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The application of big data technology in the development of smart tourism is increasingly being promoted and impacting the development trend of the tourism industry in our lives. This paper deeply explores the practical application of big data in smart ...

In the rapidly expanding field of IoT, data production has reached an unprecedented scale, providing valuable insights that accelerate decision-making processes. However, ensuring the privacy and security of this massive amount of data poses ...

• Introducing a Clustered FL approach to enhance privacy and security in handling IoT big data.
• Addressing CFL challenges like multi-distribution data, cluster similarity, and class imbalance.
• MDSPFL allows local datasets to follow ...

Photovoltaic (PV) systems are increasingly being deployed. The efficient operation of a PV system not only requires the PV system to function in its expected power generation capacity but also to jointly optimize with downstream systems, e.g., buildings, ...

This paper proposes a new framework to provide the ML forecasting model for model predictive control (MPC) in building HVAC systems. Buildings typically encompass multiple contexts, such as different types of rooms, each with distinct requirements for ...

Traditional carbon accounting methods, e.g., Life Cycle Assessment (LCA), heavily rely on extensive data collection and expert knowledge, which is labor-intensive and time-consuming. We develop a system named CarbonReveal to achieve automatic embodied ...

Machine learning-based building load forecasting (BLF) is crucial for the building automation community, and numerous ML models have been developed for this purpose. However, a significant challenge arises when promoting these models for deployment in ...

Online Building Load Forecasting (BLF) is a scheme that designs a model update strategy to continuously update the deployed ML-based BLF model to adapt to changes in the distribution of data. Many online BLF schemes have recently been developed. However, ...

Federated learning is a promising privacy-preserving learning paradigm in which multiple clients can collaboratively learn a model with their image data kept local. For protecting data ownership, personalized watermarks are usually added to the image ...

We propose Interpretable Neural Radiance Fields (InNeRF) for generalizable 3D scene representation and rendering. In contrast to previous image-based rendering, which used two independent working processes of pooling-based fusion and MLP-based rendering, ...

Non-coding RNAs (ncRNAs) are critical regulators in diverse biological processes and pathological mechanisms. Their prospective utility as biomarkers for early disease detection and prognostic evaluation has attracted substantial scholarly interest. ...

Federated learning (FL) is a distributed machine learning approach in which many clients contribute to learning a single global model in a privacy-preserving manner on the server side. Most existing FL works all follow the fundamental assumption ...

• Designing a federated learning framework for dual-heterogeneous and noisy clients.
• HDHRFL hierarchically aggregates clients with different computational and network capabilities.
• HDHRFL handles heterogeneous client environments, ...

This paper investigates the safety-critical formation control problem with disturbance rejection for switching uncertain EL MASs in the presence of multiple stationary/moving obstacles. A safety-critical control method is proposed for achieving a ...

• This paper extends the CBF-based control method from a single Euler-Lagrange system to Euler-Lagrange Multi-agent systems.
• Both disturbances and reference signals are generated by an exosystem, which has strong universality and ...

This paper studies the performance trade-off in a multi-user backscatter communication (BackCom) system for integrated sensing and communications (ISAC), where the multi-antenna ISAC transmitter sends excitation signals to power multiple single-antenna ...

Federated learning (FL), as a distributed machine learning paradigm, enables on-device model training and inference without data updates or privacy breaches, promoting edge intelligence in Industrial Internet of Things (IIoTs) applications. ...

Fuzzy relation equations are commonly utilized to describe the fuzzy relationship between the antecedent and the consequent parts of complex data environment, and play a vital role in fuzzy system modeling. An interesting topic is to determine ...

• The aim is to form a granular augmentation of fuzzy relation equations which is developed with different t-s compositions.
• A two-phase development of granular fuzzy relation equations fundamentally contributes to pursue optimal ...

Taking advantage of the burgeoning zeroing neural network (ZNN) and the widely used fuzzy logic system (FLS), a novel double integral noise-tolerant fuzzy ZNN (DINTFZNN) model for solving the time-varying Sylvester matrix equation (TVSME) is proposed in ...

Existing network verifiers can efficiently identify failure-induced bugs. However, an equally-important concern is identification of external-routes-induced bugs, which has not been well addressed. Comprehensively reasoning about external routes is ...

This brief communication finds a clear and universal inequality of authors’ reference reuse behaviour. We observe that a few references are reused many times in an author’s oeuvre while most of his or her references only occur in the reference list for ...

The ability to learn compact, high-quality, and easy-to-optimize representations for visual data is paramount to many applications such as novel view synthesis and 3D reconstruction. Recent work has shown substantial success in using tensor networks to ...