In this paper, by considering the fuzzy nature of the data in real-life problems, single machine scheduling problems with fuzzy processing time and multiple objectives are formulated and an efficient genetic algorithm...In this paper, by considering the fuzzy nature of the data in real-life problems, single machine scheduling problems with fuzzy processing time and multiple objectives are formulated and an efficient genetic algorithm which is suitable for solving these problems is proposed. As illustrative numerical examples, twenty jobs processing on a machine is considered. The feasibility and effectiveness of the proposed method have been demonstrated in the simulation.展开更多
We consider a scheduling problem involving a single processor utilized by two customers with constant deteriorating jobs, i.e., jobs whose processing times are an increasing function of their starting times. Tradition...We consider a scheduling problem involving a single processor utilized by two customers with constant deteriorating jobs, i.e., jobs whose processing times are an increasing function of their starting times. Traditionally, such scenarios are modeled by assuming that each customer has the same criterion. In practice, this assumption may not hold. Instead of using a single criterion, we examine the implications of minimizing an aggregate scheduling objective function in which jobs belonging to different customers are evaluated with their individual criteria. We examine three basic scheduling criteria： minimizing makespan, minimizing maximum lateness, and minimizing total weighted completion time. We demonstrate all the scheduling problems considered are polynomially solvable.展开更多
Climate change is the most serious causes and has a direct impact on biodiversity.According to the world’s biodiversity conservation organization,rep-tile species are most affected since their biological and ecologic...Climate change is the most serious causes and has a direct impact on biodiversity.According to the world’s biodiversity conservation organization,rep-tile species are most affected since their biological and ecological qualities are directly linked to climate.Due to a lack of time frame in existing works,conser-vation adoption affects the performance of existing works.The proposed research presents a knowledge-driven Decision Support System(DSS)including the assisted translocation to adapt to future climate change to conserving from its extinction.The Dynamic approach is used to develop a knowledge-driven DSS using machine learning by applying an ecological and biological variable that characterizes the model and mitigation processes for species.However,the frame-work demonstrates the huge difference in the estimated significance of climate change,the model strategy helps to recognize the probable risk of threatened spe-cies translocation to future climate change.The proposed system is evaluated using various performance metrics and this framework can comfortably adapt to the decisions support to reintroduce the species for conservation in the future.展开更多
The Proportion of animal skidding in forest operations in Heilongjiang forest region increased signiticantly in recent years. This paper at first demonstrated and analyzed the development of the animal skidding and ma...The Proportion of animal skidding in forest operations in Heilongjiang forest region increased signiticantly in recent years. This paper at first demonstrated and analyzed the development of the animal skidding and machine skidding, then, evaluated these two means of ground.skidding currently runing in this region both individually and comprehensively under the following criteria: operation efficiency or operation cost, degree of damage to soil and residual stands, accident rate, and natural regeneration. Finally, according to the results of synthetic assessments, cIassitications of operation conditions suitable to each of skidding measures were recommended quqntitatively with considerations of multiple evaluation criteria.展开更多
In this paper,we provide a new approach for intelligent traffic transportation in the intelligent vehicular networks,which aims at collecting the vehicles’locations,trajectories and other key driving parameters for t...In this paper,we provide a new approach for intelligent traffic transportation in the intelligent vehicular networks,which aims at collecting the vehicles’locations,trajectories and other key driving parameters for the time-critical autonomous driving’s requirement.The key of our method is a multi-vehicle tracking framework in the traffic monitoring scenario..Our proposed framework is composed of three modules:multi-vehicle detection,multi-vehicle association and miss-detected vehicle tracking.For the first module,we integrate self-attention mechanism into detector of using key point estimation for better detection effect.For the second module,we apply the multi-dimensional information for robustness promotion,including vehicle re-identification(Re-ID)features,historical trajectory information,and spatial position information For the third module,we re-track the miss-detected vehicles with occlusions in the first detection module.Besides,we utilize the asymmetric convolution and depth-wise separable convolution to reduce the model’s parameters for speed-up.Extensive experimental results show the effectiveness of our proposed multi-vehicle tracking framework.展开更多
