- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Online Gauss-Newton-based parallel-pipeline method for real-time in-situ laser ranging
摘要： Over the last few decades, there has been considerable concern over the multifactory manufacturing environments owing to globalization. Numerous studies have indicated that ?exible job-shop scheduling problems (FJSPs) and the distributed and FJSPs (DFJSPs) belong to NP-hard puzzle. The allocation of jobs to appropriate factories or ?exible manufacturing units is an essential task in multifactory optimization scheduling, which involves the consideration of equipment performance, technology, capacity, and utilization level for each factory or manufacturing unit. Several variables and constraints should be considered in the encoding problem of DFJSPs when using genetic algorithms (GAs). In particular, it has been reported in the literature that the traditional GA encoding method may generate infeasible solutions or illegal solutions; thus, a specially designed evolution process is required. However, in such a process, the diversity of chromosomes is lost. To overcome this drawback, this paper proposes a re?ned encoding operator that integrates probability concepts into a real-parameter encoding method. In addition, the length of chromosomes can be substantially reduced using the proposed algorithm, thereby, saving computation space. The proposed re?ned GA algorithm was evaluated with satisfactory results through two-stage validation; in the ?rst stage, a classical DFJSP was adopted to show the effectiveness of the algorithm, and in the second stage, the algorithm was used to solve a real-world case. The real-world case involved the use of historical data with 100 and 200 sets of work orders of a fastener manufacturer in Taiwan. The results were satisfactory and indicated that the proposed re?ned GA algorithm could effectively overcome the con?icts caused by GA encoding algorithms.
关键词： scheduling problems,Genetic algorithms,distributed and ?exible job-shop,probability-based encoding operator,?exible job-shop
更新于2025-09-19 17:13:59
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Anisotropic Impedance Surface Enabled Low-Profile Broadband Dual-Circularly-Polarized Multi-Beam Reflect-Arrays for Ka-Band Applications
摘要： Self-organizing network (SON) mechanisms reduce operational expenditure in cellular networks while enhancing the offered quality of service. Within a SON, self-healing aims to autonomously solve problems in the radio access network and to minimize their impact on the user. Self-healing comprises automatic fault detection, root cause analysis, fault compensation, and recovery. This paper presents a root cause analysis system based on fuzzy logic. A genetic algorithm is proposed for learning the rule base. The proposed method is adapted to the way of reasoning of troubleshooting experts, which ease knowledge acquisition and system output interpretation. Results show that the obtained results are comparable or even better than those obtained when the troubleshooting experts define the rules, with the clear benefit of not requiring the experts to define the system. In addition, the system is robust, since fine tuning of its parameters is not mandatory.
关键词： genetic algorithms,self-organizing networks (SONs),Fuzzy systems,troubleshooting,root cause analysis,self-healing,supervised learning
更新于2025-09-19 17:13:59
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Research on real-time BER estimation in satellite downlink
摘要： Natural ecosystems exhibit complex dynamics of interacting species. Man-made ecosystems exhibit similar dynamics and, in the case of mobile app stores, can be said to perform optimization as developers seek to maximize app downloads. This work aims to understand stability and instability within app store dynamics and how it affects ?tness. The investigation is carried out with AppEco, a model of the iOS App Store, which was extended for this paper and updated to model the store from 2008 to 2014. AppEco models apps containing features, developers who build the apps, users who download apps according to their preferences, and an app store that presents apps to the users. It also models developers who use commonly observed strategies to build their apps: innovator, milker, optimizer, copycat, and ?exible (the ability to choose any strategy). Results show that despite the success of the copycat strategy, there is a clear stable state for low proportion of copycats in developer populations, mirroring results in theoretical biology for producer–scrounger systems. The results also show that the best ?tness is achieved when the evolutionary optimizer (as producer) and copycat (as scrounger) strategies coexist together in stable proportions.
关键词： producer–scrounger systems,computational modeling,app stores,evolutionary ecosystem model,mobile developers,genetic algorithms,Agent-based simulation
更新于2025-09-19 17:13:59
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[IEEE IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Yokohama, Japan (2019.7.28-2019.8.2)] IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Structural Optimization of Receiving System Based on Optimal Field of View for Shallow Sea Laser Measurement
摘要： Recently, mobile networking systems have been designed with more complexity of infrastructure and higher diversity of associated devices and resources, as well as more dynamical formations of networks, due to the fast development of current Internet and mobile communication industry. In such emerging mobile heterogeneous networks (HetNets), there are a large number of technical challenges focusing on the efficient organization, management, maintenance, and optimization, over the complicated system resources. In particular, HetNets have attracted great interest from academia and industry in deploying more effective solutions based on artificial intelligence (AI) techniques, e.g., machine learning, bio-inspired algorithms, fuzzy neural network, and so on, because AI techniques can naturally handle the problems of large-scale complex systems, such as HetNets towards more intelligent and automatic-evolving ones. In this paper, we discuss the state-of-the-art AI-based techniques for evolving the smarter HetNets infrastructure and systems, focusing on the research issues of self-configuration, self-healing, and self-optimization, respectively. A detailed taxonomy of the related AI-based techniques of HetNets is also shown by discussing the pros and cons for various AI-based techniques for different problems in HetNets. Opening research issues and pending challenges are concluded as well, which can provide guidelines for future research work.
