Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annu...Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing map(SOM) artificial neural network(ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product(GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANNis suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.展开更多
Due to rapid development in software industry, it was necessary to reduce time and efforts in the software development process. Software Reusability is an important measure that can be applied to improve software deve...Due to rapid development in software industry, it was necessary to reduce time and efforts in the software development process. Software Reusability is an important measure that can be applied to improve software development and software quality. Reusability reduces time, effort, errors, and hence the overall cost of the development process. Reusability prediction models are established in the early stage of the system development cycle to support an early reusability assessment. In Object-Oriented systems, Reusability of software components (classes) can be obtained by investigating its metrics values. Analyzing software metric values can help to avoid developing components from scratch. In this paper, we use Chidamber and Kemerer (CK) metrics suite in order to identify the reuse level of object-oriented classes. Self-Organizing Map (SOM) was used to cluster datasets of CK metrics values that were extracted from three different java-based systems. The goal was to find the relationship between CK metrics values and the reusability level of the class. The reusability level of the class was classified into three main categorizes (High Reusable, Medium Reusable and Low Reusable). The clustering was based on metrics threshold values that were used to achieve the experiments. The proposed methodology succeeds in classifying classes to their reusability level (High Reusable, Medium Reusable and Low Reusable). The experiments show how SOM can be applied on software CK metrics with different sizes of SOM grids to provide different levels of metrics details. The results show that Depth of Inheritance Tree (DIT) and Number of Children (NOC) metrics dominated the clustering process, so these two metrics were discarded from the experiments to achieve a successful clustering. The most efficient SOM topology [2 × 2] grid size is used to predict the reusability of classes.展开更多
We investigated the intraseasonal variability of equatorial Pacific subsurface temperature and its relationship with El Nino-Southern Oscillation(ENSO) using Self-Organizing Maps(SOM) analysis.Variation in intraseason...We investigated the intraseasonal variability of equatorial Pacific subsurface temperature and its relationship with El Nino-Southern Oscillation(ENSO) using Self-Organizing Maps(SOM) analysis.Variation in intraseasonal subsurface temperature is mainly found along the thermocline.The SOM patterns concentrate in basin-wide seesaw or sandwich structures along an east-west axis.Both the seesaw and sandwich SOM patterns oscillate with periods of 55 to 90 days,with the sequence of them showing features of equatorial intraseasonal Kelvin wave,and have marked interannual variations in their occurrence frequencies.Further examination shows that the interannual variability of the SOM patterns is closely related to ENSO;and maxima in composite interannual variability of the SOM patterns are located in the central Pacific during CP El Nino and in the eastern Pacific during EP El Nino.The se results imply that some of the ENSO forcing is manife sted through changes in the occurrence frequency of intraseasonal patterns,in which the change of the intraseasonal Kelvin wave plays an important role.展开更多
Self-organizing map(SOM) proposed by Kohonen has obtained certain achievements in solving the traveling salesman problem(TSP).To improve Kohonen SOM,an effective initialization and parameter modification method is dis...Self-organizing map(SOM) proposed by Kohonen has obtained certain achievements in solving the traveling salesman problem(TSP).To improve Kohonen SOM,an effective initialization and parameter modification method is discussed to obtain a faster convergence rate and better solution.Therefore,a new improved self-organizing map(ISOM)algorithm is introduced and applied to four traveling salesman problem instances for experimental simulation,and then the result of ISOM is compared with those of four SOM algorithms:AVL,KL,KG and MSTSP.Using ISOM,the average error of four travelingsalesman problem instances is only 2.895 0%,which is greatly better than the other four algorithms:8.51%(AVL),6.147 5%(KL),6.555%(KG) and 3.420 9%(MSTSP).Finally,ISOM is applied to two practical problems:the Chinese 100 cities-TSP and102 counties-TSP in Shanxi Province,and the two optimal touring routes are provided to the tourists.展开更多
To overcome the problem that a single feature can not reflect the state of machinery in different stages,a method of vibration feature fusion based on self-organizing map(SOM) is presented.Minimum quantization error(M...To overcome the problem that a single feature can not reflect the state of machinery in different stages,a method of vibration feature fusion based on self-organizing map(SOM) is presented.Minimum quantization error(MQE) is obtained unsupervised based on SOM network.And trend information of the MQE curve is extracted by the wavelet packet to enhance state differentiating.Experimental flat is designed for bearing accelerating fatigue.And experimental results show that the method of vibration feature fusion based on SOM can reflect the state of machinery in different stages effectively.展开更多