The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, th...The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, the high detail and volume of information provided actually encumbers the automation of the mapping process, at least for the level of automation required to map systematically wildfires on a national level. This paper proposes a fully automated methodology for mapping burn scars using Sentinel-2 data. Information extracted from a pair of Sentinel-2 images, one pre-fire and one post-fire, is jointly used to automatically label a set of training patterns via two empirical rules. An initial pixel-based classification is derived using this training set by means of a Support Vector Machine (SVM) classifier. The latter is subsequently smoothed following a multiple spectral-spatial classification (MSSC) approach, which increases the mapping accuracy and thematic consistency of the final burned area delineation. The proposed methodology was tested on six recent wildfire events in Greece, selected to cover representative cases of the Greek ecosystems and to present challenges in burned area mapping. The lowest classification accuracy achieved was 92%, whereas Matthews correlation coefficient (MCC) was greater or equal to 0.85.展开更多
Several parallel sorting techniques on different architectures have been studied for many years. Due to the need for faster systems in today＇s world, parallelism can be used to accelerate applications. Nowadays, para...Several parallel sorting techniques on different architectures have been studied for many years. Due to the need for faster systems in today＇s world, parallelism can be used to accelerate applications. Nowadays, parallel operations are used to solve computer problems such as sort and search, which result in a reasonable speed. Sorting is one of the most important operations in computing world. The authors always try to find the best in different areas which the premier is speedup. In this paper, the authors issued a sort with O（logn） time complexity on PRAM EREW （Parallel Random Access Machine Exclusive Read Exclusive Write）. The algorithm is designed in a manner that keeps the tradeoff between the number of processor elements in the architecture and execution time. The simulation of the algorithm proves the theoretical analysis of the algorithm. The results of this research can be utilized in developing faster embedded systems. Sorting on Centralized Diamond （SOCD） algorithm is issued on the novel Centralized Diamond architecture which takes the advantages of Single Instruction Multiple Data （SIMD） architecture. This architecture and the sort on it are intuitive and optimal.展开更多
基金supported by the National Natural Science Foundation of China(NNSFC)(the grant No.60274043)supported by the National High-tech Research&Development Project(863)(the grant No.2002AA412610)
文摘In this paper, by considering the fuzzy nature of the data in real-life problems, single machine scheduling problems with fuzzy processing time and multiple objectives are formulated and an efficient genetic algorithm which is suitable for solving these problems is proposed. As illustrative numerical examples, twenty jobs processing on a machine is considered. The feasibility and effectiveness of the proposed method have been demonstrated in the simulation.
文摘We consider a scheduling problem involving a single processor utilized by two customers with constant deteriorating jobs, i.e., jobs whose processing times are an increasing function of their starting times. Traditionally, such scenarios are modeled by assuming that each customer has the same criterion. In practice, this assumption may not hold. Instead of using a single criterion, we examine the implications of minimizing an aggregate scheduling objective function in which jobs belonging to different customers are evaluated with their individual criteria. We examine three basic scheduling criteria： minimizing makespan, minimizing maximum lateness, and minimizing total weighted completion time. We demonstrate all the scheduling problems considered are polynomially solvable.
文摘Climate change is the most serious causes and has a direct impact on biodiversity.According to the world’s biodiversity conservation organization,rep-tile species are most affected since their biological and ecological qualities are directly linked to climate.Due to a lack of time frame in existing works,conser-vation adoption affects the performance of existing works.The proposed research presents a knowledge-driven Decision Support System(DSS)including the assisted translocation to adapt to future climate change to conserving from its extinction.The Dynamic approach is used to develop a knowledge-driven DSS using machine learning by applying an ecological and biological variable that characterizes the model and mitigation processes for species.However,the frame-work demonstrates the huge difference in the estimated significance of climate change,the model strategy helps to recognize the probable risk of threatened spe-cies translocation to future climate change.The proposed system is evaluated using various performance metrics and this framework can comfortably adapt to the decisions support to reintroduce the species for conservation in the future.