关键词： ant colony optimization,self-organization networks,heterogeneous networks,genetic algorithms,Artificial intelligence
更新于2025-09-19 17:13:59
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[IEEE TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) - Kochi, India (2019.10.17-2019.10.20)] TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) - Bias stress induced threshold voltage shift in buckled thin film transistors
摘要： We present a ?exible, easy-to-expand digital signal processing method for detecting heart rate (HR) for cardiac vibration signals of ?ber Bragg grating (FBG) sensor. The FBG-based method of measuring HR is possible to use during the magnetic resonance imaging procedure, which is its unique advantage. Our goal was to design a detection method with plurality of parameters and to subject these parameters to genetic algorithm optimization technique. In effect, we arrived at a method that is well able to deal with much distorted signals with low SNR. We proved that the method we developed allows automatic adjustment to the shape of the waves of signal carrying useful information about the moments of heartbeat. Thus, we can easily adapt our technique to the analysis of signals, which contains information on HR, from sensors employing different techniques of strain detection. The proposed method has the capabilities of analyzing signals in semi-real-time (online) with beat-to-beat resolution, signi?cantly low delay, and negligible computational power requirements. We veri?ed our method on recordings in a group of seven subjects. Veri?cation included over 6000 heartbeats (82 min 47 s of recordings). The root-mean-square error of our method does not exceed 6.0 bpm.
关键词： heart rate (HR),parallel computing,Ballistocardiographic (BCG) signal,?ber Bragg gratings (FBGs),genetic algorithms (GAs)
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 16th International Conference on Group IV Photonics (GFP) - Singapore, Singapore (2019.8.28-2019.8.30)] 2019 IEEE 16th International Conference on Group IV Photonics (GFP) - Light Emission Driven by Fano Resonances in Symmetry-Breaking Silicon Metasurface
摘要： Over the last few decades, there has been considerable concern over the multifactory manufacturing environments owing to globalization. Numerous studies have indicated that ?exible job-shop scheduling problems (FJSPs) and the distributed and FJSPs (DFJSPs) belong to NP-hard puzzle. The allocation of jobs to appropriate factories or ?exible manufacturing units is an essential task in multifactory optimization scheduling, which involves the consideration of equipment performance, technology, capacity, and utilization level for each factory or manufacturing unit. Several variables and constraints should be considered in the encoding problem of DFJSPs when using genetic algorithms (GAs). In particular, it has been reported in the literature that the traditional GA encoding method may generate infeasible solutions or illegal solutions; thus, a specially designed evolution process is required. However, in such a process, the diversity of chromosomes is lost. To overcome this drawback, this paper proposes a re?ned encoding operator that integrates probability concepts into a real-parameter encoding method. In addition, the length of chromosomes can be substantially reduced using the proposed algorithm, thereby, saving computation space. The proposed re?ned GA algorithm was evaluated with satisfactory results through two-stage validation; in the ?rst stage, a classical DFJSP was adopted to show the effectiveness of the algorithm, and in the second stage, the algorithm was used to solve a real-world case. The real-world case involved the use of historical data with 100 and 200 sets of work orders of a fastener manufacturer in Taiwan. The results were satisfactory and indicated that the proposed re?ned GA algorithm could effectively overcome the con?icts caused by GA encoding algorithms.
关键词： Genetic algorithms,probability-based encoding operator,?exible job-shop,distributed and ?exible job-shop,scheduling problems
更新于2025-09-19 17:13:59
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[IEEE 2019 Innovations in Power and Advanced Computing Technologies (i-PACT) - Vellore, India (2019.3.22-2019.3.23)] 2019 Innovations in Power and Advanced Computing Technologies (i-PACT) - Ultra-high Negative Dispersion Compensating Index Guiding Single Mode Octagonal Photonic Crystal Fiber: Design and Analysis
摘要： Self-organizing network (SON) mechanisms reduce operational expenditure in cellular networks while enhancing the offered quality of service. Within a SON, self-healing aims to autonomously solve problems in the radio access network and to minimize their impact on the user. Self-healing comprises automatic fault detection, root cause analysis, fault compensation, and recovery. This paper presents a root cause analysis system based on fuzzy logic. A genetic algorithm is proposed for learning the rule base. The proposed method is adapted to the way of reasoning of troubleshooting experts, which ease knowledge acquisition and system output interpretation. Results show that the obtained results are comparable or even better than those obtained when the troubleshooting experts define the rules, with the clear benefit of not requiring the experts to define the system. In addition, the system is robust, since fine tuning of its parameters is not mandatory.
