Selective Memory Recursive Least Squares: Recast Forgetting into Memory in RBF Neural Network Based Real-Time Learning

In radial basis function neural network (RBFNN) based real-time learning tasks, forgetting mechanisms are widely used such that the neural network can keep its sensitivity to new data. However, with forgetting mechanisms, some useful knowledge will get lost simply because they are learned a long time ago, which we refer to as the passive knowledge forgetting phenomenon. To address this problem, this paper proposes a real-time training method named selective memory recursive least squares (SMRLS) in which the classical forgetting mechanisms are recast into a memory mechanism. Different from the forgetting mechanism, which mainly evaluates the importance of samples according to the time when samples are collected, the memory mechanism evaluates the importance of samples through both temporal and spatial distribution of samples. With SMRLS, the input space of the RBFNN is evenly divided into a finite number of partitions and a synthesized objective function is developed using synthesized samples from each partition. In addition to the current approximation error, the neural network also updates its weights according to the recorded data from the partition being visited. Compared with classical training methods including the forgetting factor recursive least squares (FFRLS) and stochastic gradient descent (SGD) methods, SMRLS achieves improved learning speed and generalization capability, which are demonstrated by corresponding simulation results.Comment: 12 pages, 15 figure

Real-Time Progressive Learning: Mutually Reinforcing Learning and Control with Neural-Network-Based Selective Memory

Memory, as the basis of learning, determines the storage, update and forgetting of the knowledge and further determines the efficiency of learning. Featured with a mechanism of memory, a radial basis function neural network (RBFNN) based learning control scheme named real-time progressive learning (RTPL) is proposed to learn the unknown dynamics of the system with guaranteed stability and closed-loop performance. Instead of the stochastic gradient descent (SGD) update law in adaptive neural control (ANC), RTPL adopts the selective memory recursive least squares (SMRLS) algorithm to update the weights of the RBFNN. Through SMRLS, the approximation capabilities of the RBFNN are uniformly distributed over the feature space and thus the passive knowledge forgetting phenomenon of SGD method is suppressed. Subsequently, RTPL achieves the following merits over the classical ANC: 1) guaranteed learning capability under low-level persistent excitation (PE), 2) improved learning performance (learning speed, accuracy and generalization capability), and 3) low gain requirement ensuring robustness of RTPL in practical applications. Moreover, the RTPL based learning and control will gradually reinforce each other during the task execution, making it appropriate for long-term learning control tasks. As an example, RTPL is used to address the tracking control problem of a class of nonlinear systems with RBFNN being an adaptive feedforward controller. Corresponding theoretical analysis and simulation studies demonstrate the effectiveness of RTPL.Comment: 16 pages, 15 figure

Port Logistics Integration: Challenges and ‎Approaches

The competitiveness of a seaport highly depends on its efficiency especially in terms of logistics practices, functions and activities and how all that is integrated with those of the other players in the supply chain.Despite the well-articulated importance of ports and terminals in integrated logistics, research on the success factors of port logistics integration remains scattered. The objective of this paper is to provide a review of the literature and based on this; provide an insight into how seaports and terminals may improve their logistics integration. A structured analysis of 78 papers published in Scopus indexed journals in logistics, supply chain and port management during the period 2000-2018 is conducted. A multidimensional conceptual framework for port logistics integration is proposed to incorporate the role of the three infrastructural variables emerging from the recent developments in the port logistics environment. The literature review has found the logistics process and operations, information integration, value-added services, and logistics practices being influential factors in logistics integration. Based on the research discussion and conclusion which is drawn from the literature review offer a basis for future research, both in respect of research approaches, concept definition and the select of theoretical foundation. The framework could be more detailed on each factor and consider actors perspective. Further testing and examination of the framework are needed for the validity of the results. This study questioned current literature in port logistics integration, highlight role infrastructural factors and the actor's role in the port logistics chain

Punctuality Improvement in Australian Rail Freight Network by Transit Time Management

Abstract: With rapid development in product globalization and just-in-time production over the last two decades, area-specific reliable, responsive and customer-oriented rail freight services are in demand and are of increasing interest. Having a proper understanding of underlying factors in the evaluation of the quality of rail freight services is a key challenge in the short-term and long-term regional and metropolitan freight mobility planning, particularly within the context of a competitive rail freight market as in Australia. Among the fundamental attributes of rail freight services, transit time and reliability/punctuality are of utmost importance, which also tend to be inevitably correlated. This paper discusses the potential opportunities for service improvement in the Australian non-bulk interstate network through managing the underlying factors. The paper also addresses the conditions under which these factors can be combined to enhance the utilisation and efficiency of rail freight services in the national rail infrastructure. Citation: Ghaderi, H. & Namazi-Rad, M-R. (2014). Punctuality Improvement in Australian Rail Freight Network by Transit Time Management. In: Campbell P. and Perez P. (Eds), Proceedings of the International Symposium of Next Generation Infrastructure, 1-4 October 2013, SMART Infrastructure Facility, University of Wollongong, Australia

