Since the atmospheric correction is a necessary preprocessing step of remote sensing image before detecting green tide, the introduced error directly affects the detection precision. Therefore, the detection method of...Since the atmospheric correction is a necessary preprocessing step of remote sensing image before detecting green tide, the introduced error directly affects the detection precision. Therefore, the detection method of green tide is presented from Landsat TM/ETM plus image which needs not the atmospheric correction. In order to achieve an automatic detection of green tide, a linear relationship（y =0.723 x＋0.504） between detection threshold y and subtraction x（x=λnir–λred） is found from the comparing Landsat TM/ETM plus image with the field surveys.Using this relationship, green tide patches can be detected automatically from Landsat TM/ETM plus image.Considering there is brightness difference between different regions in an image, the image will be divided into a plurality of windows（sub-images） with a same size firstly, and then each window will be detected using an adaptive detection threshold determined according to the discovered linear relationship. It is found that big errors will appear in some windows, such as those covered by clouds seriously. To solve this problem, the moving step k of windows is proposed to be less than the window width n. Using this mechanism, most pixels will be detected[n/k]×[n/k] times except the boundary pixels, then every pixel will be assigned the final class（green tide or sea water） according to majority rule voting strategy. It can be seen from the experiments, the proposed detection method using multi-windows and their adaptive thresholds can detect green tide from Landsat TM/ETM plus image automatically. Meanwhile, it avoids the reliance on the accurate atmospheric correction.展开更多
Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human activity.Taxi-GPS traces,acted as one kind of typical crowd sensed data,it can be used ...Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human activity.Taxi-GPS traces,acted as one kind of typical crowd sensed data,it can be used to mine the semantics of OD flows.In this paper,we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in China.The spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD flows.Then based on a novel complex network model,a semantics mining method of OD flows is proposed through compounding Points of Interests(POI)network and public transport network to the OD flows network.The propose method would offer a novel way to predict the location characteristic and future traffic conditions accurately.展开更多
The active torsion propulsion mode of a caudal fin,composed of macro fiber composites(MFC)and carbon fiber orthotropic composite material is proposed.The caudal fin is excited by the piezoelectric structure to vibrate...The active torsion propulsion mode of a caudal fin,composed of macro fiber composites(MFC)and carbon fiber orthotropic composite material is proposed.The caudal fin is excited by the piezoelectric structure to vibrate flexibly.The work principle is firstly analyzed by finite element method(FEM)and experiments.Then the caudal fin is optimized to increase the torque and improve the streamline,and the added mass effect from the water is discussed in terms of the frequency of the structure.The torsion resonance frequency is around 103 Hz in the air and decreased by 75%to 25 Hz in the water.Finally,the mean thrust is discussed and measured to be 11 mN at900V(Peak to peak)driving voltage.A flexible micro robot is developed and tested.The locomotion velocity and flow velocity is 320mm/s and 268mm/s,respectively.The results of the simulation and experiments indicate that the locomotion of the biomimetic aquatic robot has fast movement characteristics.展开更多
In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network...In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine(DBN-SVM). Sliding window(SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented.Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method's real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.展开更多
To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel c...To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel computation.To achieve highly reliable parallel computation for wireless sensor network,the network's lifetime needs to be extended.Therefore,a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the network.This paper proposes a task model and a cluster-based WSN model in edge computing.In our model,different tasks require different types of resources and different sensors provide different types of resources,so our model is heterogeneous,which makes the model more practical.Then we propose a task allocation algorithm that combines the Genetic Algorithm(GA)and the Ant Colony Optimization(ACO)algorithm.The algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be extended.The experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing.展开更多
In this paper, based on the existing research results, we obtain the unary extension 3-Lie algebras by one-dimensional extension of the known Lie algebra L. For two known 3-Lie algebras <em>H</em>, <em&...In this paper, based on the existing research results, we obtain the unary extension 3-Lie algebras by one-dimensional extension of the known Lie algebra L. For two known 3-Lie algebras <em>H</em>, <em>M</em>, the (<i>μ</i>, <i>ρ</i>, <i>β</i>)-extension of <em>H</em> through <em>M</em> is given, and the necessary and sufficient conditions for the (<i>μ</i>, <i>ρ</i>, <i>β</i>)-extension algebra of <em>H</em> through <em>M</em> being 3-Lie algebra are obtained, and the structural characteristics and properties of these two kinds of extended 3-Lie algebras are given.展开更多