关键词： troubleshooting,self-healing,genetic algorithms,self-organizing networks (SONs),root cause analysis,Fuzzy systems,supervised learning
更新于2025-09-19 17:13:59
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[IEEE 2019 34th Symposium on Microelectronics Technology and Devices (SBMicro) - Sao Paulo, Brazil (2019.8.26-2019.8.30)] 2019 34th Symposium on Microelectronics Technology and Devices (SBMicro) - Realistic Simulations and Design of GaAs Solar Cells produced by Molecular Beam Epitaxy
摘要： Recently, mobile networking systems have been designed with more complexity of infrastructure and higher diversity of associated devices and resources, as well as more dynamical formations of networks, due to the fast development of current Internet and mobile communication industry. In such emerging mobile heterogeneous networks (HetNets), there are a large number of technical challenges focusing on the efficient organization, management, maintenance, and optimization, over the complicated system resources. In particular, HetNets have attracted great interest from academia and industry in deploying more effective solutions based on artificial intelligence (AI) techniques, e.g., machine learning, bio-inspired algorithms, fuzzy neural network, and so on, because AI techniques can naturally handle the problems of large-scale complex systems, such as HetNets towards more intelligent and automatic-evolving ones. In this paper, we discuss the state-of-the-art AI-based techniques for evolving the smarter HetNets infrastructure and systems, focusing on the research issues of self-configuration, self-healing, and self-optimization, respectively. A detailed taxonomy of the related AI-based techniques of HetNets is also shown by discussing the pros and cons for various AI-based techniques for different problems in HetNets. Opening research issues and pending challenges are concluded as well, which can provide guidelines for future research work.
关键词： ant colony optimization,self-organization networks,heterogeneous networks,genetic algorithms,Artificial intelligence
更新于2025-09-19 17:13:59
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - High-Fidelity Few-Cycle Laser Pulses Generated Via Nonlinear Ellipse Rotation
摘要： Self-organizing network (SON) mechanisms reduce operational expenditure in cellular networks while enhancing the offered quality of service. Within a SON, self-healing aims to autonomously solve problems in the radio access network and to minimize their impact on the user. Self-healing comprises automatic fault detection, root cause analysis, fault compensation, and recovery. This paper presents a root cause analysis system based on fuzzy logic. A genetic algorithm is proposed for learning the rule base. The proposed method is adapted to the way of reasoning of troubleshooting experts, which ease knowledge acquisition and system output interpretation. Results show that the obtained results are comparable or even better than those obtained when the troubleshooting experts define the rules, with the clear benefit of not requiring the experts to define the system. In addition, the system is robust, since fine tuning of its parameters is not mandatory.
关键词： genetic algorithms,self-organizing networks (SONs),Fuzzy systems,troubleshooting,root cause analysis,self-healing,supervised learning
更新于2025-09-19 17:13:59
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Combined Fuzzy and Genetic Algorithm for the Optimisation of Hybrid Composite-Polymer Joints Obtained by Two-Step Laser Joining Process
摘要： In the present work, genetic algorithms and fuzzy logic were combined to model and optimise the shear strength of hybrid composite-polymer joints obtained by two step laser joining process. The first step of the process consists of a surface treatment (cleaning) of the carbon fibre-reinforced polymer (CFRP) laminate, by way of a 30 W nanosecond laser. This phase allows removing the first matrix layer from the CFRP and was performed under fixed process parameters condition. In the second step, a diode laser was adopted to join the CFRP to polycarbonate (PC) sheet by laser-assisted direct joining (LADJ). The experimentation was performed adopting an experimental plan developed according to the design of experiment (DOE) methodology, changing the laser power and the laser energy. In order to verify the cleaning effect, untreated laminated were also joined and tested adopting the same process conditions. Analysis of variance (ANOVA) was adopted to detect the statistical influence of the process parameters. Results showed that both the laser treatment and the process parameters strongly influence the joint performances. Then, an uncertain model based on the combination of fuzzy logic and genetic algorithms was developed and adopted to find the best process parameters' set able to give the maximum joint strength against the lowest uncertainty level. This type of approach is especially useful to provide information about how much the precision of the model and the process varies by changing the process parameters.
关键词： genetic algorithms,CFRP,laser cleaning,laser joining,hybrid joints,fuzzy logic
更新于2025-09-16 10:30:52