Single-photon sensors are novel devices with extremely high single-photon sensitivity and temporal resolution.However,these advantages also make them highly susceptible to noise.Moreover,single-photon cameras face sev...Single-photon sensors are novel devices with extremely high single-photon sensitivity and temporal resolution.However,these advantages also make them highly susceptible to noise.Moreover,single-photon cameras face severe quantization as low as 1 bit/frame.These factors make it a daunting task to recover high-quality scene information from noisy single-photon data.Most current image reconstruction methods for single-photon data are mathematical approaches,which limits information utilization and algorithm performance.In this work,we propose a hybrid information enhancement model which can significantly enhance the efficiency of information utilization by leveraging attention mechanisms from both spatial and channel branches.Furthermore,we introduce a structural feature enhance module for the FFN of the transformer,which explicitly improves the model's ability to extract and enhance high-frequency structural information through two symmetric convolution branches.Additionally,we propose a single-photon data simulation pipeline based on RAW images to address the challenge of the lack of single-photon datasets.Experimental results show that the proposed method outperforms state-of-the-art methods in various noise levels and exhibits a more efficient capability for recovering high-frequency structures and extracting information.展开更多
Pipeline defect detection systems collect the videos from cameras of pipeline robots,however the systems always analyzed these videos by offline systems or humans to detect the defects of potential security threats.Th...Pipeline defect detection systems collect the videos from cameras of pipeline robots,however the systems always analyzed these videos by offline systems or humans to detect the defects of potential security threats.The existing systems tend to reach the limit in terms of data access anywhere,access security and video processing on cloud.There is in need of studying on a pipeline defect detection cloud system for automatic pipeline inspection.In this paper,we deploy the framework of a cloud based pipeline defect detection system,including the user management module,pipeline robot control module,system service module,and defect detection module.In the system,we use a role encryption scheme for video collection,data uploading,and access security,and propose a hybrid information method for defect detection.The experimental results show that our approach is a scalable and efficient defection detection cloud system.展开更多
The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attr...The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.展开更多
Hybrid systems consisting of superconducting circuits and magnon systems are a promising platform for quantum technology.However,realizing high-fidelity magnon state preparation and manipulation remains an outstanding...Hybrid systems consisting of superconducting circuits and magnon systems are a promising platform for quantum technology.However,realizing high-fidelity magnon state preparation and manipulation remains an outstanding challenge due to the complexity of interactions and noise sources in hybrid systems.Here,we propose a coherence-preserving magnon state manipulation scheme.By engineering a superconducting-magnon coupling pulse and combining it with dynamical decoupling pulses,we design a composite pulse sequence.We demonstrate the manipulation and preparation of non-classical states of magnons with a fidelity of up to 98%under realistic conditions.These designs significantly improve the fidelity of manipulation and robustness to noise in hybrid systems compared to existing schemes.These results pave the way for practical applications of quantum magnonics platforms.展开更多
A hybrid model that is based on the Combination of keywords and concept was put forward. The hybrid model is built on vector space model and probabilistic reasoning network. It not only can exert the advantages of key...A hybrid model that is based on the Combination of keywords and concept was put forward. The hybrid model is built on vector space model and probabilistic reasoning network. It not only can exert the advantages of keywords retrieval and concept retrieval but also can compensate for their shortcomings. Their parameters can be adjusted according to different usage in order to accept the best information retrieval result, and it has been proved by our experiments.展开更多
We develop a design of a hybrid quantum interface for quantum information transfer （QIT）, adopting a nanome- chanical resonator as the intermedium, which is magnetically coupled with individual nitrogen-vacancy cent...We develop a design of a hybrid quantum interface for quantum information transfer （QIT）, adopting a nanome- chanical resonator as the intermedium, which is magnetically coupled with individual nitrogen-vacancy centers as the solid qubits, while eapacitively coupled with a coplanar waveguide resonator as the quantum data bus. We describe the Hamiltonian of the model, and analytically demonstrate the QIT for both the resonant interaction and large detuning cases. The hybrid quantum interface allows for QIT between arbitrarily selected individual nitrogen-vacancy centers, and has advantages of the sealability and controllability. Our methods open an alter- native perspective for implementing QIT, which is important during quantum storing or processing procedures in quantum computing.展开更多
文摘Single-photon sensors are novel devices with extremely high single-photon sensitivity and temporal resolution.However,these advantages also make them highly susceptible to noise.Moreover,single-photon cameras face severe quantization as low as 1 bit/frame.These factors make it a daunting task to recover high-quality scene information from noisy single-photon data.Most current image reconstruction methods for single-photon data are mathematical approaches,which limits information utilization and algorithm performance.In this work,we propose a hybrid information enhancement model which can significantly enhance the efficiency of information utilization by leveraging attention mechanisms from both spatial and channel branches.Furthermore,we introduce a structural feature enhance module for the FFN of the transformer,which explicitly improves the model's ability to extract and enhance high-frequency structural information through two symmetric convolution branches.Additionally,we propose a single-photon data simulation pipeline based on RAW images to address the challenge of the lack of single-photon datasets.Experimental results show that the proposed method outperforms state-of-the-art methods in various noise levels and exhibits a more efficient capability for recovering high-frequency structures and extracting information.
