2020 No. 4
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2020, (4): 1-11.
doi: 10.3969/j.issn.1000-5641.201911017
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Abstract:
We first introduce the notion of representations of a Lie supertriple system and present associated properties. We also study low dimensional cohomology and the coboundary operator of the Lie supertriple system. Lastly, we investigate the deformations and Nijenhuis operators of the Lie supertriple system by choosing suitable cohomology.
We first introduce the notion of representations of a Lie supertriple system and present associated properties. We also study low dimensional cohomology and the coboundary operator of the Lie supertriple system. Lastly, we investigate the deformations and Nijenhuis operators of the Lie supertriple system by choosing suitable cohomology.
2020, (4): 12-17.
doi: 10.3969/j.issn.1000-5641.201911018
Abstract:
In this paper, we study finite-dimensional Balinsky-Novikov superalgebras, which can be regarded as a class of superanalogues of Novikov algebras. We describe some of their properties and give the complete classification of finite-dimensional simple Balinsky-Novikov superalgebras over the field of real numbers.
In this paper, we study finite-dimensional Balinsky-Novikov superalgebras, which can be regarded as a class of superanalogues of Novikov algebras. We describe some of their properties and give the complete classification of finite-dimensional simple Balinsky-Novikov superalgebras over the field of real numbers.
2020, (4): 18-25.
doi: 10.3969/j.issn.1000-5641.201911019
Abstract:
In this paper, the existence of travelling wave solutions of a diffusive single species model with a strong generic delay kernel is established in two steps. Firstly, in the case of a species model without time delay, the existence of travelling wave solutions of the species model is obtained by using qualitative theories of ordinary differential equations. Secondly, when the time delay is greater than zero and sufficiently small, the existence of travelling wave solutions of the species model is verified by using linear chain techniques and the geometric singular perturbation theory.
In this paper, the existence of travelling wave solutions of a diffusive single species model with a strong generic delay kernel is established in two steps. Firstly, in the case of a species model without time delay, the existence of travelling wave solutions of the species model is obtained by using qualitative theories of ordinary differential equations. Secondly, when the time delay is greater than zero and sufficiently small, the existence of travelling wave solutions of the species model is verified by using linear chain techniques and the geometric singular perturbation theory.
2020, (4): 26-34.
doi: 10.3969/j.issn.1000-5641.201911015
Abstract:
Using real analysis, this paper reviews the order relations of Sándor-Yang means and single parameter harmonic (or contra-harmonic) means. Two optimal double inequalities are found.
Using real analysis, this paper reviews the order relations of Sándor-Yang means and single parameter harmonic (or contra-harmonic) means. Two optimal double inequalities are found.
2020, (4): 35-44.
doi: 10.3969/j.issn.1000-5641.201911016
Abstract:
In this paper, a class of neutral neural networks with time-varying delays was considered. First, the existence and uniqueness of equilibrium was obtained using the homeomorphic mapping theorem. Next, by constructing the corresponding Lyapunov functional and using Lyapunov stability theory, sufficient conditions for the global asymptotic stability of equilibrium were established. A comparison between our results and the existing results was made, revealing that our results can be used as criteria for judging the stability of some systems. Lastly, several numerical simulations were carried out to illustrate the results.
In this paper, a class of neutral neural networks with time-varying delays was considered. First, the existence and uniqueness of equilibrium was obtained using the homeomorphic mapping theorem. Next, by constructing the corresponding Lyapunov functional and using Lyapunov stability theory, sufficient conditions for the global asymptotic stability of equilibrium were established. A comparison between our results and the existing results was made, revealing that our results can be used as criteria for judging the stability of some systems. Lastly, several numerical simulations were carried out to illustrate the results.
2020, (4): 45-50.
doi: 10.3969/j.issn.1000-5641.201911013
Abstract:
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2020, (4): 51-63.
doi: 10.3969/j.issn.1000-5641.201911011
Abstract:
In this paper, we study the long-time behaviors of solutions for the Berger equation with white noise. By applying the asymptotic a priori estimates technique and the operator decomposition technique, the existence of random attractors is shown.
In this paper, we study the long-time behaviors of solutions for the Berger equation with white noise. By applying the asymptotic a priori estimates technique and the operator decomposition technique, the existence of random attractors is shown.
