A Lyapunov–Razumikhin approach for stability analysis of logistics networks with time-delays

Logistics network represents a complex system where different elements that are logistic locations interact with each other. This interaction contains delays caused by time needed for delivery of the material. Complexity of the system, time-delays and perturbations in a customer demand may cause unstable behaviour of the network. This leads to the loss of the customers and high inventory costs. Thus the investigation of the network on stability is desired during its design. In this article we consider local input-to-state stability of such logistics networks. Their behaviour is described by a functional differential equation with a constant time-delay. We are looking for verifiable conditions that guarantee stability of the network under consideration. Lyapunov–Razumikhin functions and the local small gain condition are utilised to obtain such conditions. Our stability conditions for the logistics network are based on the information about the interconnection properties between logistic locations and their production rates. Finally, numerical results are provided to demonstrate the proposed approach.     

Research on monitoring volleyball players’ competition load based on intelligent tracking technology

To evaluate volleyball players' sports activities using the author's own video recording system. They tested 28 volleyball players in four games for the Polish Volleyball League Plus and the Federation of Europe. Record data were analyzed for groups in matches given the average total distance covered by the volleyball players in different meetings' specialties. The research programs that extend the finals' players' distance set indicate these individual meetings' long-term trend. It is crucial to monitor the training load to essential the state together to prevent unnecessary changes. Knowledge of these variables during the volleyball season is still lacking—objective: To describe competitive volleyball players' training load and recovery status during the monitoring season. The purpose of this research is to describe and explore professional volleyball players throughout the season for Internal Training Load (ITL), recovery and body distribution. Recipe: 15 professional male Brazilian volleyball players participated. The target's three-dimensional position monitoring technology is beneficial for analysis and professional understanding of volleyball games. Still, many factors make any change in the rate of sports volleyball tracking. This article describes the image processing technology combined with 3D space matching technology. It features a particle filter, effectively enhancing volleyball tracking accuracy, tracking a game system's structure. Second, the proposed similarity prediction method is based on the properties of volleyball itself, mainly to solve supercomputing in real-time monitoring based on the similarity prediction of alkali and HSV color spaces. Finally, the simulation test results show that this method can significantly improve the tracking success rate—high efficiency and high accuracy dynamic video game tracking properties unexpected value development value volleyball.     

Queries on ontologies with temporal dependences on Allen’s extended interval logic

This paper introduces Allen’s extended interval logic whose sentences are Boolean combinations of sentences of Allen’s interval logic with metric constraints on time points. For this extended logic, a deduction method based on analytic tableaux is defined. This method is used for answering queries on ontologies specified in Allen’s extended interval logic. An example illustrating the applicability of this extended logic to the problem of workflow specification is presented.     

Employees’ attitudes towards intelligent robots: a dilemma analysis

Information Systems and e-Business Management - With the increasing adoption of robotics in professional applications, the question arises of what impact robots with more cognitive skills will have...     

A non-linear parameter identification approach with applications†

This article is concerned with the identification of unknown parameters for a proposed non-linear time-variant, multivnriable model, that is observed in an additive statistically known white Gaussian noisy environment. The estimation problem is investigated by partitioning the system into subsequent sub-systems to yield a computationally more pragmatical solution. An optimal predictor-corrector maximum-likelihood state estimator and a stochastic hill-climbing procedure are delineated for solving the identification problem. Two particular applications are furnished to illustrate the suggested technique. In the first application, the approach presented is carried out to determine the inertia constant and damping coefficient of a synchronous machine in an unsteady operation, The second application is devoted to tho evaluation of the moments of inertia for a rigid body rotating about its principal axes and subject to external random disturbances and control torques. It is highlighted for the two preceding applications that, though the equations of motion are non-linear in the unknown quantities and/or tho state variables, the solutions obtained via partitioning are expressible in a linear form.     

A novel approach to exploring company’s financial soundness: Investor’s perspective

Prediction of company’s life cycle stage change; creation of an ordered 2D map allowing to explore company’s financial soundness from a rating agency perspective; and prediction of trends of main valuation attributes usually used by investors are the main objectives of this article. The developed algorithms are based on a random forest (RF) and a nonlinear data mapping technique “t-distributed stochastic neighbor embedding”.Information from five different perspectives, namely balance, income, cash flow, stock price, and risk indicators was aggregated via proximity matrices of RF to enable exploration of company’s financial soundness from a rating agency perspective. The proposed use of information not only from companies’ financial statements but also from the stock price and risk indicators perspectives has proved useful in creating ordered 2D maps of rated companies. The companies were well ordered according to the credit risk rating assigned by the Moody’s rating agency.Results of experimental investigations substantiate that the developed models are capable of predicting short term trends of the main valuation attributes, providing valuable information for investors, with low error. The models reflect financial soundness of actions taken by company’s management team. It was also found that company’s life cycle stage change can be determined with the average accuracy of 72.7%. Bearing in mind fuzziness of the transition moment, the obtained prediction accuracy is rather encouraging.     

