Contemporary young college students are greatly impacted in the aspects of moral cognition and moral choice, which results in the weak moral will of some college students, vague moral concepts, and weak ideals and beliefs, which seriously affect the formation and development of college students’ moral quality. Therefore, the moral education evaluation model based on college students’ quality cultivation is constructed. Firstly, the present situation and defects of college students’ quality training are analyzed. Based on this, association rules in data mining method are constructed and introduced to extract valuable knowledge hidden in the data to assist education managers to make effective decisions and improve management level. Finally, the evaluation index is selected and the weighted principal component TOP-SIS model is constructed to realize the evaluation of moral education based on college students’ quality cultivation. The experimental results show that the evaluation results of the model are consistent with the actual situation, high degree of fit and freedom, and good practical performance.
{"title":"Construction of Moral Education Evaluation Model Based on Quality Cultivation of College Students","authors":"Xiao-Fang Yuan","doi":"10.1155/2022/5641782","DOIUrl":"https://doi.org/10.1155/2022/5641782","url":null,"abstract":"Contemporary young college students are greatly impacted in the aspects of moral cognition and moral choice, which results in the weak moral will of some college students, vague moral concepts, and weak ideals and beliefs, which seriously affect the formation and development of college students’ moral quality. Therefore, the moral education evaluation model based on college students’ quality cultivation is constructed. Firstly, the present situation and defects of college students’ quality training are analyzed. Based on this, association rules in data mining method are constructed and introduced to extract valuable knowledge hidden in the data to assist education managers to make effective decisions and improve management level. Finally, the evaluation index is selected and the weighted principal component TOP-SIS model is constructed to realize the evaluation of moral education based on college students’ quality cultivation. The experimental results show that the evaluation results of the model are consistent with the actual situation, high degree of fit and freedom, and good practical performance.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"23 1","pages":"5641782:1-5641782:11"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79325825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the evolution of the Internet and information technology, the era of big data is a new digital one. Accordingly, animation IP has been more and more widely welcomed and concerned with the continuous development of the domestic and international animation industry. Hence, animation video analysis will be a good landing application for computers. This paper proposes an algorithm based on clustering and cascaded SSD for object detection of animation characters in the big data environment. In the training process, the improved classification Loss function based on Focal Loss and Truncated Gradient was used to enhance the initial detection effect. In the detection phase, this algorithm designs a small target enhanced detection module cascaded with an SSD network. In this way, the high-level features corresponding to the small target region can be extracted separately to detect small targets, which can effectively enhance the detection effect of small targets. In order to further improve the effect of small target detection, the regional candidate box is reconstructed by a k-means clustering algorithm to improve the detection accuracy of the algorithm. Experimental results demonstrate that this method can effectively detect animation characters, and performance indicators are better than other existing algorithms.
{"title":"Animation Character Detection Algorithm Based on Clustering and Cascaded SSD","authors":"Yuan-Hui Wang","doi":"10.1155/2022/4223295","DOIUrl":"https://doi.org/10.1155/2022/4223295","url":null,"abstract":"With the evolution of the Internet and information technology, the era of big data is a new digital one. Accordingly, animation IP has been more and more widely welcomed and concerned with the continuous development of the domestic and international animation industry. Hence, animation video analysis will be a good landing application for computers. This paper proposes an algorithm based on clustering and cascaded SSD for object detection of animation characters in the big data environment. In the training process, the improved classification Loss function based on Focal Loss and Truncated Gradient was used to enhance the initial detection effect. In the detection phase, this algorithm designs a small target enhanced detection module cascaded with an SSD network. In this way, the high-level features corresponding to the small target region can be extracted separately to detect small targets, which can effectively enhance the detection effect of small targets. In order to further improve the effect of small target detection, the regional candidate box is reconstructed by a k-means clustering algorithm to improve the detection accuracy of the algorithm. Experimental results demonstrate that this method can effectively detect animation characters, and performance indicators are better than other existing algorithms.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"16 1","pages":"4223295:1-4223295:10"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86775437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to improve the damage feature extraction effect of prefabricated residential building components and improve the structural stability of prefabricated residential components, this paper applies BIM technology to the structural feature analysis of prefabricated residential components. Moreover, this paper adopts the simple superposition method and combines the first strength theory of material mechanics to derive the formula for calculating the cracking torque of prefabricated residential building components under compound torsion. In addition, based on the variable-angle space truss model, this paper uses a simple superposition method to derive the calculation formula for the ultimate torque of composite torsion of fabricated residential building components and applies it to the BIM fabricated residential model. Finally, this paper constructs an intelligent BIM prefabricated residential building construction damage characteristic monitoring system. Through experimental research, it can be seen that the intelligent BIM prefabricated residential building construction damage feature monitoring system proposed in this paper can monitor the damage characteristics of prefabricated residential building construction and can predict the evolution of subsequent building features.