2020, (4): 64-71.
doi: 10.3969/j.issn.1000-5641.201911029
Abstract:
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In this paper, the existence of blow-up solutions for the nonhomogeneous nonlinear Schrödinger equation is studied. First, a class of invariant sets is constructed and then the optimal Gagliardo-Nirenberg type inequality is applied; careful analysis is used to prove that for any large
2020, (4): 72-78.
doi: 10.3969/j.issn.1000-5641.201921010
Abstract:
The lunar rover is a multi-function, mobile robot equipped with a mission. Under real terrain driving conditions, in addition to selecting the optimal path from the start point to the target point, the robot should take into account the terrain, obstacles, and other influencing factors. The main influencing factors of the terrain are steep slope gradients and slope orientation; other factors are classified as slip. These greatly increase the length and time complexity of path planning as well as the overall safety of the robot. The traditional ant colony algorithm seeks the optimal solution in path planning, but it also encounters problems such as slow convergence speed, high time complexity, and unbalanced optimization. It does not consider factors such as slip and terrain when applied to lunar rover path prediction. It is easy to fall into a local optimal solution when dealing with path planning problems. This paper proposes an improved ant colony algorithm for path planning based on the slope gradient and slope orientation for 3D raster terrain. By applying a consistent pheromone heuristic factor and pheromone volatilization coefficient, changing the terrain parameters for slip prediction, and obtaining a comprehensive cost function based on slip prediction, the traditional ant colony algorithm is improved. The influence of the comprehensive cost function based on slip prediction on the path length, convergence speed, time complexity, and iteration number of the improved ant colony algorithm is analyzed. Finally, experimental simulation data is used to verify that the improved ant colony algorithm is more effective in addressing slip prediction path planning problems.
The lunar rover is a multi-function, mobile robot equipped with a mission. Under real terrain driving conditions, in addition to selecting the optimal path from the start point to the target point, the robot should take into account the terrain, obstacles, and other influencing factors. The main influencing factors of the terrain are steep slope gradients and slope orientation; other factors are classified as slip. These greatly increase the length and time complexity of path planning as well as the overall safety of the robot. The traditional ant colony algorithm seeks the optimal solution in path planning, but it also encounters problems such as slow convergence speed, high time complexity, and unbalanced optimization. It does not consider factors such as slip and terrain when applied to lunar rover path prediction. It is easy to fall into a local optimal solution when dealing with path planning problems. This paper proposes an improved ant colony algorithm for path planning based on the slope gradient and slope orientation for 3D raster terrain. By applying a consistent pheromone heuristic factor and pheromone volatilization coefficient, changing the terrain parameters for slip prediction, and obtaining a comprehensive cost function based on slip prediction, the traditional ant colony algorithm is improved. The influence of the comprehensive cost function based on slip prediction on the path length, convergence speed, time complexity, and iteration number of the improved ant colony algorithm is analyzed. Finally, experimental simulation data is used to verify that the improved ant colony algorithm is more effective in addressing slip prediction path planning problems.
2020, (4): 79-87.
doi: 10.3969/j.issn.1000-5641.201921012
Abstract:
Anomaly detection methods are widely used for applications in the field of electric power, such as equipment fault detection and abnormal electricity consumption detection. The proposed algorithm combines densities of data objects with the maximum neighborhood radius to select data points that are closer to actual cluster centers for the initial selection; this, in turn, improves random selection of the initial cluster centers. In addition, a new anomaly detection method based on an improved K-means algorithm for electric power data is proposed. Experiments show that the algorithm is more suitable in both clustering performance and anomaly detection. When this algorithm is applied to the field of electric power, abnormal data can be effectively detected.
Anomaly detection methods are widely used for applications in the field of electric power, such as equipment fault detection and abnormal electricity consumption detection. The proposed algorithm combines densities of data objects with the maximum neighborhood radius to select data points that are closer to actual cluster centers for the initial selection; this, in turn, improves random selection of the initial cluster centers. In addition, a new anomaly detection method based on an improved K-means algorithm for electric power data is proposed. Experiments show that the algorithm is more suitable in both clustering performance and anomaly detection. When this algorithm is applied to the field of electric power, abnormal data can be effectively detected.