This paper reports distinct spatio-spectral properties of Zen-meditation EEG (electroencephalograph), compared with resting EEG, by implementing unsupervised machine learning scheme in clustering the brain mappings of...This paper reports distinct spatio-spectral properties of Zen-meditation EEG (electroencephalograph), compared with resting EEG, by implementing unsupervised machine learning scheme in clustering the brain mappings of centroid frequency (BMFc). Zen practitioners simultaneously concentrate on the third ventricle, hypothalamus and corpora quadrigemina touniversalize all brain neurons to construct a <i>detached</i> brain and gradually change the normal brain traits, leading to the process of brain-neuroplasticity. During such tri-aperture concentration, EEG exhibits prominent diffuse high-frequency oscillations. Unsupervised self-organizing map (SOM), clusters the dataset of quantitative EEG by matching the input feature vector Fc and the output cluster center through the SOM network weights. Input dataset contains brain mappings of 30 centroid frequencies extracted from CWT (continuous wavelet transform) coefficients. According to SOM clustering results, resting EEG is dominated by global low-frequency (<14 Hz) activities, except channels T7, F7 and TP7 (>14.4 Hz);whereas Zen-meditation EEG exhibits globally high-frequency (>16 Hz) activities throughout the entire record. Beta waves with a wide range of frequencies are often associated with active concentration. Nonetheless, clinic report discloses that benzodiazepines, medication treatment for anxiety, insomnia and panic attacks to relieve mind/body stress, often induce <i>beta buzz</i>. We may hypothesize that Zen-meditation practitioners attain the unique state of mindfulness concentration under optimal body-mind relaxation.展开更多
With advancements in technology, personal computing devices are better adapted for and further integrated into people’s lives and homes. The integration of technology into society also results in an increasing desire...With advancements in technology, personal computing devices are better adapted for and further integrated into people’s lives and homes. The integration of technology into society also results in an increasing desire to control who and what has access to sensitive information, especially for vulnerable people including children and the elderly. With blockchain rise as a technology that can revolutionize the world, it is now possible to have an immutable audit trail of locational data over time. By controlling the process through inexpensive equipment in the home, it is possible to control whom has access to such personal data. This paper presents a block-chain based family security system for outdoor tracking and in-house monitoring of users’ activities via sensors to detect anomalies in users’ daily activities with the integration of Artificial Intelligence (AI). For outdoor tracking the locations of the consenting family members’ smart phones are logged and stored in a private blockchain which can be accessed through a node installed in the family home on a computer. The data for the whereabouts and daily activities of family members stays securely within the family unit and does not go to any third-party organizations. A Self-Organizing Maps (SOM) based smart contract is used for anomaly detection in users’ daily activities in a smart home, which notifies emergency contact or other family members in case of anomaly detection. The approach described in this paper contributes to the development of in-house data processing for outdoor tracking, and daily activities monitoring and prediction without any third-party hardware or software. The system is implemented at a small scale with one miner, two user nodes and several device nodes, as a proof of concept;the technical feasibility is discussed along with the limitations of the system. Further research will cover the integration of the system into a smart-home environment with additional sensors and multiple users, and ethical implementations of tracking, especially of vulnerable people, via the immutability of blockchain.展开更多
Recent advances in battery energy storage technologies enable increasing number of photovoltaic-battery energy storage systems(PV-BESS)to be deployed and connected with current power grids.The reliable and efficient u...Recent advances in battery energy storage technologies enable increasing number of photovoltaic-battery energy storage systems(PV-BESS)to be deployed and connected with current power grids.The reliable and efficient utilization of BESS imposes an obvious technical challenge which needs to be urgently addressed.In this paper,the optimal operation of PV-BESS based power plant is investigated.The operational scenarios are firstly partitioned using a self-organizing map(SOM)clustering based approach.The revenue optimization model is adopted for the PV-BESS power plants to determine the optimal operational modes under typical conditions for a set of considerations,e.g.power generation revenue,assessing rewards/penalties as well as peak shaving/valley filling revenue.The solution is evaluated through a set of case studies,and the numerical result demonstrates the effectiveness of the suggested solution can optimally operate the BESS with the maximal revenue.展开更多
