee for the aeroengine manufacturing industry chain.
Construction of College Student Credit Information System Based on Blockchain Technology
Authors: Xidong Liu , Yuxin Shang , Chuanling Song
Published: December 2020

In recent years, falsification of learning experience, academic fraud and other academic misconduct are common among college students. However, the data collection and query functions of various management systems used in domestic colleges and universities are relatively single, which is not enough to meet the needs of the society. With the application of blockchain technology in all walks of life, the decentralization, security, credibility and traceability of blockchain technology are highly matched with the construction of College Students’ integrity system. The research and development of College Students’ credit information system based on blockchain can effectively improve the credibility of the education system, and play a powerful role in promoting the application of education big data and innovation breakthrough.
Remote Sensing Image Scene Classification along the High-speed Railway based on Convolutional Neural Network
Authors: Gong Xunqiang , Ju Xiayi , Qian Kuan , Lu Tieding , Chen Zhiping
Published: December 2020

China’s high-speed railway operation network is huge, and the environment along the line is complex. Therefore, there are many security risks. For that reason, real-time dynamic monitoring of hidden dangers along the high-speed railway is required. Traditional manual field monitoring methods are inefficient, while remote sensing technology can provide a means for dynamic monitoring because of its large-scale, real-time and cyclical aspects. In addition, feature extraction of classification model is simple and classification accuracy is not ideal by using traditional scene classification methods. In order to effectively avoid potential safety hazards along the high-speed railway, in other words, to classify the ground objects along the high-speed railway more accurately, the remote sensing images along the high-speed railway obtained from Google Earth are taken as the research object, and three convolutional neural network (CNN) models for scene classification are constructed and their classification results are compared in this paper. The experimental results show that the classification accuracy of the three models is above 89%, and the results of the ResNet model are optimal.
Active learning for image preparation of automatic vending machine (AVM) employing transfer learning method
Authors: Li Fang , Zeng Min , Xiao Jia , Li Xiaojun , Li Yuanyuan , Hu Guosheng
Published: December 2020

In this paper, we employed active learning methods to prepare annotated images for our training of automatic vending machine (AVM) system, in order to minimize human annotation cost. Due to the tiny data of our system, transfer learning approach is used by implementing the already trained Yolov3-tiny model for COCO dataset as our training start. Also, we evaluated the effectiveness of 3 annotation strategies: smallest annotation area (SAA), largest annotation area (LAA) and moderate annotation area (MAA), for photos of top views from above. The results show that the idea of employing active learning methods to prepare annotated data is feasible. Also, the annotation strategy of MAA demonstrates the superior performance, for its enough object area and the least background area.
Research on the application of face recognition system
Authors: Mingtsung Chen , Wei Qian , Jiaqi He , zhuomin Zhao
Published: December 2020

For the unrestricted investment of face recognition technology, whether it is to protect the safety of people’s life and property or to infringe on people’s basic rights. Face recognition is the most typical one of pattern recognition. It extracts the feature information of face in different ways, compares or tests the collected information with the original information, to realise the record verification of identity or the tracking and positioning of personnel. Nowadays, face recognition technology has been widely used in everyone’s daily life. On the one hand, it brings great convenience to life, on the other hand, whether personal information security and privacy can be guaranteed. Everything has two sides, and face recognition technology is no exception. How to balance the advantages and disadvantages of privacy and security still needs to be further explored.
Design of Nursing Bed Control System Based on STM32
Authors: Zhang Huimin , Sun Hongmin , Zhang Xianda , Guo Xingzhao
Published: December 2020

In order to solve the problems of low intelligence, lack of function in the domestic nursing beds, a multifunctional intelligent nursing bed for daily care in homes and nursing homes was designed. The control system adopts STM32 series chips as the control core, uses PWM modulation to simultaneously control the working state of multiple linear drivers and solenoid valves to achieve posture adjustment. The closed-loop control of the working status of the air circuit and water circuit hardware through the pressure sensor and PID adjustment algorithm realizes the function of sewage treatment. Improve the intelligence of nursing bed through the design of voice recognition and remote communication module. The application of the control system improves the convenience of operation, control intelligence and safety and reliability of nursing bed equipment.
Research on the sterilization effect of hydrogen peroxide and equipment optimizatio
Authors: Zhang Huimin , Zhang Xianda , Sun Hongmin
Published: December 2020