2020, (4): 88-97.
doi: 10.3969/j.issn.1000-5641.201921015
Abstract:
This paper proposes a framework for extracting corporate bankruptcy-related events from ruling documents and thus extracts structured information about the related events. Combined with ruling documents, our framework uses distant supervision to generate training data; applies named entity recognition techniques to implement sequence label tagging on sentences of litigation documents; and implements event extraction with a self-defined list of event trigger words as well as an event dictionary to detect bankruptcy-related events and gather structured information. Our experimental results demonstrate the effectiveness of the framework.
This paper proposes a framework for extracting corporate bankruptcy-related events from ruling documents and thus extracts structured information about the related events. Combined with ruling documents, our framework uses distant supervision to generate training data; applies named entity recognition techniques to implement sequence label tagging on sentences of litigation documents; and implements event extraction with a self-defined list of event trigger words as well as an event dictionary to detect bankruptcy-related events and gather structured information. Our experimental results demonstrate the effectiveness of the framework.
2020, (4): 98-107.
doi: 10.3969/j.issn.1000-5641.201921011
Abstract:
Failures are unavoidable in distributed database systems. To improve the fault tolerance of distributed database systems and verify the accuracy of fault-tolerant protocols, the system should periodically run a fault injection test to artificially trigger a fault during system operations. However, the scale and complexity of distributed database systems make it difficult to fully enumerate inputs and make it impractical to explain all the behaviors that occur in the system. One of the test methods commonly used is a random combination of faults, which is simple but not complete; the other one is guided by professional knowledge and is not universally applicable. Accordingly, we adopted and revised a research prototype, called the lineage-driven fault injection (LDFI) test, that is both complete and universally applicable. We implemented the automation fault injection tool in Cedar. Experiments showed that lineage-driven fault injection tests can successfully detect system bugs caused by complex fault combinations and improve the credibility of the database; these bugs cannot be detected by random fault injection with fewer test cases.
Failures are unavoidable in distributed database systems. To improve the fault tolerance of distributed database systems and verify the accuracy of fault-tolerant protocols, the system should periodically run a fault injection test to artificially trigger a fault during system operations. However, the scale and complexity of distributed database systems make it difficult to fully enumerate inputs and make it impractical to explain all the behaviors that occur in the system. One of the test methods commonly used is a random combination of faults, which is simple but not complete; the other one is guided by professional knowledge and is not universally applicable. Accordingly, we adopted and revised a research prototype, called the lineage-driven fault injection (LDFI) test, that is both complete and universally applicable. We implemented the automation fault injection tool in Cedar. Experiments showed that lineage-driven fault injection tests can successfully detect system bugs caused by complex fault combinations and improve the credibility of the database; these bugs cannot be detected by random fault injection with fewer test cases.
2020, (4): 108-123.
doi: 10.3969/j.issn.1000-5641.201921008
Abstract:
After the new policy for college entrance examination reform was put forward in China, an increasing number of regions and senior high schools began to adopt the mobile teaching system. Compared with traditional teaching schedules, which use an executive class, this pattern further increased the challenges of scheduling, and the lack of school education resources has become more prominent. The traditional algorithm for curriculum arrangement is not suitable for solving the scheduling problem that exists with the mobile teaching system. Pure manual scheduling is not only time-consuming and laborious, but there may also be unforeseen conflicts; it is difficult to guarantee the feasibility and rationality of a curriculum. Given the characteristics of a mobile teaching system pattern, this paper presents a method for obtaining high-quality feasible solutions to deal with course scheduling. First, a method for automatically generating combinations of mobile teaching classes is proposed. Second, the improved genetic algorithm is used to solve the scheduling problem efficiently and reasonably. Experiments show that the proposed algorithm can achieve a high-quality curriculum, and the method has been applied in practical applications.
After the new policy for college entrance examination reform was put forward in China, an increasing number of regions and senior high schools began to adopt the mobile teaching system. Compared with traditional teaching schedules, which use an executive class, this pattern further increased the challenges of scheduling, and the lack of school education resources has become more prominent. The traditional algorithm for curriculum arrangement is not suitable for solving the scheduling problem that exists with the mobile teaching system. Pure manual scheduling is not only time-consuming and laborious, but there may also be unforeseen conflicts; it is difficult to guarantee the feasibility and rationality of a curriculum. Given the characteristics of a mobile teaching system pattern, this paper presents a method for obtaining high-quality feasible solutions to deal with course scheduling. First, a method for automatically generating combinations of mobile teaching classes is proposed. Second, the improved genetic algorithm is used to solve the scheduling problem efficiently and reasonably. Experiments show that the proposed algorithm can achieve a high-quality curriculum, and the method has been applied in practical applications.
