We present a faithful geometric picture for genuine tripartite entanglement of discrete, continuous, and hybrid quantum systems. We first find that the triangle relation Ei|jkα≤Ej|ikα+Ek|ijα holds for all subaddit...
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We present a faithful geometric picture for genuine tripartite entanglement of discrete, continuous, and hybrid quantum systems. We first find that the triangle relation Ei|jkα≤Ej|ikα+Ek|ijα holds for all subadditive bipartite entanglement measure E, all permutations under parties i,j,k, all α∈[0,1], and all pure tripartite states. Then, we rigorously prove that the nonobtuse triangle area, enclosed by side Eα with 0<α≤1/2, is a measure for genuine tripartite entanglement. Finally, it is significantly strengthened for qubits that given a set of subadditive and nonsubadditive measures, some state is always found to violate the triangle relation for any α>1, and the triangle area is not a measure for any α>1/2. Our results pave the way to study discrete and continuous multipartite entanglement within a unified framework.
Printed circuit board (PCB) measurement and repair is a challenging task that requires experience and expertise to perform. PCB diagnosis and repair shops employ skilled operators to carry out the corresponding measur...
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Printed circuit board (PCB) measurement and repair is a challenging task that requires experience and expertise to perform. PCB diagnosis and repair shops employ skilled operators to carry out the corresponding measurement tasks using measuring instruments (e.g., oscilloscopes, multimeters) in order to uncover the condition of a particular product. However, these tasks are often repetitive and meticulous, and additionally, the results need to be collected and carefully documented so that the gathered experience regarding the product can be re-used when the next product of the same type arrives into the shop. Nevertheless, the diagnosis of used PCBs is less researched and current flexible automation possibilities are limited. In this paper, a novel visual servoing probe test method and measurement tool are proposed to provide a flexible solution for PCB diagnosis with a higher level of automation. The aim of the approach is to reduce the burden on the operators by carrying out the repetitive measurement tasks and automatically storing the results while leaving the responsibility of measurement profile setup to the human expert. The proposed visual servo system uses manually teached-in measurement points, where template patterns are recorded using cameras, and it is capable of compensating positioning errors in the range of a couple of millimeters. The proof of concept of the proposed method is presented through motherboard measuring experiments, with a 99.7% success rate.
Myocardial Infarction (MI) is a major global health threat, where rapid and accurate diagnosis is essential for improving treatment outcomes. This study proposes MSRC-TransBLSTM, a deep learning-based hierarchical hyb...
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ISBN:
(纸本)9798400712425
Myocardial Infarction (MI) is a major global health threat, where rapid and accurate diagnosis is essential for improving treatment outcomes. This study proposes MSRC-TransBLSTM, a deep learning-based hierarchical hybrid model for the automatic detection of MI. The model combines spatial and temporal features through a hierarchical modeling strategy: multi-layer convolutional blocks and improved MSRC modules extract and optimize spatial features, strengthening the representation of both local and global features. For temporal modeling, the Transformer Encoder captures global dependencies, while the BLSTM focuses on refining local dynamics features. Experiments on the PTB-XL dataset demonstrated the model's strong performance across key metrics (Acc = 98.68%, Sen = 97.33%, F1 = 97.43%). Compared to other models, it achieves notable improvements in accuracy and feature representation, confirming its effectiveness in MI detection.
In this paper, we present a novel distributed algorithm (herein called MaxCUCL) designed to guarantee that max−consensus is reached in networks characterized by unreliable communication links (i.e., links suffering fr...
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This paper presents an FPGA-based low-power acceleration of sound source localization in HARK, open-source software for robot audition. Due to the massive matrix operations, sound source localization in HARK takes sub...
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ISBN:
(数字)9798350384147
ISBN:
(纸本)9798350384154
This paper presents an FPGA-based low-power acceleration of sound source localization in HARK, open-source software for robot audition. Due to the massive matrix operations, sound source localization in HARK takes substantial processing time in edge computing devices. To balance processing time and low power consumption, two functions in sound source localization that include many matrix operations are targeted and migrated on an FPGA SoC board called M-KUBOS. Compared to CPU-based computing on ARM Cortex A53, our implementation achieved a 2.0× speedup and 1.7× lower energy consumption.