Hydrogen peroxide sterilization is widely used in food packaging, public health and other fields. The sterilization effect is related to the vaporization spray time and nozzle parameters. This paper uses AnsysFluent software to mathematically simulate the flow field and concentration distribution of vaporized hydrogen peroxide in the sterilization space; and verify the correctness of the numerical simulation results through field experiments. UDF module is used to describe the component concentration of the nozzle, and the distribution of vaporized hydrogen peroxide is analyzed by changing the nozzle spray angle and spray speed of the equipment, and the spray time T required to reach the standard concentration in the sterilization space is the shortest and the hydrogen peroxide dosage Q is the minimum For the goal, optimize the design parameters. The results show that when the spray speed is 4.8m/s and the spray angle is -18°, the spray time required to reach the sterilization standard is the shortest, and the amount of hydrogen peroxide is the least. This article provides a theoretical basis for the improvement of vaporized hydrogen peroxide sterilization equipment.
Function Analysis of Command and Control System in Intelligent War
Authors: Chen Huangqing , Li Tingquan , Zhu Lili , Jiang Taotao
Published: December 2020

With the in-depth application of artificial intelligence and other new technologies in the military field, intelligent war has gradually emerged and shown a strong combat effectiveness. First of all, the main combat forces and functional modules of intelligent war are analyzed to grasp the characteristics of “autonomy” and “integration of cross domain operation and control” in intelligent war. Then the main functions of command and control system in intelligent war are analyzed and elaborated, focusing on the functions with human participation, the main contents of each function are stated in detail. Through the analysis of system functions, it can provide high reference value for the design and construction of intelligent command and control system in the future.
Research on Fault Diagnosis Model of Transmission Line under Lightning Stroke Based on Neural Network
Authors: ShaoJie Qu , ZhiGang Wang , YongChao Song
Published: December 2020

Transmission lines are an important part of the power system. In recent years, there have been more and more lightning trips, power outages and outages, which have become one of the hazards that threaten the stable operation of the power grid, and seriously endanger the reliability of the power grid operation. It is necessary to diagnose problems with transmission line faults. In this paper, after the information generated by the fault is processed by wavelet, it is input into the neural network fault diagnosis model as the input part, and the fault type of the transmission line is predicted and identified by the neural network operation, and the experiment is carried out through MATLAB simulation software. The prediction results show that the model is more accurate in fault diagnosis of transmission lines.
Analysis of the energetic and exergetic performance in condensers of thermal generation plant
Authors: Orjuela Abril M S , Rojas Suárez J P , Pabón León J A
Published: December 2020

In the present study, the influence of the flow parameters of the cooling water on the operating conditions of the condenser is evaluated. The above, by simulating codes that represent the thermal behavior of the equipment. The study focuses on the influence of the temperature and the flow rate of the cooling water on the pressure, heat transfer coefficient, and exergy efficiency of the condenser. Additionally, mathematical correlations are made between these operating variables. The study shows that the flow of cooling water causes a reduction in the condenser pressure. However, it does improve its heat transfer capacity. On the other hand, a 2 °C increase in temperature produces a 6% increase in condenser pressure. Pressure levels above 8.5 kPa cause a considerable reduction in the energy and exergetic efficiency of the condenser. Similarly, the exergetic efficiency of the condenser is also affected by heat losses to the environment. Additionally, the increase in temperature allows the condenser to operate at a higher exergy efficiency. Finally, the development of the correlations carried out can become a tool for evaluating the performance of the condenser under different operating conditions.
Changes to anthropogenic pressures on reach-scale rivers in South and Southeast Asia from 1990 to 2014
Authors: Liu Chang , Yang Kang , Bennett Mia M , Lu Xin , Guo Ziyan , Li Manchun
Published: December 2020

