Climatic and atmospheric properties vary significantly within a small area for a topographically diverse region like Nepal.Remote sensing can be used for large-scale monitoring of atmospheric parameters in such divers...Climatic and atmospheric properties vary significantly within a small area for a topographically diverse region like Nepal.Remote sensing can be used for large-scale monitoring of atmospheric parameters in such diverse terrains.This work evaluates the Landsat-based METRIC(Mapping Evapotranspiration at High Resolution with Internalized Calibration)model for estimating Evapotranspiration(ET)in Nepal.The slope and aspect of terrain are accounted for in our implementation,making the model suitable for regions with topographical variations.The estimations obtained from the model were compared with ground-based measurements.The root-meansquare error for hourly ET(daily ET)was 0.06 mm h-1(1.24 mm d-1),while the mean bias error was0.03 mm h-1(0.29 mm d-1).These results are comparable with results from other studies in the literature that have used the METRIC model for different regions of the world.Thus,this work validates the applicability of the METRIC model for ET estimation in a mountainous area like Nepal.Further,this implementation provides ET estimation at a very high resolution of 30 m compared to the best available resolution of 5 km in earlier works,without compromising on the accuracy.ET estimation with high resolution over a large region in Nepal has applications in agricultural planning and monitoring,among others.展开更多
Person re-identification （re-id） on robot platform is an important application for human-robot- interaction （HRI）, which aims at making the robot recognize the around persons in varying scenes. Although many effec...Person re-identification （re-id） on robot platform is an important application for human-robot- interaction （HRI）, which aims at making the robot recognize the around persons in varying scenes. Although many effective methods have been proposed for surveillance re-id in recent years, re-id on robot platform is still a novel unsolved problem. Most existing methods adapt the supervised metric learning offline to improve the accuracy. However, these methods can not adapt to unknown scenes. To solve this problem, an online re-id framework is proposed. Considering that robotics can afford to use high-resolution RGB-D sensors and clear human face may be captured, face information is used to update the metric model. Firstly, the metric model is pre-trained offline using labeled data. Then during the online stage, we use face information to mine incorrect body matching pairs which are collected to update the metric model online. In addition, to make full use of both appearance and skeleton information provided by RGB-D sensors, a novel feature funnel model （FFM） is proposed. Comparison studies show our approach is more effective and adaptable to varying environments.展开更多
This paper analyzes the semantics structure of enterprise process metric and gives guidelines to describe the semantics of metric for collecting metric data automatically. Based on domain ontology, a structure called ...This paper analyzes the semantics structure of enterprise process metric and gives guidelines to describe the semantics of metric for collecting metric data automatically. Based on domain ontology, a structure called semantic tree is defined to de- scribe the semantics relationships among measured entity, meas- urable attribute and constraints, which provides the same method to define semantics of process metrics and data elements in enterprise information model. The arithmetic to map process metrics to enterprise information model is put forward, which can compute the query conditions to retrieve process metrics data from enterprise information systems accurately. This arithmetic has been applied in an information project.展开更多
In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making d...In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making decisions based on the extracted knowledge is becoming increasingly important in all business domains. Nevertheless, high-dimensional data remains a major challenge for classification algorithms due to its high computational cost and storage requirements. The 2016 Demographic and Health Survey of Ethiopia (EDHS 2016) used as the data source for this study which is publicly available contains several features that may not be relevant to the prediction task. In this paper, we developed a hybrid multidimensional metrics framework for predictive modeling for both model performance evaluation and feature selection to overcome the feature selection challenges and select the best model among the available models in DM and ML. The proposed hybrid metrics were used to measure the efficiency of the predictive models. Experimental results show that the decision tree algorithm is the most