Maximizing profit is usually the objective of optimal control of greenhouse cultivation.However,due to the problem of“the curse of dimensionality”,the global optimization of greenhouse climate is usually difficult w...Maximizing profit is usually the objective of optimal control of greenhouse cultivation.However,due to the problem of“the curse of dimensionality”,the global optimization of greenhouse climate is usually difficult when faced with a complex dynamic model and a long cultivation period.Compared with leafy vegetables with a much simpler dynamic model and a much shorter cultivation period,the year-round tomato model usually has many more states to describe its dynamics better.To solve the year-round climate control of greenhouse tomato cultivation,a rule-based model predictive control(MPC)algorithm is raised.The innovation of this paper lies in that the setpoints of the proposed MPC algorithms are determined by the external weather and the month-averaged predictions of the tomato price.With the greenhouse climate–tomato growth dynamic model and the economic performance index,different MPC algorithms are compared with the traditional on/off control algorithm and the open field cultivation.Quantified results of yield,cost,and profit are obtained with the weather data and market data collected in Beijing.Findings of this paper showed that the year-round greenhouse tomato cultivation in Beijing is hardly profitable with the tomato price sold as an open field product(XFD price).With the tomato price sold as a high-tech greenhouse product(JD price),the higher yield guarantees a higher profit.Moreover,the simple emphasis on energy minimization cannot even guarantee a higher yield than that in the open field.A synthetical consideration of yield and cost is a prerequisite for a high profit.展开更多
The Wireless Sensor Networks(WSN)are vulnerable to assaults due to the fact that the devices connected to them have a reliable connection to the inter-net.A malicious node acts as the controller and uses a grey hole a...The Wireless Sensor Networks(WSN)are vulnerable to assaults due to the fact that the devices connected to them have a reliable connection to the inter-net.A malicious node acts as the controller and uses a grey hole attack to get the data from all of the other nodes in the network.Additionally,the nodes are dis-carding and modifying the data packets according to the requirements of the sys-tem.The assault modifies the fundamental concept of the WSNs,which is that different devices should communicate with one another.In the proposed system,there is a fuzzy idea offered for the purpose of preventing the grey hole attack from making effective communication among the WSN devices.The currently available model is unable to recognise the myriad of different kinds of attacks.The fuzzy engine identified suspicious actions by utilising the rules that were gen-erated to make a prediction about the malicious node that would halt the process.Experiments conducted using simulation are used to determine delay,accuracy,energy consumption,throughput,and the ratio of packets successfully delivered.It stands in contrast to the model that was suggested,as well as the methodologies that are currently being used,and analogue behavioural modelling.In comparison to the existing method,the proposed model achieves an accuracy rate of 45 per-cent,a packet delivery ratio of 79 percent,and a reduction in energy usage of around 35.6 percent.These results from the simulation demonstrate that the fuzzy grey detection technique that was presented has the potential to increase the net-work’s capability of detecting grey hole assaults.展开更多
Buildings and heating,ventilation and air conditioning(HVAC)systems are recognized as effective flexibility resources that can interact with the power grid to reduce electrical peak loads.Model predictive control(MPC)...Buildings and heating,ventilation and air conditioning(HVAC)systems are recognized as effective flexibility resources that can interact with the power grid to reduce electrical peak loads.Model predictive control(MPC)is a powerful approach for fully unlocking the energy flexibility of buildings.However,MPC relies on online optimization for practical engineering deployment,which imposes a significant computational burden and limits its widespread adoption.To address the challenge of computational burden,this study proposes a machine learning-enhanced lightweight rule-based control strategy(ML-RBC).The main idea of ML-RBC is to use a machine learning(ML)model to automatically tune the adjustable parameters of the rule-based controller(RBC).Specifically,the ML model learns the functional relationship between external inputs and adjustable parameters from a dataset generated by batch offline closed-loop MPC simulations.The proposed method retains inherent high computational efficiency of RBC while also achieving optimal control performance.The demand response(DR)control performance of the proposed method is evaluated using a high-fidelity co-simulation platform that integrates Spawn of EnergyPlus and Modelica.Simulation experiments are performed on a multi-zone office building equipped with a variable air volume(VAV)cooling system under time-of-use electricity pricing and day-ahead DR programs.The experimental results indicate that,compared to the baseline strategy,ML-RBC and traditional MPC achieve cost savings of 21.95%and 23.07%,respectively.Importantly,ML-RBC eliminates the need for online optimization while achieving a computational cost of less than one-thousandth that of MPC,with only a slight performance loss as the trade-off.Finally,the impact of the trajectory interpolation method in ML-RBC on control performance is discussed,revealing that different interpolation methods have a minor influence on the overall performance.展开更多