Rivers are essential to human livelihoods and agricultural production, yet human usage and irrigation are jeopardizing river sustainability. It is thus crucial to investigate the fine-scaled spatiotemporal dynamics of anthropogenic pressures on rivers. Most research, however, is conducted at the grid-scale, which impedes detailed investigations. In this study, by tracking anthropogenic pressures at the scale of river reaches (the length of river between river confluences) in South and Southeast Asia from 1990 to 2014, we provide new insights into anthropogenic pressures on river reaches using a simple and straightforward approach. We selected human usage (represented by built-up area) and irrigation (represented by irrigated area) as two fundamental indicators of anthropogenic pressure. We divided the study area into 5 × 5 km grids and calculated anthropogenic pressures on each grid to its nearest river reach. Pressures were calculated as the ratio of built-up and irrigated area to the distance between grids and reaches. Groundwater was also included to adjust for additional irrigation-induced pressures on reaches. Anthropogenic pressures on each reach were then calculated by summing pressures from the two indicators of all grids attached to it. Results indicate that >50% of reaches are affected by anthropogenic activities and that average pressures increase by ∼15% from 1990 to 2014, with hotspots concentrated in eastern Pakistan and northern India. Irrigation is the dominant pressure on ∼33% of reaches, while human usage is dominant for ∼24% of reaches. Anthropogenic pressures within transboundary river basins vary longitudinally, increasing as distance from the ocean declines. Pressures also vary significantly with reach size. Although large rivers suffer from greater anthropogenic pressures, they are rising more rapidly for small rivers. Empirically, this study reveals the increasing and heterogeneous nature of anthropogenic pressures on river reaches in South and Southeast Asia. Methodologically, it suggests that reach-scale river sustainability assessment can serve as a promising approach for researching and managing regional and transboundary rivers.
Investigation of processes occurring above laser exposed area during powder bed fusion by optical diagnostics
Authors: Podrabinnik P A , Khmyrov R S , Korotkov A D , Shtanko A E , Gusarov A V
Published: December 2020

Powder-bed fusion is accompanied by the occurrence of processes such as the formation of a vapor-gas jet, the outflow of powder particles and the movement of gas masses in the laser processing zone, which must be taken into account to improve the quality of the process. In this work, the methods of optical diagnostics of the space above the processing zone were used to identify and investigate the main occurring processes. Using the Schlieren-method, it was possible to fix the emission of powder particles from the laser exposure zone and evaluate their speed and expansion angle depending on the laser processing parameters, type of material, and particle size distribution. To study the emerging vapor-gas flows, the interferometry method was used, as a result of which a spatial change in the refractive index of the gas medium was recorded. Based on the data obtained, the velocity of the jet was calculated and the flow geometry was determined. It is also shown that metal vapor makes up the majority of the observed flows.
Modeling of an equal-wear shape of grinding wheel when working with longitudinal feed
Authors: Salova D P , Nosov N V , Salov P M
Published: December 2020

The paper proposes a model of an equal-wear shape of the working surface of grinding wheels operating in the self-sharpening mode, where the principles of system workability are used. It is established that to obtain engineering solutions, the wheel should be considered as a ceramic body that cuts into the ‘Voicht medium’. A disadvantage of the adopted model is that it does not consider the lag caused by strain. In addition, when grinding, the process temperature and the deformation rate are significantly higher than when the machine parts rub together. It is shown that the condition of equal wear of the wheel surface is provided by equal wear of the abrasive grains on the entire working surface, the density of the working grains on the elementary section of the contact surface. The paper presents conditions under which equal-wear shapes of wheels working with a longitudinal feed are provided.
Combined multi-electrode piercing of holes in hard-to-process materials
Authors: Smolentsev V , Kirillov O , Kotukov Y
Published: December 2020

The possibilities of combined piercing through and “blind” holes in hard-to-process materials are considered. The possibilities of intensifying the processing process by using multi-electrode tools are presented. A fundamentally new element of the new technology is the creation and use of a new working environment based on a rheological fluid with variable viscosity. This determines the conditions for mass removal of processing products from the hole forming site and creates conditions for process stabilization. The method is protected by a Russian patent and has been used in many factories countries. The article considers the technology of processing parts in the proposed working environment, which implements the proposed method in the form of a technological process in the manufacture of parts with holes of different cross-sections. At the same time, the use of combined multi-electrode cross-linking allowed up to 3 times to speed up the production of channels for cooling parts of the hot zone of thermal machines, and to achieve stability of cross-section sizes within ±5%. This technology expands the use of electrical processing methods in General and special engineering in the manufacture of parts from hard-to-process alloys.
Manual measurements of cutting tools on a coordinate measuring machine
Authors: Nikitina I P , Kamenev C V , Polyakov A N
Published: December 2020