Accurate delineation of urban form is essential to understand the impacts that urbanization has on the environment and regional climate.Conventional supervised classification of urban form requires a rigidly defined s...Accurate delineation of urban form is essential to understand the impacts that urbanization has on the environment and regional climate.Conventional supervised classification of urban form requires a rigidly defined scheme and high-quality sample data with class labels.Due to the complexity of urban systems,it is challenging to consistently define urban form types and collect metadata to describe them.Therefore,in this study,we propose a novel unsupervised deep learning method for urban form delineation while avoiding the limitations of conventional super-vised urban form classification methods.The novelty of the proposed method is the Multiscale Residual Convolutional Autoencoder(MRCAE),which can learn the latent representation of differ-ent urban form types.These vectors can be further used to generalize urban form types by using Self-Organizing Map(SOM)and the Gaussian Mixture Model(GMM).The proposed method is applied in the metropolitan area of Guangzhou-Foshan,China.The MRCAE model along with SOM and GMM is used to generalize the urban form types from satellite images.The physical and functional properties of each urban form type are also analyzed using several auxiliary datasets,including building footprints,Points-of-Interests(POIs)and Tencent User Density(TUD)data.The results reveal that the urban form map generated based on the MRCAE can explain 55%of the building height distribution and 55%of the building area distribution,which are 2.1%and 3.3%higher than those derived from the conventional convolutional autoencoder.As the information of urban form is essential to urban climate models,the results presented in this study can become a basis to refine the quantification of urban climate parameters,thereby introducing the urban heterogeneity to help understand the climate response of future urbanization.展开更多
Solar water heating systems have been widely used around the world.However,exposure to sunlight can overheat the device,affecting the efficiency and durability of the system.This article proposes an adaptive deck cont...Solar water heating systems have been widely used around the world.However,exposure to sunlight can overheat the device,affecting the efficiency and durability of the system.This article proposes an adaptive deck controller that protects the system from overheating without compromising the availability of domestic hot water.Solar water heaters are considered one of the most effective ways to reduce a home’s carbon footprint.They are a renewable energy source that reduces reliance on fossil fuels and saves money.Thus,this paper aims to develop a dynamic cover for solar water heaters that prevent overheating using an artificial neural network to optimize the design of control systems.Based on a self-organizing map network,the controller automatically adjusts the temperature of the solar collector through a fabric screen covering the main subsystems,depending on many parameters such as weather conditions,collector temperature and domestic hot water depending on demand.A suggested technique of four different shade percentages(0%,20%,25%or 32%)can avoid overheating and maintain the amount of hot water the home needs.Although renewable energy is free,proper controls are required to ensure maximum efficiency or proper use.In addition,the control of renewable energy leads to longer service life.展开更多
基金supported by the National Key R&D Program of China (GrantN o.2016YFC0401407)National Natural Science Foundation of China (Grant Nos. 51479003 and 51279006)
文摘Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing map(SOM) artificial neural network(ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product(GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANNis suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.
文摘Due to rapid development in software industry, it was necessary to reduce time and efforts in the software development process. Software Reusability is an important measure that can be applied to improve software development and software quality. Reusability reduces time, effort, errors, and hence the overall cost of the development process. Reusability prediction models are established in the early stage of the system development cycle to support an early reusability assessment. In Object-Oriented systems, Reusability of software components (classes) can be obtained by investigating its metrics values. Analyzing software metric values can help to avoid developing components from scratch. In this paper, we use Chidamber and Kemerer (CK) metrics suite in order to identify the reuse level of object-oriented classes. Self-Organizing Map (SOM) was used to cluster datasets of CK metrics values that were extracted from three different java-based systems. The goal was to find the relationship between CK metrics values and the reusability level of the class. The reusability level of the class was classified into three main categorizes (High Reusable, Medium Reusable and Low Reusable). The clustering was based on metrics threshold values that were used to achieve the experiments. The proposed methodology succeeds in classifying classes to their reusability level (High Reusable, Medium Reusable and Low Reusable). The experiments show how SOM can be applied on software CK metrics with different sizes of SOM grids to provide different levels of metrics details. The results show that Depth of Inheritance Tree (DIT) and Number of Children (NOC) metrics dominated the clustering process, so these two metrics were discarded from the experiments to achieve a successful clustering. The most efficient SOM topology [2 × 2] grid size is used to predict the reusability of classes.
