A Mobile High Sensitivity On-field Organophosphorous Compounds Detecting System for IoT Based Food Safety Tracking

Food safety has always been a social issue that draws great public attention. With the rapid development of wireless communication technologies and intelligent devices, more and more Internet of Things (IoT) systems are applied in the food safety tracking field. However, connection between things and information system is usually established by pre-storing information of things into RFID Tag, which is inapplicable for on-field food safety detection. Therefore, considering pesticide residue is one of the severe threaten to food safety, a new portable, high-sensitivity, low-power, on-field organophosphorus (OP) compounds detection system is proposed in this thesis to realize the on-field food safety detection. The system is designed based on optical detection method by using a customized photo-detection sensor. A Micro Controller Unit (MCU) and a Bluetooth Low Energy (BLE) module are used to quantize and transmit detection result. An Android Application (APP) is also developed for the system to processing and display detection result as well as control the detection process. Besides, a quartzose sample container and black system box are also designed and made for the system demonstration. Several optimizations are made in wireless communication, circuit layout, Android APP and industrial design to realize the mobility, low power and intelligence.

Intelligent Systems and Soft Computing for Nuclear Science and Industry, Proc. 2nd Int. FLINS Workshop, ed. D. Ruan & al., World Scientific, 1996 [Book review]

Abstract not available

A novel continuous pitch electronic wind instrument controller

We present a design for an electronic continuous pitch wind controller for musical performance. It uses a combination of linear position, magnetic reed, and air pressure sensors to generate three fully continuous control dimensions. Each control dimension is encoded and transmitted using the industry standard MIDI protocol to allow the instrument to interface with a large variety of synthesizers to control different parameters of the synthesis algorithm in real time, allowing for a high degree of expressiveness not possible with existing electronic wind instrument controllers. The first part of the thesis will provide a justification for the design of a novel instrument, and present some of the theory behind pitch representation, encoding, and transmission with respect to digital systems. The remainder of the thesis will present the particular design and explain the workings of its various subsystems.

Optimal Controller Designs for Rotating Machines - Penalising the Rate of Change of Control Forcing

In the context of active control of rotating machines, standard optimal controller methods enable a trade-off to be made between (weighted) mean-square vibrations and (weighted) mean-square currents injected into magnetic bearings. One shortcoming of such controllers is that no concern is devoted to the voltages required. In practice, the voltage available imposes a strict limitation on the maximum possible rate of change of control force (force slew rate). This paper removes the aforementioned existing shortcomings of traditional optimal control.

Injecting multiple upsets a SEU tolerant 8051 micro-controller

International audience

An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering

This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, i.e. we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, i.e. an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (i.e. the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules.

Computational Analysis of Intelligent Agents: Social and Strategic Settings

The central motif of this work is prediction and optimization in presence of multiple interacting intelligent agents. We use the phrase `intelligent agents' to imply in some sense, a `bounded rationality', the exact meaning of which varies depending on the setting. Our agents may not be `rational' in the classical game theoretic sense, in that they don't always optimize a global objective. Rather, they rely on heuristics, as is natural for human agents or even software agents operating in the real-world. Within this broad framework we study the problem of influence maximization in social networks where behavior of agents is myopic, but complication stems from the structure of interaction networks. In this setting, we generalize two well-known models and give new algorithms and hardness results for our models. Then we move on to models where the agents reason strategically but are faced with considerable uncertainty. For such games, we give a new solution concept and analyze a real-world game using out techniques. Finally, the richest model we consider is that of Network Cournot Competition which deals with strategic resource allocation in hypergraphs, where agents reason strategically and their interaction is specified indirectly via player's utility functions. For this model, we give the first equilibrium computability results. In all of the above problems, we assume that payoffs for the agents are known. However, for real-world games, getting the payoffs can be quite challenging. To this end, we also study the inverse problem of inferring payoffs, given game history. We propose and evaluate a data analytic framework and we show that it is fast and performant.

Controller system design using the coefficient diagram method

Coefficient diagram method is a controller design technique for linear time-invariant systems. This design procedure occurs into two different domains: an algebraic and a graphical. The former is closely paired to a conventional pole placement method and the latter consists on a diagram whose reading from the plotted curves leads to insights regarding closed-loop control system time response, stability and robustness. The controller structure has two degrees of freedom and the design process leads to both low overshoot closed-loop time response and good robustness performance regarding mismatches between the real system and the design model. This article presents an overview on this design method. In order to make more transparent the presented theoretical concepts, examples in Matlab®code are provided. The included code illustrates both the algebraic and the graphical nature of the coefficient diagram design method. © 2016, King Fahd University of Petroleum & Minerals.

Using Intelligent Agents to Understand Management Practices and Retail Productivity

Intelligent agents offer a new and exciting way of understanding the world of work. In this paper we apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail productivity. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents could offer potential for fostering sustainable organizational capabilities in the future. The project is still at an early stage. So far we have conducted a case study in a UK department store to collect data and capture impressions about operations and actors within departments. Furthermore, based on our case study we have built and tested our first version of a retail branch simulator which we will present in this paper.

