Pub Date : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435870
Xiuze Xia, Long Cheng
Pneumatic artificial muscle (PAM) usually exhibits strong hysteresis nonlinearity and time-varying features that bring PAM modeling and control difficulties. In this paper, an adaptive Takagi-Sugeno (T-S) fuzzy model is established based on nonlinear auto-regression moving average with exogenous input (NARMAX) structure to describe PAM’s characteristics. Experiments show that compared with other phenomenology-based models, the presented model has lower predictive error and better adaptability. Finally, a model predictive controller is designed and validated to verify the adaptive T-S fuzzy model’s practicability.
{"title":"Adaptive Takagi-Sugeno Fuzzy Model for Pneumatic Artificial Muscles","authors":"Xiuze Xia, Long Cheng","doi":"10.1109/ICACI52617.2021.9435870","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435870","url":null,"abstract":"Pneumatic artificial muscle (PAM) usually exhibits strong hysteresis nonlinearity and time-varying features that bring PAM modeling and control difficulties. In this paper, an adaptive Takagi-Sugeno (T-S) fuzzy model is established based on nonlinear auto-regression moving average with exogenous input (NARMAX) structure to describe PAM’s characteristics. Experiments show that compared with other phenomenology-based models, the presented model has lower predictive error and better adaptability. Finally, a model predictive controller is designed and validated to verify the adaptive T-S fuzzy model’s practicability.","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":"126514481","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/ICACI52617.2021.9435904
Yanwen Guo, Qiujie Wu, Xiaoping Wang
A random number generator is implemented utilizing the intrinsic stochasticity of memristor as a natural physical randomness source. The random bits are produced by cyclically switching the memristor and comparing the memristor resistive values in the high resistive state with the reference value, taking advantage of the more pronounced resistance variation in the high resistive state. Using the alternative voltage pulse scheme in the designed random number circuit, the biasness of the random numbers is largely alleviated, then the standard randomness test suite developed by NIST is used to validate the feasibility. Moreover, several memristors in parallel are considered to generate different frequency random number at the sacrifice of area overhead. The random number generation is simulated in the circuit simulated tool PSPICE. This approach improves the memristor-based stochastic circuit computer aided design.
{"title":"A Circuit Implementation of Random Number Generator Utilizing Memristor Stochasticity","authors":"Yanwen Guo, Qiujie Wu, Xiaoping Wang","doi":"10.1109/ICACI52617.2021.9435904","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435904","url":null,"abstract":"A random number generator is implemented utilizing the intrinsic stochasticity of memristor as a natural physical randomness source. The random bits are produced by cyclically switching the memristor and comparing the memristor resistive values in the high resistive state with the reference value, taking advantage of the more pronounced resistance variation in the high resistive state. Using the alternative voltage pulse scheme in the designed random number circuit, the biasness of the random numbers is largely alleviated, then the standard randomness test suite developed by NIST is used to validate the feasibility. Moreover, several memristors in parallel are considered to generate different frequency random number at the sacrifice of area overhead. The random number generation is simulated in the circuit simulated tool PSPICE. This approach improves the memristor-based stochastic circuit computer aided design.","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":"126061607","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/ICACI52617.2021.9435860
Zhi-hui Zhan, Sheng-Hao Wu, Jun Zhang
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.
{"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}
Pub Date : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435886
Binbin Yuan, Songtao Guo, Quyuan Wang
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.
{"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}
Pub Date : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435895
P. A. Ejegwa, Yuming Feng, S. Wen, Wei Zhang
By considering all the three parameters describing Pythagorean fuzzy set, we introduce a new technique of computing correlation coefficient between Pythagorean fuzzy sets from statistical perspective. The correlation coefficient value obtain via this technique shows strength of correlation between the Pythagorean fuzzy sets and indicates whether the Pythagorean fuzzy sets under consideration are related negatively or positively in contrast to other existing correlation coefficient approaches in Pythagorean fuzzy context, which only assess the strength of relationship. Certain numerical examples are considered to ascertain the authenticity of this method over similar techniques studied in intuitionistic/Pythagorean fuzzy contexts. Some pattern recognition problems are resolved with the aid of the new technique. For higher productivity sake, this technique could be approached from an object-oriented perspective.