An N-gram Chinese language model incorporating linguistic rules is presented. By constructing elements lattice, rules information is incorporated in statistical frame. To facilitate the hybrid modeling, novel methods ...An N-gram Chinese language model incorporating linguistic rules is presented. By constructing elements lattice, rules information is incorporated in statistical frame. To facilitate the hybrid modeling, novel methods such as MI-based rule evaluating, weighted rule quantification and element-based n-gram probability approximation are presented. Dynamic Viterbi algorithm is adopted to search the best path in lattice. To strengthen the model, transformation-based error-driven rules learning is adopted. Applying proposed model to Chinese Pinyin-to-character conversion, high performance has been achieved in accuracy, flexibility and robustness simultaneously. Tests show correct rate achieves 94.81% instead of 90.53% using bi-gram Markov model alone. Many long-distance dependency and recursion in language can be processed effectively.展开更多
Unmanned Aerial Vehicles(UAV)tilt photogrammetry technology can quickly acquire image data in a short time.This technology has been widely used in all walks of life with the rapid development in recent years especiall...Unmanned Aerial Vehicles(UAV)tilt photogrammetry technology can quickly acquire image data in a short time.This technology has been widely used in all walks of life with the rapid development in recent years especially in the rapid acquisition of high-resolution remote sensing images,because of its advantages of high efficiency,reliability,low cost and high precision.Fully using the UAV tilt photogrammetry technology,the construction image progress can be observed by stages,and the construction site can be reasonably and optimally arranged through three-dimensional modeling to create a civilized,safe and tidy construction environment.展开更多
The IEC 61850 standard stipulates the Substation Configuration Description Language (SCL) file as a means to define the substation equipment, IED function and also the communication mechanism for the substation area n...The IEC 61850 standard stipulates the Substation Configuration Description Language (SCL) file as a means to define the substation equipment, IED function and also the communication mechanism for the substation area network. The SCL is an eXtensible Markup Language (XML) based file which helps to describe the configuration of the substation Intelligent Electronic Devices (IED) including their associated functions. The SCL file is also configured to contain all IED capabilities including data model which is structured into objects for easy descriptive modeling. The effective functioning of this SCL file relies on appropriate validation techniques which check the data model for errors due to non-conformity to the IEC 61850 standard. In this research, we extend the conventional SCL validation algorithm to develop a more advanced validator which can validate the standard data model using the Unified Modeling Language (UML). By using the Rule-based SCL validation tool, we implement validation test cases for a more comprehensive understanding of the various validation functionalities. It can be observed from the algorithm and the various implemented test cases that the proposed validation tool can improve SCL information validation and also help automation engineers to comprehend the IEC 61850 substation system architecture.展开更多
Targeting at a reliable image matching of multiple remote sensing images for the generation of digital surface models,this paper presents a geometric-constrained multi-view image matching method,based on an energy min...Targeting at a reliable image matching of multiple remote sensing images for the generation of digital surface models,this paper presents a geometric-constrained multi-view image matching method,based on an energy minimization framework.By employing a geometrical constraint,the cost value of the energy function was calculated from multiple images,and the cost value was aggregated in an image space using a semi-global optimization approach.A homography transform parameter calculation method is proposed for fast calculation of projection pixel on each image when calculation cost values.It is based on the known interior orientation parameters,exterior orientation parameters,and a given elevation value.For an efficient and reliable processing of multiple remote sensing images,the proposed matching method was performed via a coarse-to-fine strategy through image pyramid.Three sets of airborne remote sensing images were used to evaluate the performance of the proposed method.Results reveal that the multi-view image matching can improve matching reliability.Moreover,the experimental results show that the proposed method performs better than traditional methods.展开更多
