2021 13th International Conference on Advanced Computational Intelligence (ICACI)最新文献_第2页

Adaptive Takagi-Sugeno Fuzzy Model for Pneumatic Artificial Muscles 气动人工肌肉自适应Takagi-Sugeno模糊模型

2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435870

Xiuze Xia, Long Cheng

引用次数: 2

A Circuit Implementation of Random Number Generator Utilizing Memristor Stochasticity 一种利用忆阻器随机性的随机数发生器电路实现

2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435904

Yanwen Guo, Qiujie Wu, Xiaoping Wang

引用次数: 1

A New Evolutionary Computation Framework for Privacy-Preserving Optimization 一种新的隐私保护优化进化计算框架

2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435860

Zhi-hui Zhan, Sheng-Hao Wu, Jun Zhang

{"title":"A New Evolutionary Computation Framework for Privacy-Preserving Optimization","authors":"Zhi-hui Zhan, Sheng-Hao Wu, Jun Zhang","doi":"10.1109/ICACI52617.2021.9435860","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435860","url":null,"abstract":"Evolutionary computation (EC) is a kind of advanced computational intelligence (CI) algorithm and advanced artificial intelligence (AI) algorithm. EC algorithms have been widely studied for solving optimization and scheduling problems in various real-world applications, which act as one of the Big Three in CI and AI, together with fuzzy systems and neural networks. Even though EC has been fast developed in recent years, there is an assumption that the algorithm designer can obtain the objective function of the optimization problem so that they can calculate the fitness values of the individuals to follow the “survival of the fittest” principle in natural selection. However, in a real-world application scenario, there is a kind of problem that the objective function is privacy so that the algorithm designer can not obtain the fitness values of the individuals directly. This is the privacy-preserving optimization problem (PPOP) where the assumption of available objective function does not check out. How to solve the PPOP is a new emerging frontier with seldom study but is also a challenging research topic in the EC community. This paper proposes a rank-based cryptographic function (RCF) to protect the fitness value information. Especially, the RCF is adopted by the algorithm user to encrypt the fitness values of all the individuals as rank so that the algorithm designer does not know the exact fitness information but only the rank information. Nevertheless, the RCF can protect the privacy of the algorithm user but still can provide sufficient information to the algorithm designer to drive the EC algorithm. We have applied the RCF privacy-preserving method to two typical EC algorithms including particle swarm optimization (PSO) and differential evolution (DE). Experimental results show that the RCF-based privacy-preserving PSO and DE can solve the PPOP without performance loss.","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":"126094574","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}

引用次数: 7

Joint Service Placement and Request Routing in Mobile Edge Computing Networks 移动边缘计算网络中的联合服务布局和请求路由

2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435886

Binbin Yuan, Songtao Guo, Quyuan Wang

{"title":"Joint Service Placement and Request Routing in Mobile Edge Computing Networks","authors":"Binbin Yuan, Songtao Guo, Quyuan Wang","doi":"10.1109/ICACI52617.2021.9435886","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435886","url":null,"abstract":"Mobile edge computing (MEC) is envisioned as a prospective technology that supports latency-critical and computation-intensive applications by using storage and computation resources in network edges. The advantages of this technology are trapped in limited edge cloud resources, and one of the prime challenges is how to allocate available edge cloud resources to satisfy user requests. However, previous works usually optimize service (data& code) placement and request routing simultaneously within the same timescale, ignoring the fact that frequent service replacement will incur expensive operational expenses. In this paper, we jointly optimize service placement and request routing in the MEC network for data analysis applications, under the constraints of computation and storage resource. In particular, the Cloud Radio Access Network (C-RAN) architecture is applied to pool available resources and realize load balancing among edge clouds. In addition, we adopt a two timescale framework to reduce higher operating expenses caused by frequent cross-cloud service migration. Then, we develop a greedy-based approximation algorithm for service placement subproblem and a linear programming (LP) relaxation-based heuristic algorithm for request routing subproblem, respectively. Finally, the numerical results demonstrate that our proposed solution reaches 90% of the optimal performance in services homogeneous case and 76% in services heterogeneous case.","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":"121797917","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}

引用次数: 9

Determination of Pattern Recognition Problems based on a Pythagorean Fuzzy Correlation Measure from Statistical Viewpoint 基于毕达哥拉斯模糊关联测度的模式识别问题的确定

2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435895

P. A. Ejegwa, Yuming Feng, S. Wen, Wei Zhang

引用次数: 4

A Hybrid Genetic XK-means++ Clustering Algorithm with Empty Cluster Reassignment 一种具有空簇重分配的混合遗传xk -means++聚类算法

