University of Lincoln

About: University of Lincoln is a education organization based out in Lincoln, Lincolnshire, United Kingdom. It is known for research contribution in the topics: Population & Higher education. The organization has 2341 authors who have published 7025 publications receiving 124797 citations.

A Survey on Fault Diagnosis in Wireless Sensor Networks

Abstract: Wireless sensor networks (WSNs) often consist of hundreds of sensor nodes that may be deployed in relatively harsh and complex environments. In views of hardware cost, sensor nodes always adopt relatively cheap chips, which make these nodes become error-prone or faulty in the course of their operation. Natural factors and electromagnetic interference could also influence the performance of the WSNs. When sensor nodes become faulty, they may have died which means they cannot communicate with other members in the wireless network, they may be still alive but produce incorrect data, they may be unstable jumping between normal state and faulty state. To improve data quality, shorten response time, strengthen network security, and prolong network lifespan, many studies have focused on fault diagnosis. This survey paper classifies fault diagnosis methods in recent five years into three categories based on decision centers and key attributes of employed algorithms: centralized approaches, distributed approaches, and hybrid approaches. As all these studies have specific goals and limitations, this paper tries to compare them, lists their merits and limits, and propose potential research directions based on established methods and theories.

Design and Implementation of an Anthropomorphic Hand for Replicating Human Grasping Functions

Abstract: How to design an anthropomorphic hand with a few actuators to replicate the grasping functions of the human hand is still a challenging problem. This paper aims to develop a general theory for designing the anthropomorphic hand and endowing the designed hand with natural grasping functions. A grasping experimental paradigm was set up for analyzing the grasping mechanism of the human hand in daily living. The movement relationship among joints in a digit, among digits in the human hand, and the postural synergic characteristic of the fingers were studied during the grasping. The design principle of the anthropomorphic mechanical digit that can reproduce the digit grasping movement of the human hand was developed. The design theory of the kinematic transmission mechanism that can be embedded into the palm of the anthropomorphic hand to reproduce the postural synergic characteristic of the fingers by using a limited number of actuators is proposed. The design method of the anthropomorphic hand for replicating human grasping functions was formulated. Grasping experiments are given to verify the effectiveness of the proposed design method of the anthropomorphic hand.

REVIEW - A reference data set for retinal vessel profiles

Abstract: This paper describes REVIEW, a new retinal vessel reference dataset. This dataset includes 16 images with 193 vessel segments, demonstrating a variety of pathologies and vessel types. The vessel edges are marked by three observers using a special drawing tool. The paper also describes the algorithm used to process these segments to produce vessel profiles, against which vessel width measurement algorithms can be assessed. Recommendations are given for use of the dataset in performance assessment. REVIEW can be downloaded from http://ReviewDB.lincoln.ac.uk.

Artificial Intelligence for Long-Term Robot Autonomy: A Survey

Abstract: Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics. They will assist us in our daily routines and perform dangerous, dirty, and dull tasks. However, enabling robotic systems to perform autonomously in complex, real-world scenarios over extended time periods (i.e., weeks, months, or years) poses many challenges. Some of these have been investigated by subdisciplines of Artificial Intelligence (AI) including navigation and mapping, perception, knowledge representation and reasoning, planning, interaction, and learning. The different subdisciplines have developed techniques that, when re-integrated within an autonomous system, can enable robots to operate effectively in complex, long-term scenarios. In this letter, we survey and discuss AI techniques as “enablers” for long-term robot autonomy, current progress in integrating these techniques within long-running robotic systems, and the future challenges and opportunities for AI in long-term autonomy.

Transfer Learning for Non-Intrusive Load Monitoring

Abstract: Non-intrusive load monitoring (NILM) is a technique to recover source appliances from only the recorded mains in a household. NILM is unidentifiable and thus a challenge problem because the inferred power value of an appliance given only the mains could not be unique. To mitigate the unidentifiable problem, various methods incorporating domain knowledge into NILM have been proposed and shown effective experimentally. Recently, among these methods, deep neural networks are shown performing best. Arguably, the recently proposed sequence-to-point (seq2point) learning is promising for NILM. However, the results were only carried out on the same data domain. It is not clear if the method could be generalised or transferred to different domains, e.g., the test data were drawn from a different country comparing to the training data. We address this issue in the paper, and two transfer learning schemes are proposed, i.e., appliance transfer learning (ATL) and cross-domain transfer learning (CTL). For ATL, our results show that the latent features learnt by a ‘complex’ appliance, e.g., washing machine, can be transferred to a ‘simple’ appliance, e.g., kettle. For CTL, our conclusion is that the seq2point learning is transferable. Precisely, when the training and test data are in a similar domain, seq2point learning can be directly applied to the test data without fine tuning; when the training and test data are in different domains, seq2point learning needs fine tuning before applying to the test data. Interestingly, we show that only the fully connected layers need fine tuning for transfer learning. Source code can be found at https://github.com/MingjunZhong/transferNILM .