Pub Date : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435891
Jinge Hu, Jiang Xiong, Yuming Feng, B. Onasanya
The saliency areas extracted by traditional visual saliency detection methods are not clear enough. This paper presents a visual saliency detection method based on visual center offset. On the basis of pre-segmentation of the image, the significant areas of the image are extracted by combining the color contrast, color distribution and location characteristics. Using visual center transfer to simulate the visual transfer process of human observation, the image is analyzed at multiple scales. The results indicate that this approach is efficient because ROC curve and Precision-Recall performed well.
{"title":"Visual saliency detection based on visual center shift","authors":"Jinge Hu, Jiang Xiong, Yuming Feng, B. Onasanya","doi":"10.1109/ICACI52617.2021.9435891","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435891","url":null,"abstract":"The saliency areas extracted by traditional visual saliency detection methods are not clear enough. This paper presents a visual saliency detection method based on visual center offset. On the basis of pre-segmentation of the image, the significant areas of the image are extracted by combining the color contrast, color distribution and location characteristics. Using visual center transfer to simulate the visual transfer process of human observation, the image is analyzed at multiple scales. The results indicate that this approach is efficient because ROC curve and Precision-Recall performed well.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114027307","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}
Pub Date : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435862
Lili Bai, Zerui Song, Haijie Bao, Jing-qing Jiang
In clustering, in order to find a better data clustering center, make the algorithm convergence faster and clustering results more accurate, a k-means clustering algorithm based on improved quantum particle swarm optimization algorithm is proposed. In this algorithm, the cluster center is simulated as a particle. Cloning and mutation operations are used to increase the diversity and improve the global search ability of QPSO. A suitable and stable cluster center is obtained. Finally, an effective clustering result is obtained. The algorithm is tested with UCI data set. The results show that the improved algorithm not only ensures the global convergence of the algorithm, but also obtains more accurate clustering results.
{"title":"K-means Clustering Based on Improved Quantum Particle Swarm Optimization Algorithm","authors":"Lili Bai, Zerui Song, Haijie Bao, Jing-qing Jiang","doi":"10.1109/ICACI52617.2021.9435862","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435862","url":null,"abstract":"In clustering, in order to find a better data clustering center, make the algorithm convergence faster and clustering results more accurate, a k-means clustering algorithm based on improved quantum particle swarm optimization algorithm is proposed. In this algorithm, the cluster center is simulated as a particle. Cloning and mutation operations are used to increase the diversity and improve the global search ability of QPSO. A suitable and stable cluster center is obtained. Finally, an effective clustering result is obtained. The algorithm is tested with UCI data set. The results show that the improved algorithm not only ensures the global convergence of the algorithm, but also obtains more accurate clustering results.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114245029","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}
Pub Date : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435887
Yijie Zhu, Xiaonan Luo, Shanwen Guan, Zhongshuai Wang
With the rapid development of the location service industry, various positioning technologies have emerged. Recently, the mainstream indoor positioning technologies include WiFi positioning and Bluetooth positioning. Various positioning methods have their own advantages and disadvantages due to their different positioning technologies. This paper proposes an indoor positioning method based on WiFi, Bluetooth and PDR fusion positioning. Firstly, WiFi positioning and Bluetooth positioning are achieved by improving the weighted centroid method. The WiFi and Bluetooth positioning are integrated, and the positioning result is integrated by weight adaptive constraint, which solves the problem of WiFi signal instability. The fusion positioning result and PDR positioning fusion are used to achieve fusion positioning through UKF, which solves the problem of large cumulative error in PDR positioning. The experiment proves that the WiFi, Bluetooth and PDR fusion positioning results are higher than the positioning accuracy of the individual positioning, which solves the problem that the WiFi positioning signal is unstable and the PDR cumulative error is large.