2020, (4): 124-133.
doi: 10.3969/j.issn.1000-5641.201921004
Abstract:
Hand gesture estimation is much more difficult than human pose estimation from depth images, in part because existing algorithms are unable to recognize different appearances of the same hand gesture after rotation. In this paper, an improved approach for hand gesture estimation based on in-plane image rotation is proposed. First, a convolutional neural network (CNN) was trained by datasets with an auto tagged optimum angle of rotation. Then, prior to hand gesture recognition, an in-plane image of the hand depth was processed by the predicted angle of rotation through the trained CNN model. Lastly, depth pixels were classified by random decision forest (RDF), followed by clustering to generate the hand joint position. Experiments show that this method can reduce the error between the predicted position of the hand joint and the exact position, and the accuracy of gesture estimation improves by about 4.69% from the baseline.
Hand gesture estimation is much more difficult than human pose estimation from depth images, in part because existing algorithms are unable to recognize different appearances of the same hand gesture after rotation. In this paper, an improved approach for hand gesture estimation based on in-plane image rotation is proposed. First, a convolutional neural network (CNN) was trained by datasets with an auto tagged optimum angle of rotation. Then, prior to hand gesture recognition, an in-plane image of the hand depth was processed by the predicted angle of rotation through the trained CNN model. Lastly, depth pixels were classified by random decision forest (RDF), followed by clustering to generate the hand joint position. Experiments show that this method can reduce the error between the predicted position of the hand joint and the exact position, and the accuracy of gesture estimation improves by about 4.69% from the baseline.
2020, (4): 134-146.
doi: 10.3969/j.issn.1000-5641.201921006
Abstract:
The click-through rate (CTR) prediction model is an important component of mainstream recommendation systems. The model assigns a score to recommended items according to the predicted CTR and generates an optimized scoring function which in-turn influences an item’s display strategy; this helps generate improved business conversion rates and a better user experience. Generally, CTR prediction models utilize both user and item features to predict CTR. However, structural characteristics of user behavior, such as frequency and trends, can also reflect behavioral tendencies. Given the absence of this information, this paper analyzes user behavior sequences as a time series and extracts latent features. Factorization machines are then used to learn from user/item features combined with sequence features to improve the quality of prediction. Experiments show that the sequence feature-based methods improve the performance of CTR prediction models and make CTR prediction more accurate.
The click-through rate (CTR) prediction model is an important component of mainstream recommendation systems. The model assigns a score to recommended items according to the predicted CTR and generates an optimized scoring function which in-turn influences an item’s display strategy; this helps generate improved business conversion rates and a better user experience. Generally, CTR prediction models utilize both user and item features to predict CTR. However, structural characteristics of user behavior, such as frequency and trends, can also reflect behavioral tendencies. Given the absence of this information, this paper analyzes user behavior sequences as a time series and extracts latent features. Factorization machines are then used to learn from user/item features combined with sequence features to improve the quality of prediction. Experiments show that the sequence feature-based methods improve the performance of CTR prediction models and make CTR prediction more accurate.
2020, (4): 147-155.
doi: 10.3969/j.issn.1000-5641.201921007
Abstract:
This paper proposes an online advertising feature extraction model of CNN (Convolutional Neural Networks) based on GBDT (Gradient Boosting Decision Tree) aimed at solving challenges with high-dimensional sparseness in Internet advertising data based on existing theories and technologies for click-through rate (CRT) prediction. The proposed model, CNN+, is able to extract deep, high-order features from raw data and solve the issues that convolutional neural networks face in extracting sparse and high-dimensional features. Experimental results on real datasets show that the features extracted by the CNN+ model are more effective than two other feature extraction methods studied, namely principal component analysis (PCA) and GBDT.
This paper proposes an online advertising feature extraction model of CNN (Convolutional Neural Networks) based on GBDT (Gradient Boosting Decision Tree) aimed at solving challenges with high-dimensional sparseness in Internet advertising data based on existing theories and technologies for click-through rate (CRT) prediction. The proposed model, CNN+, is able to extract deep, high-order features from raw data and solve the issues that convolutional neural networks face in extracting sparse and high-dimensional features. Experimental results on real datasets show that the features extracted by the CNN+ model are more effective than two other feature extraction methods studied, namely principal component analysis (PCA) and GBDT.