{"title":"Destruction Feature Extraction of Prefabricated Residential Building Components Based on BIM","authors":"Peili Zhao, Xiaohong Liu, Zhisheng Liang","doi":"10.1155/2022/5798625","DOIUrl":"https://doi.org/10.1155/2022/5798625","url":null,"abstract":"In order to improve the damage feature extraction effect of prefabricated residential building components and improve the structural stability of prefabricated residential components, this paper applies BIM technology to the structural feature analysis of prefabricated residential components. Moreover, this paper adopts the simple superposition method and combines the first strength theory of material mechanics to derive the formula for calculating the cracking torque of prefabricated residential building components under compound torsion. In addition, based on the variable-angle space truss model, this paper uses a simple superposition method to derive the calculation formula for the ultimate torque of composite torsion of fabricated residential building components and applies it to the BIM fabricated residential model. Finally, this paper constructs an intelligent BIM prefabricated residential building construction damage characteristic monitoring system. Through experimental research, it can be seen that the intelligent BIM prefabricated residential building construction damage feature monitoring system proposed in this paper can monitor the damage characteristics of prefabricated residential building construction and can predict the evolution of subsequent building features.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"7 1","pages":"5798625:1-5798625:12"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80511918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the further development of China's market economy, the competition faced by companies in the market has become more intense, and many companies have difficulty facing pressure and risks. Among the many types of enterprises, high-tech enterprises are the riskiest. The emergence of big data technologies and concepts in recent years has provided new opportunities for financial crisis early warning. Through in-depth study of the theoretical feasibility and practical value of big data indicators, the use of big data indicators to develop an early warning system for financial crises has important theoretical value for breaking through the stagnant predicament of financial crisis early warning. As a result of the preceding context, this research focuses on the influence of big data on the financial crisis early warning model, selects and quantifies the big data indicators and financial indicators, designs the financial crisis early warning model, and verifies its accuracy. The specific research design ideas include the following: (1) We make preliminary preparations for model construction. Preliminary determination and screening of training samples and early warning indicators are carried out, the samples needed to build the model and the early warning indicator system are determined, and the principles of the model methods used are briefly described. First, we perform a significant analysis of financial indicators and screen out early warning indicators that can clearly distinguish between financial crisis companies and nonfinancial crisis companies. (2) We analyze the sentiment tendency of the stock bar comment data to obtain big data indicators. Then, we establish a logistic model based on pure financial indicators and a logistic model that introduces big data indicators. Finally, the two models are tested and compared, the changes in the model's early warning effect before and after the introduction of big data indicators are analyzed, and the optimization effect of big data indicators on financial crisis early warning is tested.