2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435879

Chun Hua

{"title":"A Hybrid Genetic XK-means++ Clustering Algorithm with Empty Cluster Reassignment","authors":"Chun Hua","doi":"10.1109/ICACI52617.2021.9435879","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435879","url":null,"abstract":"K-means is a classical clustering algorithm in many research areas, such as, document clustering, bioinformatics, image segmentation and pattern recognition. But, K-means is sensitive to the initial choice of cluster centers. A successful modification of K-means has been introduced in the literature by improving arbitrary cluster centers in the initialization stage-called K-means++. eXploratory K-means(XK-means)is another modification of K-means, which added an exploratory disturbance onto the vector of cluster centers, so as to improve the condition of sensitivity to the initial centers and jump out of the local optimum. However, the empty clusters may appear in the process of XK-means. The efficiency of the clustering result will be damaged by these empty clusters. In this paper, we try adding exploratory disturbance in K-means++ referred to as XK-means++. The same as XK-means, empty clusters also appear in the iteration process of XK-means++. Therefore, in this paper, an empty-cluster-reassignment technique is introduced and used in XK-means++(called EXK-means++). Furthermore, we combined the EXK-means++ with genetic mechanism, obtain a GEXK-means++ clustering algorithm. The data simulation results show that GEXK-means++is promising and effective.","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":"133941229","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}

引用次数: 2

Generating High-Resolution Climate Change Projections Using Super-Resolution Convolutional LSTM Neural Networks 使用超分辨率卷积LSTM神经网络生成高分辨率气候变化预测

2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435890

C. Chou, Junho Park, Eric Chou

{"title":"Generating High-Resolution Climate Change Projections Using Super-Resolution Convolutional LSTM Neural Networks","authors":"C. Chou, Junho Park, Eric Chou","doi":"10.1109/ICACI52617.2021.9435890","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435890","url":null,"abstract":"Generating projections of climate change through extreme indices such as precipitation and temperature is crucial to evaluate their potential impacts on critical infrastructures, human health, and natural systems. However, current Earth System Models (ESMs) run at spatial resolutions of hundreds of kilometers which is too coarse to analyze localized impacts. To tackle this issue, statistical downscaling is a widely employed technique that uses historical climate observations to learn a coarse-resolution to fine-resolution mapping. Traditional statistical methods are inefficient in downscaling precipitation data and vary significantly in terms of accuracy and reliability since local climate variables such as precipitation are dependent on non-linear and complex spatio-temporal processes. To capture both spatial and temporal variabilities, we develop a Super-Resolution based Convolutional Long Short Term Memory Neural Network and test the robustness and predictability of this model on monthly precipitation data in China. We integrate original climate data from an ESM and perform downscaling on precipitation at $(1.25^{circ}times 0.9^{circ})$ to $(0.25^{circ}times 0.25^{circ})$. Experimental data indicates that our Convolutional LSTM model performs the best compared to existing methods in terms of mean squared error, relative bias, and correlation coefficient.","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":"133031418","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}

引用次数: 4

Performance of different Electric vehicle Battery packs at low temperature and Analysis of Intelligent SOC experiment 不同电动汽车电池组低温性能及智能SOC实验分析

2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435901

Le Gao, L. Cai, Yuming Feng, N. Dai, Qingshan Xu

引用次数: 1

Indoor Localization Using Bidirectional LSTM Networks 基于双向LSTM网络的室内定位

2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435876

Dong Pang, Xinyi Le

引用次数: 0

A Hybrid Obstacle Avoidance Strategy Based on PSO in Source Location 基于粒子群算法的源定位混合避障策略

2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435875

Mengshi Zhao, Pengzhan Qiu, Junqi Zhang

{"title":"A Hybrid Obstacle Avoidance Strategy Based on PSO in Source Location","authors":"Mengshi Zhao, Pengzhan Qiu, Junqi Zhang","doi":"10.1109/ICACI52617.2021.9435875","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435875","url":null,"abstract":"This paper focuses on obstacle avoidance in the source location problem, in which robots capture the signal strength and find the signal source in an unknown environment. This work proposes a particle swarm optimizer (PSO) with a hybrid obstacle avoidance strategy to solve the problem. The signal strength is considered as the fitness function for PSO to guide robots. During moving, artificial potential fields are adopted to make robots avoid obstacles and each other. A deadlock escaping strategy is put forward to deal with the constraints of concave obstacles. The weighted average velocity of a robot is employed to check whether it is stuck by an obstacle. If so, a tabu area is set to push robots out of the area and prevent them from searching the same place again. These tabu areas offer robots key information about obstacles in an unknown environment and improve robots’ ability of obstacle avoidance. The proposed algorithm is adaptive in unknown environments, meaning that no prior knowledge is needed. Simulation tests verify the effectiveness of the developed algorithm, showing satisfactory performance when dealing with concave obstacles.","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":"122916943","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}

引用次数: 0