efficient model. The higher score of HMM (m, r) = 0.47 illustrates the overall significant model that encompasses almost all the user’s requirements, unlike the classical metrics that use a criterion to select the most appropriate model. On the other hand, the ANNs were found to be the most computationally intensive for our prediction task. Moreover, the type of data and the class size of the dataset (unbalanced data) have a significant impact on the efficiency of the model, especially on the computational cost, and the interpretability of the parameters of the model would be hampered. And the efficiency of the predictive model could be improved with other feature selection algorithms (especially hybrid metrics) considering the experts of the knowledge domain, as the understanding of the business domain has a significant impact.展开更多
The algorithm of fingerprint constructing for still images based on weighted image structure model is proposed. The error correcting codes that are perfect in weighted Hamming metric are used as a base for fingerprint...The algorithm of fingerprint constructing for still images based on weighted image structure model is proposed. The error correcting codes that are perfect in weighted Hamming metric are used as a base for fingerprint constructing.展开更多
A comparative analysis of deep learning models and traditional statistical methods for stock price prediction uses data from the Nigerian stock exchange. Historical data, including daily prices and trading volumes, ar...A comparative analysis of deep learning models and traditional statistical methods for stock price prediction uses data from the Nigerian stock exchange. Historical data, including daily prices and trading volumes, are employed to implement models such as Long Short Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Autoregressive Integrated Moving Average (ARIMA), and Autoregressive Moving Average (ARMA). These models are assessed over three-time horizons: short-term (1 year), medium-term (2.5 years), and long-term (5 years), with performance measured by Mean Squared Error (MSE) and Mean Absolute Error (MAE). The stability of the time series is tested using the Augmented Dickey-Fuller (ADF) test. Results reveal that deep learning models, particularly LSTM, outperform traditional methods by capturing complex, nonlinear patterns in the data, resulting in more accurate predictions. However, these models require greater computational resources and offer less interpretability than traditional approaches. The findings highlight the potential of deep learning for improving financial forecasting and investment strategies. Future research could incorporate external factors such as social media sentiment and economic indicators, refine model architectures, and explore real-time applications to enhance prediction accuracy and scalability.展开更多
基金funded by the Second Tibetan Plateau Scientific Expedition and Research Program grant number2019QZKK0103the Strategic Priority Research Program of the Chinese Academy of Sciences grant number XDA20060101the National Natural Science Foundation of China grant numbers 41830650,91737205,and 91837208。
文摘Climatic and atmospheric properties vary significantly within a small area for a topographically diverse region like Nepal.Remote sensing can be used for large-scale monitoring of atmospheric parameters in such diverse terrains.This work evaluates the Landsat-based METRIC(Mapping Evapotranspiration at High Resolution with Internalized Calibration)model for estimating Evapotranspiration(ET)in Nepal.The slope and aspect of terrain are accounted for in our implementation,making the model suitable for regions with topographical variations.The estimations obtained from the model were compared with ground-based measurements.The root-meansquare error for hourly ET(daily ET)was 0.06 mm h-1(1.24 mm d-1),while the mean bias error was0.03 mm h-1(0.29 mm d-1).These results are comparable with results from other studies in the literature that have used the METRIC model for different regions of the world.Thus,this work validates the applicability of the METRIC model for ET estimation in a mountainous area like Nepal.Further,this implementation provides ET estimation at a very high resolution of 30 m compared to the best available resolution of 5 km in earlier works,without compromising on the accuracy.ET estimation with high resolution over a large region in Nepal has applications in agricultural planning and monitoring,among others.
基金This work is supported by the National Natural Science Foundation of China （NSFC, nos. 61340046）, the National High Technology Research and Development Programme of China （863 Programme, no. 2006AA04Z247）, the Scientific and Technical Innovation Commission of Shenzhen Municipality （nos. JCYJ20130331144631730）, and the Specialized Research Fund for the Doctoral Programme of Higher Education （SRFDP, no. 20130001110011）.
