A parameter estimation algorithm of the continuous hidden Markov model isintroduced and the rigorous proof of its convergence is also included. The algorithm uses theViterbi algorithm instead of K-means clustering use...A parameter estimation algorithm of the continuous hidden Markov model isintroduced and the rigorous proof of its convergence is also included. The algorithm uses theViterbi algorithm instead of K-means clustering used in the segmental K-means algorithm to determineoptimal state and branch sequences. Based on the optimal sequence, parameters are estimated withmaximum-likelihood as objective functions. Comparisons with the traditional Baum-Welch and segmentalK-means algorithms on various aspects, such as optimal objectives and fundamentals, are made. Allthree algorithms are applied to face recognition. Results indicate that the proposed algorithm canreduce training time with comparable recognition rate and it is least sensitive to the training set.So its average performance exceeds the other two.展开更多
The theory of shared control combines organically the control of every controlled ele-ments and with it all the controlled elements share the same control element. Applying theschemes of shared control searched by ass...The theory of shared control combines organically the control of every controlled ele-ments and with it all the controlled elements share the same control element. Applying theschemes of shared control searched by assembly programs, an integrated control of all the ele-ments is fulfilled. The distinguishing point of the method is that the maximum control output canbe obtained with the least input information. Hence it is the optimum for the conversion of com-bination states. Finally, a thared rotary valve is designed, and it is the simplest with only onegroup of control holes.展开更多
This paper introduces a groundbreaking synthesis of fundamental quantum mechanics with the Advanced Observer Model (AOM), presenting a unified framework that reimagines the construction of reality. AOM highlights the ...This paper introduces a groundbreaking synthesis of fundamental quantum mechanics with the Advanced Observer Model (AOM), presenting a unified framework that reimagines the construction of reality. AOM highlights the pivotal role of the observer in shaping reality, where classical notions of time, space, and energy are reexamined through the quantum lens. By engaging with key quantum equations—such as the Schrödinger equation, Heisenberg uncertainty principle, and Dirac equation—the paper demonstrates how AOM unifies the probabilistic nature of quantum mechanics with the determinism of classical physics. Central to this exploration is the Sequence of Quantum States (SQS) and Constant Frame Rate (CFR), which align with concepts like quantum superposition, entanglement, and wave function collapse. The model’s implications extend to how observers perceive reality, proposing that interference patterns between wave functions form the foundation of observable phenomena. By offering a fresh perspective on the interplay between determinacy and indeterminacy, AOM lays a robust theoretical foundation for future inquiry into quantum physics and the philosophy of consciousness.展开更多
This paper presents the Advanced Observer Model (AOM), a groundbreaking conceptual framework designed to clarify the complex and often enigmatic nature of quantum mechanics. The AOM serves as a metaphorical lens, brin...This paper presents the Advanced Observer Model (AOM), a groundbreaking conceptual framework designed to clarify the complex and often enigmatic nature of quantum mechanics. The AOM serves as a metaphorical lens, bringing the elusive quantum realm into sharper focus by transforming its inherent uncertainty into a coherent, structured ‘Frame Stream’ that aids in the understanding of quantum phenomena. While the AOM offers conceptual simplicity and clarity, it recognizes the necessity of a rigorous theoretical foundation to address the fundamental uncertainties that lie at the core of quantum mechanics. This paper seeks to illuminate those theoretical ambiguities, bridging the gap between the abstract insights of the AOM and the intricate mathematical foundations of quantum theory. By integrating the conceptual clarity of the AOM with the theoretical intricacies of quantum mechanics, this work aspires to deepen our understanding of this fascinating and elusive field.展开更多
A reduced-state sequence estimation(RSSE)receiver based on per-survivor processing(PSP)in conjunction with an adaptive pre-filter is proposed in this paper.In RSSEPSP,each survivor path holds an estimated value of the...A reduced-state sequence estimation(RSSE)receiver based on per-survivor processing(PSP)in conjunction with an adaptive pre-filter is proposed in this paper.In RSSEPSP,each survivor path holds an estimated value of the channel impulse response(CIR),which is updated by adaptive algorithm during the data transmission.Based on the different estimated channel values of each survivor path,corresponding pre-filters are calculated via the Levinson-Durbin algorithm,which can track the time-varying channel adaptively.Computer simulations indicate that the RSSE-PSP equalizer with the new adaptive pre-filter works much better than those with the prevenient pre-filters in ISI channel.展开更多
Higher-order patterns reveal sequential multistep state transitions,which are usually superior to origin-destination analyses that depict only first-order geospatial movement patterns.Conventional methods for higher-o...Higher-order patterns reveal sequential multistep state transitions,which are usually superior to origin-destination analyses that depict only first-order geospatial movement patterns.Conventional methods for higher-order movement modeling first construct a directed acyclic graph(DAG)of movements and then extract higher-order patterns from the DAG.However,DAG-based methods rely heavily on identifying movement keypoints,which are challenging for sparse movements and fail to consider the temporal variants critical for movements in urban environments.To overcome these limitations,we propose HoLens,a novel approach for modeling and visualizing higher-order movement patterns in the context of an urban environment.HoLens mainly makes twofold contributions:First,we designed an auto-adaptive movement aggregation algorithm that self-organizes movements hierarchically by considering spatial proximity,contextual information,and tem-poral variability.Second,we developed an interactive visual analytics interface comprising well-established visualization techniques,including the H-Flow for visualizing the higher-order patterns on the map and the higher-order state sequence chart for representing the higher-order state transitions.Two real-world case studies demonstrate that the method can adaptively aggregate data and exhibit the process of exploring higher-order patterns using HoLens.We also demonstrate the feasibility,usability,and effectiveness of our approach through expert interviews with three domain experts.展开更多
文摘A parameter estimation algorithm of the continuous hidden Markov model isintroduced and the rigorous proof of its convergence is also included. The algorithm uses theViterbi algorithm instead of K-means clustering used in the segmental K-means algorithm to determineoptimal state and branch sequences. Based on the optimal sequence, parameters are estimated withmaximum-likelihood as objective functions. Comparisons with the traditional Baum-Welch and segmentalK-means algorithms on various aspects, such as optimal objectives and fundamentals, are made. Allthree algorithms are applied to face recognition. Results indicate that the proposed algorithm canreduce training time with comparable recognition rate and it is least sensitive to the training set.So its average performance exceeds the other two.
