Showing papers in "DEStech Transactions on Computer Science and Engineering in 2019"
TL;DR: A brief history of chatbots is presented as a topic that is necessary for understanding this phenomenon, its externalities, paradoxes, and future prospects, and their recognisable history and logical development structure are summarized in the following article.
Abstract: Unquestionably, we come across conversational algorithms or chatbots more and more frequently, in increasingly everyday situations; while buying a flight ticket, or clothes from an e-shop for example. This study presents a brief history of chatbots as a topic that is necessary for understanding this phenomenon, its externalities, paradoxes, and future prospects. Chatbots, as an expanded and practically deployed AI, have their recognisable history and logical development structure, which are summed up in the following article. The article presents the logical structure of development on selected programs from Eliza to Tay and Xiaoice chatbots.
44 citations
TL;DR: Bert is used to train Chinese character embedding and connect it with Chinese radical-level representations, and put it into the BGRU-CRF model, which achieves good results in Chinese data set.
Abstract: As a basic task of NLP, named entity recognition has always been the focus of researchers. At the same time, the word vector representation which is a necessary part of many named entity recognition neural network models has been more and more important. Recently, the emergence of a new type word representation, BERT, has greatly promoted many NLP tasks. In this paper, we will use Bert to train Chinese character embedding and connect it with Chinese radical-level representations, and put it into the BGRU-CRF model. We have achieved good results in Chinese data set through a series of experiments.
22 citations
TL;DR: A method of multiobjective optimization on permutations (MOP) is offered based on the Directed Structuring Method and using Graph Theory that yields advantages in using traditional methods, as well as in developing new ones.
Abstract: A method of multiobjective optimization on permutations (MOP) is offered based on the Directed Structuring Method and using Graph Theory. Prospects of applying graph techniques are caused by representability of the feasible domain by graph vertices. It yields advantages in using traditional methods, as well as in developing new ones. Our method is a generalization of the Method for Sequential Analysis of Variants for multiobjective optimization on permutations and multi-permutations. Most problems on combinatorial configurations sets are NP-hard, and a search of an exact solution requires enumerating a factorial number of variants. To decrease it, the method includes: a choice of an unconstraint MOP method; a choice of a method for generating a sequence of feasible solutions for a constraint MOP adapted to objective function; constructing and examining a structural graph of the optimization problem; a polynomial algorithm choice for solving the problem on partially ordered vertices of the graph.
14 citations
TL;DR: The application of the new TOPSIS-MABAC model with interval neutrosophic number with extended MABAC method to rank the alternatives in multi-attribute decision making problem is presented.
Abstract: Interval neutrosophic Set is a useful tool to describe the indeterminate, inconsistent, and incomplete information. This paper presents the application of the new TOPSIS-MABAC model with interval neutrosophic number in multi-attribute decision-making problem. In this model, the combined weight of attributes is obtained based on TOPSIS method while the best alternatives by MABAC method. Firstly, some definitions of INS are given in this paper. Secondly, the objective attribute weights are determined by TOPSIS method, and then a combined attribute weight is proposed. Finally an extended MABAC method is developed to rank the alternatives in multi-attribute decision-making problem and an illustrative examples are given to demonstrate the practicality and effectiveness of this new method.
10 citations
TL;DR: Modbus-E protocol can prevent the authentication attack, man-in-the-middle attack and replay attack of the instruction by the attacker and can comprehensively improve the security of Modbus TCP communication.
Abstract: Considering the security problem of the Modbus TCP protocol, this paper proposes a secure protocol, Modbus-E. It uses symmetric key and digital signature technology to ensure the confidentiality and authentication of data. It also uses the synchronization principle and the mono-direction principle of the hash function to ensure the uniqueness of data. Through the Filtering method of "white list", it can guarantee the controllability of instruction, ultimately without any increase in communication process to achieve secure communication. Through the verification and analysis of experiment, Modbus-E protocol can prevent the authentication attack, man-in-the-middle attack and replay attack of the instruction by the attacker. Compared with existing methods, this method is more secure and can comprehensively improve the security of Modbus TCP communication.