Linear algebra has a very important application in physics and technical disciplines. This article conducted a questionnaire survey on the factors that affect the effect of linear algebra learning;the questionnaire co...Linear algebra has a very important application in physics and technical disciplines. This article conducted a questionnaire survey on the factors that affect the effect of linear algebra learning;the questionnaire contains several aspects of learning attitude, learning interest, learning methods, teaching methods, etc.;based on recycling data, cross chi-square test and multiple logistic regression analysis </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">are </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">us</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ed</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> to obtain the factors that affect the effect of linear algebra learning. The research results show that: learning methods, learning attitudes, teaching methods and elementary algebra basics are the main factors that affect the learning effect of linear algebra;among them, there are positive correlations between teaching methods, learning methods, learning attitudes and learning effects;teaching methods, learning methods 3. The three principal components of learning attitude are positively correlated. Based on the research and analysis, the following conclusions are drawn: finding a suitable learning method for the college students and maintaining a positive learning attitude are effective means to improve the linear algebra learning effect of the college students;in teaching, it is recommended to advance with the times, the teaching content and teaching methods innovate to stimulate students’ interest in learning</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">,</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> thus improv</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ing</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> the learning effect of college students’ linear algebra courses.展开更多
In recent years, with the large increase in the number of motor vehicles in colleges and universities and the lag in campus planning, the relative shortage of parking spaces on campus has become increasingly serious. ...In recent years, with the large increase in the number of motor vehicles in colleges and universities and the lag in campus planning, the relative shortage of parking spaces on campus has become increasingly serious. Taking Baoding College as an example, this article analyzes the current situation of static traffic on campus and finds out the problem of parking on campus through questionnaire surveys and field surveys. Analyze the growth trend of the number of motor vehicles based on the data, use the GM (1, 1) model and the linear fitting model to predict the number of motor vehicles in the future, and determine the size and layout of the parking lot based on the campus size, functional zoning, and road layout. The big campus-based parking system planning method based on big data can effectively solve the problems of small sample data, low accuracy, and poor timeliness of traditional methods, which improves the practicability and scientificity of planning results.展开更多
As a newly-identified protein post-translational modification, malonylation is involved in a variety of biological functions. Recognizing malonylation sites in substrates represents an initial but crucial step in eluc...As a newly-identified protein post-translational modification, malonylation is involved in a variety of biological functions. Recognizing malonylation sites in substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein malonylation. In this study, we constructed a deep learning(DL) network classifier based on long short-term memory(LSTM) with word embedding(LSTMWE) for the prediction of mammalian malonylation sites.LSTMWEperforms better than traditional classifiers developed with common pre-defined feature encodings or a DL classifier based on LSTM with a one-hot vector. The performance of LSTMWE is sensitive to the size of the training set, but this limitation can be overcome by integration with a traditional machine learning(ML) classifier. Accordingly, an integrated approach called LEMP was developed, which includes LSTMWEand the random forest classifier with a novel encoding of enhanced amino acid content. LEMP performs not only better than the individual classifiers but also superior to the currently-available malonylation predictors. Additionally, it demonstrates a promising performance with a low false positive rate, which is highly useful in the prediction application. Overall, LEMP is a useful tool for easily identifying malonylation sites with high confidence.LEMP is available at http://www.bioinfogo.org/lemp.展开更多
To ensure the safe operation of industrial digital twins network and avoid the harm to the system caused by hacker invasion,a series of discussions on network security issues are carried out based on game theory.From ...To ensure the safe operation of industrial digital twins network and avoid the harm to the system caused by hacker invasion,a series of discussions on network security issues are carried out based on game theory.From the perspective of the life cycle of network vulnerabilities,mining and repairing vulnerabilities are analyzed by applying evolutionary game theory.The evolution process of knowledge sharing among white hats under various conditions is simulated,and a game model of the vulnerability patch cooperative development strategy among manufacturers is constructed.On this basis,the differential evolution is introduced into the update mechanism of the Wolf Colony Algorithm(WCA)to produce better replacement individuals with greater probability from the perspective of both attack and defense.Through the simulation experiment,it is found that the convergence speed of the probability(X)of white Hat 1 choosing the knowledge sharing policy is related to the probability(x0)of white Hat 2 choosing the knowledge sharing policy initially,and the probability(y0)of white hat 2 choosing the knowledge sharing policy initially.When y0?0.9,X converges rapidly in a relatively short time.When y0 is constant and x0 is small,the probability curve of the“cooperative development”strategy converges to 0.It is concluded that the higher the trust among the white hat members in the temporary team,the stronger their willingness to share knowledge,which is conducive to the mining of loopholes in the system.The greater the probability of a hacker attacking the vulnerability before it is fully disclosed,the lower the willingness of manufacturers to choose the"cooperative development"of vulnerability patches.Applying the improved wolf colonyco-evolution algorithm can obtain the equilibrium solution of the"attack and defense game model",and allocate the security protection resources according to the importance of nodes.This study can provide an effective solution to protect the network security for digital twins in the industry.展开更多