针对聚类中的多视角和可解释的问题,提出多视角生成模型的可解释性聚类算法(interpretable clustering with multi-view generative model,ICMG).ICMG能够产生多个视角的聚类划分,并通过视角的语义信息对聚类结果进行定性和定量地解释....针对聚类中的多视角和可解释的问题,提出多视角生成模型的可解释性聚类算法(interpretable clustering with multi-view generative model,ICMG).ICMG能够产生多个视角的聚类划分,并通过视角的语义信息对聚类结果进行定性和定量地解释.首先,构建一种多视角生成模型(multi-view generative model,MGM),该模型使用贝叶斯程序学习(Bayesian program learning,BPL)和嵌入多视角因素的贝叶斯案例模型(multi-view Bayesian case model,MBCM)生成多个视角.其次,基于视角的匹配度进行聚类得到多种聚类方案.最后使用视角的原型和子空间所附带的语义信息定性和定量地解释聚类结果.实验结果表明:ICMG能够得到多种可解释的聚类结果,相比于传统多视角聚类算法具有较明显的优势.展开更多
Log-linear models and more recently neural network models used forsupervised relation extraction requires substantial amounts of training data andtime, limiting the portability to new relations and domains. To this en...Log-linear models and more recently neural network models used forsupervised relation extraction requires substantial amounts of training data andtime, limiting the portability to new relations and domains. To this end, we propose a training representation based on the dependency paths between entities in adependency tree which we call lexicalized dependency paths (LDPs). We showthat this representation is fast, efficient and transparent. We further propose representations utilizing entity types and its subtypes to refine our model and alleviatethe data sparsity problem. We apply lexicalized dependency paths to supervisedlearning using the ACE corpus and show that it can achieve similar performancelevel to other state-of-the-art methods and even surpass them on severalcategories.展开更多
AIM To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.METHODS Bayesian rule learning(BRL) is a rule-based classifier that uses a...AIM To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.METHODS Bayesian rule learning(BRL) is a rule-based classifier that uses a greedy best-first search over a space of Bayesian belief-networks(BN) to find the optimal BN to explain the input dataset, and then infers classification rules from this BN. BRL uses a Bayesian score to evaluate the quality of BNs. In this paper, we extended the Bayesian score to include informative structure priors, which encodes our prior domain knowledge about the dataset. We call this extension of BRL as BRL_p. The structure prior has a λ hyperparameter that allows the user to tune the degree of incorporation of the prior knowledge in the model learning process. We studied the effect of λ on model learning using a simulated dataset and a real-world lung cancer prognostic biomarker dataset, by measuring the degree of incorporation of our specified prior knowledge. We also monitored its effect on the model predictive performance. Finally, we compared BRL_p to other stateof-the-art classifiers commonly used in biomedicine.RESULTS We evaluated the degree of incorporation of prior knowledge into BRL_p, with simulated data by measuring the Graph Edit Distance between the true datagenerating model and the model learned by BRL_p. We specified the true model using informative structurepriors. We observed that by increasing the value of λ we were able to increase the influence of the specified structure priors on model learning. A large value of λ of BRL_p caused it to return the true model. This also led to a gain in predictive performance measured by area under the receiver operator characteristic curve(AUC). We then obtained a publicly available real-world lung cancer prognostic biomarker dataset and specified a known biomarker from literature [the epidermal growth factor receptor(EGFR) gene]. We again observed that larger values of λ led to an increased incorporation of EGFR into the final BRL_p model. This relevant background knowledge also led to a gain in AUC.CONCLUSION BRL_p enables tunable structure priors to be incorporated during Bayesian classification rule learning that integrates data and knowledge as demonstrated using lung cancer biomarker data.展开更多
In order to make system reliable, it should inhibit guarantee for basic service, data flow, composition of services, and the complete workflow. In service-oriented architecture (SOA), the entire software system consis...In order to make system reliable, it should inhibit guarantee for basic service, data flow, composition of services, and the complete workflow. In service-oriented architecture (SOA), the entire software system consists of an interacting group of autonomous services. Some soft computing approaches have been developed for estimating the reliability of service oriented systems (SOSs). Still much more research is expected to estimate reliability in a better way. In this paper, we proposed SoS reliability based on an adaptive neuro fuzzy inference system (ANFIS) approach. We estimated the reliability based on some defined parameter. Moreover, we compared its performance with a plain FIS (fuzzy inference system) for similar data sets and found the proposed approach gives better reliability estimation.展开更多
基金supported by Key Technology Research and Development Program of Shandong(2022CXGC020708)National Natural Science Foundation of China(32371998 and U20A2020)+2 种基金National Modern Agricultural Technology System Construction Project(CARS-23-D02)Beijing Innovation Consortium of Agriculture Research System(BAIC01-2023)the 2115 Talent Development Program of China Agricultural University.