基金the National Natural Science Foundation of China (NSFC)(Nos.41976027,41976011,41730534,41476017,41576014)the Bureau of International Cooperation Chinese Academy of Sciences (No.132B61KYSB20170005)
文摘We investigated the intraseasonal variability of equatorial Pacific subsurface temperature and its relationship with El Nino-Southern Oscillation(ENSO) using Self-Organizing Maps(SOM) analysis.Variation in intraseasonal subsurface temperature is mainly found along the thermocline.The SOM patterns concentrate in basin-wide seesaw or sandwich structures along an east-west axis.Both the seesaw and sandwich SOM patterns oscillate with periods of 55 to 90 days,with the sequence of them showing features of equatorial intraseasonal Kelvin wave,and have marked interannual variations in their occurrence frequencies.Further examination shows that the interannual variability of the SOM patterns is closely related to ENSO;and maxima in composite interannual variability of the SOM patterns are located in the central Pacific during CP El Nino and in the eastern Pacific during EP El Nino.The se results imply that some of the ENSO forcing is manife sted through changes in the occurrence frequency of intraseasonal patterns,in which the change of the intraseasonal Kelvin wave plays an important role.
文摘Self-organizing map(SOM) proposed by Kohonen has obtained certain achievements in solving the traveling salesman problem(TSP).To improve Kohonen SOM,an effective initialization and parameter modification method is discussed to obtain a faster convergence rate and better solution.Therefore,a new improved self-organizing map(ISOM)algorithm is introduced and applied to four traveling salesman problem instances for experimental simulation,and then the result of ISOM is compared with those of four SOM algorithms:AVL,KL,KG and MSTSP.Using ISOM,the average error of four travelingsalesman problem instances is only 2.895 0%,which is greatly better than the other four algorithms:8.51%(AVL),6.147 5%(KL),6.555%(KG) and 3.420 9%(MSTSP).Finally,ISOM is applied to two practical problems:the Chinese 100 cities-TSP and102 counties-TSP in Shanxi Province,and the two optimal touring routes are provided to the tourists.
文摘To overcome the problem that a single feature can not reflect the state of machinery in different stages,a method of vibration feature fusion based on self-organizing map(SOM) is presented.Minimum quantization error(MQE) is obtained unsupervised based on SOM network.And trend information of the MQE curve is extracted by the wavelet packet to enhance state differentiating.Experimental flat is designed for bearing accelerating fatigue.And experimental results show that the method of vibration feature fusion based on SOM can reflect the state of machinery in different stages effectively.
文摘This paper reports distinct spatio-spectral properties of Zen-meditation EEG (electroencephalograph), compared with resting EEG, by implementing unsupervised machine learning scheme in clustering the brain mappings of centroid frequency (BMFc). Zen practitioners simultaneously concentrate on the third ventricle, hypothalamus and corpora quadrigemina touniversalize all brain neurons to construct a <i>detached</i> brain and gradually change the normal brain traits, leading to the process of brain-neuroplasticity. During such tri-aperture concentration, EEG exhibits prominent diffuse high-frequency oscillations. Unsupervised self-organizing map (SOM), clusters the dataset of quantitative EEG by matching the input feature vector Fc and the output cluster center through the SOM network weights. Input dataset contains brain mappings of 30 centroid frequencies extracted from CWT (continuous wavelet transform) coefficients. According to SOM clustering results, resting EEG is dominated by global low-frequency (<14 Hz) activities, except channels T7, F7 and TP7 (>14.4 Hz);whereas Zen-meditation EEG exhibits globally high-frequency (>16 Hz) activities throughout the entire record. Beta waves with a wide range of frequencies are often associated with active concentration. Nonetheless, clinic report discloses that benzodiazepines, medication treatment for anxiety, insomnia and panic attacks to relieve mind/body stress, often induce <i>beta buzz</i>. We may hypothesize that Zen-meditation practitioners attain the unique state of mindfulness concentration under optimal body-mind relaxation.
基金This research was supported by School of ICT,University of Tasmania,Sandy BayWe thank the anonymous reviewers whose comments/suggestions helped improve the quality of this manuscript.
