Dynamic Detection Scheme Research Articles

Smart Underwater Pollution Detection Based on Graph-Based Multi-Agent Reinforcement Learning Towards AUV-Based Network ITS

The exploitation/utilization of marine resources and the rapid development of urbanization along coastal cities result in serious marine pollution, especially underwater diffusion pollution. It is a non-trivial task to detect the source of diffusion pollution, such that the disadvantageous effect of the pollution can be reduced. With the vision of 6G framework, we employ Autonomous Underwater Vehicle (AUV) flock and introduce the concept of AUV-based network. In particular, we utilize the Software-Defined Networking (SDN) technique to update the controllability of the AUV-based network, leading to the paradigm of SDN-enabled multi-AUVs network Intelligent Transportation Systems (SDNA-ITS). For SDNA-ITS, we utilize artificial potential field theories to model the control model. To optimize the system output, we introduce the graph-based Soft Actor-Critic (SAC) algorithm, i.e., a category of Multi-Agent Reinforcement Learning (MARL) mechanism where each AUV can be regarded as a node in a graph. In particular, we improve the optimization model based on Centralized Training Decentralized Execution (CTDE) architecture with the assistance of the SDN controller, by which each AUV can efficiently adjust its speed towards the diffusion source. Further, to achieve exact path planning for detecting the diffusion source, a dynamic detection scheme is proposed to output the united control policy to schedule the SDNA-ITS dynamically. Simulation results demonstrate that our approaches are available to detect the underwater diffusion source when the actual scenario is taken into account and perform better than some recent research products.

Topology and its detection in a dissipative Aharonov-Bohm chain

In a recent experiment, a dissipative Aharaonov-Bohm (AB) chain was implemented in the momentum space of a Bose-Einstein condensate. Formed by a series of dissipative AB rings threaded by synthetic magnetic flux, the chain exhibits the non-Hermitian skin effect, necessitating the non-Bloch band theory to account for its topology. In this work, we systematically characterize topological features of the dissipative AB chain, particularly beyond the experimentally realized parameter regime. Further, we show that an atom-injection spectroscopy is not only capable of revealing topological edge states, as has been demonstrated in the experiment, but also the general band structure of the system. We then discuss alternative dynamic detection schemes for the topological edge states. Given the generality of the model and the detection schemes, our work is helpful to future study of topological models with non-Hermitian skin effects across a variety of quantum simulators.

Tractor-trailer-train braking time sequence detection based on monocular vision

The tractor-trailer-train at the braking process prone to braking instability caused by asynchronous braking between the shafts. With respect to the lack of intelligent detection of Braking Time Sequence (BTS), a non-contact dynamic detection scheme of intelligent vehicle BTS is proposed. Based on the monocular vision principle, the edge markers of tractor-trailer train tires are identified, and the tire slip rate is solved. The noise reduction of the collected image is processed. The marker area is obtained by Blob analysis. This region at the image to be matched is identified by the template matching algorithm based on contour. The camera is calibrated by Zhang’s calibration method. In order to verify the effectiveness of the detection scheme, the real vehicle test was carried out. The test results show that the error of slip rate solution is below 4.2%.

Non-Bloch quench dynamics

We study the quench dynamics of non-Hermitian topological models with non-Hermitian skin effects. Adopting the non-Bloch band theory and projecting quench dynamics onto the generalized Brillouin zone, we find that emergent topological structures, in the form of dynamic skyrmions, exist in the generalized momentum-time domain, and are correlated with the non-Bloch topological invariants of the static Hamiltonians. The skyrmion structures anchor on the fixed points of dynamics whose existence are conditional on the coincidence of generalized Brillouin zones of the pre- and post-quench Hamiltonians. Global signatures of dynamic skyrmions, however, persist well beyond such a condition, thus offering a general dynamic detection scheme for non-Bloch topology in the presence of non-Hermitian skin effects. Applying our theory to an experimentally relevant, non-unitary quantum walk, we explicitly demonstrate how the non-Bloch topological invariants can be revealed through the non-Bloch quench dynamics.

Toward On-Demand Deep Brain Stimulation Using Online Parkinson's Disease Prediction Driven by Dynamic Detection.

In Parkinson's disease (PD), on-demand deep brain stimulation is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation, and real-time detection. The dynamic feature extraction and dynamic pattern classification are selected by evaluating a subset of feature extraction, dimensionality reduction, and classification algorithms that have been used in brain-machine interfaces. A novel dimensionality reduction technique, the maximum ratio method (MRM) is proposed, which provides the most efficient performance. In terms of accuracy and complexity for hardware implementation, a combination having discrete wavelet transform for feature extraction, MRM for dimensionality reduction, and dynamic k-nearest neighbor for classification was chosen as the most efficient. It achieves a classification accuracy of 99.29%, an F1-score of 97.90%, and a choice probability of 99.86%.

On detecting active worms with varying scan rate

Active worms have posed a major security threat to the Internet and many research efforts have focused on them. However, defending against them remains challenging due to their continuous evolution. In this paper, we study a new class of defense-oriented evolved worms, the Varying Scan Rate worm (the VSR worm in short). In order to circumvent detection by existing worm detection schemes, the VSR worm deliberately varies its scan rate according to these schemes’ weaknesses. To counteract the VSR worm, we design a new worm-detection scheme, the attack-target Distribution Entropy-based Dynamic detection scheme (DED detection for short). DED detection utilizes the attack-target distribution and robust statistical feature in conjunction with dynamic decision adaptation to distinguish worm-scan traffic from non-worm-scan traffic. We present a comparatively complete space of detection schemes and conduct extensive performance evaluations on the DED detection scheme compared with other schemes, using real-world Internet traces as background scan traffic. Our data clearly demonstrate the effectiveness of the DED detection scheme in detecting both the VSR worm and the traditional worm.

Stable dynamic detection scheme for magnetostrictive fiber-optic interferometric sensors

A stable dynamic detection scheme for magnetostrictive fiber-optic interferometric sensors is studied. The working principle is presented and the experimental verification is performed. The results show that the system employing the dynamic detection scheme has better stability and sensitivity in comparison with the system employing quadrature control technique.

Possible way to measure the Casimir force using a suspended interferometer

The accurate measurement of the Casimir force and the search for hypothetical long-range interactions are subjects of growing interest. In this paper we propose to use a suspended interferometric device to measure the Casimir effect between metallic flat surfaces. This allows us to perform the measurement at distances much larger than the ones used in previous measurements with spherical surfaces. The use of the proposed dynamic detection scheme can also help in the discrimination of the different contributions to the measured force. Some considerations about the physical information which can be obtained from this type of measurement are also made.

A lateral modulation technique for simultaneous friction and topography measurements with the atomic force microscope

A lateral sample modulation technique is presented to measure simultaneously lateral friction forces and topological features with an atomic force microscope (AFM). The employed technique allows one to use an AFM without any additional displacement sensor. This dynamic detection scheme is well suited for AFMs equipped with a fiber-optic displacement sensor. The technique and the mechanism of the contrast formation are discussed. The performance of the microscope is demonstrated by imaging flat surfaces and large corrugated films as well as low friction samples.