{"title":"Analysis of Financial Risk Early Warning Systems of High-Tech Enterprises under Big Data Framework","authors":"Maotao Lai","doi":"10.1155/2022/9055294","DOIUrl":"https://doi.org/10.1155/2022/9055294","url":null,"abstract":"With the further development of China's market economy, the competition faced by companies in the market has become more intense, and many companies have difficulty facing pressure and risks. Among the many types of enterprises, high-tech enterprises are the riskiest. The emergence of big data technologies and concepts in recent years has provided new opportunities for financial crisis early warning. Through in-depth study of the theoretical feasibility and practical value of big data indicators, the use of big data indicators to develop an early warning system for financial crises has important theoretical value for breaking through the stagnant predicament of financial crisis early warning. As a result of the preceding context, this research focuses on the influence of big data on the financial crisis early warning model, selects and quantifies the big data indicators and financial indicators, designs the financial crisis early warning model, and verifies its accuracy. The specific research design ideas include the following: (1) We make preliminary preparations for model construction. Preliminary determination and screening of training samples and early warning indicators are carried out, the samples needed to build the model and the early warning indicator system are determined, and the principles of the model methods used are briefly described. First, we perform a significant analysis of financial indicators and screen out early warning indicators that can clearly distinguish between financial crisis companies and nonfinancial crisis companies. (2) We analyze the sentiment tendency of the stock bar comment data to obtain big data indicators. Then, we establish a logistic model based on pure financial indicators and a logistic model that introduces big data indicators. Finally, the two models are tested and compared, the changes in the model's early warning effect before and after the introduction of big data indicators are analyzed, and the optimization effect of big data indicators on financial crisis early warning is tested.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"63 1","pages":"9055294:1-9055294:9"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90472415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
At present, the allocation efficiency of regional scientific and technological resources is low, and there are few research studies on social equity and economic efficiency. Therefore, this paper puts forward the allocation method of regional scientific and technological resources based on rationality. With the support of rationality perspective, the evaluation model of reform path of regional scientific and technological resource allocation is constructed to analyze the economic benefits and equity benefits of regional scientific and technological resource allocation. According to the principle of optimum allocation of regional science and technology resources, three-dimensional structure is constructed to maximize national investment and benefit and determine the optimal Pareto of resource allocation to measure the efficiency of resource allocation. The evaluation index system of the reform path of resource allocation is constructed by selecting the evaluation index of the reform path of resource allocation. The benchmark platform of big data was selected to generate data sets to be processed, and the spark on yam platform was used to submit jobs and generate spark job running data sets. The operation performance prediction model was established to optimize the configuration parameters of regional science and technology resources. The analysis results show that the designed method has high configuration capability and good effectiveness.
{"title":"Research on the Allocation Method of Regional Science and Technology Resources from the Perspective of Rationality","authors":"Huozhong Zhang, Yong-Li Zhou","doi":"10.1155/2022/7940755","DOIUrl":"https://doi.org/10.1155/2022/7940755","url":null,"abstract":"At present, the allocation efficiency of regional scientific and technological resources is low, and there are few research studies on social equity and economic efficiency. Therefore, this paper puts forward the allocation method of regional scientific and technological resources based on rationality. With the support of rationality perspective, the evaluation model of reform path of regional scientific and technological resource allocation is constructed to analyze the economic benefits and equity benefits of regional scientific and technological resource allocation. According to the principle of optimum allocation of regional science and technology resources, three-dimensional structure is constructed to maximize national investment and benefit and determine the optimal Pareto of resource allocation to measure the efficiency of resource allocation. The evaluation index system of the reform path of resource allocation is constructed by selecting the evaluation index of the reform path of resource allocation. The benchmark platform of big data was selected to generate data sets to be processed, and the spark on yam platform was used to submit jobs and generate spark job running data sets. The operation performance prediction model was established to optimize the configuration parameters of regional science and technology resources. The analysis results show that the designed method has high configuration capability and good effectiveness.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"1 1","pages":"7940755:1-7940755:12"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78510413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The study focused on the extraction of cardiovascular two-dimensional angiography sequences and the three-dimensional reconstruction based on the local threshold segmentation algorithm. Specifically, the two-dimensional cardiovascular angiography sequence was extracted first, and Gaussian smoothing was adopted for image preprocessing. Then, optimize maximum between-class variance (OSTU) was compared with the traditional two-dimensional OSTU and fast two-dimensional OSTU and applied in the segmentation of cardiovascular angiography images. It was found that the cardiovascular structure itself was continuous, the contrast agent diffused relatively evenly in the blood vessel, and the gray level of the blood vessel was also continuous. The degree of smoothness was consistent in all directions by Gaussian smoothing, avoiding the direction deviation of the smoothened image. The operation time (0.59 s) of the optimize OSTU was significantly shorter than that of traditional OSTU (35.68 s) and fast two-dimensional OSTU (6.34 s) ( P < 0.05 ). The local threshold segmentation algorithm can realize the continuous edge extraction of blood vessels and accurately reflect the stenosis of blood vessels. The results of blood vessel diameter measurement showed that the diameter from the end of blood vessel to the intersection varied linearly from 5.5 mm to 9.0 mm. In short, the optimize OSTU demonstrated good segmentation effects and fast calculation time; it successfully extracted continuous two-dimensional cardiovascular angiography images and can be used in three-dimensional reconstruction of cardiovascular images.