The development of communication technology will promote the application of Internet of Things,and Beyond 5G will become a new technology promoter.At the same time,Beyond 5G will become one of the important supports f...The development of communication technology will promote the application of Internet of Things,and Beyond 5G will become a new technology promoter.At the same time,Beyond 5G will become one of the important supports for the development of edge computing technology.This paper proposes a communication task allocation algorithm based on deep reinforcement learning for vehicle-to-pedestrian communication scenarios in edge computing.Through trial and error learning of agent,the optimal spectrum and power can be determined for transmission without global information,so as to balance the communication between vehicle-to-pedestrian and vehicle-to-infrastructure.The results show that the agent can effectively improve vehicle-to-infrastructure communication rate as well as meeting the delay constraints on the vehicle-to-pedestrian link.展开更多
With the expansion of the scale of our hospital robot laboratory, equipment use, reserves are increasing, manual management equipment information has been unable to adapt to daily management, such as data record error...With the expansion of the scale of our hospital robot laboratory, equipment use, reserves are increasing, manual management equipment information has been unable to adapt to daily management, such as data record error rate increased, the number of experiments increased, equipment loss and maintenance is not clearly defined. In order to ensure the safety and accuracy of the equipment, a system of equipment management systems was developed as a result of the conversion to the use of management systems for the recording and storage of information. The design of this system is a perfect robot laboratory equipment management system based on the development of web framework technology for equipment management functions. The back end of this system uses the Java language, the SSM framework as the main body, the database is built by MySQL relational database, the front-end page uses JSP, Bootstrap, jQuery and other technologies, and uses Eclipse project construction tools for system development. After a period of trial operation, the level of laboratory equipment management has been significantly improved.展开更多
3D face similarity is a critical issue in computer vision, computer graphics and face recognition and so on. Since Fr@chet distance is an effective metric for measuring curve similarity, a novel 3D face similarity mea...3D face similarity is a critical issue in computer vision, computer graphics and face recognition and so on. Since Fr@chet distance is an effective metric for measuring curve similarity, a novel 3D face similarity measure method based on Fr^chet distances of geodesics is proposed in this paper. In our method, the surface similarity between two 3D faces is measured by the similarity between two sets of 3D curves on them. Due to the intrinsic property of geodesics, we select geodesics as the comparison curves. Firstly, the geodesics on each 3D facial model emanating from the nose tip point are extracted in the same initial direction with equal angular increment. Secondly, the Fr@chet distances between the two sets of geodesics on the two compared facial models are computed. At last, the similarity between the two facial models is computed based on the Fr6chet distances of the geodesics obtained in the second step. We verify our method both theoretically and practically. In theory, we prove that the similarity of our method satisfies three properties： reflexivity, symmetry, and triangle inequality. And in practice, experiments are conducted on the open 3D face database GavaDB, Texas 3D Face Recognition database, and our 3D face database. After the comparison with iso-geodesic and Hausdorff distance method, the results illustrate that our method has good discrimination ability and can not only identify the facial models of the same person, but also distinguish the facial models of any two different persons.展开更多
基金The National Natural Science Foundation of China under contract Nos 41506198 and 41476101the Natural Science Foundation Projects of Shandong Province of China under contract No.ZR2012FZ003the Science and Technology Development Plan of Qingdao City of China under contract No.13-1-4-121-jch
文摘Since the atmospheric correction is a necessary preprocessing step of remote sensing image before detecting green tide, the introduced error directly affects the detection precision. Therefore, the detection method of green tide is presented from Landsat TM/ETM plus image which needs not the atmospheric correction. In order to achieve an automatic detection of green tide, a linear relationship（y =0.723 x＋0.504） between detection threshold y and subtraction x（x=λnir–λred） is found from the comparing Landsat TM/ETM plus image with the field surveys.Using this relationship, green tide patches can be detected automatically from Landsat TM/ETM plus image.Considering there is brightness difference between different regions in an image, the image will be divided into a plurality of windows（sub-images） with a same size firstly, and then each window will be detected using an adaptive detection threshold determined according to the discovered linear relationship. It is found that big errors will appear in some windows, such as those covered by clouds seriously. To solve this problem, the moving step k of windows is proposed to be less than the window width n. Using this mechanism, most pixels will be detected[n/k]×[n/k] times except the boundary pixels, then every pixel will be assigned the final class（green tide or sea water） according to majority rule voting strategy. It can be seen from the experiments, the proposed detection method using multi-windows and their adaptive thresholds can detect green tide from Landsat TM/ETM plus image automatically. Meanwhile, it avoids the reliance on the accurate atmospheric correction.
