To solve the ambiguity and uncertainty in the labeling process of power equipment corrosion datasets,a novel hierarchical annotation method(HAM)is ***,large boxes are used to label a large area covering the range of c...
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To solve the ambiguity and uncertainty in the labeling process of power equipment corrosion datasets,a novel hierarchical annotation method(HAM)is ***,large boxes are used to label a large area covering the range of corrosion,provided that the area is visually continuous and adjacent to corrosion that cannot be clearly ***,in each labeling box established in the first step,regions with distinct corrosion and relative independence are labeled to form a second layer of nested ***,a series of comparative experiments are conducted with other common annotation methods to validate the effectiveness of *** experimental results show that,with the help of HAM,the recall of YOLOv5 increases from 50.79%to 59.41%;the recall of Faster R-CNN+VGG16 increases from 66.50%to 78.94%;the recall of Faster R-CNN+Res101 increases from 78.32%to 84.61%.Therefore,HAM can effectively improve the detection ability of mainstream models in detecting metal corrosion.
Consciousness has been historically a heavily debated topic in engineering, science, and philosophy. On the contrary, awareness had less success in raising the interest of scholars in the past. However, things are cha...
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Neuromorphic photonics are processors inspired by the human brain and enabled by light (photons) instead of traditional electronics. Neuromorphic photonics and its associated concepts are experiencing a significant re...
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Neuromorphic photonics are processors inspired by the human brain and enabled by light (photons) instead of traditional electronics. Neuromorphic photonics and its associated concepts are experiencing a significant resurgence, building on foundational research from the 1980s and 1990s. This renewed momentum is driven by breakthroughs in photonic integration, nonlinear optics, and advanced materials, alongside the growing necessity of neuro-inspired computing in numerous applications of economic and societal relevance. The increasing demand for energy-efficient artificial intelligence (AI) solutions underscores the need for innovation and a cohesive vision to address key challenges, including scalability, energy efficiency, precision, and standardized performance benchmarks. Together, these efforts present an opportunity to establish a unique photonic advantage with practical, real-world applications. This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field. The roadmap introduces various approaches to embedding the high-complexity transformations central to neuromorphic computing, focusing on frequency, delay, and spectral embeddings. This is followed by a discussion of architectures of photonic neural networks (PNNs) and an in-depth analysis of methods for implementing these architectures in photonic hardware. Dedicated sections delve into integrated photonic hardware, the realization of photonic weights and memories, and the optimization of training processes for photonic neuromorphic architectures. The roadmap concludes by exploring numerous potential applications, highlighting the challenges and advances necessary to transition neuromorphic photonic computing from a primarily academic pursuit to a technolog
The regional integrated energy system is an effective way to realize energy cascade utilization and improve the flexibility and economy of the load side operation. Combine cooling heating and power (CCHP) system is a ...
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Flexible tissue modeling plays an important role in the field of telemedicine. It is related to whether the soft tissue deformation process can be accurately, real-time and vividly simulated during surgery. Most exist...
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In non-cyclic dynamic multi-objective optimization problems, the non-cyclic nature of environmental changes may cause the Pareto optimal front (PF) to be different from historical times. In addition, changes may also ...
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A global planning algorithm for intelligent vehicles is designed based on the A* algorithm, which provides intelligent vehicles with a global path towards their destinations. A distributed real-time multiple vehicle c...
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A global planning algorithm for intelligent vehicles is designed based on the A* algorithm, which provides intelligent vehicles with a global path towards their destinations. A distributed real-time multiple vehicle collision avoidance(MVCA)algorithm is proposed by extending the reciprocal n-body collision avoidance method. MVCA enables the intelligent vehicles to choose their destinations and control inputs independently,without needing to negotiate with each other or with the coordinator. Compared to the centralized trajectory-planning algorithm, MVCA reduces computation costs and greatly improves the robustness of the system. Because the destination of each intelligent vehicle can be regarded as private, which can be protected by MVCA, at the same time MVCA can provide a real-time trajectory planning for intelligent vehicles. Therefore,MVCA can better improve the safety of intelligent vehicles. The simulation was conducted in MATLAB, including crossroads scene simulation and circular exchange position simulation. The results show that MVCA behaves safely and reliably. The effects of latency and packet loss on MVCA are also statistically investigated through theoretically formulating broadcasting process based on one-dimensional Markov chain. The results uncover that the tolerant delay should not exceed the half of deciding cycle of trajectory planning, and shortening the sending interval could alleviate the negative effects caused by the packet loss to an extent. The cases of short delay(< 100100 ms) and low packet loss(< 5%) can bring little influence to those trajectory planning algorithms that only depend on V2 V to sense the context, but the unpredictable collision may occur if the delay and packet loss are further worsened. The MVCA was also tested by a real intelligent vehicle, the test results prove the operability of MVCA.
This paper investigates the high-speed train rescheduling (HSTR) problem under a partial station blockage and proposes an efficient problem-specific strengthen elitist genetic algorithm (PS-SEGA) for HSTR. An HSTR mod...
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The hottest area of indoor localization technology research right now is WiFi localization. This research provides a prediction model based on BP neural network, with the model’s weights and thresholds tuned using sn...
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ISBN:
(纸本)9781665464697
The hottest area of indoor localization technology research right now is WiFi localization. This research provides a prediction model based on BP neural network, with the model’s weights and thresholds tuned using snake optimization technique, for the demand of WiFi location fingerprint indoor localization technology. The issues that BP neural network has with easy local optimum attainment, slow convergence, and weak interference immunity are resolved. The simulation is used to confirm the viability and efficiency of the indoor WiFi localization technology method.
The incentive policies for non-fossil energy are important factors affecting the investment behavior of generation companies (GENCOs). However, the investment behavior of different types of GENCOs in the system could ...
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ISBN:
(纸本)9781665414401
The incentive policies for non-fossil energy are important factors affecting the investment behavior of generation companies (GENCOs). However, the investment behavior of different types of GENCOs in the system could be quite different, so the implementation of the policy must consider how to achieve incentive compatibility with the investment behavior of GENCOs. This article constructs a power transition simulation model that considers the investment behavior of GENCOs. Taking the wind power feed-in tariff policy as an example, it analyzes the impact of policy on the whole system’s low carbon transition pathway of the power structure and the evolution trajectory of system carbon emissions under different behavior scenarios.
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