10 citations
TL;DR: The aim of this study was to find out the competing endogenous RNA (ceRNA) of lncRNA-mRNA-miRNA regulatory network and analyze the target mRNA of low grade glioma (LGG) between different genders.
Abstract: The aim of this study was to find out the competing endogenous RNA (ceRNA) of lncRNA-mRNA-miRNA regulatory network and analyze the target mRNA of low grade glioma (LGG) between different genders. The differentially expressed mRNA, lncRNA and miRNA were screened out from the downloaded TCGA LGG data by the comparison of different genders using R package. The online tools, mircode, miRDB, miRTarBase and TargetScan, were used to analyze regulatory of lncRNA-mRNA-miRNA. The 8 DElncRNAs (IGF2-AS, TTTY14, LINC00305, XIST, LINC00276, ZFY-AS1, TTTY15 and C8orf49) and 4 DEmiRNAs (hsa-mir-122, hsa-mir-372, hsa-mir-204 and hsa-mir-206) has interacted in 18 regulation pairs. PRLR was found out as a target mRNA from 201 target mRNA of the 4 DEmiRNAs. A ceRNA network was drawn according to regulatory of 8 DElncRNAs, 4 DEmiRNAs and 1 target mRNA. Then, the target mRNA, PRLR, and its related clinical survival data were analyzed by Cancer Cell Line Encyclopedia (CCLE) and kmplot. In conclusion, we can found out the differentially expressed mRNA, lncRNA and miRNA between male patients and female patients. Among then, 8 DElncRNAs, 4 miRNAs and 1 mRNA construct a ceRNA regulatory network.
9 citations
TL;DR: Smart Agriculture Information System Based on Cloud Computing and NB-IoT designed in this paper monitors crops or farmland through information transmission equipment in real time, and records the growth status of crops, so as to adjust the planting technology and planting methods.
Abstract: The typical projects of NB-IoT technology in the agricultural Internet of Things are being planned, which has broad application prospects and development space. NB-IoT and cloud computing promotes the development of the agricultural Internet of Things and improves the development model. Smart Agriculture Information System Based on Cloud Computing and NB-IoT designed in this paper monitors crops or farmland through information transmission equipment in real time, and records the growth status of crops, so as to adjust the planting technology and planting methods.
8 citations
TL;DR: In this paper, the control methods and strategies of greenhouse microclimate are classified and summarized, and the discussion has certain reference significance for scientific and technological workers to do the related research.
Abstract: The suitable greenhouse microclimate is the necessary for the normal growth of indoor crops. However, in many countries, especially in developing countries, the greenhouse production is relatively backward and mainly used for off-season production of crops without high quality and high yield. In this paper, the control methods and strategies of greenhouse microclimate are classified and summarized. The discussion has certain reference significance for scientific and technological workers to do the related research.
8 citations
Abstract: This research explores the impact of advanced technology and green vehicles on supply chain performance in the perspective of Mexican manufacturing firms. The study used 153 firms’ data to test hypothesis. The study adopted simultaneous regression statistical method, while the findings show that advanced information technology plays an important role in supply chain improvement through greater-level of information sharing with supply chain partners, reducing the discrepancies and errors on different levels such as forecasting and scheduling. On the other hand, green transportation and vehicles reduce the supply chain cost and improve environmental performance with building strong competitive advantage through more usage of renewable energy, greater level of customer satisfaction and trust, positive image and reputation.
7 citations
TL;DR: The detection method of moving targets to acquire the images of moving zooplankton and the two factors that affect the holographic images recognition results, mean (pixel mean of images) subtraction operation and image sharpness, and the no-reference sharpness assessment based on structural similarity for holog Graphic images are discussed.