To minimize the disturbance of the Tunnel Boring Machine (TBM) cutterhead on the surrounding rock during the coal mine roadway excavation process and ensure that the cutterhead rotation speed achieves fast tracking pe...
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ISBN:
(数字)9781665464543
ISBN:
(纸本)9781665464550
To minimize the disturbance of the Tunnel Boring Machine (TBM) cutterhead on the surrounding rock during the coal mine roadway excavation process and ensure that the cutterhead rotation speed achieves fast tracking performance with minimal overshoot, we propose a direct adaptive robust control method for the cutterhead rotation speed hydraulic system based on inversion design. This method considers the strong disturbances such as loads and motion affecting the cutterhead hydraulic drive system, as well as the uncertainties in the cutterhead model. We establish the nonlinear model of the cutterhead hydraulic drive system and employ virtual control to reduce the model order. Using Lyapunov functions, we ensure the stability of the entire system and derive the control law for the cutterhead rotation speed controller, along with parameter-adaptive laws acting as parameter estimators. We validate the effectiveness of the proposed control strategy through joint simulation using AMESim and Simulink. The results show that the designed cutterhead rotation speed controller achieves high tracking accuracy and good adaptability.
The International Lunar Research Station will be established near the south pole through advanced unmanned rovers at the beginning period. The south pole of the moon has short daytime, so the efficiency of remote cont...
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ISBN:
(纸本)9798331312183
The International Lunar Research Station will be established near the south pole through advanced unmanned rovers at the beginning period. The south pole of the moon has short daytime, so the efficiency of remote control is inadequate. However, the duration and power resource usage of the lunar rover moving on the lunar surface remains uncertain because of different loading weight of collection and changes of terrain in moving. What’s more, a lunar rover needs to move back to the base before nighttime without sunlight to provide energy, while the whole time of working on the moon also needs optimization. We select to solve the planning problem with reinforcement learning (RL) due to its capability in tackling uncertainty and optimization. However, traditional reinforcement learning cannot guarantee safety with time uncertainty, resource uncertainty, and constraints due to the soft constraints in optimization. Therefore, we propose a new way through safe reinforcement learning of task planning and resource collection optimization among tasks with uncertain duration and resource collection. We consider a scenario of in-situ material utilization for the lunar base, where there are tasks of moving, charging, collecting, material delivering, and material receiving, all of which have uncertain duration in execution and every task must be done during the daytime except the charging. Resource collection is related to power consumption in moving so it will be decided according to the remaining power. We further propose an architecture on reinforcement learning to let rovers decide the next step instantaneously according to the expected task duration, the remaining time, and the remaining power. Maximizing the amount of material delivered is an optimization target in training while keeping the rovers safe to work only in the daytime without an empty battery. In our experiment, we intend that our way works well in the uncertainties, and it will lead the rover to finish tasks w
This paper develops a kind of Model Predictive control (MPC) based on the Transformer proxy model. Traditional MPC usually makes predictions via linear models which are unable to understand the interaction between var...
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Investigations of kinematic and dynamic models of tractor-trailer systems have historically been performed for stability analysis or state estimation. In this work, we present and evaluate kinematic and dynamic tracto...
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ISBN:
(数字)9798331505929
ISBN:
(纸本)9798331505936
Investigations of kinematic and dynamic models of tractor-trailer systems have historically been performed for stability analysis or state estimation. In this work, we present and evaluate kinematic and dynamic tractor-trailer models for model predictive control (MPC). We show in open-loop simulations that a kinematic and a dynamic model are equivalent at low speeds and short discretization time steps. A zero speed singularity and stiff dynamics prevents the usage of the dynamic model in control design, where discretization time steps are longer. A method of discretization is proposed to resolve the low speed feasibility of the dynamic model. In closed-loop simulations, the real-time applicability of the kinematic and dynamic models in a nonlinear MPC is verified.
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