文摘Person re-identification （re-id） on robot platform is an important application for human-robot- interaction （HRI）, which aims at making the robot recognize the around persons in varying scenes. Although many effective methods have been proposed for surveillance re-id in recent years, re-id on robot platform is still a novel unsolved problem. Most existing methods adapt the supervised metric learning offline to improve the accuracy. However, these methods can not adapt to unknown scenes. To solve this problem, an online re-id framework is proposed. Considering that robotics can afford to use high-resolution RGB-D sensors and clear human face may be captured, face information is used to update the metric model. Firstly, the metric model is pre-trained offline using labeled data. Then during the online stage, we use face information to mine incorrect body matching pairs which are collected to update the metric model online. In addition, to make full use of both appearance and skeleton information provided by RGB-D sensors, a novel feature funnel model （FFM） is proposed. Comparison studies show our approach is more effective and adaptable to varying environments.
基金Supported by the National High Technology Research and Development Program of China (863 Progam) (2006AA09A102-15)the National Major Project of Science and Technology of China (2008ZX05023-05-05)
文摘This paper analyzes the semantics structure of enterprise process metric and gives guidelines to describe the semantics of metric for collecting metric data automatically. Based on domain ontology, a structure called semantic tree is defined to de- scribe the semantics relationships among measured entity, meas- urable attribute and constraints, which provides the same method to define semantics of process metrics and data elements in enterprise information model. The arithmetic to map process metrics to enterprise information model is put forward, which can compute the query conditions to retrieve process metrics data from enterprise information systems accurately. This arithmetic has been applied in an information project.
文摘In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making decisions based on the extracted knowledge is becoming increasingly important in all business domains. Nevertheless, high-dimensional data remains a major challenge for classification algorithms due to its high computational cost and storage requirements. The 2016 Demographic and Health Survey of Ethiopia (EDHS 2016) used as the data source for this study which is publicly available contains several features that may not be relevant to the prediction task. In this paper, we developed a hybrid multidimensional metrics framework for predictive modeling for both model performance evaluation and feature selection to overcome the feature selection challenges and select the best model among the available models in DM and ML. The proposed hybrid metrics were used to measure the efficiency of the predictive models. Experimental results show that the decision tree algorithm is the most efficient model. The higher score of HMM (m, r) = 0.47 illustrates the overall significant model that encompasses almost all the user’s requirements, unlike the classical metrics that use a criterion to select the most appropriate model. On the other hand, the ANNs were found to be the most computationally intensive for our prediction task. Moreover, the type of data and the class size of the dataset (unbalanced data) have a significant impact on the efficiency of the model, especially on the computational cost, and the interpretability of the parameters of the model would be hampered. And the efficiency of the predictive model could be improved with other feature selection algorithms (especially hybrid metrics) considering the experts of the knowledge domain, as the understanding of the business domain has a significant impact.
文摘The algorithm of fingerprint constructing for still images based on weighted image structure model is proposed. The error correcting codes that are perfect in weighted Hamming metric are used as a base for fingerprint constructing.
文摘A comparative analysis of deep learning models and traditional statistical methods for stock price prediction uses data from the Nigerian stock exchange. Historical data, including daily prices and trading volumes, are employed to implement models such as Long Short Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Autoregressive Integrated Moving Average (ARIMA), and Autoregressive Moving Average (ARMA). These models are assessed over three-time horizons: short-term (1 year), medium-term (2.5 years), and long-term (5 years), with performance measured by Mean Squared Error (MSE) and Mean Absolute Error (MAE). The stability of the time series is tested using the Augmented Dickey-Fuller (ADF) test. Results reveal that deep learning models, particularly LSTM, outperform traditional methods by capturing complex, nonlinear patterns in the data, resulting in more accurate predictions. However, these models require greater computational resources and offer less interpretability than traditional approaches. The findings highlight the potential of deep learning for improving financial forecasting and investment strategies. Future research could incorporate external factors such as social media sentiment and economic indicators, refine model architectures, and explore real-time applications to enhance prediction accuracy and scalability.