Abstract: Marine zooplankton has important ecological and economic value. The observation and automatic image recognition technology of marine zooplankton is an important mean to acquire data such as species, quantity, spatial distribution and behavioral postures of zooplankton, and is an important support for marine scientific research. Digital holography has an innate advantage of refocusing and reconstruction, which is suitable for deep learning and living zooplankton recognition. In this study, a large number of holographic images was trained by using the improved YOLOv2 model, and after test, the study achieved satisfactory results: the models trained by the images with sharpness assessment score of 0.6 or higher, have precision rate above 94% and a recall rate above 88%. This study mainly discusses: (1) the detection method of moving targets to acquire the images of moving zooplankton; (2) the two factors that affect the holographic images recognition results, mean (pixel mean of images) subtraction operation and image sharpness, and the no-reference sharpness assessment based on structural similarity for holographic images; (3) the relationship between sharpness assessment index or mean subtraction and the recognition results.
7 citations
TL;DR: Aiming at six typical surface defects of hot-rolled steel strip, a method of surface defect classification based on deep learning is proposed based on the Convolutional Neural Networks model and the surface defect data set of Northeast University, which is verified by experiments.
Abstract: Surface defect is one of the important factors affecting the quality of hot-rolled steel strip. Aiming at six typical surface defects of hot-rolled steel strip, a method of surface defect classification based on deep learning is proposed. Based on the Convolutional Neural Networks model and the surface defect data set of Northeast University, the proposed method is verified by experiments. The experimental results show that the recognition rate of surface defects is up to 98.6%, and the detection speed is about 60ms, which meets the requirements of accuracy and speed in industry. The classification and recognition technology of hot strip surface defects proposed in this paper not only has certain theoretical value, but also has practical application prospects.
TL;DR: A surface defect detection method based on semantic segmentation is introduced, which uses the idea of transfer learning, and shows that the defect detection accuracy is more than 99.6% and meets the practical requirements of industrial production.
Abstract: Surface defect detection plays an important role in ensuring product quality. In view of the problem of surface defect detection in industrial production, a surface defect detection method based on semantic segmentation is introduced, which uses the idea of transfer learning. A better network model can be trained by using fewer defect samples. In addition, the defect can be classified by this method, and the defect type can be labeled, and the defect area can be obtained. In order to verify the effectiveness of the proposed method, the performance of the method is analyzed by DAGM 2007 dataset. The experimental results show that the defect detection accuracy of this method is more than 99.6% and meets the practical requirements of industrial production.
TL;DR: In this article, a questionnaire was designed, students' opinions on the current training mode were collected, and the problems and reasons of the current lack of innovation ability of college students were summarized.
Abstract: In order to further cultivate college student’s innovative ability, that OBE theory, refers to design the education goal as the student’ learning outcomes would be adopted in the study. A questionnaire was designed, students' opinions on the current training mode were collected, and the problems and reasons of the current lack of innovation ability of college students were summarized. Through the analysis of survey data, the problems and key factors that affect the promotion of college students' innovation ability in engineering practice have been obtained. Some cultivation methods for the cultivation mode of engineering practice and innovation ability are introduced specifically. The methods that are more in line with OBE education concept were adopted in the class. The results indicated that student’s mechanical innovation ability has been improved, and the students are very satisfied with these cultivation methods.
TL;DR: The experimental results show that the KRF algorithm has a better classification performance than the RF algorithm, and improves the accuracy of theRF algorithm on the indicator.
Abstract: In order to improve the indicator selection method for financial early warning, this paper combines the idea of K-fold cross-validation to improve the sampling method of Random Forest (RF) and proposes the K-fold random forest algorithm (KRF). The experimental results show that the KRF algorithm has a better classification performance than the RF algorithm, and improves the accuracy of the RF algorithm on the indicator. Finally, the importance of the selected financial indicators to the financial early warning is determined. A more scientific and accurate indicator system will provide a research basis for further financial early warning research.
