Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Control theory & Catalysis. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Adaptive Output-Feedback Fuzzy Tracking Control for a Class of Nonlinear Systems

Abstract: This paper is concerned with the problem of adaptive fuzzy tracking control via output feedback for a class of uncertain single-input single-output (SISO) strict-feedback nonlinear systems. The dynamic feedback strategy begins with an input-driven filter. By utilizing fuzzy logic systems to approximate unknown and desired control input signals directly instead of the unknown nonlinear functions, an output-feedback fuzzy tracking controller is designed via a backstepping approach. It is shown that the proposed fuzzy adaptive output controller can guarantee that all the signals remain bounded and that the tracking error converges to a small neighborhood of the origin. Simulations results are presented to demonstrate the effectiveness of the proposed methods.

Federated Learning for Internet of Things: A Comprehensive Survey

Abstract: The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing. In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration. Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. We then provide an extensive survey of the use of FL in various key IoT applications such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), smart cities, and smart industry. The important lessons learned from this review of the FL-IoT services and applications are also highlighted. We complete this survey by highlighting the current challenges and possible directions for future research in this booming area.

MASS: Masked Sequence to Sequence Pre-training for Language Generation

Abstract: Pre-training and fine-tuning, e.g., BERT, have achieved great success in language understanding by transferring knowledge from rich-resource pre-training task to the low/zero-resource downstream tasks. Inspired by the success of BERT, we propose MAsked Sequence to Sequence pre-training (MASS) for the encoder-decoder based language generation tasks. MASS adopts the encoder-decoder framework to reconstruct a sentence fragment given the remaining part of the sentence: its encoder takes a sentence with randomly masked fragment (several consecutive tokens) as input, and its decoder tries to predict this masked fragment. In this way, MASS can jointly train the encoder and decoder to develop the capability of representation extraction and language modeling. By further fine-tuning on a variety of zero/low-resource language generation tasks, including neural machine translation, text summarization and conversational response generation (3 tasks and totally 8 datasets), MASS achieves significant improvements over the baselines without pre-training or with other pre-training methods. Specially, we achieve the state-of-the-art accuracy (37.5 in terms of BLEU score) on the unsupervised English-French translation, even beating the early attention-based supervised model.

Fabrication of Flexible Metal‐Nanoparticle Films Using Graphene Oxide Sheets as Substrates

Abstract: Graphene-based sheets that possess a unique nanostructure and a variety of fascinating properties are appealing as promising nanoscale building blocks of new composites. Herein, graphene oxide sheets are used as the nanoscale substrates for the formation of silver-nanoparticle films. These silver-nanoparticle films assembled on graphene oxide sheets are flexible and can form stable suspensions in aqueous solutions. They can also be easily processed, forming macroscopic films with high reflectivity. Raman signals of graphene oxide in such hybrid films are increased by the attached silver nanoparticles, displaying surface-enhanced Raman scattering activity. The degree of enhancement can be adjusted by varying the quantity of silver nanoparticles on the graphene oxide sheets.

Fe2O3 Nanoneedles on Ultrafine Nickel Nanotube Arrays as Efficient Anode for High-Performance Asymmetric Supercapacitors

Abstract: High performance of electrochemical energy storage devices depends on the smart structure engineering of electrodes, including the tailored nanoarchitectures of current collectors and subtle hybridization of active materials. To improve the anode supercapacitive performance of Fe2O3 for high-voltage asymmetric supercapacitors, here, a hybrid core-branch nanoarchitecture is proposed by integrating Fe2O3 nanoneedles on ultrafine Ni nanotube arrays (NiNTAs@Fe2O3 nanoneedles). The fabrication process employs a bottom-up strategy via a modified template-assisted method starting from ultrafine ZnO nanorod arrays, ensuring the formation of ultrafine Ni nanotube arrays with ultrathin tube walls. The novel developed NiNTAs@Fe2O3 nanoneedle electrode is demonstrated to be a highly capacitive anode (418.7 F g−1 at 10 mV s−1), matching well with the similarly built NiNTAs@MnO2 nanosheet cathode. Contributed by the efficient electron collection paths and short ion diffusion paths in the uniquely designed anode and cathode, the asymmetric supercapacitors exhibit an excellent maximum energy density of 34.1 Wh kg−1 at the power density of 3197.7 W kg−1 in aqueous electrolyte and 32.2 Wh kg−1 at the power density of 3199.5 W kg−1 in quasi-solid-state gel electrolyte.