{"title":"Three-Dimensional Reconstruction of Two-Dimensional Cardiovascular Angiography Image Sequences by Local Threshold Segmentation Algorithm","authors":"Shenming Yu","doi":"10.1155/2022/6274903","DOIUrl":"https://doi.org/10.1155/2022/6274903","url":null,"abstract":"The study focused on the extraction of cardiovascular two-dimensional angiography sequences and the three-dimensional reconstruction based on the local threshold segmentation algorithm. Specifically, the two-dimensional cardiovascular angiography sequence was extracted first, and Gaussian smoothing was adopted for image preprocessing. Then, optimize maximum between-class variance (OSTU) was compared with the traditional two-dimensional OSTU and fast two-dimensional OSTU and applied in the segmentation of cardiovascular angiography images. It was found that the cardiovascular structure itself was continuous, the contrast agent diffused relatively evenly in the blood vessel, and the gray level of the blood vessel was also continuous. The degree of smoothness was consistent in all directions by Gaussian smoothing, avoiding the direction deviation of the smoothened image. The operation time (0.59 s) of the optimize OSTU was significantly shorter than that of traditional OSTU (35.68 s) and fast two-dimensional OSTU (6.34 s) (\u0000 \u0000 P\u0000 <\u0000 0.05\u0000 \u0000 ). The local threshold segmentation algorithm can realize the continuous edge extraction of blood vessels and accurately reflect the stenosis of blood vessels. The results of blood vessel diameter measurement showed that the diameter from the end of blood vessel to the intersection varied linearly from 5.5 mm to 9.0 mm. In short, the optimize OSTU demonstrated good segmentation effects and fast calculation time; it successfully extracted continuous two-dimensional cardiovascular angiography images and can be used in three-dimensional reconstruction of cardiovascular images.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"1 1","pages":"6274903:1-6274903:10"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78724532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As one of the core tasks in the field of natural language processing, syntactic analysis has always been a hot topic for researchers, including tasks such as Questions and Answer (Q&A), Search String Comprehension, Semantic Analysis, and Knowledge Base Construction. This paper aims to study the application of deep learning and neural network in natural language syntax analysis, which has significant research and application value. This paper first studies a transfer-based dependent syntax analyzer using a feed-forward neural network as a classifier. By analyzing the model, we have made meticulous parameters of the model to improve its performance. This paper proposes a dependent syntactic analysis model based on a long-term memory neural network. This model is based on the feed-forward neural network model described above and will be used as a feature extractor. After the feature extractor is pretrained, we use a long short-term memory neural network as a classifier of the transfer action, and the characteristics extracted by the syntactic analyzer as its input to train a recursive neural network classifier optimized by sentences. The classifier can not only classify the current pattern feature but also multirich information such as analysis of state history. Therefore, the model is modeled in the analysis process of the entire sentence in syntactic analysis, replacing the method of modeling independent analysis. The experimental results show that the model has achieved greater performance improvement than baseline methods.