TL;DR: This paper analyzes the implementation of RPN network in Faster R-CNN algorithm, optimizes the network and introduces K-Means clustering method, which has important reference significance to promote the automatic detection of sewage pipeline defects.
Abstract: Aiming at the problem of low efficiency and high labor intensity in manual inspection of drainage pipes, a method of defect inspection of drainage pipes based on optimized Faster-Rcnn algorithm is proposed. Faster R-CNN is an algorithm proposed for target detection this year, which is based on the deep learning network model of region-based recommendation network (RPN). This paper analyzes the implementation of RPN network in Faster R-CNN algorithm, optimizes the network and introduces K-Means clustering method. By clustering all Anchors in the training set and inputting the clustering results into the RPN network, the training of the network can be accelerated and the recognition accuracy of the algorithm can be improved. The experimental results show that the accuracy of the algorithm is up to 92.4%, which has great application value. This research has important reference significance to promote the automatic detection of sewage pipeline defects.
TL;DR: The paper presents a wireless temperature monitoring system of a half bridge resonant DC/DC converter for power LED lighting that allows for remote monitoring of four temperatures, but the number can be increased if necessary.
Abstract: The paper presents a wireless temperature monitoring system of a half bridge resonant DC/DC converter for power LED lighting. The proposed system allows for remote monitoring of four temperatures, but the number can be increased if necessary. The temperature monitoring console is available on any device connected to the local Wi-Fi network or to the Internet. By implementing the wireless remote monitoring, all important temperatures of the working converter can be monitored from any place with the Internet access. The presented solution is useful in laboratories developing new topologies of power converters that should be monitored and tested for a long time before any practical implementation.
TL;DR: It is shown that the gray neural network model can select the appropriate influencing factors and exclude impact factors with low relevance in the heat load forecast, improve the accuracy of heat load forecasting, and provide a theoretical basis for regional heat load forecasts.
Abstract: In this paper, the gray correlation analysis method is used to evaluate the factors affecting the district heating load, and the gray prediction is combined with the BP neural network algorithm to establish a gray neural network structure, which can screen the factors affecting the heating load and predict the heating load. The heating load forecasting and verification of a district heating load is carried out. By comparing the prediction results and errors of the gray correlation GM(1, N) model, it is shown that the gray neural network model can select the appropriate influencing factors and exclude impact factors with low relevance in the heat load forecasting, improve the accuracy of heat load forecasting, and provide a theoretical basis for regional heat load forecasting.
TL;DR: A construction method based on SOM neural network is proposed to construct the driving cycle of urban ramp, which has high clustering accuracy in the construction of the ramp driving cycle.
Abstract: In order to construct vehicle driving cycle with ramp characteristics, this paper applies SOM neural network to the construction of urban ramp driving cycle. Firstly, the data collected by the actual vehicle test is divided into micro-trips, after that the principal component analysis method is used to reduce the dimension of the selected 20 characteristic parameters, afterward the first five principal components of all the micro-trips are clustered by the SOM neural network, and then the micro-trips with the appropriate length of time are selected from each category to build a representative driving cycle with the smallest average relative error and stable slope angle-time curve. The research results show that the SOM neural network has high clustering accuracy in the construction of the ramp driving cycle. Introduction The driving cycle of the automobile is mainly used to test the performance indexes such as fuel consumption and emissions of automobiles in the development and evaluation of new technologies of automobiles, which is a common core technology of the automobile industry. The three typical driving cycles that are widely used in automotive fuel consumption testing are the US driving cycle, Japanese driving cycle and New European driving cycle(NEDC) [1]. Most of the researches on the driving cycle of automobile at home and abroad are based on the speed-time driving cycle curve. The urban ramp factors are not taken into account and cannot be used to test the performance of vehicles driving on urban ramps. Therefore, in order to construct the driving cycle of urban ramp, this paper proposes a construction method based on SOM neural network. SOM Neural Network Clustering Analysis Theory and Method The structure of SOM neural network is shown in Fig 1. It is a two-layer neural network composed of an input layer-competition layer (output layer). And it has the characteristics of unsupervised autonomous learning. Then the neurons in the competition layer of the network compete for the opportunity to respond to the input samples. Finally there is only one winning neuron, it represents the classification of the input samples[2,3]. The main process of cluster analysis consists of the following steps: 1) Initialize the network weight ∈ [0,1] ; determine the initial value of the learning rate η 0 ∈(0,1]; determine the initial field strength 0 ; set the maximum number of learning ; 2) Calculate the distance between the input vector = , , ..., and the output layer neuron as follows; d = ∑ − = 1, 2, . . . , ! (1) 3) Selecting the output layer neuron having the smallest distance from the input vector as the winning neuron; 4) Take the field strength \" = 0 ∗ $1 − % &'; adjust the nerves contained in the winning neurons and their fields (the field strength is \" ) as follows weight coefficient
TL;DR: This paper gives some software security features when design and implement secure system microcontroller software.