基金This work is supported by Shandong Provincial Natural Science Foundation,China under Grant No.ZR2017MG011This work is also supported by Key Research and Development Program in Shandong Provincial(2017GGX90103).
文摘Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human activity.Taxi-GPS traces,acted as one kind of typical crowd sensed data,it can be used to mine the semantics of OD flows.In this paper,we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in China.The spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD flows.Then based on a novel complex network model,a semantics mining method of OD flows is proposed through compounding Points of Interests(POI)network and public transport network to the OD flows network.The propose method would offer a novel way to predict the location characteristic and future traffic conditions accurately.
基金supported by the Natural Science Foundation of China(No.5175250)the Funding of Jiangsu Innovation Program for Graduate Education+2 种基金the Fundamental Research Funds for the Central Universities (No.CXLX12_0144)the Science and Technology Plan Project of Shandong Colleges and Universities (No.J16LB06)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PADA)
文摘The active torsion propulsion mode of a caudal fin,composed of macro fiber composites(MFC)and carbon fiber orthotropic composite material is proposed.The caudal fin is excited by the piezoelectric structure to vibrate flexibly.The work principle is firstly analyzed by finite element method(FEM)and experiments.Then the caudal fin is optimized to increase the torque and improve the streamline,and the added mass effect from the water is discussed in terms of the frequency of the structure.The torsion resonance frequency is around 103 Hz in the air and decreased by 75%to 25 Hz in the water.Finally,the mean thrust is discussed and measured to be 11 mN at900V(Peak to peak)driving voltage.A flexible micro robot is developed and tested.The locomotion velocity and flow velocity is 320mm/s and 268mm/s,respectively.The results of the simulation and experiments indicate that the locomotion of the biomimetic aquatic robot has fast movement characteristics.
基金supported by the National Key Research and Development Program of China(2017YFB1401300,2017YFB1401304)the National Natural Science Foundation of China(61702211,L1724007,61902203)+3 种基金Hubei Provincial Science and Technology Program of China(2017AKA191)the Self-Determined Research Funds of Central China Normal University(CCNU)from the Colleges’Basic Research(CCNU17QD0004,CCNU17GF0002)the Natural Science Foundation of Shandong Province(ZR2017QF015)the Key Research and Development Plan–Major Scientific and Technological Innovation Projects of Shandong Province(2019JZZY020101)。
文摘In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine(DBN-SVM). Sliding window(SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented.Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method's real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.
基金supported by Postdoctoral Science Foundation of China(No.2021M702441)National Natural Science Foundation of China(No.61871283)。
文摘To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel computation.To achieve highly reliable parallel computation for wireless sensor network,the network's lifetime needs to be extended.Therefore,a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the network.This paper proposes a task model and a cluster-based WSN model in edge computing.In our model,different tasks require different types of resources and different sensors provide different types of resources,so our model is heterogeneous,which makes the model more practical.Then we propose a task allocation algorithm that combines the Genetic Algorithm(GA)and the Ant Colony Optimization(ACO)algorithm.The algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be extended.The experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing.