A framework for monitoring classifiers’ performance: when and why failure occurs?

Classifier error is the product of model bias and data variance. While understanding the bias involved when selecting a given learning algorithm, it is similarly important to understand the variability in data over time, since even the One True Model might perform poorly when training and evaluation samples diverge. Thus, it becomes the ability to identify distributional divergence is critical towards pinpointing when fracture points in classifier performance will occur, particularly since contemporary methods such as tenfolds and hold-out are poor predictors in divergent circumstances. This article implement a comprehensive evaluation framework to proactively detect breakpoints in classifiers’ predictions and shifts in data distributions through a series of statistical tests. We outline and utilize three scenarios under which data changes: sample selection bias, covariate shift, and shifting class priors. We evaluate the framework with a variety of classifiers and datasets.

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Unified stabilizing controller synthesis approach for discrete-time intelligent systems with time delays by dynamic output feedback

A novel model, termed the standard neural network model (SNNM), is advanced to describe some delayed (or non-delayed) discrete-time intelligent systems com- posed of neural networks and Takagi and Sugeno (T-S) fuzzy models. The SNNM is composed of a discrete-time linear dynamic system and a bounded static nonlinear operator. Based on the global asymptotic stability analysis of the SNNMs, linear and nonlinear dynamic output feedback controllers are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based (or fuzzy) discrete-time intelligent systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Three application examples show that the SNNMs not only make controller synthesis of neural-network-based (or fuzzy) discrete-time intelligent systems much easier, but also provide a new approach to the synthesis of the controllers for the other type of nonlinear systems.     

The acceptance of ‘intelligent trade shows’: Visitors’ evaluations of IS innovation

Trade Shows (TSs) provide “high-quality information,” as thousands of specialists and experts are gathered in one place at one time. Thus, information technology systems become essential for TSs. This study explores the characteristics of TSs’ onsite Information Technology (IT). This study aim to explore the relationships among onsite IT usage, visitors’ effectiveness and perception through the innovation characteristics (i.e., relative advantage, compatibility, and complexity). The study was conducted at a representative TS in Korea and used a survey approach to empirically understand the perception of onsite IT usage. The findings suggest that the four characteristics of product intelligence are influential factors of TSs’ onsite IT. Among them, relative advantage and compatibility had positive impacts on TS effectiveness, while complexity did not. In addition, discussions of the results, theoretical and practical implications for practitioners, limitations, and suggestions for future studies are presented.     

Collision with friction; Part A: Newton’s hypothesis

This paper deals with collision with friction. In Part A, equations governing a one-point collision of planar, simple nonholonomic systems are generated. Expressions for the normal and tangential impulses, the normal and tangential velocities of separation of the colliding points, and the change of the system mechanical energy are written for three types of collision (i.e., forward sliding, sticking, etc.). These together with Routh’s semigraphical method and Coulomb’s coefficient of friction are used to show that the algebraic signs of four, newly-defined, configuration-related parameters, not all independent, span five cases of system configuration. For each, the ratio between the tangential and normal components of the velocity of approach, called α, determine the type of collision, which once found, allows the evaluation of the associated normal and tangential impulses and ultimately the changes in the motion variables. The analysis of these cases indicates that the calculated mechanical energy may increase if sticking or reverse sliding occur. In Part B, theories based on Poisson’s and Stronge’s hypotheses are presented with more encouraging results.     

Formulation of customers’ shopping path in shelf space planning: A simulation-optimization approach

Numerous studies confirm that customers’ shopping behavior can highly be managed by many in-store factors such that retail managers try to systematically consider them in order to achieve a well-established solution for shelf-space allocation problem (SSAP). To assist them, we develop an approach based on two artificial intelligence techniques to facilitate well-designed shelf space management. We propose an iterative simulation-optimization approach that integrates customers’ shopping path in the potential demand and introduces it by simulation in the optimization. A profit-based integer programming is also presented that the related computer program, being able to solve small-sized instances, applies important factors including shelf level utility, attraction of store’ zones, allocated shelf space, number of product facings, and demand substitution effects. The problem is inherently a complex and large-sized problem; therefore, we develop two algorithms: GA and hybrid GA with imperialist competitive algorithm. The experimental results prove good performance of hybrid algorithm in terms of both the solution quality and computation time. By embedding this flexible and powerful framework in an expert tool, retail managers are capable of making effective decisions.     

