Artificial Intelligence enabled Agriculture, Food and Public Robotic stacker for mechanized Distribution Loading/Unloading of food grain

 The agriculture sector faces many challenges such as crop diseases, pest infestation, water shortage, weeds and many more. These problems lead to substantial crop loss, economic loss and also causes severe environmental hazards due to the current agriculture practices. The AI and Robotics technologies have the potential to solve these problems effi...

Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers - Artificial Intelligence to Heuristics

 Computationally expensive applications, including machine learning, chemical simulations, and financial modeling, are promising candidates for noisy intermediate scale quantum (NISQ) computers. In these problems, one important challenge is mapping a quantum circuit onto NISQ hardware while satisfying physical constraints of an underlying quantum ar...

Low-Dispersion Leapfrog WCS-FDTD with Artificial Anisotropy Parameters and Simulation of Hollow Dielectric Resonator Antenna Array

 An optimized three-dimensional one-step leapfrog finite-difference time-domain (FDTD) method has been investigated, which is with a weakly conditional stability (WCS) to reduce numerical dispersion further. By introducing the artificial anisotropy parameters in a cross-correspondence manner, the phase velocity error is effectively limited without a...

IFC-BD An Interpretable Fuzzy Classifier for Boosting Explainable Artificial Intelligence in Big Data

 In current Data Science applications, the course of action has derived to adapt the system behavior for the human cognition, resulting in the emerging area of explainable artificial intelligence. Among different classification paradigms, those based on fuzzy rules are suitable solutions to stress the interpretability of the global systems. However,...

Exponential Contingency Explosion Implications for Artificial General Intelligence

 The failure of complex artificial intelligence (AI) systems seems ubiquitous. To provide a model to describe these shortcomings, we define complexity in terms of a system's sensors and the number of environments or situations in which it performs. The complexity is not looked at in terms of the difficulty of design, but in the final performance of ...

Artificial Intelligence-Aided Minimum Reactive Power Control for the DAB Converter Based on Harmonic Analysis Method

 With the aim of reducing the reactive power for the dual-active-bridge (DAB) converter, this letter proposes an artificial intelligence (AI) aided minimum reactive power control scheme based on the harmonic analysis method. Specifically, as an advanced algorithm of the deep reinforcement learning (DRL), the deep deterministic policy gradient (DDPG)...

Artificial Intelligence based Control Design for Reliable Virtual Synchronous Generators

 Virtual synchronous generator (VSG) is a promising solution for inertia support of the future electricity grid to deal with the frequency stability issues caused by the high penetration of renewable generations. However, the power variation in power electronic interface converters caused by VSG emulation increases the stress on power semiconductor ...

Applications of Artificial Intelligence on the Modeling and Optimization for Analog and Mixed-Signal Circuits A Review

 Recently, there have been many studies attempting to take advantage of advancements in Artificial Intelligence (AI) in Analog and Mixed-Signal (AMS) circuit design. Automated circuit sizing optimization and improving the accuracy of performance models are the two predominant uses of AI in AMS circuit design. This paper first introduces and explains...

The Why, What and How of Artificial General Intelligence Chip Development

 The AI chips increasingly focus on implementing neural computing at low power and cost. The intelligent sensing, automation, and edge computing applications have been the market drivers for AI chips. Increasingly, the generalisation, performance, robustness, and scalability of the AI chip solutions are compared with human-like intelligence abilitie...

Edge-Cloud Collaboration Enabled Video Service Enhancement A Hybrid Human-Artificial Intelligence Scheme

  In this paper, a video service enhancement strategy is investigated under an edge-cloud collaboration framework, where video caching and delivery decisions are made in the cloud and edge respectively. We aim to guarantee the user fairness in terms of video coding rate under statistical delay constraint and edge caching capacity constraint. A hy...

Beneficial Perturbation Network for Designing General Adaptive Artificial Intelligence Systems

  The human brain is the gold standard of adaptive learning. It not only can learn and benefit from experience, but also can adapt to new situations. In contrast, deep neural networks only learn one sophisticated but fixed mapping from inputs to outputs. This limits their applicability to more dynamic situations, where the input to output mapping...

Traffic Forecasting and Decision making of investment and construction of Tourism Highway under the Background of Artificial intelligence

 Passenger traffic fore casting and decision-making of investment and construction of the tourism highway under the background of artificial intelligence is studied in this paper. As an important information asset, big data is expected to provide people with comprehensive, accurate, and real-time business insights a...