{"title":"Natural Language Processing with Improved Deep Learning Neural Networks","authors":"YiTao Zhou","doi":"10.1155/2022/6028693","DOIUrl":"https://doi.org/10.1155/2022/6028693","url":null,"abstract":"As one of the core tasks in the field of natural language processing, syntactic analysis has always been a hot topic for researchers, including tasks such as Questions and Answer (Q&A), Search String Comprehension, Semantic Analysis, and Knowledge Base Construction. This paper aims to study the application of deep learning and neural network in natural language syntax analysis, which has significant research and application value. This paper first studies a transfer-based dependent syntax analyzer using a feed-forward neural network as a classifier. By analyzing the model, we have made meticulous parameters of the model to improve its performance. This paper proposes a dependent syntactic analysis model based on a long-term memory neural network. This model is based on the feed-forward neural network model described above and will be used as a feature extractor. After the feature extractor is pretrained, we use a long short-term memory neural network as a classifier of the transfer action, and the characteristics extracted by the syntactic analyzer as its input to train a recursive neural network classifier optimized by sentences. The classifier can not only classify the current pattern feature but also multirich information such as analysis of state history. Therefore, the model is modeled in the analysis process of the entire sentence in syntactic analysis, replacing the method of modeling independent analysis. The experimental results show that the model has achieved greater performance improvement than baseline methods.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"3 1","pages":"6028693:1-6028693:8"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78925809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Z. Hu, Xiaoran Wei, Xiaoxu Han, Guang Kou, Haoyu Zhang, Xueyi Liu, Y. Bai
Density peaks clustering (DPC) is a well-known density-based clustering algorithm that can deal with nonspherical clusters well. However, DPC has high computational complexity and space complexity in calculating local density ρ and distance δ , which makes it suitable only for small-scale data sets. In addition, for clustering high-dimensional data, the performance of DPC still needs to be improved. High-dimensional data not only make the data distribution more complex but also lead to more computational overheads. To address the above issues, we propose an improved density peaks clustering algorithm, which combines feature reduction and data sampling strategy. Specifically, features of the high-dimensional data are automatically extracted by principal component analysis (PCA), auto-encoder (AE), and t-distributed stochastic neighbor embedding (t-SNE). Next, in order to reduce the computational overhead, we propose a novel data sampling method for the low-dimensional feature data. Firstly, the data distribution in the low-dimensional feature space is estimated by the Quasi-Monte Carlo (QMC) sequence with low-discrepancy characteristics. Then, the representative QMC points are selected according to their cell densities. Next, the selected QMC points are used to calculate ρ and δ instead of the original data points. In general, the number of the selected QMC points is much smaller than that of the initial data set. Finally, a two-stage classification strategy based on the QMC points clustering results is proposed to classify the original data set. Compared with current works, our proposed algorithm can reduce the computational complexity from O n 2 to O N n , where N denotes the number of selected QMC points and n is the size of original data set, typically N ≪ n . Experimental results demonstrate that the proposed algorithm can effectively reduce the computational overhead and improve the model performance.