Determining students’ level of page viewing in intelligent tutorial systems with artificial neural network

The concept of level of page viewing (LPV) refers to the extent to which a student actively revises the pages that he or she has to study in tutorial systems. In the present study, an artificial neural network (ANN) model, which is composed of 5 inputs, 20 and 30 neurons, 2 hidden layers, and 1 output, was designed to determine the students’ LPV. After this network was trained, it was integrated into a web-based prototype teaching system, which was developed by ASP.net C# programming language. Additionally, Decision Tree method is tried to determine students’ LPV. However, this method gave wrong results according to expected LPV values. In this system, the student first studies the pages uploaded by the teacher onto the system. After studying all the pages within the scope of a topic, the student can go to the test page for evaluation purposes. LPVs of a student who wants to navigate to the test page are calculated by an ANN module added to the system. On the condition that one or more of the LPV’s are not up to the desired level, the student is not allowed to take the test and is informed of the pages with missing LPV’s so that he can re-study these pages. This prototype system developed based on ANN to determine students’ LPV is essential for intelligent tutorial systems, geared to provide intelligent assistance and guidance. The system can track the pages which the students did not study sufficiently and thus direct them to relevant pages. How much activity the students perform on each page to study is observed before they actually take the test, and the areas which should be further revised are determined much in advance.     

Dynamic leaders’ containment control of high-order multi-agent systems with state time-delay: an LMI approach

This paper addresses the containment control of a multi-agent system with dynamic leaders while considering the state time-delay and time-varying input disturbances. The agents dynamics are assumed to be high-order integrals and an unkown state time-delay with a known upper bound is considered. In order to deal with dynamic leaders and time-varying input disturbances, an appropriate distributed PI^p-type-based control protocol is proposed in this study. Thereafter, utilising the Lyapunov–Krasovskii stability theorem and based on LMI (Linear Matrix Inequality) approach, two theorems are proved which guarantee asymptotically stability of the multi-agent system with fixed and switching networks. In addition to stability assurance, the gains of the proposed protocol are achieved through solving the LMI. Finally, some numerical examples are given to demonstrate the validity of the theoretical results.     

A centre–free approach for resource allocation with lower bounds

Since complexity and scale of systems are continuously increasing, there is a growing interest in developing distributed algorithms that are capable to address information constraints, specially for solving optimisation and decision-making problems. In this paper, we propose a novel method to solve distributed resource allocation problems that include lower bound constraints. The optimisation process is carried out by a set of agents that use a communication network to coordinate their decisions. Convergence and optimality of the method are guaranteed under some mild assumptions related to the convexity of the problem and the connectivity of the underlying graph. Finally, we compare our approach with other techniques reported in the literature, and we present some engineering applications.     

Modeling the behavior of persons with mild cognitive impairment or Alzheimer’s for intelligent environment simulation

User Modeling and User-Adapted Interaction - Intelligent environments may improve the independence and quality of life of persons with mild cognitive impairment (MCI) or Alzheimer’s disease...     

A new approach to consensus problems in discrete-time multiagent systems with time-delays

In this paper, consensus problems in discrete-time multiagent systems with time-invariant delays are considered. In order to characterize the structures of communication topologies, the concept of "pre-leader-follower" decomposition is introduced. Then, a necessary and sufficient condition for state consensus is established. By this method, consensus problems in networks with a single time-delay, as well as with multiple time-delays, are studied, and some necessary and sufficient conditions for solvability of consensus problems are obtained.     

A photo interpretation approach to forest regrowth monitoring using side-looking airborne radar—Grant County,Oregon

A preliminary project was devised to study the forest monitoring potential of side-looking airborne radar. A group often photo interpreters was assembled to evaluate the forest regrowth content of a radar image. A series of 20 cutting locations was selected from U.S. Forest Service data. Categories and category associations were tabulated from each participant's results. Some success was achieved in distinguishing between five major forest categories. Little success was achieved in attempting to subdivide the image content into highly specific categories. It was concluded that the radar has the potential to segregate categories, but interpreters must possess as much radar interpretation expertise as forestry expertise.