Use of Echo360 generated materials and its impact on class attendance

Echo360 lecture capture system has become widely used in Australian universities. However, there are concerns about how Echo360 generated materials are used by students and the effects of its use on student learning. The paper draws on data from an inter-disciplinary project that aimed to investigate the role of Echo360 lecture capture system on learning and teaching at the University of Tasmania. Initial findings showed that the majority of respondents used Echo360 generated materials to help them better understand face-to-face lectures, review notes, prepare for assignments and examinations, rather than using the materials as an alternative to attending lectures. Contrary to some published findings, this study found that the availability of Echo360 generated materials did not necessary result in low class attendance. Over 86 per cent of respondents still considered face-to-face lectures to be of high value and attendance was necessary to promote their learning

Fault Diagnosis for Engine Based on Single-Stage Extreme Learning Machine

Single-Stage Extreme Learning Machine (SS-ELM) is presented to dispose of the mechanical fault diagnosis in this paper. Based on it, the traditional mapping type of extreme learning machine (ELM) has been changed and the eigenvectors extracted from signal processing methods are directly regarded as outputs of the network’s hidden layer. Then the uncertainty that training data transformed from the input space to the ELM feature space with the ELM mapping and problem of the selection of the hidden nodes are avoided effectively. The experiment results of diesel engine fault diagnosis show good performance of the SS-ELM algorithm

Research advances in adiponectin actions in nonalcoholic fatty liver disease

The incidence of nonalcoholic fatty liver disease (NAFLD) is increasing worldwide and the age of onset gradually gets younger. However, specific therapeutic interventions are lacking. The exact mechanism of NAFLD has not yet been well elucidated. Recent studies indicate that adiponectin (APN) is an adipokine with anti-inflammatory activity and is considered a hepatic protector, which plays a central role in the pathogenesis of NAFLD. Research on APN actions in NAFLD is popular around the world. This article summarizes the mechanism of APN actions in NAFLD, briefly describes the relationship of APN with nonalcoholic steatohepatitis and NAFLD-related hepatic fibrosis, and reviews related clinical studies. It is suggested that APN holds promise as a novel therapeutic target in the treatment of NAFLD and further research into the signaling pathway of APN would lead to a better understanding of its action mechanism and can provide a novel strategy for the treatment of NAFLD

Truck-to-door sequencing in multi-door cross-docking system with dock repeat truck holding patter

Cross-docking is a logistics strategy that consolidates the products of different inbound trucks according to their destinations in order to reduce the inventory, order picking, and transportation costs. It requires a high level of collaboration between inbound trucks, internal operations, and outbound trucks. This article addresses the truck-to-door sequencing problem. Truck-to-door sequencing has been studied by some researchers in different titles such as scheduling and sequencing of inbound and outbound trucks of the cross-dock center. However, previous studies have not considered repeat truck holding pattern. Therefore, it is important to determine the doors and the sequence of the inbound and outbound trucks that should be assigned in a cross-dock center. This paper focuses on optimizing truck-to-door sequencing with consideration of repeat truck holding pattern in inbound trucks in order to minimize makespan. Two methods are considered to solve this problem, including mathematical modeling and a heuristic algorithm. In the first method, a mixed integer-programming model is developed to minimize the makespan. Then, GAMS software is used to solve small-scale problems. In the second approach, a heuristic algorithm is developed to find near-optimal solutions within the shortest time possible and the algorithm is used to solve large-scale problems. The results of the mathematical model and the heuristic algorithm are slightly different and show the good quality of the presented heuristic algorithm.CC BY 4.0 DEED</p

Five-Axis Contour Error Control Based on Numerical Control Data

Improving contour accuracy is one of the significant goals of industrial machining. This paper proposes a contour error estimation and compensation algorithm for five-axis computer numerical control (CNC) machine tools based on modified numerical control (NC) codes. The expected path analyzed by NC data and the actual trajectory collected by sensors are spatially mapped by the hidden Markov model (HMM). Next, an evaluation function that hybrids the tool tip position and tool orientation change trend is proposed as the index of contour error estimation. Finally, spatial iterative learning control (ILC) is used to compensate the contour error, and high-precision machining instructions are obtained after multiple iterations. Experiments with different trajectories are performed on a five-axis platform to verify the proposed algorithm’s effectiveness. The results show that the proposed algorithm without using planned trajectories, has the same good control effect as traditional methods, which must know the planning trajectory for simple trajectories. At the same time, the method proposed in this paper has better performance than existing algorithms based on tool tip position nearest principle at sharp corners. In conclusion, on the basis of not depending on the planning trajectories, this method has a better compensation effect for the overall accuracy of trajectories and is easier to implement in industrial applications