Using intelligent agents to understand organisational behaviour

This paper introduces two ongoing research projects which seek to apply computer modelling techniques in order to simulate human behaviour within organisations. Previous research in other disciplines has suggested that complex social behaviours are governed by relatively simple rules which, when identified, can be used to accurately model such processes using computer technology. The broad objective of our research is to develop a similar capability within organisational psychology.

An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering

This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, i.e. we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, i.e. an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (i.e. the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules.

Evaluation of factors affecting adherence to asthma controller therapy in chest clinics in a sub-Saharan African setting: a cross-sectional study

Background: Adherence to controller therapy in asthma is a major concern during the management of the disease. Objective: To determine the adherence rate and identify the predictors of low adherence to asthma controller therapy. Methods: A cross-sectional study including asthma patients was conducted from November 1, 2012 to May 31, 2013 in 4 chest clinics in Cameroon. The adherence to asthma treatment was rated using Morisky Medication Adherence Scale. A multivariate logistic regression analysis was performed for the identification of factors associated with adherence to asthma treatment. Results: Among the 201 asthma patients included, 133 (66.2%) were female. The mean age of participants was 41.2 years. Sixty-one (30.3%) of the patients did not visit the chest physician during the last year prior to the study. Asthma was well controlled in 118 patients (58.7%). The prevalence of low adherence rate to asthma controller therapy was 44.8% and the absence of any chest specialist visit within the last 12 months was the only factor associated with the low adherence rate to asthma treatment (OR 5.57 ; 95% CI 2.84–10.93). Conclusion: The adherence rate to asthma controller therapy in Cameroon is low and it could be improved if scheduled visits are respected by patients.

Intelligent pipeline control - a simulation study in the automotive sector

Automotive producers are aiming to make their order fulfilment processes more flexible. Opening the pipeline of planned products for dynamic allocation to dealers/ customers is a significant step to be more flexible but the behaviour of such Virtual-Build-To-Order systems are complex to predict and their performance varies significantly as product variety levels change. This study investigates the potential for intelligent control of the pipeline feed, taking into account the current status of inventory (level and mix) and of the volume and mix of unsold products in the planning pipeline, as well as the demand profile. Five ‘intelligent’ methods for selecting the next product to be planned into the production pipeline are analysed using a discrete event simulation model and compared to the unintelligent random feed. The methods are tested under two conditions, firstly when customers must be fulfilled with the exact product they request, and secondly when customers trade-off a shorter waiting time for compromise in specification. The two forms of customer behaviour have a substantial impact on the performance of the methods and there are also significant differences between the methods themselves. When the producer has an accurate model of customer demand, methods that attempt to harmonise the mix in the system to the demand distribution are superior.

Plataforma interactiva para edifícios inteligentes com controlo preditivo

Esta dissertação desenvolve uma plataforma de controlo interactiva para edifícios inteligentes através de um sistema SCADA (Supervisory Control And Data Acquisition). Este sistema SCADA integra diferentes tipos de informações provenientes das várias tecnologias presentes em edifícios modernos (controlo da ventilação, temperatura, iluminação, etc.). A estratégia de controlo desenvolvida implementa um controlador em cascada hierárquica onde os "loops" interiores são executados pelos PLC's locais (Programmable Logic Controller), e o "loop" exterior é gerido pelo sistema SCADA centralizado, que interage com a rede local de PLC's. Nesta dissertação é implementado um controlador preditivo na plataforma SCADA centralizada. São apresentados testes efectuados para o controlo da temperatura e luminosidade de salas com uma grande área. O controlador preditivo desenvolvido tenta optimizar a satisfação dos utilizadores, com base nas preferências introduzidas em várias interfaces distribuídas, sujeito às restrições de minimização do desperdício de energia. De forma a executar o controlador preditivo na plataforma SCADA foi desenvolvido um canal de comunicação para permitir a comunicação entre a aplicação SCADA e a aplicação MATLAB, onde o controlador preditivo é executado. ABSTRACT: This dissertation develops an operational control platform for intelligent buildings using a SCADA system (Supervisory Control And Data Acquisition). This SCADA system integrates different types of information coming from the several technologies present in modem buildings (control of ventilation, temperature, illumination, etc.). The developed control strategy implements a hierarchical cascade controller where inner loops are performed by local PLCs (Programmable Logic Controller), and the outer loop is managed by the centralized SCADA system, which interacts with the entire local PLC network. ln this dissertation a Predictive Controller is implemented at the centralized SCADA platform. Tests applied to the control of temperature and luminosity in huge­area rooms are presented. The developed Predictive Controller tries to optimize the satisfaction of user explicit preferences coming from several distributed user-interfaces, subjected to the constraints of energy waste minimization. ln order to run the Predictive Controller at the SCADA platform a communication channel was developed to allow communication between the SCADA application and the MATLAB application where the Predictive Controller runs.