文摘In this paper, based on the existing research results, we obtain the unary extension 3-Lie algebras by one-dimensional extension of the known Lie algebra L. For two known 3-Lie algebras <em>H</em>, <em>M</em>, the (<i>μ</i>, <i>ρ</i>, <i>β</i>)-extension of <em>H</em> through <em>M</em> is given, and the necessary and sufficient conditions for the (<i>μ</i>, <i>ρ</i>, <i>β</i>)-extension algebra of <em>H</em> through <em>M</em> being 3-Lie algebra are obtained, and the structural characteristics and properties of these two kinds of extended 3-Lie algebras are given.
文摘Linear algebra has a very important application in physics and technical disciplines. This article conducted a questionnaire survey on the factors that affect the effect of linear algebra learning;the questionnaire contains several aspects of learning attitude, learning interest, learning methods, teaching methods, etc.;based on recycling data, cross chi-square test and multiple logistic regression analysis </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">are </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">us</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ed</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> to obtain the factors that affect the effect of linear algebra learning. The research results show that: learning methods, learning attitudes, teaching methods and elementary algebra basics are the main factors that affect the learning effect of linear algebra;among them, there are positive correlations between teaching methods, learning methods, learning attitudes and learning effects;teaching methods, learning methods 3. The three principal components of learning attitude are positively correlated. Based on the research and analysis, the following conclusions are drawn: finding a suitable learning method for the college students and maintaining a positive learning attitude are effective means to improve the linear algebra learning effect of the college students;in teaching, it is recommended to advance with the times, the teaching content and teaching methods innovate to stimulate students’ interest in learning</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">,</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> thus improv</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ing</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> the learning effect of college students’ linear algebra courses.
文摘In recent years, with the large increase in the number of motor vehicles in colleges and universities and the lag in campus planning, the relative shortage of parking spaces on campus has become increasingly serious. Taking Baoding College as an example, this article analyzes the current situation of static traffic on campus and finds out the problem of parking on campus through questionnaire surveys and field surveys. Analyze the growth trend of the number of motor vehicles based on the data, use the GM (1, 1) model and the linear fitting model to predict the number of motor vehicles in the future, and determine the size and layout of the parking lot based on the campus size, functional zoning, and road layout. The big campus-based parking system planning method based on big data can effectively solve the problems of small sample data, low accuracy, and poor timeliness of traditional methods, which improves the practicability and scientificity of planning results.
基金supported in part by funds from the Young Scientists Fund of the National Natural Science Foundation of China (Grant No.31701142 to ZC Grant No.81602621 to NH)+3 种基金the Qingdao Postdoctoral Science Foundation (Grant No.2016061 to NH)the Shandong Provincial Natural Science Foundation (Grant No.ZR2016CM14 to LL)the National Natural Science Foundation of China (Grant No.31770821 to LL)supported by the ‘‘Distinguished Expert of Overseas Tai Shan Scholar" program
文摘As a newly-identified protein post-translational modification, malonylation is involved in a variety of biological functions. Recognizing malonylation sites in substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein malonylation. In this study, we constructed a deep learning(DL) network classifier based on long short-term memory(LSTM) with word embedding(LSTMWE) for the prediction of mammalian malonylation sites.LSTMWEperforms better than traditional classifiers developed with common pre-defined feature encodings or a DL classifier based on LSTM with a one-hot vector. The performance of LSTMWE is sensitive to the size of the training set, but this limitation can be overcome by integration with a traditional machine learning(ML) classifier. Accordingly, an integrated approach called LEMP was developed, which includes LSTMWEand the random forest classifier with a novel encoding of enhanced amino acid content. LEMP performs not only better than the individual classifiers but also superior to the currently-available malonylation predictors. Additionally, it demonstrates a promising performance with a low false positive rate, which is highly useful in the prediction application. Overall, LEMP is a useful tool for easily identifying malonylation sites with high confidence.LEMP is available at http://www.bioinfogo.org/lemp.