Abstract: With the rapid growth of microcontroller applications, especially in the IoT field, more and more devices have the requirements of inter connection, the most important thing which should be took into account is the security. The TrustZone feature developed by ARM based on ARMv8-M architecture provides the hardware basis for security among devices. Based on this, software is needed for building a secure platform to satisfy the application security requirements. Device secure boot scheme can resist software malicious modification, firmware update scheme can apply security patches in time, secure storage scheme can manage the application confidential assets and can resist malicious modification, secure crypto scheme is used to ensure the secure data transmission. This paper gives some software security features when design and implement secure system microcontroller software.
TL;DR: Zhang et al. as discussed by the authors applied the economic center of gravity model to study the dynamic evolution process of Hebei's economic centre of gravity since 2004, and concluded that its economic center showed the southeast-northeastsouthwest- southeast-south moving characteristics; then the principal component analysis method was used to extract the economic development.
Abstract: With the rapid integration of Beijing, Tianjin and Hebei (Jing-Jin-Ji), the economy of Hebei Province has also achieved great development. This paper applies the economic center of gravity model to study the dynamic evolution process of Hebei's economic center of gravity since 2004, and concludes that its economic center of gravity shows the southeast-northeastsouthwest- southeast-south moving characteristics; then the principal component analysis method is used to extract the economic development. The level of economic development of openness, secondary industry and tertiary industry is the main factor affecting the economic center of gravity. As for policy recommendation, the paper suggest the regional economic open-up and improvement of industrial structure to balance regional economic development in Hebei Province.
TL;DR: In this paper, the authors used CFD to simulate the velocity flow field of the three high-inlet pipe assemblies, and the velocity cloud of different vortex fan angle were obtained.
Abstract: In order to the problem of short life of air filter in intake system due to the harsh working conditions of commercial vehicles(CV), the computational fluid dynamics (CFD) method was used to simulate the velocity flow field of the three high inlet pipe assemblies, and the velocity cloud of different vortex fan angle were obtained. These velocity cloud diagram proves that the velocity gradient is caused by the angle of the vortex fan, and the velocity gradient can coarsely separate the dust, droplet and gas. The rack experiment method was adopted for the three high inlet pipe assemblies to evaluate its the pre-filter performance. The test results show that the vortex fan at the front end of the tube plays a good role in the pre-filtration of dusts and droplets. And the pre-filtration efficiency reaches over 87%. Effectively extend the life of the air filter element.
TL;DR: Basic principles of combined cycle unit modeling are presented and mathematical model of gas turbine and steam turbine is integrated into the general structure of power system simulation software.
Abstract: Based on the analysis of the combined cycle unit dynamic characteristics and the demand of power system simulation, basic principles of combined cycle unit modeling are presented. Through mathematical derivation and reasonable simplification, mathematical model of gas turbine and steam turbine is integrated into the general structure of power system simulation software. The problem that the power system simulation software has no special combined cycle model is solved, the accuracy of the model is verified by comparing the simulation results with the field test data, and research results are helpful to improve the accuracy of power system simulation.