{"title":"Indoor Positioning Method Based on WiFi/Bluetooth and PDR Fusion Positioning","authors":"Yijie Zhu, Xiaonan Luo, Shanwen Guan, Zhongshuai Wang","doi":"10.1109/ICACI52617.2021.9435887","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435887","url":null,"abstract":"With the rapid development of the location service industry, various positioning technologies have emerged. Recently, the mainstream indoor positioning technologies include WiFi positioning and Bluetooth positioning. Various positioning methods have their own advantages and disadvantages due to their different positioning technologies. This paper proposes an indoor positioning method based on WiFi, Bluetooth and PDR fusion positioning. Firstly, WiFi positioning and Bluetooth positioning are achieved by improving the weighted centroid method. The WiFi and Bluetooth positioning are integrated, and the positioning result is integrated by weight adaptive constraint, which solves the problem of WiFi signal instability. The fusion positioning result and PDR positioning fusion are used to achieve fusion positioning through UKF, which solves the problem of large cumulative error in PDR positioning. The experiment proves that the WiFi, Bluetooth and PDR fusion positioning results are higher than the positioning accuracy of the individual positioning, which solves the problem that the WiFi positioning signal is unstable and the PDR cumulative error is large.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114920440","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}
Pub Date : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435871
R. Yang, Songtao Guo
With the continuous boom in video services and advanced computing, the requirements of mobile users for network resource and performance are rising steadily. Mobile edge computing (MEC) technology has been applied in vehicular networks (VNs) in recent years to cope with high vehicle mobility and network topology change. In this paper, we propose a group-partitioned video caching strategy algorithm (GPC) in VNs. The algorithm first partitions the video requesters and then employs the Lagrange function and Lambert function to solve the cache probability matrix as optimization variable. Correspondingly, we choose caching hit ratio and latency as cache performance evaluation metrics we take the revenue function as optimization objective, and aim to maximize the revenue value. Experimental results show that that the dual influence of video file size and cache size is a significant factor in the probability of caching. Our GPC algorithm outperforms other existing algorithms in the revenue.
{"title":"A Mobile Edge Caching Strategy for Video Grouping in Vehicular Networks","authors":"R. Yang, Songtao Guo","doi":"10.1109/ICACI52617.2021.9435871","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435871","url":null,"abstract":"With the continuous boom in video services and advanced computing, the requirements of mobile users for network resource and performance are rising steadily. Mobile edge computing (MEC) technology has been applied in vehicular networks (VNs) in recent years to cope with high vehicle mobility and network topology change. In this paper, we propose a group-partitioned video caching strategy algorithm (GPC) in VNs. The algorithm first partitions the video requesters and then employs the Lagrange function and Lambert function to solve the cache probability matrix as optimization variable. Correspondingly, we choose caching hit ratio and latency as cache performance evaluation metrics we take the revenue function as optimization objective, and aim to maximize the revenue value. Experimental results show that that the dual influence of video file size and cache size is a significant factor in the probability of caching. Our GPC algorithm outperforms other existing algorithms in the revenue.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115121995","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}
Pub Date : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435910
Chunyu Qu, Xingwang Zhao
This paper attempts to study the impact of supply-side structural reform on the total factor productivity of high-tech industries from the perspective of supply-side structural reform affecting potential total factor productivity. Potential total factor productivity is the optimal total factor productivity level that the economy is at an ideal level. To examine the impact of supply-side structural reform on total factor productivity, the most fundamental thing is to examine whether it can increase the potential total factor productivity of high-tech industries. The actual total factor productivity of high-tech industries also fluctuates based on potential total factor productivity. In the end, the impact of supply-side structural reform on potential total factor productivity is examined through the actual data of Liaoning province.
{"title":"Total Factor Productivity Analysis of High-tech Industries for Supply-side Structural Reform","authors":"Chunyu Qu, Xingwang Zhao","doi":"10.1109/ICACI52617.2021.9435910","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435910","url":null,"abstract":"This paper attempts to study the impact of supply-side structural reform on the total factor productivity of high-tech industries from the perspective of supply-side structural reform affecting potential total factor productivity. Potential total factor productivity is the optimal total factor productivity level that the economy is at an ideal level. To examine the impact of supply-side structural reform on total factor productivity, the most fundamental thing is to examine whether it can increase the potential total factor productivity of high-tech industries. The actual total factor productivity of high-tech industries also fluctuates based on potential total factor productivity. In the end, the impact of supply-side structural reform on potential total factor productivity is examined through the actual data of Liaoning province.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116150470","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}
Pub Date : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435905
Chun-Yao Jiang, Xiao-Min Hu, Wei-neng Chen
How to improve travel efficiency and alleviate traffic congestion has long been a key problem in intelligent transportation systems. Traffic signal control is a basic tool for urban traffic management. Traditionally, the optimization of traffic light schedule and the prediction of traffic flows are studied separately. In this paper, we aim to combine these two techniques together and propose an urban traffic signal control system based on traffic flow prediction. The objective is to minimize the total number of blocked vehicles at all signalized intersections in the road network. Firstly, a new framework of urban traffic control system including both traffic flow forecasting and signal control optimization is proposed. Secondly, an adaptive traffic light scheduling strategy is designed to alleviate congestion. To validate the proposed system, experiments are performed on the real-world traffic data provided by the Aliyun Tianchi platform. The comparison results show that the proposed system and the signal control optimization strategy perform well.