密度峰聚类(DPC)是一种著名的基于密度的聚类算法，可以很好地处理非球形聚类。然而，DPC在计算局部密度ρ和距离δ时具有较高的计算复杂度和空间复杂度，因此仅适用于小规模数据集。此外，对于高维数据的聚类，DPC的性能还有待提高。高维数据不仅使数据分布更加复杂，而且导致更多的计算开销。为了解决上述问题，我们提出了一种改进的密度峰聚类算法，该算法将特征约简和数据采样策略相结合。具体而言，通过主成分分析(PCA)、自动编码器(AE)和t分布随机邻居嵌入(t-SNE)自动提取高维数据的特征。其次，为了减少计算开销，我们提出了一种新的低维特征数据采样方法。首先，利用具有低差异特征的拟蒙特卡罗序列估计数据在低维特征空间中的分布;然后，根据细胞密度选择具有代表性的QMC点。接下来，选择的QMC点用于计算ρ和δ，而不是原始数据点。一般情况下，选择的QMC点的数量要比初始数据集的数量少得多。最后，提出了一种基于QMC点聚类结果的两阶段分类策略对原始数据集进行分类。与目前的工作相比，我们提出的算法可以将计算复杂度从O n2降低到O n n，其中n表示所选QMC点的数量，n表示原始数据集的大小，通常n≪n。实验结果表明，该算法可以有效地减少计算量，提高模型性能。
{"title":"Density Peaks Clustering Based on Feature Reduction and Quasi-Monte Carlo","authors":"Z. Hu, Xiaoran Wei, Xiaoxu Han, Guang Kou, Haoyu Zhang, Xueyi Liu, Y. Bai","doi":"10.1155/2022/8046620","DOIUrl":"https://doi.org/10.1155/2022/8046620","url":null,"abstract":"Density peaks clustering (DPC) is a well-known density-based clustering algorithm that can deal with nonspherical clusters well. However, DPC has high computational complexity and space complexity in calculating local density \u0000 \u0000 ρ\u0000 \u0000 and distance \u0000 \u0000 δ\u0000 \u0000 , which makes it suitable only for small-scale data sets. In addition, for clustering high-dimensional data, the performance of DPC still needs to be improved. High-dimensional data not only make the data distribution more complex but also lead to more computational overheads. To address the above issues, we propose an improved density peaks clustering algorithm, which combines feature reduction and data sampling strategy. Specifically, features of the high-dimensional data are automatically extracted by principal component analysis (PCA), auto-encoder (AE), and t-distributed stochastic neighbor embedding (t-SNE). Next, in order to reduce the computational overhead, we propose a novel data sampling method for the low-dimensional feature data. Firstly, the data distribution in the low-dimensional feature space is estimated by the Quasi-Monte Carlo (QMC) sequence with low-discrepancy characteristics. Then, the representative QMC points are selected according to their cell densities. Next, the selected QMC points are used to calculate \u0000 \u0000 ρ\u0000 \u0000 and \u0000 \u0000 δ\u0000 \u0000 instead of the original data points. In general, the number of the selected QMC points is much smaller than that of the initial data set. Finally, a two-stage classification strategy based on the QMC points clustering results is proposed to classify the original data set. Compared with current works, our proposed algorithm can reduce the computational complexity from \u0000 \u0000 O\u0000 \u0000 \u0000 \u0000 \u0000 n\u0000 \u0000 \u0000 2\u0000 \u0000 \u0000 \u0000 \u0000 \u0000 to \u0000 \u0000 O\u0000 \u0000 \u0000 N\u0000 n\u0000 \u0000 \u0000 \u0000 , where \u0000 \u0000 N\u0000 \u0000 denotes the number of selected QMC points and \u0000 \u0000 n\u0000 \u0000 is the size of original data set, typically \u0000 \u0000 N\u0000 ≪\u0000 n\u0000 \u0000 . Experimental results demonstrate that the proposed algorithm can effectively reduce the computational overhead and improve the model performance.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"8 1","pages":"8046620:1-8046620:17"},"PeriodicalIF":0.0,"publicationDate":"2022-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79727997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article comes up with a complex-valued encoding multichain seeker optimization algorithm (CMSOA) for the engineering optimization problems. The complex-valued encoding strategy and the multichain strategy are leaded in the seeker optimization algorithm (SOA). These strategies enhance the individuals’ diversity, enhance the local search, avert falling into the local optimum, and are the influential global optimization strategies. This article chooses fifteen benchmark functions, four proportional integral derivative (PID) control parameter models, and six constrained engineering problems to test. According to the experimental results, the CMSOA can be used in the benchmark functions, in the PID control parameter optimization, and in the optimization of constrained engineering problems. Compared to the particle swarm optimization (PSO), simulated annealing based on genetic algorithm (SA_GA), gravitational search algorithm (GSA), sine cosine algorithm (SCA), multiverse optimizer (MVO), and seeker optimization algorithm (SOA), the optimization ability and robustness of the CMSOA are better than those of others algorithms.