{"title":"Determination of Pattern Recognition Problems based on a Pythagorean Fuzzy Correlation Measure from Statistical Viewpoint","authors":"P. A. Ejegwa, Yuming Feng, S. Wen, Wei Zhang","doi":"10.1109/ICACI52617.2021.9435895","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435895","url":null,"abstract":"By considering all the three parameters describing Pythagorean fuzzy set, we introduce a new technique of computing correlation coefficient between Pythagorean fuzzy sets from statistical perspective. The correlation coefficient value obtain via this technique shows strength of correlation between the Pythagorean fuzzy sets and indicates whether the Pythagorean fuzzy sets under consideration are related negatively or positively in contrast to other existing correlation coefficient approaches in Pythagorean fuzzy context, which only assess the strength of relationship. Certain numerical examples are considered to ascertain the authenticity of this method over similar techniques studied in intuitionistic/Pythagorean fuzzy contexts. Some pattern recognition problems are resolved with the aid of the new technique. For higher productivity sake, this technique could be approached from an object-oriented perspective.","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":"121810174","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/ICACI52617.2021.9435879
Chun Hua
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.
{"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}
Pub Date : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435890
C. Chou, Junho Park, Eric Chou
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.
{"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}
Pub Date : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435901
Le Gao, L. Cai, Yuming Feng, N. Dai, Qingshan Xu
This manuscript summarizes the SOC (State of charge) estimation methods of electric vehicle battery packs at low temperature, analyzes the performance of common battery packs of electric vehicle and the influence of low temperature, introduces the traditional SOC estimation method and the emerging cryogenic intelligent lifting algorithms, and summarizes their advantages and disadvantages. Combined with intelligent algorithm and battery model, the development of SOC estimation of battery packs at low temperature is prospected.
本文总结了低温下电动汽车电池组SOC (State of charge)估算方法，分析了电动汽车常用电池组的性能以及低温对其的影响，介绍了传统的SOC估算方法和新兴的低温智能提升算法，并总结了它们的优缺点。结合智能算法和电池模型，展望了低温下电池组荷电状态估计的发展前景。
{"title":"Performance of different Electric vehicle Battery packs at low temperature and Analysis of Intelligent SOC experiment","authors":"Le Gao, L. Cai, Yuming Feng, N. Dai, Qingshan Xu","doi":"10.1109/ICACI52617.2021.9435901","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435901","url":null,"abstract":"This manuscript summarizes the SOC (State of charge) estimation methods of electric vehicle battery packs at low temperature, analyzes the performance of common battery packs of electric vehicle and the influence of low temperature, introduces the traditional SOC estimation method and the emerging cryogenic intelligent lifting algorithms, and summarizes their advantages and disadvantages. Combined with intelligent algorithm and battery model, the development of SOC estimation of battery packs at low temperature is prospected.","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":"114397525","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/ICACI52617.2021.9435876
Dong Pang, Xinyi Le
Indoor localization witnessed the flourishing development in location based service for indoor environments. Regarding the availability of access points (AP) and its low cost for industry popularization, one of promising tool for localization is based on WiFi fingerprints. However, because of the interference of multi-path effects, the received signal strength data (RSS) are quite possibly to have fluctuated, thus they may result in propagation errors into localization results. In order to tackle this issue, We propose refined fingerprints based bidirectional long-short-term memory (bi-LSTM) neural network to learn the key features from the tested coarse RSS data, obtaining extracted trained weights as refined fingerprints(RFs). The extracted features of refined fingerprints are capable to demonstrate strong robustness with fluctuated signals and represent the environmental properties. The effectiveness of our bi-LSTM network is substantiated in the complex indoor environment, and accuracy is remarkably improved compared with our previous algorithm and other RSS-based approaches.
{"title":"Indoor Localization Using Bidirectional LSTM Networks","authors":"Dong Pang, Xinyi Le","doi":"10.1109/ICACI52617.2021.9435876","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435876","url":null,"abstract":"Indoor localization witnessed the flourishing development in location based service for indoor environments. Regarding the availability of access points (AP) and its low cost for industry popularization, one of promising tool for localization is based on WiFi fingerprints. However, because of the interference of multi-path effects, the received signal strength data (RSS) are quite possibly to have fluctuated, thus they may result in propagation errors into localization results. In order to tackle this issue, We propose refined fingerprints based bidirectional long-short-term memory (bi-LSTM) neural network to learn the key features from the tested coarse RSS data, obtaining extracted trained weights as refined fingerprints(RFs). The extracted features of refined fingerprints are capable to demonstrate strong robustness with fluctuated signals and represent the environmental properties. The effectiveness of our bi-LSTM network is substantiated in the complex indoor environment, and accuracy is remarkably improved compared with our previous algorithm and other RSS-based 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":"133720643","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/ICACI52617.2021.9435875
Mengshi Zhao, Pengzhan Qiu, Junqi Zhang
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.
{"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}
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