文摘Maximizing profit is usually the objective of optimal control of greenhouse cultivation.However,due to the problem of“the curse of dimensionality”,the global optimization of greenhouse climate is usually difficult when faced with a complex dynamic model and a long cultivation period.Compared with leafy vegetables with a much simpler dynamic model and a much shorter cultivation period,the year-round tomato model usually has many more states to describe its dynamics better.To solve the year-round climate control of greenhouse tomato cultivation,a rule-based model predictive control(MPC)algorithm is raised.The innovation of this paper lies in that the setpoints of the proposed MPC algorithms are determined by the external weather and the month-averaged predictions of the tomato price.With the greenhouse climate–tomato growth dynamic model and the economic performance index,different MPC algorithms are compared with the traditional on/off control algorithm and the open field cultivation.Quantified results of yield,cost,and profit are obtained with the weather data and market data collected in Beijing.Findings of this paper showed that the year-round greenhouse tomato cultivation in Beijing is hardly profitable with the tomato price sold as an open field product(XFD price).With the tomato price sold as a high-tech greenhouse product(JD price),the higher yield guarantees a higher profit.Moreover,the simple emphasis on energy minimization cannot even guarantee a higher yield than that in the open field.A synthetical consideration of yield and cost is a prerequisite for a high profit.
文摘The Wireless Sensor Networks(WSN)are vulnerable to assaults due to the fact that the devices connected to them have a reliable connection to the inter-net.A malicious node acts as the controller and uses a grey hole attack to get the data from all of the other nodes in the network.Additionally,the nodes are dis-carding and modifying the data packets according to the requirements of the sys-tem.The assault modifies the fundamental concept of the WSNs,which is that different devices should communicate with one another.In the proposed system,there is a fuzzy idea offered for the purpose of preventing the grey hole attack from making effective communication among the WSN devices.The currently available model is unable to recognise the myriad of different kinds of attacks.The fuzzy engine identified suspicious actions by utilising the rules that were gen-erated to make a prediction about the malicious node that would halt the process.Experiments conducted using simulation are used to determine delay,accuracy,energy consumption,throughput,and the ratio of packets successfully delivered.It stands in contrast to the model that was suggested,as well as the methodologies that are currently being used,and analogue behavioural modelling.In comparison to the existing method,the proposed model achieves an accuracy rate of 45 per-cent,a packet delivery ratio of 79 percent,and a reduction in energy usage of around 35.6 percent.These results from the simulation demonstrate that the fuzzy grey detection technique that was presented has the potential to increase the net-work’s capability of detecting grey hole assaults.
基金supported in part by the scholarship from Ministry of Science and Technology of China(Project ID:2024YFE0199300).