2020, (4): 156-163.
doi: 10.3969/j.issn.1000-5641.201921016
Abstract:
The traditional technique of repairing anomalous electrical energy data requires large amounts of data, has a high operational cost, and results in poor timeliness by using interpolation and other statistical methods; hence, the accuracy and efficiency of repairing results are limited. In this paper, a method for repairing anomalous electrical energy data based on the Grey Model is proposed. The normal historical electrical energy data is taken as an input variable, and the time node electrical energy data at which the abnormal point is located is taken as the output variable. The ratio test and the prediction equation are used to obtain the predicted value. The electrical energy data is iteratively predicted. Finally, the accuracy of the predicted value is tested. The average relative residual of the prediction was found to be 2.182%. The original data is then modified according to the result so as to repair the electrical energy anomaly data. The model prediction and repair are carried out with the actual electrical energy data of a certain area, and the results and errors are analyzed. The feasibility of the method is subsequently verified.
The traditional technique of repairing anomalous electrical energy data requires large amounts of data, has a high operational cost, and results in poor timeliness by using interpolation and other statistical methods; hence, the accuracy and efficiency of repairing results are limited. In this paper, a method for repairing anomalous electrical energy data based on the Grey Model is proposed. The normal historical electrical energy data is taken as an input variable, and the time node electrical energy data at which the abnormal point is located is taken as the output variable. The ratio test and the prediction equation are used to obtain the predicted value. The electrical energy data is iteratively predicted. Finally, the accuracy of the predicted value is tested. The average relative residual of the prediction was found to be 2.182%. The original data is then modified according to the result so as to repair the electrical energy anomaly data. The model prediction and repair are carried out with the actual electrical energy data of a certain area, and the results and errors are analyzed. The feasibility of the method is subsequently verified.
2020, (4): 164-172.
doi: 10.3969/j.issn.1000-5641.201931009
Abstract:
In this paper, we evaluated the ecological restoration effect of transforming traditional paddy fields into ecological paddy fields in the rural wetlands of Chenhaiwei. To achieve this objective, we conducted field investigations and laboratory observations, from July 2018 to May 2019, of amphibians, benthic animals, and the water quality at a landscape lake, ecological paddy field, traditional paddy field, and residential ditch. The results showed that: ①Amphibian species decreased sequentially from the landscape lake, ecological paddy field, traditional paddy field, and residential ditch. There was not a significant difference in the number of amphibians observed between the landscape lake and the ecological paddy field, but the number of amphibians was significantly higher than those observed at the traditional paddy field and residential ditch. ②There were no significant differences in the types and numbers of benthic species among the four types of habitats in the wet season, but the ecological paddy field showed the least variety and quantity of benthic species in the dry season. ③The water quality indices of organic nitrogen and phosphorus from the ecological paddy field ranked in the middle among the four habitats, but chlorophyll-a concentration was the lowest. In general, ecological recovery improved to a certain extent after transforming the planting pattern of the rice field at Chenhaiwei, but the lack of scientific management is not conducive to species stability. We put forward suggestions for ecological management of paddy fields.
In this paper, we evaluated the ecological restoration effect of transforming traditional paddy fields into ecological paddy fields in the rural wetlands of Chenhaiwei. To achieve this objective, we conducted field investigations and laboratory observations, from July 2018 to May 2019, of amphibians, benthic animals, and the water quality at a landscape lake, ecological paddy field, traditional paddy field, and residential ditch. The results showed that: ①Amphibian species decreased sequentially from the landscape lake, ecological paddy field, traditional paddy field, and residential ditch. There was not a significant difference in the number of amphibians observed between the landscape lake and the ecological paddy field, but the number of amphibians was significantly higher than those observed at the traditional paddy field and residential ditch. ②There were no significant differences in the types and numbers of benthic species among the four types of habitats in the wet season, but the ecological paddy field showed the least variety and quantity of benthic species in the dry season. ③The water quality indices of organic nitrogen and phosphorus from the ecological paddy field ranked in the middle among the four habitats, but chlorophyll-a concentration was the lowest. In general, ecological recovery improved to a certain extent after transforming the planting pattern of the rice field at Chenhaiwei, but the lack of scientific management is not conducive to species stability. We put forward suggestions for ecological management of paddy fields.