文摘The theory of shared control combines organically the control of every controlled ele-ments and with it all the controlled elements share the same control element. Applying theschemes of shared control searched by assembly programs, an integrated control of all the ele-ments is fulfilled. The distinguishing point of the method is that the maximum control output canbe obtained with the least input information. Hence it is the optimum for the conversion of com-bination states. Finally, a thared rotary valve is designed, and it is the simplest with only onegroup of control holes.
文摘This paper introduces a groundbreaking synthesis of fundamental quantum mechanics with the Advanced Observer Model (AOM), presenting a unified framework that reimagines the construction of reality. AOM highlights the pivotal role of the observer in shaping reality, where classical notions of time, space, and energy are reexamined through the quantum lens. By engaging with key quantum equations—such as the Schrödinger equation, Heisenberg uncertainty principle, and Dirac equation—the paper demonstrates how AOM unifies the probabilistic nature of quantum mechanics with the determinism of classical physics. Central to this exploration is the Sequence of Quantum States (SQS) and Constant Frame Rate (CFR), which align with concepts like quantum superposition, entanglement, and wave function collapse. The model’s implications extend to how observers perceive reality, proposing that interference patterns between wave functions form the foundation of observable phenomena. By offering a fresh perspective on the interplay between determinacy and indeterminacy, AOM lays a robust theoretical foundation for future inquiry into quantum physics and the philosophy of consciousness.
文摘This paper presents the Advanced Observer Model (AOM), a groundbreaking conceptual framework designed to clarify the complex and often enigmatic nature of quantum mechanics. The AOM serves as a metaphorical lens, bringing the elusive quantum realm into sharper focus by transforming its inherent uncertainty into a coherent, structured ‘Frame Stream’ that aids in the understanding of quantum phenomena. While the AOM offers conceptual simplicity and clarity, it recognizes the necessity of a rigorous theoretical foundation to address the fundamental uncertainties that lie at the core of quantum mechanics. This paper seeks to illuminate those theoretical ambiguities, bridging the gap between the abstract insights of the AOM and the intricate mathematical foundations of quantum theory. By integrating the conceptual clarity of the AOM with the theoretical intricacies of quantum mechanics, this work aspires to deepen our understanding of this fascinating and elusive field.
基金Supported by the National Nature Science Foundation of China(60372057).
文摘A reduced-state sequence estimation(RSSE)receiver based on per-survivor processing(PSP)in conjunction with an adaptive pre-filter is proposed in this paper.In RSSEPSP,each survivor path holds an estimated value of the channel impulse response(CIR),which is updated by adaptive algorithm during the data transmission.Based on the different estimated channel values of each survivor path,corresponding pre-filters are calculated via the Levinson-Durbin algorithm,which can track the time-varying channel adaptively.Computer simulations indicate that the RSSE-PSP equalizer with the new adaptive pre-filter works much better than those with the prevenient pre-filters in ISI channel.
基金supported in part by the Shenzhen Science and Technology Program(No.ZDSYS20210623092007023)in part by the National Natural Science Foundation of China(No.62172398)the Guangdong Basic and Applied Basic Research Foundation(No.2021A1515011700).
文摘Higher-order patterns reveal sequential multistep state transitions,which are usually superior to origin-destination analyses that depict only first-order geospatial movement patterns.Conventional methods for higher-order movement modeling first construct a directed acyclic graph(DAG)of movements and then extract higher-order patterns from the DAG.However,DAG-based methods rely heavily on identifying movement keypoints,which are challenging for sparse movements and fail to consider the temporal variants critical for movements in urban environments.To overcome these limitations,we propose HoLens,a novel approach for modeling and visualizing higher-order movement patterns in the context of an urban environment.HoLens mainly makes twofold contributions:First,we designed an auto-adaptive movement aggregation algorithm that self-organizes movements hierarchically by considering spatial proximity,contextual information,and tem-poral variability.Second,we developed an interactive visual analytics interface comprising well-established visualization techniques,including the H-Flow for visualizing the higher-order patterns on the map and the higher-order state sequence chart for representing the higher-order state transitions.Two real-world case studies demonstrate that the method can adaptively aggregate data and exhibit the process of exploring higher-order patterns using HoLens.We also demonstrate the feasibility,usability,and effectiveness of our approach through expert interviews with three domain experts.