Coordinate measuring machines make it possible to measure the geometric parameters of precision parts, the measuring of which is not feasible with a universal hand tool. The purpose of the study was to develop a methodology for measuring the design and geometric parameters of a straight bull-nose turning tool. To test the effectiveness of the developed methodology for measuring the design and geometric parameters of the cutting tool, a full-scale experiment was conducted. During the experiment, the measurement of all the structural and geometric parameters of the cutting was carried out using hand-held measuring tools and the Wenzel XOrbit 55 coordinate measuring machine. A comparative analysis of the results of the measurements showed the folowing: all the geometric parameters of the cutting can only be measured using a coordinate measuring machine; geometric parameters measurements using a universal hand tool do not allow you to make unambiguous organizational decisions on the further operation of the cutting tool, since the measurement error is comparable to the current size tolerances, however, for such design parameters as length, height, and width of the cutting, due to the large tolerances, it is sufficient to use a universal hand measuring tool. The measurement practice using a coordinate measuring machine has shown that when performing critical measurements, the use of coordinate measuring machines, in manual mode, is not only accurate, but also more productive in comparison with a universal hand measuring tools.
Analysis and synthesis of ecological installations for recycling waste polyurethane foams
Authors: Rakov D L
Published: December 2020

Recycling and utilization of polymer materials is one of the most urgent tasks for modern science and technology. The polymeric materials polyurethanes (PUs) play a big role in different applications. The morphological approach is used as a way of designing, thinking, finding solutions and variants for the polyurethanes recycling technology. This method use as a part of the design process, especially in the conceptual design stage. This approach allows to search for innovative engineering solutions and choose the rational variants and compare them. This paper presents and discusses an morphological approach for solving the polyurethanes recycling problems.
The synthesis of Li2ZnTi3O8 and Li2Zn3Ti4O12 powders for Low Temperature Cofired Ceramic (LTCC) production
Authors: Vershinin D I , Khusainov I N , Makarov N A
Published: December 2020

The powders of different phase compositions in the Li2O-ZnO-TiO2 system were prepared. These powders can be used as major components in microelectronic devices production using Low Temperature Cofired Ceramic (LTCC) technology. The influence of factors such as the synthesis temperature and holding time on phase transformations processes and composition of synthesized powders were studied. It was determined that the synthesis of Li2ZnTi3O8 and Li2Zn3Ti4O12 compounds can be carried out at 850-900 °C. At the same time, the synthesized phases are extremely sensitive to the ratio of the initial components.
Effect of the Step in the Blow Channel of a Blast Furnace Tuyere on Improving Natural Gas Combustion
Authors: Sayfullayev S D , Efremov D B , Tarasov Yu S
Published: December 2020

The paper demonstrates the effect of the step in the blow channel and the insulating lining on the intensity of mixing natural gas with the air blast and its combustion in the blow channel, as well as on the increase in the air blast temperature at the tuyere outlet. This allows to increase natural gas consumption and reduce coke consumption.
Twist drilling SPH simulation for thrust force and torque prediction
Authors: Boldyrev I S , Topolov D I
Published: December 2020

Drilling is one of the most common processes in metalworking. The cutting forces that occur during the drilling process have a significant impact on the accuracy and quality of the holes. Uncompensated radial cutting forces lead to an increase in the diameter of the hole being machined, which reduces its accuracy. And when machining laminated materials, excessive axial cutting force leads to a stratification of the composite and reduces the quality of the hole. In this regard, the task of determining or predicting cutting forces is currently quite relevant. This article proposes a method for calculating cutting forces when drilling aluminum homogeneous and isotropic alloy 6061-T6 using smoothed particle hydrodynamics method (SPH). The calculation results are compared with calculations using empirical formulas and the results of experiments of other authors. The influence of the chip separation criterion type and material model on cutting forces during drilling were also investigated.