文摘Buildings and heating,ventilation and air conditioning(HVAC)systems are recognized as effective flexibility resources that can interact with the power grid to reduce electrical peak loads.Model predictive control(MPC)is a powerful approach for fully unlocking the energy flexibility of buildings.However,MPC relies on online optimization for practical engineering deployment,which imposes a significant computational burden and limits its widespread adoption.To address the challenge of computational burden,this study proposes a machine learning-enhanced lightweight rule-based control strategy(ML-RBC).The main idea of ML-RBC is to use a machine learning(ML)model to automatically tune the adjustable parameters of the rule-based controller(RBC).Specifically,the ML model learns the functional relationship between external inputs and adjustable parameters from a dataset generated by batch offline closed-loop MPC simulations.The proposed method retains inherent high computational efficiency of RBC while also achieving optimal control performance.The demand response(DR)control performance of the proposed method is evaluated using a high-fidelity co-simulation platform that integrates Spawn of EnergyPlus and Modelica.Simulation experiments are performed on a multi-zone office building equipped with a variable air volume(VAV)cooling system under time-of-use electricity pricing and day-ahead DR programs.The experimental results indicate that,compared to the baseline strategy,ML-RBC and traditional MPC achieve cost savings of 21.95%and 23.07%,respectively.Importantly,ML-RBC eliminates the need for online optimization while achieving a computational cost of less than one-thousandth that of MPC,with only a slight performance loss as the trade-off.Finally,the impact of the trajectory interpolation method in ML-RBC on control performance is discussed,revealing that different interpolation methods have a minor influence on the overall performance.
文摘An N-gram Chinese language model incorporating linguistic rules is presented. By constructing elements lattice, rules information is incorporated in statistical frame. To facilitate the hybrid modeling, novel methods such as MI-based rule evaluating, weighted rule quantification and element-based n-gram probability approximation are presented. Dynamic Viterbi algorithm is adopted to search the best path in lattice. To strengthen the model, transformation-based error-driven rules learning is adopted. Applying proposed model to Chinese Pinyin-to-character conversion, high performance has been achieved in accuracy, flexibility and robustness simultaneously. Tests show correct rate achieves 94.81% instead of 90.53% using bi-gram Markov model alone. Many long-distance dependency and recursion in language can be processed effectively.
文摘Unmanned Aerial Vehicles(UAV)tilt photogrammetry technology can quickly acquire image data in a short time.This technology has been widely used in all walks of life with the rapid development in recent years especially in the rapid acquisition of high-resolution remote sensing images,because of its advantages of high efficiency,reliability,low cost and high precision.Fully using the UAV tilt photogrammetry technology,the construction image progress can be observed by stages,and the construction site can be reasonably and optimally arranged through three-dimensional modeling to create a civilized,safe and tidy construction environment.
文摘The IEC 61850 standard stipulates the Substation Configuration Description Language (SCL) file as a means to define the substation equipment, IED function and also the communication mechanism for the substation area network. The SCL is an eXtensible Markup Language (XML) based file which helps to describe the configuration of the substation Intelligent Electronic Devices (IED) including their associated functions. The SCL file is also configured to contain all IED capabilities including data model which is structured into objects for easy descriptive modeling. The effective functioning of this SCL file relies on appropriate validation techniques which check the data model for errors due to non-conformity to the IEC 61850 standard. In this research, we extend the conventional SCL validation algorithm to develop a more advanced validator which can validate the standard data model using the Unified Modeling Language (UML). By using the Rule-based SCL validation tool, we implement validation test cases for a more comprehensive understanding of the various validation functionalities. It can be observed from the algorithm and the various implemented test cases that the proposed validation tool can improve SCL information validation and also help automation engineers to comprehend the IEC 61850 substation system architecture.
基金This work was supported by the National Key Research and Development Program of China[grant number 2017YFC0803802]and the National Natural Science Foundation of China[grant number 41771486].
文摘Targeting at a reliable image matching of multiple remote sensing images for the generation of digital surface models,this paper presents a geometric-constrained multi-view image matching method,based on an energy minimization framework.By employing a geometrical constraint,the cost value of the energy function was calculated from multiple images,and the cost value was aggregated in an image space using a semi-global optimization approach.A homography transform parameter calculation method is proposed for fast calculation of projection pixel on each image when calculation cost values.It is based on the known interior orientation parameters,exterior orientation parameters,and a given elevation value.For an efficient and reliable processing of multiple remote sensing images,the proposed matching method was performed via a coarse-to-fine strategy through image pyramid.Three sets of airborne remote sensing images were used to evaluate the performance of the proposed method.Results reveal that the multi-view image matching can improve matching reliability.Moreover,the experimental results show that the proposed method performs better than traditional methods.