文摘To ensure the safe operation of industrial digital twins network and avoid the harm to the system caused by hacker invasion,a series of discussions on network security issues are carried out based on game theory.From the perspective of the life cycle of network vulnerabilities,mining and repairing vulnerabilities are analyzed by applying evolutionary game theory.The evolution process of knowledge sharing among white hats under various conditions is simulated,and a game model of the vulnerability patch cooperative development strategy among manufacturers is constructed.On this basis,the differential evolution is introduced into the update mechanism of the Wolf Colony Algorithm(WCA)to produce better replacement individuals with greater probability from the perspective of both attack and defense.Through the simulation experiment,it is found that the convergence speed of the probability(X)of white Hat 1 choosing the knowledge sharing policy is related to the probability(x0)of white Hat 2 choosing the knowledge sharing policy initially,and the probability(y0)of white hat 2 choosing the knowledge sharing policy initially.When y0?0.9,X converges rapidly in a relatively short time.When y0 is constant and x0 is small,the probability curve of the“cooperative development”strategy converges to 0.It is concluded that the higher the trust among the white hat members in the temporary team,the stronger their willingness to share knowledge,which is conducive to the mining of loopholes in the system.The greater the probability of a hacker attacking the vulnerability before it is fully disclosed,the lower the willingness of manufacturers to choose the"cooperative development"of vulnerability patches.Applying the improved wolf colonyco-evolution algorithm can obtain the equilibrium solution of the"attack and defense game model",and allocate the security protection resources according to the importance of nodes.This study can provide an effective solution to protect the network security for digital twins in the industry.
基金supported by National Natural Science Foundation of China(No.61871283)the Foundation of Pre-Research on Equipment of China(No.61400010304)Major Civil-Military Integration Project in Tianjin,China(No.18ZXJMTG00170).
文摘The development of communication technology will promote the application of Internet of Things,and Beyond 5G will become a new technology promoter.At the same time,Beyond 5G will become one of the important supports for the development of edge computing technology.This paper proposes a communication task allocation algorithm based on deep reinforcement learning for vehicle-to-pedestrian communication scenarios in edge computing.Through trial and error learning of agent,the optimal spectrum and power can be determined for transmission without global information,so as to balance the communication between vehicle-to-pedestrian and vehicle-to-infrastructure.The results show that the agent can effectively improve vehicle-to-infrastructure communication rate as well as meeting the delay constraints on the vehicle-to-pedestrian link.
文摘With the expansion of the scale of our hospital robot laboratory, equipment use, reserves are increasing, manual management equipment information has been unable to adapt to daily management, such as data record error rate increased, the number of experiments increased, equipment loss and maintenance is not clearly defined. In order to ensure the safety and accuracy of the equipment, a system of equipment management systems was developed as a result of the conversion to the use of management systems for the recording and storage of information. The design of this system is a perfect robot laboratory equipment management system based on the development of web framework technology for equipment management functions. The back end of this system uses the Java language, the SSM framework as the main body, the database is built by MySQL relational database, the front-end page uses JSP, Bootstrap, jQuery and other technologies, and uses Eclipse project construction tools for system development. After a period of trial operation, the level of laboratory equipment management has been significantly improved.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos. 61702293, 61772294, and 61572078, the Open Research Fund of the Ministry of Education Engineering Research Center of Virtual Reality Application of China under Grant No. MEOBNUEVRA201601. It was also partially supported by the National High Technology Research and Development 863 Program of China under Grant No. 2015AA020506, and the National Science and Technology Pillar Program during the 12th Five-Year Plan Period of China under Grant No. 2013BAI01B03.
文摘3D face similarity is a critical issue in computer vision, computer graphics and face recognition and so on. Since Fr@chet distance is an effective metric for measuring curve similarity, a novel 3D face similarity measure method based on Fr^chet distances of geodesics is proposed in this paper. In our method, the surface similarity between two 3D faces is measured by the similarity between two sets of 3D curves on them. Due to the intrinsic property of geodesics, we select geodesics as the comparison curves. Firstly, the geodesics on each 3D facial model emanating from the nose tip point are extracted in the same initial direction with equal angular increment. Secondly, the Fr@chet distances between the two sets of geodesics on the two compared facial models are computed. At last, the similarity between the two facial models is computed based on the Fr6chet distances of the geodesics obtained in the second step. We verify our method both theoretically and practically. In theory, we prove that the similarity of our method satisfies three properties： reflexivity, symmetry, and triangle inequality. And in practice, experiments are conducted on the open 3D face database GavaDB, Texas 3D Face Recognition database, and our 3D face database. After the comparison with iso-geodesic and Hausdorff distance method, the results illustrate that our method has good discrimination ability and can not only identify the facial models of the same person, but also distinguish the facial models of any two different persons.