基金The work was supported in part by the Fundamental Research Funds for the Central Universities(2016QJ04)Yue Qi Young Scholar Project of CUMTB,the State Key Laboratory of Coal Resources and Safe Mining(SKLCRSM16KFD04,SKLCRSM16KFD03)+3 种基金the Natural Science Foundation of China(61601466)the Natural Science Foundation of Beijing,China(8162035)the National Key R&D Program of China(2018YFC0807801)the National Training Program of Innovation and Entrepreneurship for Undergraduates(C201804970).
文摘Pipeline defect detection systems collect the videos from cameras of pipeline robots,however the systems always analyzed these videos by offline systems or humans to detect the defects of potential security threats.The existing systems tend to reach the limit in terms of data access anywhere,access security and video processing on cloud.There is in need of studying on a pipeline defect detection cloud system for automatic pipeline inspection.In this paper,we deploy the framework of a cloud based pipeline defect detection system,including the user management module,pipeline robot control module,system service module,and defect detection module.In the system,we use a role encryption scheme for video collection,data uploading,and access security,and propose a hybrid information method for defect detection.The experimental results show that our approach is a scalable and efficient defection detection cloud system.
基金Anhui Province Natural Science Research Project of Colleges and Universities(2023AH040321)Excellent Scientific Research and Innovation Team of Anhui Colleges(2022AH010098).
文摘The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12304401 and 11974336)the National Key Research and Development Program of China(Grant No.2017YFA0304100)。
文摘Hybrid systems consisting of superconducting circuits and magnon systems are a promising platform for quantum technology.However,realizing high-fidelity magnon state preparation and manipulation remains an outstanding challenge due to the complexity of interactions and noise sources in hybrid systems.Here,we propose a coherence-preserving magnon state manipulation scheme.By engineering a superconducting-magnon coupling pulse and combining it with dynamical decoupling pulses,we design a composite pulse sequence.We demonstrate the manipulation and preparation of non-classical states of magnons with a fidelity of up to 98%under realistic conditions.These designs significantly improve the fidelity of manipulation and robustness to noise in hybrid systems compared to existing schemes.These results pave the way for practical applications of quantum magnonics platforms.
文摘A hybrid model that is based on the Combination of keywords and concept was put forward. The hybrid model is built on vector space model and probabilistic reasoning network. It not only can exert the advantages of keywords retrieval and concept retrieval but also can compensate for their shortcomings. Their parameters can be adjusted according to different usage in order to accept the best information retrieval result, and it has been proved by our experiments.
基金Supported by the National Natural Science Foundation of China under Grant No 11305021the Fundamental Research Funds for the Central Universities of China under Grants Nos 3132014229 and 3132014328
文摘We develop a design of a hybrid quantum interface for quantum information transfer （QIT）, adopting a nanome- chanical resonator as the intermedium, which is magnetically coupled with individual nitrogen-vacancy centers as the solid qubits, while eapacitively coupled with a coplanar waveguide resonator as the quantum data bus. We describe the Hamiltonian of the model, and analytically demonstrate the QIT for both the resonant interaction and large detuning cases. The hybrid quantum interface allows for QIT between arbitrarily selected individual nitrogen-vacancy centers, and has advantages of the sealability and controllability. Our methods open an alter- native perspective for implementing QIT, which is important during quantum storing or processing procedures in quantum computing.