{"title":"A Complex-Valued Encoding Multichain Seeker Optimization Algorithm for Engineering Problems","authors":"Shaomi Duan, Huilong Luo, Haipeng Liu","doi":"10.1155/2022/8249030","DOIUrl":"https://doi.org/10.1155/2022/8249030","url":null,"abstract":"This article comes up with a complex-valued encoding multichain seeker optimization algorithm (CMSOA) for the engineering optimization problems. The complex-valued encoding strategy and the multichain strategy are leaded in the seeker optimization algorithm (SOA). These strategies enhance the individuals’ diversity, enhance the local search, avert falling into the local optimum, and are the influential global optimization strategies. This article chooses fifteen benchmark functions, four proportional integral derivative (PID) control parameter models, and six constrained engineering problems to test. According to the experimental results, the CMSOA can be used in the benchmark functions, in the PID control parameter optimization, and in the optimization of constrained engineering problems. Compared to the particle swarm optimization (PSO), simulated annealing based on genetic algorithm (SA_GA), gravitational search algorithm (GSA), sine cosine algorithm (SCA), multiverse optimizer (MVO), and seeker optimization algorithm (SOA), the optimization ability and robustness of the CMSOA are better than those of others algorithms.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"28 1","pages":"8249030:1-8249030:35"},"PeriodicalIF":0.0,"publicationDate":"2022-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82521717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The present work serves to improve the stable cooperation relationship among subjects of supply chain such as enterprises, farmers, intermediary organizations, and retailers and enhance the governance and optimization of agricultural product supply chain, thus strengthening the competitiveness of China’s agricultural industry. The supply chain governance of agricultural products is taken as the research object. Initially, the stabilities of two supply chain organization modes, “company and farmer” and “company, intermediary organization and farmer,” are analyzed by static game analysis. Then, based on the above analysis and the characteristics of blockchain institutional technology, a detailed analyzation is made on the mechanism of supply chain of agricultural products governance based on blockchain technology. Finally, the functional framework of agricultural supply chain governance is designed based on the basic framework of blockchain technology, and analyzation is made on the trust mechanism and contract mechanism of agricultural supply chain governance based on blockchain technology. The research results show that problems such as information and cognitive constraints in agricultural supply chain governance cannot be completely solved only through the evolution of blockchain organizational structure and the supply of governance mechanism, and speculative behavior will still appear. Optimizing the governance of supply chain of agricultural products based on blockchain technology can realize the transformation of its governance scenario. Meanwhile, the blockchain technologies such as deintermediation, demistrust, and intelligent contract play an important role in the process of agricultural supply chain governance, which can make it change in many aspects such as organization mode, application operation, and governance mechanism. The rapid development of new generation information technologies such as blockchain, the Internet of Things, and computer technology makes it possible to comprehensively digitize economic activities such as production and transaction in the supply chain of agricultural products. The present work combines the technical logic of blockchain digital governance with the institutional logic of agricultural product supply chain governance and tries to solve the instability problems caused by imperfect organization, lack of trust, and incomplete contract in agricultural product supply chain governance with the characteristics of blockchain such as deintermediation, demistrust, and intelligent contract.
{"title":"Supply Chain Governance of Agricultural Products under Big Data Platform Based on Blockchain Technology","authors":"Wei Guo, Kai Yao","doi":"10.1155/2022/4456150","DOIUrl":"https://doi.org/10.1155/2022/4456150","url":null,"abstract":"The present work serves to improve the stable cooperation relationship among subjects of supply chain such as enterprises, farmers, intermediary organizations, and retailers and enhance the governance and optimization of agricultural product supply chain, thus strengthening the competitiveness of China’s agricultural industry. The supply chain governance of agricultural products is taken as the research object. Initially, the stabilities of two supply chain organization modes, “company and farmer” and “company, intermediary organization and farmer,” are analyzed by static game analysis. Then, based on the above analysis and the characteristics of blockchain institutional technology, a detailed analyzation is made on the mechanism of supply chain of agricultural products governance based on blockchain technology. Finally, the functional framework of agricultural supply chain governance is designed based on the basic framework of blockchain technology, and analyzation is made on the trust mechanism and contract mechanism of agricultural supply chain governance based on blockchain technology. The research results show that problems such as information and cognitive constraints in agricultural supply chain governance cannot be completely solved only through the evolution of blockchain organizational structure and the supply of governance mechanism, and speculative behavior will still appear. Optimizing the governance of supply chain of agricultural products based on blockchain technology can realize the transformation of its governance scenario. Meanwhile, the blockchain technologies such as deintermediation, demistrust, and intelligent contract play an important role in the process of agricultural supply chain governance, which can make it change in many aspects such as organization mode, application operation, and governance mechanism. The rapid development of new generation information technologies such as blockchain, the Internet of Things, and computer technology makes it possible to comprehensively digitize economic activities such as production and transaction in the supply chain of agricultural products. The present work combines the technical logic of blockchain digital governance with the institutional logic of agricultural product supply chain governance and tries to solve the instability problems caused by imperfect organization, lack of trust, and incomplete contract in agricultural product supply chain governance with the characteristics of blockchain such as deintermediation, demistrust, and intelligent contract.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"77 1","pages":"4456150:1-4456150:16"},"PeriodicalIF":0.0,"publicationDate":"2022-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85402353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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