{"title":"An Urban Traffic Signal Control System Based on Traffic Flow Prediction","authors":"Chun-Yao Jiang, Xiao-Min Hu, Wei-neng Chen","doi":"10.1109/ICACI52617.2021.9435905","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435905","url":null,"abstract":"How to improve travel efficiency and alleviate traffic congestion has long been a key problem in intelligent transportation systems. Traffic signal control is a basic tool for urban traffic management. Traditionally, the optimization of traffic light schedule and the prediction of traffic flows are studied separately. In this paper, we aim to combine these two techniques together and propose an urban traffic signal control system based on traffic flow prediction. The objective is to minimize the total number of blocked vehicles at all signalized intersections in the road network. Firstly, a new framework of urban traffic control system including both traffic flow forecasting and signal control optimization is proposed. Secondly, an adaptive traffic light scheduling strategy is designed to alleviate congestion. To validate the proposed system, experiments are performed on the real-world traffic data provided by the Aliyun Tianchi platform. The comparison results show that the proposed system and the signal control optimization strategy perform well.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128049416","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}
Pub Date : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435908
Jianzhen Xiao, Siyuan Feng, Yunong Zhang
Population issues are critical to national development, social stability and resource allocation. Policy-makers hope to Figure out population factors such as birth rate, size, and demographic structure to make policies that are conducive to longterm development of a country. Therefore, population projection has always been highly valued by many government workers and scholars. Compared with other traditional population projection methods, the weights and structure determination (WASD) neuronet does not require a vast knowledge of demography to achieve excellent performance. In this work, we substantiate the WASD neuronet by numerical experiments, which show the excellent performance of the WASD neuronet. Then, we make short-term and mid-term projections of Germany population and also make comparisons with other mainstream population projection methods. Finally, this paper presents a reasonable tendency of Germany population, i.e., declining slightly in near future but growing gently in one decade.
人口问题对国家发展、社会稳定和资源配置至关重要。决策者希望弄清出生率、人口规模、人口结构等人口因素，制定有利于国家长期发展的政策。因此，人口预测一直受到许多政府工作人员和学者的高度重视。与其他传统的人口预测方法相比，WASD (weights and structure determination)神经网络不需要大量的人口统计学知识就能达到优异的效果。在本工作中，我们通过数值实验验证了WASD神经网络，表明了WASD神经网络的优异性能。然后对德国人口进行了短期和中期预测，并与其他主流人口预测方法进行了比较。最后，本文提出了德国人口的合理趋势，即近期人口略有下降，但未来10年人口将缓慢增长。
{"title":"Numerical and Graphical Results of Germany Population Projection Using WASD Neuronet","authors":"Jianzhen Xiao, Siyuan Feng, Yunong Zhang","doi":"10.1109/ICACI52617.2021.9435908","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435908","url":null,"abstract":"Population issues are critical to national development, social stability and resource allocation. Policy-makers hope to Figure out population factors such as birth rate, size, and demographic structure to make policies that are conducive to longterm development of a country. Therefore, population projection has always been highly valued by many government workers and scholars. Compared with other traditional population projection methods, the weights and structure determination (WASD) neuronet does not require a vast knowledge of demography to achieve excellent performance. In this work, we substantiate the WASD neuronet by numerical experiments, which show the excellent performance of the WASD neuronet. Then, we make short-term and mid-term projections of Germany population and also make comparisons with other mainstream population projection methods. Finally, this paper presents a reasonable tendency of Germany population, i.e., declining slightly in near future but growing gently in one decade.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126350463","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}
Non-Negative matrix factorization (NMF) is a popular research problem in data dimensional reduction. Conventional NMF approaches cannot achieve a subspace made up of binary codes from the high-dimensional data space. To address the above-mentioned problem, we propose a method based on nonnegative matrix factorization to generate a low-dimensional subspace made up of binary codes from the high-dimensional data. The problem can be mathematically expressed as a 0-1 integer mixed optimization problem. For this purpose, We put forward a method based on discrete cyclic coordination descent to obtain a local optimal solution. Experiments show that our means can obtain the better clustering ability than conventional non-negative matrix factorization and its variant approaches.