文摘针对聚类中的多视角和可解释的问题,提出多视角生成模型的可解释性聚类算法(interpretable clustering with multi-view generative model,ICMG).ICMG能够产生多个视角的聚类划分,并通过视角的语义信息对聚类结果进行定性和定量地解释.首先,构建一种多视角生成模型(multi-view generative model,MGM),该模型使用贝叶斯程序学习(Bayesian program learning,BPL)和嵌入多视角因素的贝叶斯案例模型(multi-view Bayesian case model,MBCM)生成多个视角.其次,基于视角的匹配度进行聚类得到多种聚类方案.最后使用视角的原型和子空间所附带的语义信息定性和定量地解释聚类结果.实验结果表明:ICMG能够得到多种可解释的聚类结果,相比于传统多视角聚类算法具有较明显的优势.
文摘Log-linear models and more recently neural network models used forsupervised relation extraction requires substantial amounts of training data andtime, limiting the portability to new relations and domains. To this end, we propose a training representation based on the dependency paths between entities in adependency tree which we call lexicalized dependency paths (LDPs). We showthat this representation is fast, efficient and transparent. We further propose representations utilizing entity types and its subtypes to refine our model and alleviatethe data sparsity problem. We apply lexicalized dependency paths to supervisedlearning using the ACE corpus and show that it can achieve similar performancelevel to other state-of-the-art methods and even surpass them on severalcategories.
基金Supported by National Institute of General Medical Sciences of the National Institutes of Health,No.R01GM100387
文摘AIM To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.METHODS Bayesian rule learning(BRL) is a rule-based classifier that uses a greedy best-first search over a space of Bayesian belief-networks(BN) to find the optimal BN to explain the input dataset, and then infers classification rules from this BN. BRL uses a Bayesian score to evaluate the quality of BNs. In this paper, we extended the Bayesian score to include informative structure priors, which encodes our prior domain knowledge about the dataset. We call this extension of BRL as BRL_p. The structure prior has a λ hyperparameter that allows the user to tune the degree of incorporation of the prior knowledge in the model learning process. We studied the effect of λ on model learning using a simulated dataset and a real-world lung cancer prognostic biomarker dataset, by measuring the degree of incorporation of our specified prior knowledge. We also monitored its effect on the model predictive performance. Finally, we compared BRL_p to other stateof-the-art classifiers commonly used in biomedicine.RESULTS We evaluated the degree of incorporation of prior knowledge into BRL_p, with simulated data by measuring the Graph Edit Distance between the true datagenerating model and the model learned by BRL_p. We specified the true model using informative structurepriors. We observed that by increasing the value of λ we were able to increase the influence of the specified structure priors on model learning. A large value of λ of BRL_p caused it to return the true model. This also led to a gain in predictive performance measured by area under the receiver operator characteristic curve(AUC). We then obtained a publicly available real-world lung cancer prognostic biomarker dataset and specified a known biomarker from literature [the epidermal growth factor receptor(EGFR) gene]. We again observed that larger values of λ led to an increased incorporation of EGFR into the final BRL_p model. This relevant background knowledge also led to a gain in AUC.CONCLUSION BRL_p enables tunable structure priors to be incorporated during Bayesian classification rule learning that integrates data and knowledge as demonstrated using lung cancer biomarker data.
文摘In order to make system reliable, it should inhibit guarantee for basic service, data flow, composition of services, and the complete workflow. In service-oriented architecture (SOA), the entire software system consists of an interacting group of autonomous services. Some soft computing approaches have been developed for estimating the reliability of service oriented systems (SOSs). Still much more research is expected to estimate reliability in a better way. In this paper, we proposed SoS reliability based on an adaptive neuro fuzzy inference system (ANFIS) approach. We estimated the reliability based on some defined parameter. Moreover, we compared its performance with a plain FIS (fuzzy inference system) for similar data sets and found the proposed approach gives better reliability estimation.