文摘The Proportion of animal skidding in forest operations in Heilongjiang forest region increased signiticantly in recent years. This paper at first demonstrated and analyzed the development of the animal skidding and machine skidding, then, evaluated these two means of ground.skidding currently runing in this region both individually and comprehensively under the following criteria: operation efficiency or operation cost, degree of damage to soil and residual stands, accident rate, and natural regeneration. Finally, according to the results of synthetic assessments, cIassitications of operation conditions suitable to each of skidding measures were recommended quqntitatively with considerations of multiple evaluation criteria.
基金This work was supported in part by the Beijing Natural Science Foundation(L191004)the National Natural Science Foundation of China under No.61720106007 and No.61872047+1 种基金the Beijing Nova Program under No.Z201100006820124the Funds for Cre ative Research Groups of China under No.61921003,and the 111 Project(B18008).
文摘In this paper,we provide a new approach for intelligent traffic transportation in the intelligent vehicular networks,which aims at collecting the vehicles’locations,trajectories and other key driving parameters for the time-critical autonomous driving’s requirement.The key of our method is a multi-vehicle tracking framework in the traffic monitoring scenario..Our proposed framework is composed of three modules:multi-vehicle detection,multi-vehicle association and miss-detected vehicle tracking.For the first module,we integrate self-attention mechanism into detector of using key point estimation for better detection effect.For the second module,we apply the multi-dimensional information for robustness promotion,including vehicle re-identification(Re-ID)features,historical trajectory information,and spatial position information For the third module,we re-track the miss-detected vehicles with occlusions in the first detection module.Besides,we utilize the asymmetric convolution and depth-wise separable convolution to reduce the model’s parameters for speed-up.Extensive experimental results show the effectiveness of our proposed multi-vehicle tracking framework.
文摘The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, the high detail and volume of information provided actually encumbers the automation of the mapping process, at least for the level of automation required to map systematically wildfires on a national level. This paper proposes a fully automated methodology for mapping burn scars using Sentinel-2 data. Information extracted from a pair of Sentinel-2 images, one pre-fire and one post-fire, is jointly used to automatically label a set of training patterns via two empirical rules. An initial pixel-based classification is derived using this training set by means of a Support Vector Machine (SVM) classifier. The latter is subsequently smoothed following a multiple spectral-spatial classification (MSSC) approach, which increases the mapping accuracy and thematic consistency of the final burned area delineation. The proposed methodology was tested on six recent wildfire events in Greece, selected to cover representative cases of the Greek ecosystems and to present challenges in burned area mapping. The lowest classification accuracy achieved was 92%, whereas Matthews correlation coefficient (MCC) was greater or equal to 0.85.
文摘Several parallel sorting techniques on different architectures have been studied for many years. Due to the need for faster systems in today＇s world, parallelism can be used to accelerate applications. Nowadays, parallel operations are used to solve computer problems such as sort and search, which result in a reasonable speed. Sorting is one of the most important operations in computing world. The authors always try to find the best in different areas which the premier is speedup. In this paper, the authors issued a sort with O（logn） time complexity on PRAM EREW （Parallel Random Access Machine Exclusive Read Exclusive Write）. The algorithm is designed in a manner that keeps the tradeoff between the number of processor elements in the architecture and execution time. The simulation of the algorithm proves the theoretical analysis of the algorithm. The results of this research can be utilized in developing faster embedded systems. Sorting on Centralized Diamond （SOCD） algorithm is issued on the novel Centralized Diamond architecture which takes the advantages of Single Instruction Multiple Data （SIMD） architecture. This architecture and the sort on it are intuitive and optimal.