{"title":"Non-negative Matrix Factorization for Binary Space Learning","authors":"Meng Zhang, Xiangguang Dai, Xiangqin Dai, Nian Zhang","doi":"10.1109/ICACI52617.2021.9435889","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435889","url":null,"abstract":"Non-Negative matrix factorization (NMF) is a popular research problem in data dimensional reduction. Conventional NMF approaches cannot achieve a subspace made up of binary codes from the high-dimensional data space. To address the above-mentioned problem, we propose a method based on nonnegative matrix factorization to generate a low-dimensional subspace made up of binary codes from the high-dimensional data. The problem can be mathematically expressed as a 0-1 integer mixed optimization problem. For this purpose, We put forward a method based on discrete cyclic coordination descent to obtain a local optimal solution. Experiments show that our means can obtain the better clustering ability than conventional non-negative matrix factorization and its variant approaches.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114585837","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}
Pub Date : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435912
Siwen Liu, Yanlong Zhang, Huanqing Wang
In this paper, the tracking control problem is researched for nonlinear systems in the presence of the uncertain smooth functions, the strict-feedback form, input delay and input dead-zone. The problem of input delay and input dead-zone is handled by designing an improved auxiliary system. With the help of the adaptive backstepping control way and fuzzy logic systems (FLSs), an adaptive fuzzy control technique is presented, which makes the boundedness of all signals and ensures the output signal for the considered system track the preset reference signal. A practical example is presented to prove the effectiveness of the control technique proposed in this paper.
{"title":"Tracking Control for Nonlinear Systems with Input Delay and Dead-Zone via Adaptive Fuzzy Backstepping Approach","authors":"Siwen Liu, Yanlong Zhang, Huanqing Wang","doi":"10.1109/ICACI52617.2021.9435912","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435912","url":null,"abstract":"In this paper, the tracking control problem is researched for nonlinear systems in the presence of the uncertain smooth functions, the strict-feedback form, input delay and input dead-zone. The problem of input delay and input dead-zone is handled by designing an improved auxiliary system. With the help of the adaptive backstepping control way and fuzzy logic systems (FLSs), an adaptive fuzzy control technique is presented, which makes the boundedness of all signals and ensures the output signal for the considered system track the preset reference signal. A practical example is presented to prove the effectiveness of the control technique proposed in this paper.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126971277","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}
Pub Date : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435881
Tong Zhou, Yuxuan Li, Zhiyong Yang
In this paper, based on the classic coupled map car-following model [11], a new control car-following model is presented with the consideration of the delay memory driving behavior. The stability condition of the control system is obtained via control theory. Numerical simulation implies that the contained delay memory control signal can improve the stability of traffic flow. Furthermore, with the increase of control signal, the control effect of traffic system becomes better.
{"title":"Feedback control for car-following model with the consideration of the delay memory driving behavior","authors":"Tong Zhou, Yuxuan Li, Zhiyong Yang","doi":"10.1109/ICACI52617.2021.9435881","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435881","url":null,"abstract":"In this paper, based on the classic coupled map car-following model [11], a new control car-following model is presented with the consideration of the delay memory driving behavior. The stability condition of the control system is obtained via control theory. Numerical simulation implies that the contained delay memory control signal can improve the stability of traffic flow. Furthermore, with the increase of control signal, the control effect of traffic system becomes better.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126676937","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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