文摘With advancements in technology, personal computing devices are better adapted for and further integrated into people’s lives and homes. The integration of technology into society also results in an increasing desire to control who and what has access to sensitive information, especially for vulnerable people including children and the elderly. With blockchain rise as a technology that can revolutionize the world, it is now possible to have an immutable audit trail of locational data over time. By controlling the process through inexpensive equipment in the home, it is possible to control whom has access to such personal data. This paper presents a block-chain based family security system for outdoor tracking and in-house monitoring of users’ activities via sensors to detect anomalies in users’ daily activities with the integration of Artificial Intelligence (AI). For outdoor tracking the locations of the consenting family members’ smart phones are logged and stored in a private blockchain which can be accessed through a node installed in the family home on a computer. The data for the whereabouts and daily activities of family members stays securely within the family unit and does not go to any third-party organizations. A Self-Organizing Maps (SOM) based smart contract is used for anomaly detection in users’ daily activities in a smart home, which notifies emergency contact or other family members in case of anomaly detection. The approach described in this paper contributes to the development of in-house data processing for outdoor tracking, and daily activities monitoring and prediction without any third-party hardware or software. The system is implemented at a small scale with one miner, two user nodes and several device nodes, as a proof of concept;the technical feasibility is discussed along with the limitations of the system. Further research will cover the integration of the system into a smart-home environment with additional sensors and multiple users, and ethical implementations of tracking, especially of vulnerable people, via the immutability of blockchain.
文摘Recent advances in battery energy storage technologies enable increasing number of photovoltaic-battery energy storage systems(PV-BESS)to be deployed and connected with current power grids.The reliable and efficient utilization of BESS imposes an obvious technical challenge which needs to be urgently addressed.In this paper,the optimal operation of PV-BESS based power plant is investigated.The operational scenarios are firstly partitioned using a self-organizing map(SOM)clustering based approach.The revenue optimization model is adopted for the PV-BESS power plants to determine the optimal operational modes under typical conditions for a set of considerations,e.g.power generation revenue,assessing rewards/penalties as well as peak shaving/valley filling revenue.The solution is evaluated through a set of case studies,and the numerical result demonstrates the effectiveness of the suggested solution can optimally operate the BESS with the maximal revenue.
基金supported by the National Key R&D Program of China[grant number 2019YFA0607201 and 2017YFA0604401]the National Natural Science Foundation of China[grant number 41871306]+1 种基金the Guangdong Natural Science Funds for Distinguished Young Scholar[grant number 2021B1515020104]the Fundamental Research Funds for the Central Universities[grant number 20lgzd09].
文摘Accurate delineation of urban form is essential to understand the impacts that urbanization has on the environment and regional climate.Conventional supervised classification of urban form requires a rigidly defined scheme and high-quality sample data with class labels.Due to the complexity of urban systems,it is challenging to consistently define urban form types and collect metadata to describe them.Therefore,in this study,we propose a novel unsupervised deep learning method for urban form delineation while avoiding the limitations of conventional super-vised urban form classification methods.The novelty of the proposed method is the Multiscale Residual Convolutional Autoencoder(MRCAE),which can learn the latent representation of differ-ent urban form types.These vectors can be further used to generalize urban form types by using Self-Organizing Map(SOM)and the Gaussian Mixture Model(GMM).The proposed method is applied in the metropolitan area of Guangzhou-Foshan,China.The MRCAE model along with SOM and GMM is used to generalize the urban form types from satellite images.The physical and functional properties of each urban form type are also analyzed using several auxiliary datasets,including building footprints,Points-of-Interests(POIs)and Tencent User Density(TUD)data.The results reveal that the urban form map generated based on the MRCAE can explain 55%of the building height distribution and 55%of the building area distribution,which are 2.1%and 3.3%higher than those derived from the conventional convolutional autoencoder.As the information of urban form is essential to urban climate models,the results presented in this study can become a basis to refine the quantification of urban climate parameters,thereby introducing the urban heterogeneity to help understand the climate response of future urbanization.
基金The authors would like to thank the Higher Center for Research(HCR)of the Holy Spirit University of Kaslik(USEK)the National Council for Scientific Research in Lebanon(CNRS-L)for funding this project。
文摘Solar water heating systems have been widely used around the world.However,exposure to sunlight can overheat the device,affecting the efficiency and durability of the system.This article proposes an adaptive deck controller that protects the system from overheating without compromising the availability of domestic hot water.Solar water heaters are considered one of the most effective ways to reduce a home’s carbon footprint.They are a renewable energy source that reduces reliance on fossil fuels and saves money.Thus,this paper aims to develop a dynamic cover for solar water heaters that prevent overheating using an artificial neural network to optimize the design of control systems.Based on a self-organizing map network,the controller automatically adjusts the temperature of the solar collector through a fabric screen covering the main subsystems,depending on many parameters such as weather conditions,collector temperature and domestic hot water depending on demand.A suggested technique of four different shade percentages(0%,20%,25%or 32%)can avoid overheating and maintain the amount of hot water the home needs.Although renewable energy is free,proper controls are required to ensure maximum efficiency or proper use.In addition,the control of renewable energy leads to longer service life.