TL;DR: The article considers the problem of planning the optimal trajectory of the tripod robot movement and proposes to supplement the original objective function with the Euclidean metric taken with a small weighting factor.
Abstract: The article considers the problem of planning the optimal trajectory of the tripod robot movement. The movement of the output link includes working displacements that are performed for the purpose of machining the workpiece and are completely determined by the surface of the workpiece, as well as the movement of the tool to the beginning of the next stage of processing, which can be relatively free, however, taking into account working area and workpiece surface limitations. The working area is limited by the range of permissible lengths of the drive links and the sign of the Jacobian. Additional restrictions are introduced, related to the dimensions of the workpiece. Chebyshev’s metric makes a significant ambiguity in the choice of the trajectory. Therefore, it is proposed to supplement the original objective function with the Euclidean metric taken with a small weighting factor. Optimization was carried out with restrictions on the size of the working area and workpiece.
TL;DR: In this article, a new intelligent optimization algorithm is proposed to optimize the sintering terminal prediction model of support vector machines (SVM), which can accurately predict the position of the Sintering end point, greatly improve the quality and production of sinter in the steel plant.
Abstract: The sintering burn through point (BTP) is the most important parameter in the sintering process, which is usually used to evaluate the quality of sintered products. However, due to the sintering process is a complex physical and chemical reaction process, the specific sintering end point cannot be detected. In this paper, the fireworks algorithm based on genetic algorithm (GA-FWA) is applied to optimize the parameters in support vector machines(SVM). A new intelligent optimization algorithm is proposed to optimize the sintering terminal prediction model of support vector machines. The model uses MATLAB software to test the actual production data of a steel factory. Through a large number of experiments, the average relative error of the experimental results is 0.0778%, and the average absolute error is 0.0843. The accuracy of the experimental results is obviously higher than the other methods of predicting the sintering BTP. It can accurately predict the position of the sintering end point, greatly improve the quality and production of the sinter in the steel plant, and also prolong the service life of the sintering equipment and reduce the production cost.
TL;DR: In this paper, the authors conduct correlation analysis of emergency big data, construct predictive analysis models for various emergencies, and establish data link relationships between emergencies and emergency decision-making through big data technology to support emergency analysis and decision.
Abstract: The emergency prediction analysis and emergency decision-making are still largely dependent on the actual experience of relevant personnel involved in emergency response. The judgment of emergencies is greatly influenced by subjective factors, and the lack of relevant data and information for auxiliary analysis and Models and methods of decision making. Conduct correlation analysis of emergency big data, construct predictive analysis models for various emergencies, and establish data link relationships between emergencies and emergency decisionmaking through big data technology to support emergency analysis and decision.
TL;DR: The SLAM mobile robot platform is designed and developed and Cartographer is used as the SLAM algorithm to realize the mapping and localization of the robot in the unknown environment, and the new hardware design architecture is proposed and the bottom control module is designed to be applied to the autonomous mobile robot.
Abstract: Aiming at some problems existing in the development and industrialization of intelligent autonomous mobile robot, the SLAM mobile robot platform is designed and developed in this paper. Cartographer is used as the SLAM algorithm to realize the mapping and localization of the robot in the unknown environment. What’s more, the new hardware design architecture is proposed, and the bottom control module is designed to be applied to the autonomous mobile robot. On the one hand, the design can reduce the workload of the non-algorithm part of the main control unit and improve the real-time of mapping. On the other hand, it solves the problem of communication clogging and enhances the compatibility of the system. Finally, an autonomous test platform is built to test the mapping and localization functions of the robot. The results of mapping, computational complexity and robustness of Cartographer SLAM and Hector SLAM algorithms are analyzed.
TL;DR: In this article, a convolutional neural network was constructed to recognize formula symbols, and determined the optimal parameters of the network through a large number of comparative experiments, and two convolution layers and sampling layers deepened the number of network layers.
Abstract: Printed mathematical formula recognition is a topic research region in OCR. But the diversity of fonts and sizes of mathematical symbols, as well as incorrect segmentation and stroke destruction of touching symbols lead to the difficulty of feature extraction and low recognition rate. In this paper, we constructed a convolutional neural network to recognize formula symbols, and determined the optimal parameters of the network through a large number of comparative experiments. Two convolution layers and sampling layers deepened the number of network layers and improved the recognition rate to a certain extent. Convolution kernels with fixed size extracted gradient information effectively, and ReLU activation function and dropout connection mode reduced the degree of over-fitting and gained the better generalization ability of the network. The experimental results show that the presented method can improve the recognition of printed formula symbols.
TL;DR: In this article, the authors present the issue of determining tactical and technical premises for proposed standards of calibres of ground-to-air missiles, and draw attention to the benefits for Polish society resulting from the development of an anti-aircraft system for the Narew program by Polish companies.
Abstract: The article presents the issue of determining tactical and technical premises for proposed standards of calibres of ground-to-air missiles. The possession of the missile-to-air missile systems by the state is not an end in itself, or a way of mutual allied settlements, and is a requirement of the modern battlefield. In connection with the decisions made in the Wisła program, only the Narew program implemented by Polish enterprises can improve the air defense capabilities of the country and operational forces. The proposal for the standardization of ground-toair missiles is aimed at defining the requirements for these missiles to such an extent as to increase the sense of security of citizens in peacetime, while, in wartime, the loss of human life and infrastructure will be reduced. During military operations, the state of air defense is no longer of such importance for the state authorities, because the authorities are evacuated in the face of threats, because they are the greatest asset of the nation. In the case of combat operations, the standardization of arms systems is the basis for logistical security. The purchase of rocket equipment for half an hour of combat is pointless, because, during this time, there will be no direct contact with the opponent, while the potential opponent probably has supplies for many hours of combat activities. If it is planned to start combat operations on its own, it makes sense, because, after the firing of the first volley, there will be no more. Therefore, the authors draw attention to the benefits for Polish society resulting from the development of an anti-aircraft system for the Narew programme by Polish companies and serving primarily to secure the air ______________ 1 Military University of Technology, 2 Sylwestra Kaliskiego Street, 00 – 908 Warsaw 46, Poland 2 Institute of Precision Mechanics, 3 Duchnicka Street, 01-796 Warsaw, Poland 3 MindMade Sp. z o.o., 3 Pl. Konstytucji, 00-647 Warsaw, Poland
TL;DR: Several commonly used microwave testing technology are introduced, comparative analysis of the various technical principle, difference and error sources with examples of typical are provided to provide a reference for future selection of sensor workers.
Abstract: Ranging is the basis of intelligent driving technology for vehicles. The accuracy and reliability of ranging are directly related to the safety and reliability of intelligent driving. Sensors are the basis of ranging, and various types of sensors have different characteristics due to their different structures and characteristics. This paper introduces several commonly used microwave testing technology, comparative analysis of the various technical principle, difference and error sources with examples of typical, provide a reference for future selection of sensor workers. 1
TL;DR: Based on the provincial panel data of China from 2008 to 2016, this article made an empirical study on the relationship between channel power and price fluctuation of agricultural products, and the results showed that: Channel power has a significant impact on price fluctuations of fresh agricultural products.
Abstract: Based on the provincial panel data of China from 2008 to 2016, this paper makes an empirical study on the relationship between channel power and price fluctuation of agricultural products. The results show that: Channel power has a significant impact on price fluctuation of fresh agricultural products. Specifically, relative scale power has a significant positive effect on the price fluctuation of fresh agricultural products, and relative operation power has a significant negative effect on the price fluctuation of fresh agricultural products, while channel power has no significant effect on the price fluctuation of grain agricultural products. Therefore, it is necessary to optimize the structure of